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. 2020 Jun;19(6):e13149.
doi: 10.1111/acel.13149. Epub 2020 May 3.

Determinants of accelerated metabolomic and epigenetic aging in a UK cohort

Oliver Robinson  1 , Marc Chadeau Hyam  1 , Ibrahim Karaman  1 , Rui Climaco Pinto  1 , Mika Ala-Korpela  2   3 , Evangelos Handakas  1 , Giovanni Fiorito  1   4   5 , He Gao  1 , Andy Heard  1 , Marjo-Riitta Jarvelin  1   6   7 , Matthew Lewis  8 , Raha Pazoki  1   7 , Silvia Polidoro  1   5 , Ioanna Tzoulaki  1 , Matthias Wielscher  1 , Paul Elliott  1 , Paolo Vineis  1   5
Affiliations

Determinants of accelerated metabolomic and epigenetic aging in a UK cohort

Oliver Robinson et al. Aging Cell. 2020 Jun.

Abstract

Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.

Keywords: DNA methylation; accelerated aging; affective mood disorders; metabolomics; molecular biology of aging; risk factors.

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Conflict of interest statement

All authors have no conflicts of interest to declare.

Figures

FIGURE 1
FIGURE 1
Summary of metabolomic age prediction. (a) Distribution of Pearson's correlation coefficient (r) between chronological and predicted age across bootstrapped test sets. (b) Metabolomic age plotted against chronological age. (c) Distribution of metabolomic age acceleration scores
FIGURE 2
FIGURE 2
Metabolomic age plotted against chronological age across different independent study areas. Seven models in this analysis were trained separately on data from participants in six out of the seven study areas and validated with data in the remaining study area shown
FIGURE 3
FIGURE 3
Metabolic network visualisation of significantly enriched pathways based on the manually curated KEGG global metabolic network (Chong et al., 2018). The metabolites of significantly enriched pathways are represented as nodes on the network. Empty nodes represent compounds identified from the feature list by Mummichog but not significant, while solid nodes represent significantly enriched features. Note not all metabolites from the KEGG global network are displayed
FIGURE 4
FIGURE 4
Associations between risk factors of premature mortality and age acceleration scores. Models adjusted for sex, ethnicity, study centre, income, hypertension, diabetes, BMI, smoking, alcohol intake, physical activity, and fruit, vegetable, meat and fish consumption. Bars show 95% confidence intervals. N (met) and N (epi) columns indicate number analysed for each category for metabolomic age and DNA methylation age measures, respectively
See this image and copyright information in PMC

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