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Background: Metabolomic analysis aids in the identification of novel biomarkers by revealing the metabolic dysregulations underlying cardiovascular disease (CVD) aetiology. The aim of this study was to evaluate which metabolic biomarkers could add value for the prognosis of CVD events using meta-analysis.Methods: The PRISMA guideline was followed for the systematic review. For the meta-analysis, biomarkers were included if they were tested in multivariate prediction models for fatal CVD outcomes. We grouped the metabolites in biological classes for subgroup analysis. We evaluated the prediction performance of models which reported discrimination and/or reclassification statistics.Results: For the systematic review, there were 22 studies which met the inclusion/exclusion criteria. For the meta-analysis, there were 41 metabolites grouped into 8 classes from 19 studies (45,420 subjects, 5954 events). A total of 39 of the 41 metabolites were significant with a combined effect size of 1.14 (1.07-1.20). For the predictive performance assessment, there were 21 studies, 54,337 subjects, 6415 events. The average change in c-statistic after adding the biomarkers to a clinical model was 0.0417 (SE 0.008).Conclusions: This study provides evidence that metabolomic biomarkers, mainly lipid species, have the potential to provide additional prognostic value. Current data are promising, although approaches and results are heterogeneous.

Aims: The Cardiac Lipid Panel (CLP) is a newly discovered panel of metabolite-based biomarkers that has previously shown to improve the diagnostic value of NT-proBNP. Little is known about its usefulness in predicting outcomes. In this study, the aim was to develop a risk score for 4-year cardiovascular death in elderly CHF patients. Methods and Results: We included 280 patients >65 years of age with CHF randomly selected from the CIBIS-ELD trial. A targeted metabolomic analysis of the three CLP biomarkers was performed on baseline serum samples. Cox proportional hazards regression was used to determine the relationship of the biomarkers with the outcome and adjusted for established risk factors (age, sex, BMI, creatinine, LDL, triglycerides, and LVEF). The CLP risk score was calculated by counting the number of biomarkers above a cut-off determined using the Youden’s indexes. During the follow-up period, mean 50 months [±8 months], 35 (18%) subjects met the primary endpoint of cardiovascular death. The area under the receiver operating curve (AUROC) for the model based on only clinical variables was 0.72, the second model with clinical variables plus NT-proBNP was 0.79, and the final model with clinical variables, NT-proBNP, plus the CLP was 0.83. Conclusions: A risk score based on a panel of 3 metabolite-based biomarkers was found to improve the prognostic value of traditional risk factors and NT-proBNP in a cohort of CHF subjects. Our next steps include validating the prognostic value of the CLP biomarker kit in order to provide a better clinical risk assessment of CHF patients.

Aims: The Cardiac Lipid Panel (CLP) is a newly discovered panel of metabolite-based biomarkers that has shown to improve the diagnostic value of N terminal pro B type natriuretic peptide (NT-proBNP). However, little is known about its usefulness in predicting outcomes. In this study, we developed a risk score for 4-year cardiovascular death in elderly chronic heart failure (CHF) patients using the CLP. Methods and results: From the Cardiac Insufficiency Bisoprolol Study in Elderly trial, we included 280 patients with CHF aged >65 years. A targeted metabolomic analysis of the CLP biomarkers was performed on baseline serum samples. Cox regression was used to determine the association of the biomarkers with the outcome after accounting for established risk factors. A risk score ranging from 0 to 4 was calculated by counting the number of biomarkers above the cut-offs, using Youden index. During the mean (standard deviation) follow-up period of 50 (8) months, 35 (18%) subjects met the primary endpoint of cardiovascular death. The area under the receiver operating curve for the model based on clinical variables was 0.84, the second model with NT-proBNP was 0.86, and the final model with the CLP was 0.90. The categorical net reclassification index was 0.25 using three risk categories: 0-60% (low), 60-85% (intermediate), and >85% (high). The continuous net reclassification index was 0.772, and the integrated discrimination index was 0.104. Conclusions: In patients with CHF, incorporating a panel of three metabolite-based biomarkers into a risk score improved the prognostic utility of NT-proBNP by predicting long-term cardiovascular death more precisely. This novel approach holds promise to improve clinical risk assessment in CHF patients.