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Do Interactions Between Gut Ecology and Environmental Chemicals Contribute to Obesity and Diabetes?

Suzanne M. Snedeker1,2, Anthony G. Hay1

1 Department of Microbiology and the Institute for Comparative and Environmental Toxicology, and, 2 Department of Food Science, Cornell University, Ithaca, New York, USA



Environ Health Perspect 120:332-339 (2012). http://dx.doi.org/10.1289/ehp.1104204 [online 31 October 2011]

Review

Abstract

Background: Gut microbiota are important factors in obesity and diabetes, yet little is known about their role in the toxicodynamics of environmental chemicals, including those recently found to be obesogenic and diabetogenic.

Objectives: We integrated evidence that independently links gut ecology and environmental chemicals to obesity and diabetes, providing a framework for suggesting how these environmental factors may interact with these diseases, and identified future research needs.

Methods: We examined studies with germ-free or antibiotic-treated laboratory animals, and human studies that evaluated how dietary influences and microbial changes affected obesity and diabetes. Strengths and weaknesses of studies evaluating how environmental chemical exposures may affect obesity and diabetes were summarized, and research gaps on how gut ecology may affect the disposition of environmental chemicals were identified.

Results: Mounting evidence indicates that gut microbiota composition affects obesity and diabetes, as does exposure to environmental chemicals. The toxicology and pharmacology literature also suggests that interindividual variations in gut microbiota may affect chemical metabolism via direct activation of chemicals, depletion of metabolites needed for biotransformation, alteration of host biotransformation enzyme activities, changes in enterohepatic circulation, altered bioavailability of environmental chemicals and/or antioxidants from food, and alterations in gut motility and barrier function.

Conclusions: Variations in gut microbiota are likely to affect human toxicodynamics and increase individual exposure to obesogenic and diabetogenic chemicals. Combating the global obesity and diabetes epidemics requires a multifaceted approach that should include greater emphasis on understanding and controlling the impact of interindividual gut microbe variability on the disposition of environmental chemicals in humans.

Key words: ADME, biotransformation enzymes, diabetes mellitus, diabetogenic, environmental chemicals, gut ecology, metabolic syndrome, microbes, microbiota, obesity, obesogen, obesogenic, persistent organic pollutants, POPs

Address correspondence to A.G. Hay, Department of Microbiology, B75C Wing Hall, Wing Dr., Cornell University, Ithaca, NY 14853 USA. Telephone: (607) 255-8471. Fax: (607) 255-3004. E-mail: agh5@cornell.edu

S.M.S. received financial support for the preparation of this manuscript from the Cornell University Atkinson Center for a Sustainable Future. She gratefully acknowledges the Cornell Department of Food Science’s generous support of her Visiting Fellowship.

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

The authors gratefully acknowledge T. Van de Wiele and R. Ley for comments on earlier versions of this manuscript.

Received 12 July 2011; accepted 31 October 2011; online 31 October 2011.

The prevalence of adult obesity in the United States has risen dramatically over the last three decades from 14.5% (Flegal et al. 1998) to over 33% (Flegal et al. 2010). Medical costs of obesity are estimated to be between $147 and $168 billion per year in the United States and account for up to 16.5% of medical care costs (Cai et al. 2010; Cawley and Meyerhoefer 2010; Finkelstein et al. 2009). Childhood rates of obesity are rising in the United States (Wang et al. 2011) and in many other countries (Wang and Lobstein 2006). Analysis of trends from 1980 to 2008 also show an increase in body mass index (BMI) (Finucane et al. 2011) and diabetes (Danaei et al. 2011) in most geographic areas surveyed world-wide. The origins of the global obesity and diabetes epidemics are multifaceted, with growing evidence that multiple environmental factors contribute to their development. This is exemplified by emerging evidence of the role of gut microbial ecology in obesity and type 1 and type 2 diabetes (Musso et al. 2011; Qin et al. 2010), as well as evidence from human studies and animal models that environmental chemicals may contribute to the development of these diseases (Baillie-Hamilton 2002; Carpenter 2008; Casals-Casas and Desvergne 2011; Grün 2010; Heindel and vom Saal 2009; La Merrill and Birnbaum 2011; Newbold et al. 2008). While interindividual variability in the gut microbiome affects the metabolism of pharmaceuticals (Clayton et al. 2009) and some environmental toxins (Dean and Ma 2007), the impact of gut ecology on the absorption, distribution, metabolism, and excretion (ADME) of xenobiotics, including obesogenic and diabetogenic chemicals, has received little to no attention. We review the scientific evidence that independently links gut ecology and environmental chemicals to obesity and diabetes, providing a framework for suggesting how these environmental factors may interact with these diseases, and identify future research needs to further our understanding of these relationships.

The Gut Microbiome and Obesity

Gut microbes outnumber human cells by a factor of 10, yet we know surprising little about many of these organisms. The metagenomic sequencing of the human microbiome reveals that there are 3.3 million nonredundant genes, with over 99% of the genes being of bacterial origin (Qin et al. 2010). This gene set is 150-times larger than the human genome. Although certain microbial species appear to be shared by groups of individuals (Arumugam et al. 2011), with > 50 species shared by 90% of the individuals studied, considerable variation occurs both in the types of microbes and in the diversity of microbial functional genes found between individuals (Qin et al. 2010). The notion of a conserved core of functional genes in the microbiome has been supported by studies in monozygote and dizygote twin pairs, though major differences in the abundance of microbes at the phylum level were observed in the microbiome of obese compared with lean twins (Turnbaugh and Gordon 2009; Turnbaugh et al. 2009a). No relationship between specific phyla and obesity has been found in a more recent study, although significant associations between obesity and inferred microbial metabolic activities such as energy harvesting and osmolyte production (based on the presence of genes predicted to encode specific enzymatic activities) were found (Arumugam et al. 2011). This is consistent with the observations of Calvani et al. (2010), who detected differences in the levels of microbial metabolites in the urine of obese compared with lean individuals.

The first suggestion that changes in adiposity may influence gut microbiota was made in a study of patients undergoing intestinal bypass (Bjorneklett et al. 1981). More recently, bariatric surgery has been shown to alter gut ecology (Furet et al. 2010; Zhang et al. 2009) and improve glycemic control in type 2 diabetics (Ahima and Sabri 2011; Meijer et al. 2011). The mechanisms by which bariatric surgery corrects hyperinsulinemia are unknown (Reed et al. 2011; Tam et al. 2011), and information on the duration of this effect is not available (Schauer and Rubino 2011). Although it is not known whether the changes in microbial populations observed in patients after bypass surgery are directly related to changes in diabetes status, some studies suggest changes in bacterial populations may be related to obesity. Changes in specific bacterial populations after bariatric surgery include a reduction in methanogenic Archaea (Furet et al. 2010; Zhang et al. 2009). Zhang et al. (2009) hypothesized that in obese patients, methanogens could accelerate the fermentation of plant polysaccharides by lowering hydrogen gas production during fermentation, leading to higher acetate production and increased energy harvesting; however, that work was based on an extremely small sample size. Others have suggested that genetic factors may play an important role in determining the levels of gut methanogens (Hansen et al. 2011).

Experiments with germ-free or antibiotic-treated animals have yielded conflicting evidence regarding a role for gut microbes in the development of obesity. In some studies, germ-free mice demonstrated resistance to diet-induced obesity when fed a Western-type high sugar and fat diet (Backhed et al. 2004, 2007), whereas a more recent study using a different mouse line (C3H) found the opposite effect (Fleissner et al. 2010). In one mouse model, major differences were observed in the proportion of the different bacterial phyla in genetically obese mice and lean mice. Genetically obese ob/ob mice had a 50% reduction in the abundance of Bacteroidetes, and a proportional increase in Firmicutes (Ley et al. 2005). A metagenomic analysis revealed that the microbiome of obese mice had a higher percentage of genes associated with energy extraction than that of lean mice (Turnbaugh et al. 2006). Further work in the ob/ob mouse model demonstrated that this trait of increased energy extraction was transferable: weight gain and total body fat was higher in germ-free mice that received gut microbiota from obese mice than from lean mice. These differences were observed even though food consumption was the same (Turnbaugh et al. 2006).

In addition to their role in energy harvesting in the gut, microbiota may also affect obesity and diabetes risk via several other mechanisms including regulation of fat storage (Backhed et al. 2004), metabolic endotoxemia-induced inflammation (Cani and Delzenne 2007; Cani et al. 2008), and levels of satiety factors such as glucagon-like peptides and leptin (Cani et al. 2009; Ravussin et al. 2011; Sanz et al. 2010). For instance, C57BL/6J wild-type mice raised from birth with conventional gut microbiota had suppressed levels of fasting-induced adipocyte factor (FIAF) and a 42% higher body weight compared with germ-free mice. The broader relevance of this observation is unclear, since no differences were found in circulating FIAF levels of conventional C3H mice fed a Western or high-fat diet as compared with germ-free mice (Fleissner et al. 2010). Gut epithelial FIAF is a lipoprotein lipase inhibitor (LPL) and repressing its expression increases LPL activity and the storage of triglycerides in adipocytes (Backhed et al. 2004).

The metabolic inflammation hypothesis is based on the observation that mice fed a high-fat diet show changes in microbiota associated with increased intestinal permeability and go on to develop metabolic endotoxemia and inflammation. In ob/ob mice, gut microbiota composition affects plasma levels of endotoxin, presumably through altered gut permeability, which then leads to endotoxemia, inflammation, and metabolic changes that may influence the risk of obesity and diabetes (Cani et al. 2008). Cani et al. (2009) observed that prebiotics in humans can influence gut microbiota, which in turn affect the levels of gut satiety factors including glucagon-like peptide 1 and peptide YY. Obese-mouse studies characterizing diet and weight loss, and human studies characterizing how microbial communities are affected by diet, suggest complex interactions exist between diet, adiposity, gut microbiota, satiety hormones levels, and inflammation (Jumpertz et al. 2011; Muegge et al. 2011; Ravussin et al. 2011; Turnbaugh et al. 2009b, 2010).

Host genetic-gut ecology links may also affect immune function and the development of the suite of changes linked with metabolic syndrome (Vijay-Kumar et al. 2010). Mice lacking Toll-like receptor 5 (TLR5), which is important in immune system recognition of bacterial antigens in the colon, are hyperphagic with increased food consumption resulting in hyperlipidemia, hypertension, insulin resistance, and increased adipocity. Transferring gut microbiota from these TLR5 knock out mice to germ-free wild-type mice also resulted in hyperphagia and many of the same symptoms of metabolic syndrome. Unlike ob/ob mice that demonstrated phylum-level differences in microbiota, the TLR5 knock out and wild-type mice had similar proportions of Bacteroidetes and Firmicutes. However, marked differences in certain bacterial species in the TLR5 knock out mice were noted compared with wild-type mice. Surprisingly, Letran et al. (2011) did not observe basal inflammation or other metabolic changes in TLR5 knock out mice, although they did note a reduction in flagellin-specific CD4 T cells following Salmonella infection. The discrepancy between these reports, which used genetically identical mice, was suggested to lie in the different microbiota colonizing the mice at the different facilities. Thus, despite the apparently different outcome, these reports illustrate the importance of the gut microbiota and its complex interaction with the immune system.

Other studies suggest that microbiota may influence weight gain or loss and adiposity in humans. Ley et al. (2006) showed that obese humans had a lower Bacteroidetes to Firmicutes ratio than lean humans, but that this ratio increased with weight loss (Ley et al. 2006). Armougom et al. (2009) also found lower levels of Bacteriodetes in obese persons, although Arumugam et al. (2011) found no difference in this ratio.

Human studies also indicate weight gain during pregnancy can affect gut microbial populations and the incidence of obesity in offspring (Collado et al. 2008, 2010). Breast- versus formula-feeding practices, and vaginal versus cesarean section delivery also appear to affect the gut ecology of infants and may have relevance for obesity (Hallstrom et al. 2004; Musso et al. 2010; Penders et al. 2005, 2006). Another analysis of human microbiomes suggested that fecal microbiota composition in infants may predict later weight gain in children (Kalliomaki et al. 2008).

While debate is ongoing about the relevance of phylum-level differences in obese and lean individuals, research in these areas is still in its early stages. In order to thoroughly test the human adiposity-gut microbe hypothesis, additional carefully controlled experiments, as well as larger epidemiological studies, are needed.

Gut Microbiome and Diabetes

In addition to the growing number of studies that suggest gut microbiota may affect the development of obesity, several studies suggest that the nature of the gut microbiota is linked to type 2 diabetes. This includes a study that found men with type 2 diabetes had significantly reduced levels of fecal Firmicutes, including Clostridia, compared with non-diabetic control subjects. Plasma glucose was positively correlated with both the ratios of Bacteroidetes to Firmicutes, and of the Bacteroides–Prevotella group to Clostridium coccoides–Eubacteria rectale group. In addition, the diabetic group also had more Betaproteobacteria than non-diabetic controls. The authors suggested that the Bacteroidetes and Proteobacteria groups may affect diabetes risk via an endotoxin-induced inflammatory response, as both are gram-negative bacteria with lipid polysaccharide outer membranes (Larsen et al. 2010). Larsen et al. (2010) is the first study to show changes in microbial populations between type 2 diabetics compared with non-diabetics, but the study is based on a small number of subjects (n = 36), and these results need to be replicated in larger studies. It should also be noted that study subjects in both the diabetic and control groups had a wide range of BMIs.

Other researchers (Membrez et al. 2008) have investigated whether gut microbiota affects glycemic control and glucose tolerance using animal models of type 2 diabetes. In ob/ob mice, a 2-week treatment with antibiotics (norfloxacin and ampicillin) decreased gut levels of both aerobic and anaerobic bacteria. Antibiotic-treated ob/ob mice had significantly improved glucose tolerance; this was attributed to multiple factors including reduced liver triglycerides, increased liver glycogen, increased plasma adiponectin, and reduced plasma lipopolysaccarides (Membrez et al. 2008). The authors suggested that changes in the microbiota improved glucose tolerance via changes in metabolic and inflammatory pathways. Rabot et al. (2010) explored whether differences in insulin resistance and glycemic control exist between germ-free mice and mice with conventional gut microbes. They found that germ-free mice fed a high-fat diet consumed fewer calories, excreted more fecal lipids, and weighed less than conventional high-fat diet–fed mice. The germ-free mice also had reduced fasting and non-fasting insulinemia and improved glucose tolerance. Rabot et al. (2010) suggested these results support a role for gut microbiota in insulin sensitivity.

Relatively few studies have evaluated the role of microbes in type 1 diabetes. Vehik and Dabelea (2011) suggested that increased gut permeability (commonly called “leaky gut”) may affect the absorption of antigens that can attack and damage pancreatic beta cells. Bosi et al. (2006) observed increased gut permeability in human subjects with type 1 diabetes. Because gut microbes can affect intestinal permeability (Garcia-Lafuente et al. 2001), gut ecology may play a role in the development of type 1 diabetes (Neu et al. 2010). Another hypothesis by which microbes may cause type 1 diabetes is by producing bacterial toxins that can directly damage or affect the function of pancreatic beta cells. Myers et al. (2003) found that injecting the mice with Streptomyces toxin, bafilomycin A1, resulted in smaller pancreatic beta cells and impaired glucose tolerance. This Streptomyces toxin can be produced by soil microbes and subsequently infect commonly consumed root vegetables such as potatoes. Other microbial toxins, such as streptozotocin, have been used to induce diabetes in an experimental mouse model (Like and Rossini 1976). Little is known about other microbial toxins that may directly attack pancreatic beta cells and affect type 1 diabetes. Kootte et al. (2011) have speculated whether manipulating gut microbiota may have therapeutic benefits for treating patients with type 2 diabetes, including whether prebiotics, postbiotics, antibiotics or even microbial transplantation might have clinical significance.

Obesogenic and Diabetogenic Environmental Chemicals

The possible role of chemical toxins in the rising rates of obesity world-wide was first proposed by Baillie-Hamilton (2002). Grün and Blumberg (2006, 2007) suggested that certain environmental pollutants, called “obesogens” can disrupt or interfere with the body’s homeostatic controls of adipogenesis, lipid metabolism, or energy balance. Adipose pathways involving nuclear receptors, such as the estrogen receptor (ER), retinoid X receptor (RXR), peroxisome proliferator-activated receptor-γ (PPAR-γ), and glucocorticoid receptors (GR), provided some of the first proposed molecular targets of environmental obesogens (Grün and Blumberg 2007). Casals-Casas and Desvergne (2011) have suggested that endocrine-disrupting chemicals that affect adipose and glucose-related pathways should be categorized into a subgroup called “metabolic disrupting chemicals.” Expanding the obesogen hypothesis, several researchers (Heindel and vom Saal 2009; Wolff et al. 2008) have proposed that environmental chemicals may act during critical windows of prepubertal and pubertal development to alter pathways involved in food intake, insulin sensitivity, lipid metabolism, and adipocyte development.

