Advertisement Banner
Skip to content

EHP

Review Volume 120 | Issue 6 | June 2012

Environ Health Perspect; DOI:10.1289/ehp.1104597

Role of Environmental Chemicals in Diabetes and Obesity: A National Toxicology Program Workshop Review

Kristina A. Thayer,1 Jerrold J. Heindel,2 John R. Bucher,3 and Michael A. Gallo4

Author Affiliations open
1Division of the National Toxicology Program, 2Division of Extramural Research, Cellular, Organs and Systems Pathobiology Branch, Office of Health Assessment and Translation, and 3Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA; 4Department of Environmental and Occupational Medicine, Environmental and Occupational Health Sciences Institute, UMDNJ–Robert Wood Johnson Medical School, Piscataway, New Jersey, USA

PDF icon PDF Version (553 KB)

  • Background: There has been increasing interest in the concept that exposures to environmental chemicals may be contributing factors to the epidemics of diabetes and obesity. On 11–13 January 2011, the National Institute of Environmental Health Sciences (NIEHS) Division of the National Toxicology Program (NTP) organized a workshop to evaluate the current state of the science on these topics of increasing public health concern.

    Objective: The main objective of the workshop was to develop recommendations for a research agenda after completing a critical analysis of the literature for humans and experimental animals exposed to certain environmental chemicals. The environmental exposures considered at the workshop were arsenic, persistent organic pollutants, maternal smoking/nicotine, organotins, phthalates, bisphenol A, and pesticides. High-throughput screening data from Toxicology in the 21st Century (Tox21) were also considered as a way to evaluate potential cellular pathways and generate -hypotheses for testing which and how certain chemicals might perturb biological processes related to diabetes and obesity.

    Conclusions: Overall, the review of the existing literature identified linkages between several of the environmental exposures and type 2 diabetes. There was also support for the “developmental obesogen” hypothesis, which suggests that chemical exposures may increase the risk of obesity by altering the differentiation of adipocytes or the development of neural circuits that regulate feeding behavior. The effects may be most apparent when the developmental exposure is combined with consumption of a high-calorie, high-carbohydrate, or high-fat diet later in life. Research on environmental chemical exposures and type 1 diabetes was very limited. This lack of research was considered a critical data gap. In this workshop review, we outline the major themes that emerged from the workshop and discuss activities that NIEHS/NTP is undertaking to address research recommendations. This review also serves as an introduction to an upcoming series of articles that review the literature regarding specific exposures and outcomes in more detail.

  • Citation: Thayer KA, Heindel JJ, Bucher JR, Gallo MA. 2012. Role of Environmental Chemicals in Diabetes and Obesity: A National Toxicology Program Workshop Review. Environ Health Perspect 120:779–789; http://dx.doi.org/10.1289/ehp.1104597

    Address correspondence to K. Thayer, Office of Health Assessment and Translation, National Toxicology Program, 530 Davis Dr., Room 2154/Mail Drop K2-04, Morrisville, NC 27560 USA. Telephone: (919) 541-5021. Fax: (919) 316-4511. E-mail: thayer@niehs.nih.gov

    M.A.G. served as workshop chair, and K.A.T. was lead in organizing the meeting and assembling background materials. J.J.H. and J.R.B. were NIEHS/NTP staff extensively involved in organizing the meeting. Members of specific breakout groups are described in the background materials for the meeting at http://ntp.niehs.nih.gov/go/36433 (see “List of Breakout Group Members,” http://ntp.niehs.nih.gov/ntp/ohat/diabet​esobesity/Wkshp/FinalAttendeesList508_01​182011.pdf).

    We gratefully acknowledge the contributions of S. Holmgren (NIEHS) for developing the literature search strategy and J. Stevens (GLP Support Services), V. Walker (NIEHS/NTP), K. Taylor (NIEHS/NTP), and A. Boyles (NIEHS/NTP) for assistance in preparing the background literature review documents. We also acknowledge the invaluable assistance of staff at the U.S. EPA National Center for Computation Toxicology, in particular D. Reif, D. Dix, K. Houck, and R. Judson.

    This review is based on deliberations that occurred at an 11–13 January 2011 workshop sponsored by the National Institute of Environmental Health Sciences (NIEHS)/Division of the National Toxicology Program (NTP), the U.S. Environmental Protection Agency (EPA), and the Food and Drug Administration National Center for Toxicological Research.

    This article is the work product of a group of employees of the NIEHS, National Institutes of Health (NIH); however, the statements, opinions, or conclusions contained therein do not necessarily represent the statements, opinions, or conclusions of NIEHS, NIH, or the U.S. government.

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

    Received: 9 October 2011
    Accepted: 1 February 2012
    Advance Publication: 1 February 2012
    Final Publication: 1 June 2012

  • PDF icon Supplemental Material (1.1 MB) PDF

The current prevalence of diabetes and obesity is unprecedented in the United States and abroad. Based on data from 2005–2008, 25.6 million, or 11.3% of all people in the United States ≥ 20 years of age, have diagnosed or undiagnosed diabetes [Centers for Disease Control and Prevention (CDC) 2011]. The total direct medical costs and indirect costs (disability, work loss, premature death) associated with diabetes in the United States during 2007 was $174 billion (CDC 2011). Another 35% of people in this age category are estimated to have pre-diabetes, a condition where blood glucose is higher than normal but not high enough to be classified as diabetes. This condition is a predictor for the development of diabetes. Approximately 11% of people with pre-diabetes developed type 2 diabetes each year during the average 3 years of follow-up in the Diabetes Prevention Program, a major clinical trial conducted to assess intervention strategies to prevent or delay the onset of diabetes in people with impaired glucose tolerance (American Diabetes Association 2011; Knowler et al. 2002). Overweight and obesity are well-known risk factors for the development of type 2 diabetes, perhaps contributing to approximately 70% of cases (Eyre et al. 2004). The prevalence of obesity worldwide had doubled since 1980 (World Health Organization 2011). In the United States, the prevalence of obesity among children and adolescents 2–19 years of age has almost tripled since 1980, and it is estimated that 16.9%, or 12.5 million, are obese (Ogden and Carroll 2010). This trend is also apparent in preschool children 2–5 years of age, where obesity increased from 5% in 1976–1980 to 10.4% in 2007–2008 (Ogden and Carroll 2010). Similarly, increased body weights have also been reported in pets and laboratory animals over the past decades (Klimentidis et al. 2010).

Excess caloric consumption and a seden-tary lifestyle are well-recognized risk factors for obes-ity and diabetes. However, there is growing interest in the contribution of “non-traditional” risk factors (e.g., environ-mental chemicals, stress, micro-nutrients, gut microbiome) to the etiology of these health conditions. Research addressing the role of environmental chemicals in diabetes and obesity has rapidly expanded in the past several years. The White House Task Force on Childhood Obesity (2010), the National Institutes of Health (NIH 2011), and the National Institute of Diabetes and Digestive and Kidney Diseases (2011) all acknowledge the growing science base in this area and cite the need for research to improve understanding of the role of environmental exposures in order to facilitate future prevention strategies. To help develop such a research strategy, the National Institute of Environmental Health Sciences (NIEHS) Division of the National Toxicology Program (NTP) organized a state-of-the-science workshop in January 2011 titled “Role of Environmental Chemicals in the Development of Diabetes and Obesity” to evaluate the literature for evidence of associations between certain chemicals and risk of diabetes and/or obesity (NTP 2011b). The specific environmental exposures evaluated were arsenic, maternal smoking during pregnancy/nicotine, organic tin compounds (“organotins”), phthalates, bisphenol A (BPA), pesticides, and various persistent organic pollutants (POPs). A diverse group of more than 50 scientists including endocrinologists, toxicologists, epidemiologists, bioinformaticists, and experts in the pathobiology of diabetes and obesity were asked to evaluate the current literature for consistency and biological plausi-bility, with the ultimate goal of providing advice to NIEHS for developing a research agenda on these emerging topics. Literature review documents, meeting presentations, and other background materials for the workshop are available online (NTP 2011b).

Overall, the existing literature was judged to provide plausibility, varying from suggestive to strong, that exposure to environmental chemicals may contribute to the epidemic of diabetes and/or obesity. This workshop review provides an overview of the major themes emerging from the workshop and describes several activities that NIEHS is undertaking to address research recommendations. This review also serves as the announcement of an upcoming series of papers to be published in Environmental Health Perspectives describing in more detail the critical assessment of the literature provided by the workshop participants.

Methods

Workshop format. The workshop format was an introductory plenary session and a series of breakout group meetings, followed by plenary sessions to disseminate and discuss the findings from individual breakout group deliberations. A series of white papers was distributed before the workshop to help focus discussion. Breakout groups were not required to reach consensus on responses to charge questions, and plenary reports were prepared to reflect the range of opinions expressed. For the individual chemicals or chemical classes, workshop participants were asked to a) evaluate the strength/weaknesses, consistency, and biological plausibility of findings reported in humans and experimental animals; b) identify the most useful and relevant end points in experimental animals, in vitro models, and screening systems to assess these diseases; and c) identify data gaps and areas for future evaluation/research. Data from the Toxicology in the 21st Century (Tox21) High-Throughput Screening (HTS) Initiative were also considered during the meeting. Experts used the data, primarily derived from phase I of the U.S. Environmental Protection Agency (EPA) ToxCast™ (U.S. EPA 2011a), to help evaluate biological plausibility as well as to develop testable predictions of which chemicals might perturb biological processes related to diabetes and obesity. Experts were also asked to suggest relevant assay targets that could be included in Tox21 in the future to better screen for perturbations of these biological processes. Obesity is a major risk factor for metabolic syndrome and type 2 diabetes. All three outcomes were reviewed in relation to the environmental exposures evaluated during the workshop, although the primary focus and context varied for specific exposures.

Literature search strategy. A PubMed (National Library of Medicine, Bethesda, MD) search strategy was developed to identify studies of xenobiotic exposures related to diabetes and obesity using both a MeSH (Medical Subject Headings)-based strategy and a keyword strategy [for a complete list of MeSH and keyword search terms, see Supplemental Material (http://dx.doi.org/10.1289/ehp.1104597)]. The keyword search was included to identify newer articles that were not yet MeSH indexed in PubMed at the time of the search. Additional details about the criteria used to determine study rele-vance will be presented in subsequent publications that focus on specific exposures.

