Monthly Archives: June 2017

Rice versus Drinking Water: Estimating the Primary Source of Arsenic in the U.S. Diet

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  • Received: 21 April 2017
    Revised: 21 April 2017
    Accepted: 21 April 2017
    Published: 30 June 2017

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Related EHP Article

Estimating Inorganic Arsenic Exposure from U.S. Rice and Total Water Intakes

Madhavi Mantha, Edward Yeary, John Trent, Patricia A. Creed, Kevin Kubachka, Traci Hanley, Nohora Shockey, Douglas Heitkemper, Joseph Caruso, Jianping Xue, Glenn Rice, Larry Wymer, and John T. Creed

Arsenic compounds are often naturally present in water and food.1,2 A new study estimates Americans’ inorganic arsenic exposures from drinking water and rice—a food that may contain arsenic—and concludes that rice consumption may account for as much inorganic arsenic exposure as drinking water in some U.S. populations.1

At high levels, long-term exposures to inorganic forms of arsenic are strongly linked to cancer3 and have been associated to a lesser extent with diabetes, lung disease, and cardiovascular disease.4 Arsenic in drinking water is almost exclusively inorganic arsenic; consequently, national and international guidelines have established drinking water exposure limits. Drinking water remains a priority in terms of reducing exposure to arsenic, with organizations including the World Health Organization and the U.S. Environmental Protection Agency (EPA) setting a limit of 10 μg/L for drinking water.1,2,5

Arsenic concentrations in foods are highly variable, and regulatory limits have not yet been established.2,6,7 Because the structure of a food can hinder the accessibility of arsenic for uptake and absorption, a person’s internal dose is not necessarily equal to the total amount of arsenic contained in the food that is eaten.8 Rice, which accumulates more arsenic than other staple foods, has been estimated to contribute approximately 20% of dietary arsenic exposure on average,9 but the quantities of arsenic in rice can vary as a function of the specific variety as well as the growing conditions and environment where it is raised.8

Photo of a woman planting rice in a flooded paddy.
Rice has the potential to accumulate arsenic from soil and irrigation water, but the form of the arsenic plays an important role in its uptake. Arsenite is chemically similar to silicon, and arsenate is an analog for phosphate. Silica and phosphate transporters in rice plants can move arsenite and arsenate, respectively, into the plant and distribute arsenic to the edible grain. Arsenite and arsenate are both inorganic forms of arsenic, which are more toxic than organic forms. © Claudine Van Massenhove/Shutterstock.

In the new study, the researchers incorporated data on arsenic levels in drinking water and rice in the United States into the Stochastic Human Exposure and Dose Simulation (SHEDS) model,10 which was developed by the EPA to estimate people’s everyday chemical exposures. Data on the arsenic content of drinking water were drawn from the Second Six-Year Study, a survey of 49,473 U.S. public water utilities serving approximately 230 million people. Dietary data came from the What We Eat in America survey, which is the dietary component of the National Health and Nutrition Examination Survey (NHANES).

Additional data incorporated into the SHEDS model were based on 54 samples of various rice types (e.g., long-grain, brown, parboiled) collected from U.S. mills by the researchers. This diverse sampling reflected arsenic variance in the rice supply chain.

Cooked samples of rice underwent dilute nitric acid extraction to liberate the total arsenic content, as well as an in vitro assay that mimics the digestive process. Processed samples were analyzed for arsenic content and identification of specific organic and inorganic compounds.

The SHEDS model output allowed the researchers to estimate that inorganic arsenic exposures attributable to drinking water and rice consumption averaged 4.2 and 1.4 μg/day, respectively. They were also able to identify groups that might have higher-than-average arsenic exposures from rice, including the grouped subpopulation “Tribal, Asian, and Pacific” (2.8 μg/day).1

Despite an estimated higher average intake of arsenic from drinking water compared with rice, about two-thirds of the drinking water samples had concentrations below the detection limit. This suggests that most people will not have greater arsenic exposure from drinking water compared with rice, says Rosalind Schoof, principal at Ramboll Environ, an environmental and health consulting firm.

Schoof, who was not associated with the study, points to the use of extensive up-to-date data and thorough rice sampling and analysis as valuable strengths of this study. She also highlights the in vitro bioaccessibility surveys and the in-depth speciation of the arsenic compounds as strengths, saying, “This study will facilitate assessing arsenic exposures and more accurately estimating inorganic exposures.”

Limitations to the study were primarily associated with the source data. For example, the dietary data, although extensive, captured only what people ate on two days about a week apart, which prevented accurate assessment of exposures at the extreme ends of consumption.

“As you go out on the tails of the statistical distribution of rice consumption rates, this two-day survey starts to become unreliable,” says study coauthor Jack Creed, a research chemist at the EPA’s National Exposure Research Laboratory. For example, if a person ate a lot of rice on one of their survey days, their rice consumption would appear to be high, even if they usually did not eat much rice. This is especially worth noting, given that adverse health effects of arsenic result from long-term exposure.

Creed notes that more information is needed on what people are actually eating, especially long-term rice consumption in subpopulations such as very young children and specific ethnic groups. “That’s what it is going to take to get to estimate the exposures that you’d like to better evaluate,” he says. NHANES recently started collecting more detailed information about Asian subpopulations with very different diets (including Chinese Americans, Indian Americans, and a composite group made up of Filipino, Vietnamese, Korean, and Japanese Americans) so that they can be more accurately assessed.11


Julia R. Barrett, MS, ELS, a Madison, Wisconsin–based science writer and editor, is a member of the National Association of Science Writers and the Board of Editors in the Life Sciences.

References

1. Mantha M, Yeary E, Trent J, Creed PA, Kubachka K, Hanley T, et al. 2017. Estimating inorganic arsenic exposure from U.S. rice and total water intakes. Environ Health Perspect 125(5):057005, PMID: 28572075, 10.1289/EHP418.

2. Carlin DJ, Naujokas MF, Bradham KD, Cowden J, Heacock M, Henry HF, et al. 2016. Arsenic and environmental health: State of the science and future research opportunities. Environ Health Perspect 124(7):890–899, PMID: 26587579, 10.1289/ehp.1510209.

3. Straif K, Benbrahim-Tallaa L, Baan R, Grosse Y, Secretan B, El Ghissassi F, et al. 2009. A review of human carcinogens—Part C: Metals, arsenic, dust, and fibres. Lancet Oncol 10(5):453–454, PMID: 19418618, 10.1016/S1470-2045(09)70134-2.

4. Gilbert-Diamond D, Cottingham KL, Gruber JF, Punchon T, Sayarath V, Gandolfi AJ, et al. 2011. Rice consumption contributes to arsenic exposure in US women. Proc Natl Acad Sci USA 108(51):20656–20660, PMID: 22143778, 10.1073/pnas.1109127108.

5. Naujokas MF, Anderson B, Ahsan H, Aposhian HV, Graziano JH, Thompson C, et al. 2013. The broad scope of health effects from chronic arsenic exposure: Update on a worldwide public health problem. Environ Health Perspect 121(3):295–302, PMID: 23458756, 10.1289/ehp.1205875.

6. Kurzuis-Spencer M, Burgess JL, Harris RB, Hartz V, Roberge J, Huang S, et al. 2014. Contribution of diet to aggregate arsenic exposures—An analysis across populations. J Expo Sci Environ Epidemiol 24(2):156–162, PMID: 23860400, 10.1038/jes.2013.37.

7. Gundert-Remy U, Damm G, Foth H, Freyberger A, Gebel T, Golka K, et al. 2015. High exposure to inorganic arsenic by food: The need for risk reduction. Arch Toxicol 89(12):2219–2227, PMID: 26586021, 10.1007/s00204-015-1627-1.

