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 1H 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.
Keywords: Biomarker; Metabolomics; Nuclear magnetic resonance spectroscopy; Random forest.
Copyright © 2018. Published by Elsevier Ltd.