Association of Alleles of Human Leukocyte Antigen Class II Genes and Severity of COVID-19 in Patients of the ‘Red Zone’ of the Endocrinology Research Center, Moscow, Russia
by
, , , , ,
Ekaterina Troshina
1, 
Marina Yukina
1,*
,
Nurana Nuralieva
1,
Evgeny Vasilyev
1,
Olga Rebrova
1,
Ravida Akhmatova
1,
Anna Ikonnikova
2,
Elena Savvateeva
2,
Dmitry Gryadunov
2
,
Galina Melnichenko
1 and
Natalia Mokrysheva
1 1
Endocrinology Research Centre, Ministry of Health of the Russian Federation, 117036 Moscow, Russia
2
Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
*
Author to whom correspondence should be addressed.
Diseases 2022, 10(4), 99; https://doi.org/10.3390/diseases10040099
Submission received: 23 September 2022
/
Revised: 30 October 2022
/
Accepted: 31 October 2022
/
Published: 2 November 2022
(This article belongs to the Special Issue COVID-19 and Global Chronic Disease II)
Abstract
:The aim of this study was to assess the correlations of clinical features of patients with moderate and severe courses of COVID-19, comorbidity (endocrine, autoimmune, cardiovascular, oncological, and pulmonary diseases), and alleles of the HLA class II system genes. One hundred COVID-19 patients hospitalized in the Endocrinology Research Centre, Moscow, Russia, were analyzed for age, gender, smoking, comorbidity, and invasive mechanical ventilation. Computer tomography was used to assess the severity of the disease. HLA-DRB1, HLA-DQA1, and HLA-DQB1 alleles were identified in samples from 100 patients and samples from 327 randomly selected individuals collected in the prepandemic period (control group). There was no association of gender, age, weight, body mass index, smoking, and comorbidity with the severity of COVID-19. Allele DQB1*06:02-8 was more common in patients (p < 0.00005), and DQB1*06:01 and DQB1*05:03 were more common in the control group (p < 0.00005, and p = 0.0011, respectively). DQB1*06:02-8 can probably be considered as predisposing to moderate and severe COVID-19, and DQB1*06:01 can be considered as protective. No association of these alleles with comorbidity was found. Our results suggest that carriers of predisposing alleles, with cardiovascular and non-autoimmune endocrine diseases, should take more stringent preventive measures, and if infected, a more aggressive COVID-19 treatment strategy should be used.
1. Introduction
Certain alleles of genes of the human leukocyte antigen (HLA) system are known to increase the risk of pathological activation of the immune system with the development of autoimmune disorders. Alleles of the HLA system genes have also been found to be associated with the risk of developing cardiovascular, pulmonary [1], and cancer diseases, as well as non-autoimmune endocrinopathies [2]. Furthermore, genomics studies have demonstrated strong associations between polymorphisms in major histocompatibility complex (MHC) and HLA genes with susceptibility to infectious diseases [3]. Different HLA genotypes, depending on the degree of induction of the T-cell response, are believed to affect the course of viral infection [4].
Recent data suggest that appropriate innate and adaptive T cell-mediated humoral and cellular immune responses could help the elimination of SARS-CoV-2, which in most cases coincides with clinical recovery [5,6]. On the other hand, an excessive cell-mediated and dysregulated innate and adaptive immune response can lead to an aggressive inflammatory reaction with the release of large amounts of pro-inflammatory cytokines. This condition, known as ‘cytokine storm’—resulting from the excessive production of cytokines by immune cells such as the innate dendritic cells, macrophages, natural killer cells, and the adaptive T and B cells—directly correlates with lung injury, adult respiratory distress syndrome, and multiple organ failure, and leads to an unfavorable prognosis [7,8]. The basic predictive risk factors for cytokine storms in COVID-19 patients include male gender, lactate dehydrogenase level, age over 40 years, the positive test result for replicative SARS-CoV-2 RNA, absolute lymphocyte count, D-dimer and ferritin levels, dynamics in the National Early Warning Score (NEWS) score, and plasma Interleukin 6IL-6 concentration [9]. However, immunogenetic variation in humans could also be an important target for clinical diagnosis and therapeutic intervention of COVID-19 [10].
Determining the effect of HLA gene variations on the course of COVID-19 may help to identify individuals at a higher risk, as well as to understand the different immune responses to the viral infection in the population. Current data about the association between particular HLA alleles/haplotypes and susceptibility or protection against COVID-19 are rather contradictory [4,11,12]. Numerous studies have reported alleles of protection or susceptibility to COVID-19 [13]. In silico analysis of the binding affinity across HLA phenotypes and viral peptides showed that harbors of HLA-B*46:01, HLA-A*11:01, -B*51:01, -C*14:02, HLA-DRB1*15:01, -DQB1*06:02, and -B*27:07 alleles are more vulnerable to SARS-CoV-2, and these mutations predispose patients to a worse disease course [14]. Shkurnikov et al. developed a risk score (RS) which was associated with the ability to present SARS-CoV-2 peptides by the HLA class I molecule set of an individual. An assessment of RS in 111 deceased patients with COVID-19 (Moscow, Russia) and 428 volunteers showed, that the presence of HLA-A*01:01 allele was associated with high risk, while HLA-A*02:01 and HLA-A*03:01 mainly contributed to low risk [15]. Conversely, the study of HLA Class I alleles in the Iranian population did not reveal any association of alleles with COVID-19 severity [16].
Given the more frequent development of severe forms of COVID-19 in patients with an aggravated history (including endocrine, cardiovascular, autoimmune [17], cancer [18], and pulmonary diseases), there may be common predisposing HLA alleles in these conditions. On the other hand, the absence of severe forms of pneumonia in some patients with comorbidities suggests that they have protective alleles. Thus, in a cohort of 306 patients with cancer, mild/moderate COVID-19 was observed in most cases (71%) [19]. It should be taken into account that other factors, such as old age, gender, and obesity, are also associated with an increased severity of the disease [20,21,22,23].
The aim of this study was to assess the correlations of clinical features of patients with moderate and severe courses of COVID-19, comorbid pathology (endocrine, autoimmune, cardiovascular, oncological, and pulmonary diseases), and alleles of the HLA class II system genes.
2. Materials and Methods
2.1. Patients and Clinical Data
A retrospective study of COVID-19 patients who were hospitalized at the Endocrinology Research Centre in 2020 (detailed patients’ characteristics and procedures are available in the Supplementary Table S1) was conducted. All patients included in the study permanently resided in Moscow or Moscow Region. No other inclusion and exclusion criteria were applied; consecutive sampling was used. The only exclusion criterion was the refusal to participate in the study. The final analysis includes 100 patients.
The diagnosis of COVID-19 was based according to Russian recommendations [24] on radiological signs [25] according to computer tomography (CT) carried out on a Revolution CT (General Electric, Boston, MA, USA) at admission. Repeated CTs were carried out in the case of disease progression and before discharge (1 to 6 times per patient: 1 time in 20% (n = 20) of patients; 2 times in 51% (n = 51) of patients; 3 times in 22% (n = 22) of patients; 4 times in 6% (n = 6) of patients; 6 times in 1% (n = 1) of patients).
The maximum lung lesion volume during the hospitalization was assessed by CT and qualified (by an expert) as CT-1 for a lesion volume <25% of the lung volume, CT-2 for 25–50% lesion, CT-3 for 51–75% lesion, and CT-4 for >75% lesion [25]. CT-1 and CT-2 cases were considered as a moderate disease, and CT-3 and CT-4 as a severe disease.
In addition to CT signs, all patients had other signs of coronavirus infection before admission, such as a positive test result for SARS-CoV-2 viral RNA in swabs from the nasopharynx and oropharynx confirmed by isothermal amplification (EMG LLC, Innopolis City, Russia) and/or an increase in C-reactive protein (CRP) above 5 mg/L and/or SpO2 below 95% using an Armed YX300 pulse oximeter. The molecular assay used in this study to detect SARS-CoV-2 RNA was validated by comparison with conventional reference real-time PCR (SARS-CoV-2/SARS-CoV Multiplex, DNA-Technology, Russia), with a sensitivity and specificity of 99.0%. The diagnosis of COVID-19 was based on CT data and at least one of the above confirmatory tests.
