Evidence map›Paper›PMID 41792861›Full record

ArticleBiomarker research2026

Comparing fourteen consensus biomarkers of aging: epigenetic pace of aging as the strongest predictor of mortality in BASE-II.

Valentin Max Vetter, Marit Philine Junge, Christian A Drevon, Thomas E Gundersen, Jan Homann, Christina M Lill, Ulman Lindenberger, Graham Pawelec, Lars Bertram, Denis Gerstorf and 1 more

Abstract read
In one paragraph

Article in Biomarker research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. Review
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

11 authors.

Valentin Max VetterCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Endocrinology and Metabolic Diseases (including Division of Lipid Metabolism), Biology of Aging working group, Augustenburger Platz 1, 13353, Berlin, Germany. valentin.vetter@charite.de.ORCID http://orcid.org/0000-0001-5003-7766
Marit Philine JungeLübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany.ORCID http://orcid.org/0009-0001-0333-3105
Christian A DrevonVitas Ltd. Oslo Science Park, Oslo, Norway.
Thomas E GundersenVitas Ltd. Oslo Science Park, Oslo, Norway.
Jan HomannInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Christina M LillInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-2805-1307
Ulman LindenbergerCenter for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
Graham PawelecInstitute of Immunology, University of Tübingen, Tübingen, Germany.
Lars BertramLübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany.ORCID http://orcid.org/0000-0002-0108-124X
Denis GerstorfDepartment of Psychology, Humboldt University Berlin, Berlin, Germany.
Ilja DemuthCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Endocrinology and Metabolic Diseases (including Division of Lipid Metabolism), Biology of Aging working group, Augustenburger Platz 1, 13353, Berlin, Germany. ilja.demuth@charite.de.ORCID http://orcid.org/0000-0002-4340-2523

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn many countries, lifespan has been increasing faster than healthspan, leading to more years spent with late-life disease and highlighting the need for reliable biomarkers to measure biological aging.

methodsWe used data from the Berlin Aging Study II (BASE-II, 60-80 years of age at baseline, average follow-up 7.4 ± 1.5 years, range 3.9-10.4, n = 1,083) to compare 14 biomarkers of aging recently consented by an expert panel for the use as outcome measures in intervention studies: physiological (insulin-like growth factor 1 (IGF-1), growth-differentiating factor-15 (DNA methylation derived, DNAmGDF15)), inflammatory (high sensitivity C-reactive protein (CRP), interleukin-6 (IL-6)), functional (muscle mass, muscle strength, hand grip strength (HGS), Timed-Up-and-Go (TUG), gait speed, standing balance test, frailty phenotype (FP), cognitive health, blood pressure), and epigenetic (epigenetic clock, DunedinPACE). Cox proportional hazard regression analyses were performed to investigate their role in prediction of all-cause as well as cause-specific mortality. Results were adjusted for age, sex, lifestyle factors, and genetic ancestry.

resultsIn adjusted models of all-cause mortality, HGS, IL-6, standing balance, cognitive health, and the epigenetic clock (DunedinPACE) statistically significantly predicted mortality, with the epigenetic clock (DunedinPACE) emerging as the strongest predictor. CRP, gait speed, IGF-1, blood pressure, muscle mass, DNAmGDF15, FP and TUG were not associated with mortality in this study. These results were corroborated in subgroup analyses stratified by cause of death. Feature selection identified a minimal biomarker set consisting of muscle mass, standing balance, and epigenetic clock (DunedinPACE) that predicted mortality with nearly the same discriminative accuracy (C-index = 0.63) as the full model including all biomarkers (C-index = 0.65).

conclusionAmong the fourteen investigated biomarkers of aging, DunedinPACE emerged with the strongest and most consistent association with mortality.

Indexed as

Biomarkers of agingCox regressionDunedinPACEEpigenetic clockLongevityMortality

Identifiers

PMID41792861
PMCPMC13063672

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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.