Targeted Metabolomic Profiling of Plasma and Survival in Heart Failure Patients

Abstract

Background Profiling of plasma metabolites to predict the course of heart failure (HF) appears promising but validation and incremental value are less established. Methods Patients meeting Framingham HF criteria with history of reduced ejection fraction were (n=1032) were randomly divided into derivation and validation cohorts (n=516 each). Amino acids, organic acids, and acylcarnitines were quantified using mass spectrometry in fasting plasma samples. We derived a prognostic metabolite profile (PMP) in the derivation cohort using Lasso-penalized Cox regression. Validity was assessed by 10-fold cross-validation in the derivation cohort, and by standard testing in the validation cohort. The PMP was analyzed as both a continuous variable (PMPscore) and dichotomized at the median (PMPcat), in univariate and multivariate models adjusted for clinical risk score and NTproBNP. Results Overall 48% of patients were African American, 35% were female, and average age was 69 years. After median follow-up of 34 months, there were 256 deaths (127 and 129 in derivation and validation cohorts, respectively). Optimized modeling defined the 13 metabolite PMP, which cross-validated as both PMPscore (hazard ratio [HR] 3.27, p<2×10−16) and PMPcat (HR=3.04, p=2.93×10−8). The validation cohort showed similar results; PMPscore HR=3.9 (p<2×10−16) and PMPcat HR=3.99 (p=3.47×10−9). In adjusted models PMP remained associated with mortality in the cross-validated derivation cohort (PMPscore HR=1.63, p=0.0029; PMPcat HR=1.47, p=0.081) and validation cohort (PMPscore HR=1.54, p=0.037; PMPcat HR=1.69, p=0.043). Conclusion Plasma metabolite profile varies across HF subgroups and is associated with survival incremental to conventional predictors. Additional investigation is warranted to define mechanisms and clinical applications.

Metabolic Profiling Associates with Disease Severity in Nonischemic Dilated Cardiomyopathy

“…5À9 However, data regarding metabolomics in nonischemic DCM are scarce, 10 whereas the metabolic profile is likely to be different from that of ischemic HF. 9 Modeling of large biologic data sets represents a unique opportunity to predict disease severity better, as compared to single biomarkers. However, statistical classification is a critical component of utilizing metabolomics data for examining the phenotypical determinants.…”

Serum untargeted metabolomic changes in response to diet intervention in dogs with preclinical myxomatous mitral valve disease

“…The metabolome encompasses a large variety of metabolites, the small intermediary molecules in metabolism. Metabolomics has been increasingly used to investigate metabolic pathways in cardiovascular diseases [23][24][25][26][27], including mitral valve disease in both dogs [9] and humans [15]. Emerging evidence points to derangements in cardiac metabolism including perturbations in mitochondrial bioenergetics and reactive oxygen species (ROS) homeostasis in failing hearts [1].…”

Incremental prognostic value of a novel metabolite‐based biomarker score in congestive heart failure patients

“…Recently, Lanfear et al identified and then validated a panel of 13 circulating metabolites as a predictor of mortality risk in HF patients after accounting for conventional clinical risk factors and NT-proBNP levels. 28 Another prospective population-based study deriving a risk score from four metabolites and validating this score in two cohorts found improved risk reclassification of CHF patients using the biomarker score, although discrimination was not significantly enhanced. 29 A meta-analysis of 18 metabolomic prediction studies of CV disease outcomes reported an average change in c-statistic of 0.0417 (standard error 0.008) after adding metabolite-based information, which is consistent with our results.…”