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在纵向流行病学样本中,衰老维度和标志物作为死亡率的相对预测指标。

Aging dimensions and markers as relative predictors of mortality in a longitudinal epidemiological sample.

作者信息

Markon Kristian E, Mann Frank D, Freilich Colin D, Cole Steve, Krueger Robert F

机构信息

University of Minnesota, Minneapolis, Minnesota, United States of America.

Stony Brook Medicine, Stony Brook, New York, USA.

出版信息

PLoS One. 2025 Jun 18;20(6):e0324156. doi: 10.1371/journal.pone.0324156. eCollection 2025.

Abstract

Measurement of aging is critical to understanding its causes and developing interventions, but little consensus exists on what components such measurements should include or how they perform in predicting mortality. The aim of this study was to identify factors of aging among a comprehensive set of indicators, and to evaluate their relative performance in predicting mortality. Measurements on 34 clinical, survey, and neuroimaging variables, along with epigenetic age markers, were obtained from two waves (2004-2021) of the Midlife in the United States (MIDUS) study. Mortality data was also available on 11875 participants, including 1908 twins. Factor analyses were used to identify aging factors, and these were used to predict mortality as of 2022. Twin data were used to model predictors of mortality within families. Factor analyses identified 9 major dimensions of aging: frailty, cognition, adiposity, glucose, blood pressure, inflammation, lipids, adaptive functioning, and neurological functioning. The strongest predictors of survival among the aging dimensions were cognition, adaptive functioning, and inflammation, and among the epigenetic markers, the decline-predictive markers (GrimAge and DunedinPACE). When entered in joint prediction models, cognition remained a significant predictor of mortality, but the epigenetic markers did not. Cognition, adaptive functioning, and inflammation remained significant predictors of mortality within twin pairs as well. Aging is a multidimensional construct, with cognition, adaptive functioning, and inflammation being the strongest predictors of survival among the aging dimensions examined. Their association with mortality is observed within families, suggesting that early developmental factors cannot entirely account for their association with survival. Interventions and assessments should prioritize cognition in measures of aging quality, along with adaptive functioning and inflammation.

摘要

衰老的测量对于理解其成因和制定干预措施至关重要,但对于此类测量应包含哪些组成部分以及它们在预测死亡率方面的表现,目前几乎没有达成共识。本研究的目的是在一组全面的指标中识别衰老因素,并评估它们在预测死亡率方面的相对表现。从美国中年(MIDUS)研究的两波数据(2004 - 2021年)中获取了34个临床、调查和神经影像变量以及表观遗传年龄标记的测量数据。还获得了11875名参与者(包括1908对双胞胎)的死亡率数据。使用因子分析来识别衰老因素,并将这些因素用于预测截至2022年的死亡率。双胞胎数据用于构建家庭内部死亡率的预测模型。因子分析确定了9个主要的衰老维度:虚弱、认知、肥胖、血糖、血压、炎症、血脂、适应性功能和神经功能。在衰老维度中,生存的最强预测因素是认知、适应性功能和炎症,在表观遗传标记中,是下降预测标记(GrimAge和DunedinPACE)。当纳入联合预测模型时,认知仍然是死亡率的显著预测因素,但表观遗传标记不是。认知、适应性功能和炎症在双胞胎对中也是死亡率的显著预测因素。衰老是一个多维度的概念,在所研究的衰老维度中,认知、适应性功能和炎症是生存的最强预测因素。在家庭内部也观察到了它们与死亡率的关联,这表明早期发育因素不能完全解释它们与生存的关联。在衰老质量的测量中,干预和评估应优先考虑认知,以及适应性功能和炎症。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c169/12176132/0bb923aa12c8/pone.0324156.g001.jpg

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