ArticleHuman genomics2023
Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging.
Article in Human genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of DNA methylation aging signatures in 17 human tissues.Nature aging · 2026Pooled it
- Embracing non-linearity in human ageing.Nature reviews. Genetics · 2026Review
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- BAGE: a Bayesian framework for age prediction based on PBMC gene expression data.BMC bioinformatics · 2026Article
- Genome-Wide DNA Methylation Patterns Predict Age in the Zebra Shark (Stegostoma tigrinum) and Provide Insight Into the Evolution of Vertebrate Aging.Molecular ecology · 2026Article
- Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches.Annual review of statistics and its application · 2026Article
- Sex-specific nonlinear DNA methylation aging trajectories reveal biomarkers of cancer risk and inflammation.Genome biology · 2026Article
- Metabolomic sweet spot clock predicts mortality and age-related diseases in the Canadian Longitudinal Study on Aging.Communications medicine · 2026Article
- Aging: a possible road toward gut microbiota pathoadaptation.Gut microbes · 2025Article
- Trends of genetic contributions on epigenetic clocks and related methylation sites with aging: A population-based adult twin study.Aging cell · 2025Article
- Applicability of epigenetic age models to next-generation methylation arrays.Genome medicine · 2024Article
- Application of a mathematical model to clarify the statistical characteristics of a pan-tissue DNA methylation clock.GeroScience · 2024Article
- Validation of biomarkers of aging.Nature medicine · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown.
resultWe propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age.
conclusionDICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.
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