Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China.
Nat Commun. 2024 Jun 10;15(1):4921. doi: 10.1038/s41467-024-49283-0.
Complicated associations between multiplexed environmental factors and aging are poorly understood. We manipulated aging using multidimensional metrics such as phenotypic age, brain age, and brain volumes in the UK Biobank. Weighted quantile sum regression was used to examine the relative individual contributions of multiplexed environmental factors to aging, and self-organizing maps (SOMs) were used to examine joint effects. Air pollution presented a relatively large contribution in most cases. We also found fair heterogeneities in which the same environmental factor contributed inconsistently to different aging metrics. Particulate matter contributed the most to variance in aging, while noise and green space showed considerable contribution to brain volumes. SOM identified five subpopulations with distinct environmental exposure patterns and the air pollution subpopulation had the worst aging status. This study reveals the heterogeneous associations of multiplexed environmental factors with multidimensional aging metrics and serves as a proof of concept when analyzing multifactors and multiple outcomes.
多重环境因素与衰老之间复杂的关联尚未被充分理解。我们使用多维指标（如表型年龄、大脑年龄和大脑体积）来操纵英国生物库中的衰老。使用加权分位数总和回归来研究多重环境因素对衰老的相对个体贡献，并用自组织图（SOM）来研究联合效应。在大多数情况下，空气污染表现出相对较大的贡献。我们还发现了公平的异质性，即相同的环境因素对不同的衰老指标的贡献不一致。颗粒物对衰老的变化贡献最大，而噪声和绿地对大脑体积有相当大的贡献。SOM 确定了五个具有不同环境暴露模式的亚群，其中空气污染亚群的衰老状况最差。这项研究揭示了多重环境因素与多维衰老指标之间的异质关联，为分析多因素和多结果提供了概念验证。
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