Evidence map›Paper›PMID 40346834›Full record

ReviewEpigenomics2025

The predictive power of profiling the DNA methylome in human health and disease.

Paraskevi Christofidou, Christopher G Bell

Abstract readReview
In one paragraph

Review in Epigenomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

2 authors.

Paraskevi ChristofidouWilliam Harvey Research Institute, Barts & The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.ORCID 0000-0002-5809-6557
Christopher G BellWilliam Harvey Research Institute, Barts & The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.ORCID 0000-0003-4601-1242

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early and accurate diagnosis significantly improves the chances of disease survival. DNA methylation (5mC), the major DNA modification in the human genome, is now recognized as a biomarker of immense clinical potential. This is due to its ability to delineate precisely cell-type, quantitate both internal and external exposures, as well as tracking chronological and biological components of the aging process. Here, we survey the current state of DNA methylation as a biomarker and predictor of traits and disease. This includes Epigenome-wide association study (EWAS) findings that inform Methylation Risk Scores (MRS), EpiScore long-term estimators of plasma protein levels, and machine learning (ML) derived DNA methylation clocks. These all highlight the significant benefits of accessible peripheral blood DNA methylation as a surrogate measure. However, detailed DNA methylation biopsy analysis in real-time is also empowering pathological diagnosis. Furthermore, moving forward, in this multi-omic and biobank scale era, novel insights will be enabled by the amplified power of increasing sample sizes and data integration.

Indexed as

DNA MethylationEpigenesis, GeneticEpigenomeBiomarkersEpigenomicsGenome-Wide Association StudyHumansMachine LearningBiomarkersbiological ageingDNA methylationepigenetic clocksepigenetic estimatorsEpigeneticsepigenomicsEWASmethylation risk scores

Identifiers

PMID40346834
PMCPMC12143711

What OpenQuestion holds

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LicenceCC BY-NC-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.