Evidence map›Paper›PMID 41272092›Full record

ArticleCommunications biology2025

Machine learning predicts lifespan and suggests underlying causes of death in aging C. elegans.

Carina C Kern, Petru Manescu, Matt Cuffaro, Catherine Au, Aihan Zhang, Hongyuan Wang, Ann F Gilliat, Sophie van Schelt, Marina Ezcurra, David Gems

Abstract read
In one paragraph

Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. A hierarchy of causes of death in senescentbioRxiv : the preprint server for biology · 2025
    Article
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.

Carina C KernInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.
Petru ManescuDepartment of Computer Science, Faculty of Engineering Sciences, University College London, London, UK.
Matt CuffaroInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.
Catherine AuInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.ORCID http://orcid.org/0000-0002-1923-9121
Aihan ZhangInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.
Hongyuan WangInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.
Ann F GilliatInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.
Sophie van ScheltInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.
Marina Ezcurra *Institute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK. m.ezcurra@kent.ac.uk.ORCID http://orcid.org/0000-0003-2739-6984
David Gems *Institute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, UK.ORCID http://orcid.org/0000-0002-6653-4676

Funding

Enhancing and expanding the CGC Strain CollectionP40OD010440 · OD · UNIVERSITY OF MINNESOTA · PI Ann E. Rougvie · 2012 to 2026
$7.5M
NIH HHS P40 OD010440RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/V011243/1Wellcome Trust 215574Wellcome Trust (Wellcome) 215574/Z/19/Z
6 · The paper itself

Abstract

Aging leads to age-related pathology that causes death, and genes affect lifespan by determining such pathology. Here we investigate how age-related pathology mediates the effect of genetic and environmental interventions on lifespan in C. elegans by means of a data-driven approach employing machine learning (ML). To this end, extensive data on how diverse determinants of lifespan (sex, nutrition, genotype, mean lifespan range: 7.5 to 40 days) affect patterns of age-related pathology was gathered. This revealed that different life-extending treatments result in distinct patterns of suppression of senescent pathology. By analysing the differential effects on mid-life pathology levels and lifespan, the ML models developed were able to predict lifespan variation, explaining 79% of the variance. Levels of pathology in the pharynx and intestine proved to be the strongest predictors of lifespan. This suggests that elderly C. elegans die predominantly from late-life disease affecting these organs. In addition, we noted profound sex differences in age-related pathology: the striking age-related pathologies in hermaphrodites affecting organs linked to reproduction are absent from males, suggesting that reproductive death may be hermaphrodite limited.

Indexed as

AgingCaenorhabditis elegansLongevityMachine LearningAnimalsCause of DeathFemaleMale

Identifiers

PMID41272092
PMCPMC12638908

What OpenQuestion holds

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Registered trials

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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.