Monthly Archives: August 2017

USAID Administrator Mark Green’s Meeting with African Union Chairperson Moussa Faki Mahamat


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Thursday, August 31, 2017

USAID Administrator Mark Green met with African Union Chairperson Moussa Faki Mahamat today in Addis Ababa to discuss partnering on economic growth, security, and sustainable development in Africa.

Urinary BPA and Phthalate Metabolite Concentrations and Plasma Vitamin D Levels in Pregnant Women: A Repeated Measures Analysis

Author Affiliations open

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

2Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA

3Division of Maternal-Fetal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

4Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA

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  • Background:
    In addition to its well-established role in maintaining skeletal health, vitamin D has essential regulatory functions in female reproductive and pregnancy outcomes. Phthalates and bisphenol A (BPA) are endocrine disruptors, and previous research has suggested that these chemical agents may disrupt circulating levels of total 25(OH)D in adults.
    Objectives:
    We investigated the relationships between repeated measures of urinary phthalate metabolites and BPA and circulating total 25(OH)D in a prospective cohort of pregnant women.
    Methods:
    The present study population includes participants (n=477) in a nested case–control study of preterm birth drawn from a prospective birth cohort of pregnant women at Brigham and Women’s Hospital in Boston, Massachusetts. Urine and blood samples were collected for biomarker measurements at median 10 wk and 26 wk of gestation.
    Results:
    In repeated measures analysis, we observed that an interquartile range (IQR) increase in urinary mono-3-carboxypropyl phthalate (MCPP) was associated with a 4.48% decrease [95% confidence interval (CI): −7.37, −1.58] in total 25(OH)D. We also detected inverse associations for metabolites of di(2-ethylhexyl) phthalate (DEHP) [percent difference (%Δ)=−2.83 to −2.16]. For BPA, we observed a nonsignificant inverse association with total 25(OH)D in the overall population. Our sensitivity analysis revealed that the associations for some metabolites (e.g., MEHP) varied by race/ethnicity, which may reflect potential differences in susceptibility. In agreement with findings from repeated measures analysis, we reported that DEHP metabolites and BPA were significantly associated with an approximate 20% increase in the odds of vitamin D deficiency (≤20 ng/mL) [odds ratio (95% CI): 1.19 (1.06, 1.35) for molar sum of DEHP metabolites and 1.22 (1.01, 1.47) for BPA] at median 10 wk and 26 wk, respectively.
    Conclusions:
    Our results provide suggestive evidence of the potential for environmental exposure to phthalates and/or BPA to disrupt circulating vitamin D levels in pregnancy. https://doi.org/10.1289/EHP1178
  • Received: 03 October 2016
    Revised: 10 May 2017
    Accepted: 12 May 2017
    Published: 31 August 2017

    Address correspondence to J.D. Meeker, Dept. of Environmental Health Sciences, University of Michigan School of Public Health, 1835 SPH I, 1415 Washington Heights, Ann Arbor, Michigan 48109-2029 USA. Telephone: (734) 764-7184. Email: meekerj@umich.edu

    Supplemental Material is available online (https://doi.org/10.1289/EHP1178).

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

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Introduction

Vitamin D is a prohormone that plays an integral role in the regulation of bone metabolism and calcium and phosphorous absorption (Holick 2007; Norman 2008). The major source of vitamin D in humans is exposure to ultraviolet B (UVB) radiation from sunlight, although it can also be obtained through dietary food sources or supplements (Thacher and Clarke 2011). Vitamin D from the skin and diet (vitamin D2 and D3) is biologically inactive and is transported to the liver where it is converted to 25-hydroxyvitamin D [25(OH)D], the circulating biomarker of vitamin D nutritional status (Norman 2008; Thacher and Clarke 2011). Further metabolism occurs in the kidneys, wherein 25(OH)D is hydroxylated to its biologically active metabolite, 1-25-dihydroxyvitamin D [1,25(OH)2D] (Norman 2008; Thacher and Clarke 2011); 1,25(OH)2D is a secosteroid hormone that initiates biological actions by interacting with its nuclear receptor at target tissues (Bikle 2014; Carlberg 2014; Haussler et al. 2013). Although it is well established that vitamin D plays an essential role in the development and maintenance of skeletal health, the presence of its nuclear receptor and metabolic enzymes in reproductive tissues, such as the placenta, uterus, and ovaries, indicates that vitamin D may also have regulatory functions in female reproductive and pregnancy outcomes (Grundmann and von Versen 2011; Luk et al. 2012; Ma et al. 2012; Pérez-López 2007).

Maintaining maternal vitamin D homeostasis in pregnancy is necessary for placentation and the maintenance of the pregnancy state as well as for normal fetal growth and development (Luk et al. 2012; Murthi et al. 2016; Ponsonby et al. 2010). Human health studies have shown that reduced levels of 25(OH)D in pregnancy are associated with various maternal and fetal complications, such as preeclampsia, spontaneous preterm birth, and restricted fetal growth (Bodnar and Simhan 2010; Bodnar et al. 2015; Murthi et al. 2016; Robinson et al. 2011). Because pregnancy represents a period of susceptibility during which slight deviations in maternal hormone levels may have detrimental maternal and fetal health consequences, pregnant women are particularly vulnerable to the effects of endocrine-disrupting chemicals.

Phthalates and bisphenol A (BPA) are industrial chemicals found in a wide range of consumer products (Meeker et al. 2009b). Exposure to these agents has been reported in pregnant women worldwide (Cantonwine et al. 2014; Casas et al. 2011; Mortensen et al. 2014; Mu et al. 2015). Both phthalates and BPA may disrupt endocrine systems, and results from epidemiological studies suggest these environmental chemicals may alter sex and thyroid hormone levels in pregnant women (Huang et al. 2007; Johns et al. 2015, 2016a; Sathyanarayana et al. 2014). Given that the active vitamin D metabolite is similar in structure to that of classic sex steroid hormones (Norman 2008), and its nuclear receptor is in the same superfamily of sex steroid and thyroid hormone receptors (Pike and Meyer 2010), it is also plausible that phthalates and/or BPA might disrupt the vitamin D endocrine axis. In our recent investigation conducted among a representative sample of U.S. adults, we reported inverse associations between urinary metabolites of di(2-ethylhexyl) phthalate (DEHP) and total 25(OH)D (Johns et al. 2016b). Urinary BPA was inversely associated with total 25(OH)D among women in our sex-stratified analyses (Johns et al. 2016b). Although our previous study showed the potential for phthalates and BPA to alter circulating levels of total 25(OH)D in adult populations, it was limited by its cross-sectional design with single biomarker measurements collected at one time point. Moreover, we are not aware of any studies that have investigated these associations in pregnant women. In the present study, we assessed the associations between environmental exposure to phthalates and BPA and plasma total 25(OH)D levels in a large, prospective cohort of pregnant women.

Methods

Study Population

The present study population includes participants in a nested case–control study of preterm birth drawn from a prospective cohort (LifeCodes) of pregnant women 18 y and older who were recruited early in gestation (<15 weeks) at Brigham and Women’s Hospital in Boston, Massachusetts. The only exclusion criterion was higher-order multiple gestations (e.g., triplets or greater). Additional details regarding recruitment and eligibility criteria are described in detail elsewhere (Ferguson et al. 2014a, 2014b; McElrath et al. 2012). In brief, participants completed a questionnaire at the initial study visit (median: 9.7 wk of gestation; range: 4.7–19.1 wk) to collect demographic characteristics (e.g., race/ethnicity, health insurance provider, educational attainment, etc.) and relevant health information (e.g., family health history, tobacco and alcohol use). Participants were followed until delivery and provided health information [e.g., body mass index (BMI)] as well as blood and urine samples for biomarker measurements at three additional study visits: visit 2 (median: 17.9 wk of gestation; range: 14.9–32.1 wk), visit 3 (median: 26.0 wk of gestation; range: 22.9–36.2 wk), and visit 4 (median: 35.1 wk of gestation; range: 33.1–38.3 wk). The present analyses were restricted to visits 1 and 3 because plasma samples collected at only these time points were assayed for total 25(OH)D.

Of the 1,181 pregnant women included in the original birth cohort who were followed until delivery and had a singleton birth, 130 women who delivered a preterm infant (<37 weeks of gestation), and 352 who delivered at or after 37 wk of gestation were included in the nested case–control population. The selection probabilities from the parent cohort population were 90.1% for cases and 33.9% for controls (Ferguson et al. 2015). In the current study, we excluded participants from this population who did not have measurements for urinary phthalate metabolites or BPA (n=1) or 25(OH)D (n=4) at either of the two study visits. The final study population (n=477) included 128 cases of preterm birth and 349 controls. The study protocols were approved by the ethics and research committees of the participating institutions, and all study participants gave written informed consent prior to participation.

Urinary Exposure Measurements

All available urine samples collected at up to two study visits during pregnancy were assayed for nine phthalate metabolites and total (free plus glucuronidated) BPA using isotope dilution-liquid chromatography-tandem mass spectrometry (ID–LC–MS/MS) at NSF International in Ann Arbor, Michigan. Additional details regarding this analytical method are described elsewhere (Lewis et al. 2013). The nine phthalate metabolites included: mono(2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), monobenzyl phthalate (MBzP), mono-n-butyl phthalate (MBP), monoisobutyl phthalate (MiBP), monoethyl phthalate (MEP), and mono (3-carboxypropyl) phthalate (MCPP). In addition to analyzing individual phthalate metabolites in our statistical analyses, we created a molar sum (μmol/L) measure of the four metabolites of DEHP (MEHP, MEHHP, MEOHP, and MECPP; ΣDEHP). Specifically, we divided each phthalate metabolite concentration by its molecular weight and took the sum of the individual concentrations. Urinary biomarker concentrations less than the limit of detection (LOD) were assigned a value of LOD divided by the square root of 2 (Hornung et al. 1990).

To adjust for urinary dilution in descriptive analyses, phthalate metabolites and BPA were standardized using specific gravity (SG) by the following equation (Meeker et al. 2009a): PSG=P [(1.015−1)/(SG−1)], where PSG is the specific gravity-adjusted exposure biomarker concentration (μg/L), P is the observed exposure biomarker concentration, 1.015 is the specific gravity population median, and SG is the specific gravity of the urine sample. In multivariable analyses, we used unadjusted urinary biomarker concentrations with SG added as a separate covariate because modeling corrected metabolite levels may introduce bias (Barr et al. 2005).

Plasma Vitamin D Measurements

All available plasma samples were assayed for total 25(OH)D, including 25(OH)D2 plus 25(OH)D3, using a DiaSorin LIAISON® chemiluminescence immunoassay (DiaSorin Inc.) at the Laboratory for Molecular Medicine (Partners Healthcare, Boston, MA) (Ersfeld et al. 2004). The detection range of the assay is 4.0–150 ng/mL, and total coefficients of variation ranged from 9.5% to 12.6%. For quality control, the laboratory uses the U.S. National Institute of Standards and Technology (NIST) level 1 protocol (Burris et al. 2014).

