Evidence map›Paper›PMID 39484440›Full record

ArticlebioRxiv : the preprint server for biology2024

When to Trust Epigenetic Clocks: Avoiding False Positives in Aging Interventions.

Daniel S Borrus, Raghav Sehgal, Jenel Fraij Armstrong, Jessica Kasamoto, John Gonzalez, Albert Higgins-Chen

Abstract readPreprint
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Daniel S BorrusDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.ORCID 0000-0002-1493-0896
Raghav SehgalProgram in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, CT, USA.ORCID 0000-0002-9387-1758
Jenel Fraij ArmstrongProgram in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, CT, USA.ORCID 0009-0001-4401-0986
Jessica KasamotoProgram in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, CT, USA.ORCID 0000-0002-4604-6093
John GonzalezDepartment of Pathology, Yale University School of Medicine, New Haven, CT, USA.ORCID 0000-0002-1020-8783
Albert Higgins-ChenDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.ORCID 0000-0003-2904-2741

Funding

QUANTITATIVE ASSESSMENT OF BIOLOGICAL AGE AND ITS APPLICATIONSR01AG065403 · NIA · YALE UNIVERSITY · PI Vadim N. Gladyshev, Albert Tzongyang Higgins-Chen · 2020 to 2026
$4.2M
Biological Underpinnings of Socioeconomic Differentials in Health and MortalityR01AG060110 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CRIMMINS, EILEEN M · 2018 to 2022
$2.6M
NIA NIH HHS R01 AG060110NIA NIH HHS R01 AG065403
6 · The paper itself

Abstract

Recent human studies have suggested that aging interventions can reduce aging biomarkers related to morbidity and mortality risk. Such biomarkers may potentially serve as early, rapid indicators of effects on healthspan. An increasing number of studies are measuring intervention effects on epigenetic clocks, commonly used aging biomarkers based on DNA methylation profiles. However, with dozens of clocks to choose from, different clocks may not agree on the effect of an intervention. Furthermore, changes in some clocks may simply be the result of technical noise causing a false positive result. To address these issues, we measured the variability between 6 popular epigenetic clocks across a range of longitudinal datasets containing either an aging intervention or an age-accelerating event. We further compared them to the same clocks re-trained to have high test-retest reliability. We find the newer generation of clocks, trained on mortality or rate-of-aging, capture aging events more reliably than those clocks trained on chronological age, as these show consistent effects (or lack thereof) across multiple clocks including high-reliability versions, and including after multiple testing correction. In contrast, clocks trained on chronological age frequently show sporadic changes that are not replicable when using high-reliability versions of those same clocks, or when using newer generations of clocks and these results do not survive multiple-testing correction. These are likely false positive results, and we note that some of these clock changes were previously published, suggesting the literature should be re-examined. This work lays the foundation for future clinical trials that aim to measure aging interventions with epigenetic clocks, by establishing when to attribute a given change in biological age to a

Indexed as

age reversalaging interventionsbiomarkersepigenetic clocksfalse positives

Identifiers

PMID39484440
PMCPMC11526921

What OpenQuestion holds

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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.