Vignoli Alessia, Fornaro Alessandra, Tenori Leonardo, Castelli Gabriele, Cecconi Elisabetta, Olivotto Iacopo, Marchionni Niccolò, Alterini Brunetto, Luchinat Claudio
Department of Chemistry "Ugo Schiff", Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.
Interuniversity Consortium for Magnetic Resonance of Metalloproteins, Sesto Fiorentino, Italy.
Front Cardiovasc Med. 2022 Apr 7;9:851905. doi: 10.3389/fcvm.2022.851905. eCollection 2022.
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 the long-term risk of death in a cohort of patients affected by HF due to dilated cardiomyopathy (DCM) using serum metabolomics nuclear magnetic resonance (NMR) spectroscopy.
A cohort of 106 patients with HF due to DCM, diagnosed and monitored between 1982 and 2011, were consecutively enrolled between 2010 and 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. The NMR serum metabolomic profiles were retrospectively analyzed to evaluate the patient's risk of death. Overall, 26 patients died during the 8-years of the study.
The metabolomic fingerprint at enrollment was powerful in discriminating patients who died (HR 5.71, = 0.00002), even when adjusted for potential covariates. The outcome prediction of metabolomics surpassed that of N-terminal pro b-type natriuretic peptide (NT-proBNP) (HR 2.97, = 0.005). Metabolomic fingerprinting was able to sub-stratify the risk of death in patients with both preserved/mid-range and reduced ejection fraction [hazard ratio (HR) 3.46, = 0.03; HR 6.01, = 0.004, respectively]. Metabolomics and left ventricular ejection fraction (LVEF), combined in a score, proved to be synergistic in predicting survival (HR 8.09, = 0.0000004).
Metabolomic analysis 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 outcomes.
心力衰竭(HF)是全球发病和死亡的主要原因。代谢组学可能有助于完善风险评估并潜在地指导心力衰竭管理,但专门的研究较少。本研究旨在使用血清代谢组学核磁共振(NMR)光谱对一组因扩张型心肌病(DCM)导致心力衰竭的患者的长期死亡风险进行分层。
1982年至2011年期间诊断并监测的106例因DCM导致心力衰竭的患者队列,于2010年至2012年连续入组,从每位参与者采集血清样本。每位患者每半年进行一次临床评估,并记录2019年最后一次随访时的生存状态。对NMR血清代谢组学谱进行回顾性分析以评估患者的死亡风险。在研究的8年中,共有26例患者死亡。
即使在对潜在协变量进行调整后,入组时的代谢组学指纹在区分死亡患者方面也很有效(风险比5.71,P = 0.00002)。代谢组学的结果预测优于N末端B型利钠肽原(NT-proBNP)(风险比2.97,P = 0.005)。代谢组学指纹能够对射血分数保留/中等范围和降低的患者的死亡风险进行亚分层[风险比(HR)分别为3.46,P = 0.03;HR 6.01,P = 0.004]。代谢组学和左心室射血分数(LVEF)相结合形成一个评分,在预测生存方面被证明具有协同作用(风险比8.09,P = 0.0000004)。
代谢组学分析-NMR能够快速且可重复地表征与心力衰竭预后不良相关的血清代谢指纹。我们的数据表明整合多个风险参数以早期识别预后不良的高风险心力衰竭患者的重要性。