Evidence map›Paper›PMID 42192279›Full record

ReviewEpigenomics2026

Human DNA methylation biomarkers in diabetic complications: from association to clinical translation.

Ishant Khurana, Ram Abou Zaki, Assam El-Osta

Abstract readReview
In one paragraph

Review in Epigenomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ishant KhuranaBaker Heart and Diabetes Institute, Epigenetics in Human Health and Disease Program, Melbourne, VIC, Australia.
Ram Abou ZakiBaker Heart and Diabetes Institute, Epigenetics in Human Health and Disease Program, Melbourne, VIC, Australia.
Assam El-OstaBaker Heart and Diabetes Institute, Epigenetics in Human Health and Disease Program, Melbourne, VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic complications are a major contributor to morbidity and mortality, yet accurately identifying individuals at highest risk remains a persistent clinical challenge. Although established clinical risk factors underpin current prediction models, their ability to forecast complication onset and progression remains limited. Long-term follow-up of landmark clinical trials revealed the phenomenon of metabolic memory, whereby early glycemic exposure leaves a lasting imprint on future complication risk, even after glycemic differences have converged. Epigenetic mechanisms are compelling mediators of this effect, as they encode stable changes in gene regulation without altering the underlying DNA sequence.Over the past decade, human studies have linked epigenetic modifications, particularly DNA methylation, to diabetic complications, yet translation into clinically useful biomarkers has been slow. This Perspective examines the current landscape of human epigenetic biomarker research, highlighting both biological insights and methodological constraints. We discuss key technological platforms, including Infiniummethylation arrays and sequencing-based approaches such as bisulfites sequencing and enzymatic methyl-sequencing (EM-seq). While earlyarray-based studies provided proof of concept, next-generation sequencing approaches offer substantially greater discovery potential.We conclude by outlining priorities for the next decade, emphasizing longitudinal cohorts, multi-omics integration, and predictive frameworks capable oftransforming epigenetic signals into meaningful clinical tools.

Indexed as

Diabetes ComplicationsDNA MethylationEpigenesis, GeneticBiomarkersHumansTranslational Research, BiomedicalBiomarkerscardiovascular diseasecell-free DNADiabetic complicationsdiabetic kidney diseaseDNA methylationgestational diabetesmetabolic memoryprecision medicine

Identifiers

PMID42192279
PMCPMC13313257

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.