Background: Prefrailty is associated with anomalies in protein metabolism; however, the serum proteomic signatures remain unclear. This study investigated protein profiles across different health statuses and evaluated their potential for the early identification of prefrailty. Methods: Older adults were categorized as robust, prefrail, and frail. Untargeted proteomic screening in a discovery cohort (n = 30) was followed by parallel reaction monitoring (PRM) validation (n = 99). Multidimensional clinical parameters and differentially expressed proteins were integrated within machine learning pipelines to refine the search for characteristic features of prefrailty. Results: 166 proteins were found to be differentially expressed across frailty statuses, with 15 significantly prefrailty-associated proteins subsequently confirmed by PRM validation. These proteins were functionally enriched in pathways related to cell signaling, protein metabolism, immune regulation, and skeletal muscle function maintenance. A Random Forest model, further assembled from gait speed, skeletal muscle mass, E3-independent E2 ubiquitin-conjugating enzyme (UBE2O), Timed Up and Go test time, alpha-actinin-3 (ACTN3), and Mini-Mental State Examination score, exhibited the most robust performance for early frailty identification among multiple algorithms compared. Conclusion: This exploratory study identified candidate serum protein biomarkers associated with prefrailty. Preliminary machine learning models incorporating UBE2O and ACTN3 suggested the feasibility of discriminating prefrailty from robust status, reflecting underlying proteomic heterogeneity among community-dwelling older adults. Keywords: frailty, pre-frailty, older adults, proteomics, machine learning
Ye et al. (Fri,) studied this question.