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Untargeted Metabolomics Profiling Reveals Perturbations in Arginine-NO Metabolism in Middle Eastern Patients with Coronary Heart Disease.
Ullah, Ehsan; El-Menyar, Ayman; Kunji, Khalid; Elsousy, Reem; Mokhtar, Haira R B; Ahmad, Eiman; Al-Nesf, Maryam; Beotra, Alka; Al-Maadheed, Mohammed; Mohamed-Ali, Vidya; Saad, Mohamad; Al Suwaidi, Jassim.
Afiliación
  • Ullah E; Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar.
  • El-Menyar A; Clinical Research, Trauma & Vascular Surgery, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar.
  • Kunji K; Department of Clinical Medicine, Weill Cornell Medical College, Doha P.O. Box 24144, Qatar.
  • Elsousy R; Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar.
  • Mokhtar HRB; Department of Cardiology, Heart Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar.
  • Ahmad E; Anti-doping Lab Qatar, Doha P.O. Box 27775, Qatar.
  • Al-Nesf M; Anti-doping Lab Qatar, Doha P.O. Box 27775, Qatar.
  • Beotra A; Department of Internal Medicine, Allergy and Immunology, Hamad General Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar.
  • Al-Maadheed M; Anti-doping Lab Qatar, Doha P.O. Box 27775, Qatar.
  • Mohamed-Ali V; Anti-doping Lab Qatar, Doha P.O. Box 27775, Qatar.
  • Saad M; Anti-doping Lab Qatar, Doha P.O. Box 27775, Qatar.
  • Al Suwaidi J; Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar.
Metabolites ; 12(6)2022 Jun 03.
Article en En | MEDLINE | ID: mdl-35736450
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Metabolites Año: 2022 Tipo del documento: Article País de afiliación: Qatar

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Metabolites Año: 2022 Tipo del documento: Article País de afiliación: Qatar