Oscar Coltell, Associate Professor and Lab Chair, Universitat Jaume I; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Spain
Associations between Next-Generation epigenetic clocks for age acceleration and serum metabolites in two cohorts with varying ages and cardiovascular risk factors
Abstract
At present, there is a tremendous interest in the aging biomarkers. These biomarkers may be genomics, epigenomics, metabolomics, proteomics, or metagenomics. Epigenomic biomarkers based on DNA methylation are the most frequently employed. They are classified into multiple generations differing in their training with machine learning algorithms to predict chronological age or age-related diseases. Likewise, metabolomics biomarkers to characterize biological aging hold immense potential, and several metabolomics signatures to predict age or age-related disease have been published. However, we are interested in the associations between serum metabolomics and the four generations of epigenetics in two independent populations differing in several factors. We investigated the associations between nuclear magnetic resonance (NMR) serum metabolomics biomarkers and several generations of epigenetic clocks: first generation (Horvath), second (PhenoAge and GrimAge); third (pace of aging: DunedinPACE) and fourth generation (causal clocks using Mendelian randomization: CausAge, AdaptAge and DamAge), computed from blood DNA-methylation using the EPICv1 array in subjects from the general population (mean age 40y) and high cardiovascular risk individuals (aged 55-75y). We observed some consistent associations between epigenomic and metabolomics biomarkers in the different populations. Thus, serum GlycA concentrations were directly associated with higher DunedinPACE, and GrimAge acceleration in both populations (P<0.05). Likewise, serum polyunsaturated fatty acids were inversely associated (P<0.05) with several epigenetic clocks in both populations. However, the direct association between branched-chain amino acids and age acceleration biomarkers was higher in young participants. Younger participants also showed a stronger inverse relationship between albumin and aging biomarkers. Therefore, population-specific metabolomic scores will be computed.