INTRODUCTION: Plasma proteins reflect the combined influence of both internal and external factors, making proteomics-based aging clocks a promising approach for quantifying the aging process. OBJECTIVE: This study aims to develop and validate a novel proteomics-based aging clock by integrating plasma proteomics with composite biomarkers. METHODS: We used a prospective cohort of 37,433 participants (median follow-up: 164.73 months) from the UK Biobank (UKB) with Olink Explore data. We calculated biological age (PhenoAge) and used the Boruta-SHAP (SHapley Additive exPlanations) algorithm to select PhenoAge-related proteins. Based on these proteins, six machine learning models were trained to develop a proteomics-based PhenoAge (ProtPhenoAge). We selected the best model as ProtPhenoAge based on the predictive capabilities of each model for PhenoAge and all-cause mortality. Phenome-wide association study (PheWAS) and Mendelian randomization (MR) explored associations between ProtPhenoAge Acceleration (ProtPhenoAgeAccel) and phenotypes. Genome-wide association study (GWAS) and colocalization analysis identified aging-associated loci. RESULTS: ) are respectively related to epigenetic aging and the well-recognized aging gene APOE. CONCLUSION: Based on genomic and phenomic evidences, ProtPhenoAge was regarded to better quantifies the aging process by overcoming the limitations of previous clocks, which failed to detect time-independent aging features. These findings suggested that ProtPhenoAge is a reliable tool to assess aging and supporting aging researches.
Wang et al. (Mon,) studied this question.