Research progress on the development and validation of the PhenoAge model

Abstract

Abstract:

Aging is a complex biological process involving multisystem functional decline. With global population aging，studying aging mechanisms and extending healthy lifespan have become key priorities in medicine and public health. Phenotypic age（PhenoAge），an innovative biological age assessment tool，is based on nine routine blood biomarkers covering metabolism，inflammation，and organ function，offering greater clinical utility and cost-effectiveness compared to DNA methylation clocks. This paper reviews the development and validation process of PhenoAge，highlighting the critical role of big data and machine learning in biomarker selection and avoiding overfitting. PhenoAge has been validated across multiple cohorts for predicting all-cause mortality，cardiovascular disease，and functional decline. However，challenges such as data heterogeneity，reproducibility，and competition with models like GrimAge persist. Looking forward，standardized data workflows and interdisciplinary collaboration can enhance PhenoAge's dynamic monitoring capabilities，advancing precision aging research，particularly in occupational health and public health policy. This paper provides theoretical support and guidance for optimizing and applying PhenoAge globally.

Key words: PhenoAge, Biological age, Aging biomarkers, Occupational health

CLC Number:

R181.2

HUANG Lei, SHAN Baorong, WANG Dayu. Research progress on the development and validation of the PhenoAge model. [J]OCCUPATION AND HEALTH, 2026, 42(15): 2148-2153.

References 38

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