Targeted Metabolomic Profiling of Plasma and Survival in Heart Failure Patients

Abstract

Objectives: This study sought to derive and validate plasma metabolite associations with survival in heart failure (HF) patients.

Background: Profiling of plasma metabolites to predict the course of HF appears promising, but validation and incremental value of these profiles are less established.

Methods: Patients (n = 1,032) who met Framingham HF criteria with a history of reduced ejection fraction 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 N-terminal pro-B-type natriuretic peptide.

Results: Overall, 48% of patients were African American, 35% were women, and the average age was 69 years. After a 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 PMPs, which was cross validated as both the 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 the validation cohort (PMPscore HR: 1.54; p = 0.037; PMPcat HR: 1.69; p = 0.043).

Conclusions: Plasma metabolite profiles varied across HF subgroups and were associated with survival incremental to conventional predictors. Additional investigation is warranted to define mechanisms and clinical applications.

Keywords: congestive heart failure; metabolomic profiling; prognosis; risk stratification.