Data extraction. Data extraction of the main findings from studies considered relevant was conducted by NTP staff in the Office of Health Assessment and Translation (OHAT). Identification of main findings was based on the following strategy. For studies that did not report a significant association between the exposure and a health outcome, data extraction for the main finding was based on the highest exposure group compared with the referent group (e.g., fourth quartile vs. first quartile). When a study reported a significant association between an exposure and a health outcome, the data extraction for the main finding was based on lowest exposure group where a statistically significant association was observed and the shape of the exposure–response relation was monotonic (e.g., third quartile vs. first quartile). Identification of main findings when associations were non-monotonic in nature was conducted on a case-by-case basis and included consideration of any statistical trend analyses that might have been conducted, consistency of the overall pattern across exposure groups, and/or consideration of the author’s interpreta-tion of the biological significance of the non-monotonic finding.

An Excel file was used to store the data extraction output. This Excel file can be used in conjunction with a new graphical display software program called Meta Data Viewer developed by S. Harris at SRA International Inc. (Durham, NC, USA) and NTP OHAT staff (Boyles et al. 2011). In brief, the graphing program allows users to sort, group, or filter studies according to exposures, health outcomes, and other characteristics and can present the main findings using a “forest plot” graphical display. The input data file for the diabetes/obesity workshop contains approximately 870 main findings from > 200 human studies. This software program was used during the workshop to visually display data but was not used to conduct quanti-tative meta-analyses. The graphing program, accompanying data file, and instructions for use are publicly accessible (see NTP 2012; Boyles et al. 2011). Meta Data Viewer is a public resource, and users are welcome to use the program and any associated NTP data files for their own purposes, including for use in publications. Assistance in using the data file and software program is available upon request.

Major Findings

Maternal smoking and nicotine. The strongest conclusion from the workshop was that nicotine likely acts as a developmental obesogen in humans. This conclusion was based on the very consistent pattern of overweight/obesity observed in epidemiology studies of children of mothers who smoked during pregnancy (Figure 1) and was supported by findings from laboratory animals exposed to nicotine during prenatal development. Crude and adjusted odds ratios (ORs) were similar within the individual epidemiological studies, suggesting that the social and behavioral characteristics that were included in models did not account for the observed differences in the prevalence of overweight (Oken et al. 2008). Two recent meta-analyses concluded there was some evidence for publication bias, but not enough to negate the overall conclusion of increased risk (Ino 2010; Oken et al. 2008). The body weight and adiposity-related changes reported in the animal studies recapitulated to a large extent those seen in children of mothers who smoke (Levin 2005; Newman et al. 1999; Oliveira et al. 2009, 2010a, 2010b; Santos-Silva et al. 2010, 2011; Somm et al. 2008; Williams and Kanagasabai 1984). The breakout group recognized that other components in cigarette smoke may also be contributing to the association between maternal smoking and childhood overweight/obesity; however, the studies of nicotine in experimental animals provided compelling evidence that nicotine alone was the causal agent.

Figure 1Figure 1 – Association between maternal smoking during pregnancy and overweight/obesity in offspring. Studies are grouped by study design and then sorted by health outcome (overweight or obesity); studies are presented alphabetically by author within the health outcome categories. Abbreviations and symbols: ♀, female; ♂, male; AK Nat, Alaskan native; ALSPAC, Avon Longitudinal Study of Parents and Children; Am Ind, American Indian; adj OR, adjusted odds ratio; BBC, British Birth Cohort; CI, confidence interval; CESAR, Central European Study on Air Pollution and Respiratory Health; CLASS, Children’s Lifestyle and School Performance study; CPP, Collaborative Perinatal Project; GDM, gestational diabetes mellitus; Gen R, Generation R study; OH, Ohio; MA, Massachusetts; milt serv, military service; nat’l, national; NCDS, National Child Development Study; NLSY, National Longitudinal Survey of Youth; PedNSS, Pediatric Nutrition Surveillance System; prev OR, prevalence odds ratio; WI, Wisconsin; WIC, Women, Infants, and Children program. aRisk estimates for bracketed statistics (i.e., [crude prev OR]) calculated based on data presented in the paper using open source epidemiology statistics software OpenEpi (Dean et al. 2011).

View larger image (TIF File)

Arsenic. The breakout group participants that evaluated this literature concluded that the existing human data were limited to sufficient in support of an association between arsenic and diabetes in populations with high exposure levels, namely, regions in Taiwan and Bangladesh with historical problems with arsenic contamination of drinking water (Figure 2). Although most members of the group considered the evidence sufficient for an association, additional research is needed to determine whether the relationship is causal. Workshop participants concluded that current evidence was insufficient for an association with diabetes and arsenic in lower–exposure areas (< 150 ppb in drinking water), such as the United States and Mexico, although recent studies with better measures of exposure and outcome provided increased evidence for an association (Coronado-Gonzalez et al. 2007; Del Razo et al. 2011; Ettinger 2009).

Figure 2Figure 2 – Association between arsenic and diabetes in areas of relatively high exposures (> 150 ppm drinking water). Studies are sorted by quality of the diagnostic from worse to better. Abbreviations: adj PR, adjusted prevalence ratio; As, arsenic; avg., average; CC, case–control; CEI, cumulative exposure index; CS, cross-sectional; FBG, fasting blood glucose; HEALS, Health Effects of Arsenic Longitudinal Study; OGTT, oral glucose tolerance test; Pros, prospective; Qn, quintile; Retro, retrospective; RR, relative risk; SMR, standardized mortality ratio; WA, Washington State. aCalculated based on data presented using open source epidemiology statistics software OpenEpi (Dean et al. 2011) for Nabi et al. (2005) and Tsai et al. (1999) or as estimated by Navas-Acien et al. (2006) for Tollestrup et al. (2003).

View larger image (TIF File)

The literature on arsenic and diabetes in experimental animals was judged inconclusive. The body of existing studies is highly diverse, with considerable variation in the duration of treatment (1 day to 2 years), routes of adminis-tration, and dose levels used in the studies. Most of the studies treated animals with sodium arsenite [As(III); arsenic tri-oxide], but other arsenicals have also been studied (Aguilar et al. 1997; Arnold et al. 2003; Hill et al. 2009; Paul et al. 2008). The studies also vary in experimental design and model systems used to assess end points relevant to diabetes as a health effect. Most of the studies were not designed to examine the diabeto-genic effects of chronic arsenic exposure. Although the literature as a whole was judged inconclusive, findings from recent studies that were designed to focus more specifically on glucose homeostasis appear consistent with those human studies that link arsenic exposure to diabetes. Supportive findings include impaired glucose tolerance in studies of mice or rats treated with As(III) for several months at drinking water concentrations from 5 to 50 ppm (Cobo and Castineira 1997; Paul et al. 2007, 2008; Wang et al. 2009). In addition, measures of insulin regulation [e.g., homeo-static model assessment (HOMA) insulin resistance)] were affected in Wistar rats treated with 3.4 mg/kg body weight/day As(III) by oral gavage for 90 days (Izquierdo-Vega et al. 2006) and in pregnant female LM/Bc/Fnn mice treated with 9.6 mg/kg As(V) by intra-peritoneal injection on gestational days 7.5 and 8.5 (Hill et al. 2009).

Most in vitro or mechanistic studies were not designed specifically to study the diabetogenic or adipogenic effects of arsenic. Nevertheless, these studies suggest several pathways by which arsenic could influence pancreatic β-cell function and insulin sensitivity, including oxidative stress and effects on glucose uptake and transport, gluconeogenesis, adipocyte differentiation, and Ca2+ signaling (reviewed by Diaz-Villasenor et al. 2007, 2008; Druwe and Vaillancourt 2010; Tseng 2004). Studies suggest that arsenic may exert adverse effects on β-cell function in vitro through several mechanisms, depending on the concentration tested (Fu et al. 2010).

Epidemiological studies of POPs and -diabetes. POPs comprise a broad class of organohalides (i.e., organochlorines, organofluorines, and organobromines). The POP literature related to diabetes and other metabolic disorders is complex, consisting of approximately 75 epidemiological studies that report hundreds of findings relating to diabetes, altered glucose homeostasis, insulin resistance, or metabolic syndrome. Often results for multiple POPs are reported in the same study. Because of time constraints at the workshop, breakout group participants focused on diabetes outcomes, although findings related to glucose homeostasis, insulin resistance, and metabolic syndrome will be summarized in supplemental materials that accompany the POPs breakout group report. The breakout group developed a quality rating for each study based primarily on the methods used to classify or measure exposure, and the diagnostic used to ascertain diabetes status. Studies received a lower rating if the diagnoses of diabetes came from death certificates, if diabetes was self–reported, if exposure was self-reported, or if exposure was not clearly measured. The breakout group then used the Meta Data Viewer program to assess patterns of association between various POPs chemicals or chemical classes and diabetes (Boyles et al. 2011).

The group concluded that there is evidence for a positive association of diabetes with certain organochlorine POPs. Initial data mining indicated the strongest associations of diabetes with trans-nonachlor, DDT (dichloro-diphenyltrichloroethane)/DDE (dichloro-diphenyldichloroethylene)/DDD (dichloro-chlorophenylethane), and dioxins/dioxin-like chemicals, including polychlorinated biphenyl (PCBs; Figure 3). In no case was the body of data considered sufficient to establish causality. The very strong exposure correlations among some POPs [correlation coefficients of 0.50–0.90 (Lee et al. 2006)] make it difficult to identify individual POPs as potential causal agents.

Figure 3Figure 3 – Association between PCBs and diabetes. Studies are grouped by study design and then sorted alphabetically by first author within each study design category. Abbreviations: CARDIA, Coronary Artery Risk Development in Young Adults; CS, cross-sectional; FBG, fasting blood glucose; IDR, incidence density ratio; IRR, incidence rate ratio; LOD, limit of detection; med, median; MI, Michigan; ND, not detected; NHANES, National Health and Nutrition Examination Survey; PBB, polybrominated biphenyl; Q, quartile; T, tertile; TEQ, toxic equivalency; WHILA, Women’s Health in the Lund Area; ww, wet weight.

