ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline
Garst, Swier Kuiper, Lieke van den Akker, Erik van den Berg, Niels Ghanbari, Mohsen Mooijaart, Simon Beekman, Marian Reinders, Marcel Slagboom, P Eline van Meurs, Joyce
Garst, Swier
Kuiper, Lieke
van den Akker, Erik
van den Berg, Niels
Ghanbari, Mohsen
Mooijaart, Simon
Beekman, Marian
Reinders, Marcel
Slagboom, P Eline
van Meurs, Joyce
Series / Report no.
Open Access
Type
Journal Article
Article
Article
Language
en
Date of publication
2026-04-10
Year of publication
Research Projects
Organizational Units
Journal Issue
Title
ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline
Translated Title
Published in
Cell Rep Methods 2026; 101405
Abstract
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 (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 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-related decline.