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

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dc.contributor.authorLima, Severina Carla Vieira Cunha
dc.contributor.authorBellot, Paula Emília Nunes Ribeiro
dc.contributor.authorBraga, Erik Sobrinho
dc.contributor.authorOmage, Folorunsho Bright
dc.contributor.authorNunes, Francisca Leide da
dc.contributor.authorLyra, Clélia Oliveira
dc.contributor.authorMarchioni, Dirce Maria Lobo
dc.contributor.authorPedrosa, Lucia Fatima Campos
dc.contributor.authorBarbosa Júnior, Fernando
dc.contributor.authorTasic, Ljubica
dc.contributor.authorEvangelista, Karine Cavalcanti Maurício Sena
dc.contributor.authorIDhttps://orcid.org/0000-0001-8268-1986pt_BR
dc.date.accessioned2025-02-06T15:55:45Z
dc.date.available2025-02-06T15:55:45Z
dc.date.issued2023
dc.description.resumoLipidomics 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.pt_BR
dc.identifier.citationBELLOT, Paula Emília Nunes Ribeiro; BRAGA, Erik Sobrinho; OMAGE, Folorunsho Bright; NUNES, Francisca Leide da Silva; LIMA, Severina Carla Vieira Cunha; LYRA, Clélia Oliveira; MARCHIONI, Dirce Maria Lobo; PEDROSA, Lucia Fatima Campos; BARBOSA JÚNIOR, Fernando; TASIC, Ljubica; EVANGELISTA, Karine Cavalcanti Maurício Sena. Plasma lipid metabolites as potential biomarkers for identifying individuals at risk of obesity-induced metabolic complications. Scientific Reports, v. 13, p. 11729, 2023. Citações:2|2. DOI: https://doi.org/10.1038/s41598-023-38703-8. Disponível em: https://www.nature.com/articles/s41598-023-38703-8. Acesso em: 29 nov. 2024.pt_BR
dc.identifier.doihttps://doi.org/10.1038/s41598-023-38703-8
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/62531
dc.languageenpt_BR
dc.publisherScientific Reportspt_BR
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/
dc.subjectLipidomicspt_BR
dc.subjectObesitypt_BR
dc.subjectLipid metabolismpt_BR
dc.subjectCardiometabolic risk factorspt_BR
dc.subjectLipidomic profilept_BR
dc.titlePlasma lipid metabolites as potential biomarkers for identifying individuals at risk of obesity-induced metabolic complicationspt_BR
dc.typearticlept_BR

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