Author:
Bellot Paula Emília Nunes Ribeiro,Braga Erik Sobrinho,Omage Folorunsho Bright,da Silva Nunes Francisca Leide,Lima Severina Carla Vieira Cunha,Lyra Clélia Oliveira,Marchioni Dirce Maria Lobo,Pedrosa Lucia Fatima Campos,Barbosa Fernando,Tasic Ljubica,Sena-Evangelista Karine Cavalcanti Maurício
Abstract
AbstractLipidomics studies have indicated an association between obesity and lipid metabolism dysfunction. This study aimed to evaluate and compare cardiometabolic risk factors, and the lipidomic profile in adults and older people. A cross-sectional study was conducted with 72 individuals, divided into two sex and age-matched groups: obese (body mass index—BMI ≥ 30 kg/m2; n = 36) and non-obese (BMI < 30 kg/m2; n = 36). The lipidomic profiles were evaluated in plasma using 1H nuclear magnetic resonance (1H-NMR) spectroscopy. Obese individuals had higher waist circumference (p < 0.001), visceral adiposity index (p = 0.029), homeostatic model assessment insulin resistance (HOMA-IR) (p = 0.010), and triacylglycerols (TAG) levels (p = 0.018). 1H-NMR analysis identified higher amounts of saturated lipid metabolite fragments, lower levels of unsaturated lipids, and some phosphatidylcholine species in the obese group. Two powerful machine learning (ML) models—k-nearest neighbors (kNN) and XGBoost (XGB) were employed to characterize the lipidomic profile of obese individuals. The results revealed metabolic alterations associated with obesity in the NMR signals. The models achieved high accuracy of 86% and 81%, respectively. The feature importance analysis identified signal at 1.50–1.60 ppm (–CO–CH2–CH2–, Cholesterol and fatty acid in TAG, Phospholipids) to have the highest importance in the two models.
Funder
Coordination of Improvement of Higher Education Personnel
INCTBio and Sao Paulo Research Foundation
National Council for Scientific and Technological Development
Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq)
Publisher
Springer Science and Business Media LLC
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