Combining machine learning and metabolomics to identify weight gain biomarkers

Combining machine learning and metabolomics to identify weight gain biomarkers

Citation Author(s):
Flavia L
Dias-Audibert
UNICAMP
Luiz C
Navarro
UNICAMP
Diogo N
Oliveira
UNICAMP
Carlos F O R
Melo
UNICAMP
Tatiane M
Guerreiro
UNICAMP
Mohamed Z
Dabaja
UNICAMP
Flavia T
Rosa
UniFil
Diego L
Petenuci
UEL
Maria A E
Watanabe
UEL
Licio A
Velloso
UNICAMP
Anderson R
Rocha
UNICAMP
Rodrigo R
Catharino
UNICAMP
Submitted by:
Luiz Claudio Navarro
Last updated:
Thu, 11/28/2019 - 09:25
DOI:
10.21227/k446-fp12
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Dataset Views:
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This dataset was used in the article "Combining machine learning and metabolomics to identify weight gain biomarkers, 2019, Flávia Luísa Dias-Audibert, Luiz Claudio Navarro, Diogo Noin de Oliveira, Jeany 1 Delafiori, Carlos Fernando Odir Rodrigues Melo, Tatiane Melina Guerreiro, Mohamed Ziad Dabaja, Flávia Troncon Rosa, Diego Lima Petenuci, Maria Angelica Ehara Watanabe, Licio Augusto Velloso, Anderson Rezende Rocha, Rodrigo Ramos Catharino" submitted to Frontiers Bioengineering and Biotechnology with review finalized on 22 Nov 2019.

Article 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 withmachine learning analyses to prospect and characterize biomarkers associated with weight gain will  pave the way for elucidating potential therapeutic and prognostic targets.

Instructions: 

WGMSML-Data folder contains the mass spectra input data for the Matlab scripts which are in WGMSML-MATLAB-SourceCode folder. WGMSML-ExecutionLogsAndPlots contains logs and plots generated by the execution of the Matlab code over the input data. Main scripts are enumerated in the order of execution.

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[1] Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino, "Combining machine learning and metabolomics to identify weight gain biomarkers", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/k446-fp12. Accessed: Dec. 14, 2019.
@data{k446-fp12-19,
doi = {10.21227/k446-fp12},
url = {http://dx.doi.org/10.21227/k446-fp12},
author = {Flavia L Dias-Audibert; Luiz C Navarro; Diogo N Oliveira; Carlos F O R Melo; Tatiane M Guerreiro; Mohamed Z Dabaja; Flavia T Rosa; Diego L Petenuci; Maria A E Watanabe; Licio A Velloso; Anderson R Rocha; Rodrigo R Catharino },
publisher = {IEEE Dataport},
title = {Combining machine learning and metabolomics to identify weight gain biomarkers},
year = {2019} }
TY - DATA
T1 - Combining machine learning and metabolomics to identify weight gain biomarkers
AU - Flavia L Dias-Audibert; Luiz C Navarro; Diogo N Oliveira; Carlos F O R Melo; Tatiane M Guerreiro; Mohamed Z Dabaja; Flavia T Rosa; Diego L Petenuci; Maria A E Watanabe; Licio A Velloso; Anderson R Rocha; Rodrigo R Catharino
PY - 2019
PB - IEEE Dataport
UR - 10.21227/k446-fp12
ER -
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. (2019). Combining machine learning and metabolomics to identify weight gain biomarkers. IEEE Dataport. http://dx.doi.org/10.21227/k446-fp12
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino, 2019. Combining machine learning and metabolomics to identify weight gain biomarkers. Available at: http://dx.doi.org/10.21227/k446-fp12.
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. (2019). "Combining machine learning and metabolomics to identify weight gain biomarkers." Web.
1. Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. Combining machine learning and metabolomics to identify weight gain biomarkers [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/k446-fp12
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. "Combining machine learning and metabolomics to identify weight gain biomarkers." doi: 10.21227/k446-fp12