Background: Diabetes is one of the world's most rapidly growing metabolic disorders. In Qatar, one in five people may develop Diabetes. Despite the existence of well known metabolic biomarkers of diabetes, there is still an urgent need to study whether such markers are different in the Qatari population and investigate the interactions between metabolites to understand the underlying metabolism of such disorder. Whereas previous studies have mainly considered identifying metabolites significantly associated with diabetes in one or two biofluids, the present study integrates plasma, urine and saliva. Moreover, it is the first study of its type to be conducted for the Qatari population. Objectives: Identifying metabolites significantly regressing with Type 2 Diabetes (T2D) in a large cohort of more than 2000 metabolites in plasma, urine and saliva, as well as studying the interactions between metabolites for understanding the underlying mechanism of such disorder in the Qatari population. Results from this study would be useful for future designation of appropriate therapies that target T2D in the Qatari population. Materials & Methods: A cohort of more than 300 Qatari subjects (from different ethnicities) was used for the study, with around 50% T2D cases. The subjects came from different background ethnicities. More than 2000 metabolites from all 3 bio-fluids were used for the study. Linear regression was used to identify significantly regressing metabolites, after correcting for covariates. Partial correlations were used to identify significant relations between metabolites, and to visualize the important biochemical pathways in T2D. Results: A large set of metabolites in all 3 bio-fluids (plasma, urine and saliva) was identified as significantly regressing with diabetes, and with around 10% metabolites not reported before in literature. Metabolic sub-networks were identified as related to different biochemical pathways. Main pathways appearing in the larger sub-networks showed the involvement of the processes of glycolysis, ketoacidosis, proteolysis, and their relations to other pathways. The sub-networks were used to reveal novel interesting relations between known biomarkers and other T2D significant metabolites.


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