View larger image (TIF File)

Peroxisome proliferator–activated receptor (PPAR) activators (organotins and phthalates). Organotins and phthalates were considered together in a breakout group session because these compounds both interact with PPARs. The PPARs are intimately involved in the regu-lation of adipocyte differentiation, production of adipokines, insulin responsiveness, and other biological processes related to glucose and lipid regulation (Janesick and Blumberg 2011; Kahn and McGraw 2010; Li et al. 2011; Wang 2010). In addition, there is the potential for coexposures to these two chemical classes because both are commonly used as plasticizers in PVC (polyvinylchloride) plastics. The extent and magnitude of exposure are assumed to be higher for phthalates than for organotins, but exposure to organotins is not well characterized (Kannan et al. 2010).

The pattern of stimulatory activity for specific PPAR receptor subtypes varies between the organotins [primarily tributyltin (TBT)] and individual phthalates, with the organotins appearing to have a stronger mechanistic profile for inducing “obesogenic” effects. The organotins are potent agonists for PPARγ as well as retinoid X receptor-α (RXRα), two receptors known to promote adipocyte differentiation in vitro when activated (Grun et al. 2006; Hiromori et al. 2009; Inadera and Shimomura 2005; Kanayama et al. 2005; le Maire et al. 2009; Nakanishi et al. 2005; Nishikawa et al. 2004). Because PPARγ and RXRα heterodimerize, organotins stimulate both parts of the heterodimer complex.

The phthalates are less potent activators of PPARγ than are organotins, with agonist activity occurring at concentrations 1,000 times higher (~ 10–100 µM vs. ~ 10–100⊇nM), and phthalates have not been identified as agonists for RXRα. In contrast to the organotins, the phthalates are more potent agonists for PPARα than for PPARγ. The organotins are not considered activators of PPARα (Blumberg B, personal communication, 28 November 2010). In rodent models, PPARα appears to mediate high-dose di(2-ethylhexyl) phthalate (DEHP)-induced body weight loss, but its role in regulating adipogenesis is less clear (Wang 2010).

Organotins. No epidemiological studies of organotin exposure and obesity or diabetes were identified during the literature search. There are poisoning incident reports, mostly in workers involved in applying the compounds for pesticide use, that describe incidents of hyperglycemia and/or glycosuria (Colosio et al. 1991; Manzo et al. 1981; reviewed by National Institute for Occupational Safety and Health 1976). Recent animal and mecha-nistic studies report stimulatory effects of TBT on adipocyte differentiation (in vitro and in vivo) and increased amount of fat tissue (i.e., larger epididymal fat pads) in adult animals exposed to TBT during fetal life (Grun and Blumberg 2006; Hiromori et al. 2009; Inadera and Shimomura 2005; Kanayama et al. 2005; Kirchner et al. 2010; Nakanishi et al. 2005). In vitro effects of TBT include increased lipid accumulation in adipocytes and increased differentiation of multipotent stromal stem cells into adipocytes (Kirchner et al. 2010). Although the organotin “obesogen” literature is relatively new, with few studies, the quality of the existing experimental studies was considered high by the breakout group.

Phthalates. Three cross-sectional human studies of exposure to phthalates were discussed by the breakout group (Boas et al. 2010; Hatch et al. 2008; Stahlhut et al. 2007). These studies reported some positive associations but did not provide sufficient evidence to conclude there is an association with diabetes or obesity. Therefore, findings suggesting the possibility of sex differences in associations and different effects of individual phthalates were considered preliminary. In these studies, the urinary phthalate metabolite monoethyl phthalate was the phthalate metabolite most often associated with higher body mass index (BMI) (Hatch et al. 2008), waist circumference (Stahlhut et al. 2007), or HOMA (Stahlhut et al. 2007). Mono-2-ethylhexyl phthalate was associated with decreased BMI in females > 12 years of age (Hatch et al. 2008).

Understanding differences in PPARα activity between humans and rodents is important with respect to understanding potential effects of phthalates on body weight and metabolic end points. Phthalate monoester metabolite concentrations required to activate human PPARα are two to three times higher than the concentrations required to activate mouse PPARα, and the maximum-fold induction is less for human PPARα than for mouse PPARα (Bility et al. 2004; Hurst and Waxman 2003; Maloney and Waxman 1999). Animals treated with relatively high doses of phthalates such as DEHP typically display decreased body weight and fat mass (Itsuki-Yoneda et al. 2007; Sakurai et al. 1978). These effects were not observed in PPARα-knockout mice (Feige et al. 2010), which suggests they are largely mediated via the PPARα agonist activities of DEHP metabolites (Feige et al. 2010; Martinelli et al. 2010). However, when the normal mouse PPARα gene was replaced with the human PPARα gene, mice treated with DEHP gained weight and had increased epididymal white adipose mass compared with wild-type animals (Feige et al. 2010). PPARγ activity is similar in rodents and humans, but stronger PPARα activity in mice compared with humans may mask effects mediated through PPARγ.

BPA. Overall, this breakout group concluded that the existing data, primarily based on animal and in vitro studies, are suggestive of an effect of BPA on glucose homeostasis, insulin release, cellular signaling in pancreatic β cells, and adipogenesis (Alonso-Magdalena et al. 2010; Miyawaki et al. 2007; Ryan et al. 2010; Somm et al. 2009). The existing human data on BPA and diabetes (Lang et al. 2008; Melzer et al. 2010) available at the time of the workshop were considered too limited to draw meaningful conclusions. Similarly, data were insufficient to evaluate BPA as a potential risk factor for childhood obesity: Only one pilot study was available at the time of the workshop (Wolff et al. 2008).

It was not possible to reach clear conclusions about BPA and obesity from the existing animal data. Although several studies report body weight gain after developmental exposure, the overall pattern across studies is inconsistent. However, breakout group participants emphasized that body weight is not considered a good measure of obesity in rodents and noted that only a few studies have assessed obesity using the preferred metrics such as fat mass, fat pad weight, and cell adipose tissue cellularity. There is inconsistency in the in vivo findings that may relate to differences in experimental designsuch as differences in diet, route of administration, and species/strain. Understanding the basis for these inconsistencies was considered a research priority. The group also noted that the mechanisms of BPA action are not fully understood but extend beyond its activity as an estrogen receptor agonist. A number of in vitro findings suggest interactions with other receptor systems involved in metabolic regulation (Wetherill et al. 2007), including anti-androgen effects at low concentrations and high binding affinity for estrogen-related receptor-γ (Takayanagi et al. 2006).

Pesticides. The pesticide breakout group concluded the epidemiological, animal, and mechanistic data support the biological plausibility that exposure to multiple classes of pesticides may affect risk factors for diabetes and obesity, although many significant data gaps remain. Some active ingredients of pesticides, and of insecticides in particular, affect neurotransmitter and/or ion channel systems that are also involved in regulating pancreatic function, including acetylcholine (e.g., organophosphate, carbamate, neonicotinoids), sodium channels (e.g., pyrethroids), γ-aminobutyric acid (e.g., organochlorine), catecholamine (e.g., amidine/-formamidine), and mitochondrial function (e.g., rotenone). This raises the possibility that these compounds might affect glucose homeostasis, at least at dose levels where they are effective as pesticides (Franklin and Wollheim 2004; Satin and Kinard 1998). Much less research has focused on whether pesticides have activities that might affect adiposity or other components for metabolic syndrome.

Case reports of hyperglycemia have been reported after poisoning incidents with a variety of pesticides, perhaps best documented for organophosphates (Agency for Toxic Substances and Disease Registry 1997; Sungur and Guven 2001) and the formamidine insecticide amitraz (Caksen et al. 2003; Elinav et al. 2005; Ertekin et al. 2002; Kennel et al. 1996; Ulukaya et al. 2001; Yilmaz and Yildizdas 2003). Type 1 diabetes is a recognized complication after accidental poisoning with the banned rodenticide Vacor (Gallanosa et al. 1981; Karam et al. 1980; Miller et al. 1978; Mindel 1986; Peters et al. 1981; Pont et al. 1979; Prosser and Karam 1978; Yoon 1990). Vacor is structurally similar to strepto-zotocin, a compound widely used to induce experimental diabetes in animals. With the exception of studies of persistent organo-chlorine pesticides such as DDT/DDE or trans-nonachlor, there are very few cohort studies of other pesticides and health conditions related to diabetes, metabolic syndrome, or adiposity.

There have been numerous reports of intoxication with organophosphate insecticides on blood glucose in laboratory animals, generally finding hyperglycemia at high dose levels (see reviews by Karami-Mohajeri and Abdollahi 2010; Rahimi and Abdollahi 2007). Recently, the focus of investigations has shifted toward studies designed to understand the consequences of developmental exposure to lower doses of organophosphates, and the long-term effects of these exposures on metabolic dysfunction, diabetes, and obesity later in life (Adigun et al. 2010a, 2010b, 2010c; Icenogle et al. 2004; Lassiter et al. 2008, 2010; Levin et al. 2002; Roegge et al. 2008; Slotkin et al. 2005, 2009; reviewed by Slotkin 2010). The general findings are that early-life exposure to otherwise subtoxic levels of organophosphates results in pre-diabetes, abnormalities of lipid metabolism, and promotion of obesity in response to increased dietary fat.

The EPA Toxicity Reference Database (ToxRefDB; U.S. EPA 2011b), was also used as a resource for the pesticide breakout group. The current version of the ToxRefDB contains detailed study and effect information on 474 chemicals, primarily the data-rich pesticide active ingredients. Many of these studies were conducted for regulatory purposes and are not available in the peer-reviewed literature. ToxRefDB was queried for chemicals that caused increased body weight (or body weight gain), increased blood glucose, and pancreatic effects, including changes in mass, adenomas, atrophy, congestion, hyperplasia, hypertrophy, inflammation, fatty change, degeneration, and cellular infiltration. Approximately 100 chemicals were causes of at least one of these effects (see NTP 2011b, appendix B). Six of the studies identified increased body weight as a result of treatment with several organophosphates, including two separate studies for fenthion, one conducted in rats and the other in mice (Table 1). Several sulfonylurea herbicides and imidazole fungicides were also identified by the ToxRefDB search. These pesticides belong to the same general chemical class as agents used to manage type 2 diabetes or that are being investigated as potential therapeutic agents.

Table 1Table 1 – Selected results from ToxRefDB search for chemicals that caused increased body weight, increased blood glucose, or pancreatic effects.

