Evidence map›Paper›PMID 41966686›Full record

ArticleCell reports methods2026

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

Swier Garst, Lieke Kuiper, Erik van den Akker, Niels van den Berg, Mohsen Ghanbari, Simon Mooijaart, Marian Beekman, Marcel Reinders, P Eline Slagboom, Joyce van Meurs

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In one paragraph

Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Swier GarstDelft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, Zuid-Holland, the Netherlands; Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
Lieke KuiperDepartment of Internal Medicine, Erasmus MC University Medical Center, 3000 CA Rotterdam, the Netherlands; Center for Prevention, Lifestyle and Health, National Institute for Public Health and Environment (RIVM), 3720 BA Bilthoven, the Netherlands.
Erik van den AkkerDelft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, Zuid-Holland, the Netherlands; Leiden Computational Biology Centre, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
Niels van den BergDepartment of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
Mohsen GhanbariDepartment of Epidemiology, Erasmus MC University Medical Center, 3000 CA Rotterdam, the Netherlands.
Simon MooijaartDepartment of Internal Medicine, Division of Gerontology and Geriatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
Marian BeekmanSection of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
Marcel ReindersDelft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, Zuid-Holland, the Netherlands. Electronic address: m.j.t.reinders@tudelft.nl.
P Eline SlagboomSection of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands. Electronic address: p.slagboom@lumc.nl.
Joyce van MeursDepartment of Internal Medicine, Erasmus MC University Medical Center, 3000 CA Rotterdam, the Netherlands; Department of Orthopaedics & Sports Medicine, Erasmus Medical Center, 3000 CA Rotterdam, the Netherlands. Electronic address: j.vanmeurs@erasmusmc.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

Indexed as

AgingBiomarkersFrailtyProteomicsAgedFemaleHumansMaleMetabolomicsUK BiobankBiomarkersagingbiomarkerCP: computational biologyCP: systems biologydeep learningElasticNetfrailtymetabolomicspredictionproteomics

Identifiers

PMID41966686
PMCPMC13198109

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.