Statistical Analyses

All analyses were performed using R (version 3.3.1; R Development Core Team). We conducted the present study using secondary variables measured under case–control sampling. To make our study population more representative of the original cohort from which the case–control sample arose (i.e., to correct for the over-representation of preterm-birth cases), we applied to all analyses inverse probability weights that represented the inverse sampling fractions for inclusion of controls (Richardson et al. 2007). The distributions of all urinary analytes were right-skewed so we transformed these data in statistical analyses using the natural logarithm (ln). The empirical histogram of total 25(OH)D approximated a normal distribution.

In descriptive analyses, we tabulated weighted means and standard deviations of total 25(OH)D by selected population characteristics. We used the R nlme package to fit unadjusted linear mixed models (LMMs) with a subject-specific random intercept, chosen based on Akaike’s information criterion (AIC), to account for intra-individual correlation of repeated measures over time. We used unadjusted LMMs to test the differences in mean 25(OH)D concentrations across categorical variables. To investigate the potential effects of gestational weight gain on total 25(OH)D concentrations, we calculated the absolute difference between maternal weight measured at visit 3 (median 26 wk) and prepregnancy, excluding those who lost weight between these two time points (n=16). We regressed repeated measures of total 25(OH)D on gestational weight gain using LMMs with subject-specific random intercepts, adjusting for gestational age at time of sample collection. We tabulated weighted selected percentiles of all urinary analytes and tested the differences in mean levels between the two study visits of sample collection (visits 1 and 3) using paired t-tests of ln-transformed concentrations. To evaluate the pairwise correlations among urinary phthalate metabolites and BPA, we calculated the Spearman correlation coefficients (rs) of specific-gravity standardized concentrations by study visit of sample collection.

In repeated measures analyses, we explored the associations between urinary biomarkers and plasma total 25(OH)D using LMMs that included subject-specific random intercepts, with 25(OH)D regressed on one analyte per model. We chose covariates based on biological and statistical considerations. We included maternal age, race/ethnicity (women who identified as white, black, or other regardless of Hispanic origin), and BMI a priori. Additional covariates—such as health insurance provider, educational attainment, season at time of sample collection, multivitamin supplement use in pregnancy, parity, fetal sex, and smoking and alcohol use in pregnancy—were added using a forward stepwise selection procedure and were retained in the final models if their inclusion resulted in ≥10% change in the main effect estimates.

Crude models included fixed effects terms for gestational age at time of sample collection (continuous) and urinary SG (continuous). Full models were additionally adjusted for maternal age (continuous), BMI at time of enrollment (continuous), race/ethnicity (white, black, other/mixed race), health-insurance provider (private, public), season at time of sample collection (winter, spring, summer, fall), and multivitamin supplement use in pregnancy (yes, no). Participants missing data on key covariates were not included in the final multivariable regression analyses. Final regression models included n=459 women (n=837 samples). All final LMMs were repeated with an interaction term to test whether the effects of phthalates and/or BPA on circulating 25(OH)D levels varied by study visit of sample collection.

Because skin pigmentation is associated with circulating 25(OH)D concentrations (Hall et al. 2010), we performed a sensitivity analysis by stratifying LMMs by race/ethnicity to investigate whether the associations between urinary exposure biomarkers and total 25(OH)D concentrations varied by race/ethnicity. We also assessed whether these effects were modified by race/ethnicity by adding an interaction term in the LMMs for the overall study population. To improve the interpretability of results yielded from models with ln-transformed predictor variables, we presented all regression results as the percent difference (%Δ) in 25(OH)D associated with an IQR (population-level) increase in urinary biomarker concentrations.

In addition to exploring associations with continuous measures of 25(OH)D, we assessed the relationships between urinary biomarkers and the odds of vitamin D deficiency, defined as total 25(OH)D concentrations ≤20 ng/mL (Holick et al. 2011). In this cross-sectional analysis, we stratified logistic regression models by time of sample collection in pregnancy and adjusted all models for the same covariates as those included in repeated measures analysis.

To explore potential nonlinear associations, we fitted generalized additive mixed effects models (GAMM) using the R mgcv package. For each model, we regressed repeated measures of total 25(OH)D on a penalized spline of urinary DEHP metabolites and BPA, with one urinary biomarker included per model. These multivariable GAMMs were adjusted for the same covariates as those included in LMMs, and included a random intercept for each subject. All associations were considered statistically significant at the 5% level.

Results

The population demographic characteristics of the nested case–control study population have been described in detail previously (Ferguson et al. 2014b). Briefly, the present study participants were predominately white and highly educated, and half of the women had a normal BMI (<25 kg/m2). The distributions of total 25(OH)D by population demographic characteristics are presented in Table 1. Mean 25(OH)D concentrations were significantly higher in all older age groups in comparison with women 18 to 24 y old and in participants who reported multivitamin supplement use during pregnancy in comparison with those who reported no supplement use. Women who identified as black or other race/ethnicity had significantly lower concentrations of 25(OH)D in comparison with concentrations in white women. Significantly lower concentrations were also reported in women who had public health insurance in comparison with private, in those who were overweight (BMI: 25–30 kg/m2) and obese (BMI: >30 kg/m2) in comparison with women who had a normal BMI, and in all lower educational levels in comparison with college graduates. Absolute weight gain (median=20.0 lbs) between measurements collected prepregnancy and at visit 3 (median 26 wk of gestation) was not associated with total 25(OH)D concentrations {β [the difference in total 25(OH)D concentration with a 1-lb increase in weight gain]=0.002; 95% CI: −0.08, 0.09} in our study population.

Table 1. Plasma 25(OH)D levels (weighted mean ± SD) by population demographic characteristics (n=477 pregnant women).
Population characteristics n (%)a Total 25(OH)D (ng/mL) p-valued
Age (years)
 18–24 52 (12) 20.2 (14.9) Ref
 25–29 95 (20) 23.7 (15.0) 0.01
 30–34 188 (39) 25.2 (13.7) <0.001
 ≥35 142 (29) 26.8 (13.7) <0.001
Race/ethnicity
 White 280 (59) 27.6 (12.6) Ref
 Black 76 (16) 19.0 (15.3) <0.001
 Other 121 (25) 21.9 (13.9) <0.001
Education levelc
 College graduate 186 (41) 26.8 (12.9) Ref
 Junior college or some college 139 (30) 25.6 (13.7) 0.03
 Technical school 76 (16) 23.0 (16.0) <0.001
 High school 66 (13) 20.0 (14.9) <0.001
Health insurance providerc
 Private (ref) 381 (81) 25.9 (13.7) Ref
 Public 84 (19) 20.0 (15.3) <0.001
BMI at initial visitc
 <25 kg/m2 249 (53) 26.8 (14.0) Ref
 25–30 kg/m2 125 (27) 24.0 (14.0) <0.001
 >30 kg/m2 100 (20) 20.5 (13.7) <0.001
Fetal sex
 Male 212 (45) 24.9 (15.5) Ref
 Female 265 (55) 24.7 (13.6) 0.61
Parity
 No previous pregnancies 214 (45) 25.2 (13.9) Ref
 One previous pregnancy 155 (34) 25.4 (15.2) 0.84
 More than one previous pregnancy 108 (21) 23.1 (14.0) 0.12
Tobacco usec
 Smoked in pregnancy 31 (6) 22.1 (15.3) Ref
 No smoking in pregnancy 440 (94) 25.0 (14.3) 0.20
Alcohol usec
 Alcohol use in pregnancy 19 (5) 25.9 (16.1) Ref
 No alcohol use in pregnancy 448 (95) 24.7 (14.3) 0.60
Multivitamin supplement usec
 Supplement use in pregnancy 324 (70) 25.9 (13.6) Ref
 No supplement use in pregnancy 147 (30) 22.2 (15.4) <0.001
Season of sample collection
 Winter (ref) 224 (27)b 22.6 (14.2) Ref
 Spring 231 (28) 24.5 (13.9) <0.001
 Summer 185 (22) 27.8 (14.6) <0.001
 Fall 197 (24) 25.1 (14.1) <0.001

Note: BMI, body mass index; SD, standard deviation; ref, reference category.

aProportions weighted by preterm birth case-control sampling probabilities to represent the general sampling population.

bSample size and weighted proportions refer to number of samples (not participants).

cMissing observations: n=10 for education level; n=12 for insurance provider; n=3 for BMI at initial visit; n=6 for tobacco use; n=10 for alcohol use; n=6 for multivitamin supplement use.

dp-Value for the difference in mean plasma total 25(OH)D concentrations in the category compared to reference (first category listed) using unadjusted linear mixed models with a random intercept for each subject.

All urinary biomarkers were highly detected in the study population, with urinary phthalate metabolites detected in at least 96% of the samples and BPA detected in 82% of the samples (Table 2). Urinary phthalate metabolites from the same parent compound were strongly correlated at both visits (rs=0.70−0.98 for DEHP metabolites) and were weaker among other metabolites (see Tables S1 and S2). Spearmen correlations were weak to moderate between BPA and phthalate metabolites (rs≤0.28). Concentrations of urinary MCPP as well as DEHP metabolites, including ΣDEHP, were significantly lower in samples collected at visit 3 (median 26 wk of gestation) in comparison with samples collected at visit 1 (median 10 wk of gestation) (Table 2). Urinary BPA did not significantly differ by study visit of sample collection. Total 25(OH)D concentrations were significantly greater in samples collected at 26 wk of gestation in comparison with those collected at 10 wk (median=25.6 ng/mL vs. 23.8 ng/mL, respectively) (Table 2).

Table 2. Weighted median [interquartile range (IQR; 25th–75th percentiles)] of urinary and plasma biomarkers by study visit of sample collection in pregnancy.
Biomarker LOD % Detectc Visit 1 (median 10 wk) Visit 3 (median 26 wk) p-Valued
# Samplesb Median (IQR) # Samplesb Median (IQR)
Urinary Exposure Biomarkersa
 BPA (μg/L) 0.4 82.0 476 1.28 (0.75, 2.08) 409 1.28 (0.84, 2.08) 0.47
 MEHP (μg/L) 1.0 96.6 474 10.1 (5.17, 24.7) 409 8.10 (4.65, 16.7) <0.01
 MEHHP (μg/L) 0.1 99.1 474 33.6 (17.4, 80.2) 409 23.9 (12.3, 50.0) <0.001
 MEOHP (μg/L) 0.1 99.2 474 16.9 (8.60, 40.3) 409 14.0 (7.23, 28.7) <0.01
 MECPP (μg/L) 0.2 99.3 474 40.6 (18.9, 107) 409 30.6 (15.0, 72.8) <0.001
 ΣDEHP (μmol/L) 474 0.37 (0.18, 0.81) 409 0.28 (0.14, 0.58) <0.001
 MBzP (μg/L) 0.2 99.4 474 6.22 (3.36, 13.4) 409 5.87 (3.34, 11.8) 0.83
 MBP (μg/L) 0.5 99.3 474 16.1 (10.8, 26.7) 409 16.1 (10.4, 25.5) 0.37
 MiBP (μg/L) 0.1 99.2 474 7.14 (4.51, 11.1) 409 7.53 (4.61, 11.6) 0.84
 MEP (μg/L) 1.0 99.4 474 124 (49.0, 362) 409 123 (47.2, 363) 0.96
 MCPP (μg/L) 0.2 97.7 474 1.68 (1.06, 3.38) 409 1.57 (0.98, 3.13) 0.01
Vitamin D
 25(OH)D (ng/mL) 4.0 100 469 23.8 (17.7, 30.0) 429 25.6 (18.1, 31.5) <0.001

Note: Analyses were weighted by preterm birth case–control sampling probabilities. LOD, limit of detection.

aUrinary analyte concentrations corrected for specific gravity.

bNumber of plasma samples per analyte varied due to limitations in sample volume.

cPercent of analyte concentrations above the detection limits.

dp-Value for difference between urinary phthalate metabolite or 25(OH)D concentrations between study visits based on a paired t-test.

