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...

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Autores principales:	Ma'mon M Hatmal, Walhan Alshaer, Ismail S Mahmoud, Mohammad A I Al-Hatamleh, Hamzeh J Al-Ameer, Omar Abuyaman, Malek Zihlif, Rohimah Mohamud, Mais Darras, Mohammad Al Shhab, Rand Abu-Raideh, Hilweh Ismail, Ali Al-Hamadi, Ali Abdelhay
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a427b8d7b3e8444a9555ce743fb60d9e
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spelling	oai:doaj.org-article:a427b8d7b3e8444a9555ce743fb60d9e2021-12-02T20:16:58ZInvestigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis.1932-620310.1371/journal.pone.0257857https://doaj.org/article/a427b8d7b3e8444a9555ce743fb60d9e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257857https://doaj.org/toc/1932-6203CD36 (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.Ma'mon M HatmalWalhan AlshaerIsmail S MahmoudMohammad A I Al-HatamlehHamzeh J Al-AmeerOmar AbuyamanMalek ZihlifRohimah MohamudMais DarrasMohammad Al ShhabRand Abu-RaidehHilweh IsmailAli Al-HamadiAli AbdelhayPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257857 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ma'mon M Hatmal Walhan Alshaer Ismail S Mahmoud Mohammad A I Al-Hatamleh Hamzeh J Al-Ameer Omar Abuyaman Malek Zihlif Rohimah Mohamud Mais Darras Mohammad Al Shhab Rand Abu-Raideh Hilweh Ismail Ali Al-Hamadi Ali Abdelhay Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis.
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	article
author	Ma'mon M Hatmal Walhan Alshaer Ismail S Mahmoud Mohammad A I Al-Hatamleh Hamzeh J Al-Ameer Omar Abuyaman Malek Zihlif Rohimah Mohamud Mais Darras Mohammad Al Shhab Rand Abu-Raideh Hilweh Ismail Ali Al-Hamadi Ali Abdelhay
author_facet	Ma'mon M Hatmal Walhan Alshaer Ismail S Mahmoud Mohammad A I Al-Hatamleh Hamzeh J Al-Ameer Omar Abuyaman Malek Zihlif Rohimah Mohamud Mais Darras Mohammad Al Shhab Rand Abu-Raideh Hilweh Ismail Ali Al-Hamadi Ali Abdelhay
author_sort	Ma'mon M Hatmal
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_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_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_sort	investigating the association of cd36 gene polymorphisms (rs1761667 and rs1527483) with t2dm and dyslipidemia: statistical analysis, machine learning based prediction, and meta-analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a427b8d7b3e8444a9555ce743fb60d9e
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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.

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