1H NMR based serum metabolic profiling reveals differentiating biomarkers in patients with diabetes and diabetes-related complication. Issue 1 (January 2019)

• Record Type: Journal Article
• Authors: Rawat, Atul; Misra, Gunjan; Saxena, Madhukar; Tripathi, Sukanya; Dubey, Durgesh; Saxena, Sulekha; Aggarwal, Avinash; Gupta, Varsha; Khan, M.Y.; Prakash, Anand
• Is Part Of: Diabetes & metabolic syndrome. Volume 13:Issue 1(2019)
• Page Start: 290
• Page End: 298
• Publication Date: 2019-01
• Subjects: Nuclear magnetic resonance spectroscopy -- Metabolomics -- Biomarker -- Random forest; NMR Nuclear Magnetic Resonance -- CPMG Carr–Purcell–Meiboom–Gill -- DB Diabetes -- DC Diabetes-related Complication/Comorbidity -- ROC Receiver operating characteristic -- AUROC area under the ROC curve -- RF Random Forest -- ESM Electronic Supplementary Material; Diabetes -- Periodicals; Metabolism -- Disorders -- Periodicals; Diabetes Mellitus -- Periodicals; Metabolic Diseases -- Periodicals; Diabète -- Périodiques; Métabolisme, Troubles du -- Périodiques; Endocrinologie -- Périodiques; Diabète -- Physiopathologie -- Périodiques; Diabetes; Metabolism -- Disorders; Electronic journals; Periodicals; 616.462
• DOI: 10.1016/j.dsx.2018.09.009
• Languages: English
• ISSNs: 1871-4021

Abstract

Abstract: Background: Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed due to lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated complication/comorbidities. Methodology: The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites in diabetes (DB = 38) vs. diabetes-related complication (DC = 35) along with the healthy control (HC = 50) subjects. A combined and pairwise analysis was performed to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t -test, AUROC analysis to examine the statistical significance of the identified metabolites. Results: The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline, and threonine). Conclusion: The 1 H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its complication on their onset or progression by determining the altered levels of the metabolites in serum.