Xin Wang1, Bhramar Mukherjee2, Sung Kyun Park3. 1. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America. 2. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America. 3. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America; Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America. Electronic address: sungkyun@umich.edu.
Abstract
BACKGROUND: Some heavy metals (e.g., arsenic, cadmium, lead, mercury) have been associated with obesity and obesity comorbidities. The analytical approach for those associations has typically focused on individual metals. There is a growing interest in evaluating the health effects of cumulative exposure to metal mixtures. OBJECTIVES: We utilized our Environmental Risk Score (ERS), a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research, to evaluate the associations of cumulative exposure to a mixture of correlated heavy metals with obesity and its comorbidities including hypertension, and type-2 diabetes mellitus (T2DM) while accounting for high degree correlations and interactions among metal mixtures components. METHODS: We examined blood and urinary markers of 18 heavy metals among 9537 adults in NHANES 2003-2014. We randomly split data into a training set for the construction of ERS (n = 6675) and a testing set for the evaluation of its statistical performance (n = 2862). ERS of heavy metal mixtures was computed for waist circumference using adaptive elastic-net (AENET) with 189 predictors including 18 main effects, 18 squared terms, and 153 pairwise interactions of heavy metals. Regression analyses with complex survey designs were performed to assess the associations of ERS with other obesity measures, hypertension and T2DM. RESULTS: 7 main effects (blood lead, blood cadmium, blood mercury, and urinary markers of monomethylarsonic acid (MMA), barium, mercury and thallium), 4 squared terms (blood cadmium, urinary cadmium, urinary antimony and urinary tungsten), and 7 pairwise interactions (blood lead & urinary cadmium, blood lead & urinary MMA, blood lead & urinary uranium, urinary cadmium & urinary MMA, urinary dimethylarsinic acid (DMA) & urinary tungsten, urinary MMA & urinary cobalt, and urinary lead & urinary antimony) were selected by AENET for construction of ERS of waist circumference-related metal mixtures. An increase in ERS from 10th percentile to 90th percentile in the overall study population was significantly associated with 4.50 kg/m2 (95% CI: 4.06, 4.94) higher BMI, 4.16 mm (95% CI: 3.56, 4.76) higher skinfold thickness, and 4.11 kg (95% CI: 0.83, 7.40) higher total body fat, independent of age, sex, race/ethnicity, education, smoking status, physical activity and NHANES cycle (Ps < 0.05). Significant associations of ERS with both hypertension and T2DM were also observed (Ps < 0.05). CONCLUSIONS: Our study suggests that cumulative exposure to heavy metals as mixtures is associated with obesity and its related chronic conditions such as hypertension and T2DM. Additional research is needed to confirm these findings in longitudinal settings.
BACKGROUND: Some heavy metals (e.g., arsenic, cadmium, lead, mercury) have been associated with obesity and obesity comorbidities. The analytical approach for those associations has typically focused on individual metals. There is a growing interest in evaluating the health effects of cumulative exposure to metal mixtures. OBJECTIVES: We utilized our Environmental Risk Score (ERS), a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research, to evaluate the associations of cumulative exposure to a mixture of correlated heavy metals with obesity and its comorbidities including hypertension, and type-2 diabetes mellitus (T2DM) while accounting for high degree correlations and interactions among metal mixtures components. METHODS: We examined blood and urinary markers of 18 heavy metals among 9537 adults in NHANES 2003-2014. We randomly split data into a training set for the construction of ERS (n = 6675) and a testing set for the evaluation of its statistical performance (n = 2862). ERS of heavy metal mixtures was computed for waist circumference using adaptive elastic-net (AENET) with 189 predictors including 18 main effects, 18 squared terms, and 153 pairwise interactions of heavy metals. Regression analyses with complex survey designs were performed to assess the associations of ERS with other obesity measures, hypertension and T2DM. RESULTS: 7 main effects (blood lead, blood cadmium, blood mercury, and urinary markers of monomethylarsonic acid (MMA), barium, mercury and thallium), 4 squared terms (blood cadmium, urinary cadmium, urinary antimony and urinary tungsten), and 7 pairwise interactions (blood lead & urinary cadmium, blood lead & urinary MMA, blood lead & urinary uranium, urinary cadmium & urinary MMA, urinary dimethylarsinic acid (DMA) & urinary tungsten, urinary MMA & urinary cobalt, and urinary lead & urinary antimony) were selected by AENET for construction of ERS of waist circumference-related metal mixtures. An increase in ERS from 10th percentile to 90th percentile in the overall study population was significantly associated with 4.50 kg/m2 (95% CI: 4.06, 4.94) higher BMI, 4.16 mm (95% CI: 3.56, 4.76) higher skinfold thickness, and 4.11 kg (95% CI: 0.83, 7.40) higher total body fat, independent of age, sex, race/ethnicity, education, smoking status, physical activity and NHANES cycle (Ps < 0.05). Significant associations of ERS with both hypertension and T2DM were also observed (Ps < 0.05). CONCLUSIONS: Our study suggests that cumulative exposure to heavy metals as mixtures is associated with obesity and its related chronic conditions such as hypertension and T2DM. Additional research is needed to confirm these findings in longitudinal settings.
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