8. Yager JW, Greene T, Schoof RA. 2015. Arsenic relative bioavailability from diet and airborne exposures: implications for risk assessment. Sci Total Environ 536:368–381, PMID: 26225742, 10.1016/j.scitotenv.2015.05.141.

9. Xue J, Zartarian V, Wang S-W, Liu SV, Georgopoulos P. 2010. Probabilistic modeling of dietary arsenic exposure and dose and evaluation with 2003–2004 NHANES data. Environ Health Perspect 118(3):345–350, PMID: 20194069, 10.1289/ehp.0901205.

10. U.S. EPA (U.S. Environmental Protection Agency). “Stochastic Human Exposure and Dose Simulation (SHEDS) to Estimate Human Exposure to Chemicals.” https://www.epa.gov/chemical-research/stochastic-human-exposure-and-dose-simulation-sheds-estimate-human-exposure. Updated 7 September 2016 [accessed 21 March 2017].

11. Konkel L. 2017. The “typical” Asian diet is anything but: Differences in dietary exposure to metals among subgroups of U.S. Asians. Environ Health Perspect 125(3):A58–A59, PMID: 28248183, 10.1289/ehp.125-A58.

Erratum: “Prenatal PBDE and PCB Exposures and Reading, Cognition, and Externalizing Behavior in Children”

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  • Received: 31 March 2017
    Revised: 31 March 2017
    Accepted: 06 April 2017
    Published: 30 June 2017

    Environ Health Perspect 125(4):746–752 (2017), https://doi.org/10.1289/EHP478

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

In the original article, Figure 2a incorrectly showed a mean of −0.34 for Full-Scale Intelligence Quotient (FSIQ) score in the fourth quartile of Sum4 polybrominated diphenyl ethers (PBDEs). The correct mean should have been −3.40. The corrected Figure 2 appears in this erratum.

Two plots indicating mean and 95 percent confidence interval scores (y-axis) across Sum sub 4 PBDEs quartile and Sum sub 4 PCBs quartile (x-axis).
Figure 2. Trend and association of child’s reading scores, FSIQ, and externalizing problems scores with prenatal Sum4PBDEs and Sum4PCBs concentrations quartiles. (a) The trend and association of child’s reading scores, FSIQ, and externalizing behavior problems scores with prenatal Sum4PBDEs; (b) The trend and association of child’s reading scores, FSIQ, and externalizing behavior problems scores with prenatal Sum4PCBs. The quartile cutoffs were <20.70, 20.70–35.64, 35.65–76.00, and ≥76.00 ng/g lipid for Sum4PBDEs, and <21.50, 21.50–31.29, 31.30–42.80, and ≥42.80 ng/g for Sum4PCBs, respectively. The score in the first quartile is the reference. Note: Adjusted for maternal age, education, race, IQ, household income, parity, married status, smoking (maternal serum cotinine), fish consumption, depression, child sex, and HOME score. FSIQ, Full-Scale Intelligence Quotient; * p<0.05, p<0.10.

In Table 3, the coefficient of FSIQ with maternal serum log10 BDE-47 was incorrectly typed as 3.7 (−8.8, 1.5). The correct coefficient should have been −3.7 (−8.8, 1.5).

These errors do not affect the analysis, study findings, or interpretation of the results. The authors regret these typographical errors.

Body size distributions signal a regime shift in a lake ecosystem

Communities of organisms, from mammals to microorganisms, have discontinuous distributions of body size. This pattern of size structuring is a conservative trait of community organization and is a product of processes that occur at multiple spatial and temporal scales. In this study, we assessed whether body size patterns serve as an indicator of a threshold between alternative regimes. Over the past 7000 years, the biological communities of Foy Lake (Montana,USA) have undergone a major regime shift owing to climate change. We used a palaeoecological record of diatom communities to estimate diatom sizes, and then analysed the discontinuous distribution of organism sizes over time. We used Bayesian classification and regression tree models to determine that all time intervals exhibited aggregations of sizes separated by gaps in the distribution and found a significant change in diatom body size distributions approximately 150 years before the identified ecosystem regime shift. We suggest that discontinuity analysis is a useful addition to the suite of tools for the detection of early warning signals of regime shifts.

Parental Occupational Exposure to Organic Solvents and Testicular Germ Cell Tumors in their Offspring: NORD-TEST Study

Author Affiliations open

1Section of Environment and Radiation, International Agency for Research on Cancer (IARC), Lyon, France

2Division of Cancer Epidemiology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

3Département Cancer et Environnement, Centre Léon Bérard, Lyon, France

4Université Claude Bernard–Lyon1, 43 Blvd. du 11 Novembre 1918, Villeurbanne, France

5Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland

6Faculty of Social Sciences, University of Tampere, Finland

7Cancer Registry of Norway, Majorstuen, Oslo, Norway

8National Institute of Occupational Health, Oslo, Norway

9Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

10Danish Cancer Society Research Center, Copenhagen, Denmark

11Finnish Institute of Occupational Health (FIOH), Helsinki, Finland

12Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

13International Research and Research Training Centre in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Copenhagen, Denmark

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  • Background:
    Testicular germ cell tumors (TGCT) were suggested to have a prenatal environmentally related origin. The potential endocrine disrupting properties of certain solvents may interfere with the male genital development in utero.
    Objectives:
    We aimed to assess the association between maternal and paternal occupational exposures to organic solvents during the prenatal period and TGCT risk in their offspring.
    Methods:
    This registry-based case control study included TGCT cases aged 14–49 y (n=8,112) diagnosed from 1978 to 2012 in Finland, Norway, and Sweden. Controls (n=26,264) were randomly selected from the central population registries and were individually matched to cases on year and country of birth. Occupational histories of parents prior to the child’s birth were extracted from the national censuses. Job codes were converted into solvent exposure using the Nordic job-Nordic Occupational Cancer Study Job-Exposure Matrix. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI).
    Results:
    Overall, no association was found between prenatal maternal exposure to solvents and TGCT risk. In subset analyses using only mothers for whom occupational information was available in the year of or in the year prior to the child’s birth, there was an association with maternal exposure to aromatic hydrocarbon solvents (ARHC) (OR=1.53; CI: 1.08, 2.17), driven by exposure to toluene (OR=1.67; CI: 1.02, 2.73). No association was seen for any paternal occupational exposure to solvents with the exception of exposure to perchloroethylene in Finland (OR=2.42; CI: 1.32, 4.41).
    Conclusions:
    This study suggests a modest increase in TGCT risk associated with maternal prenatal exposure to ARHC. https://doi.org/10.1289/EHP864
  • Received: 25 July 2016
    Revised: 16 December 2016
    Accepted: 22 December 2016
    Published: 30 June 2017

    Address correspondence to J. Schüz, Section of Environment and Radiation, International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372 Lyon CEDEX 08, France. Telephone: +33 (0)4 72 73 84 41. Email: Schuzj@iarc.fr

    Supplemental Material is available online (https://doi.org/10.1289/EHP864).

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

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  • PDF icon Supplemental Material PDF (471 KB)


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Background

Testicular germ cell tumors (TGCT) are the most common malignancy in young men aged 15 to 39 years old (Ferlay et al. 2013). The age-standardized incidence rate of testicular cancer using the world standard population has increased drastically in the past 40 years, recording an 8-fold increase in Finland (from 1.8 to 15.0 per 100,000 person-years), 3-fold increase in Sweden (from 4.6 to 15.4) and Norway (from 7.8 to 22.9), and a 1.5-fold increase in Denmark (from 12.2 to 18.2) between 1970 and 2013 (Engholm et al. 2016). This steep increase, the large geographical variation in incidence, and the distinct patterns of incidence rates in migrants (Schmiedel et al. 2010) support the role of environmental factors in the etiology of TGCT. In Europe, prediction of the future burden of testicular cancer up to 2025 suggests continuing incidence increase (Le Cornet et al. 2014). Although causes and risk factors of TGCT remain poorly understood, the uniformly increasing incidence burden from testicular cancer in Europe suggests increasing risk in men in most populations, rather than ongoing changes in the national demographic profiles.

