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Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2

BACKGROUND AND OBJECTIVE: Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship bet...

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Autores principales: Ghazizadeh, Hamideh, Shakour, Neda, Ghoflchi, Sahar, Mansoori, Amin, Saberi-Karimiam, Maryam, Rashidmayvan, Mohammad, Ferns, Gordon, Esmaily, Habibollah, Ghayour-Mobarhan, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258488/
https://www.ncbi.nlm.nih.gov/pubmed/37308948
http://dx.doi.org/10.1186/s12890-023-02495-4
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author Ghazizadeh, Hamideh
Shakour, Neda
Ghoflchi, Sahar
Mansoori, Amin
Saberi-Karimiam, Maryam
Rashidmayvan, Mohammad
Ferns, Gordon
Esmaily, Habibollah
Ghayour-Mobarhan, Majid
author_facet Ghazizadeh, Hamideh
Shakour, Neda
Ghoflchi, Sahar
Mansoori, Amin
Saberi-Karimiam, Maryam
Rashidmayvan, Mohammad
Ferns, Gordon
Esmaily, Habibollah
Ghayour-Mobarhan, Majid
author_sort Ghazizadeh, Hamideh
collection PubMed
description BACKGROUND AND OBJECTIVE: Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease. MATERIAL AND METHOD: This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35–65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection. RESULT: Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection. CONCLUSION: There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.
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spelling pubmed-102584882023-06-13 Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2 Ghazizadeh, Hamideh Shakour, Neda Ghoflchi, Sahar Mansoori, Amin Saberi-Karimiam, Maryam Rashidmayvan, Mohammad Ferns, Gordon Esmaily, Habibollah Ghayour-Mobarhan, Majid BMC Pulm Med Research BACKGROUND AND OBJECTIVE: Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease. MATERIAL AND METHOD: This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35–65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection. RESULT: Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection. CONCLUSION: There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection. BioMed Central 2023-06-12 /pmc/articles/PMC10258488/ /pubmed/37308948 http://dx.doi.org/10.1186/s12890-023-02495-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ghazizadeh, Hamideh
Shakour, Neda
Ghoflchi, Sahar
Mansoori, Amin
Saberi-Karimiam, Maryam
Rashidmayvan, Mohammad
Ferns, Gordon
Esmaily, Habibollah
Ghayour-Mobarhan, Majid
Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2
title Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2
title_full Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2
title_fullStr Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2
title_full_unstemmed Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2
title_short Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2
title_sort use of data mining approaches to explore the association between type 2 diabetes mellitus with sars-cov-2
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258488/
https://www.ncbi.nlm.nih.gov/pubmed/37308948
http://dx.doi.org/10.1186/s12890-023-02495-4
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