Miranda J Spratlen1, Maria Grau-Perez2, Jason G Umans3, Joseph Yracheta4, Lyle G Best4, Kevin Francesconi5, Walter Goessler5, Teodoro Bottiglieri6, Mary V Gamble7, Shelley A Cole8, Jinying Zhao9, Ana Navas-Acien10. 1. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA; Department of Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: mjs2376@cumc.columbia.edu. 2. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA; Fundación Investigación Clínico de Valencia-INCLIVA, Area of Cardiometabolic and Renal Risk, Valencia, Valencia, Spain; University of Valencia, Department of Statistics and Operational Research, Valencia, Valencia, Spain. 3. MedStar Health Research Institute, Hyattsville, MD, USA; Department of Medicine, Georgetown University School of Medicine, Washington, DC, USA. 4. Missouri Breaks Industries Research, Inc., Eagle Butte, SD, USA. 5. Institute of Chemistry - Analytical Chemistry, University of Graz, Austria. 6. Baylor Scott & White Research Institute, Dallas, TX, USA. 7. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA. 8. Texas Biomedical Research Institute, San Antonio, TX, USA. 9. College of Public Health and Health Professions and the College of Medicine at the University of Florida, Gainesville, FL, USA. 10. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA; Department of Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: an2737@cumc.columbia.edu.
Abstract
BACKGROUND: Inorganic arsenic exposure is ubiquitous and both exposure and inter-individual differences in its metabolism have been associated with cardiometabolic risk. A more efficient arsenic metabolism profile (lower MMA%, higher DMA%) has been associated with reduced risk for arsenic-related health outcomes. This profile, however, has also been associated with increased risk for diabetes-related outcomes. OBJECTIVES: The mechanism behind these conflicting associations is unclear; we hypothesized the one-carbon metabolism (OCM) pathway may play a role. METHODS: We evaluated the influence of OCM on the relationship between arsenic metabolism and diabetes-related outcomes (HOMA2-IR, waist circumference, fasting plasma glucose) using metabolomic data from an OCM-specific and P180 metabolite panel measured in plasma, arsenic metabolism measured in urine, and HOMA2-IR and FPG measured in fasting plasma. Samples were drawn from baseline visits (2001-2003) in 59 participants from the Strong Heart Family Study, a family-based cohort study of American Indians aged ≥14 years from Arizona, Oklahoma, and North/South Dakota. RESULTS: In unadjusted analyses, a 5% increase in DMA% was associated with higher HOMA2-IR (geometric mean ratio (GMR)= 1.13 (95% CI: 1.03, 1.25)) and waist circumference (mean difference=3.66 (0.95, 6.38). MMA% was significantly associated with lower HOMA2-IR and waist circumference. After adjustment for OCM-related metabolites (SAM, SAH, cysteine, glutamate, lysophosphatidylcholine 18.2, and three phosphatidlycholines), associations were attenuated and no longer significant. CONCLUSIONS: These preliminary results indicate that the association of lower MMA% and higher DMA% with diabetes-related outcomes may be influenced by OCM status, either through confounding, reverse causality, or mediation.
BACKGROUND: Inorganic arsenic exposure is ubiquitous and both exposure and inter-individual differences in its metabolism have been associated with cardiometabolic risk. A more efficient arsenic metabolism profile (lower MMA%, higher DMA%) has been associated with reduced risk for arsenic-related health outcomes. This profile, however, has also been associated with increased risk for diabetes-related outcomes. OBJECTIVES: The mechanism behind these conflicting associations is unclear; we hypothesized the one-carbon metabolism (OCM) pathway may play a role. METHODS: We evaluated the influence of OCM on the relationship between arsenic metabolism and diabetes-related outcomes (HOMA2-IR, waist circumference, fasting plasma glucose) using metabolomic data from an OCM-specific and P180 metabolite panel measured in plasma, arsenic metabolism measured in urine, and HOMA2-IR and FPG measured in fasting plasma. Samples were drawn from baseline visits (2001-2003) in 59 participants from the Strong Heart Family Study, a family-based cohort study of American Indians aged ≥14 years from Arizona, Oklahoma, and North/South Dakota. RESULTS: In unadjusted analyses, a 5% increase in DMA% was associated with higher HOMA2-IR (geometric mean ratio (GMR)= 1.13 (95% CI: 1.03, 1.25)) and waist circumference (mean difference=3.66 (0.95, 6.38). MMA% was significantly associated with lower HOMA2-IR and waist circumference. After adjustment for OCM-related metabolites (SAM, SAH, cysteine, glutamate, lysophosphatidylcholine 18.2, and three phosphatidlycholines), associations were attenuated and no longer significant. CONCLUSIONS: These preliminary results indicate that the association of lower MMA% and higher DMA% with diabetes-related outcomes may be influenced by OCM status, either through confounding, reverse causality, or mediation.
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