Type 2 diabetes and susceptibility to COVID-19: a machine learning analysis.

IF 2.8 3区 医学 Q3 ENDOCRINOLOGY & METABOLISM
Motahare Shabestari, Reyhaneh Azizi, Akram Ghadiri-Anari
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引用次数: 0

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.

2 型糖尿病与 COVID-19 的易感性:机器学习分析。
背景:2型糖尿病(T2DM)是2019年冠状病毒病(COVID-19)患者最常见的合并症之一。不同代谢参数之间的相互作用导致了对病毒的易感性;因此,本研究旨在通过应用机器学习方法,对作为 COVID-19 风险因素或保护因素的临床和实验室变量的重要性进行排序:本研究是一项在单一中心进行的回顾性队列研究,主要针对 T2DM 患者。患者于 2020 年 2 月 20 日至 2020 年 10 月 21 日在伊朗亚兹德的亚兹德糖尿病研究中心就诊。临床和实验室数据是在伊朗 COVID-19 大流行开始前三个月内收集的。59 名患者感染了 COVID-19,59 名患者未感染。数据集被分成 70% 的训练集和 30% 的测试集。对数据进行了主成分分析(PCA)。使用 "序列特征选择器 "选出最重要的成分,并通过线性判别分析模型进行评分。然后将 PCA 负载乘以 PCs 分数,以确定原始变量在 COVID-19 合同中的重要性:结果:高密度脂蛋白胆固醇(HDL-C)与感染病毒的风险呈强负相关,其次是肾小球滤过率(eGFR)。在 T2DM 群体中,较高水平的 HDL-C 和 eGFR 可防止感染 COVID-19。但是,BUN 与肌酐的比值没有显示出任何相关性。相反,AIP、TyG 指数和 TG 与 COVID-19 的易感性呈现出最大的正相关性,即这些因素的水平越高,感染病毒的风险就越大。舒张压、TyG-BMI 指数、MAP、BMI、体重、TC、FPG、HbA1C、Cr、收缩压、BUN 和 LDL-C 与 COVID-19 风险的正相关性分别下降:结论:血浆致动脉粥样硬化指数、甘油三酯血糖指数和甘油三酯水平是 T2DM 患者感染 COVID-19 的最重要危险因素。同时,高密度脂蛋白胆固醇是最重要的保护因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Endocrine Disorders
BMC Endocrine Disorders ENDOCRINOLOGY & METABOLISM-
CiteScore
4.40
自引率
0.00%
发文量
280
审稿时长
>12 weeks
期刊介绍: BMC Endocrine Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of endocrine disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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