Research On The Correlation Between Severity Of COVID-19 And Common Comorbidities And Potential Related Genes

Objective1.The clinical information of COVID-19 patients,including age,gender and common comorbidities,was retrospectively analyzed to explore the correlation between the above clinical information and the severity of COVID-19.In order to find the risk factors of severe COVID-19,and provide evidence for early clinical intervention,improvement of treatment effect and reduction of mortality.2.With the help of bioinformatics technology,we will search for genes associated with COVID-19 and common comorbidities,as well as the relationship between the expression and polymorphism of genes associated with severe COVID-19,and analyze the association mechanism between genes associated with COVID-19,so as to provide reference for further research to clarify the indicator genes of COVID-19severity.This will provide evidence for further revealing the mechanism of association between severe COVID-19 and common comorbidities,and contribute to screening of high-risk groups,vaccine and drug development.Methods1.Retrospective analysis of correlation between COVID-19 and comorbidities1.1 Analysis of basic clinical information of COVID-19 patientsThe age,sex,severity of COVID-19,comorbidities and other distribution characteristics of 1490 COVID-19 patients in a hospital in Wuhan from January 28 to April 1,2020 were retrospectively analyzed.1.2 Comparative analysis of gender,age and comorbidities of COVID-19 patientsFor 1490 COVID-19 patients,the composition of age,comorbidities and disease severity of patients of different genders was analyzed,and the composition of comorbidities of patients of different severity was analyzed.Significance tests were performed by Mann-Whitney U andχ~2 tests.1.3 Intergroup and intra-group comparative analysis of different age groups of COVID-19 patientsCOVID-19 patients were divided into four groups according to who age classification criteria:children,young adults,middle-aged and elderly.The gender,severity of disease and composition of comorbidities among patients of different age groups were compared and analyzed,and the severity of disease and composition of comorbidities among male and female patients of different age groups were compared and analyzed.χ~2 test was used for significance test.1.4 Analysis of risk factors for COVID-19The independent risk factors for severe COVID-19 were identified by binary Logistic multifactor regression analysis.2.Bioinformatics analysis of COVID-19 potential associated genes with common comorbiditiesNCBI,OMIM,KMDB/Mutation View and Disgenet databases were used to collect the genes related to COVID-19 and its three common comorbidities,including hypertension,DM and CAD.The intersection genes of COVID-19 and the three comorbidities were obtained by using the online platform of We Chat.The GO annotation and KEGG pathway analysis of the intersection genes were performed by loading Bioconductor package with R software.Mapping intersection genes in key pathways using KEGG Mapping tool.The STRING database was used for PPI analysis.Using Cytoscape software to draw PPI network visualization graphics and Identification of key genes in PPI networks using Cyto Hubba to analyze the association mechanism between associated genes and severity COVID-19.Results1.Retrospective analysis of correlation between COVID-19 and comorbidities1.1 Analysis of basic clinical information of COVID-19 patientsThe 1490 COVID-19 patients included ranged in age from 9 to 100 years,with a median age of 60 years,and 57.7%were over 60 years old.Female and male accounted for 58.9%and 41.1%,respectively.Non-severe and severe COVID-19accounted for 97.2%and 2.8%,respectively.There were 403 cases of COVID-19patients with comorbidities,and the three most common comorbidities were hypertension(254),diabetes(114),and coronary artery disease(65).1.2 Comparative analysis of gender,age and comorbidities of COVID-19 patientsThe median age of both male and female patients was 60 years old,P=0.743,and the difference was not statistically significant.The proportion of severe COVID-19 in the female group and the male group was 2.2%and 3.6%,respectively,P=0.097,with no statistically significant difference.Among the top 10 comorbidities,there was no gender difference in the other 9 diseases except prostatic hyperplasia.There was no significant difference in gender composition between severe and non-severe groups.Median age in the severe group was 72 years,significantly higher than 59 years in the non-severe group.The proportion of basic diseases in non-severe group and severe group was 25.9%and 65.9%,respectively,the difference was statistically significant(χ~2=32.178,P<0.001).The proportion of hypertension,DM and CAD in severe group was significantly higher than that in non-severe group,the difference was statistically significant(P<0.05).1.3 Intergroup and intra-group comparative analysis of different age groups of COVID-19 patientsComparison of gender,severity and composition of comorbidities among different age groups:there was no difference in composition of male and female among young(251,16.8%),middle-aged(484,32.5%)and elderly(750,50.3%)groups.There were no serious cases in the young group,few serious cases in the middle-aged group,and the proportion of serious cases in the elderly group was significantly higher than that in the first two groups(P<0.001).The proportion of COVID-19 patients with comorbidities was as follows:elderly group(34.8%)>middle-aged group(24.0%)>young group(10.4%),P<0.001,and the difference was statistically significant.Comparison of gender,severity and composition of comorbidities within each age group:no severe cases were found in the youth group,which could not be analyzed.In the middle-aged group,the proportion of men with severe COVID-19was significantly higher than that of women(P=0.021);The proportion of hypertension in male(17.7%)was significantly higher than that in female(10.6%)(P=0.025).In the elderly group,there was no significant difference in the severity of COVID-19 and the composition of comorbidities between men and women.The proportion of serious patients with comorbidities in middle-aged and elderly groups was significantly higher than that in non-serious patients.1.4 Analysis of risk factors for COVID-19Risk factors influencing the severity of COVID-19 were analyzed by binary Logistic regression.The results showed that increased age and hypertension were independent risk factors for severe COVID-19.The OR value(95%CI)of age was1.07(1.04,1.10),P<0.01,and the OR value(95%CI)of hypertension was 2.19(1.04,4.63),P=0.039.2.Bioinformatics analysis of COVID-19 potential associated genes with common comorbidities2.1 Obtaining results of intersection genesThe number of genes related to COVID-19,hypertension,diabetes and coronary artery disease collected by using the database was 150,733,743 and 692,respectively.A total of 11 genes were identified as intersections between COVID-19 and three common comorbidities,including TLR4,NLRP3,MBL2,IL6,IL1RN,IL1B,CX3CR1,CCR5,AGT,ACE,and F2.2.2 GO and KEGG enrichment analysis resultsGO and KEGG enrichment analysis was performed on the 11 overlapped genes above,and the results showed that related genes were enriched to 302 GO items and29 signal pathways.GO enrichment results showed that the associated genes were mainly involved in acute inflammatory response,regulatory transcription factors,cytokine activity,JAK-STAT receptor signaling pathway and other immune and metabolic regulatory processes.KEGG enrichment results showed that the associated genes were mainly involved in the processes of coronavirus diseases-COVID-19,cardiovascular diseases,immune diseases,endocrine and metabolic diseases,etc.2.3 PPI network analysis resultsThe PPI network was generated by the STRING database with a total of 11nodes and 20 edges.Cyto Hubba analysis screened IL6 and AGT as the core genes.IL6 and AGT were in the central position in PPI and connected with 8 and 5intersected genes,respectively.Conclusions1.Retrospective analysis of correlation between COVID-19 and comorbiditiesThrough a retrospective analysis of the correlation between COVID-19 and comorbidities,the following conclusions were drawn in this study.1.1 Gender may not be associated with age,severity and comorbidities of COVID-19.1.2 The severity of COVID-19 may be related to age and comorbidities.With age increasing,the comorbidities of patients with COVID-19 may be an important factor affecting their severity.Elderly patients with comorbidities may be more susceptible to severe COVID-19.1.3 Age increase and hypertension are independent risk factors for COVID-19severity.1.4 There is no difference in the severity of COVID-19 between other middle-aged groups and the youth group,and the sample study needs to be further expanded.1.5 There is no evidence in this study that basic diseases except hypertension are independent risk factors for severe COVID-19,which may be related to the higher proportion of hypertension in the included cases compared with other basic diseases,which needs further verification by large samples.2.Bioinformatics analysis of COVID-19 potential associated genes with common comorbiditiesA total of 11 overlapping genes of COVID-19 and its three most common comorbidities,including TLR4,NLRP3,MBL2,IL6,IL1RN,IL1B,CX3CR1,CCR5,AGT,ACE,and F2,are potential predictive genes of COVID-19 severity,and are helpful for detection of high-risk populations.In addition,the analysis of the associated genes may affect the severity of COVID-19 by involving the renin-angiotensin system,regulation of the activity of cellular inflammatory factors,coagulation,and immune recognition processes,which is conducive to the prevention,control and treatment of COVID-19.