While many epidemiological studies have investigated risk factors for TGCT, findings have been inconclusive. The early age at TGCT diagnosis, together with established associations with congenital malformations such as cryptorchidism (Cook et al. 2010; Kratz et al. 2010) and with birth characteristics such as birth order, parity, and low birth weight (Cook et al. 2009, 2010), suggest a prenatal origin of TGCT. Also, at a recent international consensus meeting, it was concluded that the common precursor cells of TGCT (both seminomas and nonseminomas) have fetal characteristics, including embryonic markers (Moch et al. 2016). However, the majority of published epidemiological studies have focused on adulthood exposures (Béranger et al. 2013).

According to the testicular dysgenesis syndrome (TDS) hypothesis, TGCT, cryptorchidism, hypospadias, and reduced sperm count originate from a common fetal disorder hampering the normal male testicular development (Skakkebaek et al. 2016). Exposure to endocrine-disrupting chemicals (EDCs) has been suggested as a plausible factor for initiating TDS through interference with gonadal development by mimicking hormones or interfering with hormone production (Sharpe 2006). One epidemiological study found the concentration of persistent organic pollutants such as polychlorinated biphenyl (PCB), hexachlorobenzene (HCB), and polybrominated diphenyl ethers (PBDEs) to be significantly higher for mothers of testicular cancer cases compared to mothers of controls (Hardell et al. 2003, 2006).

Widely used solvents such as toluene, trichloroethylene, and perchloroethylene have possible endocrine disrupting properties (Brouwers et al. 2009), and might also interact with the masculinization process in utero (Sharpe 2006). Furthermore, benzene and trichloroethylene were classified as carcinogenic to humans (Group 1) (IARC 2012, 2014), and perchloroethylene (also called tetrachloroethylene) was classified as probably carcinogenic to humans (Group 2A) by the International Agency for Research on Cancer (IARC 2014). Organic solvents are used for extracting, dissolving, or suspending materials such as fats, waxes, and resins that are not soluble in water. Organic solvents can be found in various commonly used products such as paints, adhesives, cosmetics, glues, and plastics (Verma and Rana 2009), resulting in widespread exposures. Solvent exposure is particularly high in specific occupations and industries, such as plastic product workers, chemical and food industry, well drillers, laundry workers, and rubber product workers (Brouwers et al. 2009; Kauppinen et al. 2009).

Little is known about the role of fetal exposures to organic solvents in TGCT development (Béranger et al. 2013). Only one study, based on a small number of cases, investigated parental occupations that were potentially exposed to hydrocarbons (aircraft mechanics, engine repairers, machinists, light truck drivers, gas and service station attendants, painters, and printers) and found no association with TGCT in the offspring (Kardaun et al. 1991).

Given the TDS hypothesis and the endocrine properties of some solvents such as perchloroethylene (European Commission DG ENV 2000), we assessed parental occupational exposure to solvents during the prenatal period and the risk of developing TGCT in the offspring in a large-scale population-based case–control study linking the registries of the Nordic countries, the NORD-TEST study (Le Cornet et al. 2015).

Material and Methods

Study Population

The NORD-TEST study, which has been described previously in detail (Le Cornet et al. 2015), is a registry-based case–control study nested within the populations of Denmark, Finland, Norway, and Sweden. All residents of the Nordic countries are registered with a unique personal identity code, allowing linkage between registries for tracking personal medical information and occupational status for research purposes. The occupational information is based on job codes derived from censuses in Finland, Norway, and Sweden and on industry codes derived from the Supplementary Pension Fund’s database in Denmark. As we expected that the harmonization of data could result in uncertainty in solvent exposure estimates for Denmark, we decided to exclude data from Denmark from the present pooled analysis. All testicular cancer cases diagnosed between ages 14 and 49 y old from 1988–2012 in Finland, 1978–2010 in Norway, and 1979–2011 in Sweden were extracted from the respective nationwide cancer registry, using the following International Classification of Diseases (ICD) codes: ICD-7: 178 (WHO 1955); ICD-8 and ICD-9: 186 (WHO 1968, 1978); and ICD-O-3: C62 (WHO 2000). Four controls were randomly selected from the central population registries and were individually matched to cases by year and country of birth. Cases and their matched controls were eligible when they were free of any previous neoplasm (except nonmelanoma skin cancer) and, for controls, were alive on the date of testicular cancer diagnosis of their matched case. The parental occupational codes (Finland, Norway, and Sweden) prior to the cases’ or the controls’ birth had to be known for at least one of the parents to be included in the analysis. The final sample included 8,112 cases and 26,264 controls: 3,769 cases with four matched controls, 2,816 cases with three matched controls, 1,213 cases with two matched controls, and 314 cases with one matched control.

Exposure Assessment

Job codes for both parents were retrieved from the last census conducted prior to the child’s birth and the first census conducted after the child’s birth. The Nordic Occupational Cancer Study Job-Exposure Matrix (NOCCA-JEM), constructed by a team of experts in 2009 (Kauppinen et al. 2009), was used to assign parental exposures. The NOCCA-JEM converts the occupational codes into country- and time-specific quantitative exposure estimates of solvents. The parental exposure estimates were calculated as the product of the proportion of exposed workers (P) within an occupation and the mean level of exposure to the agent among the exposed workers (L) (Kauppinen et al. 2014). The solvent exposure is expressed in parts per million (ppm) in the workroom air for the working day. Censuses were mandatory for the whole population in the Nordic countries and took place every 5 y in Sweden (1960–1990, except for the 1965 census) and in Finland (1970–1990), and every 10 y in Norway (1960–1990). The occupational exposure to solvents was estimated based on the occupational titles recorded in the census in the year of the child’s birth or the last census before the child’s birth.

Parental exposures available in NOCCA-JEM include the following six individual solvents: benzene, toluene, perchloroethylene, methylene chloride, trichloroethylene, and 1,1,1-trichloroethane. Additionally, three groups of solvents were created, which include the following: aromatic hydrocarbon solvents (ARHC, combining exposure to benzene and toluene), chlorinated hydrocarbon solvents (CHC, combining exposure to trichloroethanes, trichloroethylene, perchloroethylene, and methylene chloride), and “any solvent” exposure, combining exposure to the six individual solvents.

Additional data obtained are as follows: testicular cancer family history was retrieved for fathers and brothers of cases and controls from the cancer registries, and information on diagnosis and/or surgery of cryptorchidism, hypospadias, and inguinal hernia in cases and controls was extracted from the Medical Birth Registry and the Hospital Discharge Registry, as well as for Finland, from the registry of congenital malformations. Further details have been described previously (Le Cornet et al. 2015).

Statistical Methods

Pairwise correlations between binary solvents exposure variables were calculated using the phi coefficient. Bivariate analysis was used to assess associations of TGCT with the following factors: father’s history of testicular cancer; brother’s history of testicular cancer; personal history of inguinal hernia, hypospadias, and cryptorchidism; age of father at childbirth; and age of mother at childbirth. Conditional logistic regression was used to estimate the strength of the associations given by odds ratios (OR) and the corresponding 95% confidence intervals (CI).