Results from repeated measures analysis using multivariable LMMs are reported in Table 3. Similar associations were observed between weighted and unweighted analyses (see Table S3). We detected inverse associations between DEHP metabolites and total 25(OH)D, with the strongest associations observed for MEHP (%Δ=−2.76; 95% CI: −5.50, −0.01), MEHHP (%Δ=−2.83; 95% CI: −5.60, −0.06), and MEOHP (%Δ=−2.64; 95% CI: −5.28, −0.01). We also found a significant inverse association between MCPP and 25(OH)D, where an IQR increase in urinary MCPP was associated with a 4.48% decrease in total 25(OH)D (95% CI: −7.37, −1.58). For BPA, we observed a nonsignificant inverse association (%Δ=−2.16; 95% CI: −5.78, 1.45). Our interaction analysis using multivariable LMMs revealed no statistically significant interactions between any of the urinary biomarkers measured and study visit of sample collection (p-value for interaction terms=for BPA, 0.17; and for phthalates, ranged from 0.36 for MiBP to 0.98 for MEHP) (data not shown).

Table 3. Repeated measures analysis: Percent difference in plasma 25(OH)D associated with an interquartile range (IQR) increase in urinary exposure biomarker concentrations.
Urinary biomarker IQR %Δ (95% CI) p-Value
BPA (μg/L) 1.94 −2.16 (−5.78, 1.45) 0.24
MEHP (μg/L) 17.6 −2.76 (−5.50, −0.01) 0.049
MEHHP (μg/L) 60.2 −2.83 (−5.60, −0.06) 0.046
MEOHP (μg/L) 30.0 −2.64 (−5.28, −0.01) 0.049
MECPP (μg/L) 84.1 −2.25 (−5.31, 0.80) 0.15
ΣDEHP (μmol/L) 0.67 −2.54 (−5.42, 0.34) 0.08
MBzP (μg/L) 13.6 0.88 (−3.29, 5.05) 0.68
MBP (μg/L) 25.4 −2.37 (−5.99, 1.26) 0.20
MiBP (μg/L) 11.0 −0.16 (−4.18, 3.86) 0.94
MEP (μg/L) 336 −0.12 (−3.61, 3.36) 0.94
MCPP (μg/L) 3.02 −4.48 (−7.37, −1.58) <0.01

Note: Analyses weighted by preterm birth case–control sampling probabilities. Linear mixed models include a random intercept for each subject and are adjusted for specific gravity (continuous), maternal age (continuous), BMI at enrollment (continuous), gestational age at time of sample collection (continuous), race (black, white, other/mixed race), insurance provider (private, public), season at time of sample collection (winter, spring, summer, fall), multivitamin supplement use in pregnancy (yes, no).

In our sensitivity analysis, associations from race/ethnicity-stratified models were largely inverse (Table 4). An IQR increase in BPA was inversely associated with total 25(OH)D in white women (%Δ=−4.79; 95% CI: −9.78, 0.20; based on data for 274 women and 506 samples) but did not appear to be associated with total 25(OH)D among women who identified as black (%Δ=−0.60; 95% CI: −8.72, 7.51; 71 women and 121 samples) or other race/ethnicity (%Δ=0.99; 95% CI: −5.62, 7.59; 114 women and 210 samples). There were no significant differences in the associations for BPA by race/ethnicity, based on p-values for interaction terms (Table 4). Among women who identified as other race/ethnicity, DEHP metabolites were inversely associated with total 25(OH)D, with a significant association observed for MEHP (%Δ=−8.16; 95% CI: −13.4, −2.88) in comparison with a null association for white women (%Δ=−0.18; 95% CI: −3.67, 3.31; p-interaction other race/ethnicity vs. white=<0.01). A weak inverse association for MEHP was observed among black women (%Δ=−1.39; 95% CI: −9.38, 6.60; p-interaction black vs. white=0.06). IQR increases in MCPP were associated with a ∼8 percent decrease in total 25(OH)D in women of other race/ethnicity (%Δ=−8.33; 95% CI: −15.4, −1.26) and in black women (%Δ=−8.11; 95% CI: −20.1, 3.86), in comparison with a weaker inverse association estimated for white women (%Δ=−3.47; 95% CI: −8.94, 2.01; p-interaction other race/ethnicity vs. white=0.08; p-interaction black vs. white=0.06).

Table 4. Race/ethnicity-stratified repeated measures analysis: Percent difference in total 25(OH)D associated with an interquartile range (IQR) increase in urinary exposure biomarker concentrations.
Urinary biomarker IQR White women n=274 women; 506 samples Black women n=71 women; 121 samples Other race/ethnicity n=114 women; 210 samples Black vs. white Other vs. white
%Δ (95% CI) p-Value %Δ (95% CI) p-Value %Δ (95% CI) p-Value p-Interactiona p-Interactiona
BPA (μg/L) 1.94 −4.79 (−9.78, 0.20) 0.06 −0.60 (−8.72, 7.51) 0.88 0.99 (−5.62, 7.59) 0.77 0.97 0.71
MEHP (μg/L) 17.6 −0.18 (−3.67, 3.31) 0.92 −1.39 (−9.38, 6.60) 0.73 −8.16 (−13.4, −2.88) <0.01 0.06 <0.01
MEHHP (μg/L) 60.2 −2.11 (−4.98, 0.75) 0.15 0.34 (−6.95, 7.64) 0.93 −4.33 (−9.16, 0.50) 0.08 0.27 0.10
MEOHP (μg/L) 30.0 −2.20 (−5.66, 1.25) 0.21 −0.58 (−9.12, 7.96) 0.90 −5.19 (−10.9, 0.51) 0.08 0.26 0.11
MECPP (μg/L) 84.1 −1.05 (−4.60, 2.50) 0.56 −4.28 (−13.0, 4.43) 0.34 −3.96 (−9.51, 1.59) 0.17 0.047 0.11
ΣDEHP (μmol/L) 0.67 −1.61 (−5.51, 2.28) 0.42 −2.52 (−11.9, 6.85) 0.60 −5.78 (−12.1, 0.49) 0.07 0.09 0.06
MBzP (μg/L) 13.6 4.02 (−0.97, 9.01) 0.12 −1.54 (−12.0,8.95) 0.78 −5.27 (−12.6, 2.08) 0.16 0.14 <0.01
MBP (μg/L) 25.4 −4.61 (−10.5, 1.23) 0.12 2.05 (−11.4, 15.5) 0.77 −3.41 (−11.2, 4.37) 0.39 0.97 0.48
MiBP (μg/L) 11.0 −3.61 (−8.98, 1.77) 0.19 2.08 (−6.97, 11.1) 0.65 2.79 (−4.93, 10.5) 0.48 0.88 0.96
MEP (μg/L) 336 −2.24 (−5.13, 0.64) 0.13 3.42 (−2.18, 9.02) 0.24 1.87 (−2.93, 6.67) 0.45 0.12 0.41
MCPP (μg/L) 3.02 −3.47 (−8.94, 2.01) 0.22 −8.11 (−20.1, 3.86) 0.19 −8.33 (−15.4, −1.26) 0.02 0.06 0.08

Note: Analyses weighted by preterm birth case-control sampling probabilities. Linear mixed models include a random intercept for each subject and are adjusted for specific gravity (continuous), maternal age (continuous), BMI at enrollment (continuous), gestational age at time of sample collection (continuous), insurance provider (private, public), season at time of sample collection (winter, spring, summer, fall), multivitamin supplement use in pregnancy (yes, no).

ap-value for the interaction between ln-transformed urinary biomarkers and race/ethnicity.

In our analysis of vitamin D deficiency by study visit of sample collection, we estimated that approximately 35% (n=160) of women were vitamin D deficient at visit 1 (median 10 wk of gestation) and 30% (n=117) at visit 3 (median 26 wk of gestation) (Table 5). We reported from our stratified logistic regression models that a unit increase in urinary DEHP metabolites was associated with a 12% to 19% increase in the odds of vitamin D deficiency at visit 1 [odds ratios (OR)=1.12; 95% CI: 1.00, 1.25 for MEHP to OR=1.19; 95% CI: 1.07, 1.34 for MEOHP]. The direction of these relationships remained at visit 3, although none of the estimates were statistically significant. We also found a significant positive association for MiBP at visit 1 (OR=1.25; 95% CI: 1.04, 1.52). For BPA, we observed a significant increase in the odds of vitamin D deficiency only at visit 3 (OR=1.22; 95% CI: 1.01, 1.47). Also at visit 3, we reported statistically significant elevated odds ratios for MBzP (OR=1.27; 95% CI: 1.08, 1.50) and MBP (OR=1.22; 95% CI: 1.03, 1.45).

Table 5. Adjusted odds ratios (95% CI) of vitamin D deficiency (≤20 ng/mL) associated with a unit increase in urinary biomarkers.
Urinary biomarkers Odds ratio (95% CI) p-Value
Visit 1: Median 10 weeks (n=160 vitamin D deficient women, 292 controls)
 BPA 1.04 (0.87, 1.25) 0.65
 MEHP 1.12 (1.00, 1.25) 0.06
 MEHHP 1.19 (1.06, 1.33) <0.01
 MEOHP 1.19 (1.07, 1.34) <0.01
 MECPP 1.16 (1.03, 1.30) 0.01
 ΣDEHP 1.19 (1.06, 1.35) <0.01
 MBZP 0.95 (0.83, 1.09) 0.49
 MBP 0.96 (0.81, 1.14) 0.62
 MIBP 1.25 (1.04, 1.52) 0.02
 MEP 0.94 (0.84, 1.04) 0.21
 MCPP 1.01 (0.89, 1.14) 0.88
Visit 3: Median 26 weeks (n=117 vitamin D deficient women, 268 controls)
 BPA 1.22 (1.01, 1.47) 0.04
 MEHP 1.12 (0.97, 1.28) 0.11
 MEHHP 1.14 (1.00, 1.30) 0.05
 MEOHP 1.13 (1.00, 1.29) 0.06
 MECPP 1.05 (0.92, 1.18) 0.48
 ΣDEHP 1.10 (0.96, 1.26) 0.18
 MBZP 1.27 (1.08, 1.50) <0.01
 MBP 1.22 (1.03, 1.45) 0.02
 MIBP 1.10 (0.91, 1.32) 0.33
 MEP 0.92 (0.83, 1.02) 0.10
 MCPP 1.05 (0.90, 1.21) 0.54

Note: Analyses weighted by preterm birth case-control sampling probabilities. Logistic regression models are adjusted for specific gravity (continuous), maternal age (continuous), BMI at enrollment (continuous), gestational age at time of sample collection (continuous), insurance provider (private, public), season at time of sample collection (winter, spring, summer, fall), multivitamin supplement use in pregnancy (yes, no).

