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.

衰老是一个多方面且复杂的生理过程，其特征是功能逐渐下降，导致疾病和死亡率的易感性增加。虽然实际年龄是与年龄相关的健康状况的一个强有力的危险因素，但个体的衰老轨迹存在相当大的异质性，这表明生物年龄可以提供对衰老过程更细致的理解。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基础。本文通过提出生物年龄的全面操作化、引入用于预测生物年龄的“AccelerAge”框架以及引入先前未充分利用的评估措施来评估生物年龄预测器的性能来解决这些局限性。 AccelerAge 框架基于加速故障时间 (AFT) 模型，直接模拟衰老候选预测因子对个体生存时间的影响，与将衰老视为时钟的流行比喻相一致。我们使用模拟数据以及来自英国生物银行和莱顿长寿研究的数据，将基于 AccelerAge 框架的预测器与基于 GrimAge 预测器的预测器进行比较，GrimAge 预测器被认为是性能最佳的生物年龄预测器之一。我们的方法旨在为生物年龄时钟建立强大的统计基础，从而能够对个人的衰老状况进行更准确和可解释的评估。