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Published on: June 16, 2019
Untargeted Metabolomics Profiling Reveals Perturbations in Arginine-NO Metabolism in Middle Eastern Patients with
Ehsan Ullah1, Ayman El-Menyar2,3, Khalid Kunji1
1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar.
Insights
This study identified key metabolic biomarkers for coronary heart disease (CHD) in Middle Eastern populations. A metabolite risk score effectively predicts CHD, paving the way for new diagnostic tools.
Area of Science:
- Cardiovascular Research
- Metabolomics
- Population Health
Background:
- Coronary heart disease (CHD) is a leading cause of mortality in Middle Eastern populations.
- Existing research on CHD metabolic fingerprints lacks diversity, particularly in understudied Middle Eastern cohorts.
- There is an urgent need for specific biomarkers to understand CHD mechanisms and develop targeted therapies.
Purpose of the Study:
- To identify distinct metabolic profiles and biomarkers associated with CHD in a Middle Eastern population.
- To explore functional metabolic pathway alterations in individuals with CHD.
- To develop and validate a predictive model for CHD risk using metabolomic data.
Main Methods:
- A case-control study involving 1001 CHD patients and 2999 controls from the Middle East.
- Untargeted metabolomics analysis to profile 1159 metabolites.
- Univariate and pathway enrichment analyses, alongside machine learning for metabolite risk score (MRS) development.
Main Results:
- 511 metabolites showed significant differences between CHD patients and controls (FDR p < 0.05).
- Enriched pathways included D-arginine and D-ornithine metabolism, glycolysis, branched-chain fatty acid metabolism, and sphingolipid metabolism (FDR p < 10−300).
- The developed Metabolite Risk Score (MRS) demonstrated high discriminative power for CHD (AUC = 0.99).
Conclusions:
- This study is the first in the Middle East to identify novel and known circulating metabolites and metabolic pathways linked to CHD.
- A targeted panel of metabolites can effectively differentiate between CHD cases and controls.
- The findings support the potential use of a metabolite-based panel as a diagnostic and predictive tool for CHD in this population.
Abstract:
Coronary heart disease (CHD) is a major cause of death in Middle Eastern (ME) populations, with current studies of the metabolic fingerprints of CHD lacking in diversity. Identification of specific biomarkers to uncover potential mechanisms for developing predictive models and targeted therapies for CHD is urgently needed for the least-studied ME populations. A case-control study was carried out in a cohort of 1001 CHD patients and 2999 controls. Untargeted metabolomics was used, generating 1159 metabolites. Univariate and pathway enrichment analyses were performed to understand functional changes in CHD. A metabolite risk score (MRS) was developed to assess the predictive performance of CHD using multivariate analysis and machine learning. A total of 511 metabolites were significantly different between the CHD patients and the controls (FDR p < 0.05). The enriched pathways (FDR p < 10−300) included D-arginine and D-ornithine metabolism, glycolysis, oxidation and degradation of branched chain fatty acids, and sphingolipid metabolism. MRS showed good discriminative power between the CHD cases and the controls (AUC = 0.99). In this first study in the Middle East, known and novel circulating metabolites and metabolic pathways associated with CHD were identified. A small panel of metabolites can efficiently discriminate CHD cases and controls and therefore can be used as a diagnostic/predictive tool.