Results from our analysis in which we evaluated nonlinear associations using penalized splines for urinary biomarkers in GAMM models are presented in Figure 1. All multivariable associations were found to be linear.

Five graphs plotting total 25(OH)D level (y-axis) across the urinary biomarkers BPA, MEHP, MEHHP, MEOHP, and MECPPs.
Figure 1. GAMM results for urinary DEHP metabolites and BPA (μg/L) and total 25(OH)D (ng/mL), adjusted for specific gravity, maternal age, BMI at enrollment, gestational age at time of sample collection, race, insurance provider, season at time of sample collection, multivitamin supplement use in pregnancy. Analyses weighted by preterm birth case–control sampling probabilities.

Discussion

In a secondary analysis of 477 pregnant women drawn from a nested case–control study of preterm birth, we found that repeated measures of certain urinary phthalate metabolites, specifically DEHP metabolites and MCPP, were inversely associated with circulating total 25(OH)D levels. A nonsignificant inverse association between urinary BPA and total 25(OH)D was observed in the overall population analysis. Associations varied by race/ethnicity and estimates for white women were more precise than those for black or for women identifying as other race/ethnicity due to differences in the numbers of women in each group. In agreement with findings from repeated measures analysis, we reported that DEHP metabolites and BPA were significantly associated with an approximate 20% increase in the odds of vitamin D deficiency at median 10 wk (ΣDEHP: OR=1.19; 95% CI:1.06, 1.35) and 26 wk (BPA: OR=1.22; 95% CI:1.01, 1.47), respectively.

We are aware of one previous analysis that has investigated the associations of exposure to phthalates and/or BPA on the vitamin D endocrine system in humans (Johns et al. 2016b). Our results for DEHP metabolites in the current analysis are consistent with those previously reported in a representative sample of U.S. adults 20 y and older (Johns et al. 2016b). In our earlier study utilizing data from participants in the National Health and Nutrition Examination Survey (NHANES) 2005–2010, we found significant inverse associations between urinary DEHP metabolites, including ΣDEHP and circulating total 25(OH)D in adult men and women (Johns et al. 2016b). Furthermore, our exposure–response analysis in that previous NHANES study revealed inverse trends between quintiles of individual DEHP metabolites and total 25(OH)D (Johns et al. 2016b). GAMM model estimates for the present study population also supported linear associations between increasing exposure to DEHP metabolites and decreasing total 25(OH)D concentrations.

For BPA, we previously reported a statistically significant inverse association with total 25(OH)D when analyses were restricted to women alone (Johns et al. 2016b). The direction of this relationship is similar to those presented in the repeated measures analysis among the overall population of pregnant women in our current study. In race/ethnicity-stratified models, the magnitude of association was larger among white (%Δ=−4.79; 95% CI: −9.78, 0.20) than among women identifying as black (%Δ=−0.60; 95% CI: −8.72, 7.51) or other race/ethnicity (%Δ=0.99; 95% CI: −5.62, 7.59). These results may reflect racial differences in behaviors, lifestyle factors, and/or metabolic processes that were not captured in the present analyses, thereby potentially leading to residual confounding.

In pregnancy, the fetus relies solely on maternal levels of 25(OH)D, which in turn is converted to 1,25(OH)2D by a series of hydroxylation steps initiated by cytochrome P450 enzymes found in the fetal-placental unit (Bikle 2014; Rosen et al. 2012). Currently, there is a lack of a consensus on the threshold used to define optimal (or sufficient) serum 25(OH)D concentrations in pregnancy (Thorne-Lyman and Fawzi 2012; Urrutia and Thorp 2012). Furthermore, the optimal threshold may vary by gestational age as the clinical outcomes associated with reduced 25(OH)D likely differ across pregnancy (Aghajafari et al. 2013; Lucas et al. 2013). Although the data are somewhat conflicting due to the heterogeneity across human health studies, results from meta-analyses suggest that vitamin D insufficiency in pregnancy may be associated with various adverse maternal and neonatal outcomes (e.g., gestational diabetes, preeclampsia, infection, and restricted fetal growth) (Aghajafari et al. 2013; Wei 2014). Some of these effects may be explained by the regulatory role of 1,25(OH)2D in trophoblast function (Nguyen et al. 2015) and in responding to inflammation and infection in the placenta (Liu et al. 2011). Although the magnitude of estimated differences in 25(OH)D and odds ratios for vitamin D deficiency were relatively small in our analyses, on a population-level these decrements may have significant public health implications, especially if there is a causal association between vitamin D deficiency and adverse maternal and neonatal outcomes. Future research is required to determine the public health impact of subclinical changes in circulating 25(OH)D across diverse populations of pregnant women.

Although mechanistic studies are lacking, it is plausible that phthalates and BPA may directly and/or indirectly influence the vitamin D endocrine system at multiple points along its axis. The vitamin D endocrine system is principally regulated by: a) 1,25(OH)2D, which down-regulates its own production; b) parathyroid hormone, which in response to low serum calcium levels stimulates hydroxylation enzymes in the kidney to convert 25(OH)D to its active metabolite; c) serum calcium and phosphate levels; and d) fibroblast growth factor 23 (Henry 2011; Norman 2008). Several animal studies have shown that BPA may disturb calcium metabolism by inducing or inhibiting the renal expression of a vitamin D–dependent calcium-binding protein, calbindin-D9k (CaBP-9k) (Kim et al. 2013; Otsuka et al. 2012) as well as decreasing serum calcium levels (Otsuka et al. 2012) in pregnant mice. However, similar effects have not been reported for phthalates (Hong et al. 2005). These agents may also indirectly influence the vitamin D endocrine system through their effects on the metabolic enzymes involved in the conversion of cutaneous vitamin D to its active metabolite. Animal and in vitro studies have demonstrated that phthalates and BPA can alter the expression of cytochrome P450 enzymes involved in steroid and/or thyroid hormone metabolism (Liu et al. 2015; Mathieu-Denoncourt et al. 2015; Quesnot et al. 2014; Sekaran and Jagadeesan 2015). Moreover, increased messenger RNA (mRNA) expression of CYP27B1, the enzyme involved in converting 25(OH)D to its active metabolite, was observed in mice treated with BPA (Otsuka et al. 2012). Similar studies assessing the effects of phthalates on enzymes involved in the metabolism of vitamin D have not been conducted to date, and future research is required to elucidate the potential actions of these chemicals on additional components of the vitamin D endocrine system (e.g., vitamin D–binding protein, metabolic enzymes, parathyroid hormone regulation, etc.).

It is also possible that certain lifestyle or physiological factors may partially mediate the associations observed in our study. For example, although studies investigating the relationships between phthalate and BPA exposure and physical activity are lacking, recent animal studies suggest that exposure to endocrine-disrupting chemicals such as phthalates and BPA may reduce or alter voluntary physical activity in mice (Johnson et al. 2015; Schmitt et al. 2016). Because physical activity has been positively associated with 25(OH)D concentrations in pregnant women (Moon et al. 2015; Woolcott et al. 2016), physical activity may be one possible mechanism through which maternal phthalate and/or BPA exposure might contribute to decreased concentrations of 25(OH)D. In the current study, we did not collect data on physical activity from our participants. Additional analyses are required to confirm the role (if any) that physical activity plays in these relationships. It is also possible that phthalate and/or BPA exposure may influence circulating 25(OH)D levels through maternal weight gain in pregnancy. Previous research suggests that exposure to phthalates and BPA may be associated with increased weight gain in women (Song et al. 2014) and that a greater gestational weight gain may lead to a decline in 25(OH)D concentrations in pregnancy (Moon et al. 2015). However, in our study population, absolute weight gain, defined as the difference in measurements collected at visit 3 (median 26 wk of gestation) and prepregnancy, was not associated with repeated measures of 25(OH)D. Further animal and human health studies are needed to characterize the potential vitamin D–disruptive properties of phthalates and BPA and to identify their specific mechanisms of action in pregnancy.

One of the main strengths of our study included repeated measures of both the exposures and outcome of interest, which allowed for the use of statistical modeling techniques to more precisely detect the subtle associations of exposure. Nevertheless, our study has several potential limitations. Although the reference assay for measuring 25(OH)D is liquid chromatography tandem mass spectrometry (LC-MS/MS), its time-consuming and laborious procedures limit the efficiency of this method in clinical settings, in comparison with automated immunoassays (Hollis 2010; Wagner et al. 2009). The DiaSorin LIAISON® immunoassay, the assay utilized in the present study, is a widely used method in both clinical and research settings (Burris et al. 2015; Hollis 2010) and has shown excellent agreement with LC-MS/MS methods [concordance correlation coefficient (CCC)=0.95] (Farrell et al. 2012). Additionally, although we adjusted our statistical analyses for key confounding variables (e.g., season of sample collection, multivitamin supplement use, and race/ethnicity), we lacked data on dietary food intake and the frequency of use of vitamin D supplements and sunscreen. Concerning the dietary food intake, the dominant exposure pathway for phthalates such as DEHP is ingestion of contaminated food (Wormuth et al. 2006), whereas the major source of vitamin D in humans is exposure to sunlight (Hall et al. 2010; Holick 2004, 2007). Dietary sources of vitamin D are limited but necessary to maintain adequate vitamin D concentrations when sunlight-induced vitamin D synthesis is impaired or in times of insufficient sunlight (Calvo et al. 2004). Few foods naturally contain vitamin D (e.g., oily fish), although in the U.S., some dairy (e.g., milk, yogurt, and cheese), cereal, and juices are fortified with vitamin D (Holick and Chen 2008). Among these foods, dairy consumption has been associated with increased concentrations of urinary DEHP metabolites (Serrano et al. 2014). Unfortunately, we do not have dietary intake data from the women included in our study. Therefore, it is possible that our results may be affected by unmeasured confounding, particularly if specific dietary sources contributed to phthalate and BPA exposure as well as total 25(OH)D concentrations in our study population. Additionally, although sunscreen use was associated with increased concentrations of urinary phthalate metabolites, particularly MBP, in children who participated in a study of 90 adult–child pairs in California from 2007 to 2009 (Philippat et al. 2015), results from a recent NHANES analysis utilizing data from 2009 to 2012 revealed that sunscreen use was not significantly associated with urinary phthalate metabolite concentrations in adults (Ferguson et al. 2016). We did not collect data on personal care product use for the present study; therefore, we cannot determine whether sunscreen use was associated with urinary phthalate metabolite concentrations in our study participants. Our study may also be limited by our exposure assessment methods. Although we analyzed up to two repeated measures of urinary phthalate metabolite and BPA concentrations per subject, a potential for nondifferential exposure misclassification exists. Additional repeated measurements of exposure may be required to sufficiently reduce bias in our analyses involving short-lived chemicals such as BPA and phthalates (Perrier et al. 2016). Finally, we performed multiple statistical comparisons, and there is the potential that some of the detected associations may have been due to chance.

Conclusions

In conclusion, biomarkers of environmental exposure to phthalates and BPA were associated with reduced circulating total 25(OH)D levels in our study population of pregnant women. Given previous research showing the adverse effects of reduced total 25(OH)D levels in pregnancy on the mother and fetus, future studies are required to confirm these findings in additional cohorts of pregnant women and to determine the potential biological mechanisms through which these agents might influence the vitamin D endocrine system.

