(2024) Type 2 diabetes and susceptibility to COVID-19: a machine learning analysis. BMC Endocrine Disorders. ISSN 14726823 (ISSN)
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Abstract
Background: Type 2 diabetes mellitus (T2DM) was one of the most prevalent comorbidities among patients with coronavirus disease 2019 (COVID-19). Interactions between different metabolic parameters contribute to the susceptibility to the virus; thereby, this study aimed to rank the importance of clinical and laboratory variables as risk factors for COVID-19 or as protective factors against it by applying machine learning methods. Method: This study is a retrospective cohort conducted at a single center, focusing on a population with T2DM. The patients attended the Yazd Diabetes Research Center in Yazd, Iran, from February 20, 2020, to October 21, 2020. Clinical and laboratory data were collected within three months before the onset of the COVID-19 pandemic in Iran. 59 patients were infected with COVID-19, while 59 were not. The dataset was split into 70 training and 30 test sets. Principal Component Analysis (PCA) was applied to the data. The most important components were selected using a ‘sequential feature selector’ and scored by a Linear Discriminant Analysis model. PCA loadings were then multiplied by the PCs’ scores to determine the importance of the original variables in contracting COVID-19. Results: HDL-C, followed by eGFR, showed a strong negative correlation with the risk of contracting the virus. Higher levels of HDL-C and eGFR offer protection against COVID-19 in the T2DM population. But, the ratio of BUN to creatinine did not show any correlation. Conversely, the AIP, TyG index and TG showed the most positive correlation with susceptibility to COVID-19 in such a way that higher levels of these factors increase the risk of contracting the virus. The positive correlation of diastolic BP, TyG-BMI index, MAP, BMI, weight, TC, FPG, HbA1C, Cr, systolic BP, BUN, and LDL-C with the risk of COVID-19 decreased, respectively. Conclusion: The atherogenic index of plasma, triglyceride glucose index, and triglyceride levels are the most significant risk factors for COVID-19 contracting in individuals with T2DM. Meanwhile, high-density lipoprotein cholesterol is the most protective factor. © The Author(s) 2024.
| Item Type: | Article |
|---|---|
| Keywords: | Atherogenic dyslipidemia COVID-19 Insulin resistance Machine learning (ML) Type 2 diabetes mellitus (T2DM) Adult Aged Cholesterol, HDL Diabetes Mellitus, Type 2 Disease Susceptibility Female Glomerular Filtration Rate Humans Iran Machine Learning Male Middle Aged Retrospective Studies Risk Factors SARS-CoV-2 cholesterol creatinine glucose hemoglobin A1c high density lipoprotein cholesterol insulin low density lipoprotein cholesterol triacylglycerol age Article atherogenic index body mass body weight cholesterol blood level clinical feature cohort analysis controlled study coronavirus disease 2019 diabetic patient diagnostic test accuracy study diastolic blood pressure discriminant analysis dyslipidemia estimated glomerular filtration rate extreme gradient boosting fasting blood glucose level gender human infection risk infection sensitivity information processing k nearest neighbor laboratory test logistic regression analysis major clinical study non insulin dependent diabetes mellitus nonhuman onset age pandemic prediction principal component analysis random forest receiver operating characteristic retrospective study risk factor support vector machine systolic blood pressure triglyceride-glucose index urea nitrogen blood level blood disease predisposition epidemiology glomerulus filtration rate Severe acute respiratory syndrome coronavirus 2 |
| Journal or Publication Title: | BMC Endocrine Disorders |
| Journal Index: | Scopus |
| Volume: | 24 |
| Number: | 1 |
| Identification Number: | https://doi.org/10.1186/s12902-024-01758-3 |
| ISSN: | 14726823 (ISSN) |
| Depositing User: | ms soheila Bazm |
| URI: | http://eprints.ssu.ac.ir/id/eprint/34024 |
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