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Exposure to Ambient Particulate Matter during Specific Gestational Periods Produces Adverse Obstetric Consequences in Mice

Author Affiliations open

1Department of Environmental Medicine, New York University School of Medicine, Tuxedo, New York, USA

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  • Background:
    Epidemiological studies associate inhalation of fine-sized particulate matter (PM2.5) during pregnancy with preterm birth (PTB) and low birth weight (LBW) but disagree over which time frames are most sensitive, or if effects are cumulative.
    Objectives:
    Our objective was to provide experimental plausibility for epidemiological observations by testing the hypothesis that exposure to PM2.5 during discrete periods of pregnancy results in PTB and LBW.
    Methods:
    For the first study, timed-pregnant B6C3F1 mice were exposed to concentrated ambient PM2.5 (CAPs) or filtered air (FA) throughout pregnancy [6 h/d from gestational day (GD) 0.5 through GD16.5]. A follow-up study examined the effects of CAPs exposure during discrete gestational periods (1: GD0.5–5.5; 2: GD6.5–14.5; 3: GD14.5–16.5; 4: GD0.5–16.5) aligning to milestones during human development.
    Results:
    In the first experiment, exposure to 160 μg CAPs/m3 throughout pregnancy decreased gestational term by 0.5 d (∼1.1  wk decrease for humans) and birth weight by 11.4% compared with FA. The follow-up experiment investigated timing of CAPs exposure (mean concentrations at 178, 193, 171, and 173 μg/m3 for periods 1–4, respectively). Pregnancy was significantly shortened (vs. FA) by ∼0.4d when exposure occurred during gestational periods 2 and 4, and by ∼0.5d if exposure occurred during period 3. Exposure during periods 1, 2, and 4 reduced birth weight by ∼10% compared with FA, and placental weight was reduced (∼8%) on GD17.5 if exposure occurred only during period 3.
    Conclusions:
    Adverse PM2.5-induced outcomes such as PTB and LBW are dependent upon the periods of maternal exposure. The results of these experimental studies could contribute significantly to air pollution policy decisions in the future. https://doi.org/10.1289/EHP1029
  • Received: 26 August 2016
    Revised: 12 December 2016
    Accepted: 23 January 2017
    Published: 27 July 2017

    Address correspondence to J. Zelikoff, Department of Environmental Medicine, New York University School of Medicine, 57 Old Forge Rd., Tuxedo, NY 10987 USA. Telephone: (845) 731-3528. Email: Judith.Zelikoff@nyumc.org

    The authors declare they have no actual or potential competing financial interests.

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Introduction

In the United States, ∼11% of all pregnancies result in preterm birth (PTB; birth prior to 37 wk gestation) (March of Dimes 2014). Although the reasons for this outcome are varied, exposure of pregnant women to elevated levels of fine-sized ambient particulate matter (PM2.5) has been identified in numerous epidemiologic studies as a contributing factor (Bell et al. 2010; Ha et al. 2014; Pereira et al. 2014; Ritz et al. 2007). Exposure to PM2.5 is not only associated with PTB but also with low birth weight (LBW; <2,500 g) as a result of restricted fetal growth in infants born early and in those carried to full term (Ha et al. 2014). The link between PM2.5 and increased risk for PTB was first reported by Xu et al. (1995) in a community-based cohort study. Since that time, epidemiological evidence strengthening the association between PM exposure and PTB and LBW continues to accumulate (Ha et al. 2014; Huynh et al. 2006; Jiang et al. 2007; Malmqvist et al. 2011; Ritz et al. 2000, 2007; Srám et al. 2005; Zhao et al. 2011). Such outcomes are also associated with increased risk for long-term health issues including eye/vision problems (O’Connor and Fielder 2007), learning disabilities (Johnson and Breslau 2000), and later-life chronic diseases including cardiovascular disease (Lewandowski et al. 2013) and type 1 and type 2 diabetes (Li et al. 2014).

A question that remains highly debated among human studies is whether timing of PM2.5 exposure during pregnancy is a relevant risk factor for PTB and/or LBW. Although a number of epidemiological studies have attempted to address this critical question, the data remain inconsistent. A case–control survey performed in 2003 and nested within a birth cohort (2,543 of 6,374 women sampled in California from a cohort of ∼58,000 births in Los Angeles County), Ritz et al. (2007) demonstrated that the occurrence of PTB is proportional to PM2.5 exposure levels during the first trimester only. A more recent epidemiological study by Pereira et al. (2014) reported that exposure of Hispanic women to PM2.5 during either the first trimester or throughout the entirety of pregnancy resulted in a greater risk for PTB than at other times during pregnancy. A study from Florida revealed that maternal exposure to PM2.5 during any point of pregnancy increased the risk for both PTB and LBW but that the second trimester was most sensitive (Ha et al. 2014). Bell et al. (2010) reported an increased risk for LBW following maternal exposure to PM2.5 derived from oil burning, but only during the third trimester. Thus, the period (or periods) of greatest sensitivity during pregnancy for PM-induced effects on gestational duration and birth weight remains unsolved.

Studies performed using animal models to examine the effects of prenatal exposure to ambient PM on fetal or gestational outcomes are limited. A study by Veras et al. (2008) demonstrated that a 24 h/d exposure of pregnant Balb/c mice to 27.5 μg/m3 PM2.5 from the start of pregnancy through gestational day (GD) 17 decreased placental weight. This change in placental weight was associated with decreased blood vessel diameter on the maternal side of the placental vasculature; capillary surface area on the fetal side of the placenta was significantly increased. The study concluded that PM-induced changes in placental perfusion were, at least in part, responsible for the observed reduction in fetal weight.

The present study was designed to establish feasibility for the epidemiologic observations that inhalation exposure during pregnancy to PM2.5 leads to PTB and LBW and to determine which (if any) gestational periods are most sensitive for PM-induced LBW, PTB, or both.

Methods

Animals

Seven- to eight-week-old female and male (for breeding purposes only) B6C3F1 mice (Jackson Laboratory) were housed in single-sex pairs upon arrival and were provided food and water ad libitum at all times except during concentrated ambient PM2.5 (CAPs) exposure. Beginning one day after arrival, estrous cycles were monitored daily for at least two complete normal estrous cycles. On the third proestrus, following two normal cycles, a single female mouse was paired overnight with one male. The next morning, confirmation of successful mating was determined by the presence of a copulatory plug and was considered GD0.5. Mated females were weighed and randomly assigned to one treatment group [i.e., filtered air (FA) control vs. CAPs] and to one of four gestational exposure periods: period 1 (GD0.5–GD5.5); period 2 (GD6.5–GD14.5); period 3 (GD14.5–GD16.5); or period 4 (GD0.5–GD16.5) (Figure 1). A group of pregnant naïve mice (n=4) remained in their home cages during the exposure period and served as chamber controls to assure that any observed effects were due solely to treatment rather than to the exposure system itself. When not being exposed, all experimental animals were housed in rooms equipped with HEPA and charcoal filters to remove any ambient particles and gaseous pollutants.

Timeline indicating four gestational exposure periods.
Figure 1. Timeline for inhalation CAPs exposure. Upon arrival, female mice were staged for phase of the estrous cycle. On the third proestrus following two normal cycles, the female was paired with a single male to breed overnight. Upon confirmation of breeding, the female was weighed and assigned to a treatment and to an exposure period. Exposures were 6 h/d, 7 d/wk. Dams were weighed daily before being placed into the exposure box if being exposed or returned to the home cage if not being exposed. Mice from all periods were either euthanized on GD17.5 or allowed to give birth as described in “Methods.” Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); NYU, New York University.

A total of three CAPs exposure experiments were performed between 2012 and 2013: The first (summer of 2012) and second (winter of 2013) exposure examined the effects of maternal CAPs exposure throughout gestation (i.e., period 4) only. The third exposure occurred during the summer of 2013 for the purpose of assessing specific gestational periods of greatest vulnerability to PM2.5.

Exposure System

A particle concentrator system was used to collect and concentrate PM2.5 for each experiment as described previously (Maciejczyk et al. 2005). Briefly, the system is a modified versatile aerosol concentration enrichment system (VACES) originally developed by Sioutas et al. (1999). The principle of VACES is “condensational growth of ambient particles followed by virtual impaction to concentrate the aerosol” (Maciejczyk et al. 2005; Sioutas et al. 1999). Ambient air was drawn through an Aerotec 2 cyclone inlet that removes the majority of particles >2.5 μm in diameter and was then passed through silica gel and carbon filters to remove excess moisture and organic pollutants. Water-soluble [e.g., sulfur dioxide (SO2)] and reactive [ozone, nitrogen oxides (NOx)] gases were removed by the system itself. The PM aerosol was then quickly chilled to ∼20°C in a condenser tube. The remaining concentrated particles were then passed over a warmed water bath to restore relative humidity similar to that of ambient air. From there, the CAPs aerosol was divided into three streams: 27% of the particle flow was directed toward Teflon™ filters housed in Harvard impactors (Air Diagnostics and Engineering Inc.) and was used for gravimetric and chemical analysis (described below); 10% of the flow was directed toward a DataRAM™ nephelometer (Thermo Electron Corporation) to allow for continuous monitoring of CAPs mass concentration; the remaining particles were streamed toward the animal exposure chamber. The same system was used for the control mice, which were exposed to house air that passed through HEPA filters, which removed ∼98% of ambient particles before entering the VACES inlet.

For each of the exposures, the target CAPs concentration was 150 μg/m3; this level is ∼10–15 times that of the ambient PM2.5 concentration usually found at the New York University (NYU) Sterling Forest (Tuxedo, NY) facility where the ambient PM2.5 was collected. The selected concentration was chosen such that a 6-h exposure period, when averaged over a 24-h period, was relevant to that measured in some U.S. urban centers (Samet et al. 2000). Because no energy generation plants or other types of industrial operations are located within 20 miles of the exposure system, CAPs produced by the system was representative of the regional PM2.5 background for the megalopolis extending from Virginia to Maine on the eastern coast of the United States. Use of the VACES system neither chemically nor physically modifies the ambient particles collected by the CAPs exposure system (Chen et al. 2005).

Mouse Exposure

Individual mice were placed into single compartments of a 32-compartment stainless steel exposure chamber. The exposure box was covered by a Plexiglass lid through which perforated aluminum tubes delivered CAPs evenly throughout the exposure box (Maciejczyk et al. 2005). Mice were weighed each morning before being placed into the exposure box; those that were not being exposed were returned to their cages after weighing. For experiment 3, mice were exposed during one of four exposure periods. To reduce possible effects from differences in PM content between exposures, mice from each period were overlapping (i.e., not in a sequential manner). A subset of pregnant mice (n=6–8) from each gestational exposure period was euthanized on GD17.5 using sodium pentobarbital (150 mg/kg, IP), and the uteri were collected and opened to collect each fetal-placental unit. After all fetal–placental units were excised, the amniotic sacs were carefully opened, the umbilical cords were severed at the fetal-umbilical attachment site, and the umbilical cords and amniotic membranes were dissected away from the placenta. The position of the fetal-placental unit within the uterus was not recorded for these studies. All fetuses and placentas recovered from each dam were weighed, and fetal crown-to-rump length (CRL) was determined using digital calipers. The remaining timed-pregnant dams (n=8–17) in each exposure period were permitted to give birth, and the day of parturition was recorded; each neonate was weighed, and CRL was measured at birth and daily for 21 d, at which point they were weaned.

Starting on GD18.5, cages were checked for the presence of pups starting at 0800 hours. If present, pups were immediately counted and weighed. Alternatively, if no pups were present at that time, mice were checked every 2–3 hours until midafternoon. If pups were not observed on any given day by 1630 hours, dams were checked again the following morning. To avoid data biasing, neonatal weights were collected only after milk was viewed by eye in their stomachs; pups that had not been fed weighed less than those that had been nursed. In circumstances where litters contained more than 10 pups at birth, the number of neonates was culled to 10 on postnatal day (PND) 0. On PND10 and PND21, neonatal anogenital distance (AGD) was measured in both male and female offspring. All procedures using animals were approved by the New York University School of Medicine Institutional Animal Care and Use Committee.

Genetic Sexing of Fetal Mice

Sexing of GD17.5 fetal mice followed the same protocol as previously described (Blum Het al. 2012). Briefly, a 1-mm section of the fetal tail was clipped from each fetus after weighing, placed into a microcentrifuge tube containing 100 μL digestion buffer [25 mM sodium hydroxide (NaOH)/0.2 mM ethylenediaminetetraacetic acid (EDTA), pH 12.0], and incubated at 95°C for 1 h. Once digested, 100 μL of neutralization buffer (40 mM Tris, pH 5.5) was added to each tube and thoroughly mixed by vortexing. Undigested material was separated via centrifugation (1,000×g for 10 min), and the supernatant was collected and diluted 1:100 in ultrapure water. The diluted DNA sample was used as a template for duplex polymerase chain reaction (PCR) using primers for interleukin-3 and the sex-determining region of chromosome Y (SRY) gene. PCR products were separated using 1% agarose gel electrophoresis in tris-acetate-EDTA buffer and were visualized using ethidium bromide staining and ultraviolet light illumination.

Elemental Characterization and Mass Concentration of Collected PM2.5 Particles

Using preweighed Teflon filters (37 mm, 0.2 μm pore size; Pall), the mass concentration of CAPs was determined daily; the particle concentration from filtered air (FA) was determined on a weekly basis. Particle-laden filters were equilibrated overnight in a temperature/humidity-controlled weigh room (21°C±0.5°C and 40±5% relative humidity) and were weighed gravimetrically on an MT5 microbalance (Mettler Toledo). Filters from every third exposure day, as well as lot-matched unexposed blank control filters, were analyzed by X-ray fluorescence spectroscopy (XRF) to determine elemental content using an ARL™ Quant’X EDXRF Analyzer (ThermoScientific).

Statistical Analyses

In all cases, the dam was the experimental unit (Table 1 details sample sizes across experiments and exposure periods). Gestational days of birth, birth weights, fetal body weight, CRL, placental weight, weight-to-length ratio, and anogenital distance were compared using analysis of variance (ANOVA). For the first two experiments, the main effect was treatment. Because no statistical differences were observed across exposure periods for FA-exposed dams in experiment 3, data from all FA-treated dams in this experiment were pooled for statistical analyses and graphical presentations. For data generated from experiment 3, the main effects tested were treatment and exposure period, along with the interaction effect of treatment×exposure period. For measurements of body weight gain (percent change from birth and percent change day-over-day), data from days postpartum were compared between the four CAPs exposure periods and the pooled FA control. When statistically significant differences were observed (ANOVA p-value<0.05), post hoc testing was performed using Fisher’s Least Significant Difference (LSD) to identify differences between treatments in the case of experiment 1, or between individual CAPs exposure periods, or in comparison to the pooled FA control in experiment 3. Comparisons of offspring sex ratios between exposure periods, between treatments, or between exposure periods and treatments were performed using χ2 analysis. All statistical comparisons were performed using SAS (v.9.1.3; SAS Insitute Inc.). Data presented are the means±standard error (SE) unless otherwise stated.

Table 1. Experimental sample sizes for each treatment in each experiment.
Experiment Treatment Total sample size Number of dams used for GD17.5 Number of dams used for PTB/LBW
Experiment 1 Naïve 4 0 4
FA 10 0 10
CAPs 15 0 15
Experiment 2 FA 22 0 22
CAPs 22 0 22
Experiment 3 Period 1 10 4 6
FA Period 2 10 4 6
Period 3 13 4 9
Period 4 10 5 5
CAPs Period 1 13 5 8
Period 2 13 5 8
Period 3 13 5 8
Period 4 16 5 11
Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); FA, fitered air; GD, gestation day; LBW, low birth weight; PTB, preterm birth.

Results

Physicochemical Analyses of CAPs

Concentrations of CAPs varied moderately between each of the three experiments (Table 2). For the first and second exposures, pregnant mice were exposed to CAPs throughout gestation (GD0.5–GD16.5). The mean CAPs concentration for the first experiment was 15.2 times greater than ambient air levels and 44.3 times higher than FA levels. The CAPs mass concentration for the second experiment was 24.6 times higher than ambient air and 29.1 times higher than FA. For the third experiment, pregnant mice were exposed to CAPs only during one of four preselected gestational exposure periods in an overlapping manner. Compared with the ambient PM2.5 concentrations measured during these same periods, the mean CAPs concentrations were 15.3-, 16.6-, 14.8-, and 14.9 times higher for periods 1–4, respectively; compared with FA, CAPs concentrations were 65.7-, 71.3-, 63.4-, and 64.2 times higher for the same gestational periods.

Table 2. Average daily CAPs concentrations.
Experiment 1a Experiment 2b Experiment 3c
Treatment Period 4(GD 0.5 – 16.5) Period 4(GD 0.5 – 16.5) Period 1(GD 0.5 – 5.5) Period 2(GD 6.5 – 14.5) Period 3(GD 14.5 – 16.5) Period 4(GD 0.5 – 16.5)
FA 3.7±1.7d 3.9±2.6 2.7±1.6
CAPs 163.8±100.0 113.4±93.7 177.5±104.7 192.5±96.2 171.3±94.1 173.4±92.2
Ambiente 10.9±6.5 4.7±3.4 11.6±6.1
Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); FA, fitered air; GD, gestation day.

aExperiment 1 occurred during summer 2012.

bExperiment 2 occurred during winter 2013.

cExperiment 3 occurred during summer 2013.

dValues are mean daily concentrations (μg/m3)±standard deviation (SD) for each particular gestational time frame.

eAmbient concentrations provided for comparison only.

Elemental analyses were performed on particle-laden filters collected every third exposure day from all three experiments; the results are shown in Table 3. Elemental levels, with the exceptions of copper (Cu), zinc (Zn), bromine (Br), and lead (Pb), were greater during the summer exposures than during the winter months. For both summer experiments (experiments 1 and 3), the 10 most abundant CAPs-associated elements were sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), sulfur (S), potassium (K), calcium (Ca), titanium (Ti), iron (Fe), and bromine (Br). For the winter exposure (experiment 2), the most abundant CAPs-associated elements were the same as those measured during the summer except that Al and Si were replaced by Cu and Zn. Elements collected on filters collected from the FA exposure line did not show significant variability across the three experiments.

Table 3. Concentrations of various elements found on collection filters identified by XRF.
Element FA: Summer 2012 FA: Winter 2013 FA: Summer 2013 Ambientb: Summer 2012 Ambient: Winter 2013 Ambient: Summer 2013 CAPs: Summer 2012 CAPs: Winter 2013 CAPs: Summer 2013
Sodium (Na) 16.8±10.0a −12.3±25.2 56.9±32.6 188.3±161.6 110.0±60.6 162.6±88.0 1411.6±1871.8 1154.5±601.1 1593.6±1003.2
Magnesium (Mg) 0.7±5.1 −1.0±5.4 15.8±8.6 45.4±30.3 20.0±11.1 46.7±25.8 349.6±305.1 253.5±253.5 510.8±353.8
Aluminum (Al) −9.4±11.1 40.7±75.9 4.7±11.6 10.3±22.5 −5.2±12.1 46.5±96.9 241.2±205.1 −34.0±70.0 755.5±1589.6
Silicon (Si) 0.0±0.0 0.0±0.0 0.0±0.0 1.1±3.2 0.0±0.0 50.1±186.4 329.2±573.5 0.0±0.0 1376.5±3426.5
Sulfur (S) 10.2±4.9 13.1±0.8 10.9±4.5 686.0±755.6 402.3±109.2 1101.1±803.8 10057.4±12697.3 6750.3±2262.1 18000.9±13109.9
Potassium (K) 4.3±1.5 1.4±0.6 1.5±2.6 31.6±11.4 33.4±8.5 52.0±33.9 398.2±204.1 493.0±74.6 708.7±474.2
Calcium (Ca) 8.7±6.0 5.1±2.6 4.0±1.7 34.7±26.5 22.5±10.1 40.9±32.5 445.7±335.9 294.9±139.6 538.9±516.2
Titanium (Ti) 3.2±1.8 1.6±1.1 2.7±1.2 14.1±5.3 14.6±3.8 17.7±12.5 347.7±132.8 472.5±177.5 1000.5±1110.0
Vanadium (V) 0.1±0.4 0.3±0.0 0.1±0.3 1.3±1.4 0.3±1.0 0.7±1.0 4.1±11.3 3.9±8.7 7.2±7.8
Chromium (Cr) 0.7±0.4 −0.2±0.4 0.0±0.3 −0.3±2.3 −0.4±1.2 −0.2±0.9 2.4±7.3 −1.8±4.2 4.5±8.3
Manganese (Mn) 0.0±0.2 0.2±0.1 −0.1±0.3 1.3±1.2 0.5±1.0 1.3±1.2 14.8±8.8 11.9±8.5 23.2±18.5
Iron (Fe) 5.4±5.3 2.9±1.4 0.3±0.5 35.4±18.6 19.9±4.4 58.3±62.4 473.0±248.2 292.1±62.3 905.5±1067.9
Nickel (Ni) 0.2±0.1 0.2±0.4 0.2±0.3 1.0±0.6 0.3±0.7 1.6±3.4 8.7±5.0 7.7±3.4 7.7±6.1
Copper (Cu) 0.5±0.3 0.7±0.3 0.4±0.5 1.8±0.9 0.8±0.5 1.5±1.5 11.6±7.3 27.1±22.8 21.0±9.6
Zinc (Zn) 1.4±0.4 0.7±0.8 1.7±2.5 4.1±1.4 4.7±3.3 9.4±17.3 48.7±31.1 80.2±31.9 70.9±39.7
Bromine (Br) 0.1±0.6 0.4±1.5 0.4±0.9 6.1±8.2 8.3±8.2 6.3±6.1 108.9±119.6 214.5±109.6 156.1±84.2
Strontium (Sr) 0.1±0.4 0.2±0.4 0.3±0.5 0.3±1.3 0.6±0.8 0.8±1.2 6.8±5.2 7.7±7.8 13.9±11.8
Barium (Ba) −0.3±0.9 2.1±5.0 1.4±2.0 3.0±5.5 −1.0±1.6 1.9±5.1 7.5±28.0 5.1±18.3 25.9±44.2
Erbium (Er) 0.8±0.5 1.0±0.5 0.3±0.6 1.1±1.6 0.4±1.0 0.8±1.7 5.3±6.6 8.3±7.4 13.8±8.2
Lutetium (Lu) 0.6±0.9 0.6±0.9 0.6±0.6 1.4±1.4 2.6±1.2 2.7±3.8 18.6±9.3 24.4±6.9 24.9±11.0
Lead (Pb) −0.2±0.1 0.2±0.5 0.0±0.2 −0.2±0.8 0.4±0.9 1.6±1.3 12.7±14.7 17.1±9.7 15.4±10.8
Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); FA, fitered air; GD, gestation day; XRF, X-ray fluorescence spectroscopy.

aData presented are mean ng/m3±standard deviation (SD) from filters collected during each exposure. Each mean represents 4–18 filters across each respective exposure period that were analyzed via XRF as described in “Methods.”

bData presented for ambient are for informational purposes only. No mice were exposed to these particles.

Exposure of Pregnant Mice to CAPs Results in PTB and LBW

In the first experiment (summer 2012), pregnant mice exposed to CAPs (163.8 μg CAPs/m3) throughout gestation (GD0.5–GD16.5) demonstrated a 0.5-d reduction (p=0.0018) in gestational duration compared with both naïve and FA-exposed mice (Figure 2A). A significant (p=0.0059) decrease (11.4%) in birth weight was also observed for offspring born prematurely (Figure 2B). There were no significant differences in gestational duration or birth weight between naïve and FA-exposed groups, demonstrating that exposure to CAPs, specifically, rather than confinement stress, was responsible for the observed effects. The results from the second experiment (winter 2012), also encompassing CAPs exposure throughout gestation, supported the PTB and LBW findings from the first exposure despite the difference in season. In this case, pregnant mice exposed to CAPs at a lower concentration than in the first experiment (113.4 μg/m3 vs. 163.8 μg/m3, respectively) had a reduction of ∼0.3 d (p=0.0423) in pregnancy duration compared with FA-exposed mice (Figure 2C) that was also associated with an 8.8% decrease (p=0.0005) in average litter birth weight (Figure 2D).

Bar graphs A and C with confidence intervals plotting gestational day (y-axis) across treatment groups, namely, naïve, control, and CAPS, and control and CAPS, respectively, (x-axis). Bar graphs B and D with confidence intervals plotting birth weight in milligrams (y-axis) across treatment groups, namely, naïve, control, and CAPS, and control and CAPS, respectively, (x-axis).
Figure 2. Maternal exposure to inhaled CAPs results in preterm birth and low birth weight. Dams were exposed to CAPs during period 4 (GD0.5–16.5) and were allowed to give birth. Data are from experiment 1 (A, B) and experiment 2 (C, D). In experiment 1, some naïve dams (n=4) were used to control for changes resulting from the exposure system. Data for experiment 1 are the means±standard error (SE) from n=10 (FA) or n=15 (CAPs); for experiment 2, n=22 for each treatment. In all panels, the treatment effect is significant [analysis of variance (ANOVA) p<0.05]. Bars in panels A and B with different letters are significantly different based on Fisher’s Least Significant Difference (LSD) post hoc testing. Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); FA, filtered air. *p<0.05 based on ANOVA.

Assessments of litter sizes and sex ratios were performed for all experiments (Table 4). Across all experiments and treatments, the average litter size was 8.3 pups per litter, with an overall range of 3–12. Within each experiment, there were no significant differences (p>0.05) between treatments for litter size, numbers of a given sex (determined by ANOVA) or sex ratios (determined by χ2 analysis). In experiment 3, no statistically significant differences were observed across exposure periods within treatment groups or between treatment groups for each period.

Table 4. Average litter size and sex breakdown by experiment, treatment, and exposure period.
Experiment/treatment Treatment/gestational period Litter size Mean number of males Mean number of females % Male % Female
Experiment 1 Naïve 8.2±1.5a 4.3±1.7 4.0±0.8 50.0±14.3 50.0±14.3
FA 7.8±1.9 3.4±0.7 4.3±2.0 45.8±14.3 54.2±20.7
CAPs 7.9±2.1 3.9±1.2 3.8±2.0 51.8±16.1 48.2±17.3
Experiment 2 FA 8.7±1.3 4.2±1.3 4.5±1.6 49.0±15.4 51.0±15.4
CAPs 8.3±1.3 3.8±1.7 4.5±1.7 45.9±16.4 54.1±16.4
Experiment 3 Period 1 9.0±0.6 4.2±1.5 4.8±1.3 46.1±15.0 53.9±15.0
FA Period 2 8.5±1.6 5.0±1.6 3.2±1.8 62.7±18.5 37.3±18.5
Period 3 9.0±1.3 5.0±1.4 4.5±1.4 52.5±12.9 47.5±12.9
Period 4 8.4±0.7 4.4±1.7 4.0±2.0 53.0±22.0 47.0±22.0
Across periods 8.7±1.1 4.6±1.5 4.2±1.7 53.2±16.9 46.8±16.9
CAPs Period 1 8.7±1.0 4.0±1.8 5.0±1.9 44.6±21.5 55.4±21.5
Period 2 8.3±1.0 4.9±1.7 3.4±1.8 59.5±20.8 40.5±20.8
Period 3 7.2±2.7 4.6±0.9 2.6±1.5 65.6±16.6 34.4±16.6
Period 4 8.3±1.3 4.0±1.2 4.0±1.7 50.9±17.7 49.7±17.7
Across periods 8.1±1.7 4.4±1.4 3.8±1.8 57.6±19.7 45.4±19.7
Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); FA, fitered air.

aData presented are means±standard deviation (SD) from all litters generated in these experiments. Fetal mice were sexed using polymerase chain reaction (PCR) as described in “Methods,” and neonatal mice were sexed by visual observation on postnatal day 8. Exposure treatment groups were assessed within experiment, and no significant differences were observed.

Effects of Exposure of Pregnant Mice to CAPs on Fetal Weight, Fetal CRL, and Placental Weight (Experiment 3)

Based on the observations from the first two experiments, follow-up studies were performed in the third experiment that focused on fetal, neonatal, and placental parameters. Fetal body weights examined on GD17.5 in experiment 3 revealed that effects were dependent (p=0.0115) upon the period during pregnancy when exposure to CAPs occurred (Figure 3A). Fetuses collected at GD17.5 from dams exposed to CAPs during only the fetal growth period (period 3) and throughout gestation (period 4) at similar CAPs concentrations (171.3 vs. 173.4 μg/m3, respectively) were 8.1% and 7.7% lighter, respectively, than GD-matched counterparts from FA-exposed dams. Moreover, maternal CAPs exposure during periods 1 (177.5 μg/m3), 3, and 4 led to significant (p=0.0468) decreases in CRL of 2.7%, 5.0%, and 1.8%, respectively (Figure 3B). In addition, maternal exposure to CAPs resulted in significant (p=0.0138) changes in placental weight. Maternal exposure to CAPs during the fetal growth period alone (period 3) resulted in an 8.1% decrease in placental weight. When exposure to CAPs occurred throughout pregnancy (period 4), a 3.8% increase in placental weight was observed (Figure 3C). Exposures that occurred only during placentation/organogenesis (period 2) had no effect on fetal weight, CRL, or placental weight.

Bar graphs A, B, and C with confidence intervals plotting fetal body weight in milligrams, crown to rump length in millimeters, and placental weight in milligrams (y-axis), respectively, across exposure to filtered air and four gestational exposure periods (x-axis).
Figure 3. Exposure of pregnant mice to CAPs during different exposure periods (experiment 3) is associated with decreased body weight (A), decreased CRL (B), and altered placental weight (C) on GD17.5. The results from analysis of variance (ANOVA) showed significant differences (p<0.05) among the groups for each endpoint which was followed by Fisher’s Least Significant Difference (LSD) post hoc testing to determine differences compared with FA. Data are the means±standard error (SE) from n=5 dams from each CAPs exposure period or n=16 from the pooled FA control dams. Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); CRL, crown-to-rump length; FA, filtered air; GD, gestational day. *p<0.05 compared with FA dams based on post hoc testing.

Effects of Exposure of Pregnant Mice to CAPs on Newborn Body Weight and CRL

In the third experiment carried out in the summer of 2014, pregnant mice were exposed to CAPs during one of four gestational periods. CAPs exposure caused an exposure period–dependent decrease (p=0.0003) in gestational duration. As shown in Figure 4A, no change in gestational duration was observed for mice born to mothers exposed only before implantation (period 1). In contrast, offspring from dams exposed to CAPs during either organogenesis (period 2) or growth (period 3) or throughout gestation (period 4) demonstrated reduced gestational duration of 0.4, 0.5, or 0.4 d, respectively. Birth weights were significantly (p=0.0003) reduced by 10.3%, 9.8%, and 10.3% (compared with controls) following maternal exposure to CAPs during periods 1, 2, and 4, respectively, whereas exposure to CAPs only during the fetal growth period had no effect on birth weight (Figure 4B).

Bar graphs A, B, C, and D with confidence intervals plotting day of birth in gestational day, mean birth weight in milligrams, mean birth length in millimeters, and mean weight/length ratio in milligrams per millimeter (y-axis), respectively, across exposure to filtered air and four gestational exposure periods (x-axis).
Figure 4. Maternal exposure to inhaled CAPs during different periods of pregnancy in experiment 3 as described in “Methods” are associated with PTB (A), LBW (B), decreased CRL (C) and decreased SGA (D). The results from analysis of variance (ANOVA) showed significant differences (p<0.05) among the groups for each end point; ANOVA was followed by Fisher’s Least Significant Difference (LSD) post hoc testing to determine differences compared with FA. Data are the means±standard error (SE) from n=8–11 dams for CAPs-exposed mice during periods 1 – 4. Because no differences were observed among the four periods for FA control values, the values were pooled (n=26). Note: CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); CRL, crown-to-rump length; FA, filtered air; LBW, low birth weight; PTB, preterm birth; SGA, size for gestational age. *p<0.05 compared with FA dams based on post hoc testing.

At birth, CRL was significantly (p<0.0001) decreased irrespective of the maternal exposure period in experiment 3 (Figure 4C). CRLs were reduced by 4.0%, 3.9%, 3.3%, and 4.6% for CAPs exposure periods 1–4, respectively. Decreased size-for-gestational age (SGA; weight/length) was observed in offspring born to dams exposed only during periods 1, 2, and 4; differences in SGA were reflected by decreases of 7.4%, 6.1%, and 6.0%, respectively, compared with FA-exposed mice (p=0.0054). Exposure of dams to CAPs only during period 3 had no effect on neonatal size for gestational age (Figure 4D).

Maternal CAPs Exposure Did Not Alter Postnatal Weight Gain in Neonatal Offspring (Experiment 3)

The effects of prenatal CAPs exposure on neonatal weight gain were dependent upon the method used to calculate the outcome. When weight gain was calculated as increase in body weight over time relative to birth weight, no differences (p>0.05) in growth rates were observed compared with the FA control (Figure 5A). Furthermore, calculation of day-to-day percentage body weight gain also revealed no clear CAPs exposure–related effects (p>0.05) (Figure 5B).

Line graphs A and B with confidence interval plotting body weight gain and weight gain (y-axis), respectively, across days postpartum (x-axis) exposed to filtered air and four gestational exposure periods.
Figure 5. Exposure of pregnant mice does not affect growth rates of offspring (experiment 3). Neonatal body weight gain was computed as a percentage over birth weight (A) or daily body weight gain (percent day-to-day gain) (B). Analysis of percent weight gain compared to birth weight (A) showed no significant differences by ANOVA (p>0.05) for the interaction of treatment and time. Comparison of weight gain day-to-day (B) also revealed no significant differences among the groups when data were analyzed by day postpartum. Data are means±SE from 8–11 dams for each CAPs exposure Period and 26 dams for the pooled FA controls.

Effects of CAPs Exposure on Body Length and AGD on PND10 and PND21 (Experiment 3)

On PND10, male offspring born to dams that were exposed to CAPs only prior to implantation (period 1) displayed a 2.9% decrease (p=0.0138) in CRL compared with time-matched FA-exposed dams (Figure 6A). In contrast, CRL was significantly (p=0.0263) increased by 1.8% at PND21 in males born to dams exposed to CAPs during mid- to late pregnancy (i.e., period 3) (Figure 6B).

Bar graphs A and B with confidence intervals plotting body length in millimeters ranging from 36 to 44 (in 6A) and from 46 to 58 (in 6B), (y-axis) across exposure to filtered air and four gestational exposure periods (x-axis). Bar graphs C and D with confidence intervals plotting anogenital distance in millimeters ranging from 1 to 4 (in 4C) and 7.5 to 10 (in 4D) (y-axis) across exposure to filtered air and four gestational exposure periods (x-axis).
Figure 6. Exposure of pregnant mice to CAPs during different pregnancy periods results in alterations in CRL and AGD in male offspring on PND10 and PND21. CRLs of male offspring were measured on PND10 and PND21 (A, B), and AGDs were measured at these same time points (C, D). The results from analysis of variance (ANOVA) showed significant differences (p<0.05) among the groups for each end point; ANOVA was followed by Fisher’s Least Significant Difference (LSD) post hoc testing to determine specific differences among the groups. Data presented are the means±standard error (SE) from n=8−11 dams for each CAPs exposure period and n=26 dams for the pooled FA controls. Bars with different letters are significantly different from one another (p<0.05). Note: AGD, anogenital distance; CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); CRL, crown-to-rump length; FA, filtered air; PND, postnatal day.

Compared with the FA-exposed controls, offspring from mothers exposed to CAPs during early and mid-pregnancy (i.e., periods 1 and 2) had significantly (p=0.0088) shorter AGDs (10.8%) at PND10. The reduction in AGD for male offspring born to dams exposed to CAPs throughout pregnancy (period 4) was slightly less dramatic (8.8%), although the reduction remained significant (Figure 6C). The CAPs-induced reduction in AGD observed in male offspring at PND10 persisted (p=0.0063) until PND21, but only for those offspring whose mothers were exposed either before implantation or throughout gestation (periods 1 and 4), in which case AGDs were reduced by 5.4% and 4.3%, respectively (Figure 6D).

Similar to that observed for male offspring, maternal CAPs exposure caused significant (p=0.0403) decreases in female CRL. Exposure during period 1 decreased CRL in female offspring on PND10 by 2.4% (Figure 7A). However, by weaning on PND21, female CRL was indistinguishable (p=0.2519) from that of their sex-matched control offspring (Figure 7B). The differences in anogenital distance in female offspring in response to maternal CAPs exposure were more dramatic than those observed in their age-matched male counterparts because AGD was significantly (p=0.0001) reduced by exposure during all exposure periods (Figure 7C). However, AGDs reached control values by PND21, but only in female offspring exposed to CAPs during mid- and late pregnancy (periods 2 and 3); CAPs-induced changes in AGD in offspring exposed during early pregnancy (period 1) and throughout gestation (period 4) persisted (p=0.0490) over time (Figure 7D).

Bar graphs A and B with confidence intervals plotting body length in millimeters ranging from 36 to 44 (in 7A) and from 46 to 58 (in 7B), respectively, (y-axis) across exposure to filtered air and four gestational exposure periods (x-axis). Bar graphs C and D with confidence intervals plotting anogenital distance in millimeters ranging from 1 to 4 (in 7C) and 7.5 to 10 (in 7D) (y-axis) across exposure to filtered air and four gestational exposure periods (x-axis).
Figure 7. Exposure of pregnant mice to CAPs during different pregnancy periods results in alterations in CRL and AGD in female offspring on PND10 and PND21. CRLs of female offspring were measured on PND10 and PND21 (A, B), and AGDs were measured at these same time points (C, D). The results from analysis of variance (ANOVA) showed significant differences (p<0.05) among the groups for each endpoint; ANOVA was followed by Fisher’s Least Significant Difference (LSD) post hoc testing to determine specific differences among the groups. Data presented are the means±standard error (SE) from n=8−11 dams for each CAPs exposure period and n=26 dams for the pooled FA controls. Bars with different letters are significantly different from one another (p<0.05). Note: AGD, anogenital distance; CAPs, concentrated ambient PM2.5 (fine-sized particulate matter); CRL, crown-to-rump length; FA, filtered air; PND, postnatal day.

Discussion

There were two main goals of these studies: a) Provide experimental evidence to support the human epidemiologic literature linking both PTB and LBW to inhalation exposure of PM2.5 during pregnancy at concentrations relevant to many urban centers; b) determine whether CAPs-induced PTB, LBW, or both were linked to exposure during a specific gestational period. The 24-h National Ambient Air Quality Standard (NAAQS) for PM2.5 concentration established in 2012 by the U.S. Environmental Protection Agency (EPA) is 35 μg PM2.5/m3 (U.S. EPA 2012, 2013). Although the time-weighted average (TWA) CAPs concentrations used in some of these experiments exceeded the U.S. EPA standard (the concentration in experiment 1 was 41 μg/m3 and that in experiment 3 ranged from 42.8–48.2 μg/m3 over the designated periods), the CAPs levels are nevertheless relevant to many U.S. and global cities that often exceed the NAAQS. In 2006, >200 U.S. counties were surveyed, and of these, 53 had 24-h PM2.5 levels that exceeded the standard (Yip et al. 2011). Many cities throughout the world also have documented levels of PM2.5 far exceeding the U.S EPA standard. For example, the daily average PM2.5 concentration for Beijing, China in 2013 was 90 μg PM2.5/m3 (Huang et al. 2014), and >10 other Chinese cities registered even higher concentrations.

In addition to respiratory and cardiovascular health concerns associated with exposure to elevated PM2.5 levels, epidemiologic data demonstrate an association between exposure to ambient PM2.5 and obstetric consequences including PTB and LBW (Lewandowski et al. 2013; Li et al. 2014). Given the numbers of women of reproductive age worldwide who are exposed daily to elevated PM2.5 levels, studies such as these are critical for informing health policy and for better understanding the mechanisms behind these comorbidities.

The gestational time frames selected for PM2.5 exposures in these studies were based on recommendations by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) testing guidelines (http://www.ich.org) for predicting reproductive/developmental toxicity in animals. These same time points are highly translatable to humans and represent times during human pregnancy when the developing offspring is most vulnerable to toxic insult (Figure 8). Each specific gestational period of mouse development/growth selected for study represents a critical time period during pregnancy, including a) fertilization and implantation (period 1); b) placental development/vascularization/nutrient transport and embryonic organogenesis (period 2); and (c) placental maturation and rapid fetal growth (period 3). A fourth gestational exposure period that covered all three of the abovementioned periods was also included in experiment 3. In the studies here, period 1 (i.e., GD0.5–GD5.5) corresponds to GD0–GD7–12 in humans, which is the time period during which preimplantation events occur. Period 2 in the mouse (GD6.5–GD14.5) encompasses postimplantation events, including formation and maturation of the placenta and the completion of organogenesis, that occur in humans through the 12th–14th week of gestation, defining the first trimester. The second and third trimesters of human pregnancy align with period 3 (GD14.5 to parturition range) in mice as rapid fetal growth occurs, and the lungs become fully functional.

Tabular representation of mice gestational days, events, and human gestational days for the first, second, and third trimester.
Figure 8. Alignment of mouse reproductive timeline to that of humans from the beginning of pregnancy through parturition. This table is based on Theiller stages of mouse development (Theiler 1989) and Carnegie stages of human development (O’Rahilly and Müller 2010).

Normal gestation in humans is 38–40 wk, and birth is considered preterm if it occurs before 37 wk. For the particular mouse strain used here, normal term is approximately 19–19.5 days. Shortening the mouse gestational term by 0.5 d, as seen following maternal exposure to PM2.5 during the entire gestational period, corresponds to an approximately 1-wk decrease in humans, thus placing them into the preterm category.

The magnitude of decreases in pregnancy duration and birth weight observed in the summer exposures (experiments 1 and 3) compared with that observed in the winter exposure (experiment 2) suggests that particle concentration and relative compositions are important. In this study, the metal composition (both absolute and relative) and the particle mass differed depending on the season in which the mice were exposed. Schwab et al. (2004) reported that PM2.5 concentrations from various regions in the state of New York vary throughout the year. Early studies by Thurston et al. (1994) also reported that metal components of PM2.5 can show seasonal fluctuations between winter and summer.

Many of the PM2.5-associated metals identified in the present study have been implicated as risk factors for LBW in the northeastern and mid-Atlantic regions of the United States. Positive associations have been shown with each interquartile increase of Al, Ca, Ni, Ti, and Zn, with risk ratios ranging from 3.0 for Ca {46 ng/m3 [95% confidence interval (CI): 1.36–4.3]} to 5.7 for nickel (Ni) [6 μg/m3 (95% CI 2.7–8.8)] (Ebisu and Bell 2012). In full-term infants, LBW was associated with maternal exposure to PM2.5 with the following average levels of metallic components: vanadium (V) (4.3 ng/m3), S (0.83 μg/m3), Fe (0.16 μg/m3), Ti (10 ng/m3), manganese (Mn) (3.3 ng/m3), Br (4.4 ng/m3), Zn (15 ng/m3), and Cu (9 ng/m3) (Basu et al. 2014). In our toxicological study, the aforementioned metal concentrations associated with epidemiological studies were exceeded in all experiments (with the exception of V in experiment 2), suggesting that PM-associated metals could be playing a role in the observed toxicity. However, further research is necessary to better understand the role of PM-associated metals in causing LBW in the present scenario.

In this study, PTB was associated not only with CAPs exposure throughout pregnancy but also with exposure during particular gestational periods. When CAPs exposure occurred only prior to blastocyst implantation, no effects were observed on gestational duration compared with the control. This result supports epidemiologic findings suggesting that PM2.5-induced PTB is associated with exposure occurring later in pregnancy (i.e., during the second or third trimester) (Ha et al. 2014). However, these findings are in contrast to those of Symanski et al. (2014) who demonstrated that exposure to a 10 μg/m3 increase in PM2.5 concentration during the first 4 wk of pregnancy, the time of human blastocyst implantation, was associated with a 73% increased risk for PTB. A study by Rapazzo et al. (2014) revealed that risk for PTB was most closely associated with exposure to PM2.5 during the fourth week of gestation (i.e., just after implantation, corresponding to the early part of period 2 in the present study); exposure to PM2.5 during the week of birth and during the last two weeks before birth in that study also resulted in early delivery. The authors concluded that exposures to PM2.5 around the time of implantation or near birth were the highest risk for PTB.

In the present study, maternal exposure to PM2.5 during any gestational period other than the fetal growth phase (period 3) resulted in LBW. Harris et al. (2014) correlated PM2.5 concentrations with LBW and found that U.S. states with the highest PM2.5 concentrations such as New York (average PM2.5 concentration of 13 μg/m3) also had the highest rates of LBW (2.6%). In contrast, Utah and Minnesota (average PM2.5 concentration of 9 μg PM2.5/m3) had the lowest rates of LBW (2.1% and 1.9%, respectively). The same study also showed that in New York State, LBW was linked to PM2.5 exposure levels during each of the three trimesters as well as to full-term exposure. For all states examined, the highest risk for LBW was associated with exposure during the first trimester [odds ratio (OR)=1.018 (95% CI 1.013, 1.022)] and full-term exposure [OR=1.030 (95% CI 1.022, 1.037)], with exposure during the second and third trimesters resulting in a lower risk. Our experimental animal data support the human epidemiologic studies demonstrating that maternal exposure to high PM2.5 levels between implantation and the end of the second trimester in humans is the most sensitive time frame for suppressing birth weight.

Following implantation, placentation is the next major milestone during fetal development. In the present study, placental weight was decreased significantly with maternal exposure to PM2.5 during the gestational window of rapid fetal growth (i.e., period 3). In contrast, exposure to PM2.5 throughout gestation increased placental weight compared with FA controls. To our knowledge, these findings are the first to demonstrate that PM2.5-induced changes in placental weight are based upon the timing of exposure in an animal model. In a study by Veras et al. (2008), whole-body exposure of mice to PM2.5 (24-h average level of 27.5 μg/m3) from traffic in São Paulo, Brazil, before breeding (exposed 24 h/d from 20 d of age to 6 wk of age) or during pregnancy alone decreased fetal weight (∼23%) on GD18. In that study, decreased fetal weight was associated with reduced vasculature volumes, luminal diameters, and surface areas of the blood spaces on the maternal face of the placenta. The authors suggested that exposure to traffic-related PM2.5, either before conception or immediately after breeding, caused restrictions in maternal blood circulation through the placenta, which led to reduced birth weights. Increased fetal capillary surface area observed in that study was considered by the authors to be a result of the release of fetal “factors” that enhanced blood circulation through the placenta or enlargement of the surface areas available for nutrient exchange, or a combination of the two, to compensate for maternal vasoconstriction that may have resulted from PM2.5-induced inflammation (de Melo et al. 2015). Because the mouse placenta continues to grow throughout fetal development, mechanisms similar to those described above may have been responsible for the placental changes observed in our study. It is possible that maternal blood circulation to the placenta experienced greater restriction in mice that began their exposure in period 3 owing to increased amounts of maternal systemic inflammatory mediators.

In contrast to our observations from period 3, placentas from dams exposed to PM2.5 throughout pregnancy (i.e., period 4) were heavier than those recovered from their FA control counterparts on GD17.5. As suggested by Veras et al. (2008), increased placental weight could have resulted from signals received from the fetus leading to an increased size of the nutrient exchange domains and an increased perfusion rate from the dam’s circulation as a mechanism to prevent intrauterine growth restriction.

Alternatively, many PM2.5 components contribute to oxidative stress that may have an impact on the function of the placenta. A recent study by Saenen et al. (2016) showed that exposure to a 7.5 μg/m3 increase in PM2.5 concentration during the second trimester in human pregnancies was associated with a 1.4% decrease in placental leptin gene methylation. Decreased methylation generally results in increased transcription of the methylated gene. Because leptin is a hormone involved in the proliferation and survival of placental trophoblast cells (Maymó et al. 2011), it may play a role in the alterations in placental weight observed in periods 3 and 4 (GD14.5–16.5 and GD0.5–16.5, respectively) in the present study. Additional studies are required to determine the potential role of leptin in this model.

Given that maternal exposure to PM2.5 resulted in both PTB and LBW in this study, the observed subsequent lack of effects on postnatal growth rate was surprising. Human studies have shown that small-for-gestational-age size at birth is associated with increased risk of cardiovascular disease and type 2 diabetes in adulthood (Barker et al. 1989; Barker et al. 1990; Barker et al. 1993a, b; Phipps et al. 1993). In utero exposure to PM2.5, which has independently been shown to predispose children to these same later-life outcomes (Johnson and Breslau 2000; Lewandowski et al. 2013; Li et al. 2014), could, in combination with small-for-gestational-age size, pose a synergistic increase in risk for these same obstetric consequences. A recent study by Janssen et al. (2016) showed a link between human exposure to an 8.2 μg/m3 increase in PM2.5 exposure levels in the third trimester and decreased thyroid stimulating hormone (TSH) levels and free thyroxine to triiodothyronine ratio (T4/T3) in cord blood. The decrease in free T4 in cord blood was linked to a 56-g decrease in average birth weight. This finding differs from those in the present study, where exposure that occurred before the equivalent of the third trimester was associated with LBW. However, GD17.5 fetuses from dams exposed either throughout gestation or only during the third trimester analog were significantly lighter. Additional studies are warranted to determine the possible role for thyroid hormones in LBW due to PM2.5 exposure.

In the present study, AGD in male offspring was reduced on PND10 and PND21 following maternal exposure to PM2.5 throughout and early during gestation. In males, AGD is an indirect measure of total androgen exposure (both endogenous and exogenous) during fetal development; typically, the greater the exposure in utero to androgens, the greater the AGD (McIntyre et al. 2001). Shortening of the AGD, as was observed in this study following exposure during specific periods of development, has been used as an indicator of developmental exposure to antiandrogens such as phthalates (Foster 2006; Gray et al. 2006; Swan 2008). In humans, a shorter AGD in males has been linked to reductions in semen quality as defined by alterations in sperm morphology, motility, and total sperm per ejaculate (Mendiola et al. 2011; Swan et al. 2005). Interestingly, it has been observed in this laboratory (J.L.B. et al. 2013, unpublished work) that sperm numbers/motility were decreased in adult offspring in response to CAPs exposure throughout gestation at similar inhaled concentrations.

Increased AGD in females is also regulated by the secretion of androgens in utero (Wolf et al. 2002). In the present study, maternal exposure to CAPs early in and throughout pregnancy resulted in decreased AGD in females on PND10 that persisted through PND21. Although the underlying mechanism (or mechanisms) for such a finding is as yet unknown, AGD has been positively associated with the number of recruited ovarian follicles in women (Mendiola et al. 2012). In rat litters, female siblings with longer AGDs had greater pituitary responsiveness to gonadotropin releasing hormone than their sisters with shorter AGDs (Faber et al. 1993). The results from those studies suggest that changes associated with altered AGD in females brought about by in utero exposure to PM2.5 may result in reduced fertility in the female offspring.

The present study has several limitations. In our study, the mice were exposed to higher PM2.5 concentrations than would ordinarily be observed in human epidemiology studies, and it is not clear if the effects seen with short-term high-concentration exposures emulate those seen in constant, long-term exposures that could be experienced under conditions of pregnancy. However, when concentration is calculated based on the breathing rates of both species, the dose to the lung was only ∼5 times greater in the mouse than in pregnant women. Additionally, PM2.5 composition has been shown to vary from place to place with possible temporal variations within a single location. This study attempted to account for seasonal variation by performing experiments only in the summer and winter and to limit temporal effects by exposing the mice during all exposure periods at the same time so there would not be a bias in the event of a particularly high ambient air pollution day. Thus, although the confines of the study are recognized, the results of this animal study represent an important step forward in understanding the effects of maternal exposure to particulate air pollution across the gestational time span.

Conclusions

The study described herein presents biological feasibility for the epidemiologic studies demonstrating the adverse effects of inhaled particulate matter from air pollution on pregnancy-related outcomes. Moreover, these studies demonstrate the usefulness of a pregnant mouse model for studying the developmental consequences of exposure to PM2.5. Such a model eliminates many confounding variables that often cloud human studies; it also provides the opportunity to confine exposures to a particular gestational time period, making data interpretation easier. The results of this study also contribute to a better understanding of how and to what extent exposure periods play a role in predicting gestational outcomes.

Based on the findings here, exposure to PM2.5 before implantation is not related to PTB, whereas maternal exposure postimplantation appears to pose a credible risk. In contrast, LBW appears to be linked with PM2.5 exposure that occurs any time before the completion of embryogenesis. These animal studies suggest that exposure to high levels of inhaled PM2.5 during pregnancy poses a risk for obstetric consequences during most gestational periods. Although it is difficult to avoid exposure to air pollution during pregnancy, certain interventions including the use of home air filters and air conditioners could help mitigate the risk for adverse pregnancy outcomes.

Acknowledgments

The authors wish to acknowledge the contributions of C. Hoffman-Budde for assistance with the animal exposures and necropsies and M. Zhong for assistance with the X-ray fluorescence spectroscopy (XRF) analysis. Funding was provided by the March of Dimes (21-F12-13) and the New York University National Institutes of Health/National Institute of Environmental Health Sciences (NYU NIEHS) Center (ES000260).

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Ambient Coarse Particulate Matter and the Right Ventricle: The Multi-Ethnic Study of Atherosclerosis

Author Affiliations open

1Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, USA

2Department of Medicine, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA

3Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA

4Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, USA

5Department of Occupational and Environmental Health, University of Iowa, Iowa City, Iowa, USA

6Department of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA

7Radiology and Imaging Sciences, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland, USA

8Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

9Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, USA

10Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA

11Department of Civil and Environmental Engineering, University of Washington College of Engineering, Seattle, Washington, USA

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  • Background:
    Coarse particulate matter (PM10–2.5) is primarily mechanically generated and includes crustal material, brake and tire wear, and biological particles. PM10–2.5 is associated with pulmonary disease, which can lead to right ventricular (RV) dysfunction. Although RV characteristics have been associated with combustion-related pollutants, relationships with PM10–2.5 remain unknown.
    Objectives:
    To quantify cross-sectional associations between RV dysfunction and PM10–2.5 mass and components among older adults and susceptible populations.
    Methods:
    We used baseline cardiac magnetic resonance images from 1,490 participants (45–84 y old) from the Multi-Ethnic Study of Atherosclerosis and assigned 5-y residential concentrations of PM10–2.5 mass, copper, zinc, phosphorus, silicon, and endotoxin, using land-use regression models. We quantified associations with RV mass, end-diastolic volume, and ejection fraction after control for risk factors and copollutants using linear regression. We further examined personal susceptibility.
    Results:
    We found positive associations of RV mass and, to a lesser extent, end diastolic volume with PM10–2.5 mass among susceptible populations including smokers and persons with emphysema. After adjustment for copollutants, an interquartile range increase in PM10–2.5 mass (2.2 μg/m3) was associated with 0.5 g (95% CI: 0.0, 1.0), 0.9 g (95% CI: 0.1, 1.7), and 1.4 g (95% CI: 0.4, 2.5) larger RV mass among former smokers, current smokers, and persons with emphysema, respectively. No associations were found with healthy individuals or with ejection fraction.
    Conclusions:
    Alterations to RV structure may represent a mechanism by which long-term PM10–2.5 exposure increases risks for adverse respiratory and cardiovascular outcomes, especially among certain susceptible populations. https://doi.org/10.1289/EHP658
  • Received: 14 June 2016
    Revised: 24 February 2017
    Accepted: 16 March 2017
    Published: 27 July 2017

    Address correspondence to S. D. Adar, University of Michigan School of Public Health, 1415 Washington Heights, SPH II-5539, Ann Arbor, MI 48109 USA. Telephone: (734) 615-9207; Email: sadar@umich.edu

    Supplemental Material is available online (https://doi.org/10.1289/EHP658).

    S.M.K. receives nonfinancial support from the American College of Clinical Pharmacology and the American Thoracic Society; personal fees from the European Respiratory Journal; and grants from Actelion, Gilead, GeNO, and Bayer that are unrelated to the submitted work.

    All other authors declare they have no actual or potential competing financial interests.

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Introduction

Air pollution is a well-established risk factor for adverse respiratory outcomes, including chronic lung diseases (Andersen et al. 2011; Karakatsani et al. 2003; Lindgren et al. 2009; Schikowski et al. 2005; Sunyer 2001), hospitalizations (Chen et al. 2005) and death (Dockery et al. 1993; Pope et al. 2002). Most recently, it has been estimated that for 2013 worldwide ambient particulate matter (PM) pollution accounts for nearly 170,000 deaths and nearly 4 million disability-adjusted life years (DALYs) due to chronic respiratory disease (Forouzanfar et al. 2015; IHME 2016).

A common sequela of chronic lung disease is the development of pulmonary hypertension and impairments to the heart, including right ventricular (RV) dysfunction (Freixa et al. 2013). The right ventricle pumps blood through the lungs to allow for its oxygenation. Then the oxygen-rich blood flows to the left ventricle for subsequent distribution to all tissues of the human body. Changes in RV structure and function can therefore result in many similar clinical sequelae of left ventricular (LV) changes, including dyspnea, exercise intolerance, lower-extremity edema, and (at advanced stages) severe heart failure (Voelkel et al. 2006). Although the left ventricle is vulnerable to increased pressures during ejection due to systemic hypertension or valvular disease, reduced blood supply, and hypoxia, the right ventricle may be similarly affected by changes in lung function [e.g., chronic obstructive pulmonary disorder (COPD)], LV function, and hypoxia (e.g., sleep disordered breathing). The RV has been thought to respond to this increased load through structural changes such as hypertrophy (i.e., thickening of the ventricle leading to increased mass), chamber dilation leading to greater end-diastolic volume, and lowered pumping efficiency (i.e., reduced ejection fraction) (Polak et al. 1983; Shah et al. 1986). Although these three manifestations of RV dysfunction are most likely in severe stages of lung disease, the right ventricle can also be affected early in lung disease (Hilde et al. 2013). RV dysfunction has public health importance because it has been linked to poor outcomes among persons with and without preexisting disease, such as COPD and cardiovascular disease (Burgess et al. 2002; France et al. 1988; Kawut et al. 2012).

Long-term exposures to air pollution are believed to affect the same biological mechanisms that lead to COPD and cardiovascular disease. There is evidence that air pollution is associated with greater inflammation (Adar et al. 2015b) and reduced vessel compliance (Brook et al. 2014; Krishnan et al. 2013; Mills et al. 2005); such evidence suggests a plausible link to RV function. In fact, two studies from the Multi-Ethnic Study of Atherosclerosis (MESA) recently linked long-term exposures to two combustion-related air pollutants: nitrogen dioxide (NO2) (Leary et al. 2014) and fine PM (aerodynamic diameter <2.5 μm, PM2.5) (Aaron et al. 2016) to greater RV hypertrophy and lower function. Although PM in the coarse fraction (aerodynamic diameter between 2.5 and 10 μm, PM10–2.5) has also been associated with adverse respiratory end points (Adar et al. 2014; Brunekreef and Forsberg 2005), no study has investigated associations between PM10–2.5 and RV characteristics. Understanding the health impacts of PM10–2.5 independent of other pollutants, including PM10–2.5 and NO2 has importance, given that the U.S. Environmental Protection Agency (EPA) is interested in regulating PM10–2.5 levels but has struggled with insufficient data in the general population as well as among susceptible individuals (U.S. EPA 2009). Because PM10–2.5 is generated by very different diverse processes, ranging from crustal material to brake and tire wear, a lack of information on associations between health and indicators of different PM10–2.5 sources represents another important gap in the literature.

To expand the literature on the health implications of PM10–2.5 and to better understand environmental risk factors of RV dysfunction, we aimed to quantify cross-sectional associations between PM10–2.5 and measures of RV function among older adults and susceptible subpopulations. We approached this goal using individual-level long-term estimates of PM10–2.5 mass and selected source-specific components with multiple measures of RV structure (mass, end-diastolic volume) and function (ejection fraction) in participants of MESA. Some of these results have been previously reported in the form of an abstract (Adar et al. 2015a).

Methods

Study Population

Initiated in 2000, MESA is a multicenter, prospective study examining the progression of subclinical cardiovascular disease among an ethnically diverse population of 6,814 subjects (45–84 y old) who were free of known cardiovascular disease at baseline (Bild et al. 2002). In this analysis, we restricted reporting to participants from Chicago, Illinois, St. Paul, Minnesota, and Winston-Salem, North Carolina, who were part of the MESA Coarse ancillary study (n=3,295). The MESA Coarse study conducted intensive sampling of PM10–2.5 concentrations in three of the MESA sites chosen to reflect PM10–2.5 variability. We further restricted to those who had cardiac magnetic resonance images (MRI) interpreted for RV morphology as part of the MESA RV ancillary study (n=1,851). After excluding those with missing exposures and covariates, our final sample was 1,490 persons (Figure S1).

All protocols described herein received approval from local and national institutional review boards. Participants also provided informed consent.

Right Ventricle Characteristics

The MESA RV study obtained measures of RV function using cardiac MRIs performed at the baseline exam (Natori et al. 2006). These measures include RV mass at end-diastole, end-diastolic volume, and ejection fraction (Bluemke et al. 2008; Chahal et al. 2010). These measures were estimated by two independent analysts using QMASS software (version 4.2; Medis), is described elsewhere (Chahal et al. 2010). Based on random, blinded rereads from approximately 230 scans, the inter-reader intraclass correlation coefficients were 0.89, 0.96, and 0.80 for RV mass, end-diastolic volume, and ejection fraction, respectively (Kawut et al. 2011).

Exposure Assessment

We used site-specific land-use regression spatial prediction models derived from project-specific PM10–2.5 measurements and geographic data to predict concentrations of PM10–2.5 at subjects’ residences. Details of these models have been previously published (Zhang et al. 2014). Briefly, we conducted two spatially intensive 2-wk monitoring campaigns of integrative PM10 and PM2.5 samples using paired Harvard Personal Exposure Monitors (HPEMs) in each of three MESA Coarse sites. In each city, approximately 60 locations were targeted to cover the greatest geographic space. Additionally, the locations were selected to capture the variability of hypothesized characteristics associated with PM10–2.5 mass and components (e.g., vegetation, distance to roads). All samples were weighed in a temperature- and relative humidity-controlled chamber, analyzed for elements by X-ray fluorescence spectroscopy, and total PM10–2.5 mass and that of chemical components were calculated by difference (U.S. EPA 2009). The specific components of interest were copper, zinc, phosphorus, and silicon as consistent indicators for motor vehicle brake wear, tire wear, fertilized soil/agriculture, and crustal material across all study sites, respectively (Sturtz et al. 2014). We also examined a fifth component of PM10–2.5, endotoxin, a major component of the outer membrane of Gram-negative bacteria. Endotoxin was chosen due to its capability to induce inflammation and modulate immune responses (Hadina et al. 2008) and its association with airway disease (Schwartz et al. 1995). We separately derived spatial prediction models for PM10–2.5 mass and each component using many geographic variables, including land use, population density, vegetation, impervious surface, roadways, railways, and airports, as well as spatial correlation. The cross-validated (CV) R2 for the site-specific models of PM10–2.5 and chemical species ranged from 0.3 to 0.9. As described elsewhere (Zhang et al 2014), the models performed best for copper (CV R2, 0.5–0.9) and generally worse for endotoxin (CV R2=0.3–0.4). For our statistical modeling, we selected 5–y average concentrations weighted according to subjects’ residential history preceding subjects’ MRI.

Exposures to PM2.5 and NO2 were also estimated for each participant using spatiotemporal models derived from project-specific measurements, land-use characteristics, as well as regulatory monitoring data in the MESA Air study (Gill et al. 2011; Szpiro et al. 2010).

Covariates

All covariates, with the exception of airflow limitation, were assessed at baseline. These included sociodemographic and behavior information obtained via interview, and anthropometric measurements, left ventricle function, and laboratory data from the clinical exam. Comorbidities of hypertension and diabetes were also defined based on blood pressure or glucose measurements, respectively, self-reported medication use, and doctor diagnosis (Genuth et al. 2003; JNC 1997). Through the MESA Lung ancillary study, we had data on percent emphysema from computed tomography (CT) scans (Hoffman et al. 2009) and spirometry (Hankinson et al. 2010). The MESA Neighborhood Study developed a neighborhood socioeconomic scale (NSES) for each participant based on a principal components analysis of 2000 census tract data (U.S. Census Bureau 2002), including median household income, percent of persons in tract with at least a high school degree and median home value (Hajat et al. 2013).

Statistical Analysis

Multivariable linear regression models were used to quantify adjusted cross-sectional associations between PM10–2.5 and continuous measures of our RV outcomes. All models were adjusted for age, race/ethnicity (White, Chinese, Black, and Hispanic), sex, education (less than high school, high school/some college, college or more), NSES, height, weight, cigarette smoking history (never, former, current), pack-years of smoking (0 pack-y, 0<pack-y≤10, 10<pack-y≤20, greater than 20), second-hand smoke exposure, hypertension (JNC 1997), diabetes (according to the 2003 American Diabetes Association Fasting Criteria Algorithm: normal, impaired fasting glucose, untreated diabetes, treated diabetes), cholesterol, study site, and an interaction of study site with NSES. Age, height, weight, NSES, and cholesterol were modeled as continuous; all other variables were modeled as categorical. In secondary models, we examined the linearity of these associations using splines and the robustness of our results to adjustment for PM2.5 and NO2 in two pollutant models. In secondary models of the chemical species of PM10–2.5 we also adjusted for total mass as a covariate using a constituent residual model (Mostofsky et al. 2012). We used interaction terms to assess effect modification by age, sex, race/ethnicity, smoking status, emphysema (defined as percent of emphysema-like lung based on CT scans that were greater than the upper limit of normal (Hoffman et al. 2014)), and airflow limitation (FEV1/FVC<0.7). All reported estimates were scaled to the interquartile range (IQR) for each pollutant/species: PM10–2.5 (2.2 μg/m3), copper (4 ng/m3), zinc (11 ng/m3), phosphorous (6 ng/m3), silicon (0.13 μg/m3), endotoxin (0.08 EU/m3), NO2 (7.0 ppb), and PM2.5 (3.8 μg/m3).

In sensitivity analyses, we restricted our analyses to participants who were residentially stable (lived at their current residence for 10 y or longer) and examined additional control for hypertension, diabetes, and cholesterol, as well as measures of LV function and lung disease as potential mediators.

The data analysis for this paper was generated using SAS (version 9.4; SAS Institute Inc.) and R (version 3.3.2; R Development Core Team).

Results

The mean age of the sample at baseline was 61 y; nearly 9% had physician-diagnosed asthma, and 7% had emphysema based on their CT scans (Table 1). Although participants in this sample were more likely to be Chinese, less likely to be black, and more likely to have a graduate degree than the full MESA Coarse cohort, these individuals were otherwise quite similar. Importantly, they did not differ with respect to their air pollution levels for all pollutants except zinc, which was approximately 10% lower in the study sample (Table S1).

Table 1. Descriptive characteristics of the MESA Coarse participants at the baseline examination (2000–2002), by study site.
Characteristics Total Winston-Salem St. Paul Chicago
n 1490 457 536 497
61.1±10.0 62.4±9.6 59.4±10.0 61.9±10.1
Age (y, %)
 45–54 477 (32%) 124 (27%) 196 (37%) 157 (32%)
 55–64 437 (29%) 128 (28%) 173 (32%) 136 (27%)
 65–74 404 (27%) 150 (33%) 119 (22%) 135 (27%)
 75–84 172 (12%) 55 (12%) 48 (9%) 69 (14%)
Female 795 (53%) 253 (55%) 278 (52%) 264 (53%)
Race/ethnicity
 White 828 (56%) 277 (61%) 327 (61%) 224 (45%)
 Chinese 158 (11%) 0 (0%) 0 (0%) 158 (32%)
 Black 293 (20%) 178 (39%) 0 (0%) 115 (23%)
 Hispanic 211 (14%) 2 (0%) 209 (39%) 0 (0%)
Education
 <High school 400 (27%) 128 (28%) 205 (38%) 67 (13%)
 High school/some college 440 (30%) 135 (30%) 191 (36%) 114 (23%)
 ≥College 650 (44%) 194 (42%) 140 (26%) 316 (64%)
Smoking status
 Never 744 (50%) 225 (49%) 255 (48%) 264 (53%)
 Former 556 (37%) 171 (37%) 200 (37%) 185 (37%)
 Current 190 (13%) 61 (13%) 81 (15%) 48 (10%)
≥10 y in neighborhood 1033 (69%) 281 (61%) 381 (71%) 371 (75%)
Health
 BMI (kg/m2) 27.7±5.0 28.2±5.0 28.9±4.9 26.0±4.7
 Cholesterol (mg/dl) 195.3±36.0 189.1±34.7 201.5±38.9 194.2±32.7
 Hypertension 584 (39%) 232 (51%) 167 (31%) 185 (37%)
 Diabetic 2.3 (1.0) 1.4 (1.0) 2.0 (1.0) 3.8 (1.0)
 % Emphysema (−950 HU)a 81% (0) 37% (0) 71% (0) 134% (0)
 Airflow limitationb 220 (22%) 66 (26%) 57 (19%) 97 (22%)
 Emphysema 97 (7%) 15 (3%) 46 (9%) 36 (7%)
 Asthma 130 (9%) 36 (8%) 51 (10%) 43 (9%)
 Left Ventricular end-diastolic Mass (g) 147.5±39.0 145.7±38.5 154.6±38.5 141.4±38.8
RV Outcomes
 RV mass (g) 21.6±4.7 21.2±4.3 22.7±4.9 20.8±4.5
 RV ejection fraction (%) 70.3±6.7 69.1±7.0 70.2±6.3 71.4±6.5
 RV end-diastolic volume (mL) 127.6±33.2 122.9±30.5 135.1±35.0 123.9±32.2
Pollutants
  PM10–2.5 (μg/m3) 4.9±1.6 3.7±1.2 5.3±1.8 5.5±1.2
  Copper (ng/m3) 4.4±2.5 2.5±0.8 3.5±0.8 7.1±2.4
  Zinc (ng/m3) 9.0±9.6 3.1±1.6 5.1±1.2 18.5±11.5
  Silicon (μg/m3) 0.4±0.1 0.4±0.0 0.5±0.1 0.4±0.1
  Phosphorous (ng/m3) 15.9±3.6 19.7±2.2 12.9±1.9 15.6±2.7
  Endotoxin (EU/m3) 0.1±0.1 0.0±0.0 0.1±0.0 0.0±0.1
  PM2.5 (μg/m3) 14.6±2.1 15.5±0.9 12.3±1.4 16.1±1.4
  NO2 (ppb) 14.7±5.1 10.3±2.5 13.5±2.2 20.2±4.1
Note: Values given as n (%) or mean±standard deviation. BMI, body mass index; NO2, nitrogen dioxide; PM2.5, particulate matter <2:5 μm in diameter; PM10–2.5, particulate matter between 2.5 and 10 μm in diameter; RV, right ventricular.

aEmphysema is defined as the percent emphysema via computed tomography scan greater than the upper limit of normal.

aAirflow limitation is defined as an FEV1/FVC<0.7 and was available on only 974 participants.

Average PM10–2.5 mass concentrations were lowest for Winston-Salem (3.7 μg/m3) but similar in St. Paul (5.3 μg/m3) and Chicago (5.5 μg/m3). St. Paul had the largest intracity variation (standard deviation: 1.8 μg/m3 in St. Paul vs. 1.2 μg/m3 for Chicago and Winston-Salem). With respect to the chemical components, the highest average concentrations of the two traffic-related markers of copper and zinc were in Chicago, whereas Winston-Salem had the highest concentrations of phosphorus. Mean endotoxin levels were generally low (≤0.1 EU/m3) across all locations. In all locations, we observed modest to high correlations (0.46–0.89) between the traffic-related pollutants of copper, zinc, and NO2. In addition, PM2.5 and NO2 were also correlated (>0.6) in all locations. Although the other pollutants did not demonstrate consistent patterns across sites, there were notable (>0.6) correlations between most pollutants in Chicago (Table S2).

Among all participants, RV mass was positively associated with PM10–2.5 mass, copper, phosphorus, and silicon in single-pollutant models (Table 2). After controlling for PM2.5 and NO2, however, which were themselves associated with RV mass, the association with copper was eliminated and associations with PM10–2.5 mass, phosphorus, and silicon were blunted. Apart from copper, adjustment for PM10–2.5 mass did not strongly affect associations with any chemical components (Figure S2). Results were also robust to more and less control for potential intermediate factors such as hypertension, cholesterol, diabetes, emphysema, airflow limitation, and LV mass and function (Figure S3).

Table 2. Associations between PM10–2.5 mass and RV structure and function in single and multipollutant models.
Mass (g) Volume (mL) Ejection Fraction (%)
Model Difference 95% CI p–Value Difference 95% CI p–Value Difference 95% CI p–Value
PM10–2.5
 Single Pollutant Model 0.3 0.0, 0.5 0.06 0.4 −1.3, 2.2 0.63 −0.1 −0.6, 0.4 0.75
 + PM2.5 0.2 −0.1, 0.5 0.14 0.3 −1.5, 2.2 0.74 −0.1 −0.6, 0.4 0.76
 + NO2 0.2 −0.1, 0.5 0.22 0.4 −1.5, 2.3 0.68 −0.1 −0.6, 0.4 0.72
Cu
 Single Pollutant Model 0.3 −0.2, 0.8 0.20 0.6 −2.5, 3.6 0.71 0.1 −0.7, 1.0 0.75
 + PM2.5 0.0 −0.5, 0.5 0.93 0.0 −3.4, 3.3 0.99 0.5 −0.5, 1.4 0.32
 + NO2 −0.2 −0.8, 0.5 0.56 −0.1 −4.3, 4.1 0.96 0.4 −0.8, 1.6 0.56
Zn
 Single Pollutant Model 0.0 −0.3, 0.3 0.90 −0.6 −2.6, 1.3 0.51 −0.1 −0.6, 0.5 0.81
 + PM2.5 −0.2 −0.5, 0.1 0.16 −1.1 −3.2, 0.9 0.27 0.1 −0.5, 0.6 0.85
 + NO2 −0.3 −0.7, 0.0 0.09 −1.4 −3.6, 0.9 0.24 −0.1 −0.7, 0.6 0.81
P
 Single Pollutant Model 0.5 0.0, 1.0 0.03 0.5 −2.6, 3.6 0.75 −0.1 −0.9, 0.8 0.87
 + PM2.5 0.2 −0.3, 0.7 0.41 −0.4 −3.6, 2.9 0.83 0.0 −0.9, 1.0 0.93
 + NO2 0.3 −0.2, 0.8 0.25 0.0 −3.4, 3.4 0.99 −0.1 −1.0, 0.9 0.91
Si
 Single Pollutant Model 0.4 0.1, 0.7 0.01 0.8 −1.1, 2.8 0.41 −0.2 −0.7, 0.4 0.54
 + PM2.5 0.2 −0.2, 0.5 0.36 0.2 −2.0, 2.4 0.86 0.0 −0.6, 0.6 0.95
 + NO2 0.3 −0.1, 0.6 0.19 0.7 −1.8, 3.1 0.59 −0.2 −0.9, 0.4 0.49
Endotoxin
 Single Pollutant Model −0.1 −0.5, 0.2 0.49 −0.2 −2.4, 1.9 0.82 −0.1 −0.7, 0.5 0.67
 + PM2.5 0.1 −0.3, 0.4 0.64 0.1 −2.1, 2.4 0.91 −0.4 −1.0, 0.3 0.26
 + NO2 0.0 −0.4, 0.3 0.89 −0.1 −2.4, 2.1 0.90 −0.2 −0.8, 0.5 0.59
NO2
 Single Pollutant Model 0.5 0.1, 0.9 0.01 0.8 −1.8, 3.5 0.54 0.0 −0.8, 0.7 0.93
 + PM10–2.5 0.4 0.0, 0.9 0.06 0.6 −2.2, 3.5 0.66 0.0 −0.8, 0.8 0.96
 + PM2.5 0.2 −0.3, 0.8 0.38 0.3 −3.1, 3.8 0.84 0.3 −0.7, 1.3 0.53
PM2.5
 Single Pollutant Model 1.0 0.4, 1.6 0.001 1.8 −2.0, 5.6 0.36 −0.6 −1.7, 0.4 0.25
 + PM10–2.5 0.9 0.3, 1.5 0.003 1.7 −2.3, 5.6 0.41 −0.6 −1.7, 0.5 0.28
 + NO2 0.8 0.0, 1.5 0.043 1.5 −3.4, 6.4 0.56 −0.9 −2.3, 0.5 0.19
Note: All models adjusted for age, race, gender, height, weight, neighborhood socioeconomic scale (NSES), NSES, education, smoking status, pack-years, second-hand smoke exposure, hypertension, diabetes, total cholesterol, study site, and site by NSES interaction. Associations scaled to interquartile range (IQR) IQR of pollutant: PM10–2.5 (2.2 μg/m3), Cu (4 ng/m3), Zn (11 ng/m3), P (6 ng/m3), Si (0.13 μg/m3), endotoxin (0.08 EU/m3), PM2.5 (3.8 μg/m3), NO2 (7.0 ppb). CI, confidence interval; Cu, copper; NO2, nitrogen dioxide; P, phosphorous; PM2.5, particulate matter <2:5 μm in diameter; PM10–2.5, particulate matter between 2.5 and 10 μm in diameter; Si, silicon; Zn, zinc.

Analysis of effect modification suggested that associations between PM10–2.5 and RV mass were present in several susceptible populations. These subgroups included: former and current smokers in comparison with nonsmokers (p-value for interaction=0.02), persons with emphysema in comparison with persons without emphysema (p-value for interaction=0.02), and residentially stable participants in comparison with participants who had lived at their residences for less than 10 y (p-value for interaction=0.15). These associations remained even after adjustment for PM2.5 and NO2 concentrations (Figure 1) and after adjustment for emphysema (results not shown).

Forest plot indicating RV mass in grams per IQR increase in PM sub 10-2.5 for the following categories: overall, race/ethnicity (White, Chinese, AA, and Hispanic), age categories (45-54, 55-64, 65-74, and 75-84), smoking status (Never, Former, and Current), airflow limitation (No and Yes), emphysema (No and Yes), and greater than 10 years in neighborhood (No and Yes).
Figure 1. Effect modification of associations between PM10–2.5 concentrations and RV end-diastolic mass after control for PM10–2.5 and NO2 [g per interquartile range (IQR) of pollutant, 95% confidence interval (CI)].

*Interaction was statistically significant (p<0.05). Note: PM2.5, particulate matter with aerodynamic diameter between 2.5 and 10 μm; NO2, nitrogen dioxide.

Although the size and direction of the associations between PM10–2.5 mass and silicon with RV end-diastolic volume were consistent with RV mass, the confidence intervals were very wide and indistinguishable from no association (Table 2). As with RV mass, associations with RV end-diastolic volume were strongest among current smokers, participants with emphysema, and particpants who were residentially stable, although the precision of these estimates remained large (Figure S4). No associations were observed with ejection fraction in the full cohort or in any subpopulation evaluated.

Discussion

Among a population-based cohort from three U.S. metropolitan areas, we found suggestive evidence of associations between PM10–2.5 and RV structure after adjustment for confounding by PM2.5 and NO2. Positive associations between total PM10–2.5 mass concentrations and RV hypertrophy and, to a lesser extent, dilation were driven by relationships among former and current smokers, persons with advanced emphysema, and participants who were residentially stable. Associations were not found among other participants. No associations were found with RV ejection fraction among any group.

This study adds to the literature by expanding our understanding of the health implications of PM10–2.5 and the environmental risk factors for RV dysfunction. After adjustment for other risk factors such as smoking, height, weight, and co-pollutants previously associated with RV dysfunction, we observed the most robust associations for PM10–2.5 mass with a 1.4 g (95% CI: 0.4, 2.5) and 0.9 g (95% CI: 0.1, 1.7) larger RV mass among persons with emphysema and current smokers, respectively, per 2.2 μg/m3. These associations were on the same order of magnitude as those reported for PM2.5 in the full cohort (Aaron et al. 2016) and in the MESA Coarse cities. These differences are also comparable to differences in RV mass for participants 5 kg/m2 apart in BMI (Chahal et al. 2012) and may be clinically relevant, given that MESA participants with RV hypertrophy have double to triple the risk of heart failure or cardiovascular death (Kawut et al. 2012).

Mechanisms through which PM10–2.5 exposures might likely affect the right ventricle (Voelkel et al. 2006) include the restructuring of the pulmonary vasculature, increases in RV load (Zangiabadi et al. 2014), hypoxia, inflammation (Chaouat et al. 2009), and autonomic dysfunction (Wensel et al. 2009; Wrobel et al. 2012). Support for these mechanisms comes from a previous study of healthy Mexican children that reported greater pulmonary arterial pressures and serum levels of the vasoconstrictive protein endothelin-1 with larger long-term PM concentrations (Calderón-Garcidueñas et al. 2007). Toxicological research has similarly demonstrated enhanced vasoconstriction and impaired vasodilation of pulmonary arterioles in healthy animals and in animals with chronic bronchitis exposed to PM (Faustini et al. 2012; Peel et al. 2005). Interestingly, the associations with RV mass were robust to control for hypertension, emphysema, airflow limitation, and LV mass and function, suggesting that these factors may not be critical intermediates of our observed associations. However, it is difficult to conclusively assess mediation in this study given our cross-sectional design and the possibility that only advanced cases of emphysema or airflow limitation are critical intermediates, which are limited in number in this population. Our overall null associations with RV ejection fraction were similar to findings in a previous analysis (Kawut et al. 2012) where only RV mass was independently associated with cardiovascular death. These data could suggest that RV hypertrophy is an earlier indicator of increased pressure in the RV than RV ejection fraction, though this has yet to be clearly demonstrated.

Although the observed association between PM10–2.5 and RV appeared to be independent of PM10–2.5-associated lung damage, the interaction with emphysema suggests that individuals with preexisting lung damage may be more susceptible to long-term PM10–2.5 exposures. This susceptibility is plausible, given that persons with COPD have greater deposition and less mucociliary clearance of particles from their lungs relative to healthy individuals (Bennett et al. 1997; Brown et al. 2002). It is also consistent with epidemiological evidence of enhanced vulnerability of persons with respiratory conditions to short-term air pollution exposures (Faustini et al. 2012; Peel et al. 2005; Sacks et al. 2011), though the findings of the few studies to examine chronic lung disease as an effect modifier of long-term exposures to air pollution have been mixed (Andersen et al. 2012; Jerrett et al. 2009).

We also observed positive associations between RV mass and PM10–2.5 concentrations among participants who smoke or who have a history of smoking, independent of their emphysema status. One possible explanation may be that individuals who smoke or have smoked are more susceptible to the effects of air pollution because of smoking’s ability to increase inflammation and vasoconstriction (Akishima et al. 2007) and alter immune function, among other effects. However, epidemiologic evidence has also been mixed regarding the interaction between smoking and air pollution (Pope et al. 2011), suggesting that more research is necessary to understand this relationship. In addition, some caution is warranted about the generalizability of these findings as the smokers in MESA are generally healthier than the average smoking population due to our restriction to older adults without cardiovascular disease at baseline.

Our study is not without limitations. First, due to its cross-sectional design, our findings only provide evidence of potential associations that warrant further evaluation. Reverse causality is unlikely, however, and we have adjusted our models for a rich set of personal characteristics to account for between-person differences. Second, despite the highly innovative exposure assessment used, our exposure models are entirely spatial in nature and are assumed to capture the key differences in concentrations across space at different times. Our finding that associations were larger and more precise among persons living at their residences for >10 y may, however, suggest that our overall results may be biased towards the null due to inaccuracies in long-term exposures for some participants. On the other hand, compared with individuals who lived in their neighborhood for <10 y, residentially stable participants were more likely to be older, have hypertension, and have advanced emphysema, suggesting that these individuals may have been susceptible for other reasons. Another issue is that our models varied in predictive power by pollutant. Thus, differences in the observed strength of association between pollutants may be causal or could simply reflect different measurement errors. For example we found significant associations with PM2.5 and NO2, which, compared with PM10–2.5, had substantially better predictive ability due to a greater number of measurements that were collected over a longer period of time. In contrast, no associations were found with endotoxin, which had the lowest CV R2 in our prediction models. This finding may be the result of smaller errors for PM2.5 and NO2 that make them less likely to be biased toward the null in individual pollutant and multipollutant models. Finally, although the exposure estimation methods used in this study allow for individual assessment of outdoor concentrations, we do not have estimates of indoor or personal concentrations.

Despite these limitations, this work has many important strengths. The MESA cohort is an extremely well-characterized population with detailed and standardized measures of outcomes and covariates. The availability of RV measures is unique in such a large sample. Another distinction in this study is our exposure assessment, which improves on existing epidemiology studies of long-term exposures to PM10–2.5 in the United States. Our model predicts fine-scale spatial variability in exposures using a model derived from intensive air pollution monitoring campaigns in each study community. This methodology is in contrast to previous studies that have relied exclusively on data from relatively sparse national monitoring networks to estimate exposures to PM10–2.5 (Lipfert et al. 2006; McDonnell et al. 2000; Pope et al. 2002; Puett et al. 2009; Puett et al. 2011). We were also able to control for copollutants (PM2.5 and NO2) and demonstrated independent associations with PM10–2.5. The availability of chemical component data has particularly important implications for regulatory purposes, given that PM10–2.5 is generated by both natural and anthropogenic sources. This inclusion has important implications for regulatory purposes, given that PM10–2.5 is generated by both natural and anthropogenic sources.

Conclusion

This cross-sectional study provided some evidence of a positive association between long-term residential PM10–2.5 concentrations and RV mass among persons with a history of tobacco-smoke exposures and persons with severe emphysema. If replicated by future work, our findings could suggest a possible mechanism for observed associations between PM10–2.5 exposures and mortality from respiratory disease.

Acknowledgments

This work was supported by supported by grants RD 833741010 and RD 83169701 from the U.S. Environmental Protection Agency (EPA) and the National Institutes of Health (NIH) (R01 HL086719). MESA was further supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute and by grants UL1-TR-000040 and UL1-RR-025005 from NCRR. MESA RV was funded by NIH R01-HL086719. MESA Lung was supported by NIH-R01-HL077612 and RC1 HL100543. MESA Neighborhood was supported by 2R01 HL071759. One author (P.S.T.) was supported by NIH P30 ES005605 and another (J.D.K.) by P30 ES07033 and K24 ES013195. Although funded by the U.S. EPA, this publication has not been formally reviewed by the U.S. EPA, and the views expressed in this document are solely the views of the authors. The U.S. EPA also does not endorse any products or commercial services mentioned in this publication.

The authors acknowledge the other investigators, staff, and participants of MESA and MESA Air for their valuable contributions to this work. A full list of MESA investigators and institutions is located at http://www.mesa-nhlbi.org.

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Serum Vaccine Antibody Concentrations in Adolescents Exposed to Perfluorinated Compounds

Author Affiliations open

1Department of Environmental Medicine, University of Southern Denmark, Odense, Denmark

2Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

3Paediatric Clinic, Rigshospitalet – National University Hospital, Copenhagen, Denmark

4Department of Occupational Medicine and Public Health, Faroese Hospital System, Tórshavn, Faroe Islands, Denmark

5Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark

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  • Background:
    Postnatal exposure to perfluorinated alkylate substances (PFASs) is associated with lower serum concentrations of specific antibodies against certain childhood vaccines at 7 y.
    Objectives:
    We prospectively followed a Faroese birth cohort to determine these associations at 13 y.
    Methods:
    In 516 subjects (79% of eligible cohort members) who were 13 years old, serum concentrations of PFASs and of antibodies against diphtheria and tetanus were measured and were compared with data from the previous examination at 7 y. Multiple regression analyses and structural equation models were applied to determine the association between postnatal PFAS exposures and antibody concentrations.
    Results:
    Serum concentrations of PFASs and antibodies generally declined from 7 y to 13 y. However, 68 subjects had visited the emergency room and had likely received a vaccination booster, and a total of 202 children showed higher vaccine antibody concentrations at 13 y than at 7 y. Therefore, separate analyses were conducted after exclusion of these two subgroups. Diphtheria antibody concentrations decreased at elevated PFAS concentrations at 13 y and 7 y; the associations were statistically significant for perfluorodecanoate (PFDA) at 7 y and for perfluorooctanoate (PFOA) at 13 y, both suggesting a decrease by ∼25% for each doubling of exposure. Structural equation models showed that a doubling in PFAS exposure at 7 y was associated with losses in diphtheria antibody concentrations at 13 y of 10–30% for the five PFASs. Few associations were observed for anti-tetanus concentrations.
    Conclusions:
    These results are in accord with previous findings of PFAS immunotoxicity at current exposure levels. https://doi.org/10.1289/EHP275
  • Received: 09 February 2016
    Revised: 06 June 2016
    Accepted: 12 July 2016
    Published: 26 July 2017

    Address correspondence to P. Grandjean, HSPH-EOME, 401 Park Dr., 3E L3-045, Boston, MA 02215 USA. Telephone: 617-384-8907. Email: pgrandjean@health.sdu.dk

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Perfluorinated alkylate substances (PFASs) have a wide range of applications in water-, soil-, and stain-resistant coatings for clothing and other textiles and in oil-resistant coatings for food wrapping materials, and their use for >60 y has resulted in worldwide exposure to these persistent compounds (Lindstrom et al. 2011). Epidemiological research on possible adverse effects in exposed populations has intensified only in recent years (Grandjean and Clapp 2015; Steenland et al. 2014).

As a measure of depressed immune function, antibody response to routine childhood vaccinations has been identified as a sensitive indicator of elevated PFAS exposures in children (Grandjean et al. 2012; Granum et al. 2013). Whereas pre-booster antibody concentrations at 5 y of age appeared to be affected by both current and prenatal exposures (Grandjean et al. 2012), concentrations at 7 y of age showed inverse associations mainly with postnatal exposures (Grandjean et al. 2012; Mogensen et al. 2015). Decreased antibody responses to vaccinations have also been reported in PFAS-exposed adults (Kielsen et al. 2015; Looker et al. 2014). In addition, elevated PFAS exposures seem to increase the risk of common infections in children (Granum et al. 2013). Prenatal exposures did not appear to affect childhood hospitalization rates (Fei et al. 2010), but the quality of the exposure assessment in the study has recently been called into doubt (Bach et al. 2015). Because serum PFAS concentrations are often highly correlated, epidemiological studies have not identified which PFASs are mainly responsible for the phenomenon, and some studies have therefore modeled total PFAS exposure (Grandjean et al. 2012; Mogensen et al. 2015). Results from in vitro studies of human leukocytes support the immunotoxic potential of several PFASs, including perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and perfluorodecanoate (PFDA) (Corsini et al. 2012), and animal models suggest that immune depression may occur at serum concentrations of PFOA and PFOS similar to or slightly above those reported in exposed human populations (DeWitt et al. 2012).

Immune functions are affected by many different kinds of stimuli (MacGillivray and Kollmann 2014), and specific antibody concentrations vary between children with similar vaccination records; the main determinant of increased concentrations is recent booster vaccinations (Capua et al. 2013). Although developmental exposure to polychlorinated biphenyls (PCBs) can also reduce vaccine responses (Heilmann et al. 2010), PCB concentrations were only weakly associated with PFASs, and adjustment for prenatal and early postnatal PCB exposure did not materially affect the PFAS associations with antibody concentrations (Grandjean et al. 2012).

We have extended our follow-up of the Faroese birth cohort to the age of 13 y, where we most recently observed strong negative associations between postnatal serum PFAS concentrations and antibody concentrations at the age of 7 y (Grandjean et al. 2012; Mogensen et al. 2015). We maintained our focus on tetanus and diphtheria because these vaccines are toxoids that trigger complex immune system responses involving both T cells and B cells (Schatz et al. 1998). Our previous research showed that prenatal exposures were particularly linked to the pre-booster antibody concentration at 5 y of age, and the concomitant exposure was associated with the response to the 5-y booster, whereas antibody concentrations at 7 y depended mainly on the current exposure levels (Grandjean et al. 2012; Mogensen et al. 2015). We therefore focused on the PFAS exposures reflected in serum concentrations from the two most recent examinations.

Methods

Study Population

A birth cohort of 656 children was compiled from births at the National Hospital in Tórshavn in the Faroe Islands during 1997–2000 to explore childhood immune function and the impact on vaccination efficacy (Grandjean et al. 2012). Faroese children receive vaccinations against diphtheria and tetanus at 3 mo, 5 mo, and 12 mo, with a booster at 5 y, as part of the government-supported health care system. All children received the same amount of vaccine and associated alum adjuvant from the same source, although additional vaccines (pertussis and polio) were added to the booster during the project period (Grandjean et al. 2012). The study protocol was approved by the Faroese ethics review committee and by the institutional review board at Harvard T.H. Chan School of Public Health; written informed consent was obtained from all mothers.

All cohort members were invited for a follow-up examination at 13 y that included a physical examination and blood sampling. Information from the children’s mandatory vaccination cards was copied, and a questionnaire on past medical history and current health status also included questions about vaccinations administered since the previous examination and whether the child had visited the emergency room. Because tetanus vaccination (which includes the diphtheria toxoid) in the emergency room is a routine procedure that may not have been recorded, hospital records did not contain further information on vaccinations that could be used for the purposes of the present study.

The PFAS concentrations were measured in serum that was obtained at the clinical examinations and then frozen at –80°C shortly after separation. We performed online solid-phase extraction and analysis using high-pressure liquid chromatography with tandem mass spectrometry (Grandjean et al. 2012). Within-batch and between-batch imprecision levels (assessed by the coefficient of variation) were ∼5% or better for all analytes. Results with excellent accuracy have been obtained in regular comparisons organized by the German Society of Occupational Medicine. The PFASs quantified were perfluorohexanesulfonic acid (PFHxS), PFOA, PFOS, perfluorononanoate (PFNA), and PFDA.

Serum concentrations of major polychlorinated biphenyl congeners were available from maternal pregnancy serum; the sum of the three major congeners was used as an indicator of the total PCB exposure as on previous occasions (Grandjean et al. 2012).

Serum concentrations of immunoglobulin G (IgG) antibodies were measured by the vaccine producer (Statens Serum Institut, Copenhagen, Denmark) using an enzyme-linked immunosorbent assay for tetanus and, for diphtheria, a Vero cell–based neutralization assay using 2-fold dilutions of the serum. For both assays, calibration was performed using both international and local standard antitoxins. The methods were the same as those used for previous examinations (Grandjean et al. 2012).

Statistical Methods

Owing to skewed distributions, the antibody concentrations and the serum PFAS concentrations measured at 7 y and 13 y were all log2-transformed before the models were applied. Initial analyses were based on separate multiple linear regressions with age-13 antibody concentration as the dependent variable and serum PFAS concentration as a predictor along with age and sex as mandatory covariates while also considering age-5 booster type [i.e., co-immunization with other vaccines (Grandjean et al. 2012)]. We included adjustment for prenatal PCB exposure in separate analyses. We also performed covariate-adjusted logistic regression analyses to determine the odds ratios for each age-13 vaccine antibody concentration being below the clinically protective level of 0.1 IU/mL with a doubled PFAS exposure. Using structural equation models (Mogensen et al. 2015), we determined the associations of the age-7 PFAS concentrations with the specific antibody levels at 13 y. We modeled these associations as an indirect effect (via the antibody result at 7 y) and as a total effect. The total effect has the same interpretation as a linear regression model without the adjustment for the antibody level at 7 y, but it provides a more stable measure with better error control. The indirect effect is the loss in antibody concentration at 13 y due to increases in PFAS at 7 y being associated with lower antibody concentrations at that age, with these antibody concentrations being associated with the levels at 13 y. In addition, in a separate model, we ignored the direct path from PFAS exposure at 7 y to the antibody concentration at 13 y. All models were adjusted for sex and age as covariates, and incomplete observations were included assuming that information was missing at random, thus allowing calculations based on the maximum likelihood principle (Rubin 1987).

For comparison, we also applied linear mixed models to ascertain the effects of age-7 serum PFAS concentrations on the antibody outcomes at 7 and 13 y. Interaction between exposure and age was included, and the results were again adjusted for both age and sex. The assumption of linear dose–response associations was verified by allowing for a more flexible relationship between the PFAS and antibody concentrations in generalized additive models using cubic regression splines with three knots (Hastie and Tibshirani 1990); no significant deviation from linearity was found. Because both PFAS concentrations and antibody outcomes were log2-transformed, we expressed the regression coefficients as the change in the antibody concentration in percent for each doubling of the exposure.

Results

A total of 516 children (78.7% of the cohort members) participated in the age-13 examinations, and 587 (89.0%) participated either at 7 y or at 13 y or both. The characteristics of the children who provided sufficient serum for analyses at 13 y are shown in Table 1. PFOS was by far the most prevalent PFAS, with a median serum concentration at 13 y of 6.7 ng/mL, a 56% decrease since 7 y. PFOA and PFNA decreased to a similar extent. Although the correlation between the age-7 and age-13 concentrations of the same PFAS was even closer than the correlation between the different PFASs at each age (r≤0.85 at 13 y and≤0.62 at 7 y), the close correlations prevented meaningful adjustment for concomitant PFAS exposures. In contrast, the PCB concentration in maternal pregnancy serum correlated poorly with the child’s serum PFAS concentrations at 7 y (r between −0.06 and 0.15) and 13 y (r between 0.11 and 0.20).

Table 1. Characteristics of children who contributed serum-antibody concentrations at the two follow-up examinations.
Total cohort (n=587) No ER visit (n=519) No ER visits and no antibody increase (n=317)
Variable n Summary n Summary n Summary
Sex, n (%) (Girls) 587 (100%) 278 (47.4%) 519 (100%) 238 (45.9%) 317 (100%) 147 (46.4%)
Booster type 1, n (%) (yes) 575 (98.0%) 412 (71.7%) 509 (98.1%) 367 (72.1%) 311 (98.1%) 209 (67.2%)
Age, median (IQR) (years)
 Current examination 516 (87.9%) 13.2 (12.9; 13.6) 448 (86.3%) 13.2 (12.9; 13.6) 317 (100%) 13.3 (13.0; 13.6)
 Age-7 examination 565 (96.3%) 7.5 (7.5; 7.6) 497 (95.8%) 7.5 (7.5; 7.6) 317 (100%) 7.5 (7.5; 7.6)
Antibody concentrations, median (IQR) (IU/mL)
 Anti-diphtheria at 13 y 515 (87.7%) 0.1 (0.0; 0.2) 447 (86.1%) 0.1 (0.0; 0.2) 317 (100%) 0.1 (0.0; 0.2)
 Anti-diphtheria at 7 y 459 (78.2%) 0.8 (0.4; 1.6) 391 (75.3%) 0.8 (0.4; 1.6) 317 (100%) 0.8 (0.4; 1.6)
 Anti-tetanus at 13 years 515 (87.7%) 0.6 (0.3; 1.8) 447 (86.1%) 0.5 (0.3; 1.2) 317 (100%) 0.5 (0.2; 1.0)
 Anti-tetanus at 7 y 459 (78.2%) 1.8 (0.6; 4.5) 391 (75.3%) 2.1 (1.1; 5.2) 317 (100%) 2.3 (1.3; 5.5)
PFAS concentrations, median (IQR) (ng/mL)
 PFOS at 13 years 515 (87.7%) 6.7 (5.2; 8.5) 447 (86.1%) 6.7 (5.3; 8.5) 317 (100%) 6.8 (5.4; 8.7)
 PFOS at 7 y 488 (83.1%) 15.3 (12.4; 19.0) 420 (80.9%) 15.3 (12.4; 19.0) 312 (98.4%) 15.5 (12.9; 18.9)
 PFOA at 13 y 515 (87.7%) 2.0 (1.6; 2.5) 447 (86.1%) 2.0 (1.5; 2.5) 317 (100%) 2.0 (1.6; 2.6)
 PFOA at 7 y 488 (83.1%) 4.4 (3.5; 5.7) 420 (80.9%) 4.4 (3.6; 5.7) 312 (98.4%) 4.4 (3.5; 5.5)
 PFHxS at 13 y 515 (87.7%) 0.4 (0.3; 0.5) 447 (86.1%) 0.4 (0.3; 0.5) 317 (100%) 0.4 (0.3; 0.5)
 PFHxS at 7 y 488 (83.1%) 0.5 (0.4; 0.7) 420 (80.9%) 0.5 (0.4; 0.7) 312 (98.4%) 0.5 (0.4; 0.7)
 PFNA at 13 y 515 (87.7%) 0.7 (0.6; 0.9) 447 (86.1%) 0.7 (0.6; 0.9) 317 (100%) 0.8 (0.6; 1.0)
 PFNA at 7 y 488 (83.1%) 1.1 (0.9; 1.5) 420 (80.9%) 1.1 (0.9; 1.5) 312 (98.4%) 1.1 (0.9; 1.5)
 PFDA at 13 y 515 (87.7%) 0.3 (0.2; 0.4) 447 (86.1%) 0.3 (0.2; 0.4) 317 (100%) 0.3 (0.2; 0.4)
 PFDA at 7 y 488 (83.1%) 0.4 (0.2; 0.6) 420 (80.9%) 0.4 (0.2; 0.5) 312 (98.4%) 0.4 (0.2; 0.5)

Note: ER, emergency room; IQR, interquartile range; IU, international units; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

On average, both antibody concentrations showed clear decreases during the six-year period from 7 y to 13 y, although they were stronger for diphtheria (Table 1). At 13 y, 207 (39.4%) and 103 (19.6%) children had antibody concentrations <0.1 IU/mL for diphtheria and for tetanus, respectively. However, not all children showed the anticipated decrease in antibody concentrations. Scatter plots of the correlation between antibody concentrations at 7 y and 13 y show that concentrations increased between the two examinations in many cohort subjects (Figure 1). An increase in antibody concentration could likely be a result of additional antigen exposure, most likely because some subjects had received an unscheduled booster dose. Six of the children had received an additional booster at the project clinic because of their very low antibody concentrations after the age-5 booster. Questionnaire information further revealed that 68 cohort members had visited the emergency room where they likely received a tetanus booster shot, and indeed, many of them had elevated antibody concentrations at age 13 (Figure 1). Nevertheless, a total of 202 children did not show the anticipated decrease in antibody concentrations between 7 y and 13 y, most of whom were not known to have been vaccinated after 5 y. Statistical analyses were therefore performed on the whole group; after exclusion of subjects with a record of having visited the emergency room or otherwise having received an additional booster (no-ER group); and, for comparison purposes, after exclusion of the 202 subjects who for unknown reason did not show the anticipated decrease in antibody concentrations between 7 y and 13 y. These three groups were fairly similar with regard to sex, age, and PFAS exposure (Table 1).

Antibody concentrations in international units per milliliters are plotted on the y-axis for the 13-year-olds and on the x-axis for 7-year-olds according to whether or not there was a known visit at ER. The size of the dots increase with the number of measurements.
Figure 1. Scatter plot showing paired antibody concentrations for diphtheria (left) and tetanus (right) in children examined at both 7 y and 13 y.

Multiple regression analyses showed a uniformly inverse association between anti-diphtheria antibody concentrations at 13 y and the PFAS concentrations at either 7 y or 13 y (Table 2). The tendencies were the strongest after exclusion of subjects with a history of an additional booster, and an approximately 25% decrease for each doubling in serum PFOA concentration at 13 y and a 24% decrease for each doubling in serum PFDA concentration at 7 y were both statistically significant. However, tetanus antibody concentrations, which had decreased much less than diphtheria antibody concentrations, tended to show positive associations, one of which was statistically significant (Table 3). As expected (Grandjean et al. 2012), adjustment for developmental PCB exposure had no appreciable effect on these associations; therefore, PCB was not considered further. Logistic regression analyses of the results, many of which were close to the 0.1 IU/mL limit, in most cases showed odds close to 1 for the antibody concentrations being below the clinically protective level (data not shown). However, for diphtheria, a doubled concomitant serum PFOA concentration showed odds ratios of 1.47 (95% CI: 1.03, 2.14; p=0.038) and 1.71 (95% CI: 1.15, 2.55; p=0.008) for a nonprotective antibody level in the total study group and in the no-ER group, respectively. Further, PFDA at 7 y showed statistically significant odds ratios of 1.39 (95% CI: 1.05, 1.85; p=0.023) and 1.54 (95% CI: 1.13, 2.12; p=0.007) for diphtheria in the same two groups, respectively; no other associations reached statistical significance.

Table 2. Linear regression models of changes in anti-diphtheria concentrations at 13 y associated with serum PFAS concentrations 13 y and 7 y adjusted for sex, age at antibody assessment, and booster type.
Total cohort (n=587) No booster or ER visit (n=519) No booster or ER visit and no antibody increase (n=317)
PFAS (ng/mL) n Change 95% CI p-value n Change 95% CI p-value n Change 95% CI p-value
PFAS concentrations, 13 y
 PFOS 505 −8.6 −27.7, 15.6 0.454 439 −10.5 −29.8, 14.3 0.374 311 −0.6 −24.5, 30.9 0.965
 PFOA 505 −17.5 −35.6, 5.8 0.129 439 −25.3 −42.5, −3.0 0.029 311 −17.8 −38.0, 9.0 0.173
 PFHxS 505 −5.5 −22.9, 15.8 0.583 439 −10.9 −27.7, 9.8 0.279 311 −0.2 −20.4, 25.0 0.984
 PFNA 505 −4.5 −24.2, 20.2 0.693 439 −6.6 −26.7, 19.0 0.579 311 −3.7 −25.8, 25.2 0.780
 PFDA 505 −3.7 −22.0, 18.9 0.726 439 −3.5 −22.5, 20.3 0.754 311 −4.4 −24.9, 21.8 0.716
PFAS concentrations, 7 y
 PFOS 427 −23.8 −43.2, 2.3 0.070 361 −25.6 −45.4, 1.4 0.061 306 −10.8 −35.6, 23.5 0.490
 PFOA 427 −4.1 −25.4, 23.3 0.742 361 −9.2 −30.7, 18.8 0.480 306 −2.7 −26.4, 28.5 0.845
 PFHxS 427 −10.2 −25.7, 8.5 0.264 361 −16.3 −31.3, 2.0 0.077 306 −5.9 −23.4, 15.4 0.556
 PFNA 427 −11.3 −27.4, 8.5 0.243 361 −13.6 −30.6, 7.5 0.190 306 −7.0 −25.6, 16.1 0.519
 PFDA 427 −21.5 −34.4, −6.0 0.008 361 −24.2 −37.5, −8.0 0.005 306 −19.7 −34.0, −2.2 0.029

Notes: The change in the antibody concentration is expressed in percent per doubling of the serum PFAS concentration at the two different ages. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

Table 3. Linear regression models of changes in anti-tetanus concentrations at 13 y associated with serum-PFAS concentrations at 13 y and 7 y adjusted for sex, age at antibody assessment, and booster type.
Total cohort (n=587) No ER visit (n=519) No ER visit and no antibody increase (n=317)
PFAS (ng/mL) n Change 95% CI p-value n Change 95% CI p-value n Change 95% CI p-value
PFAS concentrations, 13 y
 PFOS 505 22.2 −12.4, 70.3 0.237 439 23.4 −7.0, 63.7 0.144 311 14.8 −8.7, 44.4 0.236
 PFOA 505 3.3 −27.3, 46.9 0.856 439 −5.6 −30.5, 28.1 0.710 311 −16.1 −33.7, 6.3 0.145
 PFHxS 505 8.7 −18.5, 45.0 0.568 439 19.3 −6.4, 52.1 0.153 311 1.8 −15.6, 22.9 0.851
 PFNA 505 15.2 −16.9, 59.7 0.394 439 5.1 −20.7, 39.3 0.727 311 11.6 −10.3, 38.8 0.324
 PFDA 505 18.7 −11.8, 59.8 0.258 439 6.9 −17.2, 38.0 0.607 311 18.0 −3.5, 44.4 0.106
PFAS concentrations, 7 y
 PFOS 427 30.0 −16.1, 101.4 0.240 361 45.4 1.2, 108.8 0.043 306 2.7 −21.8, 34.8 0.849
 PFOA 427 9.4 −24.7, 58.9 0.637 361 2.9 −25.0, 41.1 0.859 306 −4.9 −24.6, 20.0 0.671
 PFHxS 427 14.8 −13.3, 52.2 0.334 361 25.2 −0.6, 57.7 0.057 306 −11.3 −25.2, 5.2 0.167
 PFNA 427 31.0 −2.7, 76.4 0.075 361 23.1 −4.6, 59.0 0.110 306 11.9 −7.1, 34.7 0.235
 PFDA 427 36.8 4.7, 78.7 0.022 361 25.1 −0.4, 57.0 0.054 306 3.5 −12.3, 22.2 0.682

Notes: The change in the antibody concentration is expressed in percent per doubling of the serum PFAS concentration at the two different ages. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

In the first structural equation model, we included an indirect effect mediated by the exposure at 7 y via the antibody concentration at that age. Tables 4 and 5 show the indirect effect and the total effect observed in this model. For diphtheria, all associations were inverse, and all five PFASs showed statistically significant inverse associations in the no-ER group. Tendencies were weaker in the total group and after exclusion of subjects without decreasing antibody concentrations. For tetanus, some inverse associations were observed, although none was significant. A second model without a direct effect fitted the data equally well and showed a similar, though stronger, indirect effect for diphtheria via the age-7 antibody concentration. These tendencies also became clear for tetanus, where statistically significant indirect effects were now apparent in the no-ER group, both for PFOA (–24.2; 95% CI: −41.1, −2.4) and for PFHxS (–25.1; 95% CI: −38.9, −8.3). Finally, the results obtained with the linear mixed models were similar to those obtained with the first structural equation model.

Table 4. Effects of serum-PFAS concentrations at 7 y on anti-diphtheria antibody concentrations at 7 y and 13 y adjusted for age and sex in structural equation models.
Total cohort (n=587) No ER visit (n=519) No ER visit and no antibody increase (n=317)
PFAS (ng/mL) Effect Change 95% CI p-value Change 95% CI p-value Change 95% CI p-value
PFOS Indirect −29.7 −44.3, −11.3 0.003 −32.8 −47.9, −13.3 0.002 −22.3 −40.3, 1.2 0.061
Total −26.5 −45.7, −0.5 0.046 −31.1 −49.8, −5.4 0.021 −16.0 −38.8, 15.4 0.282
PFOA Indirect −17.7 −32.24, −0.0 0.050 −19.8 −35.4, −0.5 0.045 −15.0 −31.8, 5.9 0.147
Total −4.3 −26.0, 23.8 0.739 −9.4 −31.1, 19.2 0.481 −5.8 −27.8, 22.9 0.661
PFHxS Indirect −13.4 −25.9, 1.2 0.071 −16.2 −29.3, −0.6 0.042 −8.6 −22.6, 7.9 0.289
Total −12.0 −28.0, 7.5 0.211 −19.5 −34.7, −0.7 0.043 −8.0 −24.6, 12.4 0.415
PFNA Indirect −20.7 −31.8, −7.8 0.003 −23.3 −35.3, −9.0 0.002 −18.1 −31.6, −1.9 0.030
Total −15.6 −31.1, 3.2 0.099 −17.4 −33.7, 2.8 0.087 −9.4 −27.0, 12.5 0.371
PFDA Indirect −19.6 −28.8, −9.2 <0.001 −20.7 −30.7, −9.2 0.001 −17.0 −27.8, −4.7 0.008
Total −22.5 −34.5, −8.3 0.003 −25.1 −37.3, −10.4 0.002 −19.8 −32.6, −4.6 0.013

Notes: The change in the anti-diphtheria concentration is expressed in percent per doubling of the age-7 serum PFAS concentration. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

Table 5. Effects of serum-PFAS concentrations at 7 y on anti-tetanus antibody concentrations at 7 y and 13 y adjusted for age and sex in structural equation models.
Total cohort (n=587) No ER visit (n=519) No ER visit and no antibody increase (n=317)
PFAS (ng/mL) Effect Change 95% CI p-value Change 95% CI p-value Change 95% CI p-value
PFOS Indirect 3.2 −5.7, 12.9 0.492 −2.2 −6.5, 2.4 0.347 −1.4 −21.7, 24.2 0.906
Total 26.3 −17.2, 92.6 0.278 42.9 −2.8, 110.0 0.069 2.1 −21.5, 32.9 0.877
PFOA Indirect 6.0 −1.9, 14.4 0.138 −3.2 −7.7, 1.5 0.181 −16.0 −30.7, 1.8 0.075
Total 11.3 −22.3, 59.3 0.560 1.9 −27.3, 42.6 0.915 −7.2 −25.5, 15.6 0.505
PFHxS Indirect 7.7 0.6, 15.3 0.033 −3.7 −8.1, 0.8 0.107 −12.4 −24.2, 1.2 0.072
Total 14.1 −13.1, 49.8 0.342 25.0 −2.3, 59.8 0.075 −12.1 −25.4, 3.7 0.127
PFNA Indirect 1.0 −4.7, 6.9 0.739 −1.1 −3.8, 1.6 0.416 4.2 −10.9, 21.9 0.604
Total 28.9 −3.1, 71.5 0.081 23.6 −6.0, 62.5 0.129 12.7 −5.8, 34.8 0.190
PFDA Indirect −0.8 −5.4, 4.0 0.729 0.1 −1.8, 2.0 0.936 7.8 −4.7, 21.8 0.231
Total 27.5 −0.3, 62.9 0.053 16.7 −7.7, 47.5 0.196 2.7 −11.1, 18.5 0.721

Notes: The change in the anti-tetanus concentration is expressed in percent per doubling of the age-7 serum PFAS concentration. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

Discussion

The present prospective study was performed in adolescents to ascertain PFAS-associated decreases in antibody responses to their childhood vaccines. Owing to numerous unscheduled booster vaccinations, the data were censored to remove cohort members with records of emergency room visits. In this subgroup, multiple regression results showed that diphtheria antibody concentrations decreased at elevated serum PFAS concentrations at 13 y and 7 y; the associations were statistically significant for PFDA at 7 y and for PFOA at 13 y, both suggesting a decrease by ∼25% for each doubling of exposure. The findings after exclusion of all subjects without a decrease in antibody concentrations were less clear. Structural equation models showed that a doubling of PFAS exposure at 7 y was associated with statistically significant losses in diphtheria antibody concentrations at 13 y of 10–30% for the five PFASs. Few associations were observed for anti-tetanus concentrations. However, more advanced modeling showed negative associations for tetanus with regard to the age-7 PFAS exposure. Owing to the intercorrelations between the serum PFAS concentrations, further analysis of the possible role of individual PFASs was not pursued, and the observed associations may reflect the effects of the PFAS mixtures.

Although the present study aimed to obtain prospective data on the associations between PFAS exposure and vaccine antibody concentration over an extended period, an antibody concentration at a particular point in time does not represent the complete trajectory of changes and may not be representative of the protection against the specific disease in the long term. The present prospective study is apparently the first to elucidate the temporal changes in antibody concentrations in relation to PFAS immunotoxicity through to adolescence. A major obstacle in such observational studies is that children and adolescents who visit the emergency room for cuts and other injuries are routinely administered a tetanus booster, which also affects diphtheria because both toxoids are present in the vaccine, thereby interfering with the study design. We therefore chose to exclude subjects who had a record of an emergency room visit (to obtain no-ER group). For comparison, we performed parallel calculations after excluding >200 subjects who had not revealed the anticipated decrease in antibody concentrations between 7 y and 13 y. With antibody concentrations that decrease to different degrees over time and with PFAS exposure levels that likewise show decreases, multiple regression analyses of the bivariate data sets need to be complemented by more-advanced modeling.

Our study is limited to PFAS exposure assessments at two points during childhood at an interval of approximately six years. Although the elimination half-lives of the PFASs are 2–5 y (Bartell et al. 2010; Olsen et al. 2007), the two widely separated measurements may not fully characterize the childhood exposure profile. Because exposures during childhood are likely to vary (Kato et al. 2009; Lindstrom et al. 2011), it is possible that serial serum PFAS analyses would provide stronger evidence for PFAS immunotoxicity. However, given that a decreased antibody concentration is most likely due to past exposures, rather than to current, lower exposures (Mogensen et al. 2015), we chose to rely on the age-7 exposure levels and to include both antibody measurements at 7 y and 13 y in the assessment. At age 7, concurrent serum PFAS concentrations showed strong inverse associations with vaccine antibody concentrations, and inclusion of age-5 PFAS concentrations slightly strengthened these tendencies (Mogensen et al. 2015).

The 5-y booster vaccination is in principle the last booster vaccination that a child receives, and long-term protection is therefore intended. Our previous results (Grandjean et al. 2012) showed that at 7 y, many children had antibody concentrations below the level assumed to provide the desired protection. The number of children with antibody concentrations below the desired level had substantially increased by age 13 and was similar to the number of children with antibody concentrations below the level of protection at age 5 before the booster. These observations support the justification for routinely supplying a booster vaccination in the emergency room in connection with any injury that may have involved the slightest risk of tetanus. Unfortunately, such preventive measures add variance to the present study design, which relied on all children being vaccinated at the same age and with the same antigen dose. The study assumption was therefore violated, and it proved to be difficult to adjust for additional immunizations at different ages given the incomplete knowledge of booster administrations. Accordingly, the results clearly show the strongest associations between PFAS exposure and antibody concentrations in cohort subjects who were not known to have visited the emergency room or to otherwise have received a booster. However, Figure 1 also shows an increased antibody concentration at 13 y in many cohort members, possibly resulting from a booster that had not been recorded. Exclusion of all cohort members without an apparent decrease in antibody concentration at 13 y may have more than remedied this problem because it most likely excluded too many subjects, thereby attenuating the statistical power. As might be expected, the results for the most restricted subgroup are somewhat weaker than those for the less restricted cohort. Nevertheless, the increased antibody concentration in children who had received a booster suggests that any adverse influences of PFAS exposure could be remedied by repeated antigen challenge, although such intervention might not compensate for any other adverse effects associated with PFAS immunotoxicity.

As in previous studies (Grandjean et al. 2012; Mogensen et al. 2015), we found stronger associations for diphtheria than for tetanus. The former also exhibited much greater decreases from 7 y to 13 y, most likely because the diphtheria toxoid is a weaker antigen than tetanus and is therefore more easily affected by PFAS-depressed immune function. The exact magnitude of the serum antibody concentration may not be clinically important, but very low levels will result in poor or absent protection. With many antibody concentrations being close to the assumed clinically protective level of 0.1 IU/mL, logistic regression showed only weak tendencies for antibody levels below the limit to be associated with serum PFAS concentrations. Similarly, imprecision of antibody assessments, particularly at concentrations below or close to the clinically protective concentration, may have biased the regression analyses toward the null.

In support of our findings of PFAS immunotoxicity, a study of 99 Norwegian children at 3 y found that the maternal serum PFOA concentrations were associated with decreased vaccine responses in the children, particularly toward rubella vaccine, as well as increased frequencies of common cold and gastroenteritis (Granum et al. 2013). However, PFOS and PFOA concentrations in serum from 1,400 pregnant women from the Danish National Birth Cohort were not associated with the total hospitalization rate for a variety of infectious diseases in 363 of the children up to an average age of 8 y (Fei et al. 2010). However, the validity of this study has been questioned owing to the poor stability of the serum PFAS measurements (Bach et al. 2015); such imprecision of the exposure assessment could easily bias the results toward the null (Grandjean and Budtz-Jørgensen 2010).

In adults, PFOA exposure from contaminated drinking water was associated with lower serum concentrations of total IgA and IgE (in females only), although not of total IgG (Fletcher et al. 2009). More specifically, a reduced antibody titer increase after influenza vaccination was found in the most highly exposed subjects (Looker et al. 2014). Further, a small intervention study of 12 adults showed that the time-dependent increase in vaccine-specific antibody concentrations decreased at higher PFAS exposures (Kielsen et al. 2015). Overall, support is building for the notion that PFAS exposure is associated with deficient immune function. Although diphtheria and perhaps tetanus may not be likely hazards in the Faroese and in the residents of many other countries, the strongly decreased antibody concentrations likely reflect an immunological deficit. Because optimal immune system function is crucial for health, the associations identified should be regarded as adverse. We recently calculated benchmark dose levels to estimate the magnitude of exposure limits that would protect against the immunotoxicity observed. The results suggested that the present exposure limits may be too high by a factor of 100 (Grandjean and Budtz-Jørgensen 2013). The present study extends the previous findings of deficient antibody responses in this cohort at younger ages and therefore adds support to the notion that substantially strengthened prevention of PFAS exposure is indicated.

Conclusions

The results of the present study are in accord with previous findings of PFAS immunotoxicity at current exposure levels, although the results of this observational study are affected by concomitant decreases in concentrations of both PFASs and antibodies and by known and suspected booster vaccinations during the eight-year interval since the routine 5-y booster.

Acknowledgments

This study was supported by the National Institute of Environmental Health Sciences, National Institutes of Health (ES012199); the U.S. Environmental Protection Agency (R830758); the Danish Council for Strategic Research (09-063094); and the Danish Environmental Protection Agency as part of the environmental support program DANCEA (Danish Cooperation for Environment in the Arctic). The authors are solely responsible for all results and conclusions, which do not necessarily reflect the position of any of the funding agencies.

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Serum Vaccine Antibody Concentrations in Adolescents Exposed to Perfluorinated Compounds

Author Affiliations open

1Department of Environmental Medicine, University of Southern Denmark, Odense, Denmark

2Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

3Paediatric Clinic, Rigshospitalet – National University Hospital, Copenhagen, Denmark

4Department of Occupational Medicine and Public Health, Faroese Hospital System, Tórshavn, Faroe Islands, Denmark

5Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark

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  • Background:
    Postnatal exposure to perfluorinated alkylate substances (PFASs) is associated with lower serum concentrations of specific antibodies against certain childhood vaccines at 7 y.
    Objectives:
    We prospectively followed a Faroese birth cohort to determine these associations at 13 y.
    Methods:
    In 516 subjects (79% of eligible cohort members) who were 13 years old, serum concentrations of PFASs and of antibodies against diphtheria and tetanus were measured and were compared with data from the previous examination at 7 y. Multiple regression analyses and structural equation models were applied to determine the association between postnatal PFAS exposures and antibody concentrations.
    Results:
    Serum concentrations of PFASs and antibodies generally declined from 7 y to 13 y. However, 68 subjects had visited the emergency room and had likely received a vaccination booster, and a total of 202 children showed higher vaccine antibody concentrations at 13 y than at 7 y. Therefore, separate analyses were conducted after exclusion of these two subgroups. Diphtheria antibody concentrations decreased at elevated PFAS concentrations at 13 y and 7 y; the associations were statistically significant for perfluorodecanoate (PFDA) at 7 y and for perfluorooctanoate (PFOA) at 13 y, both suggesting a decrease by ∼25% for each doubling of exposure. Structural equation models showed that a doubling in PFAS exposure at 7 y was associated with losses in diphtheria antibody concentrations at 13 y of 10–30% for the five PFASs. Few associations were observed for anti-tetanus concentrations.
    Conclusions:
    These results are in accord with previous findings of PFAS immunotoxicity at current exposure levels. https://doi.org/10.1289/EHP275
  • Received: 09 February 2016
    Revised: 06 June 2016
    Accepted: 12 July 2016
    Published: 26 July 2017

    Address correspondence to P. Grandjean, HSPH-EOME, 401 Park Dr., 3E L3-045, Boston, MA 02215 USA. Telephone: 617-384-8907. Email: pgrandjean@health.sdu.dk

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Perfluorinated alkylate substances (PFASs) have a wide range of applications in water-, soil-, and stain-resistant coatings for clothing and other textiles and in oil-resistant coatings for food wrapping materials, and their use for >60 y has resulted in worldwide exposure to these persistent compounds (Lindstrom et al. 2011). Epidemiological research on possible adverse effects in exposed populations has intensified only in recent years (Grandjean and Clapp 2015; Steenland et al. 2014).

As a measure of depressed immune function, antibody response to routine childhood vaccinations has been identified as a sensitive indicator of elevated PFAS exposures in children (Grandjean et al. 2012; Granum et al. 2013). Whereas pre-booster antibody concentrations at 5 y of age appeared to be affected by both current and prenatal exposures (Grandjean et al. 2012), concentrations at 7 y of age showed inverse associations mainly with postnatal exposures (Grandjean et al. 2012; Mogensen et al. 2015). Decreased antibody responses to vaccinations have also been reported in PFAS-exposed adults (Kielsen et al. 2015; Looker et al. 2014). In addition, elevated PFAS exposures seem to increase the risk of common infections in children (Granum et al. 2013). Prenatal exposures did not appear to affect childhood hospitalization rates (Fei et al. 2010), but the quality of the exposure assessment in the study has recently been called into doubt (Bach et al. 2015). Because serum PFAS concentrations are often highly correlated, epidemiological studies have not identified which PFASs are mainly responsible for the phenomenon, and some studies have therefore modeled total PFAS exposure (Grandjean et al. 2012; Mogensen et al. 2015). Results from in vitro studies of human leukocytes support the immunotoxic potential of several PFASs, including perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and perfluorodecanoate (PFDA) (Corsini et al. 2012), and animal models suggest that immune depression may occur at serum concentrations of PFOA and PFOS similar to or slightly above those reported in exposed human populations (DeWitt et al. 2012).

Immune functions are affected by many different kinds of stimuli (MacGillivray and Kollmann 2014), and specific antibody concentrations vary between children with similar vaccination records; the main determinant of increased concentrations is recent booster vaccinations (Capua et al. 2013). Although developmental exposure to polychlorinated biphenyls (PCBs) can also reduce vaccine responses (Heilmann et al. 2010), PCB concentrations were only weakly associated with PFASs, and adjustment for prenatal and early postnatal PCB exposure did not materially affect the PFAS associations with antibody concentrations (Grandjean et al. 2012).

We have extended our follow-up of the Faroese birth cohort to the age of 13 y, where we most recently observed strong negative associations between postnatal serum PFAS concentrations and antibody concentrations at the age of 7 y (Grandjean et al. 2012; Mogensen et al. 2015). We maintained our focus on tetanus and diphtheria because these vaccines are toxoids that trigger complex immune system responses involving both T cells and B cells (Schatz et al. 1998). Our previous research showed that prenatal exposures were particularly linked to the pre-booster antibody concentration at 5 y of age, and the concomitant exposure was associated with the response to the 5-y booster, whereas antibody concentrations at 7 y depended mainly on the current exposure levels (Grandjean et al. 2012; Mogensen et al. 2015). We therefore focused on the PFAS exposures reflected in serum concentrations from the two most recent examinations.

Methods

Study Population

A birth cohort of 656 children was compiled from births at the National Hospital in Tórshavn in the Faroe Islands during 1997–2000 to explore childhood immune function and the impact on vaccination efficacy (Grandjean et al. 2012). Faroese children receive vaccinations against diphtheria and tetanus at 3 mo, 5 mo, and 12 mo, with a booster at 5 y, as part of the government-supported health care system. All children received the same amount of vaccine and associated alum adjuvant from the same source, although additional vaccines (pertussis and polio) were added to the booster during the project period (Grandjean et al. 2012). The study protocol was approved by the Faroese ethics review committee and by the institutional review board at Harvard T.H. Chan School of Public Health; written informed consent was obtained from all mothers.

All cohort members were invited for a follow-up examination at 13 y that included a physical examination and blood sampling. Information from the children’s mandatory vaccination cards was copied, and a questionnaire on past medical history and current health status also included questions about vaccinations administered since the previous examination and whether the child had visited the emergency room. Because tetanus vaccination (which includes the diphtheria toxoid) in the emergency room is a routine procedure that may not have been recorded, hospital records did not contain further information on vaccinations that could be used for the purposes of the present study.

The PFAS concentrations were measured in serum that was obtained at the clinical examinations and then frozen at –80°C shortly after separation. We performed online solid-phase extraction and analysis using high-pressure liquid chromatography with tandem mass spectrometry (Grandjean et al. 2012). Within-batch and between-batch imprecision levels (assessed by the coefficient of variation) were ∼5% or better for all analytes. Results with excellent accuracy have been obtained in regular comparisons organized by the German Society of Occupational Medicine. The PFASs quantified were perfluorohexanesulfonic acid (PFHxS), PFOA, PFOS, perfluorononanoate (PFNA), and PFDA.

Serum concentrations of major polychlorinated biphenyl congeners were available from maternal pregnancy serum; the sum of the three major congeners was used as an indicator of the total PCB exposure as on previous occasions (Grandjean et al. 2012).

Serum concentrations of immunoglobulin G (IgG) antibodies were measured by the vaccine producer (Statens Serum Institut, Copenhagen, Denmark) using an enzyme-linked immunosorbent assay for tetanus and, for diphtheria, a Vero cell–based neutralization assay using 2-fold dilutions of the serum. For both assays, calibration was performed using both international and local standard antitoxins. The methods were the same as those used for previous examinations (Grandjean et al. 2012).

Statistical Methods

Owing to skewed distributions, the antibody concentrations and the serum PFAS concentrations measured at 7 y and 13 y were all log2-transformed before the models were applied. Initial analyses were based on separate multiple linear regressions with age-13 antibody concentration as the dependent variable and serum PFAS concentration as a predictor along with age and sex as mandatory covariates while also considering age-5 booster type [i.e., co-immunization with other vaccines (Grandjean et al. 2012)]. We included adjustment for prenatal PCB exposure in separate analyses. We also performed covariate-adjusted logistic regression analyses to determine the odds ratios for each age-13 vaccine antibody concentration being below the clinically protective level of 0.1 IU/mL with a doubled PFAS exposure. Using structural equation models (Mogensen et al. 2015), we determined the associations of the age-7 PFAS concentrations with the specific antibody levels at 13 y. We modeled these associations as an indirect effect (via the antibody result at 7 y) and as a total effect. The total effect has the same interpretation as a linear regression model without the adjustment for the antibody level at 7 y, but it provides a more stable measure with better error control. The indirect effect is the loss in antibody concentration at 13 y due to increases in PFAS at 7 y being associated with lower antibody concentrations at that age, with these antibody concentrations being associated with the levels at 13 y. In addition, in a separate model, we ignored the direct path from PFAS exposure at 7 y to the antibody concentration at 13 y. All models were adjusted for sex and age as covariates, and incomplete observations were included assuming that information was missing at random, thus allowing calculations based on the maximum likelihood principle (Rubin 1987).

For comparison, we also applied linear mixed models to ascertain the effects of age-7 serum PFAS concentrations on the antibody outcomes at 7 and 13 y. Interaction between exposure and age was included, and the results were again adjusted for both age and sex. The assumption of linear dose–response associations was verified by allowing for a more flexible relationship between the PFAS and antibody concentrations in generalized additive models using cubic regression splines with three knots (Hastie and Tibshirani 1990); no significant deviation from linearity was found. Because both PFAS concentrations and antibody outcomes were log2-transformed, we expressed the regression coefficients as the change in the antibody concentration in percent for each doubling of the exposure.

Results

A total of 516 children (78.7% of the cohort members) participated in the age-13 examinations, and 587 (89.0%) participated either at 7 y or at 13 y or both. The characteristics of the children who provided sufficient serum for analyses at 13 y are shown in Table 1. PFOS was by far the most prevalent PFAS, with a median serum concentration at 13 y of 6.7 ng/mL, a 56% decrease since 7 y. PFOA and PFNA decreased to a similar extent. Although the correlation between the age-7 and age-13 concentrations of the same PFAS was even closer than the correlation between the different PFASs at each age (r≤0.85 at 13 y and≤0.62 at 7 y), the close correlations prevented meaningful adjustment for concomitant PFAS exposures. In contrast, the PCB concentration in maternal pregnancy serum correlated poorly with the child’s serum PFAS concentrations at 7 y (r between −0.06 and 0.15) and 13 y (r between 0.11 and 0.20).

Table 1. Characteristics of children who contributed serum-antibody concentrations at the two follow-up examinations.
Total cohort (n=587) No ER visit (n=519) No ER visits and no antibody increase (n=317)
Variable n Summary n Summary n Summary
Sex, n (%) (Girls) 587 (100%) 278 (47.4%) 519 (100%) 238 (45.9%) 317 (100%) 147 (46.4%)
Booster type 1, n (%) (yes) 575 (98.0%) 412 (71.7%) 509 (98.1%) 367 (72.1%) 311 (98.1%) 209 (67.2%)
Age, median (IQR) (years)
 Current examination 516 (87.9%) 13.2 (12.9; 13.6) 448 (86.3%) 13.2 (12.9; 13.6) 317 (100%) 13.3 (13.0; 13.6)
 Age-7 examination 565 (96.3%) 7.5 (7.5; 7.6) 497 (95.8%) 7.5 (7.5; 7.6) 317 (100%) 7.5 (7.5; 7.6)
Antibody concentrations, median (IQR) (IU/mL)
 Anti-diphtheria at 13 y 515 (87.7%) 0.1 (0.0; 0.2) 447 (86.1%) 0.1 (0.0; 0.2) 317 (100%) 0.1 (0.0; 0.2)
 Anti-diphtheria at 7 y 459 (78.2%) 0.8 (0.4; 1.6) 391 (75.3%) 0.8 (0.4; 1.6) 317 (100%) 0.8 (0.4; 1.6)
 Anti-tetanus at 13 years 515 (87.7%) 0.6 (0.3; 1.8) 447 (86.1%) 0.5 (0.3; 1.2) 317 (100%) 0.5 (0.2; 1.0)
 Anti-tetanus at 7 y 459 (78.2%) 1.8 (0.6; 4.5) 391 (75.3%) 2.1 (1.1; 5.2) 317 (100%) 2.3 (1.3; 5.5)
PFAS concentrations, median (IQR) (ng/mL)
 PFOS at 13 years 515 (87.7%) 6.7 (5.2; 8.5) 447 (86.1%) 6.7 (5.3; 8.5) 317 (100%) 6.8 (5.4; 8.7)
 PFOS at 7 y 488 (83.1%) 15.3 (12.4; 19.0) 420 (80.9%) 15.3 (12.4; 19.0) 312 (98.4%) 15.5 (12.9; 18.9)
 PFOA at 13 y 515 (87.7%) 2.0 (1.6; 2.5) 447 (86.1%) 2.0 (1.5; 2.5) 317 (100%) 2.0 (1.6; 2.6)
 PFOA at 7 y 488 (83.1%) 4.4 (3.5; 5.7) 420 (80.9%) 4.4 (3.6; 5.7) 312 (98.4%) 4.4 (3.5; 5.5)
 PFHxS at 13 y 515 (87.7%) 0.4 (0.3; 0.5) 447 (86.1%) 0.4 (0.3; 0.5) 317 (100%) 0.4 (0.3; 0.5)
 PFHxS at 7 y 488 (83.1%) 0.5 (0.4; 0.7) 420 (80.9%) 0.5 (0.4; 0.7) 312 (98.4%) 0.5 (0.4; 0.7)
 PFNA at 13 y 515 (87.7%) 0.7 (0.6; 0.9) 447 (86.1%) 0.7 (0.6; 0.9) 317 (100%) 0.8 (0.6; 1.0)
 PFNA at 7 y 488 (83.1%) 1.1 (0.9; 1.5) 420 (80.9%) 1.1 (0.9; 1.5) 312 (98.4%) 1.1 (0.9; 1.5)
 PFDA at 13 y 515 (87.7%) 0.3 (0.2; 0.4) 447 (86.1%) 0.3 (0.2; 0.4) 317 (100%) 0.3 (0.2; 0.4)
 PFDA at 7 y 488 (83.1%) 0.4 (0.2; 0.6) 420 (80.9%) 0.4 (0.2; 0.5) 312 (98.4%) 0.4 (0.2; 0.5)

Note: ER, emergency room; IQR, interquartile range; IU, international units; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

On average, both antibody concentrations showed clear decreases during the six-year period from 7 y to 13 y, although they were stronger for diphtheria (Table 1). At 13 y, 207 (39.4%) and 103 (19.6%) children had antibody concentrations <0.1 IU/mL for diphtheria and for tetanus, respectively. However, not all children showed the anticipated decrease in antibody concentrations. Scatter plots of the correlation between antibody concentrations at 7 y and 13 y show that concentrations increased between the two examinations in many cohort subjects (Figure 1). An increase in antibody concentration could likely be a result of additional antigen exposure, most likely because some subjects had received an unscheduled booster dose. Six of the children had received an additional booster at the project clinic because of their very low antibody concentrations after the age-5 booster. Questionnaire information further revealed that 68 cohort members had visited the emergency room where they likely received a tetanus booster shot, and indeed, many of them had elevated antibody concentrations at age 13 (Figure 1). Nevertheless, a total of 202 children did not show the anticipated decrease in antibody concentrations between 7 y and 13 y, most of whom were not known to have been vaccinated after 5 y. Statistical analyses were therefore performed on the whole group; after exclusion of subjects with a record of having visited the emergency room or otherwise having received an additional booster (no-ER group); and, for comparison purposes, after exclusion of the 202 subjects who for unknown reason did not show the anticipated decrease in antibody concentrations between 7 y and 13 y. These three groups were fairly similar with regard to sex, age, and PFAS exposure (Table 1).

Antibody concentrations in international units per milliliters are plotted on the y-axis for the 13-year-olds and on the x-axis for 7-year-olds according to whether or not there was a known visit at ER. The size of the dots increase with the number of measurements.
Figure 1. Scatter plot showing paired antibody concentrations for diphtheria (left) and tetanus (right) in children examined at both 7 y and 13 y.

Multiple regression analyses showed a uniformly inverse association between anti-diphtheria antibody concentrations at 13 y and the PFAS concentrations at either 7 y or 13 y (Table 2). The tendencies were the strongest after exclusion of subjects with a history of an additional booster, and an approximately 25% decrease for each doubling in serum PFOA concentration at 13 y and a 24% decrease for each doubling in serum PFDA concentration at 7 y were both statistically significant. However, tetanus antibody concentrations, which had decreased much less than diphtheria antibody concentrations, tended to show positive associations, one of which was statistically significant (Table 3). As expected (Grandjean et al. 2012), adjustment for developmental PCB exposure had no appreciable effect on these associations; therefore, PCB was not considered further. Logistic regression analyses of the results, many of which were close to the 0.1 IU/mL limit, in most cases showed odds close to 1 for the antibody concentrations being below the clinically protective level (data not shown). However, for diphtheria, a doubled concomitant serum PFOA concentration showed odds ratios of 1.47 (95% CI: 1.03, 2.14; p=0.038) and 1.71 (95% CI: 1.15, 2.55; p=0.008) for a nonprotective antibody level in the total study group and in the no-ER group, respectively. Further, PFDA at 7 y showed statistically significant odds ratios of 1.39 (95% CI: 1.05, 1.85; p=0.023) and 1.54 (95% CI: 1.13, 2.12; p=0.007) for diphtheria in the same two groups, respectively; no other associations reached statistical significance.

Table 2. Linear regression models of changes in anti-diphtheria concentrations at 13 y associated with serum PFAS concentrations 13 y and 7 y adjusted for sex, age at antibody assessment, and booster type.
Total cohort (n=587) No booster or ER visit (n=519) No booster or ER visit and no antibody increase (n=317)
PFAS (ng/mL) n Change 95% CI p-value n Change 95% CI p-value n Change 95% CI p-value
PFAS concentrations, 13 y
 PFOS 505 −8.6 −27.7, 15.6 0.454 439 −10.5 −29.8, 14.3 0.374 311 −0.6 −24.5, 30.9 0.965
 PFOA 505 −17.5 −35.6, 5.8 0.129 439 −25.3 −42.5, −3.0 0.029 311 −17.8 −38.0, 9.0 0.173
 PFHxS 505 −5.5 −22.9, 15.8 0.583 439 −10.9 −27.7, 9.8 0.279 311 −0.2 −20.4, 25.0 0.984
 PFNA 505 −4.5 −24.2, 20.2 0.693 439 −6.6 −26.7, 19.0 0.579 311 −3.7 −25.8, 25.2 0.780
 PFDA 505 −3.7 −22.0, 18.9 0.726 439 −3.5 −22.5, 20.3 0.754 311 −4.4 −24.9, 21.8 0.716
PFAS concentrations, 7 y
 PFOS 427 −23.8 −43.2, 2.3 0.070 361 −25.6 −45.4, 1.4 0.061 306 −10.8 −35.6, 23.5 0.490
 PFOA 427 −4.1 −25.4, 23.3 0.742 361 −9.2 −30.7, 18.8 0.480 306 −2.7 −26.4, 28.5 0.845
 PFHxS 427 −10.2 −25.7, 8.5 0.264 361 −16.3 −31.3, 2.0 0.077 306 −5.9 −23.4, 15.4 0.556
 PFNA 427 −11.3 −27.4, 8.5 0.243 361 −13.6 −30.6, 7.5 0.190 306 −7.0 −25.6, 16.1 0.519
 PFDA 427 −21.5 −34.4, −6.0 0.008 361 −24.2 −37.5, −8.0 0.005 306 −19.7 −34.0, −2.2 0.029

Notes: The change in the antibody concentration is expressed in percent per doubling of the serum PFAS concentration at the two different ages. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

Table 3. Linear regression models of changes in anti-tetanus concentrations at 13 y associated with serum-PFAS concentrations at 13 y and 7 y adjusted for sex, age at antibody assessment, and booster type.
Total cohort (n=587) No ER visit (n=519) No ER visit and no antibody increase (n=317)
PFAS (ng/mL) n Change 95% CI p-value n Change 95% CI p-value n Change 95% CI p-value
PFAS concentrations, 13 y
 PFOS 505 22.2 −12.4, 70.3 0.237 439 23.4 −7.0, 63.7 0.144 311 14.8 −8.7, 44.4 0.236
 PFOA 505 3.3 −27.3, 46.9 0.856 439 −5.6 −30.5, 28.1 0.710 311 −16.1 −33.7, 6.3 0.145
 PFHxS 505 8.7 −18.5, 45.0 0.568 439 19.3 −6.4, 52.1 0.153 311 1.8 −15.6, 22.9 0.851
 PFNA 505 15.2 −16.9, 59.7 0.394 439 5.1 −20.7, 39.3 0.727 311 11.6 −10.3, 38.8 0.324
 PFDA 505 18.7 −11.8, 59.8 0.258 439 6.9 −17.2, 38.0 0.607 311 18.0 −3.5, 44.4 0.106
PFAS concentrations, 7 y
 PFOS 427 30.0 −16.1, 101.4 0.240 361 45.4 1.2, 108.8 0.043 306 2.7 −21.8, 34.8 0.849
 PFOA 427 9.4 −24.7, 58.9 0.637 361 2.9 −25.0, 41.1 0.859 306 −4.9 −24.6, 20.0 0.671
 PFHxS 427 14.8 −13.3, 52.2 0.334 361 25.2 −0.6, 57.7 0.057 306 −11.3 −25.2, 5.2 0.167
 PFNA 427 31.0 −2.7, 76.4 0.075 361 23.1 −4.6, 59.0 0.110 306 11.9 −7.1, 34.7 0.235
 PFDA 427 36.8 4.7, 78.7 0.022 361 25.1 −0.4, 57.0 0.054 306 3.5 −12.3, 22.2 0.682

Notes: The change in the antibody concentration is expressed in percent per doubling of the serum PFAS concentration at the two different ages. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

In the first structural equation model, we included an indirect effect mediated by the exposure at 7 y via the antibody concentration at that age. Tables 4 and 5 show the indirect effect and the total effect observed in this model. For diphtheria, all associations were inverse, and all five PFASs showed statistically significant inverse associations in the no-ER group. Tendencies were weaker in the total group and after exclusion of subjects without decreasing antibody concentrations. For tetanus, some inverse associations were observed, although none was significant. A second model without a direct effect fitted the data equally well and showed a similar, though stronger, indirect effect for diphtheria via the age-7 antibody concentration. These tendencies also became clear for tetanus, where statistically significant indirect effects were now apparent in the no-ER group, both for PFOA (–24.2; 95% CI: −41.1, −2.4) and for PFHxS (–25.1; 95% CI: −38.9, −8.3). Finally, the results obtained with the linear mixed models were similar to those obtained with the first structural equation model.

Table 4. Effects of serum-PFAS concentrations at 7 y on anti-diphtheria antibody concentrations at 7 y and 13 y adjusted for age and sex in structural equation models.
Total cohort (n=587) No ER visit (n=519) No ER visit and no antibody increase (n=317)
PFAS (ng/mL) Effect Change 95% CI p-value Change 95% CI p-value Change 95% CI p-value
PFOS Indirect −29.7 −44.3, −11.3 0.003 −32.8 −47.9, −13.3 0.002 −22.3 −40.3, 1.2 0.061
Total −26.5 −45.7, −0.5 0.046 −31.1 −49.8, −5.4 0.021 −16.0 −38.8, 15.4 0.282
PFOA Indirect −17.7 −32.24, −0.0 0.050 −19.8 −35.4, −0.5 0.045 −15.0 −31.8, 5.9 0.147
Total −4.3 −26.0, 23.8 0.739 −9.4 −31.1, 19.2 0.481 −5.8 −27.8, 22.9 0.661
PFHxS Indirect −13.4 −25.9, 1.2 0.071 −16.2 −29.3, −0.6 0.042 −8.6 −22.6, 7.9 0.289
Total −12.0 −28.0, 7.5 0.211 −19.5 −34.7, −0.7 0.043 −8.0 −24.6, 12.4 0.415
PFNA Indirect −20.7 −31.8, −7.8 0.003 −23.3 −35.3, −9.0 0.002 −18.1 −31.6, −1.9 0.030
Total −15.6 −31.1, 3.2 0.099 −17.4 −33.7, 2.8 0.087 −9.4 −27.0, 12.5 0.371
PFDA Indirect −19.6 −28.8, −9.2 <0.001 −20.7 −30.7, −9.2 0.001 −17.0 −27.8, −4.7 0.008
Total −22.5 −34.5, −8.3 0.003 −25.1 −37.3, −10.4 0.002 −19.8 −32.6, −4.6 0.013

Notes: The change in the anti-diphtheria concentration is expressed in percent per doubling of the age-7 serum PFAS concentration. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

Table 5. Effects of serum-PFAS concentrations at 7 y on anti-tetanus antibody concentrations at 7 y and 13 y adjusted for age and sex in structural equation models.
Total cohort (n=587) No ER visit (n=519) No ER visit and no antibody increase (n=317)
PFAS (ng/mL) Effect Change 95% CI p-value Change 95% CI p-value Change 95% CI p-value
PFOS Indirect 3.2 −5.7, 12.9 0.492 −2.2 −6.5, 2.4 0.347 −1.4 −21.7, 24.2 0.906
Total 26.3 −17.2, 92.6 0.278 42.9 −2.8, 110.0 0.069 2.1 −21.5, 32.9 0.877
PFOA Indirect 6.0 −1.9, 14.4 0.138 −3.2 −7.7, 1.5 0.181 −16.0 −30.7, 1.8 0.075
Total 11.3 −22.3, 59.3 0.560 1.9 −27.3, 42.6 0.915 −7.2 −25.5, 15.6 0.505
PFHxS Indirect 7.7 0.6, 15.3 0.033 −3.7 −8.1, 0.8 0.107 −12.4 −24.2, 1.2 0.072
Total 14.1 −13.1, 49.8 0.342 25.0 −2.3, 59.8 0.075 −12.1 −25.4, 3.7 0.127
PFNA Indirect 1.0 −4.7, 6.9 0.739 −1.1 −3.8, 1.6 0.416 4.2 −10.9, 21.9 0.604
Total 28.9 −3.1, 71.5 0.081 23.6 −6.0, 62.5 0.129 12.7 −5.8, 34.8 0.190
PFDA Indirect −0.8 −5.4, 4.0 0.729 0.1 −1.8, 2.0 0.936 7.8 −4.7, 21.8 0.231
Total 27.5 −0.3, 62.9 0.053 16.7 −7.7, 47.5 0.196 2.7 −11.1, 18.5 0.721

Notes: The change in the anti-tetanus concentration is expressed in percent per doubling of the age-7 serum PFAS concentration. CI, confidence interval; ER, emergency room; PFAS, perflourinated alkylate substance; PFDA, perfluorodecanoate; PFHxS, perfluorohexanesulfonic acid; PFNA, perfluorononanoate; PFOA, perflourooctanoate; PFOS, perfluorooctane sulfonate.

Discussion

The present prospective study was performed in adolescents to ascertain PFAS-associated decreases in antibody responses to their childhood vaccines. Owing to numerous unscheduled booster vaccinations, the data were censored to remove cohort members with records of emergency room visits. In this subgroup, multiple regression results showed that diphtheria antibody concentrations decreased at elevated serum PFAS concentrations at 13 y and 7 y; the associations were statistically significant for PFDA at 7 y and for PFOA at 13 y, both suggesting a decrease by ∼25% for each doubling of exposure. The findings after exclusion of all subjects without a decrease in antibody concentrations were less clear. Structural equation models showed that a doubling of PFAS exposure at 7 y was associated with statistically significant losses in diphtheria antibody concentrations at 13 y of 10–30% for the five PFASs. Few associations were observed for anti-tetanus concentrations. However, more advanced modeling showed negative associations for tetanus with regard to the age-7 PFAS exposure. Owing to the intercorrelations between the serum PFAS concentrations, further analysis of the possible role of individual PFASs was not pursued, and the observed associations may reflect the effects of the PFAS mixtures.

Although the present study aimed to obtain prospective data on the associations between PFAS exposure and vaccine antibody concentration over an extended period, an antibody concentration at a particular point in time does not represent the complete trajectory of changes and may not be representative of the protection against the specific disease in the long term. The present prospective study is apparently the first to elucidate the temporal changes in antibody concentrations in relation to PFAS immunotoxicity through to adolescence. A major obstacle in such observational studies is that children and adolescents who visit the emergency room for cuts and other injuries are routinely administered a tetanus booster, which also affects diphtheria because both toxoids are present in the vaccine, thereby interfering with the study design. We therefore chose to exclude subjects who had a record of an emergency room visit (to obtain no-ER group). For comparison, we performed parallel calculations after excluding >200 subjects who had not revealed the anticipated decrease in antibody concentrations between 7 y and 13 y. With antibody concentrations that decrease to different degrees over time and with PFAS exposure levels that likewise show decreases, multiple regression analyses of the bivariate data sets need to be complemented by more-advanced modeling.

Our study is limited to PFAS exposure assessments at two points during childhood at an interval of approximately six years. Although the elimination half-lives of the PFASs are 2–5 y (Bartell et al. 2010; Olsen et al. 2007), the two widely separated measurements may not fully characterize the childhood exposure profile. Because exposures during childhood are likely to vary (Kato et al. 2009; Lindstrom et al. 2011), it is possible that serial serum PFAS analyses would provide stronger evidence for PFAS immunotoxicity. However, given that a decreased antibody concentration is most likely due to past exposures, rather than to current, lower exposures (Mogensen et al. 2015), we chose to rely on the age-7 exposure levels and to include both antibody measurements at 7 y and 13 y in the assessment. At age 7, concurrent serum PFAS concentrations showed strong inverse associations with vaccine antibody concentrations, and inclusion of age-5 PFAS concentrations slightly strengthened these tendencies (Mogensen et al. 2015).

The 5-y booster vaccination is in principle the last booster vaccination that a child receives, and long-term protection is therefore intended. Our previous results (Grandjean et al. 2012) showed that at 7 y, many children had antibody concentrations below the level assumed to provide the desired protection. The number of children with antibody concentrations below the desired level had substantially increased by age 13 and was similar to the number of children with antibody concentrations below the level of protection at age 5 before the booster. These observations support the justification for routinely supplying a booster vaccination in the emergency room in connection with any injury that may have involved the slightest risk of tetanus. Unfortunately, such preventive measures add variance to the present study design, which relied on all children being vaccinated at the same age and with the same antigen dose. The study assumption was therefore violated, and it proved to be difficult to adjust for additional immunizations at different ages given the incomplete knowledge of booster administrations. Accordingly, the results clearly show the strongest associations between PFAS exposure and antibody concentrations in cohort subjects who were not known to have visited the emergency room or to otherwise have received a booster. However, Figure 1 also shows an increased antibody concentration at 13 y in many cohort members, possibly resulting from a booster that had not been recorded. Exclusion of all cohort members without an apparent decrease in antibody concentration at 13 y may have more than remedied this problem because it most likely excluded too many subjects, thereby attenuating the statistical power. As might be expected, the results for the most restricted subgroup are somewhat weaker than those for the less restricted cohort. Nevertheless, the increased antibody concentration in children who had received a booster suggests that any adverse influences of PFAS exposure could be remedied by repeated antigen challenge, although such intervention might not compensate for any other adverse effects associated with PFAS immunotoxicity.

As in previous studies (Grandjean et al. 2012; Mogensen et al. 2015), we found stronger associations for diphtheria than for tetanus. The former also exhibited much greater decreases from 7 y to 13 y, most likely because the diphtheria toxoid is a weaker antigen than tetanus and is therefore more easily affected by PFAS-depressed immune function. The exact magnitude of the serum antibody concentration may not be clinically important, but very low levels will result in poor or absent protection. With many antibody concentrations being close to the assumed clinically protective level of 0.1 IU/mL, logistic regression showed only weak tendencies for antibody levels below the limit to be associated with serum PFAS concentrations. Similarly, imprecision of antibody assessments, particularly at concentrations below or close to the clinically protective concentration, may have biased the regression analyses toward the null.

In support of our findings of PFAS immunotoxicity, a study of 99 Norwegian children at 3 y found that the maternal serum PFOA concentrations were associated with decreased vaccine responses in the children, particularly toward rubella vaccine, as well as increased frequencies of common cold and gastroenteritis (Granum et al. 2013). However, PFOS and PFOA concentrations in serum from 1,400 pregnant women from the Danish National Birth Cohort were not associated with the total hospitalization rate for a variety of infectious diseases in 363 of the children up to an average age of 8 y (Fei et al. 2010). However, the validity of this study has been questioned owing to the poor stability of the serum PFAS measurements (Bach et al. 2015); such imprecision of the exposure assessment could easily bias the results toward the null (Grandjean and Budtz-Jørgensen 2010).

In adults, PFOA exposure from contaminated drinking water was associated with lower serum concentrations of total IgA and IgE (in females only), although not of total IgG (Fletcher et al. 2009). More specifically, a reduced antibody titer increase after influenza vaccination was found in the most highly exposed subjects (Looker et al. 2014). Further, a small intervention study of 12 adults showed that the time-dependent increase in vaccine-specific antibody concentrations decreased at higher PFAS exposures (Kielsen et al. 2015). Overall, support is building for the notion that PFAS exposure is associated with deficient immune function. Although diphtheria and perhaps tetanus may not be likely hazards in the Faroese and in the residents of many other countries, the strongly decreased antibody concentrations likely reflect an immunological deficit. Because optimal immune system function is crucial for health, the associations identified should be regarded as adverse. We recently calculated benchmark dose levels to estimate the magnitude of exposure limits that would protect against the immunotoxicity observed. The results suggested that the present exposure limits may be too high by a factor of 100 (Grandjean and Budtz-Jørgensen 2013). The present study extends the previous findings of deficient antibody responses in this cohort at younger ages and therefore adds support to the notion that substantially strengthened prevention of PFAS exposure is indicated.

Conclusions

The results of the present study are in accord with previous findings of PFAS immunotoxicity at current exposure levels, although the results of this observational study are affected by concomitant decreases in concentrations of both PFASs and antibodies and by known and suspected booster vaccinations during the eight-year interval since the routine 5-y booster.

Acknowledgments

This study was supported by the National Institute of Environmental Health Sciences, National Institutes of Health (ES012199); the U.S. Environmental Protection Agency (R830758); the Danish Council for Strategic Research (09-063094); and the Danish Environmental Protection Agency as part of the environmental support program DANCEA (Danish Cooperation for Environment in the Arctic). The authors are solely responsible for all results and conclusions, which do not necessarily reflect the position of any of the funding agencies.

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Bartell SM, Calafat AM, Lyu C, Kato K, Ryan PB, Steenland K. 2010. Rate of decline in serum PFOA concentrations after granular activated carbon filtration at two public water systems in Ohio and West Virginia. Environ Health Perspect 118(2):222–228, PMID: 20123620, 10.1289/ehp.0901252.

Capua T, Katz JA, Bocchini JA, Jr. 2013. Update on adolescent immunizations: Selected review of U.S. recommendations and literature. Curr Opin Pediatr 25(3):397–406, PMID: 23652687, 10.1097/MOP.0b013e328360dc63.

Corsini E, Sangiovanni E, Avogadro A, Galbiati V, Viviani B, Marinovich M, et al. 2012. In vitro characterization of the immunotoxic potential of several perfluorinated compounds (PFCs). Toxicol Appl Pharmacol 258(2):248–255, PMID: 22119708, 10.1016/j.taap.2011.11.004.

DeWitt JC, Peden-Adams MM, Keller JM, Germolec DR. 2012. Immunotoxicity of perfluorinated compounds: Recent developments. Toxicol Pathol 40(2):300–311, PMID: 22109712, 10.1177/0192623311428473.

Fei C, McLaughlin JK, Lipworth L, Olsen J. 2010. Prenatal exposure to PFOA and PFOS and risk of hospitalization for infectious diseases in early childhood. Environ Res 110(8):773–777, PMID: 20800832, 10.1016/j.envres.2010.08.004.

Fletcher T, Steenland K, Savitz D. 2009. Status report: PFOA and immune biomarkers in adults exposed to PFOA in drinking water in the mid Ohio Valley. In C8 Science Panel Report. http://www.c8sciencepanel.org/pdfs/Status_Report_C8_and_Immune_markers_March2009.pdf [accessed 5 July 2017].

Grandjean P, Andersen EW, Budtz-Jørgensen E, Nielsen F, Mølbak K, Weihe P, et al. 2012. Serum vaccine antibody concentrations in children exposed to perfluorinated compounds. JAMA 307(4):391–397, PMID: 22274686, 10.1001/jama.2011.2034.

Grandjean P, Budtz-Jørgensen E. 2010. An ignored risk factor in toxicology: The total imprecision of exposure assessment. Pure Appl Chem 82(2):383–391, PMID: 20419070, 10.1351/PAC-CON-09-05-04.

Grandjean P, Budtz-Jørgensen E. 2013. Immunotoxicity of perfluorinated alkylates: Calculation of benchmark doses based on serum concentrations in children. Environ Health 12(1):35, PMID: 23597293, 10.1186/1476-069X-12-35.

Grandjean P, Clapp R. 2015. Perfluorinated alkyl substances: Emerging insights into health risks. New Solut 25(2):147–163, PMID: 26084549, 10.1177/1048291115590506.

Granum B, Haug LS, Namork E, Stølevik SB, Thomsen C, Aaberge IS, et al. 2013. Pre-natal exposure to perfluoroalkyl substances may be associated with altered vaccine antibody levels and immune-related health outcomes in early childhood. J Immunotoxicol 10(4):373–379, PMID: 23350954, 10.3109/1547691x.2012.755580.

Hastie TJ, Tibshirani RJ. 1990. Generalized additive models (monographs on statistics and applied probability 43). Boca Raton, FL:Chapman and Hall/CRC Press.

Heilmann C, Budtz-Jørgensen E, Nielsen F, Heinzow B, Weihe P, Grandjean P. 2010. Serum concentrations of antibodies against vaccine toxoids in children exposed perinatally to immunotoxicants. Environ Health Perspect 118(10):1434–1438, PMID: 20562056, 10.1289/ehp.1001975.

Kato K, Calafat AM, Wong LY, Wanigatunga AA, Caudill SP, Needham LL. 2009. Polyfluoroalkyl compounds in pooled sera from children participating in the National Health and Nutrition Examination Survey 2001–2002. Environ Sci Technol 43(7):2641–2647, PMID: 19452929, 10.1021/es803156p.

Kielsen K, Shamim Z, Ryder LP, Nielsen F, Grandjean P, Budtz-Jorgensen E, et al. 2015. Antibody response to booster vaccination with tetanus and diphtheria in adults exposed to perfluorinated alkylates. J Immunotoxicol 12(2):270–273, PMID: 26181512, 10.3109/1547691X.2015.1067259.

Lindstrom AB, Strynar MJ, Libelo EL. 2011. Polyfluorinated compounds: past, present, and future. Environ Sci Technol 45(19):7954–7961, PMID: 21866930, 10.1021/es2011622.

Looker C, Luster MI, Calafat AM, Johnson VJ, Burleson GR, Burleson FG, et al. 2014. Influenza vaccine response in adults exposed to perfluorooctanoate and perfluorooctanesulfonate. Toxicol Sci 138(1):76–88, PMID: 24284791, 10.1093/toxsci/kft269.

MacGillivray DM, Kollmann TR. 2014. The role of environmental factors in modulating immune responses in early life. Front Immunol 5:434, PMID: 25309535, 10.3389/fimmu.2014.00434.

Mogensen UB, Grandjean P, Heilmann C, Nielsen F, Weihe P, Budtz-Jørgensen E. 2015. Structural equation modeling of immunotoxicity associated with exposure to perfluorinated alkylates. Environ Health 14:47, PMID: 26041029, 10.1186/s12940-015-0032-9.

Olsen GW, Burris JM, Ehresman DJ, Froehlich JW, Seacat AM, Butenhoff JL, et al. 2007. Half-life of serum elimination of perfluorooctanesulfonate, perfluorohexanesulfonate, and perfluorooctanoate in retired fluorochemical production workers. Environ Health Perspect 115(9):1298–1305, PMID: 17805419, 10.1289/ehp.10009.

Rubin DB. 1987. Multiple Imputation for Nonresponse in Surveys. New York, NY:John Wiley & Sons.

Schatz D, Ellis T, Ottendorfer E, Jodoin E, Barrett D, Atkinson M. 1998. Aging and the immune response to tetanus toxoid: diminished frequency and level of cellular immune reactivity to antigenic stimulation. Clin Diagn Lab Immunol 5(6):894–896.

Steenland K, Savitz DA, Fletcher T. 2014. Commentary: class action lawsuits: can they advance epidemiologic research?. Epidemiology 25(2):167–169, PMID: 24487199, 10.1097/EDE.0000000000000067.

Updating the NIEHS Strategic Plan

Author Affiliations open

1Director, National Institute of Environmental Health Sciences, and

2Director, National Toxicology Program, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA. Email: birnbaumls@niehs.nih.gov

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  • Published: 26 July 2017

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In 2011, the National Institute of Environmental Health Sciences (NIEHS) undertook a multipronged process to develop a five-year strategic plan. We incorporated four key elements into this process to maximize the usefulness of its outcome as a guide for NIEHS and for the global environmental health research community. It was consultative in seeking the combined wisdom of a wide range of relevant experts who were both internal and external to NIEHS. It was innovative in making use of both a public website where interested people could submit and rate “Visionary Ideas” and an Open Space meeting format designed to encourage nontraditional thinking about our field. It was inclusive in encouraging the participation of diverse stakeholders through an extensive awareness campaign and in offering multiple access points into the process. And it was transparent, using a public commenting forum, meeting summaries, a dedicated website to provide information throughout the process, and a publicly accessible home for the plan once it was published (https://www.niehs.nih.gov/about/strategicplan/index.cfm). The process for developing our next plan, which we began in June with an announcement at our National Advisory Environmental Health Science Council meeting, will incorporate these same elements.

Our investment in the 2011–2012 process resulted in a strategic plan (NIEHS/NIH 2012) that captured the scientific and training priorities of the diverse disciplines that comprise environmental health while setting new paths of exploration in areas such as data technologies, emerging threats, and economic evaluation. The plan articulated our mission and vision and set goals in 11 discrete areas supported by the crosscutting themes of Collaborative and Innovative Approaches and Knowledge Management. NIEHS immediately set about putting the 2012–2017 plan into action by creating teams to devise ways to implement the goals and integrate their priorities across the Institute’s divisions and programs.

As NIEHS approaches the end of its 2012–2017 Strategic Plan (NIEHS/NIH 2012), it is not only appropriate but also critical that we review the efforts expended, the progress made, and the shortfalls remaining in the pursuit of its goals. As a U.S. government organization, we have a responsibility to account for our stewardship of the public’s resources and trust. As a leader in environmental health, both profession and practicality require that we share the knowledge we generate across the global research, public health, and biomedical research communities to advance our field. Such reviews also offer us the opportunity to inform, refine, and expand future goals with the advantage of hindsight. Our current strategic plan (NIEHS/NIH 2012) has functioned as an invaluable roadmap for NIEHS and for the entire field of environmental health.

The evidence to support this assertion has been collected and documented on our strategic plan implementation website (https://www.niehs.nih.gov/about/strategicplan/implementation/index.cfm), which I invite you to explore. This site details accomplishments including 52 research funding opportunity announcements; more than 18,000 publications from NIEHS-funded work; new collaborations on topics as diverse as children’s environmental health, metabolomics, and Zika; new tools and resources to facilitate both research and its dissemination; and landmark convening of experts to focus on some of the most complex environmental health issues both in the United States and around the world. Although they are extensive, these pages are only highlights because not every possible outcome of the Strategic Plan can be assessed. And just as with effects of environmental exposures, the impacts of our research and programs are often measurable only over time, so evaluation is an ongoing process. Nevertheless, I believe that the evidence bears me out when I say that the level to which we have sought to use this Strategic Plan to guide, focus, and integrate efforts across NIEHS over the last five years is unprecedented.

The momentum of these successes propels us forward as we develop a plan to guide our next five years. We begin, as always, by seeking broad input. To this end, we have created an online survey, “Trends & Insights: Next Steps for NIEHS” (https://www.research.net/r/niehs_strategic_plan), through which anyone can provide feedback on the existing Strategic Plan as well as offer any other relevant comments. This survey will be available until 11 August 2017, and the results, along with input collected in other venues, will help to inform a draft Strategic Plan that we anticipate making available for review later this year.

I invite you to join NIEHS in this strategic planning process as we continue to provide global leadership that is focused on ensuring ongoing support for environmental health science, that are flexible enough that we can take advantage of new paradigms and capacities, and most importantly, that are fit for the purpose of achieving our mission: to discover how the environment affects people in order to promote healthier lives.

Reference

NIEHS/NIH (National Institute of Environmental Health Sciences/National Institutes of Health). 2012. “Advancing Science, Improving Health: A Plan for Environmental Health Research.” NIH Publication 12-7935. https://www.niehs.nih.gov/about/strategicplan/strategicplan2012_508.pdf [accessed 17 July 2017].

Location Is Everything: The Pollutants in Yellowfin Tuna Depend on Where It’s Caught

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  • Published: 25 July 2017

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Related EHP Article

Geographic Differences in Persistent Organic Pollutant Levels of Yellowfin Tuna

Sascha C.T. Nicklisch, Lindsay T. Bonito, Stuart Sandin, and Amro Hamdoun

Fish is a highly nutritious food, but it can also be a dietary source of persistent organic pollutants (POPs).1 In a new study in EHP, researchers investigated the extent to which contaminant levels within a single commercially important fish species varied depending on where the fish was caught.2 Their results suggest that capture location may be an important yet overlooked variable when assessing the risk of exposure to POPs from eating wild fish.

Governmental fish consumption advisories suggest limiting intake of certain fish species to reduce human exposures to POPs.3 These potentially harmful chemicals accumulate in body fat, and larger animals farther up the food chain tend to have higher levels. That is why characteristics, such as a fish’s species, fat content, body size, and trophic level, are used as predictors of pollutant load.4

Photo of a yellowfin tuna on a dock.
A fisherman unloads yellowfin tuna at a market in Semporna, Malaysia. Because these large predators do not migrate widely, their body burdens of persistent organic pollutants (POPs) are a good indicator of how polluted their home waters might be. © shahreen/Shutterstock.

However, pollutant levels are not uniform across the world’s oceans. Instead, POPs form a patchy distribution. Many of the fish species widely consumed in the United States, such as tuna, are harvested throughout the world’s oceans, says the study’s senior author Amro Hamdoun, a professor of biology at the Scripps Institution of Oceanography, University of California, San Diego. Geographic variation in pollutant levels in ocean waters could therefore affect the safety of fish that people eat.

Researchers from the Scripps Institute of Oceanography, led by Sascha Nicklisch, analyzed POPs in the dorsal muscle fillets of 117 wild-caught yellowfin tuna from 12 capture locations in the Atlantic, Eastern Pacific, Western Pacific, and Indian oceans. These commonly eaten fish are found around the world, but they do not travel far,5 making them a likely sentinel of geographic variation in POP levels.2

The authors found that levels of pollutants in tuna varied between sites by a factor of 36. Fish caught in the offshore waters of North America and Europe had pollutant levels that were, on average, more than an order of magnitude higher than fish caught in the waters of Asia and Oceania. The 10 most contaminated fish, which were found in the Northeast Pacific Ocean, the Gulf of Mexico, and the Northeast Atlantic Ocean, had POP levels of 20–29 ng/g wet weight of fish. The 10 least contaminated fish, taken from the Northwest and Southwest Pacific Ocean, the South China Sea, and the Indian Ocean, had pollutant levels of 0.2–0.4 ng/g wet weight.2

The researchers also measured specific POPs belonging to a group of chemicals they have dubbed transporter interfering compounds (TICs). In earlier work, they identified TICs as compounds that may interfere with transporter proteins, hampering their ability to eliminate foreign substances from the body.6 In the current study, the researchers found that the concentrations and distribution of TICs reflected those of total POPs. Hamdoun says, “TICs could help explain why certain chemicals are so persistent in body tissue,” although future research is needed to better understand the connection.

To assess the impact of geographic variation on risk-based consumption advice, the researchers calculated monthly meal recommendations based on POP levels in individual fish. Based on their estimates, most of the yellowfin tuna they caught would have been safe to eat in unlimited quantities, says Hamdoun. However, 9 of the 10 fish captured in the Northeast Atlantic Ocean and 5 of the 8 fish caught in the Gulf of Mexico contained POP levels that would trigger health advisories even for people who eat less fish than currently recommended by the American Heart Association (AHA) and U.S. Department of Agriculture (USDA). USDA dietary guidelines suggest that people eat 12 ounces of cooked fish per week,7 while the AHA recommends two 3.5-ounce servings of cooked fish each week.8

The new findings expand on earlier studies that pointed out that capture location might be an important factor in the contaminant load in fish. In one study, researchers used skipjack tuna to assess the distribution of POPs, including polychlorinated biphenyls and organochlorine pesticides in different parts of the Pacific Ocean.9 In another, farm-raised salmon from Europe had higher levels of organochlorine pesticides than those raised in North or South America.10 However, these studies were limited geographically, compared with the current study.

Unlike previous studies of farmed and wild-caught fish, Hamdoun and colleagues did not always find that POP levels correlated with the size or body fat percentage of the fish; in many cases, he says, fatty fish from a clean site were found to have lower levels of POPs than lean fish from a more polluted site. Based on these findings, Hamdoun concludes that lipid content alone is not enough to predict pollutant load when comparing fish from different sites.

According to David Carpenter, a public health physician at State University of New York at Albany who has studied POPs in farmed and wild-caught salmon and Great Lakes fish, these results are unexpected, since POPs accumulate in lipids. Carpenter, who was not involved in the current study, says it is possible that too few fish were captured at each location to suss out statistically significant differences between bigger and smaller fish at each site.

Overall, says Carpenter, the new study adds to mounting evidence that “where a fish comes from is an important factor to consider in determining likely contaminant loads and fish consumption advisories.” Unfortunately, he adds, the new findings are likely to frustrate consumers who seldom receive information about capture location when purchasing seafood at supermarkets and restaurants.


Lindsey Konkel is a New Jersey–based journalist who reports on science, health, and the environment.

References

1. Schecter A, Haffner D, Colacino J, Patel K, Päpke O, Opel M, et al. 2010. Polybrominated diphenyl ethers (PBDEs) and hexabromocyclodecane (HBCD) in composite U.S. food samples. Environ Health Perspect 118(3):357–362, PMID: 20064778, 10.1289/ehp.0901345.

2. Nicklisch SCT, Bonito LT, Sandin S, Hamdoun A. 2017. Geographic differences in persistent organic pollutant levels of yellowfin tuna. Environ Health Perspect 125(6):067014, PMID: 28686554, 10.1289/EHP518.

3. U.S. Environmental Protection Agency. 2000. Guidance for assessing chemical contaminant data for use in fish advisories. In: Risk Assessment and Fish Consumption Limits, Vol 2. 3rd edition. Washington, DC:Office of Science and Technology, Office of Water, U.S. Environmental Protection Agency. https://www.epa.gov/sites/production/files/2015-06/documents/volume2.pdf [accessed 21 February 2017].

4. Elskus A, Collier TK, Monosson E. 2005. Interactions between lipids and persistent organic pollutants in fish. In: Biochemistry and Molecular Biology of Fishes, Vol. 6. Mommsen TP, Moon TW, eds. San Diego, CA:Elsevier, 119–152.

5. Block BA, Jonsen ID, Jorgensen SJ, Winship AJ, Shaffer SA, Bograd SJ, et al. 2011. Tracking apex marine predator movements in a dynamic ocean. Nature 475(7354):86–90, PMID: 21697831, 10.1038/nature10082.

6. Nicklisch SCT, Rees SD, McGrath AP, Gökirmak T, Bonito LT, Vermeer LM, et al. 2016. Global marine pollutants inhibit P-glycoprotein: environmental levels, inhibitory effects, and cocrystal structure. Sci Adv 2(4):e1600001, PMID: 27152359, 10.1126/sciadv.1600001.

7. U.S. Department of Agriculture. 2015. Dietary Guidelines for Americans 2015–2020. 8th edition. Washington, DC:U.S. Department of Agriculture. https://health.gov/dietaryguidelines/2015/resources/2015-2020_Dietary_Guidelines.pdf [accessed 21 February 2017].

8. American Heart Association. 2016. Fish and Omega-3 Fatty Acids. Updated 6 October 2016. Dallas:TX:American Heart Association. http://www.heart.org/HEARTORG/HealthyLiving/HealthyEating/HealthyDietGoals/Fish-and-Omega-3-Fatty-Acids_UCM_303248_Article.jsp#.WKxzwBIrLJw [accessed 21 February 2017].

9. Ueno D, Takahashi S, Tanaka H, Subramanian AN, Fillmann G, Nakata H, et al. 2003. Global pollution monitoring of PCBs and organochlorine pesticides using skipjack tuna as a bioindicator. Arch Environ Contam Toxicol 45(3):378–389, PMID: 14674591, 10.1007/s00244-002-0131-9.

10. Hites RA, Foran JA, Carpenter DO, Hamilton MC, Knuth BA, Schwager SJ. 2004. Global assessment of organic contaminants in farmed salmon. Science 303(5655):226–229, PMID: 14716013, 10.1126/science.1091447.

Race/Ethnicity, Socioeconomic Status, Residential Segregation, and Spatial Variation in Noise Exposure in the Contiguous United States

Author Affiliations open

1Robert Wood Johnson Foundation Health & Society Scholars Program, University of California, San Francisco and University of California, Berkeley, California, USA

2Department of Environmental Science, Policy, and Management, and the School of Public Health; University of California, Berkeley, California, USA

3Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, Colorado, USA

4Natural Sounds and Night Skies Division, Natural Resource Stewardship and Science Directorate, National Park Service, Fort Collins, Colorado, USA

5Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

6Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute; Departments of Epidemiology and Environmental Health, Harvard TH Chan School of Public Health; Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA

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  • Background:
    Prior research has reported disparities in environmental exposures in the United States, but, to our knowledge, no nationwide studies have assessed inequality in noise pollution.
    Objectives:
    We aimed to a) assess racial/ethnic and socioeconomic inequalities in noise pollution in the contiguous United States; and b) consider the modifying role of metropolitan level racial residential segregation.
    Methods:
    We used a geospatial sound model to estimate census block group–level median (L50) nighttime and daytime noise exposure and 90th percentile (L10) daytime noise exposure. Block group variables from the 2006–2010 American Community Survey (ACS) included race/ethnicity, education, income, poverty, unemployment, homeownership, and linguistic isolation. We estimated associations using polynomial terms in spatial error models adjusted for total population and population density. We also evaluated the relationship between race/ethnicity and noise, stratified by levels of metropolitan area racial residential segregation, classified using a multigroup dissimilarity index.
    Results:
    Generally, estimated nighttime and daytime noise levels were higher for census block groups with higher proportions of nonwhite and lower-socioeconomic status (SES) residents. For example, estimated nighttime noise levels in urban block groups with 75% vs. 0% black residents were 46.3 A-weighted decibels (dBA) [interquartile range (IQR): 44.3–47.8 dBA] and 42.3 dBA (IQR: 40.4–45.5 dBA), respectively. In urban block groups with 50% vs. 0% of residents living below poverty, estimated nighttime noise levels were 46.9 dBA (IQR: 44.7–48.5 dBA) and 44.0 dBA (IQR: 42.2–45.5 dBA), respectively. Block groups with the highest metropolitan area segregation had the highest estimated noise exposures, regardless of racial composition. Results were generally consistent between urban and suburban/rural census block groups, and for daytime and nighttime noise and robust to different spatial weight and neighbor definitions.
    Conclusions:
    We found evidence of racial/ethnic and socioeconomic differences in model-based estimates of noise exposure throughout the United States. Additional research is needed to determine if differences in noise exposure may contribute to health disparities in the United States. https://doi.org/10.1289/EHP898
  • Received: 01 August 2016
    Revised: 06 February 2017
    Accepted: 06 February 2017
    Published: 25 July 2017

    Address correspondence to J.A. Casey, University of California, Berkeley, 13B University Hall, Berkeley, CA 94610 USA. Telephone: 541-760-8477; Email: joanacasey@berkeley.edu

    Supplemental Material is available online (https://doi.org/10.1289/EHP898).

    The authors declare they have no actual or potential competing financial interests.

    Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact ehponline@niehs.nih.gov. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

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    PDF icon Supplemental Table of Contents PDF (99 KB)

Introduction

A growing body of evidence links environmental noise—a biologic stressor usually generated by mechanized sources: transportation, industry, power generation, power tools, and air-conditioning—to hearing loss and other health outcomes (Basner et al. 2014). The human body initially reacts to noise with activation of the central nervous system, even while asleep. This can result in release of stress hormones and increased blood pressure, heart rate, and cardiac output (Evans et al. 1995; Lercher 1996). While individual noise sensitivities differ, the World Health Organization (WHO) estimated a “no observed effect level” for average outdoor nighttime noise of 30 A-weighted decibels (dBA) based on evidence that sleep is not disturbed by noise below 30 (dBA) (WHO 2009). The Federal Highway Administration noise abatement criteria near hospitals and schools is 70 dBA, a recommendation that balances health, communication, and economic interests (U.S. DOT 2015). Exposure to these noise levels has been associated with impaired cognitive performance (Clark et al. 2012) and behavioral problems in children (Hjortebjerg et al. 2016), as well as hypertension (van Kempen and Babisch 2012), type 2 diabetes (Sørensen et al. 2013), cardiovascular disease (Gan et al. 2012), and reduced birth weight (Gehring et al. 2014). The WHO (2011) has estimated >1 million disability adjusted life years are lost annually in Western Europe due to environmental noise, attributable primarily to annoyance and sleep disturbance. The WHO calculation was based on estimated noise exposures and previous research on associations between noise and health outcomes.

Environmental noise is typically measured as sound pressure level, a logarithmic quantity expressed in decibels (dB); for example, an increase of 3 dB is a doubling of sound energy. With every 5.5-dB increase, the proportion of individuals highly annoyed by residential noise exposure appears to double (ANSI 2003). Measurements of sound pressure level are commonly adjusted by A-weighting to reflect how humans perceive sound across frequency, denoted as dBA (Murphy and King 2014). Because sound levels vary over time, metrics describing the statistical behavior of the variation are utilized. The energy average, or equivalent, indicator is abbreviated Leq. Multiple exceedance levels are used to characterize magnitude, rate of occurrence, and duration of environmental noise. The L50 is the noise level exceeded half of the time, whereas the L10 is the level exceeded 10% of the time.

Like other exposures, the impact of noise varies by intensity, duration, and frequency. Noise sensitivity or degree of reactivity to the same level of noise can differ from person to person and by source of noise (Janssen et al. 2011; van Kamp et al. 2004). Time of day may also play a role, such that associations between noise and health outcomes appear to be stronger for noise exposure during the night vs. day (Basner et al. 2014). Despite evidence of noise-related adverse health effects, the most recent nationwide noise pollution estimates were made by the U.S. Environmental Protection Agency (U.S. EPA) in 1981 (Simpson and Bruce 1981). By extrapolating the U.S. EPA’s 1981 estimate of the prevalence of noise exposure to the current U.S. population, Hammer et al. (2014) estimated that 145.5 million Americans experience annual noise levels that exceed those recommended to protect public health with an adequate margin of safety. Moreover, the distribution of noise is not uniform across communities, and some groups may have heightened vulnerability to noise (van Kamp and Davies 2013). The spatial distribution of noise exposure may contribute to health disparities seen in the United States and elsewhere.

A body of environmental justice literature from the United States suggests that air pollution and exposure to hazardous waste often follows a social gradient such that racial/ethnic minorities, the poor, and the undereducated endure greater exposure (Bell and Ebisu 2012; Hajat et al. 2015; Mohai and Saha 2007). A more limited body of scholarship from Europe frames environmental injustices by social categories, but not usually race/ethnicity, finding, for example, that those in the 10% most deprived areas in England are the most exposed to chemical, metal, and waste facilities (Laurent 2011; Walker et al. 2005). Researchers theorize that in the United States, communities of color and the poor are disproportionately exposed to environmental hazards due to a variety of factors, including weak regulatory enforcement in marginalized neighborhoods and lack of capacity to engage in land use decision-making, which may contribute to the concentration of potentially hazardous mobile and stationary emission sources in these communities (Morello-Frosch 2002; Pulido 2000).

Only a few studies have evaluated community-level inequality in exposure to estimated noise pollution. Studies in Minneapolis and St. Paul, Minnesota (Nega et al. 2013) and Montreal, Quebec, Canada (Carrier et al. 2016; Dale et al. 2015) found that lower neighborhood socioeconomic status (SES) or a higher proportion of minority race/ethnicities was associated with higher noise levels. Outside the United States and Canada, results have been mixed and more focused on SES than race/ethnicity as an explanatory variable. In Hong Kong, Lam and Chan (2008) reported a weak, but statistically significant, association between lower income and educational attainment and higher noise exposure at the street block level. Haines et al. (2002) estimated noise exposure at 123 schools near the Heathrow Airport in the United Kingdom, and found in a subanalysis that a higher proportion of students eligible for free lunch was associated with higher noise exposure. In nearby Birmingham, a higher proportion of black residents at the enumeration district level was weakly associated with estimated daytime noise levels (Brainard et al. 2004). In Marseilles, France, census blocks with intermediate levels of deprivation had the highest estimated exposure to road noise, whereas in Berlin, Germany, there was no straightforward association between SES and noise exposure at the planning unit level (Lakes et al. 2014).

At the individual level, one study in Wales, United Kingdom (Poortinga et al. 2008) and another in Germany (Kohlhuber et al. 2006) found that lower SES participants reported more neighborhood noise. However, in Paris, France, individuals living in neighborhoods with the highest housing values and highest levels of educational attainment also had the highest estimated noise exposures (Havard et al. 2011).

To our knowledge, no prior studies have evaluated demographic disparities in noise pollution across the United States or considered how racial segregation (an indicator of metrowide social inequality) is associated with overall noise levels. Prior U.S.–based studies have found increased racial segregation associated with more air pollution (Bravo et al. 2016; Jones et al. 2014), ambient air toxins (Morello-Frosch and Jesdale 2006; Rice et al. 2014), and less tree canopy cover (Jesdale et al. 2013). In highly segregated metropolitan areas in the United States, political power is asymmetrical along racial, ethnic, and economic lines. Further, segregation spatially binds communities of color and working class residents through the concentration of poverty, lack of economic opportunity, and exclusionary housing development and lending policies (Massey and Denton 1993). These power differences may lead to disparities in environmental hazard exposures, including noise, as more powerful residents influence decisions about the siting of undesirable land uses in ways that are beneficial to them (Cushing et al. 2015; Morello-Frosch and Lopez 2006). Because segregation can make it easier for more powerful communities to displace hazardous land uses onto disadvantaged communities where regulations may not be consistently enforced, this scenario can lead to higher pollution overall (Ash et al. 2013). Segregation may also lead to spatial segmentation between neighborhoods, workplaces, and basic services, resulting in more driving, longer commute times, and higher levels of mobile source pollution (Ash et al. 2013; Morello-Frosch and Jesdale 2006).

In the present study, we utilized a nationwide noise model (Mennitt and Fristrup 2016) to a) estimate differences in noise exposure along racial/ethnic and socioeconomic lines; and to b) examine whether segregation modifies the association between race/ethnicity and noise exposure across the contiguous United States.

Methods

We conducted a cross-sectional analysis to investigate the spatial distribution of demographic characteristics at the census block group level in relation to noise exposure across the contiguous United States. Prior work identified U.S. block group–level socioeconomic measures as a relevant spatial scale for measuring socioeconomic inequality (Krieger et al. 2003). In 2010, the study area (i.e., the contiguous United States) contained 216,331 block groups—statistical divisions of census tracts generally containing 600 to 3,000 people—that we assigned to 933 Core Based Statistical Areas (CBSAs) based on the location of their centroid, using 2010 TIGER/Line shapefiles (U.S. Census Bureau 2010a, 2010b). CBSAs are counties grouped by common commuting patterns. We excluded 1,669 block groups without residents or that were missing data on any socioeconomic variable (0.8%) and 557 block groups missing noise exposure estimates (0.3%). We then designated block groups located in CBSAs containing >100,000 people as urban (n=175,373) and those located elsewhere as suburban/rural (n=38,732).

Dependent Variables

Exposure to noise was based on a previously published geospatial model of environmental sound levels (Mennitt and Fristrup 2016). Similar to land use regression models of air quality, expected noise exposure was modeled using empirical acoustical data and geospatial features such as topography, climate, hydrology, and anthropogenic activity. The acoustical data included 1.5 million h of long-term (durations of ≥25 d at natural sites and in 14 U.S. cities, and ≥30 d near all U.S. airports) measurements from 492 urban and rural sites located across the contiguous United States during 2000–2014. The explanatory variables fell into seven groups (location, climatic, landcover, hydrological, anthropogenic, temporal, and equipment) and are described in detail elsewhere (Sherrill 2012). Cross-validation procedures were used to evaluate model performance and identify variables with predictive power. The method utilized random forest, a tree-based machine-learning algorithm, to perform the regression. A cross-validation procedure was used to evaluate the accuracy of national scale projections (see Table S1; Mennitt and Fristrup 2016). The resulting geospatial sound model enabled mapping of ambient sound levels at 270-m resolution. We then used the zonal statistics function in ArcGIS (version 10.4; Esri) to estimate the mean sound level in each block group across the contiguous United States.

We examined three metrics of sound pressure from anthropogenic sources to assess the robustness of findings at different times of day and different levels of noise: a) A-weighted L10 (representing the loudest transient events or proximate sources) during the daytime; b) A-weighted L50 (median of the data, representing typical sound levels) during the daytime; and c) A-weighted L50 during the nighttime. All levels were projected for the summer season in order to maintain temporal consistency across noise estimates. Daytime was defined as 0700 to 1900 hours and nighttime as 1900 to 0700 hours.

Independent Variables

We used block group-level data from the 5-y 2006–2010 American Community Survey (ACS) to characterize area-level race/ethnicity and socioeconomic conditions (NHGIS Database). We used self-identified race/ethnicity to generate variables for the proportion of the population in each block group that fell in five race/ethnicity categories: Non-Hispanic American Indian, Non-Hispanic Asian, Non-Hispanic black, and Non-Hispanic white, and Hispanic (any race), referred to as American Indian, Asian, black, white, and Hispanic for the duration. Other block group–level variables included total population, population density (defined as number of people per km2), age (defined as percent of population <5 y old), and those selected to describe multiple dimensions of neighborhood socioeconomic context (Kawachi and Berkman 2003). We characterized neighborhood socioeconomic context at the block group level by: low educational attainment (defined as percent of adults ≥25 y of age with <high school education), median household income (defined as block group median income in dollars in the past 12 mo), poverty (defined as percent of individuals with income below the Census Bureau poverty threshold based on family size and number of children), civilian family unemployment (defined as percent of families with ≥1 family member unemployed), housing tenure (defined as percent of households comprised of renters or owners), and linguistic isolation (defined as percent of households where no one >14 y speaks English “very well”). Housing tenure may reflect residential instability as well as area-level income and wealth. Linguistically isolated households may face racial discrimination and reduced access to public services and ability to engage with regulatory processes (Gee and Ponce 2010). Some variables, like housing tenure, may have differential meaning in urban vs. rural settings, and, therefore, we conducted analyses stratified by urban/rural status (Bertin et al. 2014). To test the hypothesis that segregation was associated with higher levels of noise for all race/ethnic groups, we calculated a CBSA-level segregation measure (Sakoda 1981) for urban block groups only, using 5-y 2006–2010 ACS data (Jesdale et al. 2013). Our focus on urban areas was aimed at facilitating comparisons with prior studies on segregation and environmental hazards (Bravo et al. 2016; Jones et al. 2014; Morello-Frosch and Jesdale 2006; Rice et al. 2014). The multigroup dissimilarity index (Dm) characterizes the residential distribution of Non-Hispanics: Asians, blacks, and whites; and Hispanics (any race) among block groups located in CBSAs. Dm ranges from 0 to 1 and represents the proportion of the racial/ethnic minority population that would need to change block groups within a metro area to achieve an even distribution (Massey and Denton 1989). Block groups located in the same CBSA received the same Dm value. We selected Dm, a metro-level indicator of social inequality, because prior research indicated that land use decision-making tends to be regionally rooted (Morello-Frosch 2002; Pastor et al. 2000).

Statistical Analysis

We used weights to extrapolate block group–level noise estimates to the individual, family, and household level across the United States. For each characteristic of interest (e.g., non-Hispanic blacks), we used the wquantile function in R (version 3.2.3; R Development Core Team) to compute the weighted 25th, 50th, and 75th percentiles of noise.

We evaluated the association between 12 socioeconomic variables and log–transformed L50 nighttime, L50 daytime, and L10 daytime noise exposure by specifying 12 separate regression models for each noise metric, controlled for population size and population density. We checked the residuals from ordinary least square models and found evidence of spatial autocorrelation using Moran’s I statistic (p<0.001 indicating clustering; data not shown). Therefore, we implemented a spatial econometric approach in R using the spdep package (Bivand and Piras 2015). To discriminate between spatial autocorrelation in the error terms vs. the noise values themselves, we used a Lagrange multiple diagnostic test (implemented with lm.LMtests in spdep). p-values from this test suggested that a spatial error model was preferable to a spatial lag model for our data. A spatial error model specifies a linear relationship between the independent variable and the dependent variable, but unlike a traditional ordinary least squares model, errors are not assumed to be independent and identically distributed; rather, they are distributed according to a spatial autoregressive process:

where y is an n×1 vector of log L50 or L10 daytime or nighttime sound pressure, and n is the total number of block groups; X is an n×j matrix of independent variables, and j is the number of independent variables; W is an n×n spatial weights matrix; e∼N(0, σ2I); and λ is a spatial autoregressive coefficient (Anselin 2002). In addition to adjustment for block group population (continuous) and population density (continuous, individuals/km2), all models included polynomial terms for the independent variable of interest to allow for nonlinearity. We hypothesized that nonwhite race/ethnicities would experience higher overall levels of noise and steeper slopes as the percent minority increased in more segregated CBSAs. Therefore, we conducted stratified analyses [i.e., Dm<0.4 (low to moderate), 0.4 to <0.5 (high), 0.5 to <0.6 (very high), and ≥0.6 (extreme)] (Jesdale et al. 2013; Morello-Frosch and Jesdale 2006) from which American Indians were excluded due to small numbers in urban areas. In all analyses, we selected the number of polynomial terms (up to 10) using likelihood ratio testing by adding polynomial terms until the improvement in model fit was no longer significant at α=0.05. This procedure was completed separately for urban/rural and for L50 nighttime/L10 and daytime/L50 daytime models. In the results section, we refer to an association as nonlinear and statistically significant when likelihood ratio testing indicated that the final model with polynomial terms was a significantly better fit at the α=0.05 level than the model without the predictor. When the final model only included a first-degree predictor, we refer to it as a linear association; statistical significance (at the α=0.05 level) was determined using a t-test. We present results as scatterplots of fitted values with locally weighted smoothing functions (LOESS lines) to aid in interpretability. We did not predict noise exposure at specific values of the independent variables because predicting at specified populations and population densities would ignore model-derived weights applied to each census block to account for spatial correlations. Instead, we report median and interquartile ranges to summarize the distribution of predicted values for each noise metric according to block group race/ethnicity and socioeconomic characteristics.

In order to calculate W, the spatial weight matrix, in the main analysis, we defined queen-based neighbors (i.e., only block groups that share a common border or a single common point were considered neighbors) and S-coding scheme weights (i.e., variance-stabilizing weights) (Bivand et al. 2008; Tiefelsdorf et al. 1999). Because the choice of neighbors and weights can influence model fit and coefficient estimates, we performed several sensitivity analyses. First, we used distance-based neighbors, where block groups within 5 km of a block group centroid were classified as neighbors. Second, we applied a W-coding scheme for weights (i.e., row-standardized). Third, we combined the first two sensitivity analyses.

In additional sensitivity analyses, we simultaneously adjusted models for poverty, education, and housing tenure to assess whether these three dimensions of neighborhood socioeconomic context were confounders. All statistical analysis was performed in R (version 3.2.3; R Development Core Team) using Amazon Web Services and mapping was conducted in QGIS (version 2.12.0; QGIS, http://qgis.osgeo.org).

Results

The analysis spanned the contiguous United States and included 933 CBSAs and 214,105 block groups—175,373 urban and 38,732 suburban/rural—after excluding 2,226 block groups missing census or noise variables (Figure 1). The urban block groups contained 257,192,214 individuals, and the rural block groups 45,999,315 individuals. There was a concentration of poverty, nonwhite individuals, and low educational attainment in the South and Southwest. Urban block groups, compared to suburban/rural areas, had, on average, more racial/ethnic minorities (38.0% vs. 19.3%), more renter-occupied homes (34.6% vs. 26.9%), and slightly lower levels of poverty (13.3% vs. 16.8%) [Table 1 (urban) and Table S2 (suburban/rural)]. We observed moderate correlations (Spearman’s ρ=0.2–0.4) between many of the independent variables, for example, ρ=0.20, 0.25, and −0.43 between proportion in poverty and proportions unemployed, linguistically isolated, and of white race, respectively, in urban areas (see Figure S1). Individuals, households, and families in urban areas with lower SES had, on average, higher nighttime and daytime noise (Table 1). For example, in urban areas, the median L10 daytime noise estimated for households in the lowest quartile of median income (≤$39,224) was 54.5 dBA [interquartile range (IQR): 52.4–56.5 dBA] compared to 52.6 dBA (IQR: 50.4–54.5) estimated for households with median income >$39,224. Racial residential segregation was common; 83.4% of the urban study population resided in segregated CBSAs (Dm>0.4) (Table 1), with the most segregated regions in the Northeast and Midwest (Figure 1). Individuals living in more segregated CBSAs, compared to those living in CBSAs with Dm≤0.4, had higher L50 nighttime [median=44.5 dBA (IQR: 42.5–46.7 dBA) vs. 42.9 (IQR: 39.2–45.2 dBA)] and L50 daytime [median=48.2 dBA (IQR: 45.7–50.5 dBA) vs. 46.7 dBA (IQR: 41.5–49.4 dBA)] noise levels (Table 1).

Six spatial distribution maps of USA
Figure 1. Spatial distribution of (A) anthropogenic L50 nighttime noise; (B) population density; (C) racial residential segregation (urban CBSAs only); (D) non-Hispanic, nonwhite race/ethnicity; (E) poverty; and (F) <high school education (deciles) at the block group-level in the contiguous United States estimated from 2006–2010 American Community Survey data; 2010 shapefiles used to generate these maps downloaded from the NHGIS site: http://www.nhgis.org.
Table 1. Distribution of anthropogenic L50 nighttime, L50 daytime, and L10 daytime noise among urban residents by race/ethnicity and socioeconomic characteristics at the block group level from the 2006–2010 American Community Survey.
Median (IQR) anthropogenic noise, dBAa
Characteristic Total, n (%) L50 nighttime L50 daytime L10 daytime
Total population 254,328,850 (100) 44.3 (42.1–46.5) 48.0 (45.1–50.3) 52.9 (50.7–55.0)
 Population<5 y 17,112,446 (6.7) 44.5 (42.3–46.7) 48.1 (45.4–50.5) 53.0 (50.9–55.0)
 Population≥5 y 237,216,404 (93.3) 44.3 (42.1–46.5) 48.0 (45.0–50.3) 52.9 (50.7–55.0)
Race/ethnicityb
 Hispanic 44,095,827 (17.3) 45.6 (43.3–47.5) 49.5 (47.5–52.3) 54.1 (52.3–56.0)
 Non-Hispanic
 American Indian 1,209,132 (0.5) 42.9 (37.9–45.7) 46.1 (37.8–49.7) 51.5 (44.8–54.4)
 Asian 13,081,414 (5.1) 45.4 (43.9–47.1) 49.1 (47.4–51.1) 54.0 (52.4–55.7)
 Black 32,935,749 (13.0) 45.6 (43.8–47.6) 49.7 (47.6–52.6) 54.2 (52.4–56.3)
 White 157,730,767 (62.0) 43.6 (41.3–45.7) 47.1 (43.3–49.2) 52.3 (49.6–54.2)
 Income≤poverty threshold 33,194,588 (13.3) 45.2 (42.8–47.5) 49.2 (46.6–52.2) 54.0 (51.7–56.1)
 Income>poverty thresholdc 216,181,346 (86.7) 44.2 (42.0–46.3) 47.9 (44.9–50.0) 52.8 (50.6–54.8)
CBSA-level segregation
 0.14≤Dm<0.40 42,124,233 (16.6) 42.9 (39.2–45.2) 46.7 (41.5–49.4) 51.9 (48.0–54.3)
 0.40≤Dm<0.70 212,204,617 (83.4) 44.5 (42.5–46.7) 48.2 (45.7–50.5) 53.1 (51.1–55.1)
Total population≥25 y 35,298,009 (100) 44.6 (42.4–46.8) 48.5 (45.7–50.9) 53.3 (51.2–55.4)
 <High school education 5,837,943 (16.5) 45.4 (43.0–47.6) 49.4 (46.7–52.3) 54.1 (51.8–56.1)
 ≥High school education 29,460,066 (83.5) 44.4 (42.3–46.6) 48.3 (45.6–50.7) 53.2 (51.1–55.3)
Total households 95,455,047 (100) 44.3 (42.2–46.5) 48.1 (45.2–50.4) 52.0 (50.9–55.1)
 Median household income (USD)
 Quartile 1 ($2,868–$39,229) 23,863,693 (25.0) 45.6 (43.2–47.7) 49.8 (47.3–52.8) 54.5 (52.4–56.5)
 Quartiles 2–4 ($39,230–$249,896) 71,591,354 (75.0) 44.0 (41.8–46.0) 47.6 (44.5–49.6) 52.6 (50.4–54.5)
 Linguistically isolated households 5,140,332 (5.4) 45.9 (43.9–47.9) 50.4 (48.2–53.3) 54.8 (53.0–56.8)
 Nonlinguistically isolated households 90,314,715 (94.6) 44.2 (42.1–46.4) 48.0 (45.0–50.2) 52.9 (50.7–54.9)
 Housing tenure
 Renter-occupied homes 32,996,266 (34.6) 45.3 (43.3–47.4) 49.5 (47.2–52.3) 54.2 (52.3–56.3)
 Owner-occupied homes 62,458,781 (65.4) 43.8 (41.6–45.9) 47.4 (43.9–49.4) 52.5 (50.0–54.3)
Total families 63,521,803 44.1 (41.9–46.3) 47.7 (44.6–49.9) 52.7 (50.4–54.7)
Unemployed families 3,343,134 (5.3) 44.5 (42.3–46.8) 48.2 (45.4–50.6) 53.1 (51.0–55.2)
Employed families 60,180,669 (94.7) 44.1 (41.9–46.2) 47.7 (44.5–49.9) 52.7 (50.4–54.7)
Note: CBSA, Core Based Statistical Area; dBA, A-weighted decibels; IQR, interquartile range.

aPopulation-weighted by block group population (population<5 y, and race/ethnicity), by number of families (unemployment), by households (household income and linguistic isolation, and renters/owners), by population for whom poverty status was determined (poverty), and by population≥25 y (<high school education).

bRace/ethnicity does not sum to total; 5,275,961 individuals were of mixed or other race/ethnicity.

c4,952,916 people did not have poverty status determined and thus are not included in the poverty summary.

Median population-weighted anthropogenic L50 nighttime, L50 daytime, and L10 daytime noise levels in urban block groups were 44.3 dBA (IQR: 42.1–46.5 dBA), 48.0 dBA (IQR: 45.1–50.3 dBA), and 52.9 dBA (IQR: 50.7–55.0 dBA) (Table 1) compared to 38.8 dBA (IQR: 33.7–42.2 dBA), 37.0 dBA (IQR: 32.8–43.2 dBA), and 43.7 dBA (IQR: 40.2–49.6) in suburban/rural block groups (Table S1), respectively. While the noise metrics differed in magnitude, the spatial distribution was similar with noise levels highest in major metropolitan areas and lowest in the western United States (Figure 1, Figure S2). Overall, estimated noise levels were highest for Asians, blacks, Hispanics, and those of lower SES (Table 1, Table S2).

Figure 2 presents the results from adjusted spatial error models of the association between our 12 racial/ethnic and sociodemographic variables and nighttime L50 noise in urban and suburban/rural block groups. Daytime L50 and L10 results are available in Figures S3 and S4. Patterns of associations between each predictor and each noise metric were generally consistent in both urban and rural census blocks; as the proportion of nonwhite and low-SES individuals, families, and households increased, estimated nighttime and daytime noise increased. Table S4 summarizes the polynomial terms used for predictors in spatial error models.

Figure 2A comprises 12 plots indicating L sub 50 nighttime noise in decibels (y-axis) for urban block groups across percentage less than 5 y (log scale), percentage less than high school, income ($1000, log scale), percentage of poverty, percentage of unemployed, percentage of American Indians, percentage of Asians, percentage of Black, percentage of Hispanic, percentage of Whites, percentage of linguistically isolated, and percentage of renter-occupied (x-axis). Likewise, Figure 2B comprises plots of similar data for suburban/rural block groups.
Figure 2. Race/ethnicity and socioeconomic characteristics and anthropogenic L50 nighttime noise in (A) urban block groups (n=175,373); and (B) suburban/rural block groups (n=38,732). The figure displays the fitted values (points) showing the relationship between noise and each of 12 demographic characteristics adjusted for block group population and population density and using a queen neighbor definition and variance-stabilizing weights. Four of the plots [%<5 y, median household income (in thousands)], % unemployed, and % linguistically isolated) use a log scale x-axis as noted on the figure. The LOESS line was only estimated when there were >100 observations.

We observed significant associations between increased proportions of Asian, black, and Hispanic individuals and higher levels of noise in urban and suburban/rural areas, with the exception of Hispanic ethnicity in rural areas, which was not associated with noise. In urban block groups that contained 75% black residents, the median (interquartile range) nighttime noise level was 46.3 dBA (IQR: 44.3–47.8), while in block groups with 0% black residents, the level was 42.3 dBA (IQR: 40.4–45.5) (Table S3). Conversely, in urban block groups that contained 75% white, the median noise level was 44.8 dBA (IQR: 43.0–46.5), which rose to 47.0 dBA (IQR: 45.4–48.7) in block groups with 0% white residents. For race/ethnicity, the models with best fit all included polynomial terms (Table S4), except for Hispanic race/ethnicity, where we estimated a linear association between nighttime noise in both urban and suburban/rural block groups. Estimated urban nighttime noise increased from 43.7 dBA (IQR: 40.1–45.9) in block groups with 0% Hispanics to 46.5 dBA (IQR: 43.7–48.3) within block groups with 75% Hispanics (Table S3). For American Indian populations, we generally observed reduced noise as the percent of American Indians increased. Particularly for nighttime noise, the best model fit suggested shape of the association was fairly flat until 7–8% of the population was American Indian, and then there were more rapid reductions in noise levels. In contrast, the best model fit suggested a steeper slope (i.e., more rapid increases in estimated noise) for Asian and black populations at the lower tail of the distribution. For example, nighttime noise was estimated to be about 1.3 dBA higher in block groups containing 0% compared to 10% black individuals, but only 0.2 dBA higher in block groups containing 50% compared to 60% black individuals.

Block groups with higher proportions of individuals with less than a high school education, living in poverty, linguistically isolated, renting, and with a higher proportion of children <5 y were generally associated with higher nighttime and daytime noise levels (Figure 2, Figure S3–S4). For example, urban block groups with 50% of residents in poverty had nighttime noise levels, on average, 3 dBA higher than block groups with 0% (Table S3). The highest levels of noise were estimated in the block groups with the lowest median income; from there, noise levels declined until the median income, where noise levels plateaued.

Figure 3 shows the associations between race/ethnicity and L50 nighttime noise across segregation categories (see Table S5 for summary of polynomial terms). Three patterns emerged: first, that across all CBSAs and race/ethnicities, increasing segregation was associated with increased nighttime noise; second, that across all levels of CBSA-level segregation, block groups with higher proportions of Asian, Hispanic, and black residents generally had higher levels of exposure to nighttime L50 noise than those with higher proportions of white residents; and third, that the estimated curve shapes (i.e., the LOESS line of the fitted values) remained similar across levels of segregation, with the exception of Hispanics in the least segregated CBSAs, where there was no estimated increase in nighttime noise as the proportion of Hispanics increased above 25%. Results for L50 and L10 daytime noise were similar (Figures S6 and S7), but differences were less pronounced by level of segregation.

Four line graphs plotting L sub 50 nighttime noise in decibels (y-axis) across percentage of Asians, Black, Hispanic, and Whites (x-axis), where the D sub m is less than 0.4, 0.4 to less than 0.5, 0.5 to less than 0.6, and greater than 0.6.
Figure 3. Race/ethnicity and anthropogenic L50 nighttime noise in urban block groups (n=175,373), stratified by multigroup racial/ethnic segregation (Dm) for (A) Asians; (B) blacks; (C) Hispanics; and (D) whites. American Indians were excluded due to small numbers in urban areas. The figure displays the LOESS line of the fitted values from 16 (i.e., four categories of segregation and four race/ethnicities) separate spatial error models adjusted for block group population and population density and using a queen neighbor definition and variance-stabilizing weights. The LOESS line was only estimated when there were >100 observations.

Simultaneous adjustment for poverty, education, and housing tenure in our main analysis had little effect on race/ethnicity and SES estimates in suburban/rural block groups, except for <5 y and education, which, after adjustment, were no longer statistically significant (data not shown). In urban block groups, after adjustment for poverty, education, and housing tenure, the associations for <5 y, Hispanic race/ethnicity, education, and unemployment became nonsignificant (data not shown).

In the primary analysis, we defined queen neighbors and used variance-stabilizing weights. We also conducted sensitivity analyses using three additional neighbor/weight combinations (i.e., queen/W-coding, distance/S-coding, and distance/W-coding). Estimated associations were similar for all neighbor/weight combinations across both urban and suburban/rural block groups (Figure S5).

Discussion

Our findings suggest inequality in the spatial distribution of noise pollution along racial/ethnic and socioeconomic lines across the contiguous United States. Multiple indicators of neighborhood socioeconomic context were associated with increased night and daytime noise, including poverty, unemployment, linguistic isolation, and a high proportion of renters and those not completing high school. Block groups with higher proportions of Asians, blacks, and Hispanics had higher levels of noise, but relationships were rarely linear. The magnitude of these differences may be relevant for health outcomes (Basner et al. 2014); for example, we estimated that census block groups containing 25% black residents were exposed to a median 3.0 dBA higher nighttime noise than those with 0% black residents. In general, for all race/ethnicity groups evaluated, estimated noise exposures were higher for CBSAs with higher levels of racial segregation. However, disparities persisted between block groups with relatively high proportions of white (>50%) compared to relatively low proportions, except for block groups with high proportions of Hispanics in the least segregated CBSAs. As an example, the median nighttime noise level in block groups containing 75% of each race/ethnicity in the most segregated CBSAs were estimated to be 48.9 dBA, 46.9 dBA, 47.0 dBA, and 45.3 dBA for Asian, black, Hispanic, and white race/ethnicity, respectively.

Early indications of inequality in noise pollution in the United States came from the U.S. EPA in the 1970s. Survey respondents of higher SES tended to live in quieter neighborhoods and reported hearing fewer airplanes, traffic, and people’s voices, but more motorcycles, garden power tools, and sports cars (U.S. EPA 1977). More recently, in nearly 2000 block groups in the Twin Cities in Minnesota, Nega et al. (2013) modeled 24-h average traffic noise using data on roadways, traffic volume, building height, airplane flight paths, and other information. They reported significantly increased traffic noise as block group median household income and housing value fell and the proportion of non-white residents and persons aged >18 y increased, results from a spatial error model that simultaneously adjusted for all four independent variables. Nega et al. (2013) joined a handful of others to account for spatial dependence in their models when assessing inequality in noise pollution, with heterogeneous results (Bocquier et al. 2013; Carrier et al. 2016; Havard et al. 2011). Carrier et al. (2016) modeled mean 24-h traffic noise levels in 7,456 city blocks in Montreal, Canada, and used spatial error models to estimate associations with race/ethnicity and SES at the city block level. Like us, they observed increasing noise levels with an increasing proportion of low-income and nonwhite individuals. In Marseilles, France, Bocquier et al. (2013) reported that census blocks of intermediate SES (defined by a deprivation index constructed from 17 variables) had the highest modeled noise levels. In Paris, France, Havard et al. (2011) found an inverse relationship where modeled noise levels were higher in a 250-m buffer surrounding the residences of individuals with more education and higher valued homes. Desire of individuals to live near transportation networks may explain the inverse relationship. Indeed, among nearly 2 million individuals in Rome, Cesaroni et al. (2010) found that higher area SES and individual education were associated with increased traffic in a 150-m buffer around participants’ homes, except in the city center, where traffic density was highest, and less affluent neighborhoods and individuals were closer to roads. We observed indications of the same phenomenon; the relationship between median household income and noise was U-shaped in both urban and rural areas.

There is a broad literature concerning variation in air pollution in relation to social factors (Bell and Ebisu 2012; Hajat et al. 2015; Miranda et al. 2011). Our results are consistent with and may partially overlap this literature, due to co-occurrence of noise and air pollution. However, despite co-occurrence, noise and air pollution have only been moderately correlated (Spearman’s correlation coefficients 0.3–0.6) in New York City, London, and Vancouver (Davies et al. 2009; Fecht et al. 2016; Ross et al. 2011), and correlations did not differ by deprivation in London (Fecht et al. 2016). Furthermore, epidemiologic studies have reported associations between noise and multiple health outcomes after adjusting for air pollution, including associations with cognition and behavioral problems in children (Clark et al. 2012; Hjortebjerg et al. 2016), birth weight (Gehring et al. 2014), cardiovascular mortality (Gan et al. 2012), and diabetes (Sørensen et al. 2013).

To our knowledge, no prior studies have reported a positive association between noise levels and a community-level measure of social inequality, in this case, racial segregation. This observation is consistent with Boyce’s power-weighted decision theory (Boyce 1994) that social inequalities are associated with the distribution of environmental pollution, perhaps due to political power imbalances between the wealthy and the poor. It is also consistent with recent U.S. literature reporting positive associations between modeled air pollution and community segregation (Bravo et al. 2016; Jones et al. 2014; Rice et al. 2014). In the case of environmental noise, spatial segmentation of neighborhoods, workplaces, and basic service locations due to CBSA-level racial segregation may increase vehicle miles traveled (Morello-Frosch and Jesdale 2006), which could, in turn, contribute to noise pollution. This situation could create a feedback loop in which worse noise pollution catalyzes segregation (Bjørnskau 2005).

Our study suggests racial disparities in noise exposure, and noise has previously been linked to a number of negative health outcomes, including hypertension and sleep difficulties (Haralabidis et al. 2008; Münzel et al. 2014; Muzet 2007). Future work is needed to estimate how much differences in noise exposure may explain racial disparities in noise-related health outcomes. Disadvantaged populations may also have increased susceptibility to noise. For example, a recent German study reported that among 3,300 participants free from depressive symptoms at baseline, annual average noise exposure >55 dBA was associated with depressive symptoms at the 5-y follow-up, but only among those with <13 y of education (Orban et al. 2016). Worse quality housing, increased exposure to indoor noise, and comorbid conditions may help explain this disparity (Evans and Marcynyszyn 2004). Wealthier individuals may have greater ability to invest in noise abatement technologies (e.g., triple-paned windows, air-conditioning), and thus may have lower actual exposures to noise than less affluent individuals living in census blocks with the same estimated level of noise exposure. While living near urban centers may provide benefits like access to public transit and cultural assets, in some cities, the accompanying road and rail traffic may also increase the level of outdoor noise (Fyhri and Klaeboe 2006).

For the past 40 y, many noise studies have relied on a simple relationship between population density and community noise exposure to estimate ambient noise in the absence of in situ measurements (Schomer et al. 2011). In addition to population density, our geospatial sound model incorporated multiple explanatory variables describing the type and intensity of human activity, but did not include demographic descriptors. Unfortunately, we were only able to assess the distribution of noise at the block group level in this cross-sectional study. We could not identify the processes and procedures—for example, regulatory policies, neighborhood economic sorting, or land use decisions—that might explain inequality in noise levels, nor could we explain why some groups appeared more exposed to noise than others. We were unable to examine individual modifying characteristics, such as housing quality, work environment and location, prevalent illness, or propensity to noise sensitivity (Stansfeld and Shipley 2015). Additionally, our noise model did not differentiate between various anthropogenic sources of sound, which may have differential health effects (Basner et al. 2011). Although the noise model performed well (R2≥0.8), our prediction of outdoor noise is likely to contain measurement error (Mennitt and Fristrup 2016). Finally, our analysis did not include Leq estimates (i.e., a measure of equivalent continuous sound often used for noise standards). Therefore, we are unable to make comparisons to established WHO or U.S. EPA noise guidelines or to the majority of epidemiologic studies (Basner et al. 2014).

Despite these limitations, our study made several contributions. We characterized noise pollution across the contiguous United States for the first time since the early 1980s. The use of polynomial terms in this large sample allowed us to characterize nonlinear associations without assuming a constant slope over all values of each predictor. We accounted for spatial autocorrelation and implemented a spatial error model to avoid violating modeling assumptions. We defined neighbors and weights in multiple ways, given prior evidence that changing these definitions can impact inference (Bivand et al. 2008). Our sensitivity analyses demonstrated the stability of our results. Regardless of neighbor/weight definitions, we estimated similar relationships between race/ethnicity, SES, and noise almost universally, with the highest estimated noise levels in block groups with higher percentages of minorities and lower SES individuals.

Conclusion

Our analysis of estimated outdoor noise exposures in census block groups throughout the contiguous United States found evidence of higher noise exposures in census block groups characterized by lower SES and higher proportions of American Indian, Asian, black, and Hispanic residents. These associations were stronger in more racially segregated communities. Differences in noise exposure may have implications for more fully understanding drivers of environmental health disparities in the United States.

Acknowledgments

We thank B. Jesdale for assembling the segregation data and the University of California, Berkeley Statistical Consulting Service for statistical support. This work was supported by the Robert Wood Johnson Foundation Health & Society Scholars program (J.A.C.), the National Cancer Institute Award K99CA201542 (P.J.), and the Hewlett and Kellogg and Foundations (R. M.-F.).

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Maiden Voyage of the Collaborative Cross Mouse: Exploring Variability in Animals’ Response to Perchloroethylene

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Related EHP Article

Characterization of Variability in Toxicokinetics and Toxicodynamics of Tetrachloroethylene Using the Collaborative Cross Mouse Population

Joseph A. Cichocki, Shinji Furuya, Abhishek Venkatratnam, Thomas J. McDonald, Anthony H. Knap, Terry Wade, Stephen Sweet, Weihsueh A. Chiu, David W. Threadgill, and Ivan Rusyn

Researchers have spent more than a decade perfecting an alternative laboratory mouse model—the Collaborative Cross (CC)—that is designed to mimic the genetic diversity of the human population.1 This issue of Environmental Health Perspectives contains the first published toxicology study to be performed with CC mice: an exploration of how the toxic effects of perchloroethylene vary from animal to animal.2

Most inbred mouse strains are genetically identical, which means that individuals from the same strain respond similarly to the same chemical exposure. This limits researchers’ ability to study how differences in metabolism and other factors influence the effects of exposure and to understand how effects might vary in genetically diverse human populations.

To address that shortcoming, researchers began to develop the CC model in 2002. First, they identified eight “founder” strains from the three major laboratory and wild subspecies of Mus musculus, otherwise known as the house mouse. They crossbred these strains and then crossbred their offspring.

This unique breeding process generated inbred CC strains that each have a random sampling of the genetic diversity contained in the founder mice. Although individuals from a given CC strain are genetically identical to one another, the strains themselves are genetically distinct. Therefore, using a mouse population comprising multiple CC strains produces a facsimile of a genetically diverse human population. Several dozen genetically unique CC strains have been commercialized since the breeding effort began.3

The eight founder strains

Collaborative Cross mouse strains were developed by crossbreeding eight “founder” strains of mice. Using a mouse population comprising multiple Collaborative Cross strains produces a facsimile of a genetically diverse human population. © Jennifer Torrance.

For the present study, males from 45 CC strains were exposed to perchloroethylene, or perc, an industrial solvent that is also a common environmental contaminant. Perc is metabolized primarily to trichloroacetate (TCA), which has been shown to cause liver cancer in certain strains of rodents.4 The goal of the study was to look for strain-specific differences in perc’s toxicokinetics (i.e., how much perc and how much TCA accumulate in various organs) and its toxicodynamics (i.e., the effects caused by perc and TCA at target sites within the body).

Investigators from Texas A&M University gave the animals a single high oral dose of 1,000 mg/kg body weight. After 1, 2, 4, 12, or 24 hours, they euthanized one animal from each strain and collected serum and tissue samples for analysis. According to their results, the toxicokinetics of perc and TCA varied significantly from one strain to the next. The amounts in liver, for instance, spanned a roughly 8-fold difference from the strain with the highest level to the strain with the lowest level.

The toxicodynamics results, meanwhile, were less straightforward. The study’s first author, Joseph Cichocki, a postdoctoral research fellow at Texas A&M’s Department of Veterinary Integrative Biosciences, says that liver effects from perc were previously attributed to the formation of TCA, which in turn triggers peroxisome proliferator–activated receptors (PPARs), including PPARα, in exposed cells.5 PPARs are known to be involved in cell proliferation and in the onset of liver cancer in mice.6

Cichocki and his colleagues homed in on two PPARα-induced genes—Acox1 and Cyp4a10—and found that their induction was highly variable in the different strains. According to Cichocki, it had previously been assumed that Acox1 and Cyp4a10 expression levels would rise proportionately with increasing doses of perc. However, their results showed that this was not the case: Some mice had low Acox1 and Cyp4a10 expression levels despite being exposed to high doses of the chemical; in other mice, the opposite was true. The implication, Cichocki says, is that perc doses may not reliably predict the magnitude of their toxic effects. He asserts that additional factors, some that are related to toxicokinetics and others that are not, are responsible for interindividual variability in susceptibility to perc-induced toxicity.

“Our study was not without limitations,” Cichoki says. “But the takeaway message is that by using CC mice, we could characterize and quantify interindividual variability across the population.”

Steven Munger, an assistant professor at Jackson Laboratory, where development of the CC mouse first began, says that traditional single-strain toxicology studies provide only a limited view of the potential responses to a given chemical. “With Cichocki’s study, we’ve seen a distribution that looks a lot more like what you’d expect to see in exposed humans,” he says. “Some strains are outliers for particular phenotypes, just as a minority of people might exhibit a response that many others do not. And we still need to account for these people in our risk assessments.”

Cichocki explains that risk assessors ordinarily use default “uncertainty factors” to estimate interindividual variation in chemical effects. By providing a glimpse into the biological nature of that variation, experts anticipate that experiments with CC mice could enable risk assessors to replace those default values with real data. “This is what we’re concluding with our paper,” Cichocki says. “We’re trying to generate the experimental data for population-level variability that regulators need.”


Charles W. Schmidt, MS, an award-winning science writer from Portland, ME, writes for Scientific American, Science, various Nature publications, and many other magazines, research journals, and websites.

References

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2. Cichocki JA, Furuya S, Venkatratnam A, McDonald TJ, Knap AH, Wade T, et al. 2017. Characterization of variability in toxicokinetics and toxicodynamics of tetrachloroethylene using the Collaborative Cross mouse population. Environ Health Perspect 125(5):057006, PMID: 28572074, 10.1289/EHP788.

3. Schmidt CW. 2015. Diversity Outbred: a new generation of mouse model. Environ Health Perspect 123(3):A64–A67, PMID: 25730842, 10.1289/ehp.123-A64.

4. IARC (International Agency for Research on Cancer). 2014. Trichloroethylene, tetrachloroethylene, and some other chlorinated agents. Monogr Eval Carcinog Risk Hum 106:1–525, http://monographs.iarc.fr/ENG/Monographs/vol106/mono106.pdf [accessed 13 July 2017].

5. Maloney EK, Waxman DJ. 1999. trans-Activation of PPARα and PPARγ by structurally diverse environmental chemicals. Toxicol Appl Pharmacol 161(2):209–218, PMID: 10581215, 10.1006/taap.1999.8809.

6. Bakiri L, Wagner EF. 2013. Mouse models for liver cancer. Mol Oncol 7(2):206–223, PMID: 23428636, 10.1016/j.molonc.2013.01.005.

Northern Trek: The Spread of Ixodes scapularis into Canada

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  • Published: 24 July 2017

    Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact ehponline@niehs.nih.gov. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

Related EHP Article

Expansion of the Lyme Disease Vector Ixodes scapularis in Canada Inferred from CMIP5 Climate Projections

Michelle McPherson, Almudena García-García, Francisco José Cuesta-Valero, Hugo Beltrami, Patti Hansen-Ketchum, Donna MacDougall, and Nicholas Hume Ogden

For a decade Nicholas Ogden, a researcher at the National Microbiology Laboratory of the Public Health Agency of Canada, has tracked the northern expansion of the deer tick (Ixodes scapularis), the vector for Lyme disease. He has found a strong correlation between rising winter temperatures and the spread of the tick population.1,2 Now Ogden has collaborated with Hugo Beltrami, Canada Research Chair in Climate Dynamics at St. Francis Xavier University, and other researchers to forecast the range expansion of I. scapularis under a greater number of possible climate scenarios.3

Lyme disease was first identified in coastal Connecticut in 1976, and the bacterium that causes it, Borrelia burgdorferi, was isolated in 1982.4 Eastern Canada’s first infected ticks were found on the Ontario shore of Lake Erie in the early 1990s.5 The ticks have since expanded their range farther north into Ontario and parts of Manitoba, Quebec, New Brunswick, and Nova Scotia, Ogden says. The number of reported Lyme disease cases in Canada is rising steadily, from 144 in 2009 to 917 in 2015.6

The inference that temperature thresholds have a strong impact on tick survival fits with a growing body of evidence showing that the ranges of Ixodes ticks in Europe are limited by cold temperatures.7 In a 2014 study, Ogden and his colleagues used a single climate model to forecast the spread of the tick into Canada.1 They concluded that climatic conditions suitable for I. scapularis populations to expand steadily northward would likely occur during the coming century.

Photo of an Ixodes scapularis tick on a blade of grass.

Ticks do not jump, fly, or drop onto passersby. Instead, they wait on vegetation with their front legs raised in a “questing” pose. When an appropriate host brushes past, the tick hitches a ride and attaches itself for a blood meal. © Juniors Bildarchiv GmbH/Alamy Stock Photo.

The basic needs of I. scapularis include woodland habitat and an assortment of vertebrate hosts to bite. After hatching, ticks pass through three life stages and require a blood meal to fuel their development from one to the next. As larvae and nymphs, the ticks most often obtain these meals from white-footed mice or other small rodents, although they occasionally latch onto other creatures—a raccoon, a bird, or an unfortunate human. Adult ticks feed primarily on white-tailed deer.8

Deer are growing in numbers and expanding their range to the north, as are white-footed mice.9 In addition, Ogden says, recent warming has occurred in southern Canadian regions with new influxes of ticks, which are moving in a geographic pattern consistent with temperature being an important factor in their becoming established.

Under even the most optimistic scenario, in which the increase in global average temperature is limited to 1.5°C above preindustrial temperatures, the authors’ models showed Lyme disease continuing to spread in Canada. They conclude that people in Nova Scotia and in southern Ontario—home to more than 85% of the provincial population—will need to be aware of and adapt to the risk of bites from infected ticks. Under the worst-case scenario modeled, in which global greenhouse gas emissions are not curtailed, the authors estimate I. scapularis will spread into northern Ontario, a region not yet colonized by deer ticks.

“This study is an extension of previous work published in 20141 showing the predicted expansion of the distribution of the Lyme disease tick vector into Canada,” says Maria Diuk-Wasser, a professor at Columbia University who focuses on the emergence of vector-borne diseases. “Although the results are not qualitatively different, it represents an improvement on the previous study by incorporating the full range of and most up-to-date climate models and emission scenarios.” Importantly, she says, the new study accounts for the inherent uncertainty in such models and scenarios but also indicates that an increased risk can be expected in any event.

Canada’s public health officials track the leading edge of the tick’s range expansion in several ways: through laboratory identification of ticks found on patients by doctors and veterinarians; by conducting surveys in which a large cloth is dragged across a woodland floor, picking up any ticks that are questing for a host to bite; and by compiling data on reported cases of human infection. The Canadian government has provided information on how to avoid tick bites and identify the symptoms of Lyme disease.10 “We’ll all need to participate in adapting to the tick’s arrival,” says Ogden.


Sharon Levy based in Humboldt County, CA, has covered ecology, evolution, and environmental science since 1993. She is at work on the book The Marsh Builders: Wetlands in the Fight for Clean Water.

References

1. Ogden N, Radojević M, Wu X, Duvvuri VR, Leighton PA, Wu J. 2014. Estimated effects of projected climate change on the basic reproductive number of the Lyme disease vector Ixodes scapularis. Environ Health Perspect 122(6):631–638, PMID: 24627295, 10.1289/ehp.1307799.

2. Gabriele-Rivet V, Arsenault J, Badcock J, Cheng A, Edsall J, Goltz J, et al. 2015. Different ecological niches for ticks of public health significance in Canada. PLoS One 10(7):e0131282, PMID: 26131550, 10.1371/journal.pone.0131282.

3. McPherson M, García-García A, Cuesta-Valero FJ, Beltrami H, Hansen-Ketchum P, MacDougall D, et al. 2017. Expansion of the Lyme disease vector Ixodes scapularis in Canada inferred from CMIP5 climate projections. Environ Health Perspect 125(5):057008, PMID: 28599266, 10.1289/EHP57.

4. Steere A, Coburn J, Glickstein L. 2004. The emergence of Lyme disease. J Clin Invest 113(8):1093–1101, PMID: 15085185, 10.1172/JCI200421681.

5. Barker IK, Surgeoner GA, Artsob H, McEwen SA, Elliott LA, Campbell GD, et al. 1992. Distribution of the Lyme disease vector, Ixodes dammini (Acari: Ixodidae) and isolation of Borrelia burgdorferi in Ontario, Canada. J Med Entomol 29(6):1011–1022, PMID: 1460617, 10.1093/jmedent/29.6.1011.

6. Government of Canada. Surveillance of Lyme Disease [website]. Updated 20 September 2016. https://www.canada.ca/en/public-health/services/diseases/lyme-disease/surveillance-lyme-disease.html [accessed 6 March 2017].

7. Ostfeld RS, Brunner JL. 2015. Climate change and Ixodes tick-borne diseases of humans. Philos Trans R Soc Lond B Biol Sci 370(1665):20140051, PMID: 25688022, 10.1098/rstb.2014.0051.

8. U.S. Centers for Disease Control and Prevention. Lifecycle of Blacklegged Ticks [website]. Updated 15 November 2011. https://www.cdc.gov/lyme/transmission/blacklegged.html [accessed 6 March 2017].

9. Simon JA, Marrotte RR, Desrosiers N, Fiset J, Gaitan J, Gonzalez A, et al. 2014. Climate change and habitat fragmentation drive the occurrence of Borrelia burgdorferi, the agent of Lyme disease, at the northeastern limit of its distribution. Evol Appl 7(7):750–764, PMID: 25469157, 10.1111/eva.12165.

10. Government of Canada. Lyme Disease [website]. Updated 17 February 2017. https://www.canada.ca/en/public-health/services/diseases/lyme-disease.html [accessed 6 March 2017].

Weekly Personal Ozone Exposure and Respiratory Health in a Panel of Greek Schoolchildren

Author Affiliations open

12nd Pulmonary Department, ATTIKON University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece

2Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece

3Pulmonary Department, G. Papanikolaou Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece

4School of Chemical Engineering, National Technical University of Athens, Athens, Greece

5Department of Primary Care & Public Health Sciences and Environmental Research Group, King’s College, London, UK

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  • Background:
    The association of ozone exposure with respiratory outcomes has been investigated in epidemiologic studies mainly including asthmatic children. The findings reported had methodological gaps and inconsistencies.
    Objectives:
    We aimed to investigate effects of personal ozone exposure on various respiratory outcomes in school-age children generally representative of the population during their normal activities.
    Methods:
    We conducted a panel study in a representative sample of school-age children in the two major cities of Greece, Athens and Thessaloniki. We followed 188, 10- to 11-y-old, elementary school students for 5 wk spread throughout the 2013–2014 academic year, during which ozone was measured using personal samplers. At the end of each study week, spirometry was performed by trained physicians, and the fractional concentration of nitric oxide in exhaled air (FeNO) was measured. Students kept a daily time–activity–symptom diary and measured PEF (peak expiratory flow) using peak flow meters. Mixed models accounting for repeated measurements were applied.
    Results:
    An increase of 10 μg/m3 in weekly ozone concentration was associated with a decrease in FVC (forced vital capacity) and FEV1 (forced expiratory volume in 1 s) of 0.03 L [95% confidence interval (CI): −0.05, −0.01] and 0.01 L (95% CI: −0.03, 0.003) respectively. The same increase in exposure was associated with a 11.10% (95% CI: 4.23, 18.43) increase in FeNO and 19% (95% CI: −0.53, 42.75) increase in days with any symptom. The effect estimates were robust to PM10 adjustment. No inverse association was found between ozone exposure and PEF.
    Conclusions:
    The study provides evidence that airway inflammation and the frequency of respiratory symptoms increase, whereas lung function decreases with increased ozone exposure in schoolchildren. https://doi.org/10.1289/EHP635
  • Received: 09 June 2016
    Revised: 08 March 2017
    Accepted: 13 March 2017
    Published: 21 July 2017

    Please address correspondence to A. Karakatsani, 2nd Pulmonary Department, ATTIKON University Hospital, School of Medicine, National and Kapodistrian University of Athens, 1, Rimini St., 124 62 Haidari, Greece. Telephone: 30-210-5831184. Email: annakara@otenet.gr, akarakats@med.uoa.gr

    The authors declare they have no actual or potential competing financial interests.

    Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact ehponline@niehs.nih.gov. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

Introduction

Ozone (O3), a very reactive gas and strong oxidant, is found as a secondary pollutant in the troposphere. Although, its presence in the stratosphere is essential for life because it filters harmful ultraviolet radiation, increased concentrations of O3 in the air we breathe have been linked to adverse health effects mainly concerning the respiratory system (WHO 2013). A number of controlled human exposure studies have demonstrated changes in FEV1 and respiratory symptoms (Adams 2006; Horstman et al. 1990; Kim et al. 2011) as well as lung inflammation (Devlin et al. 1991; Kim et al. 2011), but those included healthy adults, not children. In an attempt to investigate the acute effects of ozone under real-world exposures several camp studies have been conducted involving sequential measurements of lung function and ambient ozone measurements on children attending summer camps (Burnett et al.1990; Higgins et al. 1990; Raizenne et al.1989; Spektor et al. 1988; Spektor et al. 1991). However, despite the evidence they provide of an association between daily outdoor O3 concentrations and decreased lung function (Kinney et al. 1996), the lack of personal measurements, variations in data reporting and analysis as well as concerns about potential confounding by other pollutants that may co-vary with O3, limit their use in risk assessment. Therefore, so far there is lack of studies conducted in children under real living conditions, at current concentrations of ozone, using personal exposure assessment.

Children’s lungs are considered to be more sensitive and vulnerable to air pollution as they have, even at rest, a larger surface area for body size compared with adults and are still growing. Children breathe more air per unit of body weight and have different breathing patterns than adults that alter the deposition and toxicity of inhaled gases and particles (Arcus-Arth and Blaisdell 2007; Bateson and Schwartz 2008; Warren et al. 1990). Moreover, because they spend more time outdoors and are more active, they have higher ventilation rates leading to increased intake and deposition of pollutants (Bateson and Schwartz 2008). So far, epidemiological studies on the effects of ozone on children’s lung function and respiratory symptoms have mainly focused on asthmatics and have reported inconsistent results (Castro et al. 2009; Declercq and Macquet 2000; Jalaludin et al. 2000; Just et al. 2002; Li et al. 2012; Mortimer et al. 2002; Scarlett et al. 1996). However, response to ozone exposure displays individual variability that cannot be explained by underlying morbidity (Schelegle et al. 2012; WHO 2013), and thus, from a public health point of view, it is critical to determine to what extent ambient O3 exposure is responsible for adverse respiratory effects in the general population of children.

We conducted a panel study (Respiratory Effects of Ozone Exposure in children; RESPOZE) in a representative sample of the general population of schoolchildren in the two major cities of Greece, Athens (state capital) and Thessaloniki. Greece provides a unique opportunity to study O3 health effects because of its sunny, warm weather as well as the high concentrations of precursor pollutant emissions encountered in some of its cities.

In this paper we examine the association of weekly personal ozone measurements with lung function parameters, airway inflammation, and respiratory symptoms.

Materials and Methods

Study Design: Subjects

A panel study was conducted, in the two largest and most polluted cities of Greece, Athens and Thessaloniki (total population of about 4 million). The research was approved by the Ethics Committee of the National and Kapodistrian University of Athens, and it complied with all relevant national and local regulations. The study population consisted of all children in the fifth grade (age 10–11 y) in state elementary schools representative of the general population of students in that age group. State schools were chosen because all attending students are required to live in the school neighborhood. A two-stage sampling design was applied. First, state elementary schools located near a fixed monitoring site (within 2 km), were identified, and permission to implement the project was obtained from the Ministry of Education. In the second stage, the research team visited all schools and informed fifth grade school children in class about the project. Informed consent was obtained from the parents of children willing to participate and the final sample was formed. We had estimated that a sample of 200 school children (100 from Athens and 100 from Thessaloniki) would provide power >90% to detect a decrease in FEV1 (forced expiratory volume in 1 s) of 0.14% per 10 μg/m3 in O3 exposure, at the 5% significance level, considering that the overall proportion of variance explained would be 41%. In each city 60% of the sample was drawn from high-O3 areas (suburbs), to ensure that we had enough relatively highly exposed children, and 40% from low-O3 areas (city centers, where ozone is scavenged by primary pollutants). All field work was implemented during the academic year 2013–2014 and consisted of five weeks of intensive follow-up per study participant: two in the fall period, one in winter, two in spring/summer. During each period, we used a staged entry of the participants for practical reasons. As a result, the period of data collection was longer, and thus, the likelihood for uncontrolled factors or unexpected events to influence the association between air pollution and health decreased (Roemer et al. 2000). Before the start of the field work, trained interviewers visited the schoolchildren’s families at home to fill in an extensive questionnaire with information on demographic, life style, and residential characteristics and the medical history of the child. During this visit they also provided a peak flow meter (Mini-Wright, Clement Clarke International Ltd.) and trained the student on how to use it. A team of three field workers, including one pediatrician or pulmonologist, visited each school twice for each field work week. At the first visit an O3 personal sampler (Ogawa USA) and a time–activity diary (TAD) were given to the participating students. At the second visit, a week later (same weekday), the O3 personal sampler and completed TADS were collected, spirometry was performed, the fractional concentration of nitric oxide in exhaled air (FeNO) was measured and a 24-h dietary recall questionnaire was filled in by interview. [See “TAD-PEF (Peak Expiratory Flow): Symptoms”; “Spirometry”; “FeNO”; “Ozone Exposure Assessment”].

TAD-PEF (Peak Expiratory Flow): Symptoms

Participating schoolchildren were instructed to complete a TAD collecting information on his or her location (at home, outdoors, indoors, but not at home and in transport) at 15-min intervals for each day of the field work period. In the TAD, they additionally recorded a total of nine PEF recordings per day, performed with the Mini-Wright peak flow meter three times every morning, afternoon, and night before the use of any medication. In addition, at the end of each day they were asked to record all their daily symptoms, namely cough, wheezing, breathing difficulties (dyspnea), fever, and stuffy nose; school absenteeism; and daily medication use (yes vs. no, as well as identification of medicine).

Spirometry

Spirometry was performed at school at the end of each of the five intensive field work weeks. Thus, a total of five spirometric assessments were available per schoolchild. All spirometry maneuvers were performed by the same pulmonologists or by the specially trained pediatrician in a separate office provided by each school that was quiet, well ventilated and had a standard temperature independent of season. Children were tested in a seated position and with nose clips in place. Portable spirometers (Spiropalm, Cosmed Srl, Italy) compatible with ATS (American Thoracic Society) and ERS (European Respiratory Society) requirements were used (Miller et al. 2005). At least two technically satisfactory maneuvers had to be performed with a difference between the two highest values of FVC (forced vital capacity) and FEV1 of <5% of each other. The best FVC and FEV1 were recorded independently of the order of the maneuver, whereas the values for the other parameters were chosen from the maneuver having the largest sum of FVC and FEV1 (Miller et al. 2005). PEF and FEF25–75% were also recorded.

FeNO

A measurement of FeNO was performed at school at the end of each field study week just before spirometry in the same room where spirometry was performed (see above), thus, a total of five measurements were available per schoolchild. The NIOX MINO (Aerocrine, Solna, Sweden), an instrument that has also a scrubber to eliminate ambient NO from the subject’s sample, was used for all measurements according to American Thoracic Society (ATS) and European Respiratory Society (ERS) recommendations (ATS/ERS 2005).

Ozone Exposure Assessment

To assess O3 weekly exposure, each student was provided with a personal Ogawa O3 sampler (Ogawa & Co. USA Inc., Pompano Beach, FL) at the first visit of each field work week and was instructed on how to wear and handle it. The sampler was collected during the second visit at the end of the week. One sampler was placed in the outdoor space of each participating school also providing a weekly measurement. Additionally, concentrations of daily ambient O3 as well as PM10 (particulate matter with aerodynamic diameter <10 μm) and NO2 (nitrogen dioxide) were obtained by the nearest fixed monitoring site from the state network (http://www.ypeka.gr/).

Confounder Data

A possible learning or fatigue effect as well as a growth factor throughout the academic year was taken into account by including a time trend as a variable taking values 1–5 for each consecutive week (week ID). Additionally, all models were adjusted for sex, city (i.e., Thessaloniki vs. Athens) and area (high- vs. low-ozone area, as used in the sampling frame), father’s education (years) as socioeconomic index, outdoor ambient temperature (°C, weekly average and alternatively same day temperature), consumption of antioxidant foods (yes/no, in the corresponding 24-h recall), time spent outdoors (hours/day, from data recorded in the corresponding week TAD), and medication use (yes/no, during the corresponding week). Height (cm) and weight (kg) were adjusted in models with lung function outcomes. Models were additionally adjusted for PM10 levels from the nearest fixed monitoring site.

Quality Assurance and Quality Control

Standard operating procedures (SOPs) were applied for air pollution and health measurements. A training workshop for all field workers was organized before the start of the fieldwork. It should be noted that especially for the TAD, which are demanding on the subject’s time and accuracy, scrupulous data quality procedures were applied as well as controls with manual inspections of the data and cross references from the field workers’ progress diaries.

Statistical Analyses

Random effects models, incorporating a random intercept, Stata Statistical Software (Release 12; StataCorp LP), were used to account for the repeated measurements, for each child. We applied multiple linear regression models when we investigated O3 effects on continuous variables: PEF measurements from the Mini-Wright (taken as the average of seven daily values calculated as the mean of the three maximum values from the morning, afternoon, and evening measurements), spirometry indices FVC, FEV1, PEF, and FEF25–75% (forced expiratory flow at 25–75%) as well as FeNO measurements, which were log-transformed because the distribution was skewed. Poisson models were used for counts: the number of days per week when any symptom occurred and the number of days when the child was absent from school. In all models, we adjusted for all the potential confounders mentioned above. Because ozone concentrations are higher in spring–summer, we repeated the analysis for this period only. In addition, because asthmatic children may present special characteristics, we repeated the analysis excluding this subgroup using two alternative definitions. The first definition was based on parents’ reporting a doctor-diagnosed asthma. The second definition further included those students having FEV1/FVC and/or FEF25−75%<LLN (lower limit of normal) at least once during the study period (provided that all spirometry maneuvers were performed according to the project’s protocol), which provides evidence of obstructive ventilatory defect (Lougheed et al. 2012; Lum and Stocks 2010) and may denote (undiagnosed) asthma. We also conducted additional sensitivity analyses in healthy children only, excluding alternatively the two groups (asthmatic and asthmatic plus those presenting obstructive ventilatory defect) according to the definitions above.

Results are presented per 10-μg/m3 increase in O3 levels, representing approximately an interquartile range of the weekly measurements and thus, allowing comparability with previously published results based on measurements from fixed sites monitors. In sensitivity analyses we tested for heterogeneity between children, which included adding random slopes to our models.

Results

The final sample included 188 school-age children (97 in Athens and 91 in Thessaloniki). Table 1 shows personal, medical, socioeconomic characteristics, and respiratory health indices of the study population, by city and ozone concentration area (as used for the sampling scheme), during the academic year 2013–2014. Based on demographic data children residing in the two cities were similar. Parents reported a doctor-diagnosed asthma for 21 schoolchildren (7 in Athens and 14 in Thessaloniki). Of the remaining 167 children without a reported history of asthma, 22 (10 in Athens and 12 in Thessaloniki) had FEV1/FVC and/or FEF25–75%<LLN (lower limit of normal) for at least once during the study period.

Table 1. Personal, medical, socioeconomic characteristics, and respiratory health indices (presented as 5-wk averages) of the study population, by city and ozone concentration area.
Athens Thessaloniki
Ozone concentration areaa
Characteristic/respiratory health outcome Low (n=37) High (n=60) Low (n=33) High (n=58)
Boys [n (%)] 22 (59.5) 28 (46.7) 14 (42.4) 29 (50.0)
Age (y) 10.3±0.3 10.3±0.3 10.4±0.4 10.4±0.3
Height (cm) 147.2±6.7 143.5±7.7 145.9±9.5 144.3±7.3
BMI (kg/m2) 18.2±2.8 18.5±3.6 18.0±3.4 18.0±2.8
Father’s education (y) 14.0±2.8 15.2±3.7 15.3±3.4 14.1±3.3
Working father [yes; n (%)] 29 (78.4) 55 (91.7) 27 (81.8) 52 (89.7)
Asthma [n (%)] 2 (5.4) 5 (8.3) 4 (12.1) 10 (17.2)
FVC (L) 2.5±0.4 2.4±0.3 2.6±0.4 2.5±0.4
FEV1 (L) 2.2±0.4 2.1±0.3 2.2±0.4 2.2±0.4
PEF (L/s) 4.7±0.8 4.8±0.8 4.9±0.9 4.8±0.9
FEF25–75% (L/s) 2.6±0.7 2.5±0.5 2.6±0.8 2.5±0.6
FeNO (ppb) 17.7±17.8 15.6±12.3 15.7±13.8 16.5±15.5
PEF-Mini-Wrightb (L/min) 293.5±53.9 287.8±45.3 300.8±55.8 297.5±58.3
Students with any symptom during the study periodc [n (%)] 29 (78.4) 40 (66.7) 27 (84.4) 45 (79.0)
Students with at least 1 d absence during the study period [n (%)] 18 (48.7) 31 (51.7) 12 (37.5) 22 (38.6)

Note: Data are presented as mean±SD or n (%), unless otherwise stated.

aThis definition of high- and low-concentration areas was based on previous years and used as a basis for the sampling procedure.

bWeekly averages from daily peak expiratory flow values (measured with the personal Mini-Wright peak flow meter) were used.

cAt least one symptom during the study period.

In Table 2 descriptive characteristics of the air pollutants and temperature by city and high- or low- O3 area (as used in the sampling scheme) are presented. O3 personal measurements were much lower than outdoor measurements at schools or fixed sites, reflecting the amount of time spent indoors, where ozone concentrations are lower. Furthermore, O3 as well as PM10 concentrations and temperature were higher in Athens than in Thessaloniki. Personal ozone and outdoor school ozone concentrations were in a range of 25–40% higher in high-versus low-ozone areas in both cities. In Athens, PM10 concentrations were lower by 20% in high-compared with low-ozone areas but, in contrast, in Thessaloniki they were 5% higher in high-ozone compared with low-ozone areas. Therefore, in Athens there was a larger contrast in both O3 and PM10 concentrations between high- and low-ozone areas. In additional, contrasts were higher in outdoor school measurements compared with personal measurements.

Table 2. Weekly air pollution and temperature levels by city.
Athens Thessaloniki
Ozone concentration area
Environmental indicator Low (n=37) High (n=60) Low (n=32) High (n=57)
O3 personal measurements (μg/m3) 8.2±6.7 10.8±7.8 4.7±4.8 5.9±6.6
O3 outdoor at schools (μg/m3) 45.9±14.7 64.3±20.1 35.2±20.7 45.6±19.4
O3 at fixed sites (μg/m3) 24.6±13.8 63.8±16.4 36.3±16.7 41.3±18.5
PM10 at fixed sitesa (μg/m3) 28.9±7.4 23.1±7.1 18.9±3.8 21.0±9.3
Temperature (°C) 17.6±2.5 17.6±2.5 14.6±3.9 13.7±3.8

Note: Data are presented as mean±SD. The high- or low-O3 concentration areas were used for the sampling scheme.

aAverage of 24-h values.

Concerning the assessment of health effects, a negative association between weekly O3 exposure and FVC was observed (both in the one and two pollutants models including PM10). An increase of 10 μg/m3 in weekly O3 concentration was associated with a decrease in FVC of 0.03 L or 30 mL (95% CI: −0.05 L, −0.01 L). When we restricted the analyses to the spring–early summer period the negative effect of O3 on FVC persisted. The results concerning the weekly O3 effect on FEV1 were similar. A 10-μg/m3 increase in weekly O3 concentration was associated with a decrease in FEV1 of 0.01 L or 10 mL (95% CI: −0.03 L, 0.003 L). Peak expiratory flow, as assessed by spirometry, and FEF25–75%, were not associated with weekly O3 exposure in any period of analyses (Table 3). Weekly averages from daily values of PEF, taken with the Mini-Right flow meter, were positively associated with O3 and this association remained when we restricted the analyses to the spring–early summer period (Table 3). Inclusion of PM10 did not change the above-mentioned results.

Table 3. Mean change in lung function indices associated with weekly increase of 10 μg/m3 in O3 exposure measured by personal monitors in 178 school children.
O3 effect per 10 μg/m3
All study periods Spring–early summer period
Health outcome Models β-coefficient (95% CI) Wald statistic β-coefficient (95% CI) Wald statistic
Spirometry indices
 FVC (L) Ozone only −0.03 (−0.05, −0.01)* 7.25* −0.02 (−0.04, 0.003) 2.95
Ozone+PM10a −0.03 (−0.05, −0.004)* 5.55* −0.02 (−0.04, 0.01) 2.43
 FEV1 (L) Ozone only −0.01 (−0.03, 0.003) 2.65 −0.02 (−0.04, 0.01) 1.70
Ozone+PM10a −0.01 (−0.03, 0.01) 0.70 −0.01 (−0.04, 0.01) 1.26
 PEF (L/s) Ozone only 0.004 (−0.07, 0.08) 0.01 −0.05 (−0.14, 0.04) 0.71
Ozone+PM10a 0.02 (−0.06, 0.10) 0.23 −0.05 (−0.14, 0.04) 0.62
 FEF25–75% (L/s) Ozone only 0.01 (−0.04, 0.05) 0.07 0.03 (−0.04, 0.09) 0.39
Ozone+PM10a 0.02 (−0.02, 0.07) 1.05 0.03 (−0.04, 0.10) 0.41
Self measured peak expiratory flow (Mini-Wright)
 PEFb (L/min) Ozone only 4.80 (1.17, 8.43)* 6.73* 3.45 (−1.14, 8.05) 2.17
Ozone+PM10a 5.34 (1.62, 9.06)* 8.08* 3.37 (−1.39, 8.13) 2.03
Exhaled nitric oxide fraction (FeNO) log-transformed
FeNO (ppb)c Ozone only 11.10 (4.23, 18.43)* 10.43* 11.79 (2.61, 21.80)* 6.49*
Ozone+PM10a 9.48 (2.46, 16.98)* 7.17* 11.77 (2.54, 21.82)* 6.41*

Note: Results of mixed models adjusting for sex, height, weight, exposure area (low/high), study area (Athens/Thessaloniki), years of father’s education, air temperature (7-d average), mean time spent outdoors daily, citrus fruits consumption (yes/no), and week ID.

a7-d average, measurements from nearest fixed site.

bWeekly averages from daily values (daily value is the average of three maximum values of morning, noon, and night measurements).

cIn this model we did not adjust for height, weight, and week ID.

*p<0.05%.

Analysis of the association of FeNO with O3 showed that a 10-μg/m3 increase in weekly personal O3 concentration was associated with an increase in FeNO values by 11.10% (95% CI: 4.23%, 18.43%). The direction of the effect and the magnitude of the association was practically the same after adjusting for PM10 [9.5% (95% CI: 2.46%, 16.98%)] (Table 3). Associations with FeNO remained significant when we restricted the analysis in the spring–early summer study period 11.79% (95% CI: 2.54%, 21.82%).

An increase of 19% (95% CI: −0.53%, 42.75%), (21%, 95% CI: 0.42%, 45.66%, after adjusting for PM10) in the number of days with at least one symptom within each of the five weeks was associated with a 10-μg/m3 increase in weekly ozone exposure (Table 4). The association was smaller and nonsignificant when only the spring–summer period was considered (data not shown). No association between weekly O3 personal exposure with “any absenteeism” (i.e., the number of days the student was absent from school during each of the five weeks) was observed (Table 4). In sensitivity analyses, the direction of the effects remained unchanged when the groups of asthmatic, asthmatic plus those having obstructive ventilatory defect, or healthy children were analyzed separately. We observed no significant heterogeneity in the effects between children when we allowed for random slopes in our models (data not shown). Results remained unchanged when the same day temperature was included in the models. In addition, no effect modification by area (high or low ozone) or city (Athens or Thessaloniki) was detected.

Table 4. Association between the number of days with any respiratory symptom or absenteeism with weekly O3 exposure measured with personal monitors in the panel of 178 school children.
Health outcome Model All study periods % change per 10 μg/m3 O3 (95% CI) Wald statistic
Number of days with any symptoma Ozone only 19.16 (−0.53, 42.75) 3.62
Ozone+PM10b 20.94 (0.42, 45.66)* 4.02*
Number of days of absenteeismc Ozone only −28.95 (−55.25, 12.79) 2.10
Ozone+PM10b −31.32 (−56.88, 9.39) 2.50

Note: Results of Poisson models adjusting for sex, exposure area (low/high), study area (Athens/Thessaloniki), years of father’s education, air temperature (7-d average), mean time spent outdoors daily, citrus fruits consumption (yes/no), and week ID.

aNumber of days within the week that any symptom occurred.

b7-d average, measurements from nearest fixed site.

cNumber of days within the week that the student was absent from school.

*p<0.05%.

Discussion

In this panel study, we found consistent associations between increased weekly O3 personal exposure and a decrease mainly in FVC and also FEV1, in 10-y-old school children following their normal daily schedules not modified by their participation in the study. We also observed corresponding increases in the number of days that any respiratory symptom occurred and in FeNO, a marker of airway inflammation. Our sample included asthmatic children (about 7%), but separate analyses by asthmatic and nonasthmatic children did not show a modification in the effect estimates. The students were sampled from low- and high-O3 areas and from two major cities, but no effect modification by area or city was detected.

Previous longitudinal studies investigating the effect of short-term exposure to O3 on children’s respiratory health involved mainly asthmatic children and relied on fixed monitoring sites rather than on personal measurements (Li et al. 2012). Most studies used PEF as lung function parameter. Moreover, among the few panel studies concerning healthy children no one, to the best of our knowledge, combined inflammatory response and change in lung function with personal measurements of O3 exposure (Barraza-Villarreal et al. 2008; Chen et al. 2015).

One very consistent and robust finding in our study is the increase in FeNO that persisted after controlling for PM10, probably reflecting the capacity of O3 to induce airway inflammation (Mudway and Kelly 2000) even in healthy children. Very few studies investigated exposure to O3 and FeNO levels. A panel study, involving school children living in Mexico City, demonstrated an association of FeNO with acute exposure to traffic-related air pollutants in both asthmatics and nonasthmatics (Barraza-Villarreal et al. 2008). However, the 8-h moving average outdoor concentration of O3 was associated with FeNO in asthmatic children only. Another study investigated the effects of exposure to several pollutants on FeNO in asthmatic children in the Mexican-U.S. border and found respiratory effects attributed to PM (several size fractions) and NO2 but not of O3 (Sarnat et al. 2012). Exposure assessment in these studies was based on fixed monitoring sites and passive samplers located in schools, resulting perhaps in less accurate assessment of O3 exposure.

Furthermore, we found significant negative associations, robust after adjusting for potential confounders and more strongly during warmer days, between O3 weekly personal exposure and FVC as well as FEV1 (but of borderline significance).

Based on our results, the mean decrease found in FVC (0.0 3L, per 10-μg/m3 increase in personal O3 exposure) may be of clinical significance if repeated exposures lead to a more permanent adverse effect. Moreover, the range of personal exposure measurements is about 40 μg/m3, depending not only on ambient concentrations but also on the child’s time spent outdoors, and in the highest exposure range the weekly decrease in FVC may be clinically relevant. Concerning our findings on FeNO, it follows that a 20% increase in FeNO is associated with 20-μg/m3 increase in O3 personal exposure (an exposure contrast well within the range of measured exposures from 1 to 42 μg/m3) considered to indicate an effect that may have clinical importance (Dweik et al. 2011).

An unexpected finding of our study was the significant positive associations of O3 with the weekly averages of daily PEF self-assessed values using Mini-Wright peak flow meters. PEF is the most usual measurement in panel studies evaluating the effect of air pollution on children’s lung function but results have been inconsistent across studies. Some panel studies reported PEF decrements in healthy (Declercq and Macquet 2000; Höppe et al. 2003; Li et al. 2012) and asthmatic school children (Just et al. 2002; Li et al. 2012; Mortimer et al. 2002; Romieu et al.1997) as well as enhanced daily PEF variability in the asthmatics (Just et al. 2002; Li et al. 2012), whereas others found no effect (Peacock et al. 2003; Scarlett et al. 1996). Likewise, in the older studies of children attending summer camps in the United States and Canada, inconsistent results associating ambient O3 exposure with PEF were observed, with the largest study (at Pine Springs Ranch, east of Los Angeles) reporting a positive and statistically significant association (Kinney et al. 1996). In a Brazilian study, Castro et al. (2009) observed a protective effect of O3 to school children’s PEF. Recently, Altuǧ et al. (2014) showed that PEF levels were negatively associated with weekly average O3 levels only in children without upper respiratory tract complaints.

Two panel studies examined the effect of O3 exposure on FEV1 in asthmatic children and found no effect (Dales et al. 2009; Delfino et al. 2004). Both of them relied on self-measured FEV1 by handheld electronic devices. More consistent findings have been observed in the few studies using spirometry performed by trained personnel. In a reanalysis of six summer camp studies, a consistent decrease in FEV1 has been found associated with increased ambient exposure to O3 (Kinney et al, 1996). In the Mexican study (Barraza-Villarreal et al. 2008), using fixed site outdoor measurements, the investigators report negative associations with FVC and FEV1, although none reached the nominal level of significance. In a study in Taiwan (Chang et al. 2012), based also on fixed site measurements for the assessment of O3 exposure, a significant negative association was observed with FVC and a nonsignificant negative association with FEV1. The decrements, we observed mainly in FVC and also in FEV1, are also consistent with previous experimental studies in young adults. The main reason for this O3-induced decrease is believed to be impaired inspiration as a result of stimulation of airway receptors causing a reduction in the level of inflation achieved at full inspiration (Blomberg et al. 1999; Hazucha et al. 1989; Kjærgaard et al. 2004). Based on all the above, our results support that ambient O3 exposure affects inspiratory capacity and is related to airway inflammation.

In the present study, we also found a significant increase in respiratory symptoms after increased exposure to O3. This is a consistent finding among studies that investigate O3 effects at different time scales, mostly in asthmatic children. Thus, Declerq and Macquet (2000), Delfino et al. (2002), Escamilla-Nuñez et al. (2008), Just et al. (2002), Mortimer et al. (2002), and Schlink et al. (2002) found an association of O3 exposure and increase in respiratory symptoms, but few others found no effect (Jalaludin et al. 2004; Ostro et al. 2001). In our study asthmatic children were not found to be more susceptible than nonasthmatic children and this has been reported by other studies as well (Ward et al. 2002).

Our study has certain limitations. Our sample included children who agreed to participate from randomly selected schools; however, they did not form a random sample from the students of their class. This may have introduced some selection bias because children from more educated families or families more sensitive to environmental problems may have been preferentially included. However, we think that this did not bias our results away from the null given that the students attending a specific school are socioeconomically comparable (they live in the neighborhood) and the percentage of asthmatic children in our sample is comparable to that reported for the population (Papadopoulou et al. 2011). Another limitation of our project is the fact that assessment of exposure to pollutants other than O3 was not based on personal measurements but was estimated using the nearest fixed site monitors. This might have led to residual confounding, especially taking into account exposure to PM. We believe that the probability of residual confounding is limited because the correlations of O3 with PM10 and NO2 was not the same across the high- and low-O3 areas and between the two cities. Furthermore, PM is more homogeneously distributed and penetrates indoors making the fixed site and personal measurements less different from those of O3. Absenteeism might be a source of bias to the null for the estimated effects. During the field work, on each of the days our researchers visited the schools for spirometry, only a few children were absent from school (0–4 children each time, except during the last summer week in Thessaloniki when 17 children could not be found because specific schools had end-of-year activities), and it may be hypothesized that some of these children might have had a respiratory problem because the reason for the absence was not reported by the school. Another limitation of our study is the inability to study children during the hottest months of the year, July and August, because of school holidays. These months are also characterized by high-O3 concentrations. We were not able to reach schoolchildren during the holidays, but it should be mentioned that most families in Greece take their holidays during this time and the population of children in the cities is greatly reduced.

An advantage of the present study is the use of personal O3 monitoring. The higher contrast observed in outdoor school measurements compared with personal ones denotes the importance of relying on personal measurements when assessing O3 exposure health effects on school children. Another advantage is the spirometry performed by physicians, which provides more accurate and valid results compared with self-monitoring. This may explain our findings associating ozone exposure with the weekly FVC and FEV1, provided through spirometry, whereas an analysis of daily PEF measured by peak flow meters, handled by the same sample of students, did not provide significant results (Samoli et al. 2017).

Among the strengths of our study is the study design, as panel studies allow great flexibility in investigating multiple exposures and multiple outcomes; the location, as Greece is a warm, sunny country with inhabitants exposed to high-O3 concentrations; the large number of students and the relatively long period of follow up; the limited number of participants lost to follow-up or with incomplete data; the personal O3 monitoring; and the wealth of data collected over a variety of respiratory morbidity indices using standardized procedures. Moreover, our results were robust after controlling for particles and under several sensitivity/subgroup (e.g., after considering only the asthmatic schoolchildren) analyses.

Conclusion

The study provides evidence that airway inflammation and the frequency of respiratory symptoms increase while lung function decreases with increased O3 exposure in school children generally representative of the population of children 10- to 11-y-old in two large Southern European cities, where high- ozone concentrations are observed. Schoolchildren should be considered at a moderate risk of suffering respiratory health adverse effects following short-term exposure to the current levels of ambient O3, in line with what has been found in controlled human exposure studies at ambient concentrations. Focusing on asthmatic children may lead to an underestimation of the real burden of O3 induced respiratory adverse events.

Acknowledgments

The work was cofunded by the European Commission and the Greek government through the National Strategic Reference Framework 2007–2013 contract ref RESPOZE-children/2248.

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The Prevalence of Extended-Spectrum Beta-Lactamase-Producing Multidrug-Resistant Escherichia Coli in Poultry Chickens and Variation According to Farming Practices in Punjab, India

Author Affiliations open

1Center for Disease Dynamics, Economics & Policy, Washington, DC, USA

2Public Health Foundation of India, New Delhi, Delhi, India

3Dept. of Veterinary Population Medicine, University of Minnesota, St. Paul, Minnesota, USA

4School of Public Health and Zoonoses, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India

5Dept. of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA

6SRL Diagnostics, FORTIS Hospital, Noida, Uttar Pradesh, India

7SRL Diagnostics, Mumbai, Maharashtra, India

8Veterinary & Animal Husbandry Extension, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India

9Princeton Environmental Institute, Princeton, New Jersey, USA

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  • Background:
    Agricultural use of antimicrobials in subtherapeutic concentrations is increasing in response to the rising demand for food animal products worldwide. In India, the use of antimicrobials in food animal production is unregulated. Research suggests that many clinically important antimicrobials are used indiscriminately. This is the largest study to date in India that surveys poultry production to test for antimicrobial resistance and the occurrence of extended-spectrum β-lactamases (ESBLs) modulated by farming and managerial practices.
    Objectives:
    Our goal was to survey poultry production for resistance to eleven clinically relevant antimicrobials and phenotypic occurrence of ESBLs as modulated by farming and managerial practices.
    Methods:
    Eighteen poultry farms from Punjab were surveyed, and 1,556 Escherichia coli isolates from 530 birds were tested for susceptibility to 11 antimicrobials using the disk diffusion method and validated using VITEK 2 (bioMérieux, Marcy-L’Étoile, France). Samples from 510 of these birds were phenotypically tested for ESBL production using the combination disk method and confirmed using VITEK 2. Generalized linear mixed models were used to infer differences in resistance profiles associated with different farming practices and facility types.
    Results:
    Resistance profiles were significantly different between broiler and layer farms. Broiler farms were 2.2 [ampicillin (AMP), p=0.017] to 23 [nalidixic acid (NX), p<0.001] times more likely to harbor resistant E. coli strains than layer farms. Adjusting for farm type (broiler vs. layer), the odds of resistance (although not statistically significant) to all antimicrobials except nitrofurantoin (NIT) were higher in independent facilities (IUs) as compared to contracted facilities (CFs). Increased prevalence of multidrug resistance (MDR; 94% compared to 60% in layers), including prevalence of ESBL-producing strains (87% compared to 42% in layers), was observed in broiler farms.
    Conclusions:
    Our findings suggest that unregulated use of clinically relevant antimicrobials in Indian broiler and layer farms may contribute to the emergence of resistance and support the need to curb the nontherapeutic use of medically important antimicrobials in food animal production. https://doi.org/10.1289/EHP292
  • Received: 31 March 2016
    Revised: 16 December 2016
    Accepted: 17 December 2016
    Published: 20 July 2017

    Address correspondence to R. Laxminarayan and C. Brower, Center for Disease Dynamics, Economics & Policy, 1400 Eye St. NW, #500, Washington, DC 20005 USA. Telephone: 202-939-3300. Email: ramanan@cddep.org; charlie.brower@gmail.com

    Supplemental Material is available online (https://doi.org/10.1289/EHP292).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Medically important antimicrobials are used extensively in food animal production for disease prevention (e.g., prophylaxis and metaphylaxis), treatment, and growth promotion. It is estimated that two-thirds of antimicrobials produced globally are consumed in the livestock sector (CDDEP 2015). Numerous studies suggest that the widespread use of agricultural antimicrobials contributes to increased clinical resistance to antimicrobials (Chang et al. 2015; Marshall and Levy 2011; Silbergeld et al. 2008). Since antimicrobials are routinely added to animal feeds, bacterial populations are repeatedly exposed to subtherapeutic doses ideal for the emergence and spread of antimicrobial resistance (Chang et al. 2015; Marshall and Levy 2011; Silbergeld et al. 2008; You and Silbergeld 2014). Nearly every class of antimicrobial is used in agriculture, including many closely related to clinically relevant antimicrobials, such as penicillins, cephalosporins, fluoroquinolones, tetracyclines, sulfonamides, and aminoglycosides (Marshall and Levy 2011; Schwarz et al. 2001; Silbergeld et al. 2008). The extensive use of antimicrobials in agriculture results in human exposure to antimicrobial-resistant bacteria via direct and indirect pathways. These include exposure via direct contact with livestock or contaminated food products, indirect gene transfer across bacterial species, and the widespread release of antimicrobial-resistant pathogens into the environment (Silbergeld et al. 2008). This raises serious human health concerns, since the occurrence of cross-resistance between antimicrobials of the same class is highly likely.

The use of antimicrobials in subtherapeutic concentrations is increasing in response to heightened demand for food animal products worldwide, particularly in South and Southeast Asia, due to rising incomes (Teillant et al. 2015; Van Boeckel et al. 2015). The global average annual consumption of antimicrobials in food animals was conservatively estimated in 2010 to be 63,151 (±1,560) tons. In chickens, this corresponds to a consumption of antimicrobials per kilogram of animal produced of approximately 148 Mg·kg−1 (Van Boeckel et al. 2015). Global antimicrobial consumption in food animal production is expected to rise by 67% by 2030, driven primarily by BRICS (Brazil, Russia, India, China, and South Africa) countries shifting to large-scale, intensive farming operations where antimicrobials are used routinely in subtherapeutic doses. Antimicrobial consumption in food animal production in India is projected to grow by 312%, a concerning development further compounded by India’s status as the largest consumer of antimicrobials of humans (Van Boeckel et al. 2014, 2015).

A recent study of antimicrobial residues in chicken meat sold for human consumption in New Delhi, India, found that of the 70 chicken meat samples tested, 40% contained antimicrobial residues (Sahu and Saxena 2014). The most common antimicrobials detected were enrofloxacin (20%), ciprofloxacin (14.3%), doxycycline (14.3%), oxytetracycline (11.4%), and chlortetracycline (1.4%). The high use of fluoroquinolones detected is particularly concerning, given the importance of this broad-spectrum agent in human clinical medicine. Fluoroquinolones were banned for veterinary use in poultry in the United States in 2005 in response to numerous studies linking its use in treating respiratory diseases in poultry to the emergence and spread of fluoroquinolone resistance in humans and animals (Endtz et al. 1991; Nelson et al. 2007). In 2006, the European Union banned the use of antimicrobials for growth promotion in food animals in response to evidence suggesting growth promoters drove the emergence and spread of resistance (European Commission 2005).

In light of evidence of antimicrobial use in Indian poultry production and the projected 312% increase in agricultural antimicrobial consumption over the next 15 years, this study seeks to understand the prevalence of resistance [and, in particular, extended-spectrum β-lactamase (ESBL) production] in Escherichia coli and other Enterobacteriaceae in poultry and how farm operational and managerial practices in poultry production influence resistance emergence. Previous studies reported a high prevalence of ESBL-producing Enterobacteriaceae in poultry production (Dierikx et al. 2013b, 2013a; Kar et al. 2015; Laube et al. 2013). Moreover, ESBL-producing pathogens are resistant to numerous antimicrobials and are associated with longer hospital stays and other negative clinical outcomes in humans (Lautenbach et al. 2001). Specifically, this study investigates how resistance profiles vary between broiler and layer farms, between independent (IUs) and contracted facilities (CFs), and among farms reporting antimicrobial use (including for growth promotion) and those that do not.

This study compares various farm (i.e., broiler vs. layer) and facility types (i.e., contracted vs. independent), as these are the primary types of poultry operations in Punjab. Additionally, past research has reported that broiler farms tend to use more antimicrobials and harbor a higher level of resistance than layer farms (van den Bogaard et al. 2002). Higher antimicrobial usage in broilers is reasonable, considering the need to sustain rapid growth of chickens over short periods of time (∼35–50 days), whereas layer farms typically use fewer antimicrobials to sustain consistent egg production over longer periods of time (∼52–56 weeks). Moreover, unlike independent facilities, contracted farms are owned by large-scale poultry producers and follow a strict production process established by the producer, wherein all input materials (e.g., day-old chicks, feed, antimicrobials, etc.) are supplied by the contracting firm. Contracted farms are obligated to adhere to all instructions and protocols from the contracting firm. Given the differences in farming practices among these types of operations, it is necessary to understand how resistance prevalence varies to appropriately target policy interventions where they would have the greatest impact.

Materials and Methods

Sampling Protocol

Eighteen poultry farms (nine layers and nine broilers) were randomly selected from a list of farms provided by Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, Punjab, India. Layer and broiler farming operations were categorized as CFs or IUs, based on the nature of their contractual agreements with large-scale poultry producers. In this cross-sectional design, all samples were collected over a 5-month period from July to November 2014. All farms were located within six districts across the state of Punjab, India.

Sixty cloacal swabs were collected from 30 birds selected at random (two swabs per bird) from each farm and transported to the laboratory for isolation of E. coli (three isolates per bird) and other Enterobacteriaceae (one isolate per bird) for phenotypic detection of ESBLs. Only 20 birds were sampled from the first layer farm visited due to extreme heat causing danger to the health of the birds and only a single swab could be collected from each bird. Putative ESBL-producing Enterobacteriaceae were not isolated from these samples. Thus, 530 birds (n=530) were sampled for general susceptibility testing of E. coli, and 510 birds (n=510) were sampled for ESBL-producing Enterobacteriaceae. Since three E. coli isolates were sampled per bird, a total of 1,556 viable E. coli isolates (n=1,556) were tested for susceptibility against 11 antimicrobials covering a range of clinically relevant antimicrobial classes (tetracyclines, cephalosporins, quinolones, fluoroquinolones, penicillins, carbapenems, aminoglycosides, sulfonamides, and so on) (Figure 1).

Flow diagram.
Figure 1. Sampling framework depicting differences between on-farm and in-laboratory sampling protocols.

After sampling was complete, a 30-min structured interview covering sanitation, disease control, and antimicrobial use practices was conducted with each farm manager (see Supplemental Material, Survey). All protocols used to sample isolates from animals in this study were humane and approved by the Institutional Animal Care and Use Committee at Princeton University. Informed consent was obtained from all farmers participating in interviews, and the survey protocol was approved by the Institutional Ethics Committee at the Public Health Foundation of India.

Microbiological Methodology

Cloacal swabs were transported in a Cary-Blair transport medium (HiMedia, Mumbai, India) on ice to the laboratory for bacterial isolation. One swab from each bird was cultured on selective MacConkey Lactose agar (HiMedia, Mumbai, India), while the other was inoculated in a selective pre-enrichment broth [trypticase soy broth supplemented with 1 mg/L cefotaxime (CTX)] (HiMedia, Mumbai, India) [Diederen et al. 2012; Dierikx et al. 2013b, 2013a; EFSA Panel on Biological Hazards (BIOHAZ) 2011]. Both were incubated at 37°C under aerobic conditions for 20–24 hours.

Isolation of Escherichia coli for susceptibility testing.

Three well-isolated E. coli-like colonies (pink, doughnut-shaped) were selected at random from the MacConkey Lactose agar plate and subcultured on three separate Eosin Methylene Blue (HiMedia, Mumbai, India) agar plates at 37°C under aerobic conditions for 20–24 hours (Corry et al. 2011). Selecting bacterial isolates without selection for a specific resistance profile will detect only the most prevalent flora; thus, resistance at low levels may not be detected (Aarestrup et al. 2001). Subsequently, a single, well-isolated colony with the metallic-green sheen characteristic of E. coli on Eosin Methylene Blue from each plate was subcultured onto three separate trypticase soy agar (HiMedia, Mumbai, India) plates and incubated for 20–24 hours at 37°C under aerobic conditions for the isolation of pure cultures.

A single, well-isolated colony from each trypticase soy agar plate was then suspended in 5 mL of trypticase soy broth and incubated at 37°C until the inoculum achieved 0.5 McFarland standard (≥0.10D at 620 nm). Isolates were confirmed as E. coli by using HiIMViC biochemical test kits (HiMedia, Mumbai, India). Susceptibility to 11 drugs was tested using the Kirby-Bauer disk diffusion methodology (Clinical and Laboratory Standards Institute 2013a, 2013b) (see Table S1 for zone diameter breakpoints). All 11 antimicrobial disks used for susceptibility testing: ampicillin (AMP), gentamicin (GEN), ciprofloxacin (CIP), nitrofurantoin (NIT), co-trimoxazole (COT), tetracycline (TE), cefuroxime (CXM), imipenem (IPM), nalidixic acid (NX), chloramphenicol (C), and ceftriaxone (CTR) (HiMedia, Mumbai, India), were subjected to weekly quality control tests using the standard strain E. coli ATCC 25922 (HiMedia, Mumbai, India).

Isolation of ESBL-producing Enterobacteriaceae for phenotypic confirmation.

Following incubation, the second cloacal sample, which was inoculated in trypticase soy broth supplemented with 1 mg/L CTX (HiMedia, Mumbai, India), was vortexed and subcultured onto MacConkey agar supplemented with 1 mg/L CTX [Diederen et al. 2012; Dierikx et al. 2013b, 2013a; EFSA Panel on Biological Hazards (BIOHAZ) 2011]. After incubation for 20–24 hours at 37°C under aerobic conditions, a single, lactose-fermenting (pink-red) colony was then subcultured onto trypticase soy agar and incubated for 20–24 hours at 37°C under aerobic conditions for the isolation of pure colonies.

A single, well-isolated colony was then suspended in 5 mL of trypticase soy broth and incubated at 37°C until the inoculum achieved 0.5 McFarland standard (≥0.10D at 620 nm). Species identity was confirmed using HiIMViC biochemical test kits. Phenotypic ESBL production was tested using the combination disk method using CTX, cefotaxime–clavulanic acid (CEC), ceftazidime (CAZ), and ceftazidime–clavulanic acid (CAC) (Clinical and Laboratory Standards Institute 2013b; Dierikx et al. 2013b, 2013a). All antimicrobial disks were subjected to weekly quality control tests using the standard strain E. coli ATCC 25922, as well as an ESBL-positive Klebsiella pneumoniae isolate.

Isolate storage and quality assurance testing.

All isolates were inoculated in trypticase soy broth supplemented with 30% glycerol (v/v) (HiMedia, Mumbai, India) in cryovials and stored at −80ºC for further analysis and quality assurance testing. All 347 putative ESBL-producing Enterobacteriaceae isolates (i.e., isolates that exhibited growth on MacConkey agar supplemented with CTX) and 540 E. coli isolates for general susceptibility testing were sent to SRL Diagnostics at Fortis Hospital, Noida, Uttar Pradesh, India, for quality assurance by a separate team of researchers at an independent laboratory accredited by the National Accreditation Board for Testing and Calibration Laboratories, Government of India.

Of these isolates, all putative ESBL-producing and 395 (∼25% of total sample size, selected at random) E. coli isolates underwent species identification and susceptibility testing using VITEK 2 (bioMérieux, Marcy-L’Étoile, France). Species identification was confirmed using GN ID cards (bioMérieux, Marcy-L’Étoile, France) capable of identifying more than 150 fermentative and nonfermentative gram-negative bacilli. Antimicrobial susceptibility and phenotypic detection of ESBL production was confirmed using AST N280 cards (bioMérieux, Marcy-L’Étoile, France), which test susceptibility to the following drugs: amikacin, amoxicillin–clavulanic acid, ampicillin, cefepime, cefoperazone, sulbactam, ceftriaxone, cefuroxime, ciprofloxacin, colistin, ertapenem, gentamicin, imipenem, meropenem, nalidix acid, nitrofurantoin, piperacillin tazobactam, tigecycline, and trimethoprim/sulfamethoxazole (i.e., COT). Thus, this procedure provided quality assurance for 9 of the 11 drugs tested in this study (excluding chloramphenicol and tetracycline) as well as phenotypic detection of ESBL activity.

Statistical Analysis

Generalized linear mixed models with a logit link function were used to model the resistance profiles against farm and facility type for each antimicrobial, where the outcome for each isolate was classified as either resistant or susceptible. Isolates classified as intermediate were considered resistant for these analyses. Logistic regression models using the state of each isolate as a binary outcome (combining intermediate and resistant as nonsusceptible) with farm and facility type as the explanatory variables were used to model resistance profiles modulated by farming practices (i.e., farm or facility type). For each antimicrobial, the odds of an isolate being nonsusceptible were computed against farm and facility type. Since birds within a farm were likely to be treated similarly, random effects were incorporated in each model to account for similar resistance profiles within farms. Using the model parameters estimated from the logistic regression models, we derived prediction estimates (along with 95% prediction intervals using diagonal elements of the variance–covariance matrix of the predicted means and the estimated variance of the random intercept) for the population-based probability of resistant strains in isolates from randomly sampled broiler or layer farms, given the type of facility (contracted or independent).

Chi-square tests of independence were employed to test the difference in prevalence of ESBL-producing Enterobacteriaceae strains among farm types. Of the 11 antimicrobials tested in the study, the number of antimicrobials to which an isolate was classified as resistant was computed for every isolate. Since the number of antimicrobials did not follow a normal distribution, a categorical variable describing multidrug resistance was constructed with four levels [0=susceptible, 1=singly-resistant, 2−4=moderately multidrug resistant (MDR), >4=extremely MDR]. This multidrug resistance categorical variable was analyzed against farm (broiler vs. layer) and facility (independent vs. contracted) types using a proportional odds logistic regression model in order to account for the severity of resistance. Predicted probabilities for multidrug resistance were computed using the estimated parameters from the ordinal logistic regression model.

The resistance prevalence on farms that reported use of antimicrobials for growth promotion compared to those that did not was analyzed using logistic regression models with a random intercept for each farm. Adjusting for farm type (broiler vs. layer), ordinal logistic regressions were employed to understand the association of antimicrobial growth promoter (AGP) usage with multidrug resistance. Finally, a random effects logistic regression, adjusted for farm type, was used to analyze the association of AGP usage with the presence of ESBL-positive strains.

Sensitivity analyses were implemented to understand the effects of treating intermediate isolates as susceptible instead of resistant. Logistic regression models were employed to estimate the odds of resistance prevalence in broiler farms as compared to layer farms for each antimicrobial. Additionally, logistic regression models, stratified by farm type, were used to estimate the effect of farm size (in terms of number of birds) on resistance prevalence to each antimicrobial. All statistical analyses were carried out in R (version 3.2.1; R Foundation for Statistical Computing).

Results

Summary of Survey Results

Table S2 summarizes survey responses from 16 of the 18 farms sampled; two farms did not provide responses to detailed survey questions. In total, we sampled nine broiler farms (three contracted, six independent) and nine layer farms (three contracted, six independent). The average number of birds on each farm was 57,324 (interquartile range=12,000–40,000). Of the farms that elected to participate in the survey, all 16 reported using antimicrobials for disease treatment and prevention, while 12 (67%) reported using antimicrobials for growth promotion. Table S3 reports purpose of antimicrobial use disaggregated by farm and facility type. Tetracyclines and fluoroquinolones were the most commonly reported antimicrobials used, with nine (56%) farms reporting their use.

Overall prevalence of resistant Escherichia coli in Poultry Farms

The overall prevalence of resistance in the 1,556 E. coli isolates was analyzed across all farm (broiler vs. layer) and facility (contracted vs. independent) types. Percentages of isolates classified as susceptible, intermediate, or resistant were used to summarize resistance prevalence overall and disaggregated by farm and facility type (Table S4). A high prevalence of E. coli resistant to NX (86.1%), TE (47.0%), AMP (43.8%), COT (42.2%), and CIP (39.4%) was observed. The degree of resistance to TE, NX, AMP, and COT was consistently high across all farm and facility types. However, resistance to CIP was detected at high levels only in broiler farms. No resistance to IPM was detected among any of the farms.

Quality Assurance Validation Results

Resistance profiles of isolates against antimicrobials common to both datasets were compared. Validation analysis reveals that the original analysis conservatively underestimated the prevalence of CIP resistance by ∼15–20% across various farm and facility types (Figure 2). For all other antimicrobials, the validation analysis reveals either no differences or differences only in the classification of isolates being susceptible vs. intermediate.

Five ternary diagrams indicating broiler and layer farm types, contracted and independent facility types, and overall. The prevalence of following antibiotics is reported: ampicillin, ceftriaxone, cefuroxime, ciprofloxacin, cotrimoxazole, gentamicin, imipenem, nalidixic acid, and nitrofurantoin.
Figure 2. Ternary diagrams showing differences in resistance prevalence between original and validation data for 395 isolates against 9 common antimicrobials. Prevalence in the original data is shown as solid triangles, and those in the validation data are shown as solid dots. Each point represents a three-component vector showing the prevalence of susceptible, intermediate, and resistant isolates that sum to 100%. A point closer to a vertex (for instance, R) represents a high prevalence of the “resistant” state, also indicated by the arrows along each edge.

Resistance Profiles Modulated by Farming Practices

Figure 3A shows the odds ratio (OR) of resistance against each antimicrobial in broiler farms as compared to layer farms, adjusted for type of facility (contracted or independent). It is evident that for all antimicrobials, except NIT, the odds of finding a resistant isolate in broiler farms was at least two times greater than in layer farms (See also Table S6). Specifically, odds of E. coli resistant to antimicrobials, such as NX and CIP, was more than 10 times higher in broiler farms. Independent facilities had a significantly higher risk of E. coli resistant to C and NX as compared to contracted facilities, adjusted for farm type (Figure 3B). The results suggest that the odds of resistance (although not significant) to all antimicrobials tested, except NIT were higher in independent facilities.

Graphical representation of Odd’s ratio with confidence intervals.
Figure 3. Results from logistic regression modeling the risk of resistance prevalence against farm and facility type, with random intercepts for each farm. Left panel presents the risk (in terms of odds ratios) of Escherichia coli resistance in broiler farms relative to layer farms, adjusted for facility type. Right panel presents the same risk in independent facilities as compared to contracted facilities, adjusted for farm type. For all of these analyses, intermediate isolates are treated as resistant. The x-axis represents odds ratios in a log scale. The horizontal lines represent 95% confidence intervals for the estimated odds ratios.

This model was further used to estimate the population average probabilities of the occurrence of E. coli strains resistant or susceptible to each antimicrobial across all farm and facility types (Figure 4). A high probability of resistance to NX, CIP, and TE was observed, with higher probabilities in broiler farms, and specifically, independent broiler facilities.

Predicted probabilities are plotted (y-axis) across 10 antimicrobials by contracted and layer type; independent and layer type; contracted and broiler type; and independent and broiler type, respectively.
Figure 4. Predicted probabilities of resistance (solid blue dots) and corresponding 95% confidence intervals (blue error-bars) against 10 antimicrobials [imipenem (IPM) was not included, as no resistant isolates were detected], by farm and facility type, based on a logistic regression model with random intercepts for farm. Each isolate is assumed to be either resistant or susceptible in this analysis.

Prevalence of ESBL-Positive Strains and Multidrug Resistance

Understanding the prevalence of ESBL-positive bacteria is particularly relevant, given that infections caused by these organisms are more difficult to treat in humans. Of 510 cloacal samples collected for ESBL detection, 305 E. coli, 13 K. pneumoniae, 8 Escherichia fergusonii, 3 Proteus mirabilis, and 1 Escherichia hermannii isolate were phenotypically confirmed as ESBL-producing Enterobacteriaceae. Additionally, seven non-Enterobacteriaceae gram-negative isolates were confirmed as ESBL-positive: three Pseudomonas aeruginosa, two Bordetella trematum, and two Acinetobacter spp., and are included in our overall analysis, as they still indicate that a particular bird carried ESBL-positive flora (Table S5). The prevalence of ESBL-positive strains was significantly higher [OR=9.55, confidence interval (CI): 6.14, 14.85, p<0.0001] in broiler farms (87%) than layer farms (42%) (Figure 5A). There was no statistically significant difference in the prevalence of ESBL-positive strains (∼87%) between contracted and independent broiler farms. Contracted layer farms had a higher prevalence (49%) than independent layer farms (38%), although the difference was not statistically significant.

Figure A is a bar graph plotting percentage ESBL (y-axis) across facility types (x-axis) for the broiler and layer farm types. Figure B is a box-and-whisker plot, plotting number of antibiotics (y-axis) across facility types (x-axis) for the broiler and layer farm types. Figure C shows four panels of bar graphs plotting predicted probability for independent and contracted facilities for broiler and layer.
Figure 5. (A) Distribution of ESBL-producing status of the 510 cloacal samples, disaggregated by farm and facility type. ESBL status was tested for all Enterobacteriaceae isolated, including seven non-Enterobacteriaceae, gram-negative isolates. The figure shows proportions and associated 95% confidence intervals of ESBL-positive strains within each group. (B) Distribution of 1,556 multidrug-resistant Escherichia coli isolates by farm type and facility type. The vertical axis shows the number of antimicrobials (maximum 10) to which an isolate was resistant. The horizontal lines within the boxes indicate the median number of antibiotics to which an isolate was resistant, while the length of the box represents the interquartile range (IQR). The lower and upper limits of the whiskers represent Q1−1.5 IQR and Q3+1.5 IQR, where Q1 and Q3 are the first and third quartiles, respectively. (C) Predicted probabilities and associated 95% confidence intervals for multidrug resistance according to a proportional odds logistic regression of categories of multidrug resistance against farm and facility type.

Given that all isolates were tested against multiple antimicrobials, the prevalence of MDR E. coli strains was analyzed against farming practices (Table S6). A higher prevalence of MDR E. coli was detected on broiler farms (94%) as compared to layer farms (60%) (Figure 5B). Isolates on independent broiler farms were 15 times more likely (CI: 10.3, 21.9, p<0.001) to be multidrug resistant than those from an independent layer farm. The odds were significant yet lower in the case of contracted farming facilities (OR=5.4, CI: 4.1, 7.0, p<0.001). Predicted probabilities (Figure 5C) show that 90% of isolates were likely to be moderately or extremely resistant in broiler farms, while contracted layer farms had a lower probability (36%) of being moderately or extremely resistant.

Impact of Antimicrobial Use for Growth Promotion on Resistance Profiles

The prevalence patterns of antimicrobial resistance within certain subgroups were analyzed to better understand the impact of farming practices on resistance. Prevalence percentages within farms that reported using antimicrobials for growth promotion in poultry chickens were analyzed compared to farms that did not. Of the 16 farms that elected to participate in the survey, 75% reported using antimicrobials for growth promotion. Irrespective of the type of farming operation, our results indicate increased resistance prevalence in farms using AGPs. AGP use is correlated with significantly increased odds of resistance for all antimicrobials except CTR and NIT. Within broiler farms that reported use of AGPs, a significantly higher prevalence of resistance to CIP, C, NIT, and COT was detected. Within layer farms, significantly higher odds of resistance were observed for all antimicrobials except C, NIT, CXM, and CTR (Table 1).

Table 1 lists antimicrobials in the first column; the corresponding odds ratios stratified by farm type for overall, broilers, and layers are listed in the other columns.

Table 1. Odds ratios of increased resistance prevalence to all antibiotics for farms reporting antimicrobial use for growth promotion as compared to farms that did not report antimicrobial use for growth promotion.
Odds ratios stratified by farm type
Antimicrobial Overall Broilers Layers
Ampicillin (AMP) 1.413* (1.088, 1.839) 0.784* (0.485, 1.256) 1.460* (1.031, 2.079)
Chloramphenicol (C) 1.698* (1.179, 2.489) 2.721* (1.361, 6.040) 0.916 (0.562, 1.513)
Ciprofloxacin (CIP) 4.980** (3.792, 6.567) 2.258* (1.168, 4.189) 4.297** (2.899, 6.473)
Cotrimoxazole (COT) 4.242** (3.131, 5.809) 1.669* (1.042, 2.693) 6.638** (4.221, 10.764)
Ceftriaxone (CTR) 1.680 (0.885, 3.464) 1.284 (0.529, 3.769) 1.061 (0.363, 3.492)
Cefuroxime (CXM) 2.032* (1.130, 3.923) 1.888 (0.792, 5.479) 0.993 (0.388, 2.747)
Gentamicin (GEN) 2.559** (1.734, 3.875) 1.151 (0.694, 1.954) 3.279* (1.498, 8.190)
Nalidixic Acid (NX) 5.572** (3.761, 8.298) N/A 3.977** (2.590, 6.158)
Nitrofurantoin (NIT) 1.290 (0.984, 1.698) 2.942** (1.669, 5.457) 0.879 (0.624, 1.240)
Tetracycline (TE) 1.969** (1.490, 2.598) 1.101 (0.563, 2.036) 1.633* (1.162, 2.295)

Note: Fisher’s exact test was employed to compute the odds ratios and associated 95% confidence intervals (given in parentheses below) with the outcome as the presence or absence of resistant isolates. Odds ratios are presented for all farms (“overall”) and disaggregated by farm type (“broilers” vs. “layers”). Note that an OR could not be calculated for NX in broiler farms, as the cell frequency was zero. *p<0.05; **p<0.001.

After accounting for farm type (broiler vs. layer), our results indicate that reported AGP use had no significant association with the prevalence of ESBL-positive strains (OR=0.55, CI: 0.01, 30.58, p=0.773). In contrast, isolates from farms reporting AGP use were 2.92 times more likely (CI: 2.24, 3.81, p<0.001) to be multidrug resistant than those from farms not reporting AGP use. Moreover, in this analysis, the effect of farm type on the odds of multidrug resistance was maintained as broiler farms were still 6.17 times (CI: 4.80, 8.00, p<0.001) more likely to harbor MDR strains compared to layer farms.

Sensitivity Analysis

We analyzed the impact of treating intermediate isolates as susceptible instead of resistant on the associations described earlier, and we provide these results in the Supplemental Material (Figure S1). We did not observe any significant changes in the direction and statistical significance of associations between farming practices and patterns of antimicrobial resistance. However, in some cases (e.g., GEN, CXM, C), the associations were stronger, indicating a higher prevalence of intermediate isolates in layer farms.

We also conducted a sensitivity analysis to examine associations between farm size (number of birds) and the prevalence of resistance among different types of farms (broilers and layers). The median farm size in terms of number of birds was 15,000 in broiler farms compared to 40,000 in layer farms. With an increase in farm size by 10,000 birds, we observed that changes in the odds of resistance to all antimicrobials were not statistically significant within broiler farms. In the case of layer farms, we observed marginally lower odds of resistance to NX, CIP, and NIT with increases in farm size. These results are summarized in the Supplemental Materials (Figure S2).

Discussion

Research on antimicrobial use and resistance in food animal production in India remains a relatively new field. However, research is urgently needed given the projected large-scale increase in poultry production and antimicrobial use in the poultry sector (Brahmachari et al. 2013; Van Boeckel et al. 2015). Sarma et al. (1981) isolated E. coli from healthy and diseased fowl in Ludhiana, Punjab, India, and discovered that approximately 80% of isolates were resistant to chlortetracycline, tetracycline, oxytetracycline, and triple sulfas. Shivachandra et al. (2004) found 100% resistance to sulfadiazine and widespread resistance to amikacin, carbenicillin, erythromycin, and penicillin in Pasteurella multocida isolates from chickens and other birds from 11 separate states in India. More recently, Dhanarani et al. (2009) found extensive resistance to streptomycin (75%), erythromycin (57%), tobramycin (54%), ampicillin (50%), rifampicin (46%), and kanamycin (40%) in Staphylococcus and other bacterial isolates from poultry litter in Tamil Nadu, India.

Recent surveys suggest that 70–90% of Enterobacteriaceae in India are ESBL producers and that colonization of humans with such bacteria is widespread(Hawkey 2008; Kumarasamy et al. 2010; Mathai et al. 2002). However, researchers in India have only recently begun to investigate ESBL-producing bacteria of food animal origin. Kar et al. (2015) conducted the first systematic study on multidrug resistant ESBL-producing E. coli in food producing animals from India in which 316 E. coli isolates were collected from poultry and dairy cattle in Odisha with 18 (6%) isolates confirmed as ESBL-positive by combination disc method and ESBL E-test. A study in Hyderabad, India, isolated E. coli from 150 food samples (vegetable salad, raw egg surface, raw chicken, unpasteurized milk, and raw meat) and detected 6 (4%) ESBL producers, two of which were isolated from raw chicken samples (Rasheed et al. 2014). Another study in West Bengal, India, of 360 healthy layers and their environment did not detect ESBL production by PCR, but did record high levels of phenotypic resistance to several drugs: erythromycin (95.83%), chloramphenicol (87.52%), and cotrimoxazole (78.%) (Samanta et al. 2014).

We found a high degree of antimicrobial resistance and ESBL production in poultry facilities, which varied according to farming practices (i.e., farm and facility type). Moreover, we found a high prevalence of E. coli resistant to antimicrobials, such as CIP (fluoroquinolone), AMP (β-lactam), and tetracycline, which are commonly used in clinical settings. Broiler farms, and especially independent broiler farms, were associated with a higher prevalence of resistant E. coli strains than layer farms, corroborating past research (van den Bogaard et al. 2002). Additionally, the prevalence of ESBL-producing Entero-bacteriaceae was higher for broiler farms (87%) than layer farms (42%), and for contracted layer farms (49%) than independent layer farms (38%). These results may indicate that independent layer farms are using more drugs such as tetracyclines and fluoroquinolones, while contracted layer and broiler farms are beginning to shift to more recently developed drugs, such as third-generation cephalosporins, accounting for the higher prevalence of ESBLs. Additionally, higher odds of resistance among independent facilities may suggest that contracted facilities are employing better hygiene practices and veterinary care, which seems reasonable, considering these protocols and services are supplied by the contracting firm. In contrast, independent farmers do not have a comparable support system and could be misusing antimicrobials to a greater degree. However, it is critical that future studies identify the specific farming practices that are driving increased prevalence of resistant strains in order to mitigate the risk of spreading antimicrobial resistance.

Most of the farms surveyed in this study were large (average number of birds greater than 50,000), and all farms (that participated in the survey) reported using antimicrobials. Large flocks in small, confined areas, a lack of proper sanitation measures, and the unregulated application of broad-spectrum antimicrobials drive the spread of resistance at the farm level. The questionnaire-based surveys employed in this study revealed disturbing trends regarding the indiscriminate use of antimicrobials; all poultry farms included in this study used antimicrobials, and over half of them used antimicrobials for growth promotion rather than solely for disease prevention or treatment. Antimicrobials are often employed when broilers are being transported or held prior to slaughter to help them tolerate stress. Anecdotally, one farmer noted that antimicrobials were more effective than hygiene or sanitation measures because labor on poultry farms is unskilled, making it difficult to ensure that all hygiene procedures are followed. A majority of the farmers surveyed reported being unaware of the presence of AGPs premixed in chicken feed purchased from feed mills. Given the size and reach of these poultry farms in the retail market, the risk of exposure to resistant bacteria and antimicrobial compounds to humans is a significant concern.

This study reports a high prevalence of ESBL-producing Enterobacteriaceae in poultry farms (87% and 42% in broilers and layers, respectively). These results corroborate similar studies in broiler production facilities, but the prevalence reported here is similar or higher (Dierikx et al. 2013b, 2013a; Kar et al. 2015; Laube et al. 2013). ESBL-producing Enterobacteriaceae are highly resistant to multiple drugs, can contribute to acquired resistance through horizontal gene transfer across a wide range of bacterial species, and are associated with longer hospital stays and negative clinical outcomes in humans (Lautenbach et al. 2001; Marshall and Levy 2011; Rawat and Nair 2010; Silbergeld et al. 2008). Emerging resistance to widely used antimicrobials, such as fluoroquinolones and cephalosporins, reduces the efficacy of treating enteric, urinary tract, and skin infections, resulting in prolonged and more serious courses of illness. Enterobacteriaceae resistant to third-generation cephalosporins as a result of cephalosporin overuse in poultry production have been associated with increased human deaths in Europe (Collignon et al. 2013). In the present study, the prevalence of ESBL-producing E. coli and other Enterobacteriaceae was higher in broiler facilities, where antimicrobials are more commonly administered for growth promotion and disease prevention. This highlights the need to regulate the use of antimicrobials in these intensive farming operations, especially since researchers predict that this region will experience a major shift towards this type of farming operation over the next 15 years (Van Boeckel et al. 2015).

Direct contact with livestock colonized with resistant bacteria is the most documented route of resistance transmission from the agricultural reservoir into human populations (Bergeron et al. 2012; Jakobsen et al. 2010a, 2010b; Marshall and Levy 2011; Schmithausen et al. 2015; van den Bogaard et al. 2002; Zhang et al. 2009). These high-risk individuals provide a conduit of entry for resistant bacteria and resistance genes into the community and hospitals, where further person-to-person transmission is possible (Marshall and Levy 2011; Silbergeld et al. 2008). Of particular concern in this study was the lack of sanitation measures to prevent the transfer of resistant bacteria from animals to farm workers. Among survey respondents, 67% indicated they take no precautions when entering poultry sheds (and farm workers often do not wear closed-toe shoes); thus, the risk of colonization of farm workers is likely much higher than in other countries where strict disease control practices are implemented.

Despite extensive evidence linking the use of antimicrobials in food animal production to resistance in human populations, little has been done to address the problem in the majority of developing and developed countries worldwide. Recent publications have highlighted the rise of antimicrobial resistance and the emergence of new mechanisms of resistance in the Indian subcontinent (Kumarasamy et al. 2010). Policy makers and researchers have focused their attention on the clinical overuse and misuse of antimicrobials (to treat colds and other viral infections, for example) as drivers of resistance emergence. Furthermore, a recent review of the effectiveness of AGPs in food animal production suggests that the effects of AGPs on improving production and decreasing mortality in the poultry industry are minimal and do not offset the costs of the AGPs themselves (Cogliani et al. 2011; Engster et al. 2002; Graham et al. 2007; Sneeringer et al. 2015; Teillant et al. 2015). Moreover, it is possible to reduce the prevalence of antimicrobial resistance by placing restrictions on the use of antimicrobials in food animal production without negative impacts on productivity, as evidenced by the experience of both the poultry and pork industries in Denmark (Aarestrup et al. 2001; Levy 2014).

Notwithstanding the growing body of evidence, India has no regulatory provisions for the use of antimicrobials in cattle, chicken, or pigs raised for domestic consumption (Ganguly et al. 2011; Van Boeckel et al. 2015). The only laws on antimicrobial use in food animal production for domestic consumption mandate withdrawal of antimicrobials before processing of food animal products (Brahmachari et al. 2013). The lack of uniform regulations by the various agencies involved in poultry farming (and other food animal production industries) hinders enforcement of the appropriate use of antimicrobials. Policy actions should be implemented immediately in order to safeguard the effectiveness of antimicrobials, since antimicrobial effectiveness is a globally shared resource and responsibility (Ganguly et al. 2011).

This study analyzes a large sample size of isolates for resistance patterns and ESBL production, utilizing samples from farms following a variety of different operational and managerial practices, but only covers 18 farms located in one particular region of India. Future surveys of farms across multiple locations will be needed to obtain estimates at the district, state, and national levels. Furthermore, resistance prevalence at the farm level should be tracked longitudinally over longer periods of time to account for seasonality and to ascertain how resistance profiles are fluctuating over time. Supply of, and demand for, poultry meat and the economics of poultry farming are other critical factors that determine the proliferation of broiler farms and associated farming practices. Augmenting this study with antimicrobial sales and consumption data would help in formulating strategies to curb the rise of resistance in this growing reservoir.

This study only examines the prevalence of resistance on farms that reported using antimicrobials. Future studies should attempt to include antimicrobial-free or organic farms in their sampling frame. However, this may be difficult to accomplish in this region, as we did not encounter any such farms. Additionally, this study relies on survey data to create analytical variables used in our statistical models; all variables used were selected for analysis based on their likelihood of impacting resistance profiles and whether survey responses were reliable across all farms. For instance, we were unable to obtain detailed information on the types and amounts of antimicrobials used for all farms, since some farmers were unaware of this information or unwilling to report it. In light of these limitations, our study primarily compares resistance profiles among different farm (broiler vs. layer) and facility (independent vs. contracted) types instead of investigating specific practices influencing resistance development. Future studies should augment this survey data with observational periods on farms to better understand specific managerial and antimicrobial use practices.

Finally, given the high rates of background resistance in both human populations and the environment in India, this study cannot definitively link increased farm use of antimicrobials to increased resistance. A more detailed genetic investigation of the isolates to identify specific resistance genes could shed light on the mechanisms of resistance propagation within and among high-risk farms. Moreover, genetic analysis also would enable tracking of resistance genes in poultry birds to farmers and laborers in order to better quantify the risk of transmission from animals to humans, as well as help us understand the sources of resistance (e.g., humans, animals, environment, etc.).

Conclusion

This study presents evidence that antimicrobial resistance in E. coli and other pathogenic bacteria is correlated with particular farming practices. In particular, the results of this study revealed that broiler farms were associated with a higher prevalence of resistance, including ESBL-producing Enterobacteriaceae and multidrug resistance, than layer farms. Moreover, our findings suggest that antimicrobial use for growth promotion promoted the development of reservoirs of highly resistant bacteria on the studied farms, with potentially serious implications for human health. The prevalence of resistance to multiple antimicrobials was higher in both broiler and layer farms that used antimicrobials for growth promotion. ESBL-positive and multidrug-resistant strains are equipped with an arsenal of mechanisms that enable them to survive against last-resort treatments in human clinical settings. Furthermore, these highly resistant strains contribute to acquired resistance through horizontal gene transfer of resistance determinants to other microbial strains and species (including commensal microbes) further propagating antimicrobial resistance across various reservoirs of resistance, a threat that is both real and imminent (Marshall and Levy 2011; Silbergeld et al. 2008).

Until recently, resistance to polymyxin (a drug of last resort when other modern antimicrobials are ineffective) had only been reported to evolve via chromosomal mutations. A recent study of commensal E. coli in Chinese food animal production has identified a plasmid-mediated polymyxin resistance mechanism, MCR-1, in Enterobacteriaceae that has spread from animals to humans (detected in 1% of inpatients with infection) (Liu et al. 2015). Although we did not test for MCR-1 in this study, the emergence of such a highly mobile resistance determinant to such an important class of antimicrobials, with risk of global dissemination similar to NDM-1 (New Delhi metallo-β-lactamase), further emphasizes the need to regulate and curb antimicrobial overuse in food animal production (Liu et al. 2015). We conclude that withdrawal of nontherapeutic use of agricultural antimicrobials in India would be prudent to protect public health.

Acknowledgments

C.H.B. and R.L.’s time, as well as the cost of the study, was funded by the Global Antibiotic Resistance Partnership (funded by the Bill and Melinda Gates Foundation). C.H.B.’s time was also partially funded by the Fulbright-Nehru Fellowship. S.P. was funded by Princeton University’s Grand Challenges in Health program. We thank the poultry farmers for providing access to their farms to sample bacteria from their birds, SRL and Guru Angad Dev Veterinary and Animal Sciences University (GADVASU) for providing laboratory facilities, bioMérieux for providing us VITEK supplies, Suraj Pant for helping us create the figures presented in this article, and Manish Kakkar and Elizabeth Rogawski for helping develop the survey we administered.

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The Saliva Exposome for Monitoring of Individuals’ Health Trajectories

Author Affiliations open

1Department of Chemistry, University of Waterloo, Waterloo, Ontario, Canada

2Center for Exposure Biology, School of Public Health, University of California, Berkeley, Berkeley, California, USA

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  • Background:
    There is increasing evidence that environmental, rather than genetic, factors are the major causes of most chronic diseases. By measuring entire classes of chemicals in archived biospecimens, exposome-wide association studies (EWAS) are being conducted to investigate associations between a myriad of exposures received during life and chronic diseases.
    Objectives:
    Because the intraindividual variability in biomarker levels, arising from changes in environmental exposures from conception onwards, leads to attenuation of exposure–disease associations, we posit that saliva can be collected repeatedly in longitudinal studies to reduce exposure–measurement errors in EWAS.
    Methods:
    From the literature and an open-source saliva–metabolome database, we obtained concentrations of 1,233 chemicals that had been detected in saliva. We connected salivary metabolites with human metabolic pathways and PubMed Medical Subject Heading (MeSH) terms, and performed pathway enrichment and pathway topology analyses.
    Results:
    One hundred ninety-six salivary metabolites were mapped into 49 metabolic pathways and connected with human metabolic diseases, central nervous system diseases, and neoplasms. We found that the saliva exposome represents at least 14 metabolic pathways, including amino acid metabolism, TCA cycle, gluconeogenesis, glutathione metabolism, pantothenate and CoA biosynthesis, and butanoate metabolism.
    Conclusions:
    Saliva contains molecular information worthy of interrogation via EWAS. The simplicity of specimen collection suggests that saliva offers a practical alternative to blood for measurements that can be used to characterize individual exposomes. https://doi.org/10.1289/EHP1011
  • Received: 23 August 2016
    Revised: 08 November 2016
    Accepted: 18 November 2016
    Published: 20 July 2017

    Address correspondence to V. Bessonneau, Silent Spring Institute, 320 Nevada St., Suite 302, Newton, Massachusetts 02460 USA. Telephone: (617) 332-4288. Email: vincent.bessonneau@gmail.com

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Because genetic factors typically account for only about 18% of chronic disease risks, it is reasonable to infer that nongenetic factors (i.e., exposures) are major causes of chronic diseases (Rappaport 2016). Given the myriad exposures from both exogenous and endogenous sources that an individual experiences during life [the “exposome” (Wild 2005)], investigators are performing exposome-wide association studies (EWAS) that interrogate levels of chemicals in biospecimens to discover causes of chronic diseases (Patel et al. 2010; Rappaport 2012; Wild et al. 2013). By measuring entire classes of chemicals (e.g., small molecules, protein modifications, antigens) in archived biospecimens from incident disease cases and matched controls, EWAS can pinpoint discriminating features that then generate hypotheses for targeted follow-up studies (Rappaport 2011; Rappaport et al. 2014). For example, Hazen and coworkers employed this avenue to implicate joint microbial/human metabolism of the nutrient choline as a potentially major cause of coronary heart disease (Wang et al. 2011; Tang et al. 2013; Koeth et al. 2013).

An important challenge to designing EWAS is the intraindividual variability in levels of circulating molecules arising from changes in diet, lifestyle factors, and sources of pollutants during decades of life that precede disease onset. This within-person variability in biomarker levels leads to exposure measurement errors that attenuate causal signals and obscure disease associations (Lin et al. 2005; Sampson et al. 2013). One way to circumvent such measurement errors is to perform longitudinal studies with repeated biospecimens, collected from subjects during critical stages of life (Rappaport 2011; Robinson and Vrijeheid 2015). The most logical approach for doing this relies on prospective cohorts that archived blood or other biospecimens repeatedly from the same subjects. However, such cohorts are rare and repeated collection of blood, the main archival specimen, is difficult to perform (Hansen et al. 2007; Randell et al. 2016).

Saliva (also referred as oral fluid) is a natural filtrate of blood that contains omic features (small molecules, metals, proteins, and DNA) worthy of interrogation via EWAS. Because collection is “stress-free,” repeated specimens of saliva are routinely obtained for determination of steroid hormones in psychobiological studies (Hjortskov et al. 2004; Kajantie and Phillips 2006; Hunter et al. 2011). Sampling of saliva is straightforward and protocols are available that allow subjects to collect their own samples and ship them to a laboratory or repository.

Metabolomics is recognized as a powerful top-down approach for detecting small molecules in biological matrices (Nicholson and Wilson 2003; German et al. 2005). These small molecules can be either substrates or end products of cellular metabolism and can originate from exogenous sources via inhalation, ingestion and dermal absorption, or from endogenous processes including human and microbial metabolism. Adductomics is another top-down technique that employs modifications of blood proteins like hemoglobin or human serum albumin (HSA) to characterize exposures to reactive electrophiles that are inherently toxic but cannot be measured directly in biospecimens (Rubino et al. 2009; Li et al. 2011; Carlsson et al. 2014; Grigoryan et al. 2016; Rappaport 2012). Because blood is in equilibrium with the tissues and saliva is in equilibrium with blood, both blood and saliva represent dynamic reservoirs of small molecules that are present in the body at a given time. Given the potential utility of saliva as a biospecimen for EWAS, we will evaluate the linkages between salivary metabolites and human metabolic pathways, as well as those between these pathways and chronic diseases. We will also consider methods for collection and analysis of saliva via untargeted metabolomics and adductomics.

Methods

Saliva Metabolome

Salivary metabolites (n=1,233) were obtained from the saliva metabolome database (http://www.salivametabolome.ca/) that was recently integrated into the Human Metabolome Database (HMDB) (Wishart et al. 2013; Dame et al. 2015). This database compiles important physical, chemical and biological information of metabolites derived from peer-reviewed articles, including chemical structure, chemical class, origin, biological function, and cellular location. Normal and abnormal concentrations are also reported for metabolites previously quantified in humans as well as standard deviations of the measurements that can be used in some cases to estimate the intra- and interindividual variability. It is important to note that this database was built from a recent study by Dame et al. who combined contemporary measurements of salivary metabolites in healthy subjects with literature data derived from the healthy and/or diseased subjects (Dame et al. 2015). Those investigators measured 308 salivary metabolites in 16 healthy adult subjects sampled once and two subjects sampled three times in a day (morning, midday, and afternoon/evening). They also reported levels of an additional 708 metabolites from peer-reviewed articles. All metabolites included in the saliva metabolome database were used for analyses.

Visualization of Human Metabolic Pathways

Salivary metabolites were connected to their human metabolic pathways using the Metscape 3.1 App (Karnovsky et al. 2012) for Cytoscape 3.2.1. (Shannon et al. 2003). The network of metabolites and reactions was built using the internal Metscape database that integrates metabolomics and gene-expression data derived from all available biospecimens, and compiled in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Edinburgh Human Metabolic Network (EHMN) (Karnovsky et al. 2012). Nodes (i.e., metabolites) were colored according to their source (e.g., host, microbial). Edges connecting nodes represent KEGG and EHMN biochemical reactions. Only salivary metabolites with a KEGG IDs (196 metabolites) were retained with “human” as the model organism. The network type “compound” was used to map metabolic connections between small molecules.

Pathway Enrichment and Pathway Topology Analysis

Pathway enrichment and topology analyses of the saliva metabolome were performed using MetaboAnalyst 3.0 (Xia et al. 2015) with “Homo sapiens” as the model organism. Pathway enrichment analysis was conducted using the hypergeometric test to determine whether saliva metabolites were represented in a particular KEGG metabolic pathway more than expected by chance. p-Values≤0.05 indicate statistically significant representations of metabolic pathways. Pathway topology analysis was performed using the “relative-betweenness” centrality measure to estimate the importance of saliva metabolites relative to the biological pathway structure. Metabolite centrality is reported on a scale from 0 (isolated metabolite) to 1 (key metabolite).

Connections between the Saliva Metabolome and Human Chronic Diseases

Salivary metabolites were connected with human chronic diseases using the MetDisease App for Cytoscape 3.2.1 (Duren et al. 2014). MetDisease is a text-mining App that queried metabolites previously reported in any biospecimen as associated with PubMed Medical Subject Heading (MeSH) terms in peer-reviewed human and/or animal studies. For each disease, the total number of salivary metabolites, associated with a particular MeSH term, was determined. Salivary metabolites were queried using their KEGG IDs as identifiers and their shared names as select attributes. MeSH terms related to both chronic and nonchronic diseases (e.g., bacterial infections) were included in the network. The network of MeSH terms was built with Cytoscape 3.2.1 (Shannon et al. 2003) using the organic layer. The number of salivary metabolites associated with MeSH terms was used as the node attributes. Edges connecting nodes represented interconnections between MeSH terms.

Results and Discussion

The Saliva Metabolome

Saliva is a mixture of fluids originating mainly from the parotid, submandibular, sublingual, and minor salivary glands, and to a lesser extent from oral and nasal mucosa (de Almeida et al. 2008). Saliva consists of approximately 99% water with the remaining 1% comprised of electrolytes, mucus, cellular debris, proteins, and small molecules (Humphrey and Williamson 2001; de Almeida et al. 2008). Transfer of small molecules from blood to saliva relies on passive diffusion of lipophilic compounds and ultrafiltration of hydrophilic compounds of low molecular weight (<300–1,900 Da) (Gallardo and Queiroz 2008; de Almeida et al. 2008; Thieme 2012; Higashi 2012). The pKa of a molecule plays an important role in transfer from blood to saliva. For example, basic molecules accumulate in saliva due to ion-trapping phenomena associated with their transfer from neutral blood (pH 7.4) to acidic saliva (pH 6) (Thieme 2012). Also, several factors including disease states, diet, drug consumption, and physical activity can significantly affect saliva excretion and saliva pH can be influenced by variation in bicarbonate concentrations (Aps and Martens, 2005).

Several studies have evaluated the correlation between saliva and blood concentrations for compounds with diverse physical and chemical properties, as summarized in Table 1. The median value of the Pearson correlation coefficients was 0.92 with a range of 0.30≤r≤0.98). High correlations were observed between blood and saliva concentrations for neutral molecules that are not affected by changes in saliva pH (Hill et al. 2001; Sakaguchi and Hasegawa 2005; Juniarto et al. 2011; Gunnala et al. 2015). Lower correlations for acidic and basic compounds have been attributed to intraindividual variation in saliva pH as well as buccal contamination (for nicotine) (Fisher et al. 2013). However, these studies have been conducted only on a small subset of compounds (mainly drugs and pollutants). Because the blood–saliva transfer of small molecules relies on physical and chemical processes, more in-depth studies are needed to determine effects of pKa, polarity, physical activity, diet, and disease state.

Table 1. Blood:saliva concentration ratios and correlations for various classes of small molecules.
Compound Log Pa pKa Blood: saliva ratio (mean) Correlation coefficient (r) Reference
Therapeutic drugs
 Clozapine 3.7 15.9 3.6b 0.728 Fisher et al. 2013
 Norclozapine 3.2 15.9 3.6b 0.806 Fisher et al. 2013
 Quetiapine 2.9 15.1 3.0b 0.843 Fisher et al. 2013
 Risperidone 3.4 8.8 2.6b 0.954 Fisher et al. 2013
 9-hydroxyrisperidone 2.3 13.7 2.5b 0.640 Fisher et al. 2013
 Alprazolam 2.2 18.3 2.3 ND Gjerde et al. 2014
 Clonazepam 2.8 11.9 7.1 ND Gjerde et al. 2014
 Diazepam 2.6 2.9 27.0 ND Gjerde et al. 2014
 Nordiazepam 2.8 12.3 22.0 ND Gjerde et al. 2014
 Busulfan −0.9 NA 0.92b 0.980 Rauh et al. 2006
 Methylphenidate 1.5 8.9 0.5b ND Seçilir et al. 2013
 Mycophenolic acid 2.4 3.6 NRb 0.922 Shen et al. 2009
 Voriconazole 1.6 12.7 2.04b 0.943 Vanstraelen et al. 2015
Illicit drugs
 Cocaine 1.9 8.8 0.4b ND Moolchan et al. 2000
 Benzoylecgonine 1.7 3.1 5b ND Moolchan et al. 2000
 Ecgonine methyl ester 0.1 14.6 1b ND Moolchan et al. 2000
Pollutants
 Nicotine 0.9 8.9 NRb 0.300 Shin et al. 2002
 Cotinine 0.4 4.8 2.3b 0.980 Shin et al. 2002
 Perchlorate −0.9 −6.9 0.07c 0.927 Oldi et al. 2009
Endogenous
 Cortisol 1.8 12.6 0.87 0.933 Gunnala et al. 2015
 7-HydroxyDHEA 1.8 18.2 4.5c 0.975 Hill et al. 2001
 Androstenedione 2.9 19.0 NRb 0.781 Juniarto et al. 2011
 17-Hydroxyprogesterone 2.9 12.7 NRb 0.964 Juniarto et al. 2011
 Testosterone 2.9 19.1 NRc 0.843 Sakaguchi and Hasegawa 2005

Note: ND, not determined.

aFrom in silico prediction using ALOGPS 2.1(http://www.vcclab.org/lab/alogps/).

bPlasma:saliva ratio.

cSerum:saliva ratio.

Dame et al. (2015) recently combined results from metabolomic analyses of human saliva with a literature review of salivary metabolites. This characterization of the saliva metabolome included 1,233 small molecules, metals and ions, which is about one-fourth of the 4,549 metabolites that have been reported in human blood (Wishart et al. 2013). The difference in coverage of the two metabolomes reflects the lower metabolite concentrations observed in saliva (nM to low μM) compared with blood (μM to mM). We classified the sources of these salivary molecules and metals as follows: a) host endogenous (879), b) microbial endogenous (52), c) food (225), d) drugs (15), e) pollutants (25), and f) metals (37) (Figure 1).

Pie chart of the distribution of following sources of salivary metabolites: host endogenous: 72 percent; food: 18 percent; microbial endogenous: 4 percent; metals: 3 percent; pollutants: 2 percent; drugs: 1 percent.
Figure 1. Source category of salivary metabolites compiled from the saliva metabolome database (Dame et al. 2015).

Using Metscape, we mapped the 1,233 salivary metabolites into biological pathways (Figure 2). Metscape uses an internal database that integrates metabolomic and gene-expression data from KEGG and EHMN (Karnovsky et al. 2012). Because only 196 of the 1,233 (16%) metabolites detected in saliva were included in these databases, an additional 529 metabolites were added to the network through linkages (i.e., direct neighbor in metabolic pathways) with the 196 salivary metabolites through 49 recognized pathways. Most of these additional 529 metabolites were either phase-II conjugates or compounds tightly bound to proteins. In saliva, metabolites are mainly present in the free form (i.e., unbound fraction) due to blood-saliva transfer processes. Phase-II conjugation increases the molecular weight, acidity, and hydrophilicity of each parent compound (Figure 2A), whereas protein binding increases the size of the compound. Both of these processes limit the efficiency of blood–saliva transfer. For compounds tightly bound to proteins, such as eicosanoids (markers of inflammation) (Figure 2A), the free form represents only about 1% of the total concentration in the systemic circulation (Brodersen et al. 1990; Fujiwara and Amisaki 2013; Bessonneau et al. 2015b). Salivary levels of these compounds are very low (nM) and, therefore, difficult to detect with untargeted analyses.

Linkage pathways of salivary metabolites classified by their source, namely, host endogenous, microbial endogenous, metals, food, drugs, and pollutants. Insets A and B are provided as magnified images in the same figure.
Figure 2. Network of metabolic pathways reconstructed from metabolites detected in human saliva. Gray nodes represent metabolites that had not previously been detected in saliva but have direct neighbors in metabolic pathways. Edges represent biochemical connections between metabolites.

Next, we performed pathway enrichment and pathway topology analyses to identify the most significant pathways in the saliva exposome. Table 2 summarizes the 14 most significant metabolic pathways (p≤0.05), which prominently includes amino acid metabolism, TCA cycle, gluconeogenesis, glutathione metabolism, pantothenate and CoA biosynthesis, and butanoate metabolism.

Table 2. Metabolic pathways represented by the saliva metabolome (metaboanalyst – homosapiens) for pathways having p-values<0.05 from pathway enrichment analysis. Impact values are derived from pathway topology analysis.
Pathway Total metabolitesa Hitsb p-valuec Impactd
Alanine, aspartate, and glutamate metabolism 24 13 1.4e−4 0.88
beta-Alanine metabolism 28 13 9.8e−4 0.38
Phenylalanine metabolism 45 18 9.9e−4 0.45
Arginine and proline metabolism 77 26 1.7e−3 0.73
Nitrogen metabolism 39 15 4.0e−3 0.22
Citrate cycle (TCA cycle) 20 9 7.7e−3 0.43
Glycolysis or gluconeogenesis 31 12 9.3e−3 0.32
D-Glutamine and D-glutamate metabolism 11 6 9.7e−3 0.64
Glycine, serine, and threonine metabolism 48 16 1.4e−2 0.56
Glutathione metabolism 38 13 2.1e−2 0.35
Pantothenate and CoA biosynthesis 27 10 2.4e−2 0.35
Valine, leucine, and isoleucine biosynthesis 27 10 2.4e−2 0.36
Butanoate metabolism 40 13 3.3e−2 0.21
D-Arginine and D-ornithine metabolism 8 4 5.0e−2 0.50

aTotal number of metabolites involved in the pathway.

bNumber of salivary metabolites involved in the pathway.

cFisher’s exact test was used to measure the association between input metabolites (i.e., salivary metabolites) and pathways. p-Values≤5.0e−2 indicate that associations are less likely due to random chance.

dImpact represents, on a scale from 0 to 1, the centrality of salivary metabolites in their respective pathways.

Connections between the Saliva Metabolome and Human Diseases

In order to provide additional biological context for the saliva metabolome, we evaluated associations between salivary metabolites and MeSH terms using MetDisease. The resulting network (Figures 3 and 4) indicates that salivary metabolites were associated with most human diseases (Table 3). For example, substantial evidence from breast cancer epidemiology shows the influence of hormones at different stages of women’s development. A nested case–control study found that premenopausal concentrations of testosterone were associated with breast cancer risk (Zeleniuch-Jacquotte et al. 2012). Another case–control study reported lower salivary testosterone levels in women with breast cancer, suggesting a protective effect of testosterone (Dimitrakakis et al. 2010). The carboxylic acid ester phenylacetate and its analogs have also been linked to breast neoplasms (Sawatsri et al. 2001) due to their antiproliferative activities on human breast cancer cells. Recently, targeted metabolomics of salivary levels of polyamines such as spermidine has discriminated breast cancer patients from healthy subjects (Takayama et al. 2016). Polyamines are essential molecules for normal cell growth and regulate gene expression by modulating ligand–receptor interactions, including estradiol binding to estrogens receptors (Cervelli et al. 2014). Previous studies have attributed the proliferation of highly invasive breast cancer tumor cells to the upregulation of polyamine metabolism (Cervelli et al. 2014).

Linkage pathways indicating the MeSH terms for most human chronic diseases associated with salivary metabolites.
Figure 3. Network of PubMed Medical Subject Headings (MeSH) terms reported associated with salivary metabolites. The size of a node and text reflects the number of metabolites associated with MeSH terms. Edges represent links between MeSH terms.
Linkage pathways indicating the MeSH terms for neoplasms associated with salivary metabolites.
Figure 4. Subnetwork of neoplasm-related PubMed Medical Subject Headings (MeSH) terms reported associated with salivary metabolites. The size of a node and text reflects the number of metabolites associated with MeSH terms. Edges represent links between MeSH terms.
Table 3. Summary of the most important human chronic diseases associated with salivary metabolites.
Human chronic disease Number of salivary metabolites
Congenital, hereditary, and neonatal diseases and abnormalities 145
Nutritional and metabolic diseases 144
Central nervous system diseases 138
Neoplasms 118
Digestive system diseases 105
Urogenital diseases 103
Cardiovascular diseases 101
Immune system diseases 76
Respiratory tract diseases 69
Endocrine system diseases 64

There is also growing evidence that the human gut microbiome and its complex interactions with exogenous exposures play roles in disease processes (Jiménez et al. 2008; Orešič et al. 2008; Nicholson et al. 2012). Of the 14 metabolites of known microbial origin, 12 were associated with congenital, hereditary, and neonatal diseases, 11 with nervous system diseases, 10 with nutritional and metabolic diseases, eight with digestive system diseases, and seven each with neoplasms and urogenital and pregnancy complications.

The above analyses suggest that the saliva metabolome captures a biologically meaningful fraction of exposures that are associated with human diseases. Thus, any discriminating salivary molecules detected by EWAS should be well worth following up in targeted studies to examine sources, causality, disease mechanisms, and interventions (Rappaport 2012). We envision that creating exposure terms, similar to MeSH terms indexed to the peer-reviewed literature, would allow annotation of metabolites based on their origins and facilitate investigation of exposure sources and temporality of exposure–disease associations. For example, Scalbert et al. (2014) performed a comprehensive review of metabolites associated with specific food groups (e.g., red meat, fish, vegetables) from human dietary interventions and cross-sectional studies. They reported that many metabolites were significantly correlated (Pearson correlation r>0.3, p<0.01) with food consumption; e.g., apple consumption was associated with kaempferol, isorhamnetin, m-coumaric acid and phloretin, carrot consumption with α-carotene, and soy consumption with daidzein, genistein, isoflavones, and O-desmethylangolensin. Likewise, Rappaport et al. (2014) compiled literature values for 94 environmental chemicals and nutrients that have been measured in the general population.

Saliva as a Convenient Biospecimen for Longitudinal Studies

Saliva offers several advantages over traditional biospecimens archived in population-based studies. As with blood, saliva provides a snapshot of the internal exposome at the time of collection. However, whereas blood sampling usually requires a trained phlebotomist, saliva can be collected by the subjects themselves, using commercial kits (Shirtcliff et al. 2000; Abraham et al. 2012). This advantage should increase participation rates in cohort studies, particularly when repeated sampling is desired. In a recent study of inflammatory bowel disease, Randell et al. (2016) reported that the participation rate was significantly higher across 591 participants when they were asked to contribute a saliva sample (38% participation), compared with blood sample (23% participation). Similarly, Hansen et al. (2007) found a participation rate of 72% when individuals were asked to deliver saliva samples for DNA analysis, compared with 31% for venous blood samples collected at a medical facility. Another investigation revealed that the participation rates for collection of saliva samples and dried blood spots (DBS) were equal at 71.0% for 4,600 women (Sakhi et al. 2015). Although DBS specimens are also minimally invasive and amenable to self-collection by participants, the volume of blood is small (approximately 50 μL per drop) and the analytical matrix consists of both serum and hemolysate, thereby complicating untargeted analysis (Vuckovic 2012). Also, it is unclear whether subjects would be amenable to repeated collection of DBS, which does involve some discomfort. Thus, saliva specimens offer an attractive alternative to blood sampling for investigating individual exposomes with the high temporal resolution (i.e., repeated measurements) needed to explore health trajectories. Several studies have found low day-to-day variability in salivary levels of tightly regulated metabolites such as steroid hormones (Gann et al. 2001; Viardot et al. 2005). For example, Gann et al. (2001) observed that the intraclass correlation for peak progesterone between two consecutive menstrual cycles in healthy premenopausal women ranged from 0.72 to 0.76, indicating that cycle-to-cycle variability within women was smaller than variability across women.

Analytical Considerations

Saliva can easily be collected with a variety of commercial devices such as Salivette® or Drugwipe®. The most common approach is to collect saliva with an absorbent pad or swab, from which saliva is then recovered in the laboratory by centrifugation (Drummer 2008). Prior to sampling, saliva production can be stimulated with citric acid and by chewing of gum or sterile pads (Gröschl et al. 2008; Lund et al. 2011). This approach is often used to increase the volume of saliva collected and to control the pH so as to standardize the transfer of acidic and basic molecules. Unstimulated saliva can be collected passively with a collection tube or an oral swab (Drummer 2008). Collection devices should be selected carefully because some media, including cotton and polypropylene pads, can nonspecifically bind small molecules (Gallardo and Queiroz 2008; de Almeida et al. 2008; Gröschl et al. 2008; Higashi 2012). Given that the buccal cavity can be contaminated by components originating from previous oral ingestion (Shin et al. 2002), it is useful for subjects to refrain from eating and drinking for a few hours prior to saliva collection. Once collected, saliva samples can be easily transported by mail to the laboratory for analysis. Because a large number of salivary metabolites such as amino acids, steroids, or fatty acids are under circadian control (i.e., high diurnal variation) (Dallmann et al. 2012; Dame et al. 2015), it is important to collect repeated samples at similar times (e.g., all samples collected during early morning). Saliva is also an attractive alternative matrix for pediatric populations, due to the noninvasiveness of the sample collection. Although several collection devices have been specifically developed for children 6 mo–6 y old, more studies are required to address the safety of saliva sampling devices and procedures in young children.

Several studies have demonstrated that various classes of compounds, including illicit drugs (Lund et al. 2011), therapeutic drugs (Mendonza et al. 2006; Moore et al. 2007; Ogawa et al. 2014), pollutants (Bentley et al. 1999; Wang and Lu 2009; Gherardi et al. 2010), and endogenous steroids (Higashi et al. 2011; Alvi et al. 2013) are stable in saliva for a few days at room temperature and for at least 1 year when stored at low temperature (≤−20°C).

Sample Preparation

The strategy for preparing samples plays an important role in the quality of metabolomics data. Issues related to the quenching of metabolism prior to analysis, ion suppression during mass spectrometry and metabolite instability can adversely impact the interpretation of data from untargeted analysis. The most common techniques for preparing saliva samples for mass spectrometry are liquid–liquid extraction (LLE), protein precipitation (PP), and solid-phase extraction (SPE) (Higashi 2012). Although LLE and PP provide exhaustive extraction of small molecules, such procedures are labor intensive, difficult to automate, require multistep sample handling, and are prone to suppression or enhancement of ionization in mass spectrometric analysis. Most of metabolites in the saliva metabolome database have been obtained through LLE or PP of saliva samples. These analytical techniques often used an internal standard in order to correct for possible loss and/or degradation during sample preparation and for matrix effects during analysis.

In contrast to LLE and PP, solid-phase microextraction (SPME) offers a simple, fast, and sensitive technique for preparing saliva and other biological fluids for metabolomics analysis (Bojko et al. 2014; Bessonneau et al. 2013, 2015a). In fact, when coupled to the Concept-96 system (employing 96-well plates), thin-film SPME (TF-SPME) motivates automation of all preconditioning, extraction, rinsing, and desorption steps (Jiang and Pawliszyn 2012). For example, preparation for saliva via TF-SPME can be achieved in less than 2 min per sample and prevents matrix effects and adsorption of macromolecules compared with LLE (Bessonneau et al. 2015a).

The small size and biocompatibility of SPME materials also permits in vivo sampling of saliva (Bessonneau et al. 2015a). By placing the SPME probe in the mouth for a short period of time, this technique can be used for rapid extraction and stabilization of metabolites for analysis (Bessonneau et al. 2015b). For example, SPME coatings were able to rapidly stabilize highly reactive metabolites (i.e., eicosanoids) formed in vivo and prevented their autooxidation during storage (Bessonneau et al. 2015b). Although promising, in vivo sampling of saliva with SPME is a new technique that will require substantial validation to address variability arising from salivary flow rate, pH, sample volume, and agitation.

The Saliva Adductome

Biotransformation of dietary chemicals and pollutants leads to the formation of reactive electrophiles, including reactive oxygen and nitrogen species, aldehydes, oxiranes, and quinones that can modify DNA, proteins, and lipids and can lead to dysregulation of homeostasis (Farmer and Davoine 2007; Fritz and Petersen 2013). Because reactive species are unstable, it is difficult to measure them in their free-circulating form. However, one can detect adducts formed from reactions between reactive electrophiles and nucleophilic residues on DNA and proteins. Several targeted studies have reported correlations between adducts of reactive electrophiles from endogenous and exogenous sources with DNA (Sturla 2007,) and prominent blood proteins, that is, hemoglobin (Hb) and human serum albumin (HSA) [reviewed by Törnqvist et al. 2002; Rubino et al. 2009].

Both DNA and proteins are also present in saliva, albeit at lower concentrations than in blood. For example, HSA is found in saliva at concentrations of about 0.5 mg/mL compared with 35–50 mg/mL in serum (Wang et al. 2012; Shaila et al. 2013; Metgud and Patel 2014; Nam et al. 2015); whereas Hb is present at a 50,000-fold lower concentration in saliva (3.3 μg/mL) compared with blood (150 mg/mL) (Nomura et al. 2012). For DNA, mean salivary levels (12 μg/mL) are 2-fold lower than those in blood (26 μg/mL) (Abraham et al. 2012). Although the analysis of salivary adducts is still in its infancy, targeted studies have reported adducts of salivary DNA with electrophiles from smoking and dietary sources (Bessette et al. 2010; Chen and Lee 2014; Chen and Lin 2014), and of HSA adducts in nasal lavage fluid from workers exposed to reactive electrophiles (Kristiansson et al. 2004; Jeppsson et al. 2009).

Untargeted analysis of adducts of DNA, HAS, and Hb has motivated adductomics for untargeted characterization of exposures to reactive electrophiles (Rappaport et al. 2012). That is, an adductome represents the totality of adducts generated from reactions between reactive electrophiles and a particular nucleophilic locus on one of these molecules. By characterizing a complete adductome, it is possible to map systemic exposures that occurred over the in vivo residence time of the nucleophile, which can range from hours to months. Particular attention has been paid to mass spectrometric characterization of DNA adductomes in human tissue samples (reviewed by Balbo et al. 2014) and the blood proteins, HSA (Li et al. 2011; Grigoryan et al. 2016) and Hb (Carlsson et al. 2014).

Here, we posit that salivary HSA and Hb residence times would be similar to those in blood (i.e., 30 days for HSA and 60 days for Hb), which are much longer than those for DNA (a few days). Consequently, by performing untargeted mass spectrometry of HSA and Hb adducts in saliva samples, it should be possible to characterize systemic exposures to reactive electrophiles during 1–2 mo prior to specimen collection. With repeated saliva sampling every few years, one can construct an individual exposure history of relevance to a person’s health trajectory.

Conclusions

Saliva contains a rich set of molecular information for circulating chemicals that can be interrogated via EWAS, with metabolomics and adductomics, to discover exposure–risk factors for chronic diseases. Although the number of metabolites detected in saliva is smaller than that in blood, we can anticipate that analytical improvements will uncover many additional salivary metabolites, present at low concentrations.

Given the simplicity and noninvasiveness of specimen collection, saliva offers a practical alternative to blood for longitudinal measurements of individual’s exposomes. Several studies have shown that participants are more willing to donate saliva specimens than venous blood, and saliva can be collected by the subjects themselves. This would eventually improve participation rates in cohort studies and, therefore, generate larger sets of biospecimens.

Data-driven studies that utilize repeated omic measurements from individuals at different stages of life should reduce exposure measurement errors and thereby increase power to discover unknown causes of chronic diseases. Given the ease of collection, saliva could well be the specimen of choice for obtaining repeated samples to profile small molecules, DNA, and proteins.

Acknowledgments

Support for this work was provided by the Natural Sciences and Engineering Research Council of Canada Industrial Research program and the Canada Research Chairs program (V.B. and J.P.) and by the U.S. National Institutes of Health through grants P42ES04705 and R33CA191159 (S.M.R.).

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Pesticide Use and Age-Related Macular Degeneration in the Agricultural Health Study

Author Affiliations open

1Epidemiology Branch, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA

2Department of Ophthalmology, Duke University Medical School, Durham, North Carolina, USA

3Biostatistics and Computational Biology Branch, NIEHS, NIH, DHHS, Research Triangle Park, North Carolina, USA

4Westat, Durham, North Carolina, USA

5Social & Scientific Systems, Inc., Durham, North Carolina, USA

6Occupational and Environmental Epidemiology Branch, National Cancer Institute, NIH, DHHS, Rockville, Maryland, USA

7Department of Medicine, Duke University Medical School, Durham, North Carolina, USA

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  • Background:
    Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. Few studies have investigated its relationship to environmental neurotoxicants. In previous cross-sectional studies, we found an association between pesticide use and self-reported retinal degeneration.
    Objective:
    We evaluated the association of pesticide use with physician-confirmed incident AMD.
    Methods:
    The Agricultural Health Study (AHS) is a prospective cohort of pesticide applicators and their spouses enrolled from 1993–1997 in Iowa and North Carolina. Cohort members reported lifetime use of 50 specific pesticides at enrollment. Self-reports of incident AMD during follow-up through 2007 were confirmed by reports from participants’ physicians and by independent evaluation of retinal photographs provided by the physicians. Confirmed cases ( n=161) were compared with AHS cohort members without AMD ( n=39,108). We estimated odds ratios (ORs) and 95% confidence intervals (CIs) by logistic regression with adjustment for age, gender, and smoking.
    Results:
    AMD was associated with ever use of organochlorine [OR=2.7 (95% CI: 1.8, 4.0)] and organophosphate [OR=2.0 (95% CI: 1.3, 3.0)] insecticides and phenoxyacetate herbicides [OR=1.9 (95% CI: 1.2, 2.8)]. Specific pesticides consistently associated with AMD included chlordane, dichlorodiphenyltrichloroethane (DDT), malathion, and captan; others with notable but slightly less consistent associations were heptachlor, diazinon, phorate, 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), and 2,4-dichlorophenoxyacetic acid (2,4-D). Results were similar for men and women. Some specific pesticides were associated with both early- and late-stage AMD, but others were associated with only one stage.
    Conclusions:
    Exposures to specific pesticides may be modifiable risk factors for AMD. https://doi.org/10.1289/EHP793
  • Received: 12 July 2016
    Revised: 17 March 2017
    Accepted: 18 March 2017
    Published: 19 July 2017

    Address correspondence to F. Kamel, Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr., Bldg. 101, Room 327, PO Box 12233, MD A3-05, Research Triangle Park, NC 27709 USA. Telephone: 919-541-1581; Email: kamel@niehs.nih.gov

    Supplemental Material is available online (https://doi.org/10.1289/EHP793).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Age-related macular degeneration (AMD) is a degenerative condition of the central portion of the retina, the macula (Velez-Montoya et al. 2014). AMD is the leading cause of blindness in older individuals in developed countries, affecting >8 million U.S. residents. The early stage of the disease is often asymptomatic, but late AMD, either geographic atrophy (“dry” AMD) or the neovascular form (“wet” AMD), results in the loss of central, high-acuity vision. Factors affecting risk of early AMD may differ from those affecting progression to late-stage disease (Evans and Lawrenson 2012a; Evans and Lawrenson 2012b).

Both genetic and environmental factors play a role in the etiology of AMD (Sobrin and Seddon 2014). AMD is associated with polymorphisms in approximately 20 genes, most notably complement factor H (CFH) (Sofat et al. 2012) and the age-related maculopathy susceptibility 2/HtrA serine peptidase (ARMS2/HTRA1) locus at chromosome 10q26 (Tong et al. 2010). Smoking is associated with increased risk of AMD, and adiposity may also be important (Chakravarthy et al. 2010). However, these factors do not explain all cases of AMD.

Limited evidence suggests an association of pesticide exposure with retinal dysfunction. Several case series reported signs of macular degeneration in pesticide workers (Dementi 1994; Misra et al. 1985), and experimental studies of rodents have shown biochemical, morphological, and functional changes in the retina after systemic (Imai et al. 1983) or intraocular (Zhang et al. 2006) treatment with pesticides. Nevertheless, few epidemiologic studies have addressed this issue. The Agricultural Health Study (AHS) is a study of licensed pesticide applicators and their spouses who have been followed since enrollment in the mid-1990s. In a cross-sectional analysis of AHS data collected at enrollment, we found that self-reported prevalent retinal or macular degeneration was associated with use of fungicides and organochlorine and organophosphate insecticides in pesticide applicators (Kamel et al. 2000) and with use of fungicides in AHS spouses (Kirrane et al. 2005).

The present study extends these findings. We exploited the prospective design of the AHS to evaluate the association of pesticide use with medically confirmed incident cases of AMD, thus overcoming some limitations of our previous studies.

Methods

Population

The AHS cohort includes 52,394 private pesticide applicators (mostly farmers) and 32,345 of their spouses enrolled between 1993 and 1997 in Iowa and North Carolina. Most applicators were men (97%), most spouses were women (99%), and the race/ethnicity of most cohort members was non-Hispanic white (97%). At enrollment, participants completed self-administered questionnaires that collected information on demographics, lifestyle characteristics, medical history, lifetime pesticide use, and other farming practices. Follow-up telephone interviews were conducted in 1999–2003 and 2005–2010.

To investigate the relationship of pesticide use to AMD incidence, we conducted a case–control study nested within the AHS cohort. We used information from the two follow-up interviews to identify potential incident cases through 1 September 2007. Among 84,739 AHS cohort members, 6 had requested no further contact, 26,002 had not completed either follow-up interview, and 2,554 had died. We also excluded 13,975 persons who were <50 y old on 1 September 2007 because AMD is rare before that age, 324 who had reported retinal or macular degeneration at enrollment, and 15 for other reasons. Thus, 41,863 cohort members were eligible to participate.

Medical histories collected in the follow-up interviews included self-report of physician-diagnosed retinal or macular degeneration. We verified self-reports using information from participants’ eye-care physicians (Figure 1). We screened 552 of 886 participants who reported AMD at either follow-up; of these, 315 affirmed their diagnosis and provided permission for retrieval of medical records. In addition, we screened 257 of 442 AHS cohort members who, although not reporting AMD, did report using an Amsler grid, a self-test of vision loss sometimes recommended for patients with early signs of AMD; of these, 30 affirmed a diagnosis of AMD and provided permission for retrieval of medical records. We contacted one or more physicians for 345 potential AMD cases and obtained diagnostic information for 311 study participants. Physicians either completed a short questionnaire on AMD diagnosis, retinal pathology, and treatment or provided relevant medical records. In addition, some physicians provided retinal photographs for one or both eyes for 101 participants. An optometrist abstracted medical records, and the study ophthalmologist (E.P.) evaluated retinal photographs.

Flowchart.
Figure 1. Recruitment and validation of AMD cases.

Case Definition

Cases were participants for whom the treating physician confirmed the diagnosis of AMD with supporting pathology or for whom the study ophthalmologist diagnosed AMD from retinal photographs. Early AMD was defined by the presence of large, soft, or confluent drusen, with or without pigmentary changes. Individuals with physician reports indicating small, hard drusen as the only sign of AMD were not included as cases unless the retinal photograph provided additional evidence to support the diagnosis. Late AMD was defined by the presence of at least one of the following pathological signs: geographic atrophy, disciform scar, retinal pigment epithelial detachment, and subretinal hemorrhage. AMD unknown stage was assigned when both the physician report and the retinal photograph indicated AMD but disagreed regarding stage. Overall, there was 76% agreement between diagnoses of AMD assigned with or without the photographs; the major difference was the identification of 19 additional early cases using supporting pathology from photographs. We used the more severe diagnosis from either eye to assign a diagnosis to the participant. The final assignments were unclear diagnosis (6), no AMD (133), AMD early stage (58), AMD late stage (79), and AMD unknown stage (35), with a total of 172 AMD cases of any stage.

The controls were AHS cohort members remaining after excluding both the 172 AMD cases and 1,156 individuals identified as possible cases but not confirmed. We also excluded from analysis 10 cases whose physician-reported diagnoses occurred before enrollment in the AHS and 1 case and 1,427 controls with incomplete smoking data. The final sample included 161 incident AMD cases diagnosed between 1994 and 2007 and 39,108 controls.

Institutional review boards of the National Institutes of Health and its contractors approved the study, and all participants signified consent by completing questionnaires; written consent was obtained for release of medical records.

Pesticide Exposure

We used information on ever use of 50 specific pesticides provided by AHS cohort members at enrollment. We also combined information on specific pesticides to create four categories based on function (insecticides, herbicides, fungicides, and fumigants), three chemical classes of insecticides (organochlorines, organophosphates, and carbamates), and two chemical classes of herbicides (phenoxyacetate and triazine herbicides).

Pesticide applicators, but not spouses, provided additional information on the duration (years) and frequency (days per year) of use for 22 individual pesticides. Some applicators (53% of controls and 72% of cases included in this analysis) also completed a second questionnaire, providing information on the frequency and duration of use for 28 additional pesticides. We multiplied frequency and duration to calculate lifetime days of use for each specific pesticide and categorized the results (0, 0.01–10, 10.01–100, and >100 lifetime days), combining categories for some pesticides with sparse data. Because of potential overlap in periods of use, we did not calculate lifetime days of use for pesticide groups.

Data Analysis

We examined the association of AMD with pesticide exposure using multivariable logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). We included age on 1 September 2007 (50–69, 70–79, ≥80 y), gender, and smoking at enrollment (ever/never) in models because all are important risk factors for AMD and are associated with pesticide use. Adjustment for age using a six-level categorical variable, continuous age, or continuous age plus age squared gave estimates virtually identical to those using the three-level categorical variable. We also evaluated associations in models stratified by age (<70 vs.≥70); these models were further adjusted for age with a continuous variable. Adjusting for smoking status (never, former, or current smoking) gave results similar to those using smoking as a binary variable. We considered body mass index, education, and study site (North Carolina or Iowa) as potential confounders; none of these factors substantially altered effect estimates (<15%), so our final models included only age, gender, and smoking. Because the results were generally similar for men and women when analyzed separately, we present the results for men and women together (adjusted for gender) as our main analysis. In additional analyses, we restricted cases to individuals diagnosed with either early- or late-stage AMD (excluding those of unknown stage) and used multinomial logistic regression to compare each case group with controls. Because sun exposure may increase AMD risk, we evaluated AMD–pesticide associations in models including a variable for hours per day of sun exposure (≥3 vs.≤2).

We evaluated the association of AMD with ever use of 47 pesticides for which at least five cases reported use. We also examined associations with lifetime days of use for 38 pesticides for which users could be categorized into at least two groups with at least five cases in each; these analyses were restricted to men because few women were applicators and because spouses were not asked to provide data on frequency or duration of pesticide use. We evaluated correlations of ever-use variables between pairs of pesticides. Whenever the correlation coefficient for a pair was ≥0.25 and at least one member of the pair was associated with AMD risk, we ran an additional model including both pesticides.

To address concerns regarding possible selection bias, we conducted a quantitative bias analysis (Lash et al 2009) (for details, see Supplemental Material, “Quantitative Bias Analysis”). Briefly, individuals who were not screened were allocated to AMD case or control status based on covariate distribution and pesticide use. We then estimated ORs associated with pesticide exposure among the “complete” set of cases and controls. Separate analyses were performed for each pesticide.

We used SAS (version 9.2; SAS Institute Inc.) and data from AHS data releases REL201004.00, P2REL0506.03, P1REL0506.01, P2REL0506.03, and P3REL0707.01 (https://aghealth.nih.gov/) in our analyses.

Results

We attempted to screen 1,328 potential cases, individuals identified from the AHS cohort because they reported a diagnosis of AMD or use of an Amsler grid (Figure 1). Among those screened, 43% (237/552) of those initially reporting AMD and 88% (227/257) reporting Amsler grid use denied AMD. If all potential cases had been screened and similar proportions had denied AMD, we project that a total of 557 would have affirmed AMD. We enrolled 345 cases (62% of the projected 557), and approximately half of these were confirmed after evaluation of medical records. Comparing potential cases ( n=1,328) with those included in the analysis ( n=161; AMD confirmed, diagnosis after enrollment, smoking data available), we found that the latter were older and more likely to be ever smokers (data not shown); they were also considerably more likely to have used pesticides ≥25 lifetime days (66% and 74%, respectively) and to have ever used organochlorines (50% and 62%, respectively).

AMD risk was positively associated with age and smoking and was slightly elevated among women, those with more than a high school education, and those who consumed alcohol more frequently; AMD was not related to race/ethnicity, state, or body mass index (Table 1). Both early AMD (57 cases) and late AMD (72 cases) were associated with age and smoking; late AMD was also associated with residence in North Carolina and having more than a high school education (see Table S1). Comparing case groups with one another, late AMD cases were slightly older (χ2=3.52, p=0.17) and more likely to be from North Carolina (χ2=5.58, p=0.018) than early cases; other characteristics were similar.

Table 1. Characteristics of incident AMD cases and controls among pesticide applicators and their spouses, AHS 1993-2007.
Case Control
Characteristic n % n % ORa 95% CI
Age at enrollment in AMD study, y
 50–69 36 22 28,801 74 1.0 Reference
 70–79 82 51 8,286 21 7.9 5.3 11.7
 80+ 43 27 2,021 5 17.5 11.2 27.3
Gender
 Men 95 59 22,658 58 1.0 Reference
 Women 66 41 16,450 42 1.3 0.9 1.8
Race/ethnicity
 White, non-Hispanic 158 98 37,984 97 1.0 Reference
 Other 3 2 1,124 3 0.5 0.2 1.6
State
 Iowa 95 59 25,612 65 1.0 Reference
 North Carolina 66 41 13,496 35 0.9 0.7 1.3
Education
 ≤High school 92 59 19,877 54 1.0 Reference
 >High school 64 41 17,046 46 1.3 0.9 1.8
Ever smoker
 No 75 47 23,292 60 1.0 Reference
 Yes 86 53 15,816 40 1.8 1.3 2.5
Alcohol consumption (frequency)
 Never 77 49 16,100 42 1.0 Reference
 <1 to 3 times per mo 41 26 13,471 35 1.0 0.7 1.4
 Once a week or more 38 24 8,530 22 1.3 0.9 2.0
BMI (kg/m2)
 <25 54 34 12,123 32 1.0 Reference
 25–30 78 50 17,139 45 1.0 0.7 1.5
 >30 25 16 87,98 23 0.7 0.5 1.2

Note: AMD, age-related macular degeneration; BMI, body mass index; CI, confidence interval; OR, odds ratio.

aAll models include age, gender, and smoking.

AMD risk was elevated among ever users of insecticides and fungicides as classes but not among ever users of herbicides or fumigants (Table 2). Among chemical classes, AMD was associated with organochlorine and organophosphate insecticides and with phenoxyacetate herbicides. Specific organochlorines significantly associated with AMD were aldrin, chlordane, dichlorodiphenyltrichloroethane (DDT), dieldrin, heptachlor, and lindane; specific organophosphates significantly associated with AMD were diazinon, dichlorvos, malathion, parathion, and phorate; specific phenoxyacetate herbicides significantly associated with AMD were 2,4-dichlorophenoxyacetic acid (2,4-D), 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), and 2-propionic acid (fenoprop; 2,4,5-TP); specific chemicals in other classes significantly associated with AMD were the insecticide permethrin used on crops; the herbicide glyphosate; the fungicides benomyl and captan; and the fumigant ethylene dibromide. Adjustment for sun exposure (≥3 vs.≤2 h/d) had no effect on the AMD–pesticide association.

Table 2. Incident AMD and ever use of specific pesticides in pesticide applicators and their spouses, AHS 1993-2007.
Case Control
n % n % ORa 95% CI
Insecticides (any) 126 78 28558 73 1.6 1.1 2.5
 Organochlorines (any) 98 62 14870 38 2.7 1.8 4.0
  Aldrin 37 26 5433 15 1.5 0.95 2.3
  Chlordane 61 42 7696 21 2.4 1.7 3.6
  DDT 70 47 7728 21 2.1 1.4 3.1
  Dieldrin 21 15 1960 5 1.9 1.1 3.2
  Heptachlor 36 26 4490 12 1.9 1.2 3.0
  Lindane 32 22 5148 14 1.9 1.2 3.0
  Toxaphene 27 19 3957 11 1.5 0.9 2.3
 Organophosphates (any) 117 73 25157 64 2.0 1.3 3.0
  Chlorpyrifos 45 29 10522 27 1.3 0.9 1.9
  Coumaphos 10 7 2302 6 1.1 0.6 2.2
  Diazinon 58 40 9404 26 2.0 1.4 2.9
  Dichlorvos 18 12 3121 9 1.8 1.1 3.0
  Fonofos 20 14 5463 15 1.0 0.6 1.7
  Malathion 103 68 19889 53 2.2 1.5 3.3
  Parathion 28 20 3877 11 1.9 1.2 3.0
  Phorate 42 30 8070 22 1.7 1.1 2.6
  Terbufos 35 24 9210 25 1.1 0.7 1.7
 Other insecticides
  Aldicarb 5 4 2511 7 0.5 0.2 1.3
  Carbaryl 91 61 18890 51 1.4 0.99 2.0
  Carbofuran 31 21 7231 20 1.1 0.7 1.7
  Permethrin (crops) 16 11 3197 9 1.8 1.03 3.0
  Permethrin (animals) 11 8 3541 10 1.3 0.7 2.4
Herbicides (any) 119 74 28973 74 1.2 0.8 1.9
 Phenoxyacetate (any) 101 64 21222 55 1.9 1.2 2.8
  2,4,5-T 46 32 5841 16 2.0 1.3 3.0
  2,4,5-TP 18 13 2390 7 1.7 1.03 2.9
  2,4-D 98 62 20689 54 1.8 1.2 2.7
 Triazine (any) 79 50 19414 50 1.2 0.7 1.8
  Atrazine 74 47 17889 47 1.2 0.8 1.8
  Cyanazine 41 28 10175 28 1.3 0.9 2.0
  Metribuzin 40 28 10756 30 1.2 0.8 1.9
 Other herbicides
  Alachlor 59 39 13154 36 1.4 0.9 2.1
  Butylate 30 21 7886 22 1.2 0.7 1.8
  Chlorimuron ethyl 28 20 7887 22 1.2 0.7 1.9
  Dicamba 44 30 12012 33 1.1 0.7 1.7
  EPTC 17 12 4618 13 1.2 0.7 2.0
  Glyphosate 103 64 23493 61 1.4 0.99 2.0
  Imazethapyr 32 23 9503 26 1.1 0.7 1.8
  Metolachlor 41 28 10728 29 1.2 0.8 1.8
  Paraquat 30 20 5542 15 1.5 0.9 2.3
  Pendimethalin 31 22 9912 27 0.9 0.6 1.4
  Petroleum oil 39 27 11165 31 0.9 0.6 1.4
  Trifluralin 51 36 12775 35 1.2 0.8 1.9
Fungicides (any) 51 32 9337 24 1.5 1.04 2.1
  Benomyl 18 12 2548 7 1.7 0.99 2.8
  Captan 20 14 3009 8 2.0 1.2 3.3
  Chlorothalonil 11 7 1965 5 1.2 0.7 2.3
  Maneb 13 9 2627 7 1.1 0.6 2.0
  Metalaxyl 24 17 5459 15 1.1 0.7 1.8
Fumigants (any) 28 18 6032 16 1.0 0.6 1.5
  Carbon tetrachloride 11 8 1542 4 1.4 0.7 2.6
  Ethylene dibromide 10 7 884 2 2.8 1.5 5.6
  Methyl bromide 17 11 4106 11 0.8 0.5 1.4

Note: AHS, Agricultrual Health Study; AMD, age-related macular degeneration; BMI, body mass index; CI, confidence interval; DDT, dichlorodiphenyltrichloroethane; EPTC, S-ethyl dipropylthiocarbamate; OR, odds ratio. 2,4-D, 2,4-dichlorophenoxyacetic acid; 2,4,5-T, 2,4,5-trichlorophenoxyacetic acid; 2,4,5,-TP, 2-propionic acid (fenoprop).

aAdjusted for age, gender, and smoking.

In gender-stratified analyses, the results were generally similar for men (95 cases) and women (66 cases), although many chemicals were used by too few women to permit analysis (see Table S2). Only organochlorines as a class and the specific chemicals chlordane and malathion were significantly associated with AMD risk in both early and late AMD (see Table S3). Several other exposures—insecticides and organophosphates as classes, DDT, 2,4,5-T, and ethylene dibromide—were associated with both subtypes (OR>1.5) but significantly so only in one subtype. Associations were weaker for late than for early AMD for organochlorines and phenoxyacetate and triazine herbicides as classes and for aldrin, dieldrin, 2,4-D, cyanazine, butylate, and metolachlor (p<0.05). Associations were stronger for late than for early AMD for paraquat, petroleum oil, and benomyl.

In models stratified by age (<70 vs.≥70 y old), ORs for most pesticides were similarly elevated in both age groups, and most age-by-pesticide interactions were unimportant (p>0.8; data not shown). The exception was phenoxyacetate herbicides: as a group, these pesticides were associated with AMD in those ≥70 y old [OR=2.1 (95% CI: 1.0, 4.4)] but not in those <70 y old [OR=1.3 (95% CI: 0.7, 2.7)]; p-interaction=0.24.

Most pesticides were not strongly correlated with one another. We constructed models including each correlated pair (r>0.25), one pair per model (see Table S4). The results suggested that some pesticides (chlordane, DDT, heptachlor, diazinon, malathion, parathion, 2,4,5-T) had strong independent effects: their associations with AMD persisted in models including other pesticides. Others (aldrin, toxaphene, carbaryl, 2,4,5-TP, glyphosate, paraquat, benomyl) did not have independent effects: when modeled with other pesticides, their associations with AMD became weaker and nonsignificant. Some (dieldrin, lindane, phorate, 2,4-D) were intermediate, affected by modeling with some pesticides but not with others. The remainder were not correlated with other pesticides; therefore, their effects were presumed to be independent.

Information on lifetime days of use was available for 38 pesticides for applicators but not spouses (Table 3). Because the data were sparse, we considered a trend to be notable if ptrend<0.10. Such trends were observed for the insecticides chlordane, DDT, lindane, malathion, parathion, and phorate; the herbicides 2,4-D, alachlor, and glyphosate; and the fungicides captan and maneb/mancozeb.

Table 3. Dose–response trends for pesticide use and risk of incident AMD among male pesticide applicators, AHS 1993-2007.
Case Control
Cumulative days of use n % n % ORa 95% CI p-Value for trend
Organochlorine insecticides
 Aldrinb
  0 43 67 8,950 79 1.0 Reference
  >0–10 8 13 1,107 10 1.0 0.5 2.2
  >10–100 8 13 1,060 9 1.0 0.5 2.1
  >100 5 8 211 2 3.0 1.1 7.6 0.233
 Chlordaneb
  0 43 65 8,771 77 1.0 Reference
  >0–10 9 14 1,734 15 0.8 0.4 1.7
  >10 14 21 834 7 2.4 1.3 4.5 0.025
 DDTb
  0 30 45 8,308 73 1.0 Reference
  >0–10 9 14 1,396 12 0.9 0.4 1.8
  >10–100 19 29 1,033 9 2.3 1.3 4.2
  >100 8 12 575 5 1.9 0.8 4.2 0.011
 Heptachlorb
  0 54 81 9,708 85 1.0 Reference
  >0–10 4 6 885 8 0.6 0.2 1.6
  >10 9 13 819 7 1.4 0.7 2.8 0.670
 Lindaneb
  0 49 74 9,535 84 1.0 Reference
  >0–10 5 8 817 7 1.2 0.5 3.0
  >10–100 7 11 683 6 1.9 0.9 4.3
  >100 5 8 290 3 3.5 1.4 9.0 0.005
 Toxapheneb
  0 54 81 9,876 87 1.0 Reference
  >0–10 6 9 786 7 1.1 0.5 2.6
  >10 7 10 750 7 1.3 0.6 2.8 0.527
Organophosphate insecticides
 Chlorpyrifos
  0 52 57 12,679 57 1.0 Reference
  >0–10 15 16 3,137 14 1.3 0.7 2.3
  >10–100 15 16 4,287 19 1.1 0.6 1.9
  >100 10 11 2,081 9 1.7 0.8 3.3 0.224
 Diazinonb
  0 46 71 8,782 78 1.0 Reference
  >0–10 7 11 1,196 11 1.1 0.5 2.5
  >10 12 18 1,326 12 1.6 0.8 3.0 0.174
 Dichlorvos
  0 70 86 17,938 88 1.0 Reference
  >0–10 5 6 736 4 1.9 0.8 4.8
  >10 6 7 1,779 9 1.1 0.5 2.5 0.558
 Fonofos
  0 63 77 15,648 76 1.0 Reference
  >0–10 4 5 1,500 7 0.8 0.3 2.2
  >10 15 18 3,451 17 1.2 0.7 2.2 0.522
 Malathionb
  0 21 32 3,720 33 1.0 Reference
  >0–10 12 18 2,961 26 0.8 0.4 1.5
  >10–100 15 23 3,247 29 0.9 0.4 1.7
  >100 17 26 1,352 12 2.0 1.1 3.9 0.093
 Parathionb
  0 52 81 10,353 92 1.0 Reference
  >0–10 8 13 403 4 3.3 1.6 7.1
  >10 4 6 548 5 1.3 0.5 3.8 0.087
 Phorateb
  0 39 61 7,570 67 1.0 Reference
  >0–10 6 9 1,552 14 0.8 0.3 1.9
  >10–100 9 14 1,664 15 1.0 0.5 2.2
  >100 10 16 557 5 3.5 1.7 7.2 0.020
 Terbufos
  0 49 61 12,154 59 1.0 Reference
  >0–10 4 5 2,047 10 0.6 0.2 1.6
  >10 27 34 6,371 31 1.3 0.8 2.0 0.394
Other insecticides
 Carbofuran
  0 54 67 13,797 68 1.0 Reference
  >0–10 8 10 2,614 13 0.7 0.3 1.5
  >10–100 13 16 2,857 14 1.1 0.6 2.1
  >100 6 7 1,121 5 1.5 0.6 3.5 0.463
 Carbarylb
  0 27 40 6,178 55 1.0 Reference
  >0–10 15 22 2,042 18 1.5 0.8 2.9
  >10–100 11 16 1,728 15 1.1 0.5 2.3
  >100 15 22 1,355 12 1.9 1.0 3.6 0.101
 Permethrin (crops)
  0 67 84 17,630 87 1.0 Reference
  >0–10 8 10 1,394 7 2.1 0.99 4.4
  >10 5 6 1,287 6 1.4 0.6 3.5 0.150
Herbicides
 2,4-D
  0 13 14 4,580 21 1.0 Reference
  >0–10 10 11 2,502 11 1.4 0.6 3.2
  >10–100 26 28 6,877 31 1.4 0.7 2.8
  >100 44 47 8,116 37 2.2 1.2 4.1 0.011
 2,4,5,Tb
  0 44 69 8,833 78 1.0 Reference
  >0–10 10 16 1,430 13 1.0 0.5 2.0
  >10 10 16 1,116 10 1.2 0.6 2.4 0.635
 Alachlor
  0 30 36 8,622 42 1.0 Reference
  >0–10 12 14 2,633 13 1.4 0.7 2.8
  >10–100 19 23 5,025 25 1.2 0.7 2.2
  >100 22 27 4,176 20 1.9 1.1 3.3 0.046
 Atrazine
  0 24 26 5,566 25 1.0 Reference
  >0–10 8 9 2,686 12 0.8 0.4 1.9
  >10–100 28 30 6,561 30 1.2 0.7 2.1
  >100 34 36 7,387 33 1.5 0.9 2.5 0.111
 Butylateb
  0 49 74 7,968 70 1.0 Reference
  >0–10 5 8 1,177 10 0.8 0.3 2.0
  >10 12 18 2,246 20 1.2 0.6 2.3 0.668
 Chlorimuron ethylb
  0 47 71 7,907 69 1.0 Reference
  >0–10 12 18 2,274 20 1.2 0.6 2.2
  >10 7 11 1,240 11 1.2 0.5 2.6 0.633
 Cyanazine
  0 41 52 11,217 55 1.0 Reference
  >0–10 11 14 2,816 14 1.3 0.6 2.5
  >10–100 17 22 3,986 19 1.4 0.8 2.5
  >100 10 13 2,556 12 1.5 0.8 3.1 0.141
 Dicamba
  0 39 49 9,454 46 1.0 Reference
  >0–10 10 13 3,089 15 1.0 0.5 2.0
  >10–100 16 20 4,934 24 1.1 0.6 1.9
  >100 15 19 2,887 14 1.9 1.03 3.5 0.112
 EPTC
  0 65 82 16,124 79 1.0 Reference
  >0–10 7 9 1,902 9 1.1 0.5 2.5
  >10 7 9 2,284 11 1.0 0.5 2.2 0.917
 Glyphosate
  0 15 16 5,104 23 1.0 Reference
  >0–10 18 19 4,929 22 1.3 0.6 2.5
  >10–100 33 35 7,403 33 1.7 0.9 3.1
  >100 28 30 4,783 22 2.6 1.4 4.9 0.002
 Imazethapyr
  0 45 59 11,670 57 1.0 Reference
  >0–10 13 17 3,957 19 1.1 0.6 2.1
  >10 18 24 4,748 23 1.5 0.9 2.6 0.167
 Metolachlor
  0 41 53 10,720 52 1.0 Reference
  >0–10 10 13 2,435 12 1.2 0.6 2.5
  >10–100 13 17 4,275 21 1.0 0.5 1.8
  >100 13 17 3,078 15 1.5 0.8 2.9 0.325
 Metribuzinb
  0 40 60 6,746 59 1.0 Reference
  >0–10 13 19 2,253 20 1.2 0.6 2.3
  >10 14 21 2,389 21 1.4 0.7 2.6 0.282
 Paraquatb
  0 53 80 9,560 84 1.0 Reference
  >0–10 7 11 1,032 9 1.2 0.5 2.6
  >10 6 9 829 7 1.4 0.6 3.2 0.413
 Pendimethalinb
  0 42 66 7,250 64 1.0 Reference
  >0–10 14 22 1,924 17 1.4 0.8 2.6
  >10 8 13 2,239 20 0.8 0.4 1.8 0.980
 Petrolium oilb
  0 48 79 8,894 78 1.0 Reference
  >0–100 7 11 1,771 16 0.8 0.4 1.8
  >100 6 10 679 6 1.9 0.8 4.5 0.370
 Trifluralin
  0 35 45 9,185 45 1.0 Reference
  >0–10 9 12 1,813 9 1.3 0.6 2.7
  >10–100 15 19 4,862 24 0.9 0.5 1.7
  >100 19 24 4,657 23 1.4 0.8 2.5 0.386
Fungicides
 Captan
  0 66 81 17,971 89 1.0 Reference
  >0–10 7 9 1,436 7 1.8 0.8 3.9
  >10 8 10 677 3 2.9 1.4 6.2 0.002
 Manebb
  0 56 84 10,447 92 1.0 Reference
  >0–100 7 10 694 6 1.7 0.8 3.7
  2)100+ 4 6 239 2 2.5 0.9 6.9 0.039
 Metalaxylb
  0 54 82 9,216 81 1.0 Reference
  >0–10 5 8 723 6 1.2 0.5 3.0
  >10 7 11 1,394 12 0.9 0.4 1.9 0.822
Fumigants
 Methyl bromide
  0 77 82 18,556 84 1.0 Reference
  >0–10 6 6 923 4 1.2 0.5 2.9
  >10 11 12 2,728 12 0.8 0.4 1.5 0.582

Note: AHS, Agricultrual Health Study; AMD, age-related macular degeneration; BMI, body mass index; CI, confidence interval; DDT, dichlorodiphenyltrichloroethane; EPTC, S-ethyl dipropylthiocarbamate; OR, odds ratio. 2,4-D, 2,4-dichlorophenoxyacetic acid; 2,4,5-T, 2,4,5-trichlorophenoxyacetic acid; 2,4,5,-TP, 2-propionic acid (fenoprop).

aAdjusted for age and smoking.

bData available only for subset of applicators who completed the take-home questionnaire.

Table 4 summarizes results of the various analyses. We defined a consistent association of a pesticide with AMD as one that was evident in all five of the following: a) the ever use analysis (Table 2); b) the analysis with adjustment for correlated pesticides (see Table S4); c) the lifetime use analysis (Table 3); d) either men or women (see Table S2); and e) either early or late AMD (see Table S3). Four pesticides met these criteria fully: chlordane, DDT, malathion, and captan. Five additional pesticides met most of the criteria for consistency: phorate and 2,4-D were each significantly associated with AMD in one of two analyses with correlated pesticides, and each had evidence of dose–response; heptachlor, diazinon, and 2,4,5-T were each associated with AMD in analyses of correlated pesticides but did not have evidence of dose–response, possibly because small numbers of individuals were exposed.

Table 4. Summary of analyses of AMD and pesticide use.
Ever Use By Gender By AMD stage
Ever use Adjusted for correlated pesticides Cumulative use Men Women Early Late
(Table 2) (Table S4) (Table 3) (Table S2) (Table S3)
Insecticides (any) + ND ND + + +
 Organochlorines (any) + ND ND + + + +
  Aldrin + + +
  Chlordane + + + + + + +
  DDT + + + + + +
  Dieldrin + + +
  Heptachlor + + + +
  Lixane + + + +
  Toxaphene +
 Organophosphates (any) + ND ND + + +
  Chlorpyrifos
  Coumaphos
  Diazinon + + + + +
  Dichlorvos + + +
  Fonofos +
  Malathion + + + + + + +
  Parathion + +
  Phorate + + + +
  Terbufos
 Other insecticides
  Aldicarb
  Carbaryl + +
  Carbofuran
  Permethrin (crops) + +
  Permethrin (animals) +
Herbicides (any) ND ND
 Phenoxyacetate (any) + ND ND +
  2,4,5-T + + + +
  2,4,5-TP + +
  2,4-D + + + + +
 Other herbicides ND ND
  Alachlor +
  Atrazine +
  Butylate
  Chlorimuron ethyl
  Cyanazine
  Dicamba
  EPTC
  Glyphosate + + +
  Imazethapyr
  Metolachlor +
  Metribuzin +
  Paraquat +
  Peximethalin
  Petroleum oil
  Trifluralin
Fungicides (any) + ND ND +
  Benomyl + +
  Captan + + + + +
  Chlorothalonil
  Maneb +
  Metalaxyl
Fumigants (any) ND ND
  Carbon tetrachloride
  Ethylene dibromide + + +
  Methyl bromide

Note: AMD, age-related macular degeneration; DDT, dichlorodiphenyltrichloroethane; EPTC, S-ethyl dipropylthiocarbamate; ND, not done. Blank cells indicate that no association was present. ND indicates that analyses of correlated pesticides and cumulative use could not be tested for grouped pesticides. +, AMD was associated with the pesticide in the indicated analysis; 2,4-D, 2,4-dichlorophenoxyacetic acid ; 2,4,5-T, 2,4,5-trichlorophenoxyacetic acid; 2,4,5,-TP, 2-propionic acid (fenoprop).

We performed a quantitative bias analysis in which individuals who were not screened were allocated to AMD case or control status based on covariate distribution and pesticide use (see Table S5). The ORs were somewhat attenuated but remained elevated, and our interpretation was not qualitatively affected.

Discussion

To our knowledge, this is the first epidemiologic study to examine the relationship between specific pesticides and physician-confirmed AMD. We found associations of incident AMD with specific pesticides in several functional and chemical groups. The results were strongest and most consistent for organochlorine and organophosphate insecticides and phenoxyacetate herbicides together with specific pesticides from these classes and the fungicide captan (Table 4). Results were qualitatively similar for men and women, but some differences were apparent between early and late AMD. The present findings are consistent with results from two previous cross-sectional analyses in the AHS, which evaluated cases prevalent at enrollment (not included in the present study). These earlier studies implicated organochlorine and organophosphate insecticides and fungicides as risk factors for AMD (Kamel et al. 2000; Kirrane et al. 2005).

The etiology of AMD likely involves both genetic susceptibility and environmental exposures. AMD has been associated with polymorphisms in approximately 20 genes (Fritsche et al. 2014; Sobrin and Seddon 2014) including CFH (Sofat et al. 2012), other genes in complement pathways (Schramm et al. 2014), and genes involved in inflammation and immune regulation, lipid metabolism and transport, maintenance of the extracellular matrix, and angiogenesis (Fritsche et al. 2014; Sobrin and Seddon 2014). Smoking is positively associated with AMD (Chakravarthy et al. 2010; Sobrin and Seddon 2014), and certain dietary factors, including vitamins, minerals, and omega-3 fatty acids, are inversely associated with AMD (Sobrin and Seddon 2014; Zampatti et al. 2014). A meta-analysis of 24 studies found consistent associations with adiposity, hypertension, and cardiovascular disease (Chakravarthy et al. 2010). Genetic variation may modify associations of environmental factors such as smoking with AMD (Seddon et al. 2006; Wang et al. 2008).

Two fundamental mechanisms critical to AMD pathogenesis are inflammation and oxidative stress. The importance of the former is supported by the genetic evidence cited above and by associations of AMD with changes in inflammation biomarkers (Hong et al. 2011). High levels of oxidative stress are normally present in the retina and may be further increased by aging or environmental factors such as smoking (Handa 2012). Biomarkers of oxidative stress are elevated in patients with AMD (Zafrilla et al. 2013), and dietary antioxidants may retard AMD progression (Evans and Lawrenson 2012b; Zampatti et al. 2014). Oxidative stress may provoke the innate immune system and further increase inflammation, perhaps particularly in the presence of genetic variation in complement factors (Handa 2012).

Many specific pesticides associated with AMD are polychlorinated cyclic hydrocarbons. Of 47 specific pesticides evaluated in the ever-use analysis, 11 of 14 (79%) polychlorinated cyclic hydrocarbons were associated with AMD compared with 10 of 33 (30%) pesticides having other structures (χ2=9.3, p=0.002). Many polychlorinated cyclic hydrocarbons are persistent, perhaps because they are lipophilic, which might account both for the greater toxicity of these chemicals and for the inconsistent association of adiposity with AMD: perhaps the latter can be detected primarily in populations exposed to persistent lipophilic toxicants.

Polychlorinated cyclic hydrocarbons may activate mechanisms involved in AMD. Organochlorine insecticides and polychlorinated biphenyls (PCBs) increase both oxidative stress (Bagchi et al. 1995; Lee and Opanashuk 2004) and inflammation (Kim KS et al. 2012; Hayley et al. 2011). Pesticides from other chemical classes, including organophosphate and pyrethroid insecticides and the bipyridyl herbicide paraquat, increase oxidative stress in the retina (Cingolani et al. 2006; Rotstein et al. 2003; Yu et al. 2008). Further, many of the polycyclic pesticides associated with AMD are aromatic, and polycyclic aromatic hydrocarbons are prone to phototoxic reactions (Wielgus and Roberts 2012). These persistent lipophilic compounds could accumulate in the retina and increase oxidative stress in response to light exposure. Potentially of great importance to AMD are observations that complement pathways can be activated by some pesticides, including DDT (Dutta et al. 2008), other organochlorine insecticides (Kumar et al. 2014), malathion (Ayub et al. 2001), the pyrethroid fenvalerate (Dutta and Das 2011), and paraquat (Kim YS et al. 2012).

Risk of late AMD is greater in women than in men (Rudnicka et al. 2015), and risk factors may differ between the genders (Adams et al. 2011; Erke et al. 2014; Klein et al. 1998). We found, however, that the pesticide–AMD association was qualitatively similar in men and women, although fewer women used pesticides. Risk factors for early and late AMD may differ. For example, vitamin supplements do not appear to reduce the risk of early AMD but may delay progression to the late forms (Evans and Lawrenson 2012a; Evans and Lawrenson, 2012b). Similarly, smoking has a stronger association with late than with early AMD (McKay et al. 2011), as does adiposity (Adams et al. 2011; Klein et al. 2007). Differences in pesticide–AMD associations for early and late AMD might be attributable to chance or to confounding: for example, residual confounding by age-related factors. One would, however, expect the latter to produce greater discrepancies for pesticides with greater secular trends, such as organochlorine insecticides (which were banned in the United States beginning in the 1970s), but organophosphate insecticides and herbicides accounted for more of the differences between early and late AMD. Further, neither adjustment for age using finer-grained variables nor stratification by age affected the pesticide–AMD associations.

Because pesticides from several classes were associated with AMD, general farming-related exposures might confound pesticide–AMD associations. A possible confounder is sun exposure, which is a risk factor for AMD that is potentially related to pesticide use (Sui et al 2013). However, adjustment for hours per day of sun exposure did not alter pesticide–AMD associations.

That many potential cases were not included in the final analysis raises a concern of selection bias. Some nonparticipation may have resulted from individuals misreporting AMD on the original questionnaires because they misunderstood the question or for other reasons; this could explain why 43% of those initially reporting AMD subsequently denied the diagnosis on screening. However, we expected that most Amsler grid users would deny the diagnosis. We enrolled 62% of potential cases, but only approximately half of these were confirmed by evaluation of medical records. Overall, these results suggest primarily that self-reports are unreliable and underscore the importance of our effort to validate cases. Quantitative bias analysis (Lash et al 2009) indicated that the results were qualitatively similar after accounting for the possible case–control status of unscreened individuals, although the ORs were slightly weaker. Thus, our findings do not appear to be the result of selection bias.

A further concern is that 25% of individuals otherwise eligible for the study did not complete either follow-up interview and so could not be considered for screening. In a previous study, we found that reporting either a health condition or greater pesticide use at enrollment in the AHS was associated with slightly greater odds of participation at follow-up (Montgomery et al 2010). Thus, we are unlikely to have lost exposed cases from the study population, which might have biased our estimates to the null. Further, our previous study showed that associations of pesticide use with health conditions either in the full cohort or in those participating in the follow-up gave very similar results (Montgomery et al 2010), and in another study, we found that loss to follow-up created notable bias only in analyses of strongly related exposures and outcomes (e.g., smoking and lung cancer) and was not a general problem (Rinsky et al. 2016).

Differences in age and smoking between potential AMD cases and those included in the analysis are not surprising because these factors are known to be related to AMD. Differences in pesticide use are a greater concern; however, they undoubtedly reflect the greater age of AMD cases. Further, it is difficult to understand why greater exposure to pesticides would select for substantially greater participation. Although our previous study found a small association of ever use of pesticides with greater participation at follow-up, increasing frequency of use was associated with nonparticipation, and neither duration of use nor ever use of insecticides was related to participation, either positively or inversely (Montgomery et al 2010). Thus, greater use of pesticides among confirmed AMD cases may represent a real difference and not selection bias.

Based on the age structure and race/ethnicity of the AHS cohort, we expected to find ∼84 incident late AMD cases compared with the 72 we identified (Rudnicka et al. 2015). There are few estimates of incidence for early AMD, but we are likely to have missed more of these cases. Early cases may be less aware of their disease or less likely to report it and so would not have been contacted for screening. Our inability to identify asymptomatic early AMD cases is unlikely to have produced spurious associations unless underascertainment of early cases was related to pesticide use: an unlikely event. Further, pesticide associations with early AMD were often stronger than those with late AMD, suggesting that underascertainment of early cases may have resulted in underestimation of pesticide–AMD associations.

Strengths of the present study include its prospective design and its uniquely detailed information on the use of specific pesticides. Reporting of pesticide use by AHS applicators is generally reliable (Blair et al. 2002), and remaining misclassification would likely be nondifferential and bias associations toward the null (Blair et al. 2011). Although virtually all applicators and approximately half of spouses had used some pesticides, the use of specific chemicals varied considerably, enabling us to compare exposed and unexposed individuals within the cohort. Information was available to control for confounding by many risk factors for AMD, and the use of cases and controls from the same cohort would tend to further minimize confounding. The study was sufficiently large to evaluate exposure–response trends for many pesticides and to compare findings for men and women and for early and late AMD.

Conclusion

We found associations of AMD with use of organochlorine and organophosphate insecticides and phenoxyacetate herbicides as classes as well as with individual pesticides. Specifically, there were consistent associations with chlordane, DDT, malathion, and captan. Additional pesticides with slightly less consistent but nevertheless notable associations were heptachlor, diazinon, phorate, 2,4,5-T and 2,4-D. Overall, these results are consistent with experimental studies of mechanisms underlying AMD, including oxidative stress, inflammation, and complement activation. Our study involved a relatively small number of cases, and its novel results require replication. Nevertheless, it suggests that use of specific pesticides may be a modifiable risk factor for AMD.

Acknowledgments

B. Wujciak evaluated medical records provided by physicians. This work was supported in part by the Intramural Research Program of the National Institutes of Health/National Institute of Environmental Health Sciences (Z01-ES049030) and the National Cancer Institute (Z01-CP-1-119).

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High-Throughput Analysis of Ovarian Cycle Disruption by Mixtures of Aromatase Inhibitors

Author Affiliations open

1Models for Ecotoxicology and Toxicology Unit (DRC/VIVA/METO), Institut National de l’Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France

2S-IN Soluzioni Informatiche Srl (S-IN), Vicenza, Italy

3School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA

PDF icon PDF Version (2 MB)

  • Background:
    Combining computational toxicology with ExpoCast exposure estimates and ToxCast™ assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures.
    Objectives:
    We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women’s menstrual cycles.
    Methods:
    We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCast™ aromatase assay provided concentration–inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus–pituitary–ovarian control of ovulation in women.
    Results:
    Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, ∼10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%.
    Conclusions:
    These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment. https://doi.org/10.1289/EHP742
  • Received: 30 June 2016
    Revised: 07 December 2016
    Accepted: 24 February 2017
    Published: 19 July 2017

    Address correspondence to F.Y. Bois, INERIS, DRC/VIVA, Parc ALATA, BP 2, 60550 Verneuil en Halatte, France. Telephone: 33-344-234-385; Email: frederic.bois@ineris.fr

    Supplemental Material is available online (https://doi.org/10.1289/EHP742).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Concern is growing worldwide over the negative human health and environmental impacts of chemical pollutants that can interfere with the production, metabolism, and action of natural hormones, the so-called endocrine-disrupting chemicals (EDCs). In humans, EDCs have been linked to reproductive disorders (Sweeney et al. 2015), abnormal or delayed development in children (Schug et al. 2015), changes in immune function (Rogers et al. 2013), and cancer (Birnbaum and Fenton 2003). Exposure to mixtures of EDCs may result in effects that can depart from mere summation (Kortenkamp 2007), and human subgroups (e.g., women) may not be sufficiently protected against mixtures of EDCs by current regulatory limits (Kortenkamp 2014).

Each menstrual cycle in women involves hormonal regulation of follicular growth and maturation resulting in ovulation of a single oocyte (Falcone and Hurd 2013). The cycle is controlled by coordinated stimulations and inhibitions along the hypothalamus–pituitary–ovarian axis. Gonadotropin-releasing hormone (GnRH), secreted by the hypothalamus, stimulates the secretion of gonadotropins [follicle-stimulating hormone (FSH) and luteinizing hormone (LH)] by the anterior pituitary gland. Those hormones, in turn, regulate the secretion of ovarian hormones, such as estradiol (E2) or progesterone (P4). Exposures to EDCs that interfere directly or indirectly with any of these hormones can eventually induce infertility or other pathological outcomes. Aromatase is critical because it irreversibly converts testosterone to E2 and androstenedione to estrone, maintaining the dynamic balance between androgens and estrogens.

The objective of the present work was to explore predictively the effects of exposure to large-scale (i.e., potentially real-life) mixtures of aromatase inhibitors on the dynamics of menstrual cycling in women. We input exposure estimates from ExpoCast (Wambaugh et al. 2013) and biological effect data from ToxCast™ (Dix et al. 2007) to coupled pharmacokinetic (PK) and ovarian cycle models; this provided a quantitative mechanistic link between exposure to mixtures of EDCs and their potential adverse effects on the menstrual cycle in women. We compared the expected effects of exposures to single EDCs, as is usually considered by risk assessment provisions in different regulations, with estimated effects of cumulative and concurrent exposures.

Methods

Workflow Overview

The overall computational workflow is pictured in Figure S1. Briefly, after selecting the chemicals of interest, we sampled millions of random mixtures of chemicals using the exposure estimates provided by ExpoCast (Wambaugh et al. 2013). Both constant and time-varying exposure scenarios of an adult woman were considered. A pharmacokinetic model of intake and disposition was then used to estimate the blood concentration (over 2 y) for each chemical present in each mixture. The resulting aromatase activity inhibition was estimated using the Hill’s dose–response model parameters provided by ToxCast™ (Dix et al. 2007). A mathematical model of the hypothalamus–pituitary–ovarian hormonal events [based on a study by Chen and Ward (2014)] was used to predict the levels of E2, P4, and other quantities characterizing the ovarian cycle, for a reference cycle and following exposure to the mixtures generated. Monte Carlo sampling (Bois et al. 2010) was used to propagate uncertainties in exposure, kinetics, and dose–response relationships up to ovarian cycle perturbation.

Databases, Chemical Selection, and Mixture Sampling

ExpoCast (Wambaugh et al. 2013) provides exposure estimates, with measures of uncertainty, for 1,936 chemicals. Those exposure estimates were obtained using far-field, mass-balance human exposure models (USEtox® and RAIDAR).

In ToxCast™ (11 December 2013 release), the Tox21-aromatase-inhibition assay is a cell-based assay measuring CYP19A1 (aromatase) gene activity via a fluorescent protein reporter gene. Chemicals acting on aromatase mRNA synthesis, degradation, or translation, or on aromatase itself, should give positive results in this assay (Chen et al. 2015). For each chemical x assayed, ToxCast™ provides the geometric mean and standard error for the parameter values AC50,x (half-maximal response), Wx (exponent), Bx (baseline value) and Tx (maximum value) of a Hill function fitted to the concentration–inhibition data (scaled using the positive and negative controls’ data):

In ToxCast™, 1,102 chemicals are identified as aromatase inhibitors by the Tox21-aromatase-inhibition assay (on MCF-7 human breast cells) with a fitting that “mate basic requirements of Hill model with some minimal confidence in T and B.” Among those, 256 chemicals (matching either by CAS number or by chemical name) also had exposure estimates in ExpoCast. However, cytotoxicity has been shown to induce many false positive results in ToxCast™ (Judson et al. 2016). Of the 256 chemicals mentioned above, we kept only the 86 that had an AC90 (90% of maximal response) for aromatase inhibition lower than their cytotoxicity AC10 (10% of maximal response) (as measured by the ToxCast™ proliferation decrease assay on T47D human breast cells). The virtual mixtures generated included all of those 86 chemicals.

Exposure Modeling

ExpoCast provided the molecular mass, geometric mean, and lower and upper 95% confidence limits of the exposure rate (milligrams per kilogram per day) for each chemical present in the randomly generated mixtures. For constant exposure modeling over 10 mo, we sampled a rate for each chemical from the corresponding log-normal distribution, but with a standard deviation (SD) scaled by the square root of the number of days of exposure simulated (because a constant exposure rate should be a time average level in that case) and converted it to micromoles per kilogram per minute.

More realistic time-varying exposures over a 2-y period were also modeled [similarly to the report of Bertail et al. (2010)] using exposure windows of random length and intensity. We first sampled the number n of exposure windows for each chemical in the mixture from a scaled exponential distribution (with a rate parameter equal to 5). That yielded on average 145 exposure events over 2 y (median: 100 events, first quartile: 40 events, third quartile: 200 events). The n exposures’ start and end times were sampled uniformly over the 2-y period. The exposure rate during each of the n exposure windows was randomly sampled from the log-normal distribution given by ExpoCast, with an SD scaled by the number of days of the exposure window considered (or unscaled if the exposure lasted less than a day).

To obtain a lower bound on the effect of mixtures, we similarly simulated random nonoverlapping exposures to the 86 EDCs selected (i.e., each person was exposed to the 86 chemicals, in random order, at random times, but to only one chemical at a time).

Pharmacokinetics Modeling

For each chemical in each mixture, a one-compartment PK model was used to estimate its internal concentration (in micromoles) at steady state in the case of constant oral exposure or at any point in time in the case of varying oral exposures. Steady-state internal concentrations for chemical x were calculated as:
where Fx is the bioavailability of x (unitless), Ex is its exposure rate (in micromoles per kilogram per minute, sampled as indicated above), and Ke,x is its total body clearance rate constant (per minute). A body density of 1 was assumed.

For time-varying exposures, internal concentrations were obtained as a function of time by numerical integration of the following differential over a 2-y period (with an initial value set to zero):

We used quantitative structure-activity relationships (QSAR) to obtain central estimates of Fx and Ke,x for each of the 86 aromatase inhibitors considered. The robustness of the prediction was evaluated by examining compounds from the training set similar to the target substances, together with literature data and references.

Fx central estimates were obtained at several oral dose levels and were linearly interpolated between dose levels as needed. Beyond the dose rates of 0.001 to 10 mg/d, the Fx value at the closest bound was used.

To take into account the uncertainty affecting the QSAR-estimated PK parameters, we randomly sampled F values from beta distributions (naturally bounded between 0 and 1), with parameters calculated such that the distribution modes corresponded to the interpolated value of Fx with a coefficient of variation (CV) of 20% (for null Fx modes, a and b were set to 1 and 50, yielding a median at 0.01, a first quartile at 0.006, and a third quartile at 0.03, approximately). Ke,x values were log-normally sampled with a geometric mean equal to the central estimates obtained by QSAR and a geometric SD corresponding to a factor of 3.

Ovarian Cycle Model

We adapted the menstrual cycle model presented by Chen and Ward (2014). The model describes the inhibitory and stimulatory effects of hormones E2 and P4 on the hypothalamus–pituitary axis in women (Figure 1). The equations and definitions of all parameters used in the model are given in the Supplemental Material (see “Menstrual cycle model equations”; see also Tables S3 and S4). In the original model, E2 and P4 were assumed to be instantaneously in equilibrium between blood and the ovaries. Instead, we described the kinetics of E2 and P4 using differential equations (Equations 15–19 in the Supplemental Material, “Menstrual cycle model equations”). That modification had practically no impact on the time course of the model variables during a normal cycle [equilibrium between blood and ovaries is fast, as assumed by Chen and Ward (2014)], but it allowed us to coherently integrate the dynamic aspect of estradiol synthesis inhibition by EDCs. The three additional parameters (blood and ovarian volumes, ovarian blood flow) were obtained from the literature (see Table S3).

Conceptual diagram of the ovarian cycle model, showing the hormonal controls between the brain and the ovaries.
Figure 1. Regulatory pathways of the human menstrual cycle as implemented in the model. During the follicular phase (1: germ cells; 2: developing follicle; 3: mature follicle; 4: ovulation; 5: corpus luteum formation; 6: corpus luteum degradation), negative feedback by estradiol (E2) reduces follicle-stimulating hormone (FSH) secretion, leading to the selection of one follicle for ovulation. Gonadotropin-releasing hormone (GnRH) secretion is promoted by E2 and inhibited by progesterone (P4), inducing a luteinizing hormone (LH) peak and consecutive ovulation. E2 is mainly produced by follicles and corpus luteum and P4 by corpus luteum.

An additional variable, the ratio of disrupted over basal E2 synthesis rate constants (EDCYP19), was introduced to link the internal doses of chemicals in mixtures to aromatase inhibition. EDCYP19 was calculated using Hill’s model, parameterized with the chemical-specific values provided by ToxCast™, as a cumulative product of remaining activity for each of the m chemicals of the mixture considered:

For constant exposures, Cx was set to the steady-state internal concentrations Cx,ss. For time-varying exposures, Cx was computed by integration as explained above. The parameter Bx was set to zero because a positive inhibition with no dosage would not make sense. AC50,x, Wx, and Tx values were randomly sampled using the mean and standard error provided by ToxCast™. AC50,x was sampled from a log-normal distribution (its logarithm is actually the ToxCast™ fitted value). Wx and Tx were sampled from truncated normal distributions. Truncation was from 0 to 10 for Wx (values beyond 10 would be found for some chemicals for which Wx is poorly identified, but have no biological meaning). Truncation was from 0 to 100 for letrozole’s Tx (the positive control). For the other chemicals, truncation was from 0 to 10,000 over Tx for letrozole to properly rescale the ToxCast™ Tx values between 0 and 100.

EDCYP19 was entered as an input to the ovarian cycle model, which was then solved to obtain the time profile of its output variables over 2 y of simulated time. For constant exposures, we computed the square root of the sum of the squared Euclidean distances between a reference E2 concentration (EDCYP19 set at zero) and the perturbed concentrations (at a fixed set of times) as a summary measure of disruption.

Software Used

The ACD/Labs Percepta platform modules ACD/Oral Bioavailability and ACD/PK Explorer were used for the prediction of oral bioavailability (F) and the total body clearance rate constant (Ke), respectively (see Supplemental Material, “PK modules of ACD/Labs”; see also Table S1 and Figures S2 and S3). GNU MCSim v5.6.5 (www.gnu.org/software/mcsim) (Bois 2009) was used to build the ovarian cycle model. R v3.1.1 (R Development Core Team) with the parallel, deSolve, and EnvStats packages was used for database processing, numerical integration of the models, and graphics.

Results

Estimates of Internal Dose

The relationship between constant exposure rates and steady-state internal concentrations for the 86 EDCs considered indicates that exposures ranged from 10−8μmole/kg/d to 10−3μmole/kg/d and that the resulting steady-state internal concentrations ranged from 10−13μM to 10−3μM (see Figure S4). The exposure rates and pharmacokinetic parameters were Monte Carlo sampled as described above. For any single EDC, uncertainty is approximately a factor of 10 for exposures and approximately a factor of 1,000 for the resulting internal concentrations. For time-varying exposures, Figure S5 shows an example of a simulated random 2-y time course of internal concentration for lindane. Such profiles were obtained for each chemical in each simulated mixture.

Cycle Model Behavior

Our implementation of the ovarian cycle model proposed by Chen and Ward (2014) correctly reproduces their results. Human data from McLachlan et al. (1990) and Welt et al. (1999) on LH, FSH, E2, and P4 normal cycles are correctly simulated except for McLachlan FSH data, for which the baseline levels are not well matched. There is a large intra- and inter-subject variability in hormonal levels across women in those data sets (see Figure S6). We took the model-simulated normal cycling of E2 as the “reference cycle” in the following. Constant exposure scenarios result, at steady state, in a constant level of aromatase inhibition. In that case, perturbation depends only on that parameter (according to the model assumptions), so the distance between the perturbed and reference cycles is a useful measure of effect (see Figure 2). As aromatase inhibition increases, cycles become increasingly perturbed and exhibit chaotic features (hence the misalignment of the points in Figure 2). At 5% inhibition (95% of normal aromatase activity), cycles are shortened, baseline levels change little, and peak levels either increase or decrease less than proportionally except for LH. Simply put, the regulations dampen the effect of perturbation. At ∼10% inhibition, LH peaks disappear after approximately five cycles, and a major bifurcation in cycle patterns occurs: cycles are further shortened, baseline levels are much increased (doubled for E2 and P4, for example, even though E2 synthesis by aromatase is decreased), and peak levels mostly decreased; E2 distance to normal increases up to a maximum (Figure 2). At higher inhibition levels, the cycles increasingly dampen and disappear completely between 30% and 40% inhibition (see Figures S7–S10). Overall, according to this model, having ∼10% constant inhibition of aromatase activity in vivo leads to perturbations of the cycle, which is still under control and should be compatible with normal reproductive function. Beyond 10% inhibition, an actual disruption of the system seems to occur.

Scatter plot with a regression line plotting Euclidian distance to reference E2 (y-axis) across percentage of aromatase inhibition (x-axis).
Figure 2. Euclidean distance between an estradiol (E2) normal cycle and a perturbed cycle as a function of aromatase inhibition at steady state. Distance is computed over 2 y based on 7,301 time points (one every 144 min).

Effects of Single Chemicals

We first simulated 1,000,000 constant exposures to each of the 86 chemicals considered, taken individually. In that case, despite accounting for uncertainty in exposure levels and dose–response parameters, none induced >1% aromatase inhibition. Hence, none of those chemicals alone was able to induce a significant disruption of the ovarian cycle. Figure 3 places those chemicals on a map with the slope W of the Hill dose–response curve at AC50 and the log-margin of exposure as coordinates. The margin of exposure was defined as the ratio of the 97.5th percentile of internal concentrations over AC50. The log10-margins of the chemicals studied ranged from −10 to −1.8, indicating that for all chemicals, the high end of internal exposure concentrations was at most 1% of AC50. In that case, Equation 1 shows that the logarithm of aromatase inhibition is approximately equal to the product of W times the log-margin of exposure. The color background of the map codes for the resulting risk index (i.e., the log10-inhibitions) and ranges from <−2 (1%) to approximately −80(10−78 %), much too low to elicit changes in ovarian cycles such as those in Figure 2. Therefore, no effects can be expected from typical exposures to those chemicals when considered alone. Table 1 gives the list of the 10 chemicals for which individual risk is the highest. Note that letrozole is the reference chemical for the Tox21 aromatase inhibition assay, which is consistent with its high rank. The others are found in therapeutic drugs, agrochemicals, food contaminants, consumer products, and other materials.

Inhibition map plotting log sub 10 margin of exposure (y-axis) across slope of W at AC sub 50 (x-axis).
Figure 3. Map of the aromatase inhibitors studied over a plane defined by dose–response slope W and log-margin of exposure (see text). Colors vary linearly with powers of 10 of aromatase inhibition resulting from W and margin of exposure combinations, from red (10−2) to blue (10−80). For the chemicals–numbers correspondence, see Table S1.
Table 1. Top-ranking chemicals according to their individual risk indices. Those chemicals are in the top left corner of Figure 3.
ExpoCast name CAS number Risk indexa
Letrozole 112809-51-5 −2.99
Estrone 53-16-7 −3.41
Fulvestrant 129453-61-8 −4.30
Triflumizole 68694-11-1 −4.81
2,4,7,9-Tetramethyl-5-decyne-4,7-diol 126-86-3 −5.11
n-Methyl-2-pyrrolidone 872-50-4 −5.49
Rhodamine 6G 989-38-8 −5.51
Anastrozole 120511-73-1 −6.04
Fenvalerate 51630-58-1 −6.05
Imazalil 35554-44-0 −6.31

aRisk index=W×log10(E95/AC50), where E95 is the 95th percentile of the exposure values sampled, W is the Hill exponent, and AC50 the half-maximal response.

Effects of Mixtures of Chemicals

We generated 1,000,000 hypothetical mixtures of the 86 aromatase inhibitors studied and evaluated their global effect on E2 synthesis and the resulting ovarian cycle disruption. Figure 4 shows a histogram of the resulting inhibition levels. Depending on the (random) composition of the mixtures, and given the uncertainties on exposures and effect parameters, responses ranged from 0% to 100% inhibition, but on average were very high. Such inhibition levels may lead to perturbation of the ovarian cycle, according to the model.

Histogram of the average percent inhibition of E2 synthesis by random mixtures at constant exposure levels.
Figure 4. Histogram of the average percent inhibition of estradiol (E2) synthesis by random mixtures (n=1,000,000) of 86 aromatase inhibitors at constant exposure levels.

Real-life exposures to chemicals do not usually occur at constant levels. We simulated time-varying exposures (see Figure S5) to investigate the resulting effects on aromatase inhibition and ovarian cycle disruption. To define a lower bound on mixture effects, we first simulated fluctuating levels of exposure to EDCs, but without concomitant exposures to them. Interactions can still occur in that case because of storage in the body or because of persistent effects on the ovarian cycle. Figure 5 shows that only 0.3% of the simulated exposures caused >10% average aromatase inhibition. The maximum inhibition found was close to 50%. More realistic exposure scenarios do not prohibit concomitant exposures. In that case (Figure 6), the distribution of simulated time-averaged inhibitions is shifted toward greater effects, and average inhibitions >20% are not uncommon (yet they do not reach the extreme levels observed in Figure 4). Because inhibition changes with time, the distances between normal and perturbed cycles do not follow the pattern shown in Figure 2 (distances can be much larger), and the link between estradiol inhibition and cycle disruption is harder to establish. Examination of the time course of the dominant follicle mass (F) for 1,000 random simulations of mixtures of the 86 chemicals shows that perturbed cycles typically have a baseline shifted up (which may or may not return to normal) and an irregular succession of peaks (corresponding to ovulation) (Figure 7). The 1,000 simulations examined can be classified into four groups. In group 1 (17% of the samples), the cycles are practically normal with no baseline shifts and at most one or two missing ovulations. In group 2 (73% of cases), baseline shifts are always present but without major irregularities in ovulation. Group 3 (7% of cases) has systematic baseline shifts and frequent or prolonged anovulations. Such cycling would clearly impair fertility. In group 4 (3% of cases), disruption is catastrophic or total. Figure S11 shows the corresponding plots for E2 time courses. Judging by these plots, E2 profiles can have a shifted baseline even in normal ovulation profiles, but otherwise, the patterns are rather similar.

Histogram of the average percent inhibition of E2 synthesis by random mixtures at nonconcomitant exposure levels.
Figure 5. Histogram of the average percent inhibition of estradiol (E2) synthesis by random mixtures (n=1,000,000) of 86 aromatase inhibitors with time-varying nonconcomitant exposures. For visibility, the first histogram bar has been truncated (it represents ∼90,000 simulations). Note: E2, estradiol.
Histogram of the average percent inhibition of E2 synthesis by random mixtures at variable exposure levels.
Figure 6. Histogram of the average percent inhibition of estradiol (E2) synthesis by random mixtures (n=1,000,000) of 86 aromatase inhibitors with time-varying exposures.
Simulated time profiles plotting follicular mass in arbitrary units (y-axis) across time (x-axis).Figure 7. Typical simulated time profiles of dominant follicle mass during time-varying exposures to random mixtures of 86 aromatase inhibitors. Four classes (rows) of increasing disruption are illustrated (see text). Left column: least disrupted profile in its class; Right column: most disrupted. Responses range from regular ovulation (1A) to complete disruption (4B).

Discussion

We linked ToxCast™ data and ExpoCast estimates of exposures to (mixtures of) aromatase inhibitors and estimated their effects on the ovarian cycle in women. To our knowledge, this is the first application of computational toxicology and high-throughput testing to assessment of the combined effects of exposures to a large number of EDCs.

Our approach is predictive, and we had to make many assumptions and simplifications. ToxCast™ and ExpoCast are incomplete, and a full inventory of all the EDCs to which women are exposed is not yet available. Therefore, we were only able to look at a subset of the potential EDCs. We used human exposure estimates reported by Wambaugh et al. (2013), who gave summary statistics for the distribution of exposures to individual chemicals. This information allowed us to take the correspondingly large uncertainty into account via Monte Carlo simulations. However, we cannot differentiate between uncertainty and variability in those exposure estimates, and we cannot identify subgroups of sensitive individuals. We cannot even focus on women, our target population. More sophisticated exposure models (Isaacs et al. 2014) could help in that respect, but they still deal only with single chemicals and provide no data or estimates on coexposures. Depending on age, occupation, socioeconomic status, ethnicity, and health condition, we are exposed to different cocktails of chemicals in our diet, workplace, environment, and so forth (Tornero-Velez et al. 2012). We modeled coexposures by random sampling, either at a constant level or, more realistically, with time-varying exposure profiles and hence with time-varying mixture complexity. That approach is still imperfect, and we had to guess about the distribution of the number of exposure windows, for example. We respected the distribution of population exposure levels documented by ExpoCast, but lacking coexposure information, our estimates might be lower or higher than in reality. Efforts are ongoing to collect relevant data in, for example, the European Total Diet Study (Vin et al. 2014). An analysis of such data (Traoré et al. 2016) shows that among 153 synthetic chemicals studied in seven typical French diets (food associations), three are aromatase inhibitors according to ToxCast™: zearalenone, triadimenol, and lindane. In this regard, mixtures of ≥86 aromatase inhibitors may seem unrealistic, but only food contaminants were studied by Traoré et al. (2016).

We searched for the 86 selected aromatase inhibitors in a database of consumer products marketed in the United States (Gabb and Blake 2016). Briefly, this database was constructed by scraping product information from online retailers and currently contains 53,743 products. Twelve of the 86 aromatase inhibitors were detected in 5,701 products (representing 11% of the products in the database). [It is worth noting that none of the 86 aromatase inhibitors is among the volatile fragrance chemicals detected in consumer products (Steinemann 2015; Steinemann et al. 2011), so aromatase inhibitors are unlikely to be hidden in generic “fragrance” or “flavor” designations on consumer product labels.] Two-way combinations of these chemicals were found in 220 products, and the three-way combination of carminic acid, FD&C blue no. 1, and retinol was found in 3 products. These findings may not seem to indicate a large problem, but it is an incomplete view of combinatorial exposure. Consider that 3,660 of 4,501 makeup products (81%) in the database contain at least one of the aromatase inhibitors evaluated (carminic acid, retinol, and artificial colors are common ingredients in makeup) and that a typical consumer uses several products each day, possibly even several makeup products. This increases the likelihood of combined exposures. In addition, no readily available data address associations for all near- and far-field exposures for the 86 aromatase inhibitors, which are used in industrial or agricultural processes (18), consumer product formulations (12), biocidal applications (38), and pharmaceutical drugs (18). These usage categories are likely to be independent, so focusing on a few known associations would only give partial answers and would underestimate global risk. We are striving for a more extensive picture. To address the potential overestimation of mixture effects when generating purely random associations, we present the results of a very optimistic exposure scenario (with no coexposures at all). This scenario gives a lower bound estimate: 0.3% of exposures would lead to >10% average aromatase inhibition in women.

ToxCast™ aromatase inhibition data were obtained by exposing cells in vitro. We had no easy way to assess the in vitro kinetics of the substances assayed, and reconstruction methods (Armitage et al. 2014) require input data that we did not have. We assumed that the nominal assay concentrations were those actually experienced by the cells and that equivalent extracellular concentrations in vivo would lead to the same levels of aromatase inhibition. That is a typical assumption, but it is not necessarily correct (Coecke et al. 2013). To obtain extracellular concentrations in vivo, we estimated bioavailability and total clearance with QSAR methods and input them in a simple one-compartment PK model with oral exposure only (even though inhalation or dermal exposures might be more relevant). Again, more sophisticated [physiologically based pharmacokinetic (PBPK)] models and additional PK data would give more precise and more accurate predictions (El-Masri et al. 2016; Wambaugh et al. 2015).

On the effect side, we only considered chemicals for which the ToxCast™ dose–response parameters were estimated with reasonable confidence. This was a conservative choice, and additional chemicals would have been included if more relaxed criteria had been chosen (at the cost of lower confidence in the results). A concern with large databases such as ToxCast™ is the quality assurance for the data provided. Aromatase inhibition may not be the most sensitive toxicity end point, for example, or the action may be due to a burst effect of cytotoxicity (Judson et al. 2016). From the original set of 256 aromatase inhibitors for which we had exposure estimates and ToxCast™ data, 170 were excluded based on cytotoxicity. Those were mostly weak inhibitors (data not shown). For the screen, we kept only the 86 substances that had an aromatase inhibition AC90 lower than their cytotoxicity AC10 as measured by the ToxCast™ proliferation decrease assay on T47D human breast cells, a cell type similar to the MCF-7 used by the aromatase inhibition assays (Aka and Lin 2012). We preferred that screen to the omnibus z-score criterion, which aggregates cytotoxicity results from different cell types and species. For the remaining 86 substances, we may still have downplayed other types of toxicity. Cytotoxicity, it should be noted, is not a negligible end point, and an evaluation of the cytotoxicity of mixtures would be interesting in its own right.

The evidence provided by ToxCast™ is also not perfectly predictive of in vivo outcomes in humans. For example, the Tox21 aromatase inhibition assay uses the MCF-7 breast cancer cell line, which might not respond as normal ovary cells would. In addition, our model of the ovarian cycle (Chen and Ward 2014) describes only approximately the complex dynamic interactions between ovarian follicular growth and hormonal homeostasis. The hypothalamus and pituitary gland are treated as a single compartment, and the description of the central hormonal controls is simplified. More complex models have been proposed (Hendrix et al. 2014), but they still make many assumptions and do not seem to offer dramatically better performance. In addition, we note that we treated the parameters of the ovarian cycle as constant, when in fact they are affected by both uncertainty and variability in response to EDCs, adding to the tails of the distributions of our results. However, we did not have sufficient information to define statistical distributions for those parameters.

In terms of results, an obvious question is that of the “bad” actors, that is, the chemicals responsible for the predicted effects. The answer is provided by Figure 3 (and partially by the top-ten Table 1) because here, the impact of individual chemicals on aromatase is only conditioned by internal dose and inhibition potency. Figure 3 is a useful prioritization tool. It is rather simple to construct and does not require running the ovarian cycle (the PK model is needed). However, it does not give an answer in terms of magnitude of effect at the subject level. For that, we need the whole-body ovarian cycle model.

One of the consistent features of E2 cycle perturbation that we found is that the baseline (interovulation) levels of E2 tend to increase (to approximately twice the normal level) in response to aromatase inhibition (which implies a lower rate of E2 synthesis by aromatase). That counterintuitive feature of the complex cycle dynamic is induced by central nervous system (CNS) feedback. Beyond a certain level of inhibition, the control of E2 remains in effect (peaks are still observed) but moves the baseline to a higher value. We do not have confirmation that this is the case in women exposed to EDCs, but that would be interesting information and a potential biomarker of effects. We also note that we lack good measures of perturbation for such complex systems. We used visual inspection to classify cycles for 1,000 time-varying exposures (Figure 7). Perturbation analysis of more cycles would require more sophisticated tools. Finally, many other perturbation pathways exist for ovarian cycle disruption that were not accounted for (e.g., actions mediated by the androgen or estrogen receptor). The simplicity of our model also precludes investigation of synergistic or antagonistic effects that could result from metabolic or toxicodynamic interactions (Cheng and Bois 2011).

Conclusion

High-throughput data collection requires high-throughput analysis, extrapolation, and decision tools if we want to avoid a bottleneck and accumulation of unused data. We developed such a tool, making use of our increasing understanding of toxicity mechanisms.

This exercise in prediction suggests large data gaps. Our knowledge of exposure to actual mixtures is minimal except in a few cases (e.g., tar, tobacco smoke). For the chemicals studied here, quantitative knowledge of their routes of exposure and PK parameters is also lacking. Our knowledge of endocrine disruption mechanisms is still in its infancy. We should check, for example, the inhibition potential of the 86 substances studied here with better tests and with better characterization of the in vitro fate of the chemicals. Nevertheless, we now have a prioritized list and some reason to deepen our investigations. We should also research relevant human biomarkers of exposure and effects for validation of the results and for better actions.

The basic assumption of regulatory practice and most risk assessments is that keeping individual chemicals under control with reasonable safety factors will keep their joint effects at bay. That may not be the case. Some women have various levels of ovarian dysfunction that can be caused by various internal and external factors. Environmental chemical exposures add on to that background (National Research Council 2009; Zeise et al. 2013). We found that even though individual chemicals are likely “safe” as used now, their joint effects, when they exceed a few dozen in number, can lead to severe disruption of the ovarian cycle in women in a sizable number of cases. Obviously, this study does not provide definite proof that some fertility problems can be caused by real-life exposures to EDCs. Nevertheless, risk assessment practice and regulations should start thinking of the problem of mixtures not as an unsolvable one, but as needing a clearly laid out research agenda. In the case in point, the simple graphical map of internal dose and potency we propose could already be used for prioritization. Given the magnitude of the range of the predicted effects on ovarian cycling, while waiting for confirmation of our results, a cautionary attitude should be adopted.

Acknowledgments

The authors thank the reviewers for their helpful comments.

We thank the U.S. Environmental Protectin Agency Computational Toxicology Research team for its help in using ToxCast™ and ExpoCast. The research leading to these results received funding from the European Union’s 7th Framework Program and Cosmetics Europe [grant agreement 266835 (COSMOS)], the European Union’s Horizon 2020 research and innovation program [grant agreement 633172 (Euromix)], and the French Ministry for the Environment (PRG190).

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