AUTHOR=Vignoli Alessia , Fornaro Alessandra , Tenori Leonardo , Castelli Gabriele , Cecconi Elisabetta , Olivotto Iacopo , Marchionni Niccolò , Alterini Brunetto , Luchinat Claudio 

TITLE=Metabolomics Fingerprint Predicts Risk of Death in Dilated Cardiomyopathy and Heart Failure

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.851905

DOI=10.3389/fcvm.2022.851905

ISSN=2297-055X

ABSTRACT=Background: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. Metabolomics may help refine risk assessment and potentially guide HF management, but dedicated studies are few. This study aims at stratifying long-term risk of death in a cohort of patients affected by HF due to dilated cardiomyopathy (DCM) using serum metabolomics via nuclear magnetic resonance (NMR) spectroscopy. 
Methods: A cohort of 106 patients with HF due to DCM, diagnosed and monitored between 1982-2011, were consecutively enrolled between 2010-2012, and a serum sample was collected from each participant. Each patient underwent half-yearly clinical assessments, and survival status at the last follow-up visit in 2019 was recorded. NMR serum metabolomic profiles were retrospectively analyzed to evaluate patient risk of death. Overall, 26 patients died during the 8-years of the study.
Results: The metabolomic fingerprint at enrollment was powerful in discriminating patients who died (HR 5.71, p=0.00002), even when adjusted for potential covariates. The outcome prediction of metabolomics surpassed that of NT-proBNP (HR 2.97, p=0.005). Metabolomic fingerprinting was able to sub-stratify risk of death in patients with both preserved/mid-range and reduced ejection fraction (HR 3.46, p=0.03; HR 6.01, p=0.004, respectively). Metabolomics and LVEF, combined in a score, proved to be synergistic in predicting survival (HR 8.09, p=0.0000004).
Conclusions: Metabolomic analysis via NMR enables fast and reproducible characterization of the serum metabolic fingerprint associated with poor prognosis in the HF setting. Our data suggest the importance of integrating several risk parameters to early identify HF patients at high risk of poor outcome.