Swier Garst,[…], P Eline Slagboom, Joyce van Meurs | 18-05-2026 | Because individuals age at different rates, robust aging biomarkers are needed beyond chronological age. However, the methodological choices that shape such biomarkers have not been compared in a unified framework. This study addresses that gap by evaluating model choice, training targets, and protein selection to derive efficient proteomics-based predictors of frailty and mortality.
Many molecular aging biomarkers have been developed to capture heterogeneity in individual aging rates. Yet, systematic comparison of the modeling choices underlying these biomarkers has been limited. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (1H-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 two aging biomarkers, ProteinFrailty (ProtFI) and ProteinMortality (ProtMort), which are both ElasticNet models that use a minimal set of proteins to predict FI and mortality, respectively. In particular, 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-r Save elated decline.
Cell Rep Methods. 2026 May 18;6(5):101405. doi: 10.1016/j.crmeth.2026.101405 Save