Two clinical outcomes of COVID-19 were recorded: fatal (death) or improvement. Improvement was defined as the appearance of at least one of the following signs: normalization of CRP (5 mg/L or less), reduction in CT lung lesions, and increase in SpO2 (95% or more). After improvement, the patients were discharged from the hospital; the treatment was continued on an outpatient basis.
The following patient characteristics were analyzed: age, gender, smoking (a patient was considered a smoker if he smoked at least 100 cigarettes during his life and continues to smoke currently [26]), weight, body mass index (BMI), comorbidity according to the patient, and medical documents—endocrine (ED), cardiovascular (CVD), autoimmune (AID), pulmonary (PD), and oncological (OD) diseases; invasive mechanical ventilation (IMV) during hospitalization.
2.2. HLA Typing
HLA-DRB1, HLA-DQA1, and HLA-DQB1 alleles were identified in 100 patients and 327 randomly selected individuals in Moscow and Moscow Region (population control group, samples were collected in the prepandemic period and used only for population studies of alleles of HLA class II genes). Genomic DNA was isolated from venous blood lymphocytes. Low-resolution typing of HLA class II (DRB1, DQA1, DQB1) genes was performed using a DT-96 Real-Time PCR thermocycler (DNA-Technology, Russia) and the HLA-DRB1, HLA-DQA1, HLA-DQB1 Real-Time PCR genotyping kits (DNA-Technology, Russia) according to the manufacturer’s instructions. These tests have passed all trials and were approved for in vitro diagnostic by the Federal service for surveillance in the healthcare of the Russian Federation. The diagnostic specificity of the assays, declared by the manufacturer, is 98.7–100% in comparison with the reference sequencing. Genotyping for all loci was performed at the level of allele groups, not specific alleles.
2.3. Statistical Analysis
The Statistica v.13 software (TIBCO Software, USA) was used for data analysis. The descriptive statistics are medians (Me), minimum and maximum values for quantitative data, and absolute and relative frequencies for categorical data. The comparison of independent groups was performed by the Mann–Whitney (U) and Kruskal–Wallis (H) tests for quantitative data, Pearson’s Chi-Square, Yates’ adjusted Chi-Square (χ2), and two-sided Fisher’s exact test (FET) for categorical data. The odds ratio (OR) and 95% CI were calculated using statistical tools [27,28]. The basic significance level was 0.05, and the Bonferroni correction was applied if necessary. p-values between the Bonferroni-corrected level and 0.05 were considered as indicators of statistical tendency.
3. Results
Among the patients’ contingent, there were 12% of cases of CT-4, 47% of CT-3, 34% of CT-2, and 7% of CT-1. There were no significant differences in age and gender in the CT groups. The age of females was higher than that of males (p = 0.00084, U). There was no significant age difference between severe and moderate COVID-19 (Table 1). A total of 45% of patients were diagnosed with obesity; however, there was no significant difference in BMI and body weight in severe and moderate disease cases. There were also no significant differences between the groups in the frequencies of AID, OD, CVD, PD, and non-autoimmune ED.
IMV was performed in 3% of patients (n = 3): two females 75 and 83 years old with CT-4, and one male 67 years old with CT-3. All cases of the disease that required this type of treatment ended in death. There were no lethal outcomes during hospitalization among patients who did not undergo IMV.
The results of HLA-typing (absolute and relative allele frequencies in the entire cohort and in groups) are presented in Table 2. There were no significant differences in HLA class II gene alleles in patients with moderate–severe and severe courses of the Coronavirus infection. The following alleles of the HLA class II genes had frequencies greater than 20%: DQA1*05:01 (30%), DQB1*03:01 (21.5%), and DQB1*06:02-8 (21%) (the corresponding rows are highlighted in bold in Table 2).
Then, a comparative analysis of the frequency of alleles in the examined cohort and in a population control group of individuals from Moscow and the Moscow Region was carried out (Table 3). In the cohort of patients, the frequencies of three alleles of the DQB1 gene differ from those in the control group: the DQB1*06:02-8 allele was more frequent (RR 5.166, 95% CI (4.525; 5.166)), and the DQB1*06:01 and DQB1*05:03 alleles were less frequent. There were no significant differences in the frequencies of other alleles studied.
The results of the analysis of clinical and genetic characteristics of patients carrying the DQB1*05:03, DQB1*06:01, and DQB1*06:02-8 alleles are shown in Table 4. Patients with a suspected predisposing allele in more than 50% of the cases experienced severe COVID-19, CVD, and non-endocrine AID. One patient with both DQB1*06:01 and DQB1*06:02-8 alleles and moderate COVID-19 (CT-2) at the age of 48 had no comorbidities. The DRB1*15-DQA1*01:02-DQB1*06:02-8 was the most common haplotype in patients with the DQB1*06:02-8 allele.
The association of the COVID-19 severity with allele combinations was checked. To test this hypothesis, the absolute and relative frequencies of HLA haplotypes in the groups were compared. In the general cohort of patients, the predominance of any haplotype was not found (Table 5). The following HLA haplotypes had frequencies 10% or more: DRB1*01-DQA1*01:01-DQB1*05:01 (14%; serotype DR1), DRB1*15-DQA1*01:02-DQB1*06:02-8 (13.5%; serotype DR2), DRB1*11-DQA1*05:01-DQB1*03:01 (12.5%; serotype DR5), and DRB1*03(DRB1*17)- DQA1*05:01-DQB1*02 (10%; serotype DR17).
4. Discussion
The median age of hospitalized patients with moderate to severe COVID-19 was 58.5 years. Unlike other authors [29,30,31], we found no age correlation with the severe course of the infection. A slight predominance of males with COVID-19 [32,33,34], most likely associated with smoking, to which they are traditionally more predisposed [35], was also noted by other authors. No significant effect of gender on the severity of COVID-19 was found unlike the result of another Russian study, which included more patients [9].
As in other studies [15,36], the majority of patients in our study had comorbidities, the most common of which in the general cohort were CVD (64% of cases), non-autoimmune ED (59%), and obesity (45%). However, there was no statistically significant effect of the presence of CVD and non-autoimmune ED on the severity of the course of coronavirus infection. Patients with a severe course of the disease were characterized by higher body weight and BMI, which was also emphasized by other authors [37]. However, this correlation was not significant. It is noteworthy that all patients who underwent IMV (all of these cases ended in death) were obese (BMI 32–37.5 kg/m2). However, there was no significant effect of BMI on the severity of the course of coronavirus infection. In contrast to the study by Richardson et al. [38] which indicated a high incidence of type 2 diabetes mellitus in hospitalized patients with COVID-19 (33.8%), we detected this disease in only 12% of the patients.
Less frequently (26%), patients with COVID-19 reported a burdened history of PD without the prevalence of any particular nosology. It is noteworthy that the incidence of tuberculosis in our study was higher (4%) than the incidence of this disease (1%) in the larger cohort (12,513 patients with coronavirus infection) examined by Sy et al. [39]. This may be due to the smaller size of our sample. For this reason, no correlation was found between the presence of PD and the severity of coronavirus infection, unlike other studies [40,41].
OD, compared with CVD, non-autoimmune ED, and PD, occurred less frequently–in 5% of cases. Comparable data were reported by other authors: 3.9% in the study by Singh et al. [36] and 6% in the study by Richardson et al. [38]. The prevalence of the frequency of any OD in the general cohort, as well as its impact on the severity of COVID-19, was not found.
Concomitant AID was detected in 16% of patients. The results of our study did not confirm the earlier assumption about the development of more severe forms of coronavirus infection in patients with AID (OR 0.874, p > 0.05), which is consistent with the data of Murtas et al. [42]. These authors reported a low frequency of AID in COVID-19 (3.2%), as well as in other respiratory diseases (3.9%).
