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Abstract
BACKGROUND AND AIM: Inorganic arsenic exposure is of increasing interest as a possible risk factor for diabetes, and although there is growing research on potential toxicological mechanisms, the biology remains incompletely understood. METHODS: This is a cross-sectional, gene-environment interaction study using data from n=1,915 participants from 40 pedigrees in the Strong Heart Family Study with measured arsenic exposure (sum of inorganic and methylated urine arsenic species, dichotomized for analysis), age, sex, educational attainment, body mass index, genotypes, family relationships, fasting glucose, HOMA-IR, and HOMA-B. We conducted a multipoint variance components linkage analysis testing for arsenic-locus interactions (GxE) predicting glucose, HOMA-IR, or HOMA-B in separate models, adjusted for potential confounders of the arsenic-outcome relationships. We then tested for SNP-trait associations and SNP GxE in linkage regions. Analyses were implemented using the software Sequential Oligogenic Linkage Analysis Routines (SOLAR). RESULTS:We localized three quantitative trait loci that showed suggestive evidence for differential genetic contributions to the outcome in the presence of higher vs. lower inorganic arsenic exposure. The first was an interaction locus for fasting glucose at chromosome 17q22 (LOD 2.6 in GxE model, LOD 1.5 in standard linkage model without interaction). The second was an interaction locus for HOMA-IR at chromosome 7p13 (LOD 2.0 in GxE model, 0.4 in the standard linkage model). The third was an interaction locus for HOMA-B at chromosome 18q22.2 (LOD 2.0 in GxE model, LOD 1.7 in standard linkage model). Several SNPs showed Bonferroni-significant associations with diabetes-related traits, with the strongest evidence for rs4793861 in the MSI2 gene for glucose; however, no tested SNPs showed Bonferroni-significant interactions with arsenic. CONCLUSIONS:We found suggestive evidence for different locus-specific contributions to diabetes trait variability in the presence of higher inorganic arsenic exposure, and SNPs in these regions that were associated with diabetes traits. Further research (e.g., linkage fine-mapping) might further elucidate this biology. KEYWORDS: Environmental Epidemiology, Toxicology, Heavy Metals
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