加速年龄框架:基于时间到事件数据预测生物年龄的新统计方法

IF 7.7 1区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Marije Sluiskes, Jelle Goeman, Marian Beekman, Eline Slagboom, Erik van den Akker, Hein Putter, Mar Rodríguez-Girondo
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引用次数: 0

摘要

衰老是一个多方面、错综复杂的生理过程,其特点是机能逐渐衰退,导致对疾病的易感性和死亡率增加。虽然计时年龄是导致与年龄相关的健康问题的一个重要风险因素,但个体的衰老轨迹存在相当大的异质性,这表明生物年龄可能提供了对衰老过程更细致入微的理解。然而,生物年龄的概念缺乏明确的可操作性,导致各种生物年龄预测指标的开发缺乏坚实的统计学基础。本文针对这些局限性,提出了一个全面的生物年龄操作化方法,介绍了预测生物年龄的 "AccelerAge "框架,并引入了以前未得到充分利用的评估生物年龄预测方法性能的评价指标。加速衰老 "框架基于加速衰老时间(AFT)模型,直接模拟候选衰老预测因子对个体生存时间的影响,与将衰老比喻为时钟的流行观点相一致。我们使用模拟数据以及英国生物库和莱顿长寿研究的数据,比较了基于 AccelerAge 框架的预测因子和基于 GrimAge 预测因子的预测因子,后者被认为是表现最好的生物年龄预测因子之一。我们的方法旨在为生物年龄钟建立一个稳健的统计基础,从而对个人的衰老状况进行更准确、更可解释的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the “AccelerAge” framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual’s survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual’s aging status.

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来源期刊
European Journal of Epidemiology
European Journal of Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
21.40
自引率
1.50%
发文量
109
审稿时长
6-12 weeks
期刊介绍: The European Journal of Epidemiology, established in 1985, is a peer-reviewed publication that provides a platform for discussions on epidemiology in its broadest sense. It covers various aspects of epidemiologic research and statistical methods. The journal facilitates communication between researchers, educators, and practitioners in epidemiology, including those in clinical and community medicine. Contributions from diverse fields such as public health, preventive medicine, clinical medicine, health economics, and computational biology and data science, in relation to health and disease, are encouraged. While accepting submissions from all over the world, the journal particularly emphasizes European topics relevant to epidemiology. The published articles consist of empirical research findings, developments in methodology, and opinion pieces.
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