View larger image (TIF File)

Use of Tox21 HTS to Identify Substances of Potential Interest

Consideration of data from the Tox21 HTS Initiative played a prominent role in the workshop. Tox21 is a collaborative program between the U.S. EPA, NIEHS/NTP, NIH Chemical Genomics Center, and U.S. Food and Drug Administration (FDA) designed to research, develop, validate, and translate innovative chemical testing methods that characterize toxicity pathways (NTP 2011a). Data from phase I of ToxCast™, U.S. EPA’s contribution to Tox21 (U.S. EPA 2011a), were used to help determine the biological plausibility of reported effects and to identify chemicals that may interact with relevant mechanistic targets but have not been assessed for effects related to diabetes or obesity. In general, the ToxCast™ data often aligned with mechanistic findings in the peer-reviewed literature. For example, the organotin fentin was identified in ToxCast™ as a target for PPARγ at a relatively low concentration (U.S. EPA 2011a; search for “fentin”). Amitraz, a formamidine insecticide, is an α2-adrenoreceptor agonist (Chen and Hsu 1994; Hugnet et al. 1996; Smith et al. 1990), and this activity was identified in ToxCast™ (U.S. EPA 2011a; search for “amitraz”).

Many of the pesticides identified from ToxRefDB as causes of increased body weight, increased blood glucose, or pancreatic effects were also screened in phase I of ToxCast™, providing clues regarding potential mechanisms that may underlie the in vivo effects. Preliminary analysis of these results indicates that many pesticides have HTS “hits” that are unrelated to their classic pesticide mechanism of action but may be relevant to biological processes relevant to glucose homeostasis, insulin sensitivity, adipocyte differentiation, and lipid metabolism. However, the chemicals or chemical classes that have been most strongly or consistently associated with diabetes or obesity in humans (trans-nonachlor, 2,3,7,8-tetrachlorodibenzo-p-dioxin, DDT/DDE/DDD, PCBs, arsenic, nicotine) have not yet been screened in ToxCast™.

Data from phase I of ToxCast™ were also used to develop testable predictions of which chemicals might perturb biological processes related to diabetes and obesity. In brief, workshop participants identified relevant HTS targets for several biological processes related to diabetes and obesity (insulin signaling, islet cell function, adipocyte differentiation, and feeding behavior in Caenorhabditis elegans). The 309 chemicals tested in phase I of ToxCast™, primarily pesticide active ingredients, were then screened against these targets to identify a set of chemicals predicted to perturb these processes and others predicted to have no effect. As a follow-up activity, the NTP is initiating a targeted testing activity for a set of predicted “positives” and “negatives” using more physiologically based in vitro model systems. Experts also suggested biological assay targets that could be added to Tox21 to improve the ability to identify chemicals that may perturb metabolic processes.

Conclusions, Research Recommendations, and Next Steps

Overall, the workshop review of the existing litera-ture supports the plausibility of the “obeso-gen” hypothesis, as well as linkages between type 2 diabetes and exposures to certain chemical classes. A review of the literature indicates very little research has been directed toward understanding associations between environmental exposures and type 1 diabetes. This was considered a critical data gap. Many research questions remain, and an important goal of this workshop was to identify data gaps to stimulate focused research to move the field forward. The research recommendations included suggestions for the most appropriate end points to evaluate in human, animal, and mechanistic studies of diabetes and obesity (Tables 24). All of the breakout groups highlighted the importance of using clinically accepted measures of diabetes and overweight/obesity in the epidemiological studies (Table 4). Understanding more about the different phenotypes of obesity will require more sophisticated measurement methods because the distribution of adipose tissue can vary among individuals with the same BMI and waist circumference. Another series of recommendations was to elucidate the role(s) of effect modifiers, confounding factors, and specific genetic contributions in humans and animal models used to study these diseases (Table 3).

Table 2Table 2 – Research recommendations for health outcome assessment measures.

View larger image (TIF File)

Table 3Table 3 – Factors to consider for interactions, effect modification, and potential confounding.

View larger image (TIF File)

Table 4Table 4 – Diagnostic criteria for human studies (BMJ Group 2011).

View larger image (TIF File)

Many of the research gaps were not unique to the field of diabetes/obesity research. The workshop noted a) deficiencies in data on human exposures to many of the chemicals examined, b) the need for better biomarkers of exposure that may be related mechanistically to the disease end points, c) the need for a better understanding of the basic biology of adipocytes, β cells, and neural circuits that regulate feeding behavior in healthy and disease states, and d) the need for an appreciation of how the biology that controls body weight and metabolic set points changes with life stage. A number of the breakout groups noted the need to consider non-monotonic dose–-response relationships for environmental influences on obesity and diabetes. Also, there is a need to consider coexposures between environmental chemicals and consumption of high-calorie, high-carbohydrate, and/or high-fat diets. Finally, workshop participants found the incorporation of HTS information from the Tox21 program to be an intriguing and useful way of improving our understanding of the similarities and differences in biological actions across classes of chemicals and recommended many specific targets for further assay development to further enhance its utility.

NIEHS has already taken steps to address some of the research needs, recognizing that the work will best be accomplished through the combined efforts of the NTP, the NIEHS Division of Extramural Research and Training (DERT), and the NIEHS Division of Intramural Research. Based on the results of this workshop and the data gaps noted, the DERT released program announcements focused on improving our understanding of the role of environmental exposures in the develop-ment of obesity and diabetes (see NIEHS 2011a, 2011b). The announcements have one receipt date per year for the next 3 years. The NTP is organizing further in vitro targeted testing of some of the predictions of chemical effects from the Tox21 screening program and is specifically developing an analytical method to measure organotins in human blood because the lack of exposure data to these compounds was considered a critical research need.

We hope this workshop will stimulate further research to better understand the public health impacts of environmental influences on the increasing international prevalence of diabetes, obesity, and metabolic syndrome. We acknowledge the dedicated efforts of the workshop participants toward achieving this goal.


References

Adams AK, Harvey HE, Prince RJ. 2005. Association of maternal smoking with overweight at age 3 y in American Indian children. Am J Clin Nutr 82(2):393–398.

Adigun AA, Wrench N, Levin ED, Seidler FJ, Slotkin TA. 2010a. Neonatal parathion exposure and interactions with a high-fat diet in adulthood: adenylyl cyclase-mediated cell signaling in heart, liver and cerebellum. Brain Res Bull 81(6):605–612.

Adigun AA, Wrench N, Seidler FJ, Slotkin TA. 2010b. Neonatal dexamethasone treatment leads to alterations in cell signaling cascades controlling hepatic and cardiac function in adulthood. Neurotoxicol Teratol 32(2):193–199.

Adigun AA, Wrench N, Seidler FJ, Slotkin TA. 2010c. Neonatal organophosphorus pesticide exposure alters the developmental trajectory of cell-signaling cascades controlling metabolism: differential effects of diazinon and parathion. Environ Health Perspect 118:210–215.

Agency for Toxic Substances and Disease Registry. 1997. Toxicological Profile for Chlorpyrifos. Atlanta, GA:U.S. Department of Health and Human Services, Public Health Service. Available: http://www.atsdr.cdc.gov/ToxProfiles/tp.​asp?id=495&tid=88 [accessed 12 December 2011].

Aguilar MV, Martinez-Para MC, Gonzalez MJ. 1997. Effects of arsenic (V)–chromium (III) interaction on plasma glucose and cholesterol levels in growing rats. Ann Nutr Metab 41(3):189–195.

Al Mamun A, Lawlor DA, Alati R, O’Callaghan MJ, Williams GM, Najman JM. 2006. Does maternal smoking during pregnancy have a direct effect on future offspring obesity? Evidence from a prospective birth cohort study. Am J Epidemiol 164(4):317–325.

Alonso-Magdalena P, Vieira E, Soriano S, Menes L, Burks D, Quesada I, et al. 2010. Bisphenol A exposure during pregnancy disrupts glucose homeostasis in mothers and adult male offspring. Environ Health Perspect 118:1243–1250.

American Diabetes Association. 2011. Prediabetes FAQs. Available: http://www.diabetes.org/diabetes-basics/​prevention/pre-diabetes/pre-diabetes-faq​s.html [accessed 8 December 2011].

Arnold LL, Eldan M, van Gemert M, Capen CC, Cohen SM. 2003. Chronic studies evaluating the carcinogenicity of monomethyl-arsonic acid in rats and mice. Toxicology 190(3):197–219.

Bergmann KE, Bergmann RL, Von Kries R, Bohm O, Richter R, Dudenhausen JW, et al. 2003. Early determinants of childhood overweight and adiposity in a birth cohort study: role of breast-feeding. Int J Obes Relat Metab Disord 27(2):162–172.

Bility MT, Thompson JT, McKee RH, David RM, Butala JH, Vanden Heuvel JP, et al. 2004. Activation of mouse and human peroxisome proliferator-activated receptors (PPARs) by phthalate monoesters. Toxicol Sci 82(1):170–182.

BMJ Group. 2011. Best Practices Reference Material. Available: http://bestpractice.bmj.com/best-practic​e/welcome.html [accessed 12 December 2011].

Boas M, Frederiksen H, Feldt-Rasmussen U, Skakkebaek NE, Hegedus L, Hilsted L, et al. 2010. Childhood exposure to phthalates: associations with thyroid function, insulin-like growth factor I, and growth. Environ Health Perspect 118:1458–1464.

Boerschmann H, Pfluger M, Henneberger L, Ziegler AG, Hummel S. 2010. Prevalence and predictors of overweight and insulin resistance in offspring of mothers with gestational diabetes mellitus. Diabetes Care 33(8):1845–1849.

Boyles AL, Harris SF, Rooney AA, Thayer KA. 2011. Forest Plot Viewer: a fast, flexible graphing tool. Epidemiology 22(5):746–747.

Braun JM, Daniels JL, Poole C, Olshan AF, Hornung R, Bernert JT, et al. 2010. Prenatal environmental tobacco smoke exposure and early childhood body mass index. Paediatr Perinat Epidemiol 24(6):524–534.

Broadmeadow A, Lee P, Ashby R, et al. 1984. Princeton, NJ:Life Science Research. 104 Week Combined Toxicity and Oncogenicity Study in Dietary Administration to CD Rats: Using NF-114: Rd-84113. Report No. 83/Nis004/212. Unpublished study prepared by Life Science Research

Caksen H, Odabas D, Arslan S, Akgun C, Atas B, Akbayram S, et al. 2003. Report of eight children with amitraz intoxication. Hum Exp Toxicol 22(2):95–97.