Acknowledgments

Subject recruitment and sample collection was originally funded by Abbott Diagnostics. Funding was also provided by the NIH, NIEHS (grants R01ES018872, P42ES017198, P01ES022844, P30ES017885, and T32ES007062). Funding support for K.K.F. was provided by the Intramural Research Program of the NIH, NIEHS.

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Association of Long-Term Exposure to Transportation Noise and Traffic-Related Air Pollution with the Incidence of Diabetes: A Prospective Cohort Study

Author Affiliations open

1Centre for Psychiatry, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom

2School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada

3School of Community and Regional Planning, Faculty of Applied Science, University of British Columbia, Vancouver, British Columbia, Canada

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  • Background:
    Evidence for an association between transportation noise and cardiovascular disease has increased; however, few studies have examined metabolic outcomes such as diabetes or accounted for environmental coexposures such as air pollution, greenness, or walkability.
    Objectives:
    Because diabetes prevalence is increasing and may be on the causal pathway between noise and cardiovascular disease, we examined the influence of long-term residential transportation noise exposure and traffic-related air pollution on the incidence of diabetes using a population-based cohort in British Columbia, Canada.
    Methods:
    We examined the influence of transportation noise exposure over a 5-y period (1994–1998) on incident diabetes cases in a population-based prospective cohort study (n=380,738) of metropolitan Vancouver (BC) residents who were 45–85 y old, with 4-y of follow-up (1999–2002). Annual average transportation noise (Lden), air pollution [black carbon, particulate matter with aerodynamic diameter <2.5 μm (PM2.5), nitrogen oxides], greenness [Normalized Difference Vegetation Index (NDVI)], and neighborhood walkability at each participant’s residence were modeled. Incident diabetes cases were identified using administrative health records.
    Results:
    Transportation noise was associated with the incidence of diabetes [interquartile range (IQR) increase, 6.8 A-weighted decibels (dBA); OR=1.08 (95% CI: 1.05, 1.10)]. This association remained after adjustment for environmental coexposures including traffic-related air pollutants, greenness, and neighborhood walkability. After adjustment for coexposure to noise, traffic-related air pollutants were not associated with the incidence of diabetes, whereas greenness was protective.
    Conclusion:
    We found a positive association between residential transportation noise and diabetes, adding to the growing body of evidence that noise pollution exposure may be independently linked to metabolic health and should be considered when developing public health interventions. https://doi.org/10.1289/EHP1279
  • Received: 26 October 2016
    Revised: 07 May 2017
    Accepted: 09 May 2017
    Published: 31 August 2017

    Address correspondence to C. Clark, Ove Arup and Partners, Acoustics, 13 Fitzroy Street, London, W1T 4BQ, UK. Telephone: +44 207755 4702. Email: Charlotte.Clark@arup.com

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

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Introduction

Over the past decade, there has been increasing evidence that transportation noise exposure, such as road traffic noise, leads to poorer cardiovascular health. A recent review suggested that risk for adverse cardiovascular health outcomes, such as heart attacks and stroke, increased by 7–17% for a 10-dB increase in road traffic noise exposure (Basner et al. 2014). This increase is biologically plausible (Babisch 2014; Recio et al. 2016): Noise exposure is hypothesized to cause physiological stress reactions in individuals (Recio et al. 2016), which in turn lead to increases in cardiovascular disease risk factors such as blood pressure, blood fats, and blood glucose concentrations. These risk factors lead to increased risk of high blood pressure and arteriosclerosis (e.g., narrowing of arteries because of fat deposits) and are related to serious events such as heart attacks and strokes (Babisch 2014; Basner et al. 2014). Noise exposure at night may also interfere with sleep, which may also affect diabetes via effects on glucose regulation, appetite, and energy expenditure (Eriksson et al. 2014).

Given the widely accepted causal pathways for noise and cardiovascular health (Babisch 2014; Recio et al. 2016), we would expect to observe associations between noise exposure and metabolic risk factors. To date, relatively few studies have examined the influence of transportation noise exposure on metabolic risk factors for cardiovascular health such as body mass index (BMI), waist circumference, central obesity (Oftedal et al. 2015; Pyko et al. 2015), and blood fats (Sørensen et al. 2015), with studies suggesting small effects on these outcomes. Diabetes is another metabolic risk factor that places an enormous burden on the Canadian population (PHAC 2011) but has received only limited study. A cohort study of 57,053 Danish adults, 50–64 y old, who were exposed to annual average road traffic noise ranging from 48–70 dB found that a 10-dB higher level of road traffic noise during the 5 y preceding diagnosis was associated with an increased risk of incident diabetes identified from registry data {incidence rate ratio of 1.11 [95% confidence interval (CI) 1.05, 1.18]} after adjusting for age, sex, body mass index, waist circumference, education, air pollution (nitrogen oxides), and lifestyle characteristics (Sørensen et al. 2013).

One potential confounding factor in studies examining associations of environmental noise on diabetes is air pollution (Eze et al. 2015; Thiering and Heinrich 2015). There is robust evidence for a prospective association between air pollution and cardiovascular health, and emerging evidence suggests an association between air pollution and the incidence of type 2 diabetes (Balti et al. 2014; Thiering and Heinrich 2015; Wang et al. 2014) and diabetes-associated mortality (Li et al. 2014). The hypothesized pathological mechanism between air pollution and cardiovascular disease differs from that proposed for noise. Air pollution is thought to provoke inflammatory and oxidative stress responses, which promote a variety of pathological processes related to cardiovascular disease including thrombosis, hypercoagulability, atherosclerosis, endothelial dysfunction, mitochondrial dysfunction, and insulin resistance (Chin 2015; Thiering and Heinrich 2015). However, few studies of air pollution and diabetes have taken into account coexisting noise exposure or other potential environmental confounders such as neighborhood walkability (Creatore et al. 2016; Paquet et al. 2014; Sundquist et al. 2015) or greenness (Thiering and Heinrich 2015), which have also been found to have associations with diabetic risk factors, incidence of diabetes, and cardiovascular disease and mortality.

This study examined the influence of long-term residential exposure to transportation noise and traffic-related air pollution on the incidence of diabetes using a population-based cohort drawn from linked health administration databases in British Columbia (Canada). We have previously reported the joint influences of air pollution (Henderson et al. 2007) and noise (Gan et al. 2012b) on cardiovascular mortality (Gan et al. 2012a) in this cohort. In the present study, we also examined the impacts of exposure to neighborhood greenness and neighborhood walkability on the association of residential transportation noise and air pollution exposure with the incidence of diabetes.

Methods

Study Population

Cohort data were accessed through Population Data BC (www.popdata.bc.ca/data). British Columbia has a mandatory health insurance program that covers nearly all of the residents in the province (Chamberlayne et al. 1998). We used the Central Registry data, Physician Visit, and Hospital Discharge data sets of the BC Medical Services Plan (MSP) provided by the BC Ministry of Health (British Columbia Ministry of Health; British Columbia Ministry of Health) and vital statistics data provided by the British Columbia Vital Statistics Agency. The cohort was enumerated from the MSP central registry and comprised all metropolitan Vancouver adult residents 45–84 y old who were registered with the provincial health insurance plan and who had lived in the study region during the 5-y exposure period (January 1994–December 1998) and during a 4-y follow-up period (January 1999–December 2002). Persons missing data for more than a total of 15 mo or in more than 3 consecutive months during the exposure period were also excluded to reduce misclassification of exposures. We excluded individuals who had a diagnosis of diabetes before or during the 5-y exposure period (1994–1998) (n=55,965). The study was approved by the Behavioral Research Ethics Board of the University of British Columbia (certificate # H08-00185). Informed consent was not sought or required: Anonymized data were provided by Population Data BC, and no contact was made with individuals in the cohort.

Air Pollution and Noise Exposure Estimation

Individual-level residential exposures to transportation noise (predominantly road traffic noise but including aircraft and rail noise) and to traffic-related air pollutants were estimated using noise propagation and land-use regression models, respectively. A detailed methodology for noise exposure is described elsewhere (Gan et al. 2012b). Briefly, we used CadnaA, a model-based computer program developed by DataKustik (Greifenberg, Germany), with the following inputs. Traffic volumes were obtained from a 2003 transportation planning model, road widths were estimated as the distance between the center lines of the outermost lanes, and road type was based on the provincial Digital Road Atlas (Setton et al. 2005); each road type was automatically assigned a specific percentage of truck traffic. The model also took into account the influence of road speed limits, traffic lights at intersections, road gradients (changes in elevation along a given road), road surface (paved or loose surface), bridges (heights of the road segments above ground), buildings (height, footprint, and reflection/absorption characteristics), and topography. Aircraft noise was estimated from the Airport Authority aircraft noise exposure forecast contours for 2003 (Transport Canada 2005). Railway noise exposure assessment was based on railway operation data including length of trains, velocity, percentage of disc brakes, and number of each type of train by day, evening, and night.

Based on the data above, annual day–evening–night A-weighted equivalent continuous noise levels (Lden dbA) were calculated for a 10 m×10 m grid. The Lden metric integrates noise levels during the day (Lday, 0600 hours–1800 hours), the evening (1800 hours–2200 hours), and the night (Lnight, 2200 hours–0600 hours); it reflects increased sensitivity of residents to community noise during the evening and the night by adding a 5-dBA weight to evening noise levels and a 10-dBA weight to nighttime noise levels (WHO 2011).

Based on the estimated noise levels, we calculated an annual average noise level for each 6-character postal code area by geometrically averaging the noise levels of all 10 m×10 m grid cell values contained in a postal code area; this geometric mean was assigned to all subjects in the postal code. Noise levels were calculated from road traffic and aircraft separately and from all sources combined.

In the study region, postal code areas varied greatly in size depending on the population density: in urban areas, a postal code typically represents one high-rise building or one side of a city block; however, in rural areas, a postal code may represent a larger area. Because metropolitan Vancouver is a highly urbanized region, most postal codes represent small geographical areas: on average, a residential postal code included ∼35 individuals. In larger postal code areas, the use of the geometric mean noise level reduces bias casued by heterogeneous exposure areas because it gives heavier weighting to higher noise estimates, which are grid points closer to roads where residences are most likely situated.

The noise level (Lden dBA) was analyzed for interquartile increases in exposure (IQR=6.8 dBA). Noise level was also analyzed categorically, following the method described by Gan et al. (2012a). We compared cohort members in the 10th decile of exposure (≥70 dBA), the sixth through ninth deciles of exposure (62–69 dBA), and the second through fifth deciles of exposure (58–61 dBA) with a reference group comprising cohort members in the lowest (first) decile of exposure (≤57 dBA) (Gan et al. 2012a).

