Associations of non-essential metal mixture with biological aging and the mediating role of inflammation in Chinese older adults

非必需金属混合物与生物衰老的相关性及炎症在其中介作用在中国老年人中的研究

Authors：Yan Zhang, Yuan Wang, Xuqiu Cheng, Ziwei Tian, Yuantao Zhang, Wenyuan Liu, Xianglong Liu, Bing Hu, Fangbiao Tao, Anna Bi, Jun Wang, Linsheng Yang

Source：Environmental Pollution

DOI:10.1016/j.envpol.2025.126474

Abstract

Background:Individual non-essential metals (NMs) have been linked with biological aging. However, the effects of NM mixture and their mechanisms remain unclear.

Objective:To characterize the relationships of individual NMs and their mixture to biological aging, and to explore the mediating roles of inflammatory factors.

Methods:This cross-sectional study recruited 3251 individuals aged 60 years or above in China. Urine gallium, arsenic, cadmium, cesium, thallium, and barium were tested using ICP-MS. The Klemera-Doubal method was used to construct the KDMAge, reflecting the estimation of biological age, and ΔKDMAge, defined as the difference between KDMAge and chronological age, reflecting the deviation in aging rate. Four blood cell counts, including neutrophil, lymphocyte, platelet, and monocyte, were used to calculate inflammatory indices: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and systemic immune-inflammation index. Linear regression, generalized additive model (GAM), weighted quantile sum (WQS), quantile-based computation (QGC), and Bayesian kernel machine regression (BKMR) were employed to assess the associations between the NMs and ΔKDMAge. Mediation analysis was further performed to examine the roles of inflammatory factors.

Results:KDMAge strongly correlated with chronological age (r = 0.863). Linear regression showed significant positive associations of Gallium (β = 0.88, 95 % CI = 0.30, 1.46), arsenic (β = 1.11, 95 % CI = 0.54, 1.69), and cesium (β = 0.75, 95 % CI = 0.19, 1.30) with ΔKDMAge. GAMs further exhibited a "J-shaped" relationship for gallium, arsenic with ΔKDMAge, a linear trend for cesium, and a "U-shaped" relationship for barium. The mixture models demonstrated a positive association between the NM mixture and ΔKDMAge, with gallium, arsenic, and cesium identified as the primary contributors. Mediation analyses further suggested that neutrophil-to-lymphocyte ratio and systemic immune-inflammation index partially mediated this association.

Conclusions:The NM mixture accelerates biological aging, mainly driven by gallium, arsenic, and cesium, with partial mediation by inflammation. Future longitudinal studies are necessary to verify these findings.

Keywords:Biological aging; Inflammatory factors; Mediation; Non-essential metals; Older adults.

摘要

背景：单一非必需金属（NMs）已被证实与生物衰老相关。然而，NMs混合物的影响及其作用机制尚不明确。

目的：探讨单一NMs及其混合物与生物衰老之间的关系，并探索炎症因子在其中的介导作用。

方法：本横断面研究在中国招募了3251名60岁及以上的个体。采用ICP-MS检测尿液中的镓、砷、镉、铯、铊和钡。采用Klemera-Doubal方法构建KDMAge，反映生物学年龄的估算值，以及ΔKDMAge，定义为KDMAge与实际年龄的差值，反映衰老速率的偏差。通过中性粒细胞、淋巴细胞、血小板和单核细胞四项血细胞计数，计算炎症指标：中性粒细胞与淋巴细胞比值、血小板与淋巴细胞比值、淋巴细胞与单核细胞比值，以及全身免疫炎症指数。采用线性回归、广义加性模型（GAM）、加权分位数和（WQS）、分位数基计算（QGC）和贝叶斯核机器回归（BKMR）评估NMs与ΔKDMAge之间的关联。进一步进行中介分析以考察炎症因子的作用。

结果：KDMAge与年龄呈显著正相关（r = 0.863）。线性回归显示，镓（β = 0.88，95% CI = 0.30, 1.46）、砷（β = 1.11, 95% CI = 0.54，1.69）、铯（β = 0.75, 95% CI = 0.19, 1.30）与ΔKDMAge呈显著正相关。GAMs进一步显示，镓和砷与ΔKDMAge呈“J型”关系，铯呈线性趋势，钡呈“U型”关系。混合模型显示NM混合物与ΔKDMAge之间存在正相关关系，其中镓、砷和铯被识别为主要贡献因素。中介分析进一步表明，中性粒细胞与淋巴细胞比值和系统性免疫炎症指数部分中介了这一关联。

结论：NM混合物加速生物学衰老，主要由镓、砷和铯驱动，炎症部分介导该过程。未来需要进行纵向研究以验证这些发现。

关键词：生物学衰老；炎症因子；中介作用；非必需金属；老年人