Aimin Yang1, Simin Liu2, Zhiyuan Cheng3, Hongquan Pu4, Ning Cheng5, Jiao Ding4, Juansheng Li6, Haiyan Li4, Xiaobin Hu6, Xiaowei Ren6, Kehu Yang3, Tongzhang Zheng7, Yana Bai8. 1. Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China; Department of Epidemiology, School of Public Health, Brown University, Providence, RI, 02912, USA. 2. Department of Epidemiology, School of Public Health, Brown University, Providence, RI, 02912, USA; Department of Medicine (Endocrinology), Rhode Island Hospital and the Alpert Medical School, Brown University, Providence, RI, 02908, USA. 3. Evidence-Based Medicine Center and Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, College of Basic Medicine, Lanzhou University, Lanzhou, Gansu, 73000, China. 4. Workers' Hospital of Jinchuan Group Co., Ltd., Jinchang, Gansu, 737103, China. 5. Center of Medical Laboratory, Lanzhou University, Lanzhou, Gansu, 730000, China. 6. Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China. 7. Department of Epidemiology, School of Public Health, Brown University, Providence, RI, 02912, USA. Electronic address: tongzhang_zheng@brown.edu. 8. Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China. Electronic address: baiyana@lzu.edu.cn.
Abstract
OBJECTIVES: Environmental exposure to metals may adversely affect cardiometabolic health. However, little data are available directly evaluating the roles of metal exposure in blood glucose of which dysfunction has been linked to diabetes. We aimed to evaluate the dose-response associations between fasting plasma glucose (FPG) and multiple urinary metals including nickel, cobalt, copper, zinc, and arsenic, as well as to examine their joint effects among occupational workers. METHODS: We performed a population-based study of 464 workers in an ongoing occupational cohort study in China. Both spline and categorical analyses were used to evaluate the dose-response relationship between urinary metals levels and FPG. RESULTS: We observed the J-shaped non-linear relationships between urinary nickel (P non-linearity = 0.03) and zinc (P non-linearity < 0.01) with FPG by spline analyses. A negative linear relationship between urinary cobalt and FPG (P for nonlinearity = 0.06) was found, but no statistically significant associations between urinary copper and arsenic with FPG. In linear regression analyses, the regression coefficient for log-transferred FPG was 0.017 (95% confidence intervals [CI]: -0.003, 0.038) in the 4th quartile concentration of urinary nickel, compared with 1st quartile. The joint effects between urinary nickel and cobalt with FPG were also detected (P for interaction = 0.04). CONCLUSIONS: Multiple urinary metals, particularly nickel, zinc and cobalt, were associated with blood glucose among Chinese metal exposed workers, supporting the notion that metal exposure may play a critical role in diabetes development.
OBJECTIVES: Environmental exposure to metals may adversely affect cardiometabolic health. However, little data are available directly evaluating the roles of metal exposure in blood glucose of which dysfunction has been linked to diabetes. We aimed to evaluate the dose-response associations between fasting plasma glucose (FPG) and multiple urinary metals including nickel, cobalt, copper, zinc, and arsenic, as well as to examine their joint effects among occupational workers. METHODS: We performed a population-based study of 464 workers in an ongoing occupational cohort study in China. Both spline and categorical analyses were used to evaluate the dose-response relationship between urinary metals levels and FPG. RESULTS: We observed the J-shaped non-linear relationships between urinary nickel (P non-linearity = 0.03) and zinc (P non-linearity < 0.01) with FPG by spline analyses. A negative linear relationship between urinary cobalt and FPG (P for nonlinearity = 0.06) was found, but no statistically significant associations between urinary copper and arsenic with FPG. In linear regression analyses, the regression coefficient for log-transferred FPG was 0.017 (95% confidence intervals [CI]: -0.003, 0.038) in the 4th quartile concentration of urinary nickel, compared with 1st quartile. The joint effects between urinary nickel and cobalt with FPG were also detected (P for interaction = 0.04). CONCLUSIONS: Multiple urinary metals, particularly nickel, zinc and cobalt, were associated with blood glucose among Chinese metal exposed workers, supporting the notion that metal exposure may play a critical role in diabetes development.