Each parental occupational exposure was classified into two categories (exposed/unexposed) as well as, for the analyses of the individual solvents, into three categories (unexposed, low exposure, and high exposure). The cutoffs of exposure were specific to each solvents’ exposure, were based on their distribution among parents of controls, and were set at the 90th (paternal exposure) or 75th percentile (maternal exposure) to have enough subjects in the high exposure category (see Table S1). It should be noted that the estimation of exposure levels results in a semiquantitative variable rather than fully quantitative one, as illustrated with the example of methylene chloride exposure levels among mothers and 1,1,1-trichloroethane exposure levels among fathers in Supplementary Figure S1. This explains why in the categorization by percentile, the cutoff is not always at the exact 90th or 75th cutoff. It is also the reason why we decided in favor of categorical analyses rather than more complex modeling of dose–response trends. Models were built using the unexposed group of each solvent individually as the reference category. Age of father at birth, age of mother at birth, and family history of testicular cancer [father and/or brother(s)] were introduced as potential confounders in the models, but did not change the OR estimate with any solvent by 10% or greater.

Analyses were stratified, and the results were reported by TGCT subtype and by country. Country-specific analyses were not conducted for maternal exposures because the number of exposed cases was too small. The Wald test was used to assess the heterogeneity of effects across strata. An alternative analysis was performed where the reference group was redefined as those that were not exposed to any of the solvents evaluated. Furthermore, since there is no strong biological argument supporting a role of parental exposure before 1 y of pregnancy in the etiology of testicular cancer, a sensitivity analysis was performed to target the conception and pregnancy time, thereby reducing the potential misclassification due to job changes. Therefore, the population was restricted to both parents for whom the census was available the year of or the year prior to the child’s birth as well as to parents for whom the census before and after childbirth recorded the same occupation, assuming that the parents held this occupation also the year of and the year prior to the child’s birth.

Additionally, the Kappa coefficient was calculated to measure the degree of agreement between prenatal and adulthood occupational exposure to solvent in cases and controls.

All analyses were performed using SAS statistical package (version 9.3; SAS Institute, Inc.).

NORD-TEST has been approved by the relevant data protection and ethical committees in Finland, Norway, and Sweden, and by the IARC ethics committee.

Results

A total of 8,112 TGCT cases (seminoma and nonseminoma) and 26,264 controls were included in the study. Country-specific distributions of cases and controls have been described previously (Le Cornet et al. 2015). Table 1 shows the distributions of different characteristics for cases and controls. Overall, 94% of the cases were diagnosed between 1990 and 2012, and 81% were born between 1960 and 1980. The proportion of nonseminoma cases was 55% with the median age of 26.7 years, whereas that of seminoma was 45% with the median age of 32.2 years. About 87% of the mothers and 97% of the fathers had an occupational status known before the child’s birth, whereof 2.3% of the mothers and 13.1% of the fathers were estimated as having been exposed to solvents in their workplace. The prevalence of occupational exposure to solvents was relatively low in mothers, ranging from 0.6% for perchloroethylene to 1.1% for 1,1,1-trichloroethane. The prevalence was somewhat higher in fathers, ranging from 1.2% for perchloroethylene to 10.7% for 1,1,1-trichloroethane. Pairwise correlations between binary solvent exposures ranged from 0.00 to 0.73 for maternal exposures and from −0.01 to 0.89 for paternal exposures (Table S2). The lowest correlation was found between toluene and perchloroethylene, whereas the highest was found for trichloroethylene and 1,1,1-trichloroethane in maternal exposures and for toluene and benzene in paternal exposures.

Table 1. Demographic characteristics of the study population by case–control status and odds ratios (OR) for prenatal factors known or potentially related to testicular cancer risk.
Demographic characteristic Cases n (%) Controls n (%) OR (95% CI)
Total 8,112 26,264
Country
 Finland 1,034 (12.7) 4,030 (15.3)
 Norway 3,163 (39.0) 9,089 (34.6)
 Sweden 3,915 (48.3) 13,145 (50.0)
Birth year
 1960–1964 1,651 (20.4) 5,352 (20.4)
 1965–1969 1,489 (18.4) 4,490 (17.1)
 1970–1974 1,926 (23.7) 6,398 (24.4)
 1975–1979 1,518 (18.7) 4,848 (18.5)
 1980–1995 1,528 (18.8) 5,176 (19.7)
Diagnostic period
 1978–1989 469 (5.7)
 1990–1994 825 (10.2)
 1995–1999 1,376 (17.0)
 2000–2004 1,965 (24.2)
 2005–2009 2,517 (31.0)
 2010–2012 960 (11.8)
Histological subtype
 Seminoma 3,687 (45.4)
 Nonseminoma 4,425 (54.6)
Age of mother at childbirth
 14–20 440 (5.4) 1,500 (5.7) 1.00
 20–29 5,155 (63.6) 16,625 (63.3) 1.04 (0.93, 1.17)
 30–39 2,314 (28.5) 7,484 (28.5) 1.04 (0.92, 1.17)
 40+ 192 (2.4) 647 (2.5) 1.00 (0.82, 1.22)
 Missing 11 (0.1) 8 (0.0)
Age of father at childbirth
 14–20 85 (1.1) 250 (1.0) 1.00
 20–29 4,050 (49.9) 13,205 (50.3) 0.89 (0.69, 1.15)
 30–39 3,168 (39.1) 10,353 (39.4) 0.89 (0.69, 1.14)
 40+ 732 (9.0) 2,285 (8.7) 0.92 (0.71, 1.20)
 Missing 77 (1.0) 171 (0.7)
Father’s history of testicular cancer
 No 8,043 (99.2) 26,192 (99.7) 1.00
 Yes 69 (0.9) 72 (0.3) 2.95 (2.11, 4.12)
Brother’s history of testicular cancer
 No 8,055 (99.3) 26,230 (99.9) 1.00
 Yes 57 (0.7) 34 (0.1) 5.30 (3.45, 8.12)
Personal history of inguinal hernia
 No 6,991 (86.2) 22,820 (86.9) 1.00
 Yes 108 (1.3) 414 (1.6) 0.93 (0.75, 1.16)
 Missing 1,013 (12.5) 3,030 (11.5)
Personal history of hypospadias
 No 7,062 (87.1) 23,187 (88.3) 1.00
 Yes 37 (0.5) 47 (0.2) 2.52 (1.62, 3.90)
 Missing 1,013 (12.5) 3,030 (11.5)
Personal history of cryptorchidism
 No 7,008 (86.4) 23,115 (88.0) 1.00
 Yes 91 (1.1) 119 (0.5) 2.56 (1.94, 3.38)
 Missing 1,013 (12.5) 3,030 (11.5)

Note: CI, confidence interval; n, number; OR, odds ratio.

While personal history of inguinal hernia, age of father, and age of mother at childbirth were not associated with TGCT risk, cryptorchidism, hypospadias, and brother’s and father’s history of testicular cancer were associated with an increased TGCT risk (Table 1). When the covariates considered as potential confounders were introduced one by one in the models, the ORs corresponding to solvent exposures did not change by 10% or more. Adjusted ORs are therefore presented in the supplementary file (Table S3). Figure 1 shows the association between prenatal parental occupational exposures to solvents and the risk of TGCT in the offspring. Slightly elevated ORs were found among sons whose mothers were exposed to several individual solvents such as benzene, toluene, methylene chloride, and the grouped solvent ARHC, as illustrated in Figure 1, but none of the associations were statistically significant. When the analysis was repeated with a reference group of mothers occupationally not exposed to any solvents (Table 3), the associations remained not significant. Similarly, the analysis for low and high levels of ARHC exposure (Table 2) did not show an increased TGCT risk in the offspring. Only the low-exposure category to ARHC had a borderline significant association. However, the sensitivity analysis restricted to sons born in the year of or the year after the census, or sons of parents who held the same occupation at censuses before and after the child’s birth, showed a stronger statistically significant association between maternal prenatal exposure to ARHC and TGCT in offspring (OR=1.53; 95% CI: 1.08, 2.17, Table 4). Although ORs remained slightly elevated for several individual solvents, only toluene exposure was found significantly associated with TGCT risk (OR=1.67; 95% CI: 1.02, 2.73, Table 4) in the sensitivity analysis.

