Evaluation of the Association Between Arsenic and Diabetes

A National Toxicology Program Workshop Review

Elizabeth A. Maull; Habibul Ahsan; Joshua Edwards; Matthew P. Longnecker; Ana Navas-Acien; Jingbo Pi; Ellen K. Silbergeld; Miroslav Styblo; Chin-Hsiao Tseng; Kristina A. Thayer; Dana Loomis


Environ Health Perspect. 2012;120(12):1658-1670. 

In This Article

Conclusions and Research Needs

Overall, data from human studies included in this review support an association between inorganic arsenic and diabetes in populations with arsenic drinking-water levels of > 500 μg/L (Lai et al. 1994; Nabi et al. 2005; Rahman et al. 1998, 1999; Tsai et al. 1999; Tseng et al. 2000b; Wang et al. 2003), but the currently available evidence was considered insufficient to conclude that arsenic is associated with diabetes in individuals with low-to-moderate exposure (< 150 μg/L in drinking water). Stronger evidence of associations at lower levels of exposure based on some recent studies with better measures of outcome and exposure support the need for further research in populations with low-to-moderate exposure levels. Weaknesses noted in the epidemiological literature review included a lack of prospective studies, use of death certificates or self-reported diagnosis for ascertainment of diabetes, and ecological methods of exposure assessment. Because of these limitations, the evidence of effects at high arsenic exposure levels ranged from limited to sufficient, but did not reach the threshold for a sufficient classification.

Research needs identified as a result of this literature review are summarized in Table 3. Prospective studies in areas of lower exposure (e.g., parts of North America other than arsenic-endemic regions) with individual measurements of exposure prior to disease incidence are needed. However, the utilization of existing cohorts (such as the Strong Health Study), nested case–control designs, and follow-up of cross-sectional populations such as NHANES is also recommended. Additional consideration of the results from the recent HEALS study in Bangladesh (Chen et al. 2010), which do not align with findings from other studies in areas of moderate-to-high exposure, would also be helpful to better understand factors that influence the generalizability of associations reported based on other study populations. Research on interactions between arsenic exposure and factors such as body mass index (BMI), diet, levels of physical activity, co-exposures including metals that occur with arsenic, duration of exposure, and timing of exposure (i.e., the importance of early life or prenatal exposures) may help address this issue. In addition, future studies should include consideration of gene × environment interactions, including studies of polymorphisms in genes related to arsenic metabolism and diabetes susceptibility.

Given its well-established role as a risk factor for diabetes, the impact of obesity as a potential modifying factor needs to be better addressed, especially in countries such as the United States and Mexico where overweight and obesity are epidemic (WHO 2012). Average BMI in Bangladesh and Taiwan, where the association between arsenic exposure and diabetes was stronger, is much lower than in the United States and Mexico. For example, approximately 80% of study participants in the HEALS study in Bangladesh had a BMI of < 22 (Chen et al. 2010) whereas 68% of study participants included in the analysis of NHANES 2008 had a BMI of ≥ 25 (Navas-Acien et al. 2008). In the Mexico studies, 34–50% of participants had a BMI of > 30 (Coronado-González et al. 2007; Del Razo et al. 2011). Information on BMI was not presented in most of the studies conducted in Taiwan except for Tseng et al. (2000b), where the average BMI was 24.5 kg/m2; although as a population, the prevalence of overweight/obesity is higher in Taiwan compared to Bangladesh and lower compared to the United States (Huang 2008; WHO 2012). Many of the recent studies considered BMI as a potential confounding factor (Chen et al. 2010; Coronado-González et al. 2007; Del Razo et al. 2011; Ettinger et al. 2009; Kim and Lee 2011; Lai et al. 1994; Navas-Acien et al. 2008, 2009a; Rahman et al. 1999; Steinmaus et al. 2009a, 2009b; Tseng et al. 2000b), but the issue of obesity as an effect modifier or potential intermediate on a causal pathway between arsenic and diabetes has not been well-explored in the existing literature.

The experimental animal literature as a whole was judged inconclusive, but findings from recent studies that focus on diabetes-relevant end points appear consistent with those human studies that support a link between arsenic exposure and diabetes. Moreover, the animal studies implicate several pathways by which arsenic may influence pancreatic β-cell function and insulin sensitivity and suggest novel biomarkers for understanding pathways of response to arsenic in human populations. However, animal studies need to be designed to be relevant to human exposures in terms of internal dose. Use of specific inbred strains susceptible to diabetes and metabolic syndrome may also be informative. Application of systems toxicology approaches within the framework utilized by the NIEHS and others in studying relevance of the "toxome" [a comprehensive list of all pathways of toxicity (Hartung and McBride 2011)] to the "diabetome" [a conceptual platform placing a disease, diabetes, onto a network perspective and linking diabetes phenotypic features to all known diabetes-related genes (modified from Goh and Choi 2012)] may be innovative and stimulate new information on key signaling pathways that connect arsenic to diabetes.

Overall, animal studies need to be designed to specifically evaluate the influence of arsenic on the development of diabetes, using modern methods and well characterized end points for diabetes. Blood glucose levels, both fasting and fed, as well as insulin levels were identified as appropriate end points for animal studies. The influence of adiposity on the development of arsenic-induced diabetes could be explored more fully in animal models by quantitating fat mass and distribution in both white and brown adipose tissues.

Improved methodologies are needed for more accurate environmental exposure assessments as well as for internal dosemetrics and biologically based measurements that integrate all and differentiate among exposures, metabolites, and toxicities. Some of the newer proposed biomarkers (e.g., toe- and fingernails, saliva, buccal cells) need to be further characterized in terms of their relationships to external exposures and validated.