Evidence map›Paper›PMID 39693428›Full record

ArticleScience advances2024

Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.

Julian Mutz, Raquel Iniesta, Cathryn M Lewis

Abstract readComparative Study
In one paragraph

Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Urinary metabolic age from high-resolution nuclear magnetic resonance spectroscopy reveals longitudinal aging patterns.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Functional, molecular, and digital measurements of biological age.The Journal of clinical investigation · 2026
    Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. 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

3 authors.

Julian MutzDepartment of Biostatistics and Health Informatics, King's College London, London, United Kingdom.ORCID 0000-0001-5308-1957
Raquel IniestaDepartment of Biostatistics and Health Informatics, King's College London, London, United Kingdom.ORCID 0000-0003-1153-5417
Cathryn M LewisSocial, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.ORCID 0000-0002-8249-8476

Funding

Medical Research Council MR/R023484/1
6 · The paper itself

Abstract

Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to

Indexed as

AlgorithmsLongevityMachine LearningMetabolomicsAgedAgingBiomarkersFemaleHumansMaleMetabolomeMiddle AgedBiomarkers

Identifiers

PMID39693428
PMCPMC11654675

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.