Serum metabolomics improve risk stratification for incident heart failure

Background and Aims Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. In this study, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance (1H-NMR) spectroscopy) for incident HF. Methods We leveraged data of 68,311 individuals and > 0.8 million person-years of follow-up from the UK Biobank (UKB) cohort to assess individual metabolite associations and to train models to predict HF risk in individuals not previously considered at risk. Specifically, we (I) fitted per-metabolite COX proportional hazards (COX-PH) models to assess individual metabolite associations and (II) trained and internally validated elastic net (EN) models to predict incident HF using the serum metabolome. We benchmarked discriminative capacities against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF, PCP-HF[1][1]). Results During median follow-up of ≈ 12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF; false discovery rate (FDR)-controlled P < 0.01). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈ 80 % feature reduction). The addition of metabolomics to PCP-HF improved predictive performance (Harrel’s C: 0.768 vs. 0.755.; continuous net reclassification improvement (NRI) = 0.287; relative integrated discrimination improvement (IDI): 17.47 %). Simplified models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel’s C: 0.745 vs. 0.755, continuous NRI: 0.097, relative IDI: 13.445 %). Risk and survival stratification was improved by the integration of metabolomics. Conclusions The assessment of serum metabolomics improves incident HF risk prediction. Scores based simply on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost- and time-effective, standardizable, and scalable single-domain alternative to more complex clinical scores. ### Competing Interest Statement AMS serves as an adviser to Forcefield Therapeutics and CYTE Global Network for Clinical Research. ### Funding Statement RO is supported by a British Heart Foundation (BHF) PhD studentship (FS/19/58/34895). KT is supported by a BHF project grant (PG/20/10387). AMS is supported by the BHF (CH/1999001/11735, RG/20/3/34823, RE/18/2/34213). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: UKB ethics approval was obtained from the North West - Haydock Research Ethics Committee (Ref: 21/NW/0157; publicly available at https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethic). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes UKB data is publicly available to approved researchers at https://www.ukbiobank.ac.uk/enable-your-research. UKB data was accessed under application ID 98729. Detailed endpoint and predictor definitions can be found in Supplementary Tables 1 and 2. [1]: #ref-1