Swier Garst, Lieke M Kuiper, Erik B. van den Akker, Niels van den Berg, Mohsen Ghanbari, Simon P. Mooijaart, Marian Beekman, Marcel Reinders, P. Eline Slagboom, Joyce B. J. van Meurs
Chronological age overlooks the heterogeneity in aging. In response, a wide range of molecular aging biomarkers has been developed to better capture an individual”s aging rate. Yet, a comprehensive comparison of modeling choices in the development of these biomarkers is lacking. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics ( 1 H-NMR) data ( n =40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed ProteinFrailty (ProtFI), an elastic net model using a minimal set of proteins to predict FI. ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts ( n =995, n =500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.