For the first time in Russia, the association of the frequency of HLA class II gene polymorphisms with the severity of COVID-19 was investigated (another Russian study [15] evaluated the frequency of HLA class I gene polymorphisms). When comparing the frequency of occurrence of HLA class II alleles in patients with moderate and severe coronavirus infection, no significant differences were found. In the general cohort, the most common allele of the DQA1 gene was DQA1*05:01, while those of the DQB1 gene were DQB1*02, DQB1*03:01, DQB1*05:01, and DQB1*06:02-8. As for the DRB1 gene, no predominance of the frequency of any allele was found, but a high frequency of the DRB1*15 and DRB1*01 alleles was noted. When comparing the frequency of occurrence of alleles in the examined cohort and a population control group, a significant increase in frequency was confirmed only for the DQB1*06:02-8 allele (p < 0.00005). This allele has been shown to be associated with narcolepsy and multiple sclerosis [43], but these pathologies were not found in the examined cohort.
In contrast, the incidence of the DQB1*06:01 (p < 0.00005) and DQB1*05:03 (p = 0.0011) alleles was lower in the group of patients with COVID-19 compared to the population control group. It is noteworthy that these alleles have been shown to be associated with a predisposition to hepatitis B [44]. At the same time, the DQB1*06:02-8 allele is protective against HIV infection (providing a more effective presentation of the CD4+ virus epitope to T-lymphocytes [45]). It is likely that the process of presentation of SARS-CoV-2 antigens, due to the features of the molecular structure of the virus, differs from that of hepatitis B and HIV [4].
Thus, DQB1*06:02-8 can probably be considered as predisposing to moderate and severe COVID-19, and DQB1*06:01 can be considered as protective. Moreover, the DQB1*06:02-8 allele was one of the most frequent among other alleles of the DQB1 gene in the examined cohort of patients; it was also significantly more common in the examined cohort than in the population control group of Moscow and the Moscow Region. Since the DQB1*05:03 allele was detected only in one patient with severe COVID-19, it was impossible to draw any definite conclusion about its protective effect. Currently, we can only state that this allele has a decreased frequency of occurrence in severe COVID-19.
Our results suggest that carriers of alleles that may be associated with a high risk of developing moderate and severe forms of the disease, who have comorbidities such as CVD, non-autoimmune ED, and, especially, obesity, should take more stringent preventive measures, be considered as priority candidates for vaccination, and if infected, a more aggressive COVID-19 treatment strategy should be used. In order to confirm the presented data, as well as to continue the study of the clinical and genetic characteristics of coronavirus infection, further research on patients with mild and asymptomatic COVID-19 is required. When comparing the frequency of occurrence of HLA haplotypes in the groups, no significant differences were found. In the general cohort of patients, the following HLA haplotypes were more common: DRB1*01-DQA1*01:01-DQB1*05:01 (serotype DR1), DRB1*15-DQA1*01:02-DQB1*06:02-8 (serotype DR2), DRB1*11-DQA1*05:01-DQB1*03:01 (serotype DR5), DRB1*03-DQA1*05:01-DQB1*02 (serotype DR17), and DRB1*07-DQA1*02:01-DQB1*02 (serotype DR7). However, it was not possible to compare the haplotypes of the cohort of patients with those of a population control group. According to the literature, the DR1 serotype is associated with a high risk of HIV infection [45], DR17 determines the predisposition to type 1 diabetes mellitus, Sjögren’s syndrome, Graves’ disease [46], celiac disease [47], and DR5 determines the predisposition to juvenile idiopathic arthritis [48]. The DR7 serotype is predisposing to celiac disease [47] and protective against Graves’ disease [46], while the DR2 serotype protects against type 1 diabetes mellitus [49]. In the patients cohort we examined, there were no cases of the above diseases.
The association between HLA and COVID-19 deserves investigations on larger patient cohorts. The results of this study are based on observational data and analysis of patients in the “red zone” of the National Endocrinology Research Center, Moscow, Russia. This sample, of course, cannot characterize the population of patients with coronavirus infection as a whole. Additionally, the limitation of the present study is genotyping at the level of allele groups, not specific alleles. Moreover, only limited sets of HLA alleles as genetic markers of COVID-19 predisposition have been studied. More research is needed to access genetic changes that may play a deleterious role in the clinical course of COVID-19 patients, including analysis of the ACE2 and APOE genes, major histocompatibility complex class I genes, Toll-like receptors, and others.
5. Conclusions
There was no association of sex, age, weight, body mass index, smoking, or comorbidity with COVID-19 severity in the patient cohort studied. Allele DQB1*06:02-8 was more common in patients (p < 0.00005), and DQB1*06:01 and DQB1*05:03 were more common in the control group (p < 0.00005, and p = 0.0011, respectively). DQB1*06:02-8 can probably be considered as predisposing to moderate and severe COVID-19, and DQB1*06:01 can be considered as protective. No association of these alleles with comorbidity was found. Further research on patients with mild and asymptomatic COVID-19 is required.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diseases10040099/s1, Table S1: Clinical characteristics, anamnestic data and results of HLA typing for COVID-19 patients included in the study.
Author Contributions
Methodology, writing—review and editing, validation, supervision, project administration, E.T.; methodology, formal analysis, data curation, writing—original draft preparation, review and editing, conceptualization, M.Y.; data curation, formal analysis, writing—original draft preparation, N.N.; methodology, investigation, writing—review and editing, validation, E.V.; methodology, formal analysis, writing—review and editing, validation, O.R. and E.V.; data curation, writing—original draft preparation, R.A.; statistical analysis, validation, A.I.; resources, E.S.; writing—review and editing, funding acquisition, D.G.; validation, supervision, project administration, G.M. and N.M. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by Foundation for Scientific and Technological Development of Yugra (agreement No 2022-05-01/2022).
