Plasma lipid metabolites as potential biomarkers for identifying individuals at risk of obesity-induced metabolic complications

Lipidomics studies have indicated an association between obesity and lipid metabolism dysfunction. This study aimed to evaluate and compare cardiometabolic risk factors, and the lipidomic profle in adults and older people. A cross-sectional study was conducted with 72 individuals, divided into two s...

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Detalhes bibliográficos
Principais autores: Lyra, Clelia de Oliveira, Bellot, Paula Emília Nunes Ribeiro, Braga, Erik Sobrinho, Omage, Folorunsho Bright, Nunes, Francisca Leide da Silva, Lima, Severina Carla Vieira Cunha, Marchioni, Dirce Maria Lobo, Pedrosa, Lucia Fatima Campos, Barbosa, Fernando, Tasic, Ljubica, Evangelista, Karine Cavalcanti Maurício Sena
Formato: article
Idioma:English
Publicado em: Scientific Reports
Assuntos:
Lipid metabolites
Obesity
Biomarkers
Cardiometabolic risk
Endereço do item:https://repositorio.ufrn.br/handle/123456789/57877
http://dx.doi.org/10.1038/s41598-023-38703-8
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Resumo:Lipidomics studies have indicated an association between obesity and lipid metabolism dysfunction. This study aimed to evaluate and compare cardiometabolic risk factors, and the lipidomic profle 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 nonobese (BMI < 30 kg/m2 ; n= 36). The lipidomic profles were evaluated in plasma using 1 H nuclear magnetic resonance (1 H-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). 1 H-NMR analysis identifed 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—knearest neighbors (kNN) and XGBoost (XGB) were employed to characterize the lipidomic profle 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 identifed 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

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