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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. [ABSTRACT FROM AUTHOR]

Aims/Introduction: The progression from prediabetes to type 2 diabetes is preventable by lifestyle intervention and/or pharmacotherapy in a large fraction of individuals with prediabetes. Our objective was to develop a risk score to screen for prediabetes in the Middle East, where diabetes prevalence is one of the highest in the world. Materials and Methods: In this cross‐sectional, case–control study, we used data of 4,895 controls and 2,373 prediabetic adults obtained from the Qatar Biobank cohort. Significant risk factors were identified by logistic regression and other machine learning methods. The receiver operating characteristic was used to calculate the area under curve, cut‐off point, sensitivity, specificity, positive and negative predictive values. The prediabetes risk score was developed from data of Qatari citizens, as well as long‐term (≥15 years) residents. Results: The significant risk factors for the Prediabetes Risk Score in Qatar were age, sex, body mass index, waist circumference and blood pressure. The risk score ranges from 0 to 45. The area under the curve of the score was 80% (95% confidence interval 78–83%), and the cut‐off point of 16 yielded sensitivity and specificity of 86.2% (95% confidence interval 82.7–89.2%) and 57.9% (95% confidence interval 65.5–71.4%), respectively. Prediabetes Risk Score in Qatar performed equally in Qatari nationals and long‐term residents. Conclusions: Prediabetes Risk Score in Qatar is the first prediabetes screening score developed in a Middle Eastern population. It only uses risk factors measured non‐invasively, is simple, cost‐effective, and can be easily understood by the general public and health providers. Prediabetes Risk Score in Qatar is an important tool for early detection of prediabetes, and can help tremendously in curbing the diabetes epidemic in the region. [ABSTRACT FROM AUTHOR]