Institutional Review Board Statement
This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the local ethics committee of the Endocrinology Research Centre, Ministry of Health of Russia, Moscow (protocol number 6, date of approval 30 April 2022).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the patients for the publication of this article.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Faner, R.; Nuñez, B.; Sauleda, J.; Garcia-Aymerich, J.; Pons, J.; Crespí, C.; Milà, J.; González, J.R.; Maria Antó, J.; Agusti, A.; et al. HLA distribution in COPD patients. COPD 2013, 10, 138–146. [Google Scholar] [CrossRef] [PubMed]
- Motala, A.A.; Busson, M.; Al-Harbi, E.M.; Khuzam, M.A.; Al-Omari, E.M.; Arekat, M.R.; Almawi, W.Y. Susceptible and protective human leukocyte antigen class II alleles and haplotypes in bahraini type 2 (non-insulin-dependent) diabetes mellitus patients. Clin. Diagn. Lab. Immunol. 2005, 12, 213–217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Klebanov, N. Genetic Predisposition to Infectious Disease. Cureus 2018, 10, e3210. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Migliorini, F.; Torsiello, E.; Spiezia, F.; Oliva, F.; Tingart, M.; Maffulli, N. Association between HLA genotypes and COVID-19 susceptibility, severity and progression: A comprehensive review of the literature. Eur. J. Med. Res. 2021, 26, 84. [Google Scholar] [CrossRef]
- Feng, C.; Shi, J.; Fan, Q.; Wang, Y.; Huang, H.; Chen, F.; Tang, G.; Li, Y.; Li, P.; Li, J.; et al. Protective humoral and cellular immune responses to SARS-CoV-2 persist up to 1 year after recovery. Nat. Commun. 2021, 12, 4984. [Google Scholar] [CrossRef] [PubMed]
- Schietzel, S.; Anderegg, M.; Limacher, A.; Born, A.; Horn, M.P.; Maurer, B.; Hirzel, C.; Sidler, D.; Moor, M.B. Humoral and cellular immune responses on SARS-CoV-2 vaccines in patients with anti-CD20 therapies: A systematic review and meta-analysis of 1342 patients. RMD Open 2022, 8, e002036. [Google Scholar] [CrossRef] [PubMed]
- Ye, Q.; Wang, B.; Mao, J. The pathogenesis and treatment of the ‘Cytokine Storm’ in COVID-19. J. Infect. 2020, 80, 607–613. [Google Scholar] [CrossRef]
- Mangalmurti, N.; Hunter, C.A. Cytokine storms: Understanding COVID-19. Immunity 2020, 53, 19–25. [Google Scholar] [CrossRef]
- Shcherbak, S.G.; Anisenkova, A.Y.; Mosenko, S.V.; Glotov, O.S.; Chernov, A.N.; Apalko, S.V.; Urazov, S.P.; Garbuzov, E.Y.; Khobotnikov, D.N.; Klitsenko, O.A.; et al. Basic Predictive Risk Factors for Cytokine Storms in COVID-19 Patients. Front. Immunol. 2021, 12, 745515. [Google Scholar] [CrossRef]
- Sagris, M.; Theofilis, P.; Antonopoulos, A.S.; Oikonomou, E.; Tsioufis, K.; Tousoulis, D. Genetic Predisposition and Inflammatory Inhibitors in COVID-19: Where Do We Stand? Biomedicines 2022, 10, 242. [Google Scholar] [CrossRef]
- Kolin, D.A.; Kulm, S.; Christos, P.J.; Elemento, O. Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank. PLoS ONE 2020, 15, e0241264. [Google Scholar] [CrossRef] [PubMed]
- Gutiérrez-Bautista, J.F.; Rodriguez-Nicolas, A.; Rosales-Castillo, A.; López-Ruz, M.Á.; Martín-Casares, A.M.; Fernández-Rubiales, A.; Anderson, P.; Garrido, F.; Ruiz-Cabello, F.; López-Nevot, M.Á. Study of HLA-A, -B, -C, -DRB1 and -DQB1 polymorphisms in COVID-19 patients. J. Microbiol. Immunol. Infect. 2022, 55, 421–427. [Google Scholar] [CrossRef] [PubMed]
- Lani, R.; Senin, N.A.; AbuBakar, S.; Hassandarvish, P. Knowledge of SARS-CoV-2 Epitopes and Population HLA Types Is Important in the Design of COVID-19 Vaccines. Vaccines 2022, 10, 1606. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, A.; David, J.K.; Maden, S.K.; Wood, M.A.; Weeder, B.R.; Nellore, A.; Thompson, R.F. Human Leukocyte Antigen Susceptibility Map for Severe Acute Respiratory Syndrome Coronavirus 2. J. Virol. 2020, 16, e00510-20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shkurnikov, M.; Nersisyan, S.; Jankevic, T.; Galatenko, A.; Gordeev, I.; Vechorko, V.; Tonevitsky, A. Association of HLA Class I Genotypes With Severity of Coronavirus Disease-19. Front. Immunol. 2021, 12, 641900. [Google Scholar] [CrossRef] [PubMed]
- Basir, H.R.G.; Majzoobi, M.M.; Ebrahimi, S.; Noroozbeygi, M.; Hashemi, S.H.; Keramat, F.; Mamani, M.; Eini, P.; Alizadeh, S.; Solgi, G.; et al. Susceptibility and Severity of COVID-19 Are Both Associated with Lower Overall Viral-Peptide Binding Repertoire of HLA Class I Molecules, Especially in Younger People. Front. Immunol. 2022, 13, 891816. [Google Scholar] [CrossRef]
- Tian, J.; Yuan, X.; Xiao, J.; Zhong, Q.; Yang, C.; Liu, B.; Cai, Y.; Lu, Z.; Wang, J.; Wang, Y.; et al. Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: A multicentre, retrospective, cohort study. Lancet Oncol. 2020, 21, 893–903. [Google Scholar] [CrossRef]
- Monari, C.; Sagnelli, C.; Maggi, P.; Sangiovanni, V.; Numis, F.G.; Gentile, I.; Masullo, A.; Rescigno, C.; Calabria, G.; Megna, A.S.; et al. More severe COVID-19 in patients with active cancer: Results of a multicenter cohort study. Front. Oncol. 2021, 11, 662746. [Google Scholar] [CrossRef]
- Russell, B.; Moss, C.L.; Shah, V.; Ko, T.K.; Palmer, K.; Sylva, R.; George, G.; Monroy-Iglesias, M.J.; Patten, P.; Ceesay, M.M.; et al. Risk of COVID-19 death in cancer patients: An analysis from Guy’s Cancer Centre and King’s College Hospital in London. Br. J. Cancer 2021, 125, 939–947. [Google Scholar] [CrossRef]
- Klein, S.L.; Dhakal, S.; Ursin, R.L.; Deshpande, S.; Sandberg, K.; Mauvais-Jarvis, F. Biological sex impacts COVID-19 outcomes. PLoS Pathog. 2020, 16, 1008570. [Google Scholar] [CrossRef]
- Cai, Q.; Chen, F.; Wang, T.; Luo, F.; Liu, X.; Wu, Q.; He, Q.; Wang, Z.; Liu, Y.; Liu, L.; et al. Obesity and COVID-19 Severity in a Designated Hospital in Shenzhen, China. Diabetes Care 2020, 43, 1392–1398. [Google Scholar] [CrossRef] [PubMed]
- Gammone, M.A.; D’Orazio, N. Review: Obesity and COVID-19: A Detrimental Intersection. Front. Endocrinol. 2021, 12, 652639. [Google Scholar] [CrossRef] [PubMed]
- Asai, Y.; Nomoto, H.; Hayakawa, K.; Matsunaga, N.; Tsuzuki, S.; Terada, M.; Ohtsu, H.; Kitajima, K.; Suzuki, K.; Suzuki, T.; et al. Comorbidities as Risk Factors for Severe Disease in Hospitalized Elderly COVID-19 Patients by Different Age-Groups in Japan. Gerontology 2022, 68, 1027–1037. [Google Scholar] [CrossRef] [PubMed]
- Ministry of Health of the Russian Federation. Prevention, Diagnosis and Treatment of New Coronavirus Infection (COVID-19); Temporary Methodological Recommendations; Version 5 (08.04.2020); Ministry of Health of the Russian Federation: Moscow, Russia, 2020. [Google Scholar]
- Bernheim, A.; Mei, X.; Huang, M.; Yang, Y.; Fayad, Z.A.; Zhang, N.; Diao, K.; Lin, B.; Zhu, X.; Li, K.; et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology 2020, 295, 200463. [Google Scholar] [CrossRef] [Green Version]
- National Center for Health Statistics. Special Topics. Adult Tobacco Use Information. Available online: https://www.cdc.gov/nchs/nhis/tobacco/tobacco_glossary.htm#:~:text=Current%20smoker%3A%20An%20adult%20who,and%20who%20currently%20smokes%20cigarettes.&text=Every%20day%20smoker%3A%20An%20adult,called%20a%20%E2%80%9Cregular%20smoker%E2%80%9D (accessed on 10 January 2022).
- Rosner, B. Fundamentals of Biostatistics, 6th ed.; Thomson-Brooks/Cole: Belmont, CA, USA, 2006. [Google Scholar]
- 2-Way Contingency Table Analysis. Available online: https://statpages.info/ctab2x2.html (accessed on 1 February 2022).
- Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef]
- Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 2020, 323, 1061–1069. [Google Scholar] [CrossRef]
- Liu, Y.; Mao, B.; Liang, S.; Yang, J.W.; Lu, H.W.; Chai, Y.H.; Wang, L.; Zhang, L.; Li, Q.H.; Zhao, L.; et al. Association between age and clinical characteristics and outcomes of COVID-19. Eur. Respir. J. 2020, 55, 2001112. [Google Scholar] [CrossRef] [Green Version]
- Cheng, Y.; Luo, R.; Wang, K.; Zhang, M.; Wang, Z.; Dong, L.; Li, J.; Yao, Y.; Ge, S.; Xu, G. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int. 2020, 97, 829–838. [Google Scholar] [CrossRef]
- Chen, J.; Qi, T.; Liu, L.; Ling, Y.; Qian, Z.; Li, T.; Li, F.; Xu, Q.; Zhang, Y.; Xu, S.; et al. Clinical progression of patients with COVID-19 in Shanghai, China. J. Infect. 2020, 80, e1–e6. [Google Scholar] [CrossRef]
- Garg, S.; Kim, L.; Whitaker, M.; O’Halloran, A.; Cummings, C.; Holstein, R.; Prill, M.; Chai, S.J.; Kirley, P.D.; Alden, N.B.; et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019—COVID-NET, 14 States, March 1-30, 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 458–464. [Google Scholar] [CrossRef]
- Bwire, G.M. Coronavirus: Why Men are More Vulnerable to Covid-19 Than Women? SN Compr. Clin. Med. 2020, 2, 874–876. [Google Scholar] [CrossRef] [PubMed]
- Singh, A.K.; Misra, A. Impact of COVID-19 and comorbidities on health and economics: Focus on developing countries and India. Diabetes Metab. Syndr. 2020, 14, 1625–1630. [Google Scholar] [CrossRef] [PubMed]
- Rychter, A.M.; Zawada, A.; Ratajczak, A.E.; Dobrowolska, A.; Krela-Kaźmierczak, I. Should patients with obesity be more afraid of COVID-19? Obes. Rev. 2020, 21, e13083. [Google Scholar] [CrossRef] [PubMed]
- Richardson, S.; Hirsch, J.S.; Narasimhan, M.; Crawford, J.M.; McGinn, T.; Davidson, K.W.; the Northwell COVID-19 Research Consortium; Barnaby, D.P.; Becker, L.B.; Chelico, J.D.; et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA 2020, 323, 2052–2059. [Google Scholar] [CrossRef]
- Sy, K.T.L.; Haw, N.J.L.; Uy, J. Previous and active tuberculosis increases risk of death and prolongs recovery in patients with COVID-19. Infect. Dis. 2020, 52, 902–907. [Google Scholar] [CrossRef]
- Liang, W.; Guan, W.; Chen, R.; Wang, W.; Li, J.; Xu, K.; Li, C.; Ai, Q.; Lu, W.; Liang, H.; et al. Cancer patients in SARS-CoV-2 infection: A nationwide analysis in China. Lancet Oncol. 2020, 21, 335–337. [Google Scholar] [CrossRef]
- Wang, B.; Li, R.; Lu, Z.; Huang, Y. Does comorbidity increase the risk of patients with COVID-19: Evidence from meta-analysis. Aging 2020, 12, 6049–6057. [Google Scholar] [CrossRef]
- Murtas, R.; Andreano, A.; Gervasi, F.; Guido, D.; Consolazio, D.; Tunesi, S.; Andreoni, L.; Greco, M.T.; Gattoni, M.E.; Sandrini, M.; et al. Association between autoimmune diseases and COVID-19 as assessed in both a test-negative case-control and population case-control design. Auto Immun. Highlights 2020, 11, 15. [Google Scholar] [CrossRef]
- HLA Nomenclature. Available online: https://www.ufrgs.br/imunovet/molecular_immunology/hla.html (accessed on 15 February 2022).
- Ou, G.; Xu, H.; Yu, H.; Liu, X.; Yang, L.; Ji, X.; Wang, J.; Liu, Z. The roles of HLA-DQB1 gene polymorphisms in hepatitis B virus infection. J. Transl. Med. 2018, 16, 362. [Google Scholar] [CrossRef] [Green Version]
- Kasjko, D.; Jasinskis, V.; Eglite, J.; Golushko, J.; Sture, G.; Kalimulin, A.; Sochnevs, A.; Viksna, L. Research of HLA II Class DRB1, DQA1, DQB1 Genetic Markers in Patients with HIV Infection and AIDS. J. Adv. Med. 2014, 4, 4482–4500. [Google Scholar] [CrossRef]
- Arango, M.T.; Perricone, C.; Kivity, S.; Cipriano, E.; Ceccarelli, F.; Valesini, G.; Shoenfeld, Y. HLA-DRB1 the notorious gene in the mosaic of autoimmunity. Immunol. Res. 2017, 65, 82–98. [Google Scholar] [CrossRef] [PubMed]
- Krini, M.; Chouliaras, G.; Kanariou, M.; Varela, I.; Spanou, K.; Panayiotou, J.; Roma, E.; Constantinidou, N. HLA class II high-resolution genotyping in Greek children with celiac disease and impact on disease susceptibility. Pediatr. Res. 2012, 72, 625–630. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ombrello, M.J.; Remmers, E.F.; Tachmazidou, I.; Grom, A.; Foell, D.; Haas, J.P.; Martini, A.; Gattorno, M.; Özen, S.; Prahalad, S.; et al. HLA-DRB1*11 and variants of the MHC class II locus are strong risk factors for systemic juvenile idiopathic arthritis. Proc. Natl. Acad. Sci. USA 2015, 112, 15970–15975. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pugliese, A.; Boulware, D.; Yu, L.; Babu, S.; Steck, A.K.; Becker, D.; Rodriguez, H.; DiMeglio, L.; Evans-Molina, C.; Harrison, L.C.; et al. HLA-DRB1*15:01-DQA1*01:02-DQB1*06:02 Haplotype Protects Autoantibody-Positive Relatives from Type 1 Diabetes Throughout the Stages of Disease Progression. Diabetes 2016, 65, 1109–1119. [Google Scholar] [CrossRef]
Table 1.
Diagnostic and anamnestic data of patients included in the study (data on several patients were not available due to an inaccuracy in the collection of anamnesis).
Table 1.
Diagnostic and anamnestic data of patients included in the study (data on several patients were not available due to an inaccuracy in the collection of anamnesis).
| Clinical Feature | All Patients | Moderate COVID-19 (CT-1 or CT-2) | Severe COVID-19 (CT-3 or CT-4) | p (p0 = 0.003 after Bonferroni Correction) |
|---|---|---|---|---|
| Number of Patients | n = 100 | n = 41 | n = 59 | |
| Duration of hospital stay, days | 13 (6, 26) (median value (min, max) are presented for quantitative variables) | 11 (6, 19) | 14 (8, 26) | 0.00002 (U) |
| Fatal outcome | 3 (3%) | 0 | 3 (5.1%) | 0.267 (FET) |
| Age, years | 58.5 (34, 96) | 54 (36, 89) | 61 (34, 96) | 0.062 (U) |
| Sex, males | 52 (52%) | 18 (43.9%) | 34 (57.6%) | 0.223 (FET) |
| Age, years | ||||
| Females (n = 48) | 67 (40, 96) | 58 (40, 89) | 71 (43, 96) | 0.021 (U) |
| Males (n = 52) | 51 (34, 88) | 48 (36, 79) | 57.5 (34, 88) | 0.233 (U) |
| BMI, kg/m2 | 28.4 (16.9, 64) | 26.4 (16.9, 42,2) | 30.2 (20.8, 64) | 0.017 (U) |
| Weight, kg | 84.5 (45, 185) | 77 (45, 180) | 87 (58, 185) | 0.007 (U) |
| Smoking | 68 | 33 | 35 | - |
| Smoking during hospitalization | 5 (7%) | 3 (9.1%) | 2 (5.7%) | 0.668 (FET) |
| Of them males | 4 (80%) | 2 (67%) | 2 (100%) | 1.000 (FET) |
| Invasive mechanical ventilation, number of patients, % | 3 (3%) | 0 | 3 (5.1%) | 0.267 (FET) |
| Autoimmune diseases | 16 (16%) | 7 (17.1%) | 9 (15.3%) | 1.000 (FET) |
| Endocrine | 8 (8%) | 4 (9.8%) | 4 (6.8%) | 0.713 (FET) |
| Autoimmune thyroiditis | 8 (8%) | 4 (9.8%) | 4 (6.8%) | 0.713 (FET) |
| Nonendocrine | 8 (8%) | 3 (7.3%) | 6 (10.2%) | 0.734 (FET) |
| Bronchial asthma | 5 (5%) | 1 (2.4%) | 4 (6.8%) | 0.646 (FET) |
| Psoriasis/psoriatic arthritis | 2 (2%) | 1 (2.4%) | 1 (1.7%) | 1.000 (FET) |
| Rheumatoid arthritis | 1 (1%) | 0 | 1 (1.7%) | 1.000 (FET) |
| Guillain–Barre disease | 1 (1%) | 0 | 1 (1.7%) | 1.000 (FET) |
| Bechterew’s disease | 1 (1%) | 1 (2.4%) | 0 | 0.410 (FET) |
| Non-autoimmune endocrine diseases | 59 (59%) | 21 (51.2%) | 38 (64.4%) | 0.218 (FET) |
| Obesity | 45 (45%) | 13 (31.7%) | 32 (54.2%) | 0.040 (FET) |
| Thyroid disease | 15 (15%) | 6 (14.6%) | 9 (15.3%) | 1.000 (FET) |
| Type 2 diabetes mellitus (according to the medical history (carbohydrate metabolism disorders first detected during hospitalization were not considered)) | 12 (12%) | 5 (12.1%) | 7 (11.9%) | 1.000 (FET) |
| Diseases of the parathyroid glands | 12 (12%) | 5 (12.1%) | 7 (11.9%) | 1.000 (FET) |
| Others | 1 (1%) | 1 (2.4%) | 0 | 0.410 (FET) |
| Cardiovascular diseases | 64 (64%) | 23 (56.1%) | 41 (69.5%) | 0.206 (FET) |
| Arterial hypertension | 55 (55%) | 20 (48.8%) | 35 (59.3%) | 0.315 (FET) |
| Coronary heart disease | 21 (21%) | 8 (19.5%) | 13 (22.0%) | 0.808 (FET) |
| Atherosclerosis of the aorta, lower limb arteries | 12 (12%) | 4 (9.8%) | 8 (13.6%) | 0.757 (FET) |
| Chronic heart failure | 4 (4%) | 2 (5%) | 2 (3.4%) | 1000 (FET) |
| History of acute violation of cerebral circulation | 4 (4%) | 3 (7.3%) | 1 (1.7%) | 0.302 (FET) |
| Heart rhythm disorders | 3 (3%) | 0 | 3 (5.1%) | 0.267 (FET) |
| Others | 1 (1%) | 1 (2.4%) | 0 | 0.410 (FET) |
| Oncological disorders | 5 (5%) | 1 (2.4%) | 3 (5.1%) | 0.642 (FET) |
| Chronic leukemia | 2 (2%) | 0 | 1 (1.7%) | 1.000 (FET) |
| Breast cancer | 2 (2%) | 1 (2.4%) | 1 (1.7%) | 1.000 (FET) |
| Vulva cancer | 1 (1%) | 0 | 1 (1.7%) | 1.000 (FET) |
| Lung diseases | 26 (26%) | 11 (17%) | 16 (27.1%) | 1.000 (FET) |
| Pulmonary hypertension | 8 (8%) | 1 (2.4%) | 7 (11.9%) | 0.136 (FET) |
| Emphysema | 7 (7%) | 4 (10%) | 3 (5.1%) | 0.441 (FET) |
| Previous tuberculosis | 4 (4%) | 4 (10%) | 3 (5.1%) | 0.441 (FET) |
| Chronic bronchitis | 5 (5%) | 3 (7.3%) | 2 (3.4%) | 0.398 (FET) |
| Bronchial asthma | 5 (5%) | 1 (2.4%) | 4 (6.8%) | 0.646 (FET) |
| Decreased lung volume due to injury/surgery | 2 (2%) | 0 | 2 (3.4%) | 0.511 (FET) |
| Others | 2 (2%) | 1 (2.4%) | 1 (1.7%) | 1.000 (FET) |
Table 2.
HLA typing: frequencies of alleles of class II HLA genes in the entire cohort and in the CT groups.
Table 2.
HLA typing: frequencies of alleles of class II HLA genes in the entire cohort and in the CT groups.
| Alleles | All Patients n = 100, 200 Alleles | Moderate COVID-19 (CT-1 or CT-2) n = 41, 82 Alleles | Severe COVID-19 (CT-3 or CT-4) n = 59, 118 Alleles | p *, χ2 | |
|---|---|---|---|---|---|
| n (%) | 95% CI for Proportion | n (%) | n (%) | ||
| DRB1*01 | 28 (14%) | [9.5%; 19.6%] | 15 (18.3%) | 13 (11.0%) | 0.145 |
| DRB1*03 (DRB1*17) | 20 (10%) | [6.2%; 15.0%] | 10 (12.2%) | 10 (8.5%) | 0.388 |
| DRB1*04 | 15 (7.5%) | [4.3%; 12.1%] | 6 (7.3%) | 9 (7.6%) | 0.932 |
| DRB1*07 | 24 (12%) | [7.8%; 17.3%] | 9 (10.9%) | 15 (12.7%) | 0.689 |
| DRB1*08 | 6 (3%) | [1.1%; 6.4%] | 2 (2.4%) | 4 (3.4%) | 1.000 (FET) |
| DRB1*09 | 3 (1.5%) | [0.3%; 4.3%] | 1 (1.2%) | 2 (1.7%) | 1.000 (FET) |
| DRB1*10 | 5 (2.5%) | [0.8%; 5.7%] | 1 (1.2%) | 4 (3.4%) | 0.646 (FET) |
| DRB1*11 | 25 (12.5%) | [8.3%; 17.9%] | 12 (14.6%) | 13 (11%) | 0.447 |
| DRB1*12 | 8 (4%) | [1.7%; 7.7%] | 2 (2.4%) | 6 (5%) | 0.466 (FET) |
| DRB1*13 | 21 (10.5%) | [6.6%; 15.6%] | 9 (10.9%) | 12 (10.2%) | 0.846 |
| DRB1*14 | 3 (1.5%) | [0.3%; 4.3%] | 0 | 3 (2.5%) | 0.270 (FET) |
| DRB1*15 | 33 (16.5%) | [11.6%; 22.4%] | 10 (12.2%) | 23 (19.5%) | 0.172 |
| DRB1*16 | 9 (4.5%) | [2.1%; 8.4%] | 5 (6.1%) | 4 (3.4%) | 0.481 (FET) |
| DQA1*01:01 | 35 (17.5%) | [12.5%; 23.5%] | 16 (19.5%) | 19 (16.1%) | 0.532 |
| DQA1*01:02 | 39 (19.5%) | [14.3%; 25.7%] | 12 (14.6%) | 27 (22.9%) | 0.148 |
| DQA1*01:03 | 17 (8.5%) | [5.0%; 13.3%] | 10 (12.2%) | 7 (5.9%) | 0.101 |
| DQA1*02:01 | 24 (12%) | [7.8%; 17.3%] | 9 (10.9%) | 15 (12.7%) | 0.710 |
| DQA1*03:01 | 20 (10%) | [6.2%; 15.0%] | 7 (8.5%) | 13 (11.0%) | 0.565 |
| DQA1*04:01 | 4 (2%) | [0.6%; 5.0%] | 2 (2.4%) | 2 (1.7%) | 1.000 (FET) |
| DQA1*05:01 | 60 (30%) | [23.7%; 36.9%] | 26 (31.7%) | 34 (28.8%) | 0.660 |
| DQA1*06:01 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 1.000 (FET) |
| DQB1*02 | 38 (19%) | [13.8%; 25.1%] | 15 (18.3%) | 23 (19.5%) | 0.832 |
| DQB1*03:01 | 42 (21%) | [15.6%; 27.3%] | 15 (18.3%) | 27 (22.9%) | 0.433 |
| DQB1*03:02 | 13 (6.5%) | [3.5%; 10.9%] | 6 (7.3%) | 7 (5.9%) | 0.685 |
| DQB1*03:03 | 9 (4.5%) | [2.1%; 8.4%] | 5 (6.1%) | 4 (3.4%) | 0.481 (FET) |
| DQB1*03:04 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 1.000 (FET) |
| DQB1*03:05 | 1 (0.5%) | [0.01%; 2.8%] | 1 (1.2%) | 0 | 0.410 (FET) |
| DQB1*04:01/02 | 4 (2%) | [0.6%; 5.0%] | 2 (2.4%) | 2 (1.7%) | 1.000 (FET) |
| DQB1*05:01 | 33 (16.5%) | [11.6%; 22.4%] | 16 (19.5%) | 17 (14.4%) | 0.339 |
| DQB1*05:02/04 | 10 (5%) | [2.4%; 9.0%] | 5 (6.1%) | 5 (4%) | 0.736 (FET) |
| DQB1*05:03 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 1.000 (FET) |
| DQB1*06:01 | 5 (2.5%) | [0.8%; 5.7%] | 3 (3.7%) | 2 (1.7%) | 0.398 (FET) |
| DQB1*06:02-8 | 43 (21.5%) | [16.0%; 27.9%] | 14 (17.1%) | 29 (24.6%) | 0.204 |
* p0 = 0.0015 after Bonferroni correction.