CDC (Centers for Disease Control and Prevention). 2011. National Diabetes Data & Trends. Available: http://apps.nccd.cdc.gov/DDTSTRS/default​.aspx [accessed 12 December 2011].

Chan P. 1987. NTP Technical Report on the Toxicology and Carcinogenesis Studies of Dichlorvos (CAS No. 62-73-7) in F344/N Rats and B63F1 Mice (Gavage Studies). NTP TR 342.

Chen A, Pennell ML, Klebanoff MA, Rogan WJ, Longnecker MP. 2006. Maternal smoking during pregnancy in relation to child overweight: follow-up to age 8 years. Int J Epidemiol 35(1):121–130.

Chen T-H, Hsu WH. 1994. Inhibition of insulin release by a form-amidine pesticide amitraz and its metabolites in a rat beta-cell line: an action mediated by alpha-2 adrenoceptors, a GTP-binding protein and a decrease in cyclic AMP. J Pharmacol Exp Ther 271(3):1240–1245.

Chen Y, Ahsan H, Slavkovich V, Peltier GL, Gluskin RT, Parvez F, et al. 2010. No association between arsenic exposure from drinking water and diabetes mellitus: a cross-sectional study in Bangladesh. Environ Health Perspect 118:1299–1305.

Cobo JM, Castineira M. 1997. Oxidative stress, mitochondrial respiration, and glycemic control: clues from chronic supplementation with Cr3+ or As3+ to male Wistar rats. Nutrition 13(11–12):965–970.

Codru N, Schymura MJ, Negoita S, Rej R, Carpenter DO. 2007. Diabetes in relation to serum levels of polychlorinated biphenyls and chlorinated pesticides in adult Native Americans. Environ Health Perspect 115:1442–1447.

Colosio C, Tomasini M, Cairoli S, Foa V, Minoia C, Marinovich M, et al. 1991. Occupational triphenyltin acetate poisoning: a case report. Br J Ind Med 48(2):136–139.

Coronado-Gonzalez JA, Del Razo LM, Garcia-Vargas G, Sanmiguel-Salazar F, Escobedo-de la Pena J. 2007. Inorganic arsenic exposure and type 2 diabetes mellitus in Mexico. Environ Res 104(3):383–389.

Daly I. 1996. Princeton, NJ:Huntingdon Life Sciences. A 24-Month Oral Toxicity/Oncogenicity Study of Malathion in the Rat via Dietary Administration: Final Report: Lab Project No. 90-3641; J-11 90-3641. Unpublished study prepared by Huntingdon Life Sciences

Dean A, Sullivan K, Soe M. 2011. OpenEpi: Open Source Epidemiologic Statistics for Public Health. Version 2.3.1, updated 2011/23/06. Available: http://www.OpenEpi.com [accessed 8 December 2011].

Del Razo LM, Garcia-Vagras GG, Valenzuela OL, Hernandez Castellanos E, Sánchez-Peña LC, Currier JM, et al. 2011. Exposure to arsenic in drinking water is associated with increased prevalence of diabetes: a cross-sectional study in the Zimapan and Lagunera Regions in Mexico. Environmental Health 10(1):73; doi: 10.1186/1476-069X-10-73 [Online 24 August 2011].

Diaz-Villasenor A, Burns AL, Hiriart M, Cebrian ME, Ostrosky-Wegman P. 2007. Arsenic-induced alteration in the expression of genes related to type 2 diabetes mellitus. Toxicol Appl Pharmacol 225(2):123–133.

Diaz-Villasenor A, Burns AL, Salazar AM, Sordo M, Hiriart M, Cebrian ME, et al. 2008. Arsenite reduces insulin secretion in rat pancreatic β-cells by decreasing the calcium-dependent calpain-10 proteolysis of SNAP-25. Toxicol Appl Pharmacol 231(3):291–299.

Druwe IL, Vaillancourt RR. 2010. Influence of arsenate and arsenite on signal transduction pathways: an update. Arch Toxicol 84(8):585–596.

Dubois L, Girard M. 2006. Early determinants of overweight at 4.5 years in a population-based longitudinal study. Int J Obes (Lond) 30(4):610–617.

Durmus B, Kruithof CJ, Gillman MH, Willemsen SP, Hofman A, Raat H, et al. 2011. Parental smoking during pregnancy, early growth, and risk of obesity in preschool children: the Generation R Study. Am J Clin Nutr 94(1):164–171.

Eiben R. 1989. Pittsburgh, PA:Mobay Chemical Corporation. MAT 7484: Study of the Subchronic Toxicity to Wistar Rats (Administration in the Feed for 86 Days): Lab Project No. 99266; T1023097. Unpublished study prepared by Mobay Chemical Corporation

Eiben R. 1990. Pittsburgh, PA:Bayer Corporation. MAT 7484: Study of the Chronic Toxicity and Carcinogenicity to B6C3F1 Mice: (Administration in the Feed for 24 Months): Lab Project No. 100669; 19823; T2024097. Unpublished study prepared by Bayer Corporation

Eiben R. 1991. Pittsburgh, PA:Bayer Corporation. Methyl parathion: Study for Chronic Toxicity and Carcinogenicity in B6C3F1 Mice: Administration in the Diet over a Period of 24 Months: Lab Project No. T 4027023. Unpublished study prepared by Bayer Corporation

Elinav E, Shapira Y, Ofran Y, Hassin T, Ben-Dov IZ. 2005. Near-fatal amitraz intoxication: the overlooked pesticide. Basic Clin Pharmacol Toxicol 97(3):185–187.

Ertekin V, Alp H, Selimoglu MA, Karacan M. 2002. Amitraz poisoning in children: retrospective analysis of 21 cases. J Int Med Res 30(2):203–205.

Ettinger A. 2009. Maternal arsenic exposure in relation to maternal and child adiposity and risk factors for diabetes. Epidemiology 20(6):S234–S235.

Eyre H, Kahn R, Robertson RM. 2004. Preventing cancer, cardio-vascular disease, and diabetes: a common agenda for the American Cancer Society, the American Diabetes Association, and the American Heart Association. CA Cancer J Clin 54(4):190–207.

Feige J, Gerber A, Casals-Casas C, Yang Q, Winkler C, Bedu E, et al. 2010. The pollutant diethylhexyl phthalate regulates hepatic energy metabolism via species-specific PPARα-dependent mechanisms. Environ Health Perspect 118:234–241.

Franklin IK, Wollheim CB. 2004. GABA in the endocrine pancreas: its putative role as an islet cell paracrine-signalling molecule. J Gen Physiol 123(3):185–190.

Fu J, Woods CG, Yehuda-Shnaidman E, Zhang Q, Wong V, Collins S, et al. 2010. Low-level arsenic impairs glucose–stimulated insulin secretion in pancreatic beta-cells: involvement of cellular adaptive response to oxidative stress. Environ Health Perspect 118:864–870.

Gallanosa AG, Spyker DA, Curnow RT. 1981. Diabetes mellitus associated with autonomic and peripheral neuropathy after Vacor rodenticide poisoning: a review. Clin Toxicol 18(4):441–449.

Gillman MW, Rifas-Shiman SL, Kleinman K, Oken E, Rich-Edwards JW, Taveras EM. 2008. Developmental origins of childhood overweight: potential public health impact. Obesity 16(7):1651–1656.

Goodyer M. 1987. Harrogate, UK:Hazleton Laboratories Corporation. CGA-18809: Lifetime Oral (Dietary Administration) Oncogenicity Study in the Mouse: Lab Project No. 84 1214. Unpublished study prepared by Hazleton Laboratories Corporation

Gorog K, Pattenden S, Antova T, Niciu E, Rudnai P, Scholtens S, et al. 2009. Maternal smoking during pregnancy and childhood obesity: results from the CESAR study. Matern Child Health J 15(7):985–992.

Grun F, Blumberg B. 2006. Environmental obesogens: organotins and endocrine disruption via nuclear receptor signaling. Endocrinology 147(6): supplS50–S55.

Grun F, Watanabe H, Zamanian Z, Maeda L, Arima K, Cubacha R, et al. 2006. Endocrine-disrupting organotin compounds are potent inducers of adipogenesis in vertebrates. Mol Endocrinol 20(9):2141–2155.

Hatch EE, Nelson JW, Qureshi MM, Weinberg J, Moore LL, Singer M, et al. 2008. Association of urinary phthalate metabolite concentrations with body mass index and waist circumference: a cross-sectional study of NHANES data, 1999–2002. Environ Health 7:27; doi: 10.1186/1476-069X-7-27 [Online 3 June 2008].

Hayes R. 1989. Pittsburgh, PA:Mobay Chemical Corporation. Oncogenicity Study of Technical Grade Tribufos (DEF) with Mice: Study No. 86-271-01. Unpublished study prepared by Mobay Chemical Corporation

Hill DS, Wlodarczyk BJ, Mitchell LE, Finnell RH. 2009. Arsenate-induced maternal glucose intolerance and neural tube defects in a mouse model. Toxicol Appl Pharmacol 239(1):29–36.

Hiromori Y, Nishikawa J, Yoshida I, Nagase H, Nakanishi T. 2009. Structure-dependent activation of peroxisome proliferator–activated receptor (PPAR) γ by organotin compounds. Chem Biol Interact 180(2):238–244.

Hugnet C, Buronrosse F, Pineau X, Cadore JL, Lorgue G, Berny PJ. 1996. Toxicity and kinetics of amitraz in dogs. Am J Vet Res 57(10):1506–1510.

Hurst CH, Waxman DJ. 2003. Activation of PPARα and PPARγ by environmental phthalate monoesters. Toxicol Sci 74(2):297–308.

Icenogle LM, Christopher NC, Blackwelder WP, Caldwell DP, Qiao D, Seidler FJ, et al. 2004. Behavioral alterations in adolescent and adult rats caused by a brief subtoxic exposure to chlorpyrifos during neurulation. Neurotoxicol Teratol 26(1):95–101.

Iliadou AN, Koupil I, Villamor E, Altman D, Hultman C, Langstrom N, et al. 2010. Familial factors confound the association between maternal smoking during pregnancy and young adult offspring overweight. Int J Epidemiol 39(5):1193–1202.

