Nuclear magnetic resonance combined with genetic algorithm with linear discriminant analysis (GA-LDA) is a suitable model for discriminating urinary metabolomic profiles of individuals with glycemic disorders

Metabolomics is essential in identifying biomarkers involved in the development and progression of diabetes. The study aimed to verify the discrimination among metabolites in the urine of individuals with glycemic alterations using Nuclear Magnetic Resonance (1H NMR) and multivariate analysis. The preliminary case-control study was performed with three groups: patients with type 2 diabetes (T2D, n = 16), patients with prediabetes (PD, n = 12), and control group individuals (C, n = 11). We obtained the 24-h urine spectra using 1H NMR from 39 participants. The data were analyzed using Principal Component Analysis (PCA) and supervised analyses. We identified characteristic signals of various metabolites by comparing the chemical shift data with the literature. Our analysis revealed twenty-one distinct metabolic regions, emphasizing citrate, creatinine, glucose, urea, acetate, and glycine. The most pronounced differences were observed in individuals with T2D compared to groups C and PD. We evaluated a range of algorithms to determine the optimal model. The Genetic Algorithm with Linear Discriminant Analysis (GA-LDA) model exhibited remarkable accuracy, sensitivity, and specificity rates of 100% in discriminating between the studied groups. The 1H NMR and the GA-LDA model are promising methods for discriminating urine metabolomic profiles in case-control studies involving individuals with PD and T2D.