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.

ABSTRACT:

Introduction

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.

Methods

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.

Results

We identified characteristic signals of various metaboli

SUBMITTER: Freire de Sousa Papa AW 

PROVIDER: S-EPMC12502107 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature