Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework

DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and pr...

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Bibliographic Details
Published in:Scientific reports 2024-10, Vol.14 (1), p.24208-13, Article 24208
Main Authors: Zhou, Sheng, Chen, Jing, Wei, Shanshan, Zhou, Chengxing, Wang, Die, Yan, Xiaofan, He, Xun, Yan, Pengcheng
Format: Article
Language:English
Subjects:
631/114/1305
631/208/176/1988
Age
Aging
Aging - genetics
Biological age
Biomarkers
CpG Islands
Datasets
Deoxyribonucleic acid
DNA
DNA Methylation
Epigenetics
Experimental methods
Gender
Gene expression
Gene loci
Genomes
GO enrichment analysis
Health care
Humanities and Social Sciences
Humans
Interpretable machine learning
Learning algorithms
Machine Learning
Male
Medicine
multidisciplinary
Physiology
Prediction models
Research methodology
Science
Science (multidisciplinary)
Shapley Additive exPlanations
Statistical analysis
Statistical models
XGBoost
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Summary:DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-75586-9