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Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach
According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature sele...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689091/ https://www.ncbi.nlm.nih.gov/pubmed/36428864 http://dx.doi.org/10.3390/diagnostics12112803 |
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author | Morgan-Benita, Jorge Sánchez-Reyna, Ana G. Espino-Salinas, Carlos H. Oropeza-Valdez, Juan José Luna-García, Huizilopoztli Galván-Tejada, Carlos E. Galván-Tejada, Jorge I. Gamboa-Rosales, Hamurabi Enciso-Moreno, Jose Antonio Celaya-Padilla, José |
author_facet | Morgan-Benita, Jorge Sánchez-Reyna, Ana G. Espino-Salinas, Carlos H. Oropeza-Valdez, Juan José Luna-García, Huizilopoztli Galván-Tejada, Carlos E. Galván-Tejada, Jorge I. Gamboa-Rosales, Hamurabi Enciso-Moreno, Jose Antonio Celaya-Padilla, José |
author_sort | Morgan-Benita, Jorge |
collection | PubMed |
description | According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: “Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal” (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214–0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: “Cer(d18:1/24:1) i2”, “PC(20:3-OH/P-18:1)”, “Ganoderic acid C2”, “TG(16:0/17:1/18:1)” and “GPEtn(18:0/20:4)”. |
format | Online Article Text |
id | pubmed-9689091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96890912022-11-25 Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach Morgan-Benita, Jorge Sánchez-Reyna, Ana G. Espino-Salinas, Carlos H. Oropeza-Valdez, Juan José Luna-García, Huizilopoztli Galván-Tejada, Carlos E. Galván-Tejada, Jorge I. Gamboa-Rosales, Hamurabi Enciso-Moreno, Jose Antonio Celaya-Padilla, José Diagnostics (Basel) Article According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: “Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal” (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214–0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: “Cer(d18:1/24:1) i2”, “PC(20:3-OH/P-18:1)”, “Ganoderic acid C2”, “TG(16:0/17:1/18:1)” and “GPEtn(18:0/20:4)”. MDPI 2022-11-15 /pmc/articles/PMC9689091/ /pubmed/36428864 http://dx.doi.org/10.3390/diagnostics12112803 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Morgan-Benita, Jorge Sánchez-Reyna, Ana G. Espino-Salinas, Carlos H. Oropeza-Valdez, Juan José Luna-García, Huizilopoztli Galván-Tejada, Carlos E. Galván-Tejada, Jorge I. Gamboa-Rosales, Hamurabi Enciso-Moreno, Jose Antonio Celaya-Padilla, José Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach |
title | Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach |
title_full | Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach |
title_fullStr | Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach |
title_full_unstemmed | Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach |
title_short | Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach |
title_sort | metabolomic selection in the progression of type 2 diabetes mellitus: a genetic algorithm approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689091/ https://www.ncbi.nlm.nih.gov/pubmed/36428864 http://dx.doi.org/10.3390/diagnostics12112803 |
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