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.