Forest plot indicates odds ratios and the corresponding 95% confidence intervals for the association between prenatal parental occupational exposures to solvents and the risk of TGCT in the offspring. The results are presented separately for mothers and fathers. Odds ratios for benzene, toluene, perchloroethylene, methylene chloride, trichloroethylene, and 1,1,1-trichloroethane and three groups of solvents (aromatic hydrocarbon solvents, chlorinated hydrocarbon solvents, and “any solvent”) are shown.
Figure 1. Forest plot of the risk to develop testicular germ cell tumor after paternal or maternal exposure to solvents prior to birth. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were derived based on logistic regression analysis conditional on year and country of child’s birth, the reference category (unexposed) was the parents that were not occupationally exposed to the solvent of interest. Aromatic hydrocarbon solvents (ARHC) include benzene and/or toluene. Chlorinated hydrocarbon solvents (CHC) include methylene chloride, perchloroethylene, trichloroethylene, and/or 1,1,1-trichloroethane. Any solvent category includes at least one of the following: benzene, toluene, methylene chloride, perchloroethylene, trichloroethylene, 1,1,1-trichloroethane.
Table 2. Parental occupational exposure to solvents before the child’s birth categorized into low/high exposure and the risk of testicular germ cell tumor in the offspring.
Maternal exposure Paternal exposure
Agents Level of exposurea Cases n (%) Controls n (%) OR (95% CI)b Cases n (%) Controls n (%) OR (95% CI)b
Aromatic hydrocarbon solvents (ARHC)
Benzene Unexposed 6,932 (98.8) 22,840 (99.0) 7,315 (93.1) 23,810 (93.4)
Low 55 (0.8) 155 (0.7) 1.22 (0.89, 1.68) 267 (3.4) 862 (3.4) 1.01 (0.88, 1.16)
High 31 (0.4) 86 (0.4) 1.10 (0.71, 1.71) 273 (3.5) 824 (3.2) 1.06 (0.92, 1.22)
Toluene Unexposed 6,963 (99.2) 22,921 (99.3) 7,274 (92.6) 23,644 (92.7)
Low 40 (0.6) 110 (0.5) 1.32 (0.90, 1.93) 505 (6.4) 1,615 (6.3) 1.02 (0.92, 1.13)
High 15 (0.2) 50 (0.2) 1.01 (0.55, 1.84) 76 (1.0) 237 (0.9) 1.01 (0.77, 1.31)
Chlorinated hydrocarbon solvents (CHC)
Methylene chloride Unexposed 6,964 (99.2) 22,939 (99.4) 7,322 (93.2) 23,848 (93.5)
Low 40 (0.6) 102 (0.4) 1.36 (0.94, 1.99) 423 (5.4) 1,311 (5.1) 1.06 (0.95, 1.19)
High 14 (0.2) 40 (0.2) 1.28 (0.69, 2.40) 110 (1.4) 337 (1.3) 1.01 (0.81, 1.26)
Perchloroethylene Unexposed 6,977 (99.4) 22,956 (99.5) 7,763 (98.8) 25,220 (98.9)
Low 28 (0.4) 92 (0.4) 1.03 (0.67, 1.58) 86 (1.1) 248 (1.0) 1.11 (0.87, 1.43)
High 13 (0.2) 33 (0.1) 1.28 (0.67, 2.47) 6 (0.1) 28 (0.1) 0.67 (0.28, 1.63)
Trichloroethylene Unexposed 6,958 (99.1) 22,866 (99.1) 7,396 (94.2) 24,073 (94.4)
Low 38 (0.5) 148 (0.6) 0.85 (0.59, 1.23) 213 (2.7) 639 (2.5) 1.12 (0.96, 1.32)
High 22 (0.3) 67 (0.3) 1.06 (0.65, 1.72) 246 (3.1) 784 (3.1) 1.05 (0.91, 1.22)
1,1,1,trichloroethane Unexposed 6,937 (98.8) 22,815 (98.8) 7,017 (89.3) 22,937 (90.0)
Low 45 (0.6) 162 (0.7) 0.95 (0.68, 1.33) 380 (4.8) 1,147 (4.5) 1.10 (0.98, 1.25)
High 36 (0.5) 104 (0.5) 1.14 (0.77, 1.67) 458 (5.8) 1,412 (5.5) 1.07 (0.95, 1.19)

Note: CI, confidence interval; n, number; OR, odds ratio.

aThe exposed group was divided into two groups, based on the 75th percentile of mothers who were exposed and on the 90th percentile of fathers who were exposed.

bORs and the corresponding 95% CIs were derived based on logistic regression analysis conditional on year and country of child’s birth. The reference category (unexposed) consists of the parents who were not occupationally exposed to the solvent of interest.

Table 3. Parental occupational exposure to different solvents before childbirth and TGCT in the offspring with unexposed to any of the solvents as reference category.
Maternal exposure Paternal exposure
Exposure Cases n (%) Controls n (%) OR (95% CI)a Cases n (%) Controls n (%) OR (95% CI)a
Aromatic hydrocarbon solvents (ARHC)
Any of ARHC solventsb
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 104 (1.5) 287 (1.2) 1.23 (0.97, 1.55) 612 (7.8) 1,951 (7.7) 1.03 (0.93, 1.13)
 Exposed only to others 89 (1.3) 275 (1.2) 1.09 (0.86, 1.40) 545 (6.9) 1,666 (6.5) 1.10 (1.00, 1.22)
Benzene
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 86 (1.2) 241 (1.0) 1.18 (0.91, 1.53) 540 (6.9) 1,686 (6.6) 1.04 (0.94, 1.15)
 Exposed only to others 107 (1.5) 321 (1.4) 1.15 (0.91, 1.44) 617 (7.9) 1,931 (7.6) 1.08 (0.98, 1.19)
Toluene
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 55 (0.8) 160 (0.7) 1.22 (0.89, 1.69) 581 (7.4) 1,852 (7.3) 1.02 (0.93, 1.13)
 Exposed only to others 138 (2.0) 402 (1.7) 1.14 (0.93, 1.39) 576 (7.3) 1,765 (6.9) 1.10 (1.00, 1.22)
Chlorinated hydrocarbon solvents (CHC)
Any of CHC solventsc
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 108 (1.5) 344 (1.5) 1.06 (0.85, 1.32) 967 (12.3) 2,972 (11.7) 1.07 (0.99, 1.16)
 Exposed only to others 85 (1.2) 218 (0.9) 1.33 (1.03, 1.73) 190 (2.4) 645 (2.5) 1.00 (0.85, 1.18)
Methylene chloride
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 54 (0.8) 142 (0.6) 1.34 (0.97, 1.86) 533 (6.8) 1,648 (6.5) 1.06 (0.95, 1.17)
 Exposed only to others 139 (2.0) 420 (1.8) 1.10 (0.90, 1.35) 624 (7.9) 1,969 (7.7) 1.07 (0.97, 1.17)
Perchloroethylene
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 41 (0.6) 125 (0.5) 1.10 (0.77, 1.58) 92 (1.2) 276 (1.1) 1.08 (0.84, 1.37)
 Exposed only to others 152 (2.2) 437 (1.9) 1.18 (0.97, 1.43) 1,065 (13.6) 3,341 (13.1) 1.06 (0.98, 1.14)
Trichloroethylene
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 60 (0.9) 215 (0.9) 0.93 (0.69, 1.24) 459 (5.8) 1,423 (5.6) 1.09 (0.98, 1.22)
 Exposed only to others 133 (1.9) 347 (1.5) 1.31 (1.06, 1.61) 698 (8.9) 2,194 (8.6) 1.04 (0.95, 1.14)
1,1,1,trichloroethane
 Unexposed 6,825 (97.2) 22,519 (97.6) 6,698 (85.3) 21,879 (85.8)
 Exposed 81 (1.2) 266 (1.2) 1.03 (0.80, 1.33) 838 (10.7) 2,559 (10.0) 1.08 (1.00, 1.18)
 Exposed only to others 112 (1.6) 296 (1.3) 1.28 (1.02, 1.61) 319 (4.1) 1,058 (4.1) 1.01 (0.88, 1.14)

Note: CI, confidence interval; n, number; OR, odds ratio; TGCT, testicular germ cell tumor risk; Exposed, exposed to the solvents of interest but can also be exposed to other solvents; Exposed only to others, exposed to any other solvents but the solvent of interest; Unexposed, unexposed to any of the individual or groups of solvents.

aORs and the corresponding 95% CIs were derived based on logistic regression analysis conditional on year and country of child’s birth, the reference category (unexposed) being the parents that were occupationally unexposed to any of the individual or groups of solvents.

bBenzene and/or toluene.

cMethylene chloride, perchloroethylene, trichloroethylene, and/or 1,1,1-trichloroethane.

Table 4. Sensitivity analysis: testicular germ cell tumor risk when parents have been exposed to solvents within the year before childbirth.
Maternal exposure Paternal exposure
Exposure Cases n (%) Controls n (%) OR (95% CI)a Cases n (%) Controls n (%) OR (95% CI)a
Aromatic hydrocarbon solvents (ARHC)
Any of ARHC solventsb
 Unexposed 4,007 (98.6) 13,567 (99.0) 4,034 (91.9) 13,482 (91.8)
 Exposed 56 (1.4) 138 (1.0) 1.53 (1.08, 2.17) 354 (8.1) 1,198 (8.2) 0.98 (0.85, 1.13)
Benzene
 Unexposed 4,022 (99.0) 13,589 (99.2) 4,074 (92.8) 13,638 (92.9)
 Exposed 41 (1.0) 116 (0.8) 1.24 (0.83, 1.85) 314 (7.2) 1,042 (7.1) 0.99 (0.86, 1.15)
Toluene
 Unexposed 4,037 (99.4) 13,642 (99.5) 4,047 (92.2) 13,542 (92.2)
 Exposed 26 (0.6) 63 (0.5) 1.67 (1.02, 2.73) 341 (7.8) 1,138 (7.8) 0.99 (0.86, 1.14)
Chlorinated hydrocarbon solvents (CHC)
Any of CHC solventsc
 Unexposed 4,016 (98.8) 13,568 (99.0) 3818 (87.0) 12,904 (87.9)
 Exposed 47 (1.2) 137 (1.0) 1.17 (0.83, 1.66) 570 (13.0) 1,776 (12.1) 1.07 (0.95, 1.20)
Methylene chloride
 Unexposed 4,037 (99.4) 13,646 (99.6) 4,064 (92.6) 13,636 (92.9)
 Exposed 26 (0.6) 59 (0.4) 1.54 (0.94, 2.51) 324 (7.4) 1,044 (7.1) 1.03 (0.89, 1.19)
Perchloroethylene
 Unexposed 4,043 (99.5) 13,658 (99.7) 4,338 (98.9) 14,532 (99.0)
 Exposed 20 (0.5) 47 (0.3) 1.43 (0.83, 2.45) 50 (1.1) 148 (1.0) 1.02 (0.71, 1.46)
Trichloroethylene
 Unexposed 4,038 (99.4) 13,618 (99.4) 4,124 (94.0) 13,873 (94.5)
 Exposed 25 (0.6) 87 (0.6) 0.98 (0.62, 1.54) 264 (6.0) 807 (5.5) 1.10 (0.94, 1.28)
1,1,1,trichloroethane
 Unexposed 4,022 (99.0) 13,590 (99.2) 3,886 (88.6) 13,145 (89.5)
 Exposed 41 (1.0) 115 (0.8) 1.22 (0.84, 1.78) 502 (11.4) 1,535 (10.5) 1.09 (0.96, 1.22)
Any solventd
 Unexposed 5,076 (97.6) 16,899 (97.8) 4,732 (86.0) 15,975 (87.1)
 Exposed 127 (2.4) 378 (2.2) 1.15 (0.94, 1.42) 773 (14.0) 2,376 (12.9) 1.09 (0.99, 1.19)

Note: CI, confidence interval; n, number; OR, odds ratio.

aORs and the corresponding 95% CIs were derived based on logistic regression analysis conditional on year and country of child’s birth, the reference category (unexposed) was the parents that were not occupationally exposed to the solvent of interest.

bBenzene and/or toluene.

cMethylene chloride, perchloroethylene, trichloroethylene, and/or 1,1,1-trichloroethane.

dAt least one of the following solvents: benzene, toluene, methylene chloride, perchloroethylene, trichloroethylene, 1,1,1-trichloroethane.

Most ORs associated with paternal exposures were close to 1.0. When exposure was subdivided into categories of low and high exposures, none of the exposures in fathers were found to be associated with TGCT risk (Table 2). In the sensitivity analyses assessing the paternal occupational exposure to solvents in the year of or the year prior to the childbirth, no associations with TGCT risks in offspring were found, which is consistent with the results of the primary analysis. Stratification by country (see Table S4) indicated an increased TGCT risk in Finland with prenatal paternal exposure to perchloroethylene (OR=2.42; 95% CI: 1.32, 4.41), and the test for heterogeneity between countries was statistically significant (p=0.02).

No major differences in associations were observed between seminomas and nonseminomas for any of the maternal or paternal solvent exposures (Table S5).

Discussion

In this large-scale nested case–control study of more than 8,000 TGCT cases in Finland, Norway, and Sweden, we found little evidence of an association between parental occupational exposure to solvents and the risk of TGCT in their offspring. The exception was a statistically significant moderately increased risk with maternal exposure to ARHC and toluene when restricting the analysis to subjects whose maternal exposure information near the time of pregnancy/childbirth was available, possibly showing a stronger association than the overall analyses due to reduced exposure misclassification. Country-specific results showed no additional associations besides the association with paternal exposure to perchloroethylene, which was observed exclusively in Finland.

The finding of a role of ARHC and toluene exposure has to be interpreted with caution, as the number of mothers exposed was limited in the sensitivity analysis, and the strongest association was found in the low exposure category. Job-exposure matrices (JEMs) allocate the same level and probability of exposure to all individuals within one occupational code. The individual exposure estimates are thus prone to nondifferential misclassification, particularly when the probability of exposure within one occupational code is low. The exposure categorization (low, high) of the continuous distribution of exposure underlying random measurement error might induce a systematic nondifferential over- or underestimation (Brenner and Loomis 1994). The borderline significant OR observed for the low category of exposure of ARHC might be due to systematic nondifferential exposure misclassification. However, it might be noted that nonmonotonic dose–response curves have previously been described for exposures to EDCs in relation to various endpoints (Vandenberg et al. 2016). Also, the positive association found between maternal exposure to ARHC and TGCT in offspring might be driven by toluene exposure, since ARHC is the combination of benzene and toluene.

