Evidence map›Paper›PMID 35304597›Full record

ReviewNature reviews. Genetics2022

DNA methylation-based predictors of health: applications and statistical considerations.

Paul D Yousefi, Matthew Suderman, Ryan Langdon, Oliver Whitehurst, George Davey Smith, Caroline L Relton

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
143citing papers in PubMed, 4 pooled it
–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

143 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Identifying Key Predictors of Mid-Childhood Obesity in a Population-Based Cohort Study: An Evidence Synthesis and Predictive Modeling Study.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025
    Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
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  8. Review
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  10. DNA methylation as proxy of genetic, prenatal and perinatal psychiatric risk factors.medRxiv : the preprint server for health sciences · 2026
    Article
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  12. Article
  13. Article
  14. Review
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  20. The Impact of Environmental Pollution and Climate Change on Allergic Rhinitis and Lung Diseases.World journal of otorhinolaryngology - head and neck surgery · 2026
    Review

83 more citing papers are in PubMed but not listed here.

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.

Paul D YousefiMedical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.ORCID http://orcid.org/0000-0003-3284-3240
Matthew SudermanMedical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
Ryan LangdonMedical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
Oliver WhitehurstMedical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.ORCID http://orcid.org/0000-0002-4575-2092
George Davey SmithMedical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.ORCID http://orcid.org/0000-0002-1407-8314
Caroline L ReltonMedical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK. caroline.relton@bristol.ac.uk.

Funding

Cancer Research UK 29019Cancer Research UK C18281/A29019Medical Research Council MC_UU_00011/1Medical Research Council MC_UU_00011/5
6 · The paper itself

Abstract

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.

Indexed as

DNA MethylationNeoplasmsBiomarkersEpigenesis, GeneticGenome-Wide Association StudyHumansBiomarkers

Identifiers

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.