Language

English

Publication Date

7-7-2024

Journal

Metabolomics

DOI

10.1007/s11306-024-02141-y

PMID

38972029

PMCID

PMC12060728

PubMedCentral® Posted Date

5-8-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Background and objective: Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline.

Methods: We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites.

Results: We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet.

Conclusion: Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

Keywords

Heart Failure, Humans, Metabolomics, Male, Female, Prospective Studies, Middle Aged, Metabolome, Aged, Metabolic Networks and Pathways, Metabolomics, Heart failure, Confounding metabolites, Data-driven or Bayesian networks, Causal networks, Structural equation modeling, Genetic variation

Published Open-Access

yes

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