Evidence map›Paper›PMID 39554132›Full record

ArticlebioRxiv : the preprint server for biology2024

An Open Competition for Biomarkers of Aging.

Kejun Ying, Seth Paulson, Julian Reinhard, Lucas Paulo de Lima Camillo, Jakob Träuble, Stefan Jokiel, Biomarkers of Aging Consortium, Dane Gobel, Chiara Herzog, Jesse R Poganik and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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. Embracing non-linearity in human ageing.Nature reviews. Genetics · 2026
    Review
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Kejun YingDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-1791-6176
Seth PaulsonMethuselah Foundation, Washington, DC, USA.
Julian ReinhardGlobal Bioinformatics, Evotec SE, Hamburg, Germany.
Lucas Paulo de Lima CamilloSchool of Clinical Medicine, University of Cambridge, Cambridge, UK.
Jakob TräubleDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Stefan JokielFaculty of Physics, Ludwig Maximilian University of Munich, Munich, Germany.
Biomarkers of Aging Consortium
Dane GobelMethuselah Foundation, Washington, DC, USA.
Chiara HerzogInstitute for Biomedical Ageing Research, Universität Innsbruck and European Translational Oncology Prevention and Screening Centre, Universität Innsbruck, Innsbruck, Austria.
Jesse R PoganikDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Mahdi MoqriDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-6675-0566
Vadim N GladyshevDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

The role of hyaluronan in longevity and cancer resistance of longest-lived rodentP01AG047200 · NIA · UNIVERSITY OF ROCHESTER · PI Vadim N. Gladyshev · 2014 to 2026
$36.9M
Using causal aging biomarkers and protein design to develop novel anti-aging interventionsF99AG088431 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI YING, KEJUN · 2024 to 2024
$37k
NIA NIH HHS F99 AG088431NIA NIH HHS P01 AG047200
6 · The paper itself

Abstract

Open scientific competitions have successfully driven biomedical advances but remain underutilized in aging research, where biological complexity and heterogeneity require methodological innovations. Here, we present the results from Phase I of the Biomarkers of Aging Challenge, an open competition designed to drive innovation in aging biomarker development and validation. The challenge leverages a unique DNA methylation dataset and aging outcomes from 500 individuals, aged 18 to 99. Participants are asked to develop novel models to predict chronological age, mortality, and multi-morbidity. Results from the chronological age prediction phase show important advances in biomarker accuracy and innovation compared to existing models. The winning models feature improved predictive power and employ advanced machine learning techniques, innovative data preprocessing, and the integration of biological knowledge. These approaches have led to the identification of novel age-associated methylation sites and patterns. This challenge establishes a paradigm for collaborative aging biomarker development, potentially accelerating the discovery of clinically relevant predictors of aging-related outcomes. This supports personalized medicine, clinical trial design, and the broader field of geroscience, paving the way for more targeted and effective longevity interventions.

Identifiers

PMID39554132
PMCPMC11565782

What OpenQuestion holds

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LicenceCC BY-ND
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Registered trials

None linked

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.