Predictive value of metabolomic biomarkers for cardiovascular disease risk: a systematic review and meta-analysis

Abstract

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.