High-spatial-resolution land-use regression models were used to estimate residential exposures to air pollutants including nitrogen dioxide (NO2), nitric oxide (NO), particulate matter with aerodnamic diameter <2.5 μm (PM2.5), and black carbon in 2003. The models were built at a resolution of 10 m and then smoothed for a final resolution of 30 m. Land-use regression models can be used to assign household-level exposures in community health studies by combining information about land use (e.g., traffic indicators, population density), with air monitoring data of the urban airshed (Bertazzon et al. 2015). We have previously demonstrated the stability of the spatial component of these exposure estimates, supporting their application to the time period of interest (Wang et al. 2012). In this airshed, black carbon, based on the particle light absorption coefficient, was highly correlated with the concentration of elemental carbon measured by traditional thermal/optical reflectance (R2=0.7–0.8); 10−5/m Black carbon is approximately equivalent to 0.8 μg/m3 elemental carbon (Rich 2002). As in previous analyses of this cohort, these estimates were then temporally adjusted with regulatory air quality monitoring data to calculate monthly concentrations and average concentrations during the 5-y exposure period for each postal code area (Gan et al. 2012a; Henderson et al. 2007). IQR measures were calculated for each air pollutant. Both the noise and the air pollution estimates took residential changes of address within the exposure period into account, resulting in 5-y time-weighted exposure averages.

Greenness and Walkability

Residential greenness was measured using the satellite-derived Normalized Difference Vegetation Index (NDVI) of greenness (Hystad et al. 2014). The average greenness values were extracted for 100-m buffers around residential postal code centroids, and both yearly (1992–2002) and seasonal greenness values were calculated. Neighborhood walkability (2001) (Frank et al. 2010) is a composite index of built-environment characteristics around residential postal codes that may influence opportunities for physical activity (Frank et al. 2005; Hystad et al. 2014), including net residential density, retail floor space-to-land area ratio, land use mix, and street connectivity or intersection density, within a 1-km road network distance around each postal code centroid. High index levels indicate an environment that encourages walking, whereas low index levels represent environmental features that inhibit walking and promote driving and obesity (Frank et al. 2004).

Diabetes Case Definition

International Statistical Classification of Diseases and Related Health Problems, 9th Revision (ICD-9; WHO 1977) and International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10; WHO 2010) codes for diabetes were used to identify incident diabetes cases in the follow-up period (ICD-9 code 250, with ICD-10 coding back-translated to ICD-9 coding). We excluded subjects who had prior hospital or doctor visits for diabetes during the exposure period to identify new (incident) cases. The administrative records did not enable type 1 diabetes to be distinguished from type 2 diabetes.

Standardized Canadian definitions for identifying diabetes using administrative data sets were used in this study (Lix et al. 2008); these case definitions have been evaluated and were found to have good agreement with self-reporting of disease in the Canadian Community Health Survey (Lix et al. 2008). The diabetes case definition used was any one hospitalization for diabetes, or two physician or health care provider visits for diabetes within a 1-y period.

Covariates

Individual-level age and gender data were available from the MSP administrative health database. Neighborhood-level data from the 2001 Statistics Canada census were linked to the database to account for variance in socioeconomic status and ethnicity, both of which are potential confounders for associations of transportation noise exposure and traffic-related air pollution with diabetes. These data were available at the census dissemination area level (400–700 persons) and were assigned based on residential postal codes. Socioeconomic status was measured using neighborhood income quintiles. South Asians in Canada have a higher prevalence of diabetes than the general population (Rana et al. 2014), and residences of individuals of South Asian ethnicity may be spatially clustered. We therefore conducted sensitivity analyses with the inclusion of a census measure of neighborhoods with >10% of the population being of South Asian ethnicity. A similar analysis was conducted for Chinese ethnicity, given the high prevalence of this ethnicity in the study area.

Statistical Analyses

All analyses were performed using Stata (v.13; StataCorp LLC). Initial descriptive statistics reported the prevalence and range of exposures and outcomes. Correlations examined the associations between the noise exposure, air pollution, walkability, and greenness estimates. Initial single-exposure logistic regression analyses examined the crude univariate association for an IQR increase of each exposure (noise, NO2, NO, black carbon, PM2.5, greenness, and walkability) with diabetes and adjusted for age, gender, and area-level household income. Smoothing splines (gam function, R v.2.15.0, The R Project for Statistical Computing) were used to investigate the functional relationship between incident diabetes cases and Lden noise exposure. We examined unadjusted values and adjusted for age, gender, and area-level household income.

To assess whether the associations of noise exposure with diabetes were independent of spatially covarying air pollution, greenness, and walkability, incremental coexposure regression models were run. We first adjusted individually for each air pollutant measure, greenness, and walkability; then, we adjusted for each air pollutant that was significant in the first stage along with greenness and walkability. All models used IQR measures of the environmental exposures to enable comparison between observed associations for the different pollutants. To test the sensitivity of these final coexposure regression models for ethnicity, models were then rerun with additional adjustment for South Asian and Chinese ethnicity.

Results

The cohort comprised 380,738 individuals. Of these, 3.4% were identified as incident diabetes cases during the follow-up period (Table 1). The average transportation noise exposure (Lden) for the cohort was 63 dBA. For air pollution, the average exposures were as follows: NO2, 32.1 μg/m3; NO, 32.0 μg/m3; PM2.5, 4.1 μg/m3, and particle light absorbance, 10−5/m. The average subject age was 58 y, and 46% were male. One-fifth of the sample lived in neighborhoods where >10% of the population was of South Asian ethnicity, and nearly one-half of the sample lived in neighborhoods where >10% of the population was of Chinese ethnicity. All covariates were significantly associated with all of the exposures and outcomes (p<0.001).

Table 1. Descriptive statistics showing the exposures, outcomes, and covariates: Metropolitan Vancouver resident cohort, 45–85 y old (1994–1998), n=380,738.
Category n=380,738 Mean or % IQR Range
Exposures
 Transportation noise Lden (dBA) 63.4 6.8 <45–98.5
 Transportation noise Lden (dBA) lowest decile 36,685 9.6% ≤57
 Transportation noise Lden (dBA) 2nd–5th deciles 154,796 40.6% 58–61
 Transportation noise Lden (dBA) 6th–9th deciles 151,989 39.9% 62–69
 Transportation noise Lden (dBA) 10th decile 37,268 9.9% ≥70
NO2 (μg/m3) 32.1 8.4 14.4 – 57.8
 NO (μg/m3) 32.0 13.13 8.8–126.0
PM2.5 (μg/m3) 4.1 1.6 0 – 10.2
 Black carbon (10−5/m) 1.5 0.9 0 – 5.0
 Greenness (NDVI Index) 100 m 0.32 0.12 −0.08–0.6
 Neighborhood Walkability Index 0.31 4.3 −7.7–13.4
Outcomes
 Incident diabetes cases over 4 years 12,941 3.4%
Covariates
 Age (years) 58 17 45–83
 Male 175,219 46.0%
 Quintiles of area level household income from census
 Income: 0 55,626 14.6%
 Income: 1 65,567 17.2%
 Income: 2 73,602 19.3%
 Income: 3 84,426 22.2%
 Income: 4 101,517 26.7%
>10% South Asian population in neighborhood 74,911 19.6%
>10% Chinese population in neighborhood 183,044 48.1%

Note: dBA, A-weighted decibels; IQR, interquartile range; Lden, annual average noise exposure; NDVI, Normalized Difference Vegetation Index; NO, nitric oxide; NO2, nitrogen dioxide; PM2.5, particulate matter with aerodynamic diameter <2.5 μm.

Table 2 shows correlations between the environmental exposures in the cohort. Transportation noise exposure was most strongly correlated with black carbon (r=0.47) and NO (r=0.40) but was weakly correlated with NO2 (r=0.24) and PM2.5 (r=0.14). Greenness showed a strong negative correlation with the walkability index (r=−0.60), but both greenness and walkability showed only weak correlations with noise and with the air pollutants.

Table 2. Correlations between environmental exposures: Metropolitan Vancouver resident cohort, 45–85 y old (1994–1998), n=380,738.
Category Lden NO2 NO PM2.5 Black carbon Greenness (NDVI) Walkability index
Lden (dBA) 1.00
NO2 (μg/m3) 0.24 1.00
NO (μg/m3) 0.42 0.47 1.00
PM2.5 (μg/m3) 0.14 0.52 0.29 1.00
Black carbon (10−5/m) 0.47 0.25 0.52 0.11 1.00
Greenness (NDVI) −0.27 −0.42 −0.43 −0.36 −0.29 1.00
Walkability Index 0.16 0.38 0.38 0.28 0.14 −0.60 1.00

Notes: dBA, A-weighted decibels; Lden, annual average noise exposure; NDVI, Normalized Difference Vegetation Index; NO, nitric oxide; NO2, nitrogen dioxide.

Noise exposure was associated with incidence of diabetes after adjustment for age, gender, and area-level household income; there was an 8% increase in the incidence of diabetes with an IQR increase in noise exposure (Table 3). Because there was no a priori understanding of the shape of the relationship between exposure and outcome, we also examined noise exposure as a categorical variable (Gan et al. 2012a). Odds ratios for diabetes were 32% higher for those in the 10th decile of exposure [OR=1.32 (95% CI: 1.22, 1.43)] compared with those in the first decile. Compared with the first decile, odds ratios were 18% higher for those in the sixth–ninth deciles of noise exposure [OR=1.18 (95% CI: 1.11, 1.26)] and 9% higher for those in the second–fifth deciles of noise exposure [OR=1.09 (95% CI: 1.02, 1.06)].

Table 3. Adjusted associations of transportation noise exposures with incident diabetes cases per one interquartile range increase in exposure (single-exposure models).
Exposure Incident diabetes
Crude OR (95% CI) AOR (95% CI)
Transportation noise exposure
 Lden (dBA) 1.10 (1.08, 1.13) 1.08 (1.05, 1.10)
Traffic-related air pollution
NO2 (μg/m3 ) 1.05 (1.03, 1.07) 1.00 (0.98, 1.02)
 NO (μg/m3) 1.06 (1.05, 1.09) 1.04 (1.01, 1.05)
PM2.5 (μg/m3) 1.06 (1.05, 1.08) 1.03 (1.01, 1.05)
 Black carbon (10−5/m) 1.05 (1.03, 1.06) 1.03 (1.01, 1.04)
Greenness
 Greenness (NDVI) 100m 0.83 (0.81, 0.85) 0.90 (0.87, 0.92)
Walkability index
 Walkability 1.09 (1.06, 1.11) 1.01 (0.98, 1.04)

Note: Each row is a separate model. AOR adjusted for gender, age, area-level household income. AOR, adjusted odds ratio; CI, confidence interval; dBA, A-weighted decibels; Lden, annual average noise exposure; NDVI, Normalized Difference Vegetation Index; NO, nitric oxide; NO2, nitrogen dioxide; OR, odds ratio; PM2.5, particulate matter with aerodynamic diameter <2.5 μm.

Figures 1 and 2 show the crude and adjusted dose–response relationships between Lden and incident diabetes cases. The relationships were found to be linear for a wide range of the data with the exception of extreme exposure values. Departure from linearity with wider confidence limits was observed close to the maximum and minimum exposure estimates, where the number of events was substantially lower.

Line graph plotting smoothed response (y-axis) across noise exposure (x-axis).
Figure 1. Functional relationship between noise exposure (Lden) and incident diabetes cases: unadjusted. Line is the log odds of diabetes incidence (log base 10); gray area is the 95% confidence interval.
Line graph plotting smoothed response (y-axis) across noise exposure (x-axis).
Figure 2. Functional relationship between noise exposure (Lden) and incident diabetes cases: adjusted for age, gender, and area-level household income. Line is the log odds of diabetes incidence (log base 10); gray area is the 95% confidence interval.

