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Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis
CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516279/ https://www.ncbi.nlm.nih.gov/pubmed/34648514 http://dx.doi.org/10.1371/journal.pone.0257857 |
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author | Hatmal, Ma’mon M. Alshaer, Walhan Mahmoud, Ismail S. Al-Hatamleh, Mohammad A. I. Al-Ameer, Hamzeh J. Abuyaman, Omar Zihlif, Malek Mohamud, Rohimah Darras, Mais Al Shhab, Mohammad Abu-Raideh, Rand Ismail, Hilweh Al-Hamadi, Ali Abdelhay, Ali |
author_facet | Hatmal, Ma’mon M. Alshaer, Walhan Mahmoud, Ismail S. Al-Hatamleh, Mohammad A. I. Al-Ameer, Hamzeh J. Abuyaman, Omar Zihlif, Malek Mohamud, Rohimah Darras, Mais Al Shhab, Mohammad Abu-Raideh, Rand Ismail, Hilweh Al-Hamadi, Ali Abdelhay, Ali |
author_sort | Hatmal, Ma’mon M. |
collection | PubMed |
description | CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen’s kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen’s κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases. |
format | Online Article Text |
id | pubmed-8516279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85162792021-10-15 Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis Hatmal, Ma’mon M. Alshaer, Walhan Mahmoud, Ismail S. Al-Hatamleh, Mohammad A. I. Al-Ameer, Hamzeh J. Abuyaman, Omar Zihlif, Malek Mohamud, Rohimah Darras, Mais Al Shhab, Mohammad Abu-Raideh, Rand Ismail, Hilweh Al-Hamadi, Ali Abdelhay, Ali PLoS One Research Article CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen’s kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen’s κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases. Public Library of Science 2021-10-14 /pmc/articles/PMC8516279/ /pubmed/34648514 http://dx.doi.org/10.1371/journal.pone.0257857 Text en © 2021 Hatmal et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hatmal, Ma’mon M. Alshaer, Walhan Mahmoud, Ismail S. Al-Hatamleh, Mohammad A. I. Al-Ameer, Hamzeh J. Abuyaman, Omar Zihlif, Malek Mohamud, Rohimah Darras, Mais Al Shhab, Mohammad Abu-Raideh, Rand Ismail, Hilweh Al-Hamadi, Ali Abdelhay, Ali Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis |
title | Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis |
title_full | Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis |
title_fullStr | Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis |
title_full_unstemmed | Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis |
title_short | Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis |
title_sort | investigating the association of cd36 gene polymorphisms (rs1761667 and rs1527483) with t2dm and dyslipidemia: statistical analysis, machine learning based prediction, and meta-analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516279/ https://www.ncbi.nlm.nih.gov/pubmed/34648514 http://dx.doi.org/10.1371/journal.pone.0257857 |
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