The AccelerAge framework: A new statistical approach to predict biological age based on time-to-event data
bioRxiv (Cold Spring Harbor Laboratory)
Abstract
Abstract 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.
Authors 7
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Leiden University Medical Center
Affiliation as printed
Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Molecular Epidemiology, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center · Max Planck Institute for Biology of Ageing
Affiliation as printed
Max Planck Institute for the Biology of Ageing, Cologne, Germany
Molecular Epidemiology, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Molecular Epidemiology, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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