ArticleCurrent genomics2023
Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes.
Article in Current genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The aging epigenome: integrative analyses reveal intersection with Alzheimer's disease.GeroScience · 2026Pooled it
- Placental epigenetic clocks derived from crowdsourcing: Implications for the study of accelerated aging in obstetrics.iScience · 2025Article
- Age-related changes in DNA methylation in a sample of elderly Brazilians.Clinical epigenetics · 2025Article
- EpiAge: a next-generation sequencing-basedAging · 2025Article
- Survival Rate and Chronic Diseases of TCGA Cancer and KoGES Normal Samples by Clustering for DNA Methylation.Life (Basel, Switzerland) · 2024Article
- A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Background: In humans, age-related DNA methylation has been studied in blood, tissues, buccal swabs, and fibroblasts, and changes in DNA methylation patterns according to age and sex have been detected. To date, approximately 137,000 samples have been analyzed from 14,000 studies, and the information has been uploaded to the NCBI GEO database. Methods: A correlation between age and methylation level and longitudinal changes in methylation levels was revealed in both sexes. Here, 20 public datasets derived from whole blood were analyzed using the Illumina BeadChip. Batch effects with respect to the time differences were correlated. The overall change in the pattern was provided as the inverse of the coefficient of variation (COV). Results: Of the 20 datasets, nine were from a longitudinal study. All data had age and sex as common variables. Comprehensive details of age-, sex-, and longitudinal change-based DNA methylation levels in the whole blood sample were elucidated in this study. Conclusion: Based on 20 public DNA methylation datasets, methylation levels according to age and longitudinal changes by sex were identified and visualized using an integrated approach. The results highlight the molecular mechanisms underlying the association of sex and biological age with changes in DNA methylation, and the importance of optimal genomic information management.
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