Only one other case–control study has investigated occupations of fathers and mothers potentially exposed to hydrocarbons, but no excess risk of TGCT was observed in the offspring (Kardaun et al. 1991). However, exposure to individual chemical agents were not assessed, and the study, including 223 cases, has low statistical power. Several agents belonging to the group of ARHC solvents, such as styrene or agents deriving from benzene and toluene (pentachlorobenzene, trichlorobenzene, HCB, nitrotoluene), have been classified in the priority list of EDCs by the European Commission (European Commission DG ENV 2000). Exposure to EDCs has been suggested to contribute to the development of TDS through interference with hormone synthesis, secretion, and signaling (Cook et al. 2011; Giordano et al. 2010; Sharpe 2006). In two studies, fetal exposure to toluene and styrene was reported to be associated with a reduced synthesis and secretion of testosterone in the fetal testes and decreased weight of male reproductive organs (Ohyama et al. 2007; Tsukahara et al. 2009).

One study reported a positive association between fathers working as painters and occupationally exposed to organic solvents within the 3 months before pregnancy and birth defect risk in children (Hooiveld et al. 2006). Our results on overall exposure to solvents do not support the association observed in this study, but the two studies are difficult to compare, since in the study by Hooiveld et al. (2006), the number of children with birth defects was very small (n=48) and involved a wide range of congenital malformations and disorders. The mechanisms underlying a potential effect of paternal exposure to solvents on TGCT in offspring is unclear, but a plausible pathway has been hypothesized; notably an effect on sperm DNA, producing mutations or chromosomal abnormalities (Olshan et al. 1991). Although experimental studies have supported a potential effect of solvent exposure on the testes, none have investigated the corresponding effect of paternal exposure on the testes in the offspring (Lamb and Hentz 2006; NTP 2002; Verma and Rana 2009; Xu et al. 2004). Therefore, the association found for paternal exposure to perchloroethylene in our study requires further support from mechanistic studies. However, it should be noted that neither the prevalence of exposure nor the exposure levels among the exposed to perchloroethylene were substantially higher in Finland compared to Norway or Sweden. While the differences in ORs observed across the countries might be explained by some difference in occupational practices, we cannot exclude the possibility of an observation by chance due to multiple comparisons.

The main advantage of the NORD-TEST study is the linkage between the population-based registries from the Nordic countries representing an exceptional setting to design a retrospective study, with access to prospectively registered exposure information. The extraction of the cases diagnosed during the last 30 years in three countries provides a large sample size to analyze parental occupational exposure to organic solvents, even though the prevalence of exposure remained limited for certain solvents and exposure categories. Another strength is the use of a country-specific JEM created for Nordic countries (NOCCA-JEM) that encompasses the exposure estimates of single substances that are likely to have their own mechanisms of action in contrast to groups of substances (Pukkala et al. 2009). One of the advantages of using a JEM is that it assigns occupational exposures in a systematic and objective way, therefore avoiding differential misclassification between cases and controls; this is particularly true for the NORD-TEST study collecting occupational data via censuses, which were carried out before the diagnosis of TGCT.

One of the limitations of the NORD-TEST study is the lack of information on the time and duration of each occupation and therefore the inability to precisely target the critical time window of exposure and calculate preconception cumulative exposures. Because of the study design, we had to assume that the job at the time of census reflected the exposure at the time of conception and during intrauterine development, even though the census might be several years before the child’s birth. Nevertheless, the sensitivity analyses, including parents for whom the census was available within the year before or the year of the child’s birth or parents who held the same occupation in census prior and after the child’s birth, yielded stronger associations. In addition, NOCCA-JEM assumes homogeneity of exposure within jobs. The true variance of solvent exposure within jobs represented by equal levels of exposure might have diluted the estimated strength of a possible association. Nevertheless, our approach allowed us to compare the parents with a higher risk of solvent exposure with those with a lower risk of solvent exposure.

A recent hypothesis suggests that TGCT could arise from a combined effect of prenatal and early- and later-life exposures (McGlynn and Trabert 2012). The germ cell neoplasia in situ (Berney et al. 2016) assumed to be generated in utero by primary prenatal exposure does not become invasive until puberty following activation of the pituitary–gonadal axis (Rajpert-De 2006; Skakkebaek et al. 1987, 2001; Sonne et al. 2009). The design of NORD-TEST study did not allow collection of individual lifelong exposures. However, some of the subjects had a known occupational history. With this information, we checked whether subjects exposed to solvents during the prenatal period were likely to be exposed later in life in their occupations, but found this was not the case (as measured by the kappa statistics of agreement of categorical variables; κ≤0.1).

Conclusions

This is the first large nested case–control study investigating associations between maternal and paternal exposure to several individual organic solvents and types of them, and the risk of TGCT in their offspring. We found no evidence of any association with paternal occupational exposure to solvents, except an elevated risk observed with perchloroethylene in Finland only. For maternal exposure, a moderately elevated risk was observed among sons of mothers occupationally exposed to ARHC, particularly toluene, when analyses were restricted to mothers for whom the occupation was known in the year of or the year before childbirth. Further studies are needed, including investigations of molecular mechanisms, to better understand the observed association between maternal exposures to toluene and TGCT risk in their offspring.

Acknowledgments

This work was supported by public funding from the Lyric Grant INCa-DGOS-4664 (Institute of Cancer Research, France), the International Agency for Research on Cancer (IARC), and the Cancéropôle Lyon Auvergne Rhône-Alpes (CLARA). We would like to acknowledge M. Steding-Jessen and A. Meersohn from the Danish Cancer Society Research Center, K. Fremling from the Institute of Environmental Medicine, and Karolinska Institutet and V. Luzon from IARC for their help with the data management. The Family Cancer Database was created by linking registers maintained at Statistics Sweden and the Swedish Cancer Registry. Data from the Finnish Cancer Registry was extracted by the permission for the research No.THL/1123/5.05.00/2012. We would also like to thank the NOCCA team for approving our use of the NOCCA-JEM and their expertise on the occupational exposure in the Nordic countries.

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Cisco Releases Security Updates

Original release date: June 30, 2017

Cisco has released a security advisory to address Simple Network Management Protocol (SNMP) vulnerabilities in its IOS and IOS XE software. A remote attacker could exploit these vulnerabilities to take control of an affected system.

US-CERT encourages users and administrators to review the Cisco Security Advisory and apply the necessary workarounds until patches are released.


This product is provided subject to this Notification and this Privacy & Use policy.

USAID Acting Administrator Wade Warren Participates in Annual Tidewater Meeting on Development Assistance


English
Friday, June 30, 2017

U. S. Agency for International Development (USAID) Acting Administrator Wade Warren will travel to Lisbon, Portugal July 2-4 to participate in the 49th Annual Tidewater Meeting.

