Pedrosa, Lucia de Fátima CamposPapa, Ângela Waleska Freire de Sousa2023-04-122023-04-122022-11-25PAPA, Ângela Waleska Freire de Sousa. Estudo metabolômico da urina de indivíduos com alterações glicêmicas utilizando ressonância magnética nuclear e análises multivariadas. Orientador: Lúcia de Fátima Campos Pedrosa. 2022. 67f. Dissertação (Mestrado em Nutrição) - Centro de Ciências da Saúde, Universidade Federal do Rio Grande do Norte, Natal, 2022.https://repositorio.ufrn.br/handle/123456789/52118Metabolomics has been applied in the investigation of pathophysiological control of several diseases, such as diabetes. The metabolomic profile is determined in several biological matrices, with urine being one of the preferred ones, due to its reliability and ease of obtaining. The objective of the study was to characterize individuals with glycemic alterations and to analyze urine metabolomics using the Nuclear Magnetic Resonance ( 1H NMR) as a spectroscopic method and multivariate analysis. The study involved three groups: DM2 Group - patients with type 2 diabetes (DM2); the PD Group - patients with prediabetes (PD); and the control group (C) formed by healthy individuals. Participants were characterized in relation to demographic, clinical, lifestyle, glycemic, and lipid profiles. The 24h urine spectra were acquired from NMR, and later, the data were analyzed by Principal Component Analysis (PCA), followed by unsupervised analysis. The set of characteristic signals of the metabolites were identified based on the chemical shift data observed in the 1H NMR spectra, compared with data from the literature. A series of algorithms were tested to verify which model had better accuracy, sensitivity, and specificity. The fasting glucose, HbA1c, and HOMA-IR values of the T2D and PD groups were significantly different from the control. The PD and T2D groups had high waist circumference values. Diet composition did not differ between groups and was adequate in terms of the proportion of macronutrients and inadequate in total fiber. The set of metabolite signals based on the chemical shifts (peaks) resulted in the identification of 21 characteristic regions, among which glycine, urea, glucose, acetate, citrate, and creatinine stand out. Specifically, five signals showed significant differences in the following ppm ranges 3,27; 3,37; 3,75; 5,90 and 9,29. APC scores showed partial separation between groups. The GA-LDA model was the most responsive algorithm for the discrimination of the urine of the groups in terms of accuracy, sensitivity, and specificity. Improving strategies to discriminate metabolites in certain health or disease conditions is important for the clinical application of metabolomics.Acesso AbertoDiabetes Mellitus Tipo 2MetabolômicaUrinaRessonância magnética nuclearmasterThesisCNPQ::CIENCIAS DA SAUDE::NUTRICAO