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Background: DNA methylation (DNAm) has unique properties which makes it a potential biomarker for lifestyle-related exposures. Epigenetic clocks, particularly DNAm-based biological age predictors [epigenetic age (EA)], represent an exciting new area of clinical research and deviations of EA from chronological age [epigenetic age acceleration (EAA)] have been linked to overall health, age-related diseases, and environmental exposures., Objectives: This observational study investigates the relationships between biological aging and various dietary factors within the LifeLines-DEEP Cohort. These factors include diet quality, processed food consumption, dietary glycemic load, and intake of vitamins involved in maintaining the epigenetic homeostasis (vitamins B-9, B-12, B-6, B-2, and C)., Methods: Dietary records collected using food-frequency questionnaires were used to estimate diet quality [LifeLines Diet Score (LLDS)], measure the intake of unprocessed/ultraprocessed food according to the NOVA food classification system, and the adequacy of the dietary intake of vitamins B-9, B-12, B-2, B-6, and C. EA using Horvath, Hannum, Levine, and Horvath2 epigenetic clock models and DNAm-predicted telomere length (DNAm-TL) were calculated from DNAm data in 760 subjects. Associations between dietary factors and EAA were tested, adjusting for sex, energy intake, and body composition., Results: LLDS was associated with EAA (EAA_Horvath: β: -0.148; P = 1 × 10 -4 ; EAA_Hannum: β: -0.148; P = 9 × 10 -5 ; EAA_Levine: β: -0.174; P = 1 × 10 -5 ; and EAA_Horvath2: β: -0.176; P = 4 × 10 -6 ) and DNAm-TL (β: 0.116; P = 0.003). Particularly, EAA was associated with dietary glycemic load (EAA_Horvath: β: 0.476; P = 9 × 10 -10 ; EAA_Hannum: β: 0.565; P = 1 × 10 -13 ; EAA_Levine: β: 0.469; P = 5 × 10 -9 ; EAA_Horvath2: β: 0.569; P = 1 × 10 -13 ; and DNAmTL adjusted for age: β: -0.340; P = 2 × 10 -5 ) and different measures of food processing (NOVA classes 1 and 4). Positive EAA was also associated with inadequate intake of vitamin B-12 (EAA_Horvath: β: -0.167; P = 0.002; EAA_Hannum: β: -0.144; P = 0.007; and EAA_Horvath2: β: -0.126; P = 0.019) and C (EAA_Hannum: β: -0.136; P = 0.010 and EAA_Horvath2: β: -0.151; P = 0.005)., Conclusions: Our findings corroborate the hypothesis that nutrition plays a pivotal role in influencing epigenetic homeostasis, especially DNAm, thereby contributing to individual health trajectories and the pace of aging., Competing Interests: Conflict of interest The authors report no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Ageing is the accumulation of changes and decline of function of organisms over time. The concept and biomarkers of biological age have been established, notably DNA methylation-based clocks. The emergence of single-cell DNA methylation profiling methods opens the possibility of studying the biological age of individual cells. Here, we generate a large single-cell DNA methylation and transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages. The number of genes expressed increases with age, but gene-specific changes are small. We next develop scEpiAge, a single-cell DNA methylation age predictor, which can accurately predict age in (very sparse) publicly available datasets, and also in single cells. DNA methylation age distribution is wider than technically expected, indicating epigenetic age heterogeneity and functional differences. Our work provides a foundation for single-cell and sparse data epigenetic age predictors, validates their functionality and highlights epigenetic heterogeneity during ageing., (© 2024. The Author(s).)

Background: DNA methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse., Results: We have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age which allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the diet., Conclusions: Here we identify and characterise an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.