The level and strength of evidence (human vs. experimental animal or cell culture studies), the mechanism of action, and whether a dose–response effect or a low-dose effect (i.e., a U-shaped response curve) is observed, all vary by chemical. Although relatively few studies have examined whether environmental factors play a role in type 1 diabetes (Howard and Lee 2011), associations between the incidence of type 2 diabetes and exposure or use of a number of environmental chemicals is well supported in the human epidemiological literature for dichlorodiphenyldichloroethylene (DDE) (Codru et al. 2007; Cox et al. 2007; Lee et al. 2006; Rignell-Hydbom et al. 2009; Son et al. 2010; Turyk et al. 2009a, 2009b; Ukropec et al. 2010), hexachlorobenzene (HCB) (Codru et al. 2007; Ukropec et al. 2010), highly chlorinated polychlorinated biphenyls (PCBs) (Codru et al. 2007; Lee et al. 2006, 2010; Ukropec et al. 2010; Wang et al. 2008), dioxin (Henriksen et al. 1997; Kang et al. 2006; Michalek and Pavuk 2008), chlordane (Cox et al. 2007; Everett and Matheson 2010; Lee et al. 2006, 2007a, 2010, 2011; Son et al. 2010), and occupational exposure to agricultural insecticides and herbicides including chlordane, heptachlor, chlorpyrifos, diazinon, alachlor, cyanazine, and trichlorofon (Montgomery et al. 2008). Without mechanisms of action, however, it cannot yet be determined if all of the chemicals identified play a potential causal role, or if coexposures result in detecting some chemicals that do not have a biological effect on diabetes risk.

For other chemicals, there is a clearer picture of effects in human populations and mechanisms of action. Globally, high levels of arsenic in water supplies have been associated with increased incidence of type 2 diabetes (Chen et al. 2007; Navas-Acien et al. 2006; Rahman et al. 2009; Tseng 2007). Mechanistic studies suggest arsenic may impair insulin secretion from pancreatic beta cells and induce changes in gene expression affecting pancreatic insulin secretion and insulin resistance in peripheral tissues (Diaz-Villasenor et al. 2006, 2007). While a diabetogenic effect of another metal, cadmium, was noted in rats exposed neonatally (Merali and Singhal 1980), and suggestive evidence on fasting glucose levels in humans was reported (Schwartz et al. 2003), no mechanism of action has been identified.

Although strong evidence of a mechanism of action exists for other chemicals, few if any studies have documented whether past or current exposure levels in humans pose a risk. For example, strong mechanistic data support tributyltin as a developmental obesogen, especially for its action through nuclear receptor signaling. PPAR-γ is one of the key regulators of cell growth and differentiation of adipocytes. Tributyltin is an agonist for both PPAR-γ and the retinoid X receptor (RXR-α, -β and -γ) (Grün and Blumberg 2006; Grün et al. 2006), and tributyltin can sensitize human and mouse stromal stem cells to differentiate into adipocytes (Inadera and Shimomura 2005; Kirchner et al. 2010). Pubertal exposures in male mice cause increased body weight gain, hepatic steatosis, hyperinsulinemia, and hyperleptinemia (Zuo et al. 2011). However, the effect of environmental tributyltin exposure on related obesity disorders in human populations has not yet been investigated. More studies are needed to define the extent of human exposure from tributyltin’s use in anti-fouling marine paints and as a stabilizer in polyvinyl chloride plastics as well as its use in wallpaper, textiles, and floor coverings (Antizar-Ladislao 2008; Appel 2004; Kannan et al. 2010).

As discussed below, for several estrogenic environmental chemicals including bisphenol A (BPA), alkylphenols nonyl- and octylphenol, diethylstilbestrol (DES), and genistein, evidence from animal and tissue culture models indicate that these environmental estrogens affect a variety of other receptor-mediated, cellular, and molecular targets linked to adipose and/or glucose metabolism. GR signaling is central to adipocyte differentiation. For example, using the 3T3-L1 preadipocyte cell line, Sargis et al. (2010) demonstrated that BPA stimulated GR and increased lipid accumulation in the differentiating adipocytes. BPA can also modulate glucose transport in mouse 3Y3-F442A adipocytes, enhancing the level of a key glucose transport protein GLUT4 (Sakurai et al. 2004). Other studies have shown that BPA can suppress the release of adiponectin (which can affect insulin sensitivity and resistance) from human adipocytes or adipose explants (Hugo et al. 2008).

Evidence from animal studies on the effects of early-life BPA exposure on obesity is not consistent; effects appear to depend on route of exposure, sex, and species (Miyawaki et al. 2007; Rubin et al. 2001; Ryan et al. 2010). Human studies have not provided strong evidence of an effect on obesity for current levels of human BPA exposure (Lang et al. 2008; Melzer et al. 2010).

Using 3T3-L1 adipocytes, researchers have found that the alkylphenols octylphenol and nonylphenol up-regulate the expression of the resisten gene, which affects insulin resistance and decreases adipocyte differentiation. Male rats treated with octylphenol show increased serum levels of glucose (Lee et al. 2008). Although nonylphenol has been widely detected in human adipose tissue, a positive relationship between the measures of obesity such as BMI and adipose levels for this environmental estrogen has not been shown (Lopez-Espinosa et al. 2009).

Neonatal exposure of mice to the nonsteroidal estrogen DES results in an initial weight loss followed by an increase in body fat by 2 months of age (Newbold 2010). DES exposure increases serum leptin and triglycerides levels and changes the expression of several genes involved in fat distribution (Newbold et al. 2007). Cohort studies have not yet determined whether there is a higher incidence of obesity in DES mothers, or in their children exposed to DES in utero, even though this compound has been found to have multigenerational effects on other end points such as female cervical cancer and male urogenital malformations (Palmer et al. 2009; Troisi et al. 2007).

For the phytoestrogen genistein, effects on gene expression of adipose-related factors, including induction of phospholipase A2 group 7 and phospholipid transfer protein genes, were seen at low, but not high, doses in a mouse study (Penza et al. 2006). This suggests that for some environmental chemicals, especially those with hormonal action, low-dose effects need to be examined rather than relying on traditional high-dose response effects. U-shaped response curves have been reported for other environmental chemicals, including some congeners of polybrominated diphenyl ether (PBDE) flame retardants including PBDE-153, and certain PCBs (Lee et al. 2007b, 2011; Lim et al. 2008). The U-shaped response curves suggest that future research is needed to determine if other chemicals have low-dose effects on metabolic syndrome, obesity, and diabetes.

The effects of individual chemicals on obesity and diabetes cannot be generalized to entire classes of chemicals. For instance, type 2 diabetes–related effects of PCBs and brominated flame retardants [polybrominated biphenyls (PBBs), and PBDEs] appear to be more closely associated with highly halogenated forms of these chemicals (Everett et al. 2007; Lee et al. 2010; Lim et al. 2008). Only certain types or metabolites of phthalates appear to be associated with obesity or diabetes in humans (Hatch et al. 2008). In rodent studies, diisobutylphthalate shows some evidence of affecting obesity via PPAR pathways (Boberg et al. 2008), but evidence for other phthalates, including diethylhexyl phthalate, is less consistent (Casals-Casas et al. 2008; Feige et al. 2010).

For other chemicals such as the PPAR agonist perfluorooctanoic acid (PFOA), evidence of an association with obesity and diabetes is emerging but inconsistent. Although there is some evidence of a higher incidence of diabetes in persons who are occupationally exposed to PFOA (Lundin et al. 2009), a large-scale cross-sectional epidemiological study did not observe a relationship between PFOA levels and type 2 diabetes or fasting glucose levels (MacNeil et al. 2009). Rodent studies indicate that PFOA can be transmitted from the dam to pup during lactation (Fenton et al. 2009), and there is some evidence of PFOA being a developmental obesogen in mice (Hines et al. 2009), but studies in rats have not indicated an effect of early-life PFOA exposure on plasma insulin or leptin levels (Boberg et al. 2008).

Disposition of Environmental Chemicals

In addition to the host–microbe interactions and the direct effects of the chemicals discussed above, we suggest that microbes may affect obesity and diabetes by altering the ADME of environmental chemicals. Microbially mediated effects on ADME could include the direct activation of chemicals (Van de Wiele et al. 2005, 2010; Wallace et al. 2010), production of microbial metabolites that compete for limited host biotransformation capacity (Clayton et al. 2009; Wallace et al. 2010), alteration of host biotransformation enzyme activities (Claus et al. 2011; Meinl et al. 2009), changes in enterohepatic circulation (Meijer et al. 2006), or altered bioavailability of environmental chemicals and/or antioxidants from food (Kemperman et al. 2010; Lhoste et al. 2003; Oishi et al. 2008; van Duynhoven et al. 2010). Increased bioavailability may also result from changes in gut motility and barrier function. Although evidence indicates that the ADME of environmental chemicals may be affected by many of these microbial-mediated pathways, no studies have evaluated how the ADME of obesogenic or diabetogenic chemicals are affected by variations in the human microbiome. Thus, there is a need to determine the effect of microbes on the bioavailability of environmental chemicals, and the direct biotransformation of persistent organic pollutants (Dean and Ma 2007; Possemiers et al. 2009).

Using an in vitro model that simulates the human intestinal microbial system (biota cultured from human feces), Van de Wiele et al. (2005) demonstrated that colonic microbiota were capable of transforming polyaromatic hydrocarbons (PAHs) to the bioactive estrogenic metabolites 1-hydroxypyrene and 7-hydroxybenzo[a]pyrene, whereas stomach and small intestine digests of the PAH did not produce estrogenic metabolites. This finding suggests that colonic microbes can biotransform parent compounds directly into active metabolites. Gut microbes were found to thiolate and methylate arsenic in both human and mouse models (Pinyayev et al. 2011; Van de Wiele et al. 2010). Exposure to high levels of arsenic have been associated with both an increased risk of bladder cancer and a higher incidence of diabetes in persons living in areas with contaminated water supplies and/or seafood (Chen et al. 2007; Coronado-Gonzalez et al. 2007; Kim and Lee 2011; Navas-Acien et al. 2006; Rahman et al. 2009; Yen et al. 2007). Although these microbial model systems suggest the capacity for biotransformation of environmental chemicals in the gut, especially the colon, their impact on systemic pollutant levels is not known. The extent of biotransformation variation by the gut microbes, so called “presystemic metabolism” (Grundmann 2010) of different individuals is also not known. Despite phylogenetic diversity, the implied functional metabolic redundancy observed in the gut metagenomes of individual twins (Turnbaugh et al. 2009a) raises the question as to whether or not important differences exist between the enzymatic capacities of individuals. Although no large-scale functional studies have been done to characterize the interindividual variation in gut microbe enzymatic capacity, the available human and rodent data suggest that variations in gut microbiota affect environmental chemical disposition (McBain and MacFarlane 1998; Rowland et al. 1985). This has been indirectly established in the area of colon cancer where variation in fecal enzyme activities has been found to correlate with cancer risk (Rowland 2009).

The pharmacology literature provides valuable evidence demonstrating how chemical fate can be affected by variability in the host microbiome (Clayton et al. 2009; Sousa et al. 2008; Wallace et al. 2010; Wilson 2009). For example, a 2008 review identified 30 drugs (including chloramphenicol) that can be metabolized by gut microbiota whose metabolism shows considerable interindiviual variability depending on the presence or absence of specific bacteria genera (Sousa et al. 2008). In addition, studies have shown that variations in gut microbiota can affect the metabolism of commonly used over-the-counter drugs such as acetaminophen (Clayton et al. 2009) and that limiting gut microbial metabolism of chemotherapy drugs can reduce drug toxicity (Wallace et al. 2010).

Recent evidence suggests that acetaminophen’s metabolism and toxicity are affected by individual variations in the gut microbiome (Clayton et al. 2009). Some gut bacteria, including Clostridium difficle, metabolize tyrosine to p-cresol, which can compete with acetaminophen for sulfonation in the gut. In individuals whose gut bacteria produce high levels of p-cresol, less acetaminophen undergoes sulfonation because of competition with p-cresol and more is glucuronidated. Researchers found that the ratio of sulfonated to glucuronidated p-cresol in the urine was predictive of acetaminophen toxicity. The same markers are likely relevant to metabolism of other compounds that rely on these pathways for detoxification (Clayton et al. 2009), including many that have been suggested to be environmental obesogens or diabetogens [see Supplemental Material, Table 1 (http://dx.doi.org/10.1289/ehp.1104204)].

Another example of gut ecology–pharmaceutical interaction is the metabolism of the chemotherapeutic prodrug CPT-11. Upon administration, CPT-11 is first activated by carboxyesterases in the liver to yield toxic SN38, which in turn is glucuronidated by uridine diphosphate (UDP)-glucuronosyltransferase to nontoxic SN38G. SN38G is excreted into the bile and returned to the gut where β-glucuronidases from commensal gut bacteria remove the glucuronide. This reactivates the drug in the gut, which can in turn cause bloody diarrhea, limiting the dose that can be used in chemotherapy. To circumvent the unintended intestinal toxicity of SN38, researchers developed an inhibitor of microbial glucuronidase that was not toxic to gut microbes, but which prevented the metabolism of SN38G and thereby increased mouse tolerance to CPT-11 (Wallace et al. 2010). Microbial glucuronidase activity also has been shown to be important in activating foodborne procarcinogens in the gut (Humblot et al. 2007), further illustrating the role of this enzyme in chemical metabolism.

The above examples underscore the reasons recent reviews in the pharmacology literature have articulated the need for future drug development to include an integrated assessment of host and environmental factors, including gut microbes that affect drug disposition (Grundmann 2010; Sousa et al. 2008; Wilson 2009; Wilson and Nicholson 2009). This topic has received little attention in the toxicology literature (Possemiers et al. 2009). In this regard, pharmacometabolomics appears to be an important emerging tool for investigating how gut ecology may affect the fate of chemical toxicants and their contribution to diabetes and obesity risk. Given the established functional differences in the microbiomes of obese and lean humans (Arumugam et al. 2011), microbes may be an important source of variation in the ADME profile of obesogenic and diabetogenic chemicals and deserve increased attention.

This is especially important in light of evidence in animal models that suggest changes in gut microbiota not only affect levels of gut metabolic enzymes, but levels of hepatic enzymes as well (Claus et al. 2011; Meinl et al. 2009; Reddy et al. 1973). The ability of gut microbiota to affect levels of hepatic enzymes was initially demonstrated for carbohydrate-metabolizing enzymes. Compared with germ-free rats, conventional rats displayed a significantly higher activity of hepatic glucose-6-phosphate dehydrogenase (Reddy et al. 1973). Limited recent evidence indicates that certain phase I enzymes also can be influenced by gut microbiota. The expression and level of P450 enzymes Cyp3a11 and Cyp2c29 were significantly higher in the livers of mice with conventional gut microbiota compared with germ-free control mice (Claus et al. 2011).

Several hepatic and gut phase II enzymes also have been compared in germ-free and germ-free rats “reassociated” with conventional microbiota (Meinl et al. 2009). These include glutathione transferases, glutathione peroxidase, epoxide hydrolases, acetyltransferases, and sulfotransferases. Levels of isoenzymes were compared in the liver, small intestine, cecum, and colon of germ-free and reassociated control rats. In most cases, germ-free rats had higher levels of colonic phase I and II enzymes than control rats with conventional microbiota, although there was no effect of germ-free status on the levels of enzymes in the small intestine. Differences were, however, observed in the level of liver enzymes in germ-free rats compared with reassociated rats of both sexes, with elevations in sulfotransferases. Hepatic epoxide hydrolase was elevated in germ-free rats as well, but only in females (Meinl et al. 2009).

Levels of hepatic biotransformation enzymes can also be affected by diet-induced changes in gut microbes (Treptow-van Lishaut et al. 1999). Levels of glutathione S-transferase-π (the predominant GST isoenzyme) were lower in colon cells of germ-free rats compared with colon cells of rats with conventional microbiota. The levels of this enzyme increased 3-fold when the diet of rats with conventional microbiota was changed from a highly digestible maize starch to a poorly digestible high-amylose maize starch. The authors suggested that amylose was fermented in the colon, and may have yielded short-chain fatty acids such as n-butyrate, which may have induced GST (Treptow-van Lishaut et al. 1999). Changes in gut microbiota composition were not measured with the dietary change, so it is unknown to what extent gut microbiota may have affected induction of GST directly; however, this report does suggest that changes in gut microbial activity (fermentation) correlate with changes in this phase II enzyme that plays an important role in cellular detoxification (Di Pietro et al. 2010). The potential for diet/microbe-enhanced induction of detoxification capacity demonstrated in the colon of animal models contributes to interest in the potential detoxification and anticancer effects of both pre- and probiotics; however, further human studies are required (Genuis 2011).

Some evidence from laboratory animal studies indicates that the polyphenols quercetin and catechin may influence liver or gut levels of phase II enzymes (Lhoste et al. 2003; Wiegand et al. 2009) and that gut microbes play a role in polyphenol-mediated enzyme induction (Lhoste et al. 2003). Few studies have looked at the levels or activity of phase II biotransformation enzymes in the human gut (Peters et al. 1991; Teubner et al. 2007). Little is known concerning to what extent activities of these biotransformation enzymes are affected by the wide variations in the human microbiome. Hence, we have virtually no information on how variations in gut ecology affect human ADME capacity with respect to environmental chemicals. Understanding how other dietary components, including polyphenols, might modify gut microbial populations and levels of phase I and II enzymes, may yield important information relevant to interindividual variation in chemical metabolism (Kemperman et al. 2010; van Duynhoven et al. 2010).

Another area where little information exists is whether the enterohepatic circulation of environmental chemicals is affected by variation in gut microbial populations. Since many environmental toxicants undergo phase II metabolism [see Supplemental Material, Table 1 (http://dx.doi.org/10.1289/ehp.1104204)], those that are excreted in the bile may be further metabolized by the enzymes of gut microbiota, including glucuronidases, leading to enterohepatic circulation and increased residence time in the body (Humblot et al. 2007). The importance of this process has been demonstrated by administration of nonabsorbable fat (i.e., sucrose polyester), which decreases enterohepatic circulation and increases fecal fat excretion of the flame retardant PBDE-47 (Meijer et al. 2006). To what extent enterohepatic circulation of lipophillic persistent pollutants, including obesogenic or diabetogenic chemicals, can be influenced by variations in gut microbial populations is unclear; however, the limited information available suggests that the variability in the activity levels of relevant gut microbe enzymes may be quite high, especially for β-glucuroronidase (Rowland et al. 1986). Given the importance of microbial β-glucuronidases in enterohepatic cycling, and the enrichment of related genes (e.g., those encoding hydrolases) in obesity-associated microbiomes (Turnbaugh et al. 2009a), it is important that we understand interindividual differences in enterohepatic cycling and how they may affect the risk of obesity or diabetes.

Conclusions and Future Research Needs

Microbial populations and/or metabolic capacities are known to differ in obese and lean subjects (and in type 2 diabetes), yet we know surprisingly little about the effect of these differences on the body burden of obesogenic and diabetogenic chemicals. The ability to characterize and manipulate microbial populations in gnotobiotic mice, however, including humanizing of the rodent gut (Goodman and Gordon 2010), provide us with an unparalleled opportunity to begin exploring the impact of gut microbe variability on the disposition of environmental chemicals in humans. Future research in this area should quantify how interindividual variations in gut microbiota affect the body burden of environmental chemicals by altering a) these chemicals directly, b) the level and activity of host phase I and II enzymes, c) enterohepatic circulation of environmental chemicals, d) depletion of host detoxification capacity, and e) alterations of gut barrier function. Studies should also identify biomarkers that are predictive of impaired obesogenic and diabetogenic chemical absorption, distribution, metabolism, and excretion and assess the interaction between microbiota and developmental obesogens, including intergenerational effects. This approach will shed light on how variations in gut ecology affect the metabolism of obesogenic and diabetogenic chemicals and lead to more personalized approaches in the treatment and prevention of obesity and diabetes.

References

  1. Ahima RS, Sabri A. 2011. Bariatric surgery: metabolic benefits beyond weight loss. Gastroenterology 141(3):793–795.
  2. Antizar-Ladislao B.. 2008. Environmental levels, toxicity and human exposure to tributyltin (TBT)-contaminated marine environment. Environ Int 34(2):292–308.
  3. Appel KE. 2004. Organotin compounds: toxicokinetic aspects. Drug Metab Rev 36(3–4):763–786.
  4. Armougom F, Henry M, Vialettes B, Raccah D, Raoult D.. 2009. Monitoring bacterial community of human gut microbiota reveals an increase in Lactobacillus in obese patients and methanogens in anorexic patients. PLoS One 4(9):e7125.; doi:10.1371/journal.pone.0007125 [Online 23 September 2009]
  5. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, et al. 2011. Enterotypes of the human gut microbiome. Nature 473(7346):174–180.
  6. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, et al. 2004. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA 101(44):15718–15723.
  7. Backhed F, Manchester JK, Semenkovich CF, Gordon JI. 2007. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci USA 104(3):979–984.
  8. Baillie-Hamilton PF. 2002. Chemical toxins: a hypothesis to explain the global obesity epidemic. J Altern Complement Med 8(2):185–192.
  9. Bjorneklett A, Viddal KO, Midtvedt T, Nygaard K. 1981. Intestinal and gastric bypass. Changes in intestinal microecology after surgical treatment of morbid obesity in man. Scand J Gastroenterol 16(5):681–687.
  10. Boberg J, Metzdorff S, Wortziger R, Axelstad M, Brokken L, Vinggaard AM, et al. 2008. Impact of diisobutyl phthalate and other PPAR agonists on steroidogenesis and plasma insulin and leptin levels in fetal rats. Toxicology 250(2–3):75–81.
  11. Bosi E, Molteni L, Radaelli MG, Folini L, Fermo I, Bazzigaluppi E, et al. 2006. Increased intestinal permeability precedes clinical onset of type 1 diabetes. Diabetologia 49(12):2824–2827.
  12. Cai L, Lubitz J, Flegal KM, Pamuk ER. 2010. The predicted effects of chronic obesity in middle age on Medicare costs and mortality. Med Care 48(6):510–517.
  13. Calvani R, Miccheli A, Capuani G, Tomassini Miccheli A, Puccetti C, Delfini M, et al. 2010. Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype. Int J Obes (Lond) 34:1095–1098.; doi:. .10.1038/ijo.2010.44
  14. Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM, et al. 2008. Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet–induced obesity and diabetes in mice. Diabetes 57(6):1470–1481.
  15. Cani PD, Delzenne NM. 2007. Gut microflora as a target for energy and metabolic homeostasis. Curr Opin Clin Nutr Metab Care 10(6):729–734.
  16. Cani PD, Lecourt E, Dewulf EM, Sohet FM, Pachikian BD, Naslain D, et al. 2009. Gut microbiota fermentation of prebiotics increases satietogenic and incretin gut peptide production with consequences for appetite sensation and glucose response after a meal. Am J Clin Nutr 90(5):1236–1243.
  17. Carpenter DO. 2008. Environmental contaminants as risk factors for developing diabetes. Rev Environ Health 23(1):59–74.
  18. Casals-Casas C, Desvergne B.. 2011. Endocrine disruptors: from endocrine to metabolic disruption. Annu Rev Physiol 73:135–162.
  19. Casals-Casas C, Feige JN, Desvergne B. 2008. Interference of pollutants with PPARs: endocrine disruption meets metabolism. Int J Obes (Lond) 32: suppl 6S53–S61.; doi:10.1038/ijo.2008.207 [Online 23 December 2008]
  20. Cawley J, Meyerhoefer C. 2010. The Medical Care Costs of Obesity: An Instrumental Variables Approach. (National Bureau of Economic Research Working Paper Series, NBER Working Paper No 16467). Cambridge, MA:National Bureau of Economic Research.
  21. Chen CJ, Wang SL, Chiou JM, Tseng CH, Chiou HY, Hsueh YM, et al. 2007. Arsenic and diabetes and hypertension in human populations: a review. Toxicol Appl Pharmacol 222(3):298–304.
  22. Claus SP, Ellero SL, Berger JP, Krause L, Bruttin A, Molina J, et al. 2011. Colonization-induced host–gut microbial metabolic interaction. mBio. 2. (2). [Online 1 March 2011].
  23. Clayton TA, Baker D, Lindon JC, Everett JR, Nicholson JK. 2009. Pharmacometabonomic identification of a significant host–microbiome metabolic interaction affecting human drug metabolism. Proc Natl Acad Sci USA 106(34):14728–14733.
  24. 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.
  25. Collado MC, Isolauri E, Laitinen K, Salminen S. 2008. Distinct composition of gut microbiota during pregnancy in overweight and normal-weight women. Am J Clin Nutr 88(4):894–899.
  26. Collado MC, Isolauri E, Laitinen K, Salminen S. 2010. Effect of mother’s weight on infant’s microbiota acquisition, composition, and activity during early infancy: a prospective follow-up study initiated in early pregnancy. Am J Clin Nutr 95(2):1023–1030.
  27. 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.
  28. Cox S, Niskar AS, Narayan KM, Marcus M. 2007. Prevalence of self-reported diabetes and exposure to organochlorine pesticides among Mexican Americans: Hispanic Health and Nutrition Examination Survey, 1982–1984. Environ Health Perspect 115:1747–1752.
  29. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, et al. 2011. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 378(9785):31–40.; doi: .10.1016/S0140-6736(11)60679-X
  30. Dean JR, Ma R. 2007. Approaches to assess the oral bioaccessibility of persistent organic pollutants: a critical review. Chemosphere 68(8):1399–1407.
  31. Di Pietro G, Magno LA, Rios-Santos F. 2010. Glutathione S-transferases: an overview in cancer research. Expert Opin Drug Metab Toxicol 6(2):153–170.
  32. 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.
  33. Diaz-Villasenor A, Sanchez-Soto MC, Cebrian ME, Ostrosky-Wegman P, Hiriart M. 2006. Sodium arsenite impairs insulin secretion and transcription in pancreatic beta-cells. Toxicol Appl Pharmacol 214(1):30–34.
  34. Everett CJ, Frithsen IL, Diaz VA, Koopman RJ, Simpson WM Jr, Mainous AG III. 2007. Association of a polychlorinated dibenzo-p-dioxin, a polychlorinated biphenyl, and DDT with diabetes in the 1999–2002 National Health and Nutrition Examination Survey. Environ Res 103(3):413–418.
  35. Everett CJ, Matheson EM. 2010. Biomarkers of pesticide exposure and diabetes in the 1999–2004 National Health and Nutrition Examination Survey. Environ Int 36(4):398–401.
  36. Feige JN, 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.
  37. Fenton SE, Reiner JL, Nakayama SF, Delinsky AD, Stanko JP, Hines EP, et al. 2009. Analysis of PFOA in dosed CD-1 mice. Part 2. Disposition of PFOA in tissues and fluids from pregnant and lactating mice and their pups. Reprod Toxicol 27(3–4):365–372.
  38. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. 2009. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood) 28(5):w822–w831.
  39. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al. 2011. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 377(9765):557–567.
  40. Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. 1998. Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord 22(1):39–47.
  41. Flegal KM, Carroll MD, Ogden CL, Curtin LR. 2010. Prevalence and trends in obesity among US adults, 1999–2008. JAMA 303(3):235–241.
  42. Fleissner CK, Huebel N, Abd El-Bary MM, Loh G, Klaus S, Blaut M. 2010. Absence of intestinal microbiota does not protect mice from diet-induced obesity. Br J Nutr 104(6):919–929.
  43. Furet JP, Kong LC, Tap J, Poitou C, Basdevant A, Bouillot JL, et al. 2010. Differential adaptation of human gut microbiota to bariatric surgery-induced weight loss: links with metabolic and low-grade inflammation markers. Diabetes 59(12):3049–3057.
  44. Garcia-Lafuente A, Antolin M, Guarner F, Crespo E, Malagelada JR. 2001. Modulation of colonic barrier function by the composition of the commensal flora in the rat. Gut 48(4):503–507.
  45. Genuis SJ. 2011. Elimination of persistent toxicants from the human body. Hum Exp Toxicol 30(1):3–18.
  46. Goodman AL, Gordon JI. 2010. Our unindicted coconspirators: human metabolism from a microbial perspective. Cell Metab 12(2):111–116.
  47. Grün F.. 2010. Obesogens. Curr Opin Endocrinol Diabetes Obes 17(5):453–459.
  48. Grün F, Blumberg B.. 2006. Environmental obesogens: organotins and endocrine disruption via nuclear receptor signaling. Endocrinology 147(6): supplS50–S55.
  49. Grün F, Blumberg B.. 2007. Perturbed nuclear receptor signaling by environmental obesogens as emerging factors in the obesity crisis. Rev Endocr Metab Disord 8(2):161–171.
  50. Grün 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.
  51. Grundmann O.. 2010. The gut microbiome and pre-systemic metabolism: current state and evolving research. J Drug Metabol Toxicol 1:104.; doi:10.4172/2157-7609.1000104 [Online 30 November 2010]
  52. Hallstrom M, Eerola E, Vuento R, Janas M, Tammela O.. 2004. Effects of mode of delivery and necrotising enterocolitis on the intestinal microflora in preterm infants. Eur J Clin Microbiol Infect Dis 23(6):463–470.
  53. Hansen EE, Lozupone CA, Rey FE, Wu M, Guruge JL, Narra A, et al. 2011. Pan-genome of the dominant human gut-associated archaeon, Methanobrevibacter smithii, studied in twins. Proc Natl Acad Sci USA 108: suppl 14599–4606.
  54. 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]
  55. Heindel JJ, vom Saal FS. 2009. Role of nutrition and environmental endocrine disrupting chemicals during the perinatal period on the aetiology of obesity. Mol Cell Endocrinol 304(1–2):90–96.
  56. Henriksen GL, Ketchum NS, Michalek JE, Swaby JA. 1997. Serum dioxin and diabetes mellitus in veterans of Operation Ranch Hand. Epidemiology 8(3):252–258.
  57. Hines EP, White SS, Stanko JP, Gibbs-Flournoy EA, Lau C, Fenton SE. 2009. Phenotypic dichotomy following developmental exposure to perfluorooctanoic acid (PFOA) in female CD-1 mice: low doses induce elevated serum leptin and insulin, and overweight in mid-life. Mol Cell Endocrinol 304(1–2):97–105.
  58. Howard SG, Lee DH. 2011. What is the role of human contamination by environmental chemicals in the development of type 1 diabetes? J Epidemiol Community Health. ; doi:10.1136/jech.2011.133694 [Online 17 April 2011]
  59. Hugo ER, Brandebourg TD, Woo JG, Loftus J, Alexander JW, Ben-Jonathan N. 2008. Bisphenol A at environmentally relevant doses inhibits adiponectin release from human adipose tissue explants and adipocytes. Environ Health Perspect 116:1642–1647.
  60. Humblot C, Murkovic M, Rigottier-Gois L, Bensaada M, Bouclet A, Andrieux C, et al. 2007. Beta-glucuronidase in human intestinal microbiota is necessary for the colonic genotoxicity of the food-borne carcinogen 2-amino-3-methylimidazo[4,5-f]quinoline in rats. Carcinogenesis 28(11):2419–2425.
  61. Inadera H, Shimomura A.. 2005. Environmental chemical tributyltin augments adipocyte differentiation. Toxicol Lett 159(3):226–234.
  62. Jumpertz R, Le DS, Turnbaugh PJ, Trinidad C, Bogardus C, Gordon JI, et al. 2011. Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. Am J Clin Nutr 94(1):58–65.
  63. Kalliomaki M, Collado MC, Salminen S, Isolauri E. 2008. Early differences in fecal microbiota composition in children may predict overweight. Am J Clin Nutr 87(3):534–538.
  64. Kang HK, Dalager NA, Needham LL, Patterson DG Jr, Lees PS, Yates K, et al. 2006. Health status of Army Chemical Corps Vietnam veterans who sprayed defoliant in Vietnam. Am J Ind Med 49(11):875–884.
  65. 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.
  66. Kemperman RA, Bolca S, Roger LC, Vaughan EE. 2010. Novel approaches for analysing gut microbes and dietary polyphenols: challenges and opportunities. Microbiology 156(Pt 11):3224–3231.
  67. Kim Y, Lee BK. 2011. Association between urinary arsenic and diabetes mellitus in the Korean general population according to KNHANES 2008. Sci Total Environ 409(19):4054–4062.
  68. 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.
  69. Kootte RS, Vrieze A, Holleman F, Dallinga-Thie GM, Zoetendal EG, de Vos WM, et al. 2011. The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab. ; doi: .10.1111/j.1463-1326.2011.01483.x
  70. La Merrill M, Birnbaum LS. 2011. Childhood obesity and environmental chemicals. Mt Sinai J Med 78(1):22–48.
  71. 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.
  72. Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, et al. 2010. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5(2):e9085.; doi:10.1371/journal.pone.0009085 [Online 5 February 2010]
  73. Lee DH, Lee IK, Jin SH, Steffes M, Jacobs DR Jr. 2007a. Association between serum concentrations of persistent organic pollutants and insulin resistance among nondiabetic adults: results from the National Health and Nutrition Examination Survey 1999–2002. Diabetes Care 30(3):622–628.
  74. Lee DH, Lee IK, Porta M, Steffes M, Jacobs DR Jr. 2007b. Relationship between serum concentrations of persistent organic pollutants and the prevalence of metabolic syndrome among non-diabetic adults: results from the National Health and Nutrition Examination Survey 1999–2002. Diabetologia 50(9):1841–1851.
  75. Lee DH, Lee IK, 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.
  76. Lee DH, Steffes MW, Sjodin A, Jones RS, Needham LL, Jacobs DR Jr. 2010. Low dose of some persistent organic pollutants predicts type 2 diabetes: a nested case–control study. Environ Health Perspect 118:1235–1242.
  77. Lee DH, Steffes MW, Sjodin A, Jones RS, Needham LL, Jacobs DR Jr. 2011. Low dose organochlorine pesticides and polychlorinated biphenyls predict obesity, dyslipidemia, and insulin resistance among people free of diabetes. PLoS One 6(1):e15977.; doi:10.1371/journal.pone.0015977 [Online 26 January 2011]
  78. Lee MJ, Lin H, Liu CW, Wu MH, Liao WJ, Chang HH, et al. 2008. Octylphenol stimulates resistin gene expression in 3T3-L1 adipocytes via the estrogen receptor and extracellular signal-regulated kinase pathways. Am J Physiol Cell Physiol 294(6):C1542–C1551.
  79. Letran SE, Lee SJ, Atif SM, Flores-Langarica A, Uematsu S, Akira S, et al. 2011. TLR5-deficient mice lack basal inflammatory and metabolic defects but exhibit impaired CD4 T cell responses to a flagellated pathogen. J Immunol 186(9):5406–5412.
  80. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. 2005. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 102(31):11070–11075.
  81. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. 2006. Microbial ecology: human gut microbes associated with obesity. Nature 444(7122):1022–1023.
  82. Lhoste EF, Ouriet V, Bruel S, Flinois JP, Brezillon C, Magdalou J, et al. 2003. The human colonic microflora influences the alterations of xenobiotic-metabolizing enzymes by catechins in male F344 rats. Food Chem Toxicol 41(5):695–702.
  83. Like AA, Rossini AA. 1976. Streptozotocin-induced pancreatic insulitis: new model of diabetes mellitus. Science 193(4251):415–417.
  84. Lim JS, Lee DH, Jacobs DR Jr. 2008. Association of brominated flame retardants with diabetes and metabolic syndrome in the U.S. population, 2003–2004. Diabetes Care 31(9):1802–1807.
  85. Lopez-Espinosa MJ, Freire C, Arrebola JP, Navea N, Taoufiki J, Fernandez MF, et al. 2009. Nonylphenol and octylphenol in adipose tissue of women in Southern Spain. Chemosphere 76(6):847–852.
  86. Lundin JI, Alexander BH, Olsen GW, Church TR. 2009. Ammonium perfluorooctanoate production and occupational mortality. Epidemiology 20(6):921–928.
  87. MacNeil J, Steenland NK, Shankar A, Ducatman A. 2009. A cross-sectional analysis of type II diabetes in a community with exposure to perfluorooctanoic acid (PFOA). Environ Res 109(8):997–1003.
  88. McBain AJ, MacFarlane GT. 1998. Ecological and physiological studies on large intestine bacteria in relation to production of hydrolytic and reductive enzymes involved in formation of genotoxic metabolites. J Med Microbiol 47(5):407–416.
  89. Meijer L, Hafkamp AM, Bosman WE, Havinga R, Bergman A, Sauer PJ, et al. 2006. Nonabsorbable dietary fat enhances disposal of 2,2’,4,4’-tetrabromodiphenyl ether in rats through interruption of enterohepatic circulation. J Agric Food Chem 54(17):6440–6444.
  90. Meijer RI, van Wagensveld BA, Siegert CE, Eringa EC, Serne EH, Smulders YM. 2011. Bariatric surgery as a novel treatment for type 2 diabetes mellitus: a systematic review. Arch Surg 146(6):744–750.
  91. Meinl W, Sczesny S, Brigelius-Flohe R, Blaut M, Glatt H.. 2009. Impact of gut microbiota on intestinal and hepatic levels of phase 2 xenobiotic-metabolizing enzymes in the rat. Drug Metab Dispos 37(6):1179–1186.
  92. 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]
  93. Membrez M, Blancher F, Jaquet M, Bibiloni R, Cani PD, Burcelin RG, et al. 2008. Gut microbiota modulation with norfloxacin and ampicillin enhances glucose tolerance in mice. FASEB J 22(7):2416–2426.
  94. Merali Z, Singhal RL. 1980. Diabetogenic effects of chronic oral cadmium adminstration to neonatal rats. Br J Pharmacol 69(1):151–157.
  95. Michalek JE, Pavuk M. 2008. Diabetes and cancer in veterans of Operation Ranch Hand after adjustment for calendar period, days of spraying, and time spent in Southeast Asia. J Occup Environ Med 50(3):330–340.
  96. 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.
  97. Montgomery MP, Kamel F, Saldana TM, Alavanja MC, Sandler DP. 2008. Incident diabetes and pesticide exposure among licensed pesticide applicators: Agricultural Health Study, 1993–2003. Am J Epidemiol 167(10):1235–1246.
  98. Muegge BD, Kuczynski J, Knights D, Clemente JC, Gonzalez A, Fontana L, et al. 2011. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332(6032):970–974.
  99. Musso G, Gambino R, Cassader M.. 2010. Obesity, diabetes, and gut microbiota: the hygiene hypothesis expanded? Diabetes Care 33(10):2277–2284.
  100. Musso G, Gambino R, Cassader M.. 2011. Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu Rev Med 62:361–380.
  101. Myers MA, Hettiarachchi KD, Ludeman JP, Wilson AJ, Wilson CR, Zimmet PZ. 2003. Dietary microbial toxins and type 1 diabetes. Ann N Y Acad Sci 1005:418–422.
  102. 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.
  103. Neu J, Lorca G, Kingma SD, Triplett EW. 2010. The intestinal microbiome: relationship to type 1 diabetes. Endocrinol Metab Clin North Am 39(3):563–571.
  104. Newbold RR. 2010. Impact of environmental endocrine disrupting chemicals on the development of obesity. Hormones (Athens) 9(3):206–217.
  105. Newbold RR, Padilla-Banks E, Jefferson WN, Heindel JJ. 2008. Effects of endocrine disruptors on obesity. Int J Androl 31(2):201–208.
  106. Newbold RR, Padilla-Banks E, Snyder RJ, Phillips TM, Jefferson WN. 2007. Developmental exposure to endocrine disruptors and the obesity epidemic. Reprod Toxicol 23(3):290–296.
  107. Oishi K, Sato T, Yokoi W, Yoshida Y, Ito M, Sawada H.. 2008. Effect of probiotics, Bifidobacterium breve and Lactobacillus casei, on bisphenol A exposure in rats. Biosci Biotechnol Biochem 72(6):1409–1415.
  108. Palmer JR, Herbst AL, Noller KL, Boggs DA, Troisi R, Titus-Ernstoff L, et al. 2009. Urogenital abnormalities in men exposed to diethylstilbestrol in utero: a cohort study. Environ Health 8:37.; doi:10.1186/1476-069X-8-37 [Online 18 August 2009]
  109. Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, et al. 2006. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics 118(2):511–521.
  110. Penders J, Vink C, Driessen C, London N, Thijs C, Stobberingh EE. 2005. Quantification of Bifidobacterium spp., Escherichia coli and Clostridium difficile in faecal samples of breast-fed and formula-fed infants by real-time PCR. FEMS Microbiol Lett 243(1):141–147.
  111. Penza M, Montani C, Romani A, Vignolini P, Pampaloni B, Tanini A, et al. 2006. Genistein affects adipose tissue deposition in a dose-dependent and gender-specific manner. Endocrinology 147(12):5740–5751.
  112. Peters WH, Kock L, Nagengast FM, Kremers PG. 1991. Biotransformation enzymes in human intestine: critical low levels in the colon? Gut 32(4):408–412.
  113. Pinyayev TS, Kohan MJ, Herbin-Davis K, Creed JT, Thomas DJ. 2011. Preabsorptive metabolism of sodium arsenate by anaerobic microbiota of mouse cecum forms a variety of methylated and thiolated arsenicals. Chem Res Toxicol 24(4):475–477.
  114. Possemiers S, Grootaert C, Vermeiren J, Gross G, Marzorati M, Verstraete W, et al. 2009. The intestinal environment in health and disease—recent insights on the potential of intestinal bacteria to influence human health. Curr Pharm Des 15(18):2051–2065.
  115. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464(7285):59–65.
  116. Rabot S, Membrez M, Bruneau A, Gerard P, Harach T, Moser M, et al. 2010. Germ-free C57BL/6J mice are resistant to high-fat-diet-induced insulin resistance and have altered cholesterol metabolism. FASEB J 24(12):4948–4959.
  117. Rahman MM, Ng JC, Naidu R. 2009. Chronic exposure of arsenic via drinking water and its adverse health impacts on humans. Environ Geochem Health 31: suppl 1189–200.
  118. Ravussin Y, Koren O, Spor A, Leduc C, Gutman R, Stombaugh J, et al. 2011. Responses of gut microbiota to diet composition and weight loss in lean and obese mice. Obesity (Silver Spring). ; doi:10.1038/oby.2011.111 [Online 19 May 2011]
  119. Reddy BS, Pleasants JR, Wostmann BS. 1973. Metabolic enzymes in liver and kidney of the germfree rat. Biochim Biophys Acta 320(1):1–8.
  120. Reed MA, Pories WJ, Chapman W, Pender J, Bowden R, Barakat H, et al. 2011. Roux-en-Y gastric bypass corrects hyperinsulinemia implications for the remission of type 2 diabetes. J Clin Endocrinol Metab 96(8):2525–2531.
  121. Rignell-Hydbom A, Lidfeldt J, Kiviranta H, Rantakokko P, Samsioe G, Agardh CD, 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]
  122. Rowland IR. 2009. The role of the gastrointestinal microbiota in colorectal cancer. Curr Pharm Des 15(13):1524–1527.
  123. Rowland IR, Mallett AK, Bearne CA, Farthing MJ. 1986. Enzyme activities of the hindgut microflora of laboratory animals and man. Xenobiotica 16(6):519–523.
  124. Rowland IR, Mallett AK, Wise A. 1985. The effect of diet on the mammalian gut flora and its metabolic activities. Crit Rev Toxicol 16(1):31–103.
  125. Rubin BS, Murray MK, Damassa DA, King JC, Soto AM. 2001. Perinatal exposure to low doses of bisphenol A affects body weight, patterns of estrous cyclicity, and plasma LH levels. Environ Health Perspect 109:675–680.
  126. 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.
  127. Sakurai K, Kawazuma M, Adachi T, Harigaya T, Saito Y, Hashimoto N, et al. 2004. Bisphenol A affects glucose transport in mouse 3T3-F442A adipocytes. Br J Pharmacol 141(2):209–214.
  128. Sanz Y, Santacruz A, Gauffin P.. 2010. Probiotics in the defence and metabolic balance of the organism: Gut microbiota in obesity and metabolic disorders. Proc Nutr Soc 69:434–441.
  129. Sargis RM, Johnson DN, Choudhury RA, Brady MJ. 2010. Environmental endocrine disruptors promote adipogenesis in the 3T3-L1 cell line through glucocorticoid receptor activation. Obesity (Silver Spring) 18(7):1283–1288.; doi:10.1038/oby.2009.419 [Online 19 November 2009]
  130. Schauer PR, Rubino F. 2011. International Diabetes Federation position statement on bariatric surgery for type 2 diabetes: implications for patients, physicians, and surgeons. Surg Obes Relat Dis 7(4):448–451.
  131. Schwartz GG, Il’yasova D, Ivanova A. 2003. Urinary cadmium, impaired fasting glucose, and diabetes in the NHANES III. Diabetes Care 26(2):468–470.
  132. Son HK, Kim SA, Kang JH, Chang YS, Park SK, Lee SK, et al. 2010. Strong associations between low-dose organochlorine pesticides and type 2 diabetes in Korea. Environ Int 36(5):410–414.
  133. Sousa T, Paterson R, Moore V, Carlsson A, Abrahamsson B, Basit AW. 2008. The gastrointestinal microbiota as a site for the biotransformation of drugs. Int J Pharm 363(1–2):1–25.
  134. Tam CS, Berthoud HR, Bueter M, Chakravarthy MV, Geliebter A, Hajnal A, et al. 2011. Could the mechanisms of bariatric surgery hold the key for novel therapies?: a report from the Pennington Scientific Symposium. Obes Rev. ; doi: .10.1111/j.1467–789X.2011.00902.x
  135. Teubner W, Meinl W, Florian S, Kretzschmar M, Glatt H.. 2007. Identification and localization of soluble sulfotransferases in the human gastrointestinal tract. Biochem J 404(2):207–215.
  136. Treptow-van Lishaut S, Rechkemmer G, Rowland I, Dolara P, Pool-Zobel BL. 1999. The carbohydrate crystalean and colonic microflora modulate expression of glutathione S-transferase subunits in colon of rats. Eur J Nutr 38(2):76–83.
  137. Troisi R, Hatch EE, Titus-Ernstoff L, Hyer M, Palmer JR, Robboy SJ, et al. 2007. Cancer risk in women prenatally exposed to diethylstilbestrol. Int J Cancer 121(2):356–360.
  138. Tseng CH. 2007. Metabolism of inorganic arsenic and non-cancerous health hazards associated with chronic exposure in humans. J Environ Biol 28(2): suppl349–357.
  139. Turnbaugh PJ, Gordon JI. 2009. The core gut microbiome, energy balance and obesity. J Physiol 587(Pt 17):4153–4158.
  140. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. 2009a. A core gut microbiome in obese and lean twins. Nature 457(7228):480–484.
  141. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. 2006. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122):1027–1031.
  142. Turnbaugh PJ, Quince C, Faith JJ, McHardy AC, Yatsunenko T, Niazi F, et al. 2010. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc Natl Acad Sci USA 107(16):7503–7508.
  143. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. 2009b. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med. 6. 1. pp. 6ra–14.
  144. 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.
  145. 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.
  146. 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.
  147. Van de Wiele T, Gallawa CM, Kubachka KM, Creed JT, Basta N, Dayton EA, et al. 2010. Arsenic metabolism by human gut microbiota upon in vitro digestion of contaminated soils. Environ Health Perspect 118:1004–1009.
  148. Van de Wiele T, Vanhaecke L, Boeckaert C, Peru K, Headley J, Verstraete W, et al. 2005. Human colon microbiota transform polycyclic aromatic hydrocarbons to estrogenic metabolites. Environ Health Perspect 113:6–10.
  149. van Duynhoven J, Vaughan EE, Jacobs DM, Kemperman RA, van Velzen EJJ, Gross G, et al. 2010. Microbes and Health Sackler Colloquium: Metabolic fate of polyphenols in the human superorganism. Proc Natl Acad Sci USA 108: suppl 14531–4538.
  150. Vehik K, Dabelea D.. 2011. The changing epidemiology of type 1 diabetes: why is it going through the roof? Diabetes Metab Res Rev 27(1):3–13.
  151. Vijay-Kumar M, Aitken JD, Carvalho FA, Cullender TC, Mwangi S, Srinivasan S, et al. 2010. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 328(5975):228–231.
  152. Wallace BD, Wang H, Lane KT, Scott JE, Orans J, Koo JS, et al. 2010. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330(6005):831–835.
  153. Wang SL, Tsai PC, Yang CY, 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.
  154. Wang Y, Lobstein T.. 2006. Worldwide trends in childhood overweight and obesity. Int J Pediatr Obes 1(1):11–25.
  155. Wang YC, Gortmaker SL, Taveras EM. 2011. Trends and racial/ethnic disparities in severe obesity among US children and adolescents, 1976–2006. Int J Pediatr Obes 6(1):12–20.
  156. Wiegand H, Boesch-Saadatmandi C, Regos I, Treutter D, Wolffram S, Rimbach G.. 2009. Effects of quercetin and catechin on hepatic glutathione-S transferase (GST), NAD(P)H quinone oxidoreductase 1 (NQO1), and antioxidant enzyme activity levels in rats. Nutr Cancer 61(5):717–722.
  157. Wilson ID. 2009. Drugs, bugs, and personalized medicine: pharmacometabonomics enters the ring. Proc Natl Acad Sci USA 106(34):14187–14188.
  158. Wilson ID, Nicholson JK. 2009. The role of gut microbiota in drug response. Curr Pharm Des 15(13):1519–1523.
  159. 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.
  160. Yen CC, Lu FJ, Huang CF, Chen WK, Liu SH, Lin-Shiau SY. 2007. The diabetogenic effects of the combination of humic acid and arsenic: in vitro and in vivo studies. Toxicol Lett 172(3):91–105.
  161. Zhang H, DiBaise JK, Zuccolo A, Kudrna D, Braidotti M, Yu Y, et al. 2009. Human gut microbiota in obesity and after gastric bypass. Proc Natl Acad Sci USA 106(7):2365–2370.
  162. Zuo Z, Chen S, Wu T, Zhang J, Su Y, Chen Y, et al. 2011. Tributyltin causes obesity and hepatic steatosis in male mice. Environ Toxicol 26(1):79–85.

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