Abstract：
Previous works have shown that hematological system can be affected by exposure to lead; however, the effects of multiple metals on platelets remain elusive within the population from metal-contaminated areas. Hence, the study enrolled 609 participants, with 396 from a metal-exposed area and 213 from a control area. Platelet count (PLT), mean platelet volume (MPV), thrombocytocrit (PCT), platelet to large cell ratio (P-LCR), and platelet distribution width (PDW) were selected to evaluate platelet function. Stepwise regression and Lasso regression were utilized to identify the most influential metals. Moreover, the generalized linear model (GLM), Bayesian kernel machine regression (BKMR) models, and quantile g-computation were employed to estimate the individual or combined effects associations between 12 urinary metals and platelet indices. The results revealed all metals except vanadium, copper, strontium, and molybdenum were significantly higher in the exposed group. The GLM models indicated that urinary metals, including lead, antimony, and arsenic, exhibited associations with PLT, MPV, P-LCR, and PDW. Quantile g-computation and BKMR demonstrated negative correlations between metal mixtures and MPV as well as PDW. In conclusion, the study highlights the associations between multiple metal exposures and platelet indices, suggesting that elevated levels of the metal mixture may impede platelet activation in the population in metal-contaminated areas.
摘要：
以前的工作表明，血液系统可能会受到铅接触的影响；然而，在金属污染地区的人群中，多种金属对血小板的影响仍然难以捉摸。因此，这项研究招募了609名参与者，396来自金属暴露区域，213来自控制区域。血小板计数(PLT),平均血小板体积（MPV），血小板增多症(PCT),血小板与大细胞比率（P-LCR），选择血小板分布宽度(PDW)评价血小板功能。逐步回归和Lasso回归用于确定最有影响力的金属。此外，广义线性模型(GLM)，贝叶斯核机回归(BKMR)模型，和分位数g计算用于评估12种尿金属和血小板指数之间的个体或综合效应关联。结果显示除了钒以外的所有金属,铜，锶,和钼在暴露组中明显更高。GLM模型表明尿金属，包括铅,锑,还有砷,展示了与PLT的关联，MPV,P-LCR,和PDW。分位数g计算和BKMR证明了金属混合物与MPV以及PDW之间的负相关。总之,该研究强调了多种金属暴露与血小板指数之间的关联，这表明金属混合物水平升高可能会阻碍金属污染地区人群的血小板活化。