After adjustment for gender, age, and area-level household income, PM2.5 and black carbon were significantly associated with diabetes (Table 3): An IQR increase was associated with a 3% increase in odds for the incidence of diabetes [OR=1.03 (95% CI: 1.01, 1.05); OR=1.03 (95% CI: 1.01, 1.05) for PM2.5 and black carbon, respectively]. Similarly, an IQR increase in NO exposure increased the risk of diabetes by 4% [OR=1.04 (95% CI: 1.01, 1.05)]. NO2 and walkability were not associated with diabetes after adjustment for gender, age, and area-level household income, whereas increasing residential greenness showed a protective association [OR=0.90 (95% CI: 0.87, 0.92).

The associations between transportation noise exposure and incidence of diabetes were independent of spatially covarying air pollution, greenness, and walkability. In the models adjusted for environmental coexposures (Table 4, Model 1) an interquartile dBA increase in transportation noise exposure remained associated with a 6% increase in the odds for incidence of diabetes [OR=1.06 (95% CI: 1.03, 1.09). This association was robust to further adjustment for South Asian ethnicity (Table 4, Model 2) and Chinese ethnicity (Table 4, Model 3).

Table 4. Associations of transportation noise exposure (IQR) with incident diabetes cases, further adjusted for environmental coexposures; AOR (95% CI).
Exposures Environmental coexposures added to the model containing noise exposure (Lden)
PM2.5 only NO only Black carbon only Greenness only Walkability only PM2.5, greenness, walkability NO, greenness, walkability Black carbon, greenness, walkability
Model 1: Environmental coexposures
 Lden (dBA) 1.06 (1.06, 1.07)* 1.07 (1.04, 1.10)* 1.07 (1.04, 1.10)* 1.05 (1.03, 1.06)* 1.07 (1.06, 1.08)* 1.06 (1.03, 1.08)* 1.06 (1.03, 1.09)* 1.06 (1.03, 1.09)*
 NO (μg/m3) 1.01 (1.00, 1.04) 1.00 (0.97, 1.03)
 PM2.5 (μg/m3) 1.03 (1.02, 1.05)* 1.01 (1.00, 1.03)
 Black carbon (10−5/m) 1.01 (0.99, 1.03) 1.00 (0.98, 1.02)
 Greenness (NDVI) 0.91 (0.89, 0.94)* 0.89 (0.87, 0.92)* 0.90 (0.87, 0.92)* 0.90 (0.87, 0.93)*
 Walkability 1.01 (1.00, 1.04) 0.95 (0.91, 0.99)* 0.95 (0.92, 0.98)* 0.96 (0.95, 1.00)*
Model 2: Model 1+further adjustment for South Asian ethnicity
 Lden (dBA) 1.06 (1.01, 1.07)* 1.06 (1.03, 1.09)* 1.06 (1.01, 1.07)* 1.05 (1.01-1.06)* 1.06 (1.01, 1.07)* 1.04 (1.00, 1.06)* 1.06 (1.03, 1.09)* 1.05 (1.02, 1.08)*
 NO (μg/m3) 1.01 (1.00, 1.04) 1.00 (0.99, 1.03)
 PM2.5 (μg/m3) 1.03 (1.01, 1.04)* 1.01 (1.00, 1.03)
 Black carbon 10−5/m) 1.01 (0.99, 1.03) 1.00 (0.99, 1.02)
 Greenness (NDVI) 0.91 (0.89-0.94)* 0.89 (0.86, 0.92)* 0.90 (0.86, 0.93)* 0.90 (0.87, 0.93)*
 Walkability 1.02 (0.99, 1.04) 0.97 (0.92, 0.99)* 0.97 (0.94, 1.00) 0.97 (0.95, 0.99)*
Model 3: Model 1+further adjustment for Chinese ethnicity
 Lden (dBA) 1.06 (1.04, 1.09)* 1.07 (1.04, 1.09)* 1.06 (1.03, 1.09)* 1.05 (1.02-1.06)* 1.05 (1.01, 1.06)* 1.05 (1.03-1.08)* 1.05 (1.03, 1.08)* 1.05 (1.02, 1.08)*
 NO (μg/m3) 1.01 (1.00, 1.03) 1.00 (0.97, 1.02)
 PM2.5 (μg/m3) 1.02 (1.00, 1.04)* 1.01 (1.00-1.03)
 Black carbon 10−5/m) 1.02 (1.00, 1.05) 1.00 (0.98, 1.04)
 Greenness (NDVI) 0.93 (0.91, 0.96)* 0.90 (0.87, 0.93)* 0.90 (0.86, 0.93)* 0.90 (0.87, 0.93)*
 Walkability 0.99 (0.91, 1.00) 0.96 (0.92, 0.99)* 0.94 (0.91, 0.97) 0.96 (0.92, 0.99)*

Note: All models are adjusted for age, gender, and area-level household income. Models are presented within columns and use interquartile range (IQR) increments for every environmental exposure metric. AOR, adjusted odds ratio; CI, confidence interval; dBA, A-weighted decibels; Lden, annual average noise exposure; NDVI, Normalized Difference Vegetation Index; NO, nitric oxide; NO2, nitrogen dioxide; PM2.5, particulate matter with aerodynamic diameter <2.5 μm.

*Statistically significant (p<0.05).

Odds ratios for diabetes were 32% higher for those in the 10th (highest) decile of noise exposure (95% CI: 22%, 43%) and 18% higher for those in the sixth–ninth decile of noise exposure (95% CI: 11%, 26%) compared with those in the first decile after adjustment for age, gender, and income. These estimates were reduced in models that included other environmental exposures: Odds ratios in the highest noise decile were between 1.16 and 1.20 (Table 5). Again, associations were robust to further adjustment for South Asian ethnicity and Chinese ethnicity.

Table 5. Associations of transportation noise exposure (percentiles) with incident diabetes cases, further adjusted for environmental coexposures; AOR (95% CI).
Exposures Environmental coexposures added to the model containing noise exposure (noise percentiles)
PM2.5, greenness, walkability Black carbon, greenness, walkability NO, greenness, walkability NO2, greenness, walkability
Model 1: Environmental coexposures
 Noise: 2nd-5th percentile 1.04 (0.98, 1.12) 1.05 (0.98, 1.12) 1.05 (0.98, 1.13) 1.05 (0.98, 1.13)
 Noise: 6th-9th percentile 1.10 (1.02, 1.17)* 1.10 (1.02, 1.16)* 1.11 (1.03, 1.19)* 1.11 (1.04, 1.19)*
 Noise: 10th percentile 1.16 (1.07, 1.26)* 1.20 (1.11, 1.30)* 1.17 (1.08, 1.28)* 1.17 (1.08, 1.27)*
Model 2: Model 1+further adjustment for South Asian ethnicity
 Noise: 2nd-5th percentile 1.03 (0.96, 1.09) 1.03 (0.96, 1.11) 1.03 (0.96, 1.11) 1.03 (0.97, 1.11)
 Noise: 6th-9th percentile 1.07 (1.00, 1.14)* 1.07 (1.00, 1.14)* 1.07 (1.00, 1.14)* 1.08 (1.01, 1.15)*
 Noise: 10th percentile 1.13 (1.04, 1.23)* 1.13 (1.03, 1.22)* 1.13 (1.03, 1.22)* 1.14 (1.05, 1.25)*
Model 3: Model 1+further adjustment for Chinese ethnicity
 Noise: 2nd-5th percentile 1.03 (0.97, 1.10) 1.03 (0.96, 1.11) 1.03 (0.97, 1.11) 1.04 (0.97, 1.11)
 Noise: 6th-9th percentile 1.07 (1.00, 1.14)* 1.07 (1.00, 1.14)* 1.07 (1.00, 1.15)* 1.08 (1.01, 1.16)*
 Noise: 10th percentile 1.13 (1.04, 1.23)* 1.13 (1.03, 1.22)* 1.13 (1.04, 1.23)* 1.15 (1.06, 1.25)*

Note: All models are adjusted for age, gender, and area-level household income. Reference group is “Noise: 1st percentile.” AOR, adjusted odds ratio; CI, confidence interval; NO, nitric oxide; NO2, nitrogen dioxide.

*Statistically significant (p<0.05).

The associations of the traffic-related air pollutants with diabetes were not robust to adjustment for environmental coexposures. The association of PM2.5 with diabetes was independent of spatially covarying noise exposure but became borderline significant after further adjustment for greenness and walkability (Table 4). NO and black carbon were not associated with diabetes after adjustment for covarying noise exposure.

The protective associations of residential greenness and walkability with diabetes remained in the models adjusting for environmental coexposures. An IQR increase in greenness remained associated with a 10% reduction in odds for diabetes, and an IQR increase in walkability remained associated with a 5% reduction in odds for diabetes.

Discussion

This large-scale population-based cohort study found robust associations between residential transportation noise exposure and the incidence of diabetes. These associations were not explained by spatially varying environmental coexposures (a range of traffic-related air pollutants, greenness, walkability) or by ethnicity. Traffic-related air pollutants were not independently associated with the incidence of diabetes after adjustment for environmental coexposures. Neighborhood greenness and walkability showed protective associations with the incidence of diabetes in fully adjusted models.

Transportation Traffic Noise and the Incidence of Diabetes

This study found that an IQR increase in transportation noise exposure in the Vancouver cohort (∼7 dBA Lden increase) in the preceding 5 y was associated with a 6% increase in odds for incidence of diabetes over a 4-y period. This finding is similar to that of a previous Danish cohort study of 57,053 50–64-y-olds exposed to road traffic noise ranging from 48–70 dB Lden that found that a 10-dBA higher level of road traffic noise during the 5 y preceding diagnosis was associated with an increased risk for the incidence of diabetes [RR=1.11 (95% CI: 1.05, 1.18)] after adjusting for age, gender, BMI, waist circumference, education, air pollution (nitrogen oxides), and lifestyle characteristics (Sørensen et al. 2013). Both studies had large sample sizes, and both found a prospective association of transportation noise and incident diabetes cases after taking nitrogen oxides into account. Our study extends the knowledge base by including adjustments for other environmental exposures including walkability, greenness, and a range of traffic-related air pollutants.

Our finding of a relationship between transportation noise and the incidence of diabetes is compatible with Babisch’s hypothesized pathway (Babisch 2014) in which chronic transportation noise exposure can lead to physiological stress reactions in the endocrine system (e.g., the hypothalamic–pituitary–adrenal axis) and the sympathetic nervous system that result in hormonal changes (to, e.g., cortisol, norepinephrine, epinephrine), which in turn lead to increases in cardiovascular disease risk factors including blood pressure, blood fats, and blood glucose concentrations. Alternatively, residential transportation noise can cause sleep loss, which has metabolic consequences in terms of glucose regulation, appetite, and energy expenditure (Eriksson et al. 2014). Our findings support the hypothesized role of sleep loss and its metabolic impact as a potential explanation for the association between transportation noise and the incidence of diabetes because noise exposure was estimated over a 24-h period. However, we were unable to examine sleep loss as a moderator of our associations. Studies exploring physiological stress and sleep loss as explanations for associations of noise with diabetic (Eriksson et al. 2014) and cardiovascular outcomes remain a research priority.

