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

IF 7.6 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Pollution Pub Date : 2025-05-16 DOI:10.1016/j.envpol.2025.126474

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

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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.

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非必需金属混合物与中国老年人生物衰老和炎症介导作用的关系

个体非必需金属（NMs）与生物衰老有关。然而，纳米混合物的作用及其机制尚不清楚。目的探讨单个NMs及其混合物与生物衰老的关系，并探讨炎症因子的介导作用。方法本横断面研究在中国招募了3251名60岁及以上的老年人。采用ICP-MS检测尿中镓、砷、镉、铯、铊和钡。采用klemera - double方法构建反映生物年龄估计的KDMAge，定义为KDMAge与实足年龄之差的ΔKDMAge，反映衰老率的偏差。采用中性粒细胞、淋巴细胞、血小板和单核细胞四种血细胞计数计算炎症指数：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和全身免疫炎症指数。采用线性回归、广义加性模型（GAM）、加权分位数和（WQS）、基于分位数的计算（QGC）和贝叶斯核机回归（BKMR）来评估NMs与ΔKDMAge之间的关系。进一步进行中介分析以检验炎症因子的作用。结果skdmage与实足年龄呈显著相关（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。镓的GAMs进一步表现为“j”形关系，砷为ΔKDMAge，铯为线性趋势，钡为“u”形关系。混合模型表明纳米混合物与ΔKDMAge呈正相关，其中镓、砷和铯被确定为主要贡献者。中介分析进一步表明，中性粒细胞与淋巴细胞比率和全身免疫炎症指数部分介导了这种关联。结论纳米混合物加速生物老化，主要由镓、砷和铯驱动，部分由炎症介导。未来有必要进行纵向研究来验证这些发现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Environmental Pollution 环境科学-环境科学

CiteScore

16.00

自引率

6.70%

发文量

2082

审稿时长

2.9 months

期刊介绍： Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.