Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.
Abstract
Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to
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Research summary
The paper focuses on developing metabolomic aging clocks using machine learning algorithms, which is directly related to understanding biological aging and its implications for health and lifespan. While it presents solid research and contributes to the field of aging clocks, the impact is limited as it primarily benchmarks existing methods rather than introducing a novel approach or significant breakthrough in the understanding of aging mechanisms.
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