Predictive value of metabolomic biomarkers for cardiovascular disease risk: a systematic review and meta-analysis
| Autor: | Jasmin Radenkovic, Doris Bach, Leonhard Schleußner, Tobias Daniel Trippel, Frank Edelmann, Burkert Pieske, Muni Rubens, Peter McGranaghan, Anshul Saxena |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Oncology
medicine.medical_specialty Health Toxicology and Mutagenesis Clinical Biochemistry Subgroup analysis Disease 030204 cardiovascular system & hematology Risk Assessment Biochemistry 03 medical and health sciences 0302 clinical medicine Metabolomics Predictive Value of Tests Internal medicine medicine Humans business.industry Guideline Prognosis Precision medicine Predictive value Cardiovascular Diseases 030220 oncology & carcinogenesis Meta-analysis Disease risk business Biomarkers |
| Zdroj: | Biomarkers. 25:101-111 |
| ISSN: | 1366-5804 1354-750X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|