ReviewEpigenomics2025
DNA methylation-based health predictors: advances, applications, and perspectives.
Review in Epigenomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Article
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Exploring a targeted epigenetic clock based on mortality-associated CpGs as a potential biomarker for frailty.Clinical epigenetics · 2026Article
- Epigenetic signatures of cardiometabolic risk in men: accelerated aging and differential methylation replicated across cohorts.Clinical epigenetics · 2026Article
- Unraveling the layers of epigenetic control in respiratory antiviral defense.Journal of virology · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation (DNAm) has emerged as a powerful and dynamic biomarker for predicting health outcomes, biological aging, and disease risk. Unlike static genetic variants, DNAm is dynamic and influenced by environmental, lifestyle, and pathological factors, making it highly suitable for applications in personalized medicine. This review provides a comprehensive synthesis of recent advances in DNAm-based predictors, including epigenetic clocks, exposure biomarkers, disease risk models, and trait-specific estimators. We describe the diverse methodological frameworks underpinning these predictors, such as penalized regression, surrogate modeling and deep learning. We discuss their performance across various preprocessing strategies and study populations. Additionally, we highlight clinical and research applications, ethical considerations, and emerging challenges, such as issues of reproducibility, tissue specificity, population generalizability, and interpretability. Looking forward, we explore future directions emphasizing artificial intelligence, multiomics integration, and longitudinal modeling. By critically assessing current limitations and technological innovations, this review outlines a roadmap for advancing the development, validation, and responsible implementation of DNAm-based health predictors.
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What OpenQuestion holds
Registered trials
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.