An Evaluation of LH-Stimulated Testosterone Production by Highly Purified Rat Leydig Cells : A Complementary Screen for Steroidogenesis in the Testis

An Evaluation of LH-Stimulated Testosterone Production by Highly Purified Rat Leydig Cells: A Complementary Screen for Steroidogenesis in the Testis. 1Botteri, N., 2Suarez, J., 2Laws, S., 2Klinefelter, G.1Oak Ridge Institute for Science and Education, Oak Ridge, TN, 2 U.S. Environmental Protection Agency, ORD, NHEERL, TAD, RTP, NCThe H295R steroidogenesis assay uses an adrenocarcinoma cell line which fails to elicit LH mediated responses. This limits the assay&rsquo;s ability to detect chemicals which disrupt LH-mediated Leydig cell responses in the testis. This study evaluated whether LH-stimulated T production by purified rat Leydig cells would be altered after exposure to chemicals that failed to decrease T production in the ToxCast H295R screen. Ten chemicals negative for T inhibition in the H295R screen, were selected based on alterations in upstream substrates (deoxycorticosterone, hydroxyprogesterone) expected to result in a decrease in T. Based on earlier work, simvastatin served as our positive control. Each chemical was tested over 6 concentrations ranging from 0.1 &micro;M to 100 &micro;M. Leydig cells were cultured overnight under maximal LH stimulation. A minimum of 3 replicate experiments were conducted for each format (24 and 96 well) and chemical tested; cell viability was assessed using a live/dead cytotoxicity kit. T data were excluded if viability was less than 80% of control. Initial evaluation using a 24-well Leydig cell assay confirmed that 4 of 10 chemicals produced significant (P &lt; 0.05) concentration-related decreases in LH-stimulated T synthesis. To increase throughput and efficiency, we adopted a 96-well assay and observed that exposure to the same chemicals decreased T; in addition corticosterone decreased T. The lowest observed effective concentrations for simvastatin, metconazole, anilazine, hydroquinone and corticosterone were 0.1 &micro;M, 1 &micro;M, 3 &micro;M, 10 &micro;M, 100 &micro;M, respectively. The 96-well assay data revealed decreases in T at lower concentrations (anilazine, metconazole). Perhaps greater sensitivity of the 96-well assay is attributed to the relative increase in T production by cells in this format compared to the 24 well format (1436 vs 738 ng T/106 Leydig cells). Using similar selection criteria, additional chemicals negative for T in the H295R cell assay will be screened using the 96-well Leydig cell assay. We will also determine chemical response with and without LH stimulation. The inherently greater sensitivity afforded by cells making over 1&micro;g of T, together with the ability to capture LH-mediated alterations, makes the Leydig cell assay a reasonable complement to the H295R steroidogenic screen.This abstract does not reflect US EPA policy

Report: In 2015, US institutions awarded most doctorates ever recorded

Graph showing increase in doctorates awarded by U.S. institutions.

U.S. institutions in 2015 awarded 55,006 research doctorate degrees, the highest number ever reported, according to the Survey of Earned Doctorates (SED), an annual census of research degree recipients.

The report, published by the National Science Foundation’s (NSF) National Center for Science and Engineering Statistics (NCSES), supplies data and analysis for a vital U.S. economic interest: the American system of doctoral

More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=242416&WT.mc_id=USNSF_51&WT.mc_ev=click


This is an NSF News item.

Developmental Exposure to Mild Variable Stress: Adult Offspring Performance in Trace Fear Conditioning after Prenatal and Postnatal Stress

In utero exposure to mild variable stress has been reported to influence learning and memory formation in offspring. Our research aims to examine whether nonchemical environmental stressors will exacerbate effects to chemical exposure. This study utilized a varying stress paradigm to simulate human psychosocial stress incurred during and after pregnancy to identify phenotypic learning changes in adult offspring that are potential stress markers. We additionally wanted to compare these behavioral outcomes to rat performance induced by perinatal exposure to manganese (Mn), a neurotoxic environmental element, at 2 or 5 g/l in drinking water throughout gestation and lactation. Pregnant Long Evans rats were exposed to an unpredictable series of mild stressful events which had previously been shown to increase maternal corticosterone levels. Nonchemical stressors were presented from GD 13 through GD 21 and included varying noise, light, housing, and confinement during both sleep and wake cycles. A subgroup of offspring was also exposed to periods of maternal separation. Starting at PND 97 offspring were trained with a trace fear conditioning protocol whereby rats were exposed to a compound cue (light and tone) followed by 30 seconds (trace period) and a mild foot shock (1mA, 0.5 seconds). Five paired training sessions occurred on the first day. The following day, context and cue learning were assessed by measuring motor activity. Preliminary data suggests adult offspring learned the task and exhibited reduced movement in response to both context and cue regardless of stress or Mn exposure. Ongoing research will continue to look for treatment differences in offspring of dams concurrently exposed to Mn and prenatal stress and if there are molecular changes to RNA in the hippocampus or amygdala of adult offspring after learning the trace fear conditioning task. This is an abstract of a proposed presentation and does not necessarily reflect EPA policy.

People and Places Forum Workshop Report

In November 2015, the Twin Ports-based People and Places Work Group (PPWG) coordinated a special gathering to bring together researchers and scholars from diverse fields to discuss environment-human research, scholarship and collaboration opportunities. Hosted by the US Environmental Protection Agency (USEPA), the group approached and invited over 150 individuals from eight regional universities. The goals were to learn who was doing or interested in doing applied research on human-environment interactions, who might have students to engage in work, who might partner with the Lake Superior National Estuarine Research Reserve (Reserve), USEPA, University of Minnesota Duluth’s Natural Resource Research Institute (NRRI), Minnesota and Wisconsin Sea Grant Institutes (Sea Grant), and other partnering institutes and who might be interested in ecosystem services work in particular. A pre-gathering survey collected initial information about this community and the adapted, open-space design gathering allowed for even more data collection about potential new colleagues to engage in the work of understanding people and place in our region. This summary reviews some of findings and presents what may be considered the beginning of a network directory to encourage and facilitate interdisciplinary research and collaboration.

Life Cycle Assessment and Cost Analysis of Water and Wastewater Treatment Options for Sustainability: Influence of Scale on Membrane Bioreactor Systems

changes in drinking and wastewater infrastructure need to incorporate a holistic view of the water service sustainability tradeoffs and potential benefits when considering shifts towards new treatment technology, decentralized systems, energy recovery and reuse of treated wastewater. The main goal of this study is to determine the influence of scale on the energy and cost performance of different transitional membrane bioreactors (MBR) in decentralized wastewater treatment (WWT) systems by performing a life cycle assessment (LCA) and cost analysis. LCA is a tool used to quantify sustainability-related metrics from a systems perspective. The study calculates the environmental and cost profiles of both aerobic MBRs (AeMBR) and anaerobic MBRs (AnMBR), which not only recover energy from waste, but also produce recycled water that can displace potable water for uses such as irrigation and toilet flushing. MBRs represent an intriguing technology to provide decentralized WWT services while maximizing resource recovery. A number of scenarios for these WWT technologies are investigated for different scale systems serving various population density and land area combinations to explore the ideal application potentials. MBR systems are examined from 0.05 million gallons per day (MGD) to 10 MGD and serve land use types from high density urban (100,000 people per square mile) to semi-rural single family (2,000 people per square mile). The LCA and cost model was built with existing literature data sources, data from actual commercial units, and wastewater treatment plant design costing software simulations. The results focus on the energy demand and associated greenhouse gases (GHG) for the scenarios examined. However, a full suite of life cycle impact assessment results, including water savings, was calculated.Net energy benefits, considering the drinking water displaced by the delivered recycled water, start at the 1 MGD scale for the AeMBR and at the 5 MGD scale for the AnMBR operated at 35˚C (mesophilic). For all scales investigated, the psychrophilic AnMBR reactor operated at 20˚C results in net energy benefits. This study supports the findings from other literature that AnMBRs operated at lower reactor temperatures are a potential technology for decreasing the environmental impacts of wastewater treatment systems. When examining the energy demand results normalized to a cubic meter of water treated, all energy demand impacts decrease as the scale increases due to economies of scales. While the AnMBR operating at ambient temperature results in notable energy and GHG benefits compared to the AeMBR, the AnMBR costs remain higher than the AeMBR under all scenarios. The main driver for this is the increase in operation and maintenance labor needed to operate the anaerobic reactor and, to a lesser extent, anaerobic reactor capital costs. The study found that all impacts decrease comparatively as the population density increases due to decreased pumping distanc

Brief History of Agricultural Systems Modeling

Abstract: Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists …