NMR metabolomics-guided DNA methylation mortality predictors

Journal article (2024)

Authors

Daniele Bizzarri Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
D. Bizzarri Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
M.J.T. Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
Marcel Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
Marcel J.T. Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
M. J.T. Reinders Leiden University Medical Center, Pattern Recognition and Bioinformatics - EEMCS
Lieke Kuiper Rijksinstituut voor Volksgezondheid en Milieu (RIVM), Erasmus Medical Centre
Marian Beekman Leiden University Medical Center
Joris Deelen Leiden University Medical Center, Max Planck Institute for Biology of Ageing, University of Cologne
Joyce B.J. van Meurs Erasmus Medical Centre
Jenny van Dongen Amsterdam Reproduction and Development Research Institute, Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute
René Pool Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam
E.B. van den Akker Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
E. B. van den Akker Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Erik van den Akker Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Erik Ben van den Akker Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Erik B. van den Akker Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
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Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
More Info
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Published Date

2024

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

Background
1H-NMR metabolomics and DNA methylation in blood are widely known biomarkers predicting age-related physiological decline and mortality yet exert mutually independent mortality and frailty signals.

Methods
Leveraging multi-omics data in four Dutch population studies (N = 5238, ∼40% of which male) we investigated whether the mortality signal captured by 1H-NMR metabolomics could guide the construction of DNA methylation-based mortality predictors.

Findings
We trained DNA methylation-based surrogates for 64 metabolomic analytes and found that analytes marking inflammation, fluid balance, or HDL/VLDL metabolism could be accurately reconstructed using DNA-methylation assays. Interestingly, a previously reported multi-analyte score indicating mortality risk (MetaboHealth) could also be accurately reconstructed. Sixteen of our derived surrogates, including the MetaboHealth surrogate, showed significant associations with mortality, independent of relevant covariates.

Interpretation
The addition of our metabolic analyte-derived surrogates to the well-established epigenetic clock GrimAge demonstrates that our surrogates potentially represent valuable mortality signal.

Funding
BBMRI-NL, X-omics, VOILA, Medical Delta, NWO, ERC.