Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China; Ma(,)anshan Center for Disease Control and Provention, Ma,anshan, Anhui, 243000, China.
Environ Pollut. 2021 Sep 15;285:117150. doi: 10.1016/j.envpol.2021.117150. Epub 2021 Apr 30.
Association between long-term exposure to multiple metals and obesity remains inconclusive, and prospective evidence on the region along the Yangtze River was limited. Thus, our study aimed to examine the association of multiple metal exposure and obesity. We measured baseline urine levels of 22 metals of 982 adults living along the Yangtze River, incidence of obesity was calculated from body mass index (BMI) and waist circumference (WC) measured at follow-up survey. Cox proportional hazards models were used to examine the hazard ratios (HR) and 95% confidence interval (CI) for the association between urinary metals and obesity, and the mixing effect of metals on obesity was estimated by using quantile g-computation. In multiple-metal models, arsenic was significantly associated with BMI/obesity, with the HR in the highest quartiles of 0.33 (95% CI: 0.16, 0.69; p-trend = 0.004). The HRs for WC/obesity of arsenic and molybdenum were 0.49 (95% CI: 0.32, 0.75 for the fourth vs. first quartile; p-trend = 0.002) and 1.83 (95% CI: 1.25, 2.70; p-trend = 0.001), respectively. Quantile g-computation mixtures approach showed a significantly negative joint effect of multiple metals on WC/obesity, with the HR of 0.26 (95% CI: 0.14, 0.47; p < 0.001) when increasing all seventeen metals by one quartile. Our study suggests that all seventeen metal mixed exposure may be negatively associated with obesity. Further cohort studies are needed to confirm these findings and clarify the underlying biological mechanisms.
长期暴露于多种金属与肥胖之间的关系仍不确定，而关于长江流域的前瞻性证据有限。因此，我们的研究旨在探讨多种金属暴露与肥胖之间的关系。我们测量了 982 名居住在长江沿岸的成年人的基线尿液中 22 种金属的水平，根据随访调查中测量的体重指数（BMI）和腰围（WC）计算肥胖的发生率。我们使用 Cox 比例风险模型来检验尿液金属与肥胖之间的关联的风险比（HR）和 95%置信区间（CI），并使用分位数 g 计算来估计金属对肥胖的混合效应。在多金属模型中，砷与 BMI/肥胖显著相关，最高四分位的 HR 为 0.33（95%CI：0.16，0.69；p-trend = 0.004）。砷和钼与 WC/肥胖的 HR 分别为 0.49（95%CI：第四 vs. 第一四分位，0.32，0.75；p-trend = 0.002）和 1.83（95%CI：1.25，2.70；p-trend = 0.001）。分位数 g 计算混合效应方法表明，多种金属对 WC/肥胖的联合作用具有显著的负性，当所有 17 种金属都增加一个四分位时，HR 为 0.26（95%CI：0.14，0.47；p<0.001）。我们的研究表明，所有 17 种金属混合暴露可能与肥胖呈负相关。需要进一步的队列研究来证实这些发现并阐明潜在的生物学机制。
