Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Sunchon, Jeonnam, Republic of Korea.
Biol Trace Elem Res. 2022 Aug;200(8):3554-3571. doi: 10.1007/s12011-021-02972-z. Epub 2021 Oct 22.
Little is known about the association between a mixture of heavy metals and obesity among individuals ≥50 years of age with comorbidities. Thus, we identified the associations of serum cadmium (Cd), lead (Pb), and mercury (Hg) with obesity using linear regression models; weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted as secondary analyses. Of the 6434 subjects included in the analysis, 13.8% had obesity and 44.6% had abdominal obesity. In the logistic regression model, serum Hg was associated with obesity and abdominal obesity, and significant trends were observed for these heavy metal tertiles (p < 0.001). Serum Hg levels were also associated with body mass index (BMI) and waist circumference (WC). The WQS index was significantly associated with both obesity (OR = 1.43, 95% CI: 1.40-1.46) and abdominal obesity (β = 1.51, 95% CI: 1.48-1.54). The qgcomp index also found a significant association between heavy metals and both obesity (OR = 1.35, 95% CI: 1.12-1.63) and abdominal obesity (OR = 1.34, 95% CI: 1.12-1.60). Serum Hg was the most heavily weighed heavy metal in these models. In BKMR analysis, the overall effect of the mixture was significantly associated with obesity, BMI, and WC. Serum Hg showed positive trends and was observed as the most important factor associated with obesity, BMI, and WC. Our findings were largely robust to secondary analyses that used three novel mixture modeling approaches: WQS, qpcomp, and BKMR. Given increasing exposure to heavy metals, well-characterized cohorts of individuals aged ≥50 years are required to determine the mixed effects of heavy metals on obesity and related diseases.
对于患有合并症的 50 岁及以上人群，重金属混合物与肥胖之间的关系知之甚少。因此，我们使用线性回归模型来确定血清镉（Cd）、铅（Pb）和汞（Hg）与肥胖之间的关联；进行加权分位数总和（WQS）回归、分位数 g 计算（qgcomp）和贝叶斯核机器回归（BKMR）作为二次分析。在分析中纳入的 6434 名受试者中，13.8%患有肥胖症，44.6%患有腹型肥胖症。在逻辑回归模型中，血清 Hg 与肥胖症和腹型肥胖症相关，这些重金属三分位数存在显著趋势（p<0.001）。血清 Hg 水平与体重指数（BMI）和腰围（WC）也相关。WQS 指数与肥胖症（OR=1.43，95%CI：1.40-1.46）和腹型肥胖症（β=1.51，95%CI：1.48-1.54）显著相关。qgcomp 指数也发现重金属与肥胖症（OR=1.35，95%CI：1.12-1.63）和腹型肥胖症（OR=1.34，95%CI：1.12-1.60）之间存在显著关联。在这些模型中，血清 Hg 是权重最重的重金属。在 BKMR 分析中，混合物的整体效应与肥胖症、BMI 和 WC 显著相关。血清 Hg 呈正趋势，是与肥胖症、BMI 和 WC 相关的最重要因素。我们的研究结果在使用三种新的混合建模方法（WQS、qgcomp 和 BKMR）进行的二次分析中基本稳健。鉴于重金属暴露的增加，需要对 50 岁及以上人群进行特征明确的队列研究，以确定重金属对肥胖症和相关疾病的混合影响。
