Related Experiment Video
Updated: Jan 19, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
Published on: May 6, 2022
Associations of DNA methylation algorithm measures of aging with type 2 diabetes and mortality risk among US older
Xuetong Zhao1, Chongyu Ding2, Hui Zhang2
1School of Global Health, Chinese Centre for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China; School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Background:
DNA methylation algorithm values show promise as biomarkers for aging and adverse health outcomes, However, their comparative predictive utility for type 2 diabetes mellitus and its related mortality remains inadequately characterized. This study systematically evaluated twelve established epigenetic algorithms to address this knowledge gap.
Methods:
Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 1999-2002, we assessed twelve DNA methylation algorithms (e.g., PhenoAgeAcc, GrimAgeMortAcc, GrimAge2MortAcc) in relation to type 2 diabetes mellitus risk and mortality among 2532 participants aged 50 years or more. DNA methylation was measured using the Infinium Methylation EPIC BeadChip kit. Statistical models quantified effect estimates as odds ratios for type 2 diabetes mellitus risk and subdistribution hazard ratios for mortality, with 95% confidence intervals expressed per one-standard deviation increment in epigenetic age Acceleration metrics.
Results:
Significant associations were observed for PhenoAgeAcc, GrimAgeMortAcc, and GrimAge2MortAcc with type 2 diabetes mellitus risk, with multivariable-adjusted odds ratios (95% confidence interval) per standard deviation increase of 1.24 (1.04-1.49), 2.08 (1.39-3.13), and 2.95 (1.97-4.43), respectively. These associations remained consistent across biological sex and age subgroups (50-64 vs. ≥65 years). For mortality risk, eight algorithm measures were positively associated with type 2 diabetes mellitus mortality, with GrimAgeMortAcc and GrimAge2MortAcc showing the strongest predictive performance, with adjusted subdistribution hazard ratios (95% confidence interval) per standard deviation increase of 1.61 (1.39-1.87) and 1.69 (1.48-1.93), respectively.
Conclusions:
DNA methylation algorithm values, particularly GrimAgeMortAcc and GrimAge2MortAcc, are strongly associated with prevalent type 2 diabetes mellitus and show significant utility for mortality risk stratification, highlighting the potential of these algorithms as tools for identifying high-risk populations. These findings highlight the potential of epigenetic biomarkers in guiding targeted prevention strategies.
Related Concept Videos
Psychoneuroimmunology: Diabetes and Cancer
Epigenetic Regulation
X-chromosome...
Epigenetic Regulation
Diabetes Mellitus: Type 2 and Gestational
Diabetes: Symptoms, Diagnosis, and Complications