Inadera H, Shimomura A. 2005. Environmental chemical tri-butyl-tin augments adipocyte differentiation. Toxicol Lett 159(3):226–234.

Ino T. 2010. Maternal smoking during pregnancy and offspring obesity: meta-analysis. Pediatr Int 52(1):94–99.

Ino T, Shibuya T, Saito K, Ohtani T. 2011. Effects of maternal smoking during pregnancy on body composition in offspring. Pediatr Int 53(6):851–857.

Itsuki-Yoneda A, Kimoto M, Tsuji H, Hiemori M, Yamashita H. 2007. Effect of a hypolipidemic drug, di (2-ethylhexyl) phthalate, on mRNA-expression associated fatty acid and acetate metabolism in rat tissues. Biosci Biotechnol Biochem 71(2):414–420.

Izquierdo-Vega JA, Soto CA, Sanchez-Pena LC, De Vizcaya-Ruiz A, Del Razo LM. 2006. Diabetogenic effects and pancreatic oxidative damage in rats subchronically exposed to arsenite. Toxicol Lett 160(2):135–142.

Janesick A, Blumberg B. 2011. Minireview: PPARI3 as the target of obesogens. J Steroid Biochem Mol Biol 127(1–2):4–8.

Jørgensen ME, Borch-Johnsen K, Bjerregaard P. 2008. A cross-sectional study of the association between persistent organic pollutants and glucose intolerance among Greenland Inuit. Diabetologia 51(8):1416–1422.

Kahn BB, McGraw TE. 2010. Rosiglitazone, PPARγ, and type 2 diabetes. N Engl J Med 363(27):2667–2669.

Kanayama T, Kobayashi N, Mamiya S, Nakanishi T, Nishikawa J. 2005. Organotin compounds promote adipocyte differentiation as agonists of the peroxisome proliferator-activated receptor γ/retinoid X receptor pathway. Mol Pharmacol 67(3):766–774.

Kannan K, Takahashi S, Fujiwara N, Mizukawa H, Tanabe S. 2010. Organotin compounds, including butyltins and octyltins, in house dust from Albany, New York, USA. Arch Environ Contam Toxicol 58(4):901–907.

Karam JH, Lewitt PA, Young CW, Nowlain RE, Frankel BJ, Fujiya H, et al. 1980. Insulinopenic diabetes after rodenticide (Vacor) ingestion: a unique model of acquired diabetes in man. Diabetes 29(12):971–978.

Karami-Mohajeri S, Abdollahi M. 2010. Toxic effects of organophosphate, carbamate, and organochlorine pesticides on cellular metabolism of lipids, proteins, and carbohydrates: a comprehensive review. Hum Exp Toxicol 30(9):1119–1140.

Kennel O, Prince C, Garnier R. 1996. Four cases of amitraz poisoning in humans. Vet Hum Toxicol 38(1):28–30.

Kirchner S, Kieu T, Chow C, Casey S, Blumberg B. 2010. Prenatal exposure to the environmental obesogen tributyltin predisposes multipotent stem cells to become adipocytes. Mol Endocrinol 24(3):526–539.

Klimentidis YC, Beasley TM, Lin HY, Murati G, Glass GE, Guyton M, et al. 2010. Canaries in the coal mine: a cross-species analysis of the plurality of obesity epidemics. Proc Biol Sci 278:( 1712. ):1626–1632.

Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. 2002. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346(6):393–403.

Koshy G, Delpisheh A, Brabin BJ. 2011. Dose response association of pregnancy cigarette smoke exposure, childhood stature, overweight and obesity. Eur J Public Health 21(3):286–291.

Koupil I, Toivanen P. 2008. Social and early-life determinants of overweight and obesity in 18-year-old Swedish men. Int J Obes (Lond) 32(1):73–81.

Kowalski R, Clemens G, Jasty V, et al. 1989. Elkhart, IN:Miles Inc., Toxicology Department. A Two-Generation Reproduction Study with Fenthion (Baytex) in the Rat: Lab Project No. 99811; 1166; 8765. Unpublished study prepared by Miles Inc

Kuhle S, Allen AC, Veugelers PJ. 2010. Perinatal and childhood risk factors for overweight in a provincial sample of Canadian grade 5 students. Int J Pediatr Obes 5(1):88–96.

Lai MS, Hsueh YM, Chen CJ, Shyu MP, Chen SY, Kuo TL, et al. 1994. Ingested inorganic arsenic and prevalence of diabetes mellitus. Am J Epidemiol 139(5):484–492.

Lang IA, Galloway TS, Scarlett A, Henley WE, Depledge M, Wallace RB, et al. 2008. Association of urinary bisphenol A concentration with medical disorders and laboratory abnormalities in adults. JAMA 300(11):1303–1310.

Lassiter TL, Ryde IT, Levin ED, Seidler FJ, Slotkin TA. 2010. Neonatal exposure to parathion alters lipid metabolism in adulthood: Interactions with dietary fat intake and implications for neurodevelopmental deficits. Brain Res Bull 81(1):85–91.

Lassiter TL, Ryde IT, Mackillop EA, Brown KK, Levin ED, Seidler FJ, et al. 2008. Exposure of neonatal rats to parathion elicits sex-selective reprogramming of metabolism and alters the response to a high-fat diet in adulthood. Environ Health Perspect 116:1456–1462.

le Maire A, Grimaldi M, Roecklin D, Dagnino S, Vivat-Hannah V, Balaguer P, et al. 2009. Activation of RXR-PPAR hetero-dimers by organotin environmental endocrine disruptors. EMBO Rep 10(4):367–373.

Lee D-H, Lee I-K, Song K, Steffes M, Toscano W, Baker BA, et al. 2006. A strong dose-response relation between serum concentrations of persistent organic pollutants and diabetes: results from the National Health and Examination Survey 1999–2002. Diabetes Care 29(7):1638–1644.

Lee D-H, Steffes MW, Sjodin A, Jones RS, Needham LL, Jacobs JDR. 2010. Low dose of some persistent organic pollutants predicts type 2 diabetes: a nested case–control study. Environ Health Perspect 118:1235–1242.

Leser K, Suberg H. 1990. Pittsburgh, PA:Bayer Corporation. E 1752: Oncogenicity Study on B6C3F1 Mice (Feeding Study for Periods of up to 24 Months): Lab Project No. 100581; 19624. Unpublished study prepared by Bayer Corporation.

Levin ED. 2005. Fetal nicotinic overload, blunted sympathetic responsivity, and obesity. Birth Defects Res A Clin Mol Teratol 73(7):481–484.

Levin ED, Addy N, Baruah A, Elias A, Christopher NC, Seidler FJ, et al. 2002. Prenatal chlorpyrifos exposure in rats causes persistent behavioral alterations. Neurotoxicol Teratol 24(6):733–741.

Li X, Ycaza J, Blumberg B. 2011. The environmental obesogen tributyltin chloride acts via peroxisome proliferator activated receptor gamma to induce adipogenesis in murine 3T3-L1 preadipocytes. J Steroid Biochem Mol Biol 127:9–15.

Lina B, Til H, van Nesselrooij J, et al. 1983. Delft, Netherlands:Netherlands Organization for Applied Scientific Research. Six-Month Oral Toxicity Study with Imazalil Base-R 23979 in Rats: Report No. V 83.186/220555. Unpublished study prepared by Netherlands Organization for Applied Scientific Research

Luginbuehl H. 1980. Basel, Switzerland:Sandoz Ltd. Propetamphos: Chronic Feeding Study In Rats: Project No. 279. Unpublished study prepared by Sandoz Ltd

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

Manzo L, Richelmi P, Sabbioni E, Pietra R, Bono F, Guardia L. 1981. Poisoning by triphenyltin acetate. Report of two cases and determination of tin in blood and urine by neutron activation analysis. Clin Toxicol 18(11):1343–1353.

Martinelli MI, Mocchiutti NO, Bernal CA. 2010. Effect of di(2-ethylhexyl) phthalate (DEHP) on lipolysis and lipoprotein lipase activities in adipose tissue of rats. Hum Exp Toxicol 29(9):739–745.

Melzer D, Rice NE, Lewis C, Henley WE, Galloway TS. 2010. Association of urinary bisphenol A concentration with heart disease: evidence from NHANES 2003/06. PLoS One 5(1):e8673; doi: 10.1371/journal.pone.0008673 [Online 13 January 2010].

Miller LV, Stokes JD, Silpipat C. 1978. Diabetes mellitus and autonomic dysfunction after Vacor rodenticide ingestion. Diabetes Care 1(2):73–76.

Mindel JS. 1986. N-3-pyridylmethyl-N’-p-nitrophenylurea ocular toxicity in man and an animal model. Trans Am Ophthalmol Soc 84:389–428.

Miyawaki J, Sakayama K, Kato H, Yamamoto H, Masuno H. 2007. Perinatal and postnatal exposure to bisphenol A increases adipose tissue mass and serum cholesterol level in mice. J Atheroscler Thromb 14(5):245–252.

Mizutani T, Suzuki K, Kondo N, Yamagata Z. 2007. Association of maternal lifestyles including smoking during pregnancy with childhood obesity. Obesity (Silver Spring) 15(12):3133–3139.

Mobay Chemical Corporation. 1983. Pittsburgh, PA:Mobay Chemical Corporation. Oncogenicity Study of Technical Disulfoton on Mice. Interim report, unpublished study prepared by Mobay Chemical Corporation

Montgomery SM, Ekbom A. 2002. Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. BMJ 324(7328):26–27.

Nabi AH, Rahman MM, Islam LN. 2005. Evaluation of biochemical changes in chronic arsenic poisoning among Bangladeshi patients. Int J Environ Res Pubic Health 2(3–4):385–393.

Nakanishi T, Nishikawa J, Hiromori Y, Yokoyama H, Koyanagi M, Takasuga S, et al. 2005. Trialkyltin compounds bind retinoid X receptor to alter human placental endocrine functions. Mol Endocrinol 19(10):2502–2516.

National Institute for Occupational Safety and Health. 1976. NIOSH Criteria Documents: Criteria for a Recommended Standard: Occupational Exposure to Organotin Compounds. DHHS (NIOSH) Publication 77-115. Available: http://www.cdc.gov/niosh/77-115.html [accessed 12 December 2011].

National Institute of Diabetes and Digestive and Kidney Diseases. 2011. Diabetes Research Strategic Plan. Available: http://www2.niddk.nih.gov/AboutNIDDK/Rep​ortsAndStrategicPlanning/DiabetesPlan/Pl​anPosting.htm [accessed 12 December 2011].

Navas-Acien A, Silbergeld EK, Streeter RA, Clark JM, Burke TA, Guallar E. 2006. Arsenic exposure and type 2 diabetes: a systematic review of the experimental and epidemiological evidence. Environ Health Perspect 114:641–648.

Naylor M, Ruecker F. 1997. Creve Coeur, MO: Monsanto Corporation..Combined Chronic Toxicity/Oncogenicity Study of Mon 37500 Administered in the Diet to Sprague-Dawley Rats: Lab Project No. Ml-94-118: Ehl 94051: Rd 1353. Unpublished study prepared by Monsanto Corporation.

Newman MB, Shytle RD, Sanberg PR. 1999. Locomotor behavioral effects of prenatal and postnatal nicotine exposure in rat offspring. Behav Pharmacol 10(6–7):699–706.

NIEHS (National Institute of Environmental Health Sciences). 2011a. Program Announcement PAR-11-170. Available: http://grants.nih.gov/grants/guide/pa-fi​les/PAR-11-170.html [accessed 23 February 2012].

NIEHS (National Institute of Environmental Health Sciences). 2011b. Program Announcement PAR-11-171. Available: http://grants.nih.gov/grants/guide/pa-fi​les/PAR-11-171.html [accessed 23 February 2012].

NIH (National Institutes of Health). 2011. Strategic Plan for NIH Obesity Research. Available: http://www.obesityresearch.nih.gov/about​/strategic-plan.aspx [accessed 12 December 2011].

Nishikawa J, Mamiya S, Kanayama T, Nishikawa T, Shiraishi F, Horiguchi T. 2004. Involvement of the retinoid X receptor in the development of imposex caused by organotins in gastropods. Environ Sci Technol 38(23):6271–6276.

NTP (National Toxicology Program). 2011a. High Throughput Screening Initiative. Available: http://ntp.niehs.nih.gov/go/28213 [accessed 1 December 2011].

NTP (National Toxicology Program). 2011b. NTP Workshop: Role of Environmental Chemicals in the Development of Diabetes and Obesity. Available: http://ntp.niehs.nih.gov/go/36433 [accessed 1 December 2011].

NTP (National Toxicology Program). 2012. Meta Data Viewer. Available: http://ntp.niehs.nih.gov/go/tools_metada​taviewer [accessed 24 February 2012].

Ogden C, Carroll M. 2010. CDC-NCHS Health E-Stat. Prevalence of Obesity among Children and Adolescents: United States, Trends 1963–1965 through 2007–2008. Available: http://www.cdc.gov/nchs/data/hestat/obes​ity_child_07_08/obesity_child_07_08.htm [accessed 12 December 2011].

Oken E, Huh SY, Taveras EM, Rich-Edwards JW, Gillman MW. 2005. Associations of maternal prenatal smoking with child adiposity and blood pressure. Obes Res 13(11):2021–2028.

Oken E, Levitan EB, Gillman MW. 2008. Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis. Int J Obes (Lond) 32(2):201–210.

Oliveira E, Moura EG, Santos-Silva AP, Fagundes AT, Rios AS, Abreu-Villaca Y, et al. 2009. Short- and long-term effects of maternal nicotine exposure during lactation on body adiposity, lipid profile, and thyroid function of rat offspring. J Endocrinol 202(3):397–405.

Oliveira E, Moura EG, Santos-Silva AP, Pinheiro CR, Lima N, Nogueira-Neto J, et al. 2010a. Neonatal nicotine exposure causes insulin and leptin resistance and inhibits hypothalamic leptin signaling in adult rat offspring. J Endocrinol 206(1):55–63.

Oliveira E, Pinheiro C, Santos-Silva A, Trevenzoli I, Abreu-Villaca Y, Nogueira Neto J, et al. 2010b. Nicotine exposure affects mother’s and pup’s nutritional, biochemical and hormonal profiles during lactation in rats. J Endocrinol 205(2):159–170.

Osheroff M. 1991. Vienna, VA:Hazleton Washington Inc. RH-5992: 13 Week Dietary Toxicity Study in Rats: Final Report: Lab Project No. 417-463: 89RC-101. Unpublished study prepared by Hazleton Washington Inc

Paul DS, Devesa V, Hernandez-Zavala A, Adair BM, Walton FS, Drobna B, et al. 2008. Environmental arsenic as a disruptor of insulin signaling. In:Metal Ions in Biology and Medicine. Vol 10 (Collery P, Maynard I, Theophanides T, Khassanova L, Callery T, eds). Paris:John Libbey Eurotext, 1–7.

Paul DS, Hernandez-Zavala A, Walton FS, Adair BM, Dedina J, Matousek T, et al. 2007. Examination of the effects of arsenic on glucose homeostasis in cell culture and animal studies: development of a mouse model for arsenic-induced dia-betes. Toxicol Appl Pharmacol 222(3):305–314.

Peters KS, Tong TG, Kutz K, Benowitz NL. 1981. Diabetes mellitus and orthostatic hypotension resulting from ingestion of Vacor rat poison: endocrine and autonomic function studies. West J Med 134(1):65–68.

Pettersen J, Morrissey R. 1996. Farmington, CT:Ciba-Geigy Corporation, Environmental Health Center. 2-Year Chronic Toxicity/Oncogenicity Study with Cga-277476 Technical in Rats: Final Report: Lab Project No. F-00147. Unpublished study prepared by Ciba-Geigy Corporation

Pont A, Rubino JM, Bishop D, Peal R. 1979. Diabetes mellitus and neuropathy following Vacor ingestion in man. Arch Intern Med 139(2):185–187.

Power C, Atherton K, Thomas C. 2010. Maternal smoking in pregnancy, adult adiposity and other risk factors for cardiovascular disease. Atherosclerosis 211(2):643–648.

Power C, Jefferis BJ. 2002. Fetal environment and subsequent obesity: a study of maternal smoking. Int J Epidemiol 31(2):413–419.

Prosser PR, Karam JH. 1978. Diabetes mellitus following rodenti-cide ingestion in man. JAMA 239(12):1148–1150.

Rahimi R, Abdollahi M. 2007. A review on the mechanisms involved in hyperglycemia induced by organophosphorus pesticides. Pestic Biochem Physiol 88:115–121.

Rahman M, Tondel M, Ahmad SA, Axelson O. 1998. Diabetes mellitus associated with arsenic exposure in Bangladesh. Am J Epidemiol 148(2):198–203.

Rahman M, Tondel M, Chowdhury IA, Axelson O. 1999. Relations between exposure to arsenic, skin lesions, and glucosuria. Occup Environ Med 56(4):277–281.

Raum E, Kupper-Nybelen J, Lamerz A, Hebebrand J, Herpertz-Dahlmann B, Brenner H. 2011. Tobacco smoke exposure before, during, and after pregnancy and risk of overweight at age 6. Obesity (Silver Spring) 19(12):2411–2417.

Reilly JJ, Armstrong J, Dorosty AR, Emmett PM, Ness A, Rogers I, et al. 2005. Early life risk factors for obesity in childhood: cohort study. BMJ 330(7504):1357; doi: 10.1136/bmj.38470.670903.E0 [Online 9 June 2005].

Rignell-Hydbom A, Lidfeldt J, Kiviranta H, Rantakokko P, Samsioe G, Agardh C-D, et al. 2009. Exposure to p,p’-DDE: a risk factor for type 2 diabetes. PLoS One 4(10):e7503; doi: 10.1371/journal.pone.0007503 [Online 19 October 2009].

Roegge CS, Timofeeva OA, Seidler FJ, Slotkin TA, Levin ED. 2008. Developmental diazinon neurotoxicity in rats: later effects on emotional response. Brain Res Bull 75(1):166–172.

Rooney BL, Mathiason MA, Schauberger CW. 2010. Predictors of obesity in childhood, adolescence, and adulthood in a birth cohort. Matern Child Health J 15(8):1166–1175.

Ryan KK, Haller AM, Sorrell JE, Woods SC, Jandacek RJ, Seeley RJ. 2010. Perinatal exposure to bisphenol A and the development of metabolic syndrome in CD-1 mice. Endocrinology 151(6):2603–2612.

Rylander L, Rignell-Hydbom A, Hagmar L. 2005. A cross-sectional study of the association between persistent organochlorine pollutants and diabetes. Environ Health 4:28; doi: 10.1186/1476-069X-4-28 [Online 29 November 2005].

Sakurai T, Miyazawa S, Hashimoto T. 1978. Effects of di-(2-ethyl-hexyl)-phthalate administration on carbohydrate and fatty acid metabolism in rat liver. J Biochem 83(1):313–320.

Salsberry PJ, Reagan PB. 2005. Dynamics of early childhood overweight. Pediatrics 116(6):1329–1338.

Salsberry PJ, Reagan PB. 2007. Taking the long view: the prenatal environment and early adolescent overweight. Res Nurs Health 30(3):297–307.

Santos-Silva AP, Moura EG, Pinheiro CR, Rios AS, Abreu-Villaca Y, Passos MC, et al. 2010. Neonatal nicotine exposure alters leptin signaling in the hypothalamus–pituitary–thyroid axis in the late postnatal period and adulthood in rats. Life Sci 87(5–6):187–195.

Santos-Silva AP, Oliveira E, Pinheiro CR, Nunes-Freitas AL, Abreu-Villaca Y, Santana AC, et al. 2011. Effects of tobacco smoke exposure during lactation on nutritional and hormonal profiles in mothers and offspring. J Endocrinol 209(1):75–84.

Satin LS, Kinard TA. 1998. Neurotransmitters and their receptors in the islets of Langerhans of the pancreas: what messages do acetylcholine, glutamate, and GABA transmit? Endocrine 8(3):213–223.

Sharma AJ, Cogswell ME, Li R. 2008. Dose-response associations between maternal smoking during pregnancy and subsequent childhood obesity: effect modification by maternal race/ethnicity in a low-income US cohort. Am J Epidemiol 168(9):995–1007.

Slotkin TA. 2010. Does early-life exposure to organophosphate insecticides lead to prediabetes and obesity? Reprod Toxicol 31(3):297–301.

Slotkin TA, Brown KK, Seidler FJ. 2005. Developmental exposure of rats to chlorpyrifos elicits sex-selective hyperlipidemia and hyperinsulinemia in adulthood. Environ Health Perspect 113:1291–1294.

Slotkin TA, Lassiter TL, Ryde IT, Wrench N, Levin ED, Seidler FJ. 2009. Consumption of a high-fat diet in adulthood ameliorates the effects of neonatal parathion exposure on acetylcholine systems in rat brain regions. Environ Health Perspect 117:916–922.

Smith BE, Hsu WH, Yang PC. 1990. Amitraz-induced glucose intolerance in rats: antagonism by yohimbine but not by prazosin. Arch Toxicol 64(8):680–683.

Somm E, Schwitzgebel VM, Toulotte A, Cederroth CR, Combescure C, Nef S, et al. 2009. Perinatal exposure to bisphenol A alters early adipogenesis in the rat. Environ Health Perspect 117:1549–1555.

Somm E, Schwitzgebel VM, Vauthay DM, Camm EJ, Chen CY, Giacobino J-P, et al. 2008. Prenatal nicotine exposure alters early pancreatic islet and adipose tissue development with consequences on the control of body weight and glucose metabolism later in life. Endocrinology 149(12):6289–6299.

Squire R. 1988. Pasadena, MD:Robert A. Squire Associates Inc. An Evaluation of Vascular Proliferative Lesions in Male Wistar Rats from Project 70C0326/8241: Chronic Toxicity and Oncogenicity: Dimethoate. Unpublished study prepared by Robert A. Squire Associates Inc

Stahlhut RW, van Wijngaarden E, Dye TD, Cook S, Swan SH. 2007. Concentrations of urinary phthalate metabolites are associated with increased waist circumference and insulin resistance in adult U.S. males. Environ Health Perspect 115:876–882.

Sungur M, Guven M. 2001. Intensive care management of organophosphate insecticide poisoning. Crit Care 5(4):211–215.

Suzuki K, Ando D, Sato M, Tanaka T, Kondo N, Yamagata Z. 2009. The association between maternal smoking during pregnancy and childhood obesity persists to the age of 9–10 years. J Epidemiol 19(3):136–142.

Tai C. 1985. Basel, Switzerland:Ciba-Geigy Ltd. CGA-131036 Technical: 90-day Oral Toxicity Study in Rats: Lab Study No. 85021. Unpublished study prepared by Ciba-Geigy Ltd.

Takayanagi S, Tokunaga T, Liu X, Okada H, Matsushima A, Shimohigashi Y. 2006. Endocrine disruptor bisphenol A strongly binds to human estrogen-related receptor gamma (ERRgamma) with high constitutive activity. Toxicol Lett 167(2):95–105.

Tobia A. 1987. Newark, DE:EI DuPont de Nemours and Company, Haskell Laboratory for Toxicology and Industrial Medicine. Combined Chronic Toxicity/Oncogenicity Study with INL-5300: Long-term Feeding Study in Rats. Haskel Laboratory Report No. 61-87. Unpublished study prepared by EI DuPont de Nemours and Company.

Tollestrup K, Frost FJ, Harter LC, McMillan GP. 2003. Mortality among children residing near the American Smelting and Refining Company (ASARCO) copper smelter in Ruston, Washington. Arch Environ Health 58(11):683–691.

Tome FS, Cardoso VC, Barbieri MA, Silva AA, Simoes VM, Garcia CA, et al. 2007. Are birth weight and maternal smoking during pregnancy associated with malnutrition and excess weight among school age children? Braz J Med Biol Res 40(9):1221–1230.

Toschke AM, Koletzko B, Slikker W Jr, Hermann M, von Kries R. 2002. Childhood obesity is associated with maternal smoking in pregnancy. Eur J Pediatr 161(8):445–448.

Toschke AM, Montgomery SM, Pfeiffer U, von Kries R. 2003. Early intrauterine exposure to tobacco-inhaled products and obesity. Am J Epidemiol 158(11):1068–1074.

Toschke AM, Ruckinger S, Bohler E, Von Kries R. 2007. Adjusted population attributable fractions and preventable potential of risk factors for childhood obesity. Public Health Nutr 10(9):902–906.

Tsai SM, Wang TN, Ko YC. 1999. Mortality for certain diseases in areas with high levels of arsenic in drinking water. Arch Environ Health 54(3):186–193.

Tseng C-H. 2004. The potential biological mechanisms of arsenic–induced diabetes mellitus. Toxicol Appl Pharmacol 197(2):67–83.

Tseng C-H, Chong C-K, Heng L-T, Tseng C-P, Tai T-Y. 2000a. The incidence of type 2 diabetes mellitus in Taiwan. Diabetes Res Clin Pract 50(suppl 2)::S61–S64.

Tseng C-H, Tai T-Y, Chong C-K, Tseng C-P, Lai M-S, Lin BJ, et al. 2000b. Long-term arsenic exposure and incidence of non-insulin-dependent diabetes mellitus: a cohort study in arseniasis–hyperendemic villages in Taiwan. Environ Health Perspect 108:847–851.

Turyk M, Anderson H, Knobeloch L, Imm P, Persky V. 2009a. Organochlorine exposure and incidence of diabetes in a cohort of Great Lakes sport fish consumers. Environ Health Perspect 117:1076–1082.

Turyk M, Anderson HA, Knobeloch L, Imm P, Persky VW. 2009b. Prevalence of diabetes and body burdens of polychlorinated biphenyls, polybrominated diphenyl ethers, and p,p’-diphenyldichloroethene in Great Lakes sport fish consumers. Chemosphere 75(5):674–679.

Uemura H, Arisawa K, Hiyoshi M, Satoh H, Sumiyoshi Y, Morinaga K, et al. 2008. Associations of environmental exposure to dioxins with prevalent diabetes among general inhabitants in Japan. Environ Res 108(1):63–68.

Ukropec J, Radikova Z, Huckova M, Koska J, Kocan A, Sebokova E, et al. 2010. High prevalence of prediabetes and diabetes in a population exposed to high levels of an organochlorine cocktail. Diabetologia 53(5):899–906.

Ulukaya S, Demirag K, Moral AR. 2001. Acute amitraz intoxication in human. Intensive Care Med 27(5):930–933.

U.S. EPA (Environmental Protection Agency). 2011a. ToxCast Database (ToxCast™). Available: http://actor.epa.gov/actor/faces/ToxCast​DB/Home.jsp [accessed 12 December 2011].

U.S. EPA (Environmental Protection Agency). 2011b. ToxRefDB. Available: http://www.epa.gov/ncct/toxrefdb/ [accessed 12 December 2011].

Vasiliu O, Cameron L, Gardiner J, Deguire P, Karmaus W. 2006. Polybrominated biphenyls, polychlorinated biphenyls, body weight, and incidence of adult-onset diabetes mellitus. Epidemiology 17(4):352–359.

Verstraeten A. 1993. Beerse, Belgium:Janssen Research Foundation. Carcinogenicity Study in Swiss Mice: Imazalil Base: Final Report: Nonclinical Laboratory Study: Lab Project No. 2194: R 23979. Unpublished study prepared by Janssen Research Foundation.

von Kries R, Bolte G, Baghi L, Toschke AM, GME Study Group. 2008. Parental smoking and childhood obesity—is maternal smoking in pregnancy the critical exposure? Int J Epidemiol 37(1):210–216.

von Kries R, Toschke AM, Koletzko B, Slikker W Jr. 2002. Maternal smoking during pregnancy and childhood obesity. Am J Epidemiol 156(10):954–961.

Wang JP, Wang SL, Lin Q, Zhang L, Huang D, Ng JC. 2009. Association of arsenic and kidney dysfunction in people with diabetes and validation of its effects in rats. Environ Int 35(3):507–511.

Wang S-L, Chiou J-M, Chen C-J, Tseng C-H, Chou W-L, Wang C-C, et al. 2003. Prevalence of non-insulin-dependent diabetes mellitus and related vascular diseases in southwestern arseniasis-endemic and nonendemic areas in Taiwan. Environ Health Perspect 111:155–159.

Wang S-L, Tsai P-C, Yang C-Y, Leon Guo Y. 2008. Increased risk of diabetes and polychlorinated biphenyls and dioxins: a 24-year follow-up study of the Yucheng cohort. Diabetes Care 31(8):1574–1579.

Wang Y-X. 2010. PPARs: diverse regulators in energy metabolism and metabolic diseases. Cell Res 20(2):124–137.

Wetherill YB, Akingbemi BT, Kanno J, McLachlan JA, Nadal A, Sonnenschein C, et al. 2007. In vitro molecular mechanisms of bisphenol A action. Reprod Toxicol 24(2):178–198.

Whitaker RC. 2004. Predicting preschooler obesity at birth: the role of maternal obesity in early pregnancy. Pediatrics 114(1):e29–e36.

White House Task Force on Childhood Obesity. 2010. Report to the President: Solving the Problem of Childhood Obesity within a Generation. Available: http://www.letsmove.gov/sites/letsmove.g​ov/files/TaskForce_on_Childhood_Obesity_​May2010_FullReport.pdf [accessed 12 December 2011].

Widerøe M, Torstein V, Jacobsen G, Bakketeig L. 2003. Does maternal smoking during pregnancy cause childhood overweight? Paediatr Perinat Epidemiol 17(2):171–179.

Williams CM, Kanagasabai T. 1984. Maternal adipose tissue response to nicotine administration in the pregnant rat: effects on fetal body fat and cellularity. Br J Nutr 51(1):7–13.

Wolff MS, Britton JA, Boguski L, Hochman S, Maloney N, Serra N, et al. 2008. Environmental exposures and puberty in inner-city girls. Environ Res 107(3):393–400.

World Health Organization. 2011. Obesity and Overweight. Available: http://www.who.int/mediacentre/factsheet​s/fs311/en/index.html [accessed 12 December 2011].

Yilmaz HL, Yildizdas DR. 2003. Amitraz poisoning, an emerging problem: epidemiology, clinical features, management, and preventive strategies. Arch Dis Child 88(2):130–134.

Yoon JW. 1990. The role of viruses and environmental factors in the induction of diabetes. Curr Top Microbiol Immunol 164:95–123.


WP-Backgrounds Lite by InoPlugs Web Design and Juwelier Schönmann 1010 Wien