Traffic-Related Air Pollutants and the Incidence of Diabetes

This study found a modest association of PM2.5 with the incidence of diabetes that was attenuated by covarying noise exposure. NO and black carbon were not associated with the incidence of diabetes after adjustment for environmental coexposures. NO2 was not associated with the incidence of diabetes. Recent meta-analyses concluded that there was emerging evidence for an association between air pollution and the incidence of type 2 diabetes (Balti et al. 2014; Thiering and Heinrich 2015; Wang et al. 2014), but these studies did not take coexisting noise exposure or other environmental confounders into account. Our findings contrast with those of previous studies that suggested robust associations between air pollutants and diabetes (Balti et al. 2014; Chen et al. 2013; Krämer et al. 2010; Thiering and Heinrich 2015; Wang et al. 2014). We examined NO in addition to NO2 because NO has a different spatial distribution that is more indicative of primary traffic pollutant emissions (Henderson et al. 2007; Wang et al. 2012). Our findings suggest that NO2 and NO did not increase risk for the incidence of diabetes after taking environmental coexposures into account. Previous studies of NO2 and diabetes provided inconclusive evidence (Andersen et al. 2012; Balti et al. 2014; Krämer et al. 2010; Wang et al. 2014).

Greenness and the Incidence of Diabetes

Greenness showed a protective association with the incidence of diabetes, with an IQR increase in greenness being associated with a 10% decrease in odds for the incidence of diabetes in the fully adjusted models. Our findings are consistent with those of previous studies that suggest short-term associations between greenness and improved diabetic outcomes (Astell-Burt et al. 2014; Thiering et al. 2016). However, our study is the first to confirm the association using prospective data, and it is the first to show that the associations for greenness were independent of air pollution, in contrast with a smaller-scale study of adolescents that examined insulin resistance (Thiering et al. 2016).

Walkability and the Incidence of Diabetes

Neighborhood walkability showed a protective association with the incidence of diabetes, with an interquartile increase in walkability score being associated with a 5% decrease in odds for the incidence of diabetes in the fully adjusted models. A previous study found a protective association between neighborhood walkability and 4-y incidence of diabetes; however, this association was attenuated by adjustment for individual sociodemographic factors (Sundquist et al. 2015) and did not take environmental coexposures, including noise, air pollution, and greenness, into account (Sundquist et al. 2015). A recent Canadian time-series study found that the incidence of diabetes was lowest in neighborhoods with the highest walkability scores compared with less-walkable neighborhoods (Creatore et al. 2016), taking sociodemographic factors and distance to the nearest park into account. Our analyses suggest that associations of neighborhood walkability with the incidence of diabetes were independent of noise and air pollution.

Comparing the increased risk for the incidence of diabetes with an interquartile increase among the different environmental exposures in the fully adjusted models, the largest change in risk was observed for greenness, followed by transportation noise exposure and neighborhood walkability. Traffic-related air pollutants showed smaller influences on the incidence of diabetes than the other environmental exposures examined.

Limitations and Strengths

Individual-level environmental exposures were linked using postal codes for the residential addresses. No method can measure true exposure, and sources of error in estimated exposure include not modeling all salient features of the local environment and not taking individual factors, such as room orientation and time spent at home, into account (Gan et al. 2012a). Further limitations of the noise modeling include potential measurement error caused by the lack of data for some environmental aspects; there are no transport prediction guidelines for Canada, so we used noise prediction guidelines from Germany for road and railway noise. The use of administrative health databases meant that we were unable to take individual-level socioeconomic and diabetic risk factors (BMI, family history of diabetes, smoking history, diet, and local food environment) into account. Such factors may confound findings observed between noise and the incidence of diabetes. However, we were able to partially adjust for some of these factors by using neighborhood-level socioeconomic status (SES) measures, and the risk estimates found in the present study are similar to those found in a similar Danish study that did adjust for individual-level socioeconomic and diabetic risk factors (Sørensen et al. 2013). We have attempted to account for certain diabetic risk factors such as physical activity and obesity via adjustment for neighborhood-level walkability and greenness, but residual confounding remains a possibility. Census-based income measures used in this study do not fully capture variations in accumulated wealth. Wealthier people live in neighborhoods that are more conducive to being physically active and maintaining healthier diets. Affluent communities have higher-quality pedestrian environments and tend to be safer and to promote active living. This study was reliant on administrative records for diabetes diagnosis, which will have missed undiagnosed cases and residents who do not attend health care providers, although registration in the BC universal health care system is very high (nearly 100%). These administrative records do not distinguish between type 1 and type 2 diabetes; environmental exposures may be more important for type 2 diabetes given the importance of disease-conducive environmental factors for type 2 versus type 1 diabetes. However, when we compared a case definition of one hospitalization or two physician or health care provider visits within a 2-y period with our case definition based on a 1-y period, similar results were obtained (results not shown). In the Canadian population, the age-standardized incident rate for diabetes cases in 1999–2000 was 5.3 individuals per 1,000 population (PHAC 2011). In our sample of 380,738 individuals over 4 y, we would therefore have expected an incident case rate of 8,071 if there were no age restrictions on our sample. We actually observed 12,941 incident diabetes cases; this higher rate reflected the higher prevalence in incidence for South Asian and Chinese populations residing in the Vancouver area as well as for our older population, which was focused on the key age group for the incidence of type 2 diabetes. Therefore, our prevalence rate seems much as would be expected.

Further strengths of the study include the estimation of environmental exposures over a 5-y period (taking into account residential mobility), with follow-up for the incidence of diabetes over a 4-y period. We believe that the conclusions could be generalizable to other North American cities because similar correlations have been observed between these environmental exposures in other North American cities: for example, road traffic noise and air pollution (Allen et al. 2009); air pollution and walkability (James et al. 2015); and greenness and air pollution (Rao et al. 2014; Su et al. 2011).

Conclusion

Our study found an increasing risk of diabetes with increasing exposure to transportation noise, but not with increasing exposure to traffic-related air pollutants. The results highlight the importance of taking noise into account when planning interventions to reduce the health impact of transportation. Noise pollution was independently associated with the incidence of diabetes in adult residents of metropolitan Vancouver, British Columbia. Further studies of environmental coexposures and individual-level potential confounders are needed.

Acknowledgements

This work was funded by sabbatical leave supported by Queen Mary University of London to C.C. C.C. would also like to thank the School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia for supporting a visiting professorship during which this work was undertaken. The Border Air Quality Study was supported in part by Health Canada via an agreement with the British Columbia Centre for Disease Control. Additional support was provided by the Centre for Health and Environment Research at the University of British Columbia, funded by the Michael Smith Foundation for Health Research.

All inferences, opinions, and conclusions drawn in this research article are those of the authors, and do not reflect the opinions or policies of the Data Steward(s).

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Computer Simulation of Developmental Processes and Toxicities (SOT)

Rationale: Recent progress in systems toxicology and synthetic biology have paved the way to new thinking about in vitro/in silico modeling of developmental processes and toxicities, both for embryological and reproductive impacts. Novel in vitro platforms such as 3D organotypic culture models, engineered microscale tissues and complex microphysiological systems (MPS), together with computational models and computer simulation of tissue dynamics, lend themselves to a integrated testing strategies for predictive toxicology. As these emergent methodologies continue to evolve, they must be integrally tied to maternal/fetal physiology and toxicity of the developing individual across early lifestage transitions, from fertilization to birth, through puberty and beyond. Scope: This symposium will focus on how the novel technology platforms can help now and in the future, with in vitro/in silico modeling of complex biological systems for developmental and reproductive toxicity issues, and translating systems models into integrative testing strategies. The symposium is based on three main organizing principles: (1) that novel in vitro platforms with human cells configured in nascent tissue architectures with a native microphysiological environments yield mechanistic understanding of developmental and reproductive impacts of drug/chemical exposures; (2) that novel in silico platforms with high-throughput screening (HTS) data, biologically-inspired computational models of complex adaptive systems, and chemical structure information yields predictive understanding of developmental and reproductive impacts of drug/chemical exposures; and (3) that a combination of technologies is necessary for analytical (to understand) and theoretical (to predict) application for probing the relevant biological processes and toxicological mechanisms to inform safety assessments.

Adverse Outcome Pathway (AOP) framework for embryonic vascular disruption and developmental defects (SOT)

Vascular development commences with de novo assembly of a primary capillary plexus (vasculogenesis) followed by its expansion (angiogenesis) and maturation (angio-adaptation) into a hierarchical system of arteries and veins. These processes are tightly regulated by genetic signals and environmental factors linked to morphogenesis and microphysiology. Gestational exposure to some chemicals disrupts vascular development leading to adverse outcomes. To broadly assess consequences of gestational toxicant exposure on vascular development, an Adverse Outcome Pathway (AOP) framework was constructed that integrates data from ToxCast high-throughput screening (HTS) assays with pathway-level information from the literature and public databases. The AOP-based model resolved the ToxCast library (1065 compounds) into a matrix based on several dozen molecular functions critical for developmental angiogenesis. A sample of 38 ToxCast chemicals selected across the matrix tested model performance. Putative vascular disrupting chemical (pVDC) bioactivity was assessed by multiple laboratories utilizing diverse angiogenesis assays, including: transgenic zebrafish, complex human cell co-cultures, engineered microscale systems, and human-synthetic models. The ToxCast pVDC signature predicted vascular disruption in a manner that was chemical-specific and assay-dependent. An AOP for developmental vascular toxicity was constructed that focuses on inhibition of VEGF receptor (VEGFR2). This receptor tyrosine kinase is a master switch for angiogenic sprouting and expansion when activated by VEGFA during early development and flow-sensing angio-adaptation during later development; as such, the molecular initiating event (MIE) may be invoked by pre-receptor (VEGFA production) or post-receptor (kinase activity) mechanisms. Key downstream events include: altered endothelial function (exploratory behavior, migration, proliferation, apoptosis) leading to vascular insufficiency. This AOP is currently under internal review by OECD-EAGMST for inclusion in the AOP-Wiki [https://aopwiki.org/wiki/index.php/Aop:43]. In a regulatory context, this AOP brings new concepts for assessing adverse outcomes relevant to risk assessment and efficient use of resources for validation through predictive models linking developmental toxicity to vascular disruption. (This work does not reflect EPA policy)

In Silico Dynamics: computer simulation in a Virtual Embryo (SOT)

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrated by specific AOPs that integrate quantitative information for systems such as VEGF-mediated angiogenesis (angiodysplasia), androgen-mediated urethral closure (hypospadias) and TGFb-mediated tissue fusion (cleft palate). Computer models that virtually integrate from real (in vitro) or synthetic (in silico) data provide a platform to translate biomolecular lesions into higher-order emergent responses for predictive toxicology. (Disclaimer: this abstract does not reflect EPA policy).

Overview of NASA Ultracapacitor Technology

Abstract: NASA needed a lower mass, reliable, and safe medium for energy storage for ground-based and space applications. Existing industry electrochemical systems are limited in weight, charge rate, energy density, reliability, and safety. We chose a ceramic perovskite material for development, due to its high inherent dielectric properties, long history of use in the capacitor industry, and the safety of a solid state material.