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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers

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Author(s):
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Dias-Audibert, Flavia Luisa [1] ; Navarro, Luiz Claudio [2] ; de Oliveira, Diogo Noin [1] ; Delafiori, Jeany [1] ; Melo, Carlos Fernando Odir Rodrigues [1] ; Guerreiro, Tatiane Melina [1] ; Rosa, Flavia Troncon [3] ; Petenuci, Diego Lima [4] ; Watanabe, Maria Angelica Ehara [4] ; Velloso, Licio Augusto [5] ; Rocha, Anderson Rezende [2] ; Catharino, Rodrigo Ramos [1]
Total Authors: 12
Affiliation:
[1] Univ Estadual Campinas, Sch Pharmaceut Sci, Innovare Biomarkers Lab, Campinas - Brazil
[2] Univ Estadual Campinas, IC, RECOD Lab, Campinas - Brazil
[3] Ctr Univ Filadelfia, Londrina, Parana - Brazil
[4] Univ Estadual Londrina, Ctr Biol Sci, Lab Studies & Applicat DNA Polymorphisms, Londrina, Parana - Brazil
[5] Univ Estadual Campinas, Sch Med Sci, Dept Internal Med, Campinas - Brazil
Total Affiliations: 5
Document type: Journal article
Source: FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY; v. 8, JAN 24 2020.
Web of Science Citations: 0
Abstract

Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health, and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined with machine learning analyses to prospect and characterize biomarkers associated with weight gain will pave the way for elucidating potential therapeutic and prognostic targets. (AU)

FAPESP's process: 19/05718-3 - Determination of metabolic alterations and therapeutic potential of Zika Virus in cancer cells by mass spectrometry and artificial intelligence
Grantee:Jeany Delafiori
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)