Table 3.
Comparative analysis of frequencies of alleles of HLA class II genes in the examined cohort of patients and in the population control group.
Table 3.
Comparative analysis of frequencies of alleles of HLA class II genes in the examined cohort of patients and in the population control group.
| Frequency of Alleles in Patients Cohort | Number of Alleles in Patients Cohort | Frequency of Alleles in Control Group | Number of Alleles in Control Group | p *, Yates Corrected χ2 | |
|---|---|---|---|---|---|
| Number of Patients | n = 100 | n = 327 | - | ||
| Number of Alleles | n = 200 | n = 654 | - | ||
| DRB1*01 | 0.140 | 28 | 0.095 | 62 | 0.091 |
| DRB1*03(DRB1*17) | 0.100 | 20 | 0.075 | 49 | 0.322 |
| DRB1*04 | 0.075 | 15 | 0.115 | 75 | 0.142 |
| DRB1*07 | 0.120 | 24 | 0.143 | 94 | 0.463 |
| DRB1*08 | 0.030 | 6 | 0.018 | 12 | 0.470 |
| DRB1*09 | 0.015 | 3 | 0.007 | 5 | 0.599 |
| DRB1*10 | 0.025 | 5 | 0.013 | 8 | 0.337 |
| DRB1*11 | 0.125 | 25 | 0.142 | 93 | 0.617 |
| DRB1*12 | 0.040 | 8 | 0.028 | 18 | 0.507 |
| DRB1*13 | 0.105 | 21 | 0.142 | 93 | 0.217 |
| DRB1*14 | 0.015 | 3 | 0.023 | 15 | 0.687 |
| DRB1*15 | 0.165 | 33 | 0.132 | 86 | 0.280 |
| DRB1*16 | 0.045 | 9 | 0.067 | 44 | 0.329 |
| DQA1*01:01 | 0.175 | 35 | 0.130 | 85 | 0.137 |
| DQA1*01:02 | 0.195 | 39 | 0.190 | 124 | 0.946 |
| DQA1*01:03 | 0.085 | 17 | 0.107 | 70 | 0.442 |
| DQA1*02:01 | 0.120 | 24 | 0.143 | 94 | 0.463 |
| DQA1*03:01 | 0.100 | 20 | 0.122 | 80 | 0.463 |
| DQA1*04:01 | 0.020 | 4 | 0.015 | 10 | 0.888 |
| DQA1*05:01 | 0.300 | 60 | 0.290 | 189 | 0.833 |
| DQA1*06:01 | 0.005 | 1 | 0.003 | 2 | 0.782 |
| DQB1*02:01 | 0.190 | 38 | 0.195 | 128 | 0.939 |
| DQB1*03:01 | 0.210 | 42 | 0.240 | 157 | 0.433 |
| DQB1*03:02 | 0.065 | 13 | 0.083 | 54 | 0.510 |
| DQB1*03:03 | 0.045 | 9 | 0.033 | 22 | 0.592 |
| DQB1*03:04 | 0.005 | 1 | 0.002 | 1 | 0.958 |
| DQB1*03:05 | 0.005 | 1 | 0.017 | 11 | 0.368 |
| DQB1*04:01/02 | 0.020 | 4 | 0.022 | 14 | 0.873 |
| DQB1*05:01 | 0.165 | 33 | 0.108 | 71 | 0.044 |
| DQB1*05:02/04 | 0.050 | 10 | 0.032 | 21 | 0.333 |
| DQB1*05:03 | 0.005 | 1 | 0.068 | 44 | 0.0011 |
| DQB1*06:01 | 0.025 | 5 | 0.200 | 131 | <0.00005 |
| DQB1*06:02-8 | 0.215 | 43 | 0.000 | 0 | <0.00005 |
* p0 = 0.0015 for DQB1 after Bonferroni correction.
Table 4.
Characteristics of patients carrying presumably protective (DQB1*06:01) and presumably predisposing (DQB1*06:02-8, DQB1*05:03) alleles.
Table 4.
Characteristics of patients carrying presumably protective (DQB1*06:01) and presumably predisposing (DQB1*06:02-8, DQB1*05:03) alleles.
| DQB1*05:03 | DQB1*06:01 | DQB1*06:02-8 | |
|---|---|---|---|
| Number of Alleles | 1 | 5 | 43 |
| Number of Patients | 1 | 5 | 35 |
| Haplotypes | DRB1*14-DQA1*01:01-DQB1*05:03–1 (100%) | DRB1*15-DQA1*01:03-DQB1*06:01–5 (100%) | DRB1*15-DQA1*01:02-DQB1*06:02-8–27 (63%) DRB1*13-DQA1*01:03-DQB1*06:02-8–12 (28%) DRB1*13-DQA1*01:02-DQB1*06:02-8–3 (7%) DRB1*15-DQA1*03:01-DQB1*06:02-8–1 (2%) |
| Duration of hospital stay, days | 12 | 13 (11, 19) | 13 (7, 21) |
| Fatal outcome | 0 | 0 | 1 (2.9%) |
| Age, years | 34 | 47 (41, 65) | 58 (34, 96) |
| Sex, male | 1 (100%) | 2 (40%) | 21 (60%) |
| Age | |||
| Females | - | 43 (41, 47) | 69 (42, 96) |
| Males | 34 | 48; 65 | 51 (34, 83) |
| BMI, kg/m2 | 27.7 | 26.5 (22, 33,1) | 27.4 (16.9, 41.6) |
| Weight, kg | 82 | 80 (65, 99) | 86.5 (45, 120) |
| Smoking Smoking during hospitalization Of them males | 30 2 1 | 5 0 0 | 25 2 (8%) 1 (50%) |
| CT-1 | 0 | 0 | 2 (5.7%) |
| CT-2 | 0 | 3 (60%) | 10 (28.6%) |
| CT-3 | 0 | 2 (40%) | 17 (48.6%) |
| CT-4 | 1 (100%) | 0 | 6 (17.1%) |
| Invasive mechanical ventilation | 0 | 0 | 1 (2.9%) |
| Autoimmune diseases | 0 | 1 (20%) | 5 (14%) |
| Endocrine diseases | 0 | 1 (20%) | 3 (9%) |
| Autoimmune thyroiditis | 0 | 1 (20%) | 3 (9%) |
| Nonendocrine | 0 | 0 | 3 (9%) |
| Bronchial asthma | 0 | 0 | 3 (9%) |
| Psoriasis/ psoriatic arthritis | 0 | 0 | 0 |
| Rheumatoid arthritis | 0 | 0 | 1 (2.9%) |
| Guillain–Barre disease | 0 | 0 | 0 |
| Bechterew’s disease | 0 | 0 | 0 |
| Non-autoimmune endocrine diseases | 0 | 2 (40%) | 18 (51%) |
| Obesity | 0 | 1 (20%) | 13 (37%) |
| Thyroid disease | 0 | 1 (20%) | 6 (17%) |
| Type 2 diabetes mellitus | 0 | 0 | 4 (11%) |
| Diseases of the parathyroid glands | 0 | 0 | 2 (5.7%) |
| Others | 0 | 0 | 0 |
| Cardiovascular diseases | 0 | 0 | 21 (60%) |
| Arterial hypertension | 0 | 0 | 17 (48.6%) |
| Coronary heart disease | 0 | 0 | 6 (17.1%) |
| Atherosclerosis of the aorta, lower limb arteries | 0 | 0 | 5 (14%) |
| Chronic heart failure | 0 | 0 | 1 (2.9%) |
| History of acute violation of cerebral circulation | 0 | 0 | 0 |
| Heart rhythm disorders | 0 | 0 | 1 (2.9%) |
| Others | 0 | 0 | 0 |
| Oncological diseases | 0 | 0 | 4 (11%) |
| Chronic leukemia | 0 | 0 | 2 (5.7%) |
| Breast cancer | 0 | 0 | 1 (2.9%) |
| Vulva cancer | 0 | 0 | 1 (2.9%) |
| Lung diseases | 0 | 1 (20%) | 9 (26%) |
| Pulmonary hypertension | 0 | 1 (20%) | 1 (2.9%) |
| Emphysema | 0 | 0 | 2 (5.7%) |
| Previous tuberculosis | 0 | 0 | 2 (5.7%) |
| Chronic bronchitis | 0 | 0 | 2 (5.7%) |
| Bronchial asthma | 0 | 0 | 3 (9%) |
| Decreased lung volume due to injury/surgery | 0 | 0 | 0 |
| History of pneumonia | 0 | 0 | 1 (2.9%) |
Table 5.
HLA typing: frequencies of haplotypes of class II HLA genes in the entire cohort of patients and in the groups with moderate and severe COVID-19.
Table 5.
HLA typing: frequencies of haplotypes of class II HLA genes in the entire cohort of patients and in the groups with moderate and severe COVID-19.
| Haplotype | All Patients n = 100, 200 Alleles | Moderate COVID-19 (CT-1 or CT-2) n = 41, 82 Alleles | Severe COVID-19 (CT-3 or CT-4) n = 59, 118 Alleles | p *, Yates χ2 | |
|---|---|---|---|---|---|
| n (%) | 95% CI for Proportion | n (%) | n (%) | ||
| DRB1*01-DQA1*01:01-DQB1*05:01 | 28 (14%) | [9.5%; 19.6%] | 15 (18.3%) | 13 (11.0%) | 0.211 |
| DRB1*03(DRB1*17)-DQA1*05:01-DQB1*02 | 20 (10%) | [6.2%; 15.0%] | 10 (12.2%) | 10 (8.5%) | 0.533 |
| DRB1*04-DQA1*03:01-DQB1*03:01 | 2 (1%) | [0.1%; 3.6%] | 0 | 2 (1.7%) | 0.644 |
| DRB1*04-DQA1*03:01-DQB1*03:02 | 12 (6%) | [3.1%; 10.3%] | 6 (7.3%) | 6 (5.1%) | 0.726 |
| DRB1*04-DQA1*03:01-DQB1*03:04 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 0.854 |
| DRB1*07-DQA1*02:01-DQB1*02 | 18 (9%) | [5.42%; 13.82%] | 6 (7.3%) | 12 | 0.658 |
| DRB1*07-DQA1*02:01-DQB1*03:03 | 6 (3%) | [1.1%; 6.4%] | 4 (4.9%) | 2 (1.7%) | 0.381 |
| DRB1*08-DQA1*03:01-DQB1*03:02 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 0.854 |
| DRB1*08-DQA1*04:01-DQB1*04:01/02 | 4 (2%) | [0.6%; 5.0%] | 2 (2.4%) | 2 (1.7%) | 0.886 |
| DRB1*08-DQA1*06:01-DQB1*03:01 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 0.854 |
| DRB1*09-DQA1*03:01-DQB1*03:03 | 3 (1.5%) | [0.3%; 4.3%] | 1 (1.2%) | 2 (1.7%) | 0.750 |
| DRB1*10-DQA1*01:01-DQB1*05:01 | 5 (2.5%) | [0.8%; 5.7%] | 1 (1.2%) | 4 (3.4%) | 0.613 |
| DRB1*11-DQA1*05:01-DQB1*03:01 | 25 (12.5%) | [8.3%; 17.9%] | 12 (14.6%) | 13 (11.0%) | 0.587 |
| DRB1*11-DQA1*05:01-DQB1*03:05 | 1 (0.5%) | [0.01%; 2.8%] | 1 (1.2%) | 0 | 0.854 |
| DRB1*12-DQA1*05:01-DQB1*03:01 | 8 (4%) | [1.7%; 7.7%] | 2 (2.4%) | 6 (5.1%) | 0.567 |
| DRB1*13-DQA1*01:02-DQB1*06:02-8 | 3 (1.5%) | [0.3%; 4.3%] | 0 | 3 (2.5%) | 0.388 |
| DRB1*13-DQA1*01:03-DQB1*06:02-8 | 12 (6%) | [3.1%; 10.3%] | 7 (8.5%) | 5 (4.2%) | 0.339 |
| DRB1*13-DQA1*05:01-DQB1*03:01 | 6 (3%) | [1.1%; 6.4%] | 2 (2.4%) | 4 (3.4%) | 0.973 |
| DRB1*14-DQA1*01:01-DQB1*05:03 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 0.854 |
| DRB1*14-DQA1*01:01-DQB1*05:02/04 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 0.854 |
| DRB1*14-DQA1*05:01-DQB1*03:01 | 1 (0.5%) | [0.01%; 2.8%] | 0 | 1 (0.8%) | 0.854 |
| DRB1*15-DQA1*01:02-DQB1*06:02-8 | 27 (13.5%) | [9.1%; 19.0%] | 7 (8.5%) | 20 (16.9%) | 0.133 |
| DRB1*15-DQA1*01:03-DQB1*06:01 | 5 (2.5%) | [0.8%; 5.7%] | 3 (3.7%) | 2 (1.7%) | 0.679 |
| DRB1*16-DQA1*01:02-DQB1*05:02/04 | 9 (4.5%) | [2.1%; 8.4%] | 5 (6.1%) | 4 (3.4%) | 0.574 |
* p0 = 0.002 after Bonferroni correction.
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Troshina, E.; Yukina, M.; Nuralieva, N.; Vasilyev, E.; Rebrova, O.; Akhmatova, R.; Ikonnikova, A.; Savvateeva, E.; Gryadunov, D.; Melnichenko, G.; et al. Association of Alleles of Human Leukocyte Antigen Class II Genes and Severity of COVID-19 in Patients of the ‘Red Zone’ of the Endocrinology Research Center, Moscow, Russia. Diseases 2022, 10, 99. https://doi.org/10.3390/diseases10040099
AMA Style
Troshina E, Yukina M, Nuralieva N, Vasilyev E, Rebrova O, Akhmatova R, Ikonnikova A, Savvateeva E, Gryadunov D, Melnichenko G, et al. Association of Alleles of Human Leukocyte Antigen Class II Genes and Severity of COVID-19 in Patients of the ‘Red Zone’ of the Endocrinology Research Center, Moscow, Russia. Diseases. 2022; 10(4):99. https://doi.org/10.3390/diseases10040099
Chicago/Turabian StyleTroshina, Ekaterina, Marina Yukina, Nurana Nuralieva, Evgeny Vasilyev, Olga Rebrova, Ravida Akhmatova, Anna Ikonnikova, Elena Savvateeva, Dmitry Gryadunov, Galina Melnichenko, and et al. 2022. "Association of Alleles of Human Leukocyte Antigen Class II Genes and Severity of COVID-19 in Patients of the ‘Red Zone’ of the Endocrinology Research Center, Moscow, Russia" Diseases 10, no. 4: 99. https://doi.org/10.3390/diseases10040099
APA StyleTroshina, E., Yukina, M., Nuralieva, N., Vasilyev, E., Rebrova, O., Akhmatova, R., Ikonnikova, A., Savvateeva, E., Gryadunov, D., Melnichenko, G., & Mokrysheva, N. (2022). Association of Alleles of Human Leukocyte Antigen Class II Genes and Severity of COVID-19 in Patients of the ‘Red Zone’ of the Endocrinology Research Center, Moscow, Russia. Diseases, 10(4), 99. https://doi.org/10.3390/diseases10040099
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