Feng Xiuming, Zan Gaohui, Wei Yue, Ge Xiaoting, Cai Haiqing, Long Tianzhu, Xie Lianguang, Tong Lei, Liu Chaoqun, Li Longman, Huang Lulu, Wang Fei, Chen Xing, Zhang Haiying, Zou Yunfeng, Zhang Zhiyong, Yang Xiaobo
Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.
Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
Environ Pollut. 2023 Jan 15;317:120699. doi: 10.1016/j.envpol.2022.120699. Epub 2022 Nov 17.
Osteoporosis has become a major health problem in older women. Previous studies have linked individual metals exposure with osteoporosis, but combined effects remain inconclusive. We aimed to explore the individual and combined association between multiple metals mixture and osteoporosis risk in older Chinese women. A total of 2297 older women (aged ≥60) from the Hongshuihe region of Guangxi, southern China included. We measured 22 blood metal levels through inductively coupled plasma mass spectrometry. And osteoporosis was defined as a T score ≤ -2.5. The least absolute shrinkage and selection operator (LASSO) penalized regression, and Bayesian kernel machine regression (BKMR) models were performed to explore the association between blood metals and osteoporosis risk. Of 2297 older women, there were 829 osteoporosis and 1468 non-osteoporosis participants. The median age was 71 and 68 years old in the osteoporosis and the non-osteoporosis group, respectively. In the single-metal model, rubidium and vanadium were negatively associated with osteoporosis (P for trend = 0.02 and 0.002, respectively), and lead presented the reverse trend (P for trend = 0.01). The LASSO penalized regression model selected nine metals (calcium, cadmium, cobalt, lead, magnesium, rubidium, strontium, vanadium and zinc), which were included in the subsequent analysis. And the multiple-metal model presented a consistent trend with the single-metal model using the selected metals. Furthermore, we performed BKMR to explore the combined effect, and found an overall negative effect between metals mixture and osteoporosis risk when all the metals were fixed at 50th, and rubidium and vanadium were the main contributors. In addition, blood Rb and V were significantly negatively related to OP risk with other metals at different levels (25th, 50th and 75th percentiles). The study suggests metal mixture exposure and osteoporosis risk in older Chinese women, and further studies need to be conducted.
骨质疏松症已成为老年女性的一个主要健康问题。以往的研究将个体金属暴露与骨质疏松症联系起来，但综合影响仍无定论。我们旨在探讨多种金属混合物与中国老年女性骨质疏松症风险之间的个体及综合关联。研究纳入了来自中国南方广西红水河地区的2297名老年女性（年龄≥60岁）。我们通过电感耦合等离子体质谱法测量了22种血液金属水平。骨质疏松症定义为T值≤ -2.5。采用最小绝对收缩和选择算子（LASSO）惩罚回归以及贝叶斯核机器回归（BKMR）模型来探讨血液金属与骨质疏松症风险之间的关联。在2297名老年女性中，有829名骨质疏松症患者和1468名非骨质疏松症参与者。骨质疏松症组和非骨质疏松症组的中位年龄分别为71岁和68岁。在单金属模型中，铷和钒与骨质疏松症呈负相关（趋势P值分别为0.02和0.002），而铅呈现相反趋势（趋势P值 = 0.01）。LASSO惩罚回归模型选择了9种金属（钙、镉、钴、铅、镁、铷、锶、钒和锌），并将其纳入后续分析。多金属模型使用所选金属呈现出与单金属模型一致的趋势。此外，我们进行了BKMR以探讨综合效应，发现当所有金属固定在第50百分位数时，金属混合物与骨质疏松症风险之间总体呈负效应，铷和钒是主要贡献因素。此外，血液中的铷和钒与不同水平（第25、50和75百分位数）的其他金属存在时，与骨质疏松症风险显著负相关。该研究表明中国老年女性存在金属混合物暴露与骨质疏松症风险的关联，需要进一步开展研究。
