Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden

Abstract

Individuals of the same chronological age display different rates of biological ageing. A number of measures of biological age have been proposed which harness age-related changes in DNA methylation profiles. These include methylation-based predictors of chronological age (HorvathAge, HannumAge), all-cause mortality (DNAm PhenoAge, DNAm GrimAge) and telomere length (DNAm Telomere Length). In this study, we test the association between these epigenetic markers of ageing and the prevalence and incidence of the leading causes of disease burden and mortality in highincome countries. Furthermore, we test the clocks' relationships with phenotypic measures associated with these conditions, including spirometric and biochemical traits. We carry out these analyses in 9,537 individuals from the Generation Scotland: Scottish Family Health Study. We find that DNAm GrimAge outperforms other epigenetic clocks in its associations with self-report disease prevalence and related clinical traits. DNAm GrimAge associates with chronic obstructive pulmonary disease (COPD) prevalence (Odds Ratio = 3.29, P = 3.0 x 10 -4 ) and pulmonary spirometry tests (β = [-0.10 to -0.15], P = [1.4 x 10 -4 to 1.4 x 10 -6 ]) at study baseline after adjusting for possibly confounding risk factors including alcohol, body mass index, deprivation, education and smoking. After adjusting for these confounding risk factors, DNAm GrimAge, DNAm PhenoAge and DNAm Telomere Length, measured at study baseline, predict incidence of ICD-10-coded disease states including COPD, type 2 diabetes and cardiovascular disease after thirteen years of follow-up (Hazard Ratios = [0.80 (telomere length)to 2.19 (GrimAge)], P = [9.9 x 10 -4 , 1.9 x 10 -14 ]). Our data show that despite accounting for several possible confounding variables, epigenetic markers of ageing predict incidence of common disease.This may have significant implications for their potential utility in clinical settings to complement goldstandard methods of clinical assessment and management.

Leisure-Time and Occupational Physical Activity Associates Differently with Epigenetic Aging

“…All the utilized epigenetic aging acceleration measures have been shown to predict mortality and morbidity risk, but DNAm GrimAge acceleration stands out in the prediction accuracy ( 12 , 28 , 29 ). Previous studies have shown that DNAm GrimAge may capture the stimulus of a variety of health- and lifestyle-related factors ( 12 , 27 ).…”

Genetic and Phenotypic Evidence for the Causal Relationship Between Aging and COVID-19

“…It has been reported that risk-based biological age predictors often outperform chronological agebased estimations in tasks involving associations with risks of death, lifestyles, and diseases 27,28,43 . This may explain a stronger GrimAge association but non-significant associations of unhealthy lifestyles such as smoking (both overall and in disease-free population), and the future incidence of chronic diseases in healthy subjects [27][28][29]31,32,37,38,40 . In this work, we also established the association of BAA with the risk of non-chronic diseases, such as COVID-19 and the corresponding case fatality in the UKBB cohort independently of disease burden.…”

Genome-wide association studies identify 137 loci for DNA methylation biomarkers of ageing

“…Two such predictors, termed PhenoAge (a DNAm predictor trained on a measure that itself was trained on mortality, using 42 clinical measures and age as input features) and GrimAge (trained on mortality, including a DNAm measure of smoking as a constituent part), outperform both Hannum and Horvath clocks in predicting mortality, and are associated with various measures of morbidity and lifestyle factors [15,16]. DNAm GrimAge outperforms PhenoAge and the first generation of epigenetic clocks when it comes to predicting time to death [8,17,18].…”