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Abstract Aging biomarkers are essential for understanding and quantifying the aging process and developing targeted longevity interventions. However, validation of these tools has been hindered by the lack of standardized approaches for cross-population validation, disparate biomarker designs, and inconsistencies in dataset structures. To address these challenges, we developed Biolearn, an open-source library that provides a unified framework for the curation, harmonization, and systematic evaluation of aging biomarkers. Leveraging Biolearn, we conducted a comprehensive evaluation of various aging biomarkers across multiple datasets. Our systematic approach involved three key steps: (1) harmonizing existing and novel aging biomarkers in standardized formats; (2) unifying public datasets to ensure coherent structuring and formatting; and (3) applying computational methodologies to assess the harmonized biomarkers against the unified datasets. This evaluation yielded valuable insights into the performance, robustness, and generalizability of aging biomarkers across different populations and datasets. The Biolearn python library, which forms the foundation of this systematic evaluation, is freely available at https://Bio-Learn.github.io . Our work establishes a unified framework for the curation and evaluation of aging biomarkers, paving the way for more efficient and effective clinical validation and application in the field of longevity research.
Ying et al. (Mon,) studied this question.