Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Rd, Hefei, 230032, Anhui, China.
The Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
Biol Trace Elem Res. 2024 Jul;202(7):3005-3017. doi: 10.1007/s12011-023-03901-y. Epub 2023 Oct 10.
In reality, people are often co-exposed to multiple heavy metals; however, current research has focused on the association between individual heavy metals and inflammation. Therefore, it is more relevant to explore the combined effects of multiple heavy metal exposure on inflammation. The study included data from the National Health and Nutrition Examination Survey (NHANES), 2011-2016. The systemic immune-inflammation index (SII) was used to reflect systemic immune-inflammation status. In this study, single variable models were used to assess the linear and non-linear relationships between single heavy metal exposures and SII. To analyze the combined effect of mixed heavy metals exposure on SII, we constructed three statistical models, including weighted quantile sum (WQS) regression, quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR). The single-exposure analysis found positive associations between multiple heavy metals and SII, while mercury in blood was negatively associated with SII, and U-shaped correlations were observed between blood lead, urine barium and strontium, and SII. In the WQS model, SII increased significantly with increasing concentrations of mixed heavy metals, while consistent results in the qgcomp model, but not statistically significant. In the BKMR model, exposure to heavy metal mixtures was positively associated with SII, with mercury, cadmium, and cobalt in urine contributing the most to the mixed exposure. In addition, synergistic and antagonistic effects between heavy metals on increasing SII were found in our study. In summary, our results reveal that combined exposure to multiple heavy metals is positively associated with SII in the US adults.
实际上，人们经常会同时接触多种重金属；然而，当前的研究主要集中在单个重金属与炎症之间的关联上。因此，探索多种重金属暴露对炎症的综合影响更为相关。本研究的数据来自于 2011-2016 年的国家健康与营养调查（NHANES）。系统免疫炎症指数（SII）用于反映全身免疫炎症状态。在本研究中，采用单变量模型来评估单个重金属暴露与 SII 之间的线性和非线性关系。为了分析混合重金属暴露对 SII 的综合影响，我们构建了三个统计模型，包括加权分位数总和（WQS）回归、基于分位数的 g 计算（qgcomp）和贝叶斯核机器回归（BKMR）。单暴露分析发现，多种重金属与 SII 呈正相关，而血液中的汞与 SII 呈负相关，血液中的铅、尿液中的钡和锶与 SII 呈 U 型相关。在 WQS 模型中，随着混合重金属浓度的增加，SII 显著增加，而在 qgcomp 模型中得到了一致的结果，但没有统计学意义。在 BKMR 模型中，重金属混合物的暴露与 SII 呈正相关，尿液中的汞、镉和钴对混合暴露的贡献最大。此外，我们的研究还发现重金属对 SII 的增加存在协同和拮抗作用。总之，我们的研究结果表明，美国成年人多种重金属的联合暴露与 SII 呈正相关。
