ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly2 predicts age-related decline

Garst, S., Kuiper, L. M., van den Akker, E. B., Berg, N. v. d., Ghanbari, M., Mooijaart, S. P., Beekman, M., Reinders, M., Slagboom, P. E., van Meurs, J.

Delft University of Technology · medrxiv

Paper

Abstract

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 (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 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=996, n=500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.

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Research summary

The paper claims that the ProteinFrailty (ProtFI) model can efficiently predict age-related decline using a minimal set of proteins. This research is relevant as it addresses the heterogeneity in aging and aims to develop a biomarker that captures the biological aspects of aging, potentially leading to better understanding and interventions in age-related decline.

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