ReviewEpigenomics2026
Human DNA methylation biomarkers in diabetic complications: from association to clinical translation.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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