Impact of IL-6 rs1800795 and IL-17A rs2275913 Gene Polymorphisms on the COVID-19 Prognosis and Susceptibility in a Sample of Iranian Patients

Impact of IL-6 rs1800795 and IL-17A rs2275913 Gene Polymorphisms on the COVID-19 Prognosis and Susceptibility in a Sample of Iranian Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of IL-6 rs1800795 and IL-17A rs2275913 Gene Polymorphisms on the COVID-19 Prognosis and Susceptibility in a Sample of Iranian Patients Mostafa Khafaei, Reza Asghari, fariba zafari, morteza sadeghi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3215016/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Elevated levels of interleukin (IL)-6 and IL-17A have been linked to hyper inflammation in COVID-19 patients, and their levels are indicative of the progression of the disease. This study aimed to investigate whether single-nucleotide polymorphisms (SNPs) in IL-6 and IL-17A are linked to COVID-19 susceptibility and prognosis in Iranian patients. Methods: The study enrolled 280 COVID-19 patients, divided into 140 non-severe and 140 severe cases. Genotyping for IL-6 rs1800795 and IL-17A rs2275913 was performed using tetra primer-amplification refractory mutation system-polymerase chain reaction (tetra-ARMS-PCR). IL-6 and IL-17A circulating levels were measured using enzyme-linked immunosorbent assay (ELISA). The study also investigated predictors of COVID-19 mortality. Results: The rs1800795 GG genotype (78/140 (55.7%)) and G allele (205/280 (73.2%)) were significantly associated with a higher risk of severe COVID-19 infection (OR = 2.19, 95%CI: 1.35–3.54, P = .006 and OR = 1.79, 95%CI: 1.25–2.56, P < .001, respectively). The rs1800795 GG genotype was also significantly linked to disease mortality (OR = 1.95, 95%CI: 1.06–3.61, P = .04). In contrast, the rs2275913 GA genotype was found to be protective against severe COVID-19 (OR = 0.5, 95%CI: 0.31–0.80, P = .012), but no significant association was observed with disease mortality. Several predictors of COVID-19 mortality were identified, including INR ≥ 1.2 (OR = 2.19, 95%CI: 1.61–3.78, P = .007), D-dimer ≥ 565.5 ng/mL (OR = 3.12, 95%CI: 1.27–5.68, P = .019), respiratory rate ≥ 29 (OR = 1.19, 95%CI: 1.12–1.28, P = .001), IL-6 serum concentration ≥ 28.5 pg/mL (OR = 1.97, 95%CI: 1.942–2.06, P = .013), and IL-6 rs1800795 GG genotype (OR = 1.95, 95%CI: 1.06–3.61, P = .04). conclusion: The results of this study suggest that the rs1800795 GG genotype and G allele are associated with greater disease severity in COVID-19 patients, while the rs2275913 GA genotype is protective. These findings provide insights into the genetic and molecular mechanisms underlying COVID-19 and may inform the development of more effective therapies and prognostic tools for this disease. Polymorphism COVID-19 Interleukin-17A SARS-CoV-2 Interleukin-6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Since the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of COVID-19 cases has surpassed 390 million worldwide, with over 6 million reported deaths (source: WHO, https://covid19.who.int/ ). While significant efforts have been devoted to understanding the infection mechanism of Coronavirus Disease-2019 (COVID-19), its physiopathology remains incompletely understood. However, it is widely recognized that the cytokine storm plays a pivotal role in the increased hospitalization rates, intensive care admissions, and mortality observed in a subset of COVID-19 patients who develop severe disease [ 1 – 3 ]. The cytokine storm, characterized by an acute hyperinflammatory response, is associated with elevated serum levels of various pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α), interleukin (IL)-17A, IL-4, IL-1, IL-6, and interferon-γ (IFN-γ) [ 4 – 8 ]. This phenomenon manifests predominantly, though not exclusively, in the lungs, leading to acute respiratory distress syndrome (ARDS) in COVID-19 patients [ 9 – 11 ]. While age, body mass index (BMI), and comorbidities are established risk factors for the severity of SARS-CoV-2 infection, disease severity can still occur in patients without evident risk factors. Hence, it is plausible that other factors, such as genetic variations, contribute to disease severity [ 12 ]. Exploring the impact of single nucleotide polymorphisms (SNPs) within cytokine and chemokine genes, known to influence the remodeling of the immune response during COVID-19 infection, could shed light on the hyperinflammatory state associated with the cytokine storm in COVID-19 [ 13 ]. Notably, numerous polymorphisms in immune-related genes have been implicated in severe COVID-19 infection [ 12 – 16 ]. Since the onset of the pandemic, elevated serum concentrations of IL-6 have consistently been observed in severely ill patients with COVID-19, serving as a prominent indicator [ 17 ]. IL-6 is a pleiotropic cytokine secreted by various cell types, including macrophages, B and T lymphocytes, and dendritic cells. The increased levels of IL-6 have been closely associated with a hyperinflammatory state and the progression of acute respiratory distress syndrome (ARDS), a leading cause of mortality in coronavirus-related infections such as COVID-19, SARS, and MERS [ 18 – 21 ]. Consequently, monitoring IL-6 serum concentrations has been employed as a predictor of COVID-19 severity and mortality [ 22 – 25 ]. Moreover, recognizing the crucial role of IL-6 in the dysregulated immune response to SARS-CoV-2, numerous clinical trials have emerged to target this cytokine specifically [ 26 – 28 ]. Given the significance of IL-6 in regulating CD4 + T cells, exploring related genetic polymorphisms can provide insights into the molecular aspects of COVID-19. Among these polymorphisms, rs1800795, located on the IL-6 promoter, has been associated with IL-6 expression levels. The rs1800795 GG genotype has demonstrated protective effects against pneumococcal pneumonia and pneumonia-induced sepsis, while G alleles have been linked to worse outcomes in HCV infection [ 29 – 31 ]. A meta-analysis conducted by Ulhaq and Soraya additionally revealed that carrier status of rs1800795 is associated with elevated IL-6 production and increased severity of pneumonia [ 32 ]. Furthermore, several reports have highlighted the correlation between increased Th17 lymphocytes, elevated serum IL-17A levels, and clinical severity and progression of SARS-CoV-2 infection [ 33 , 34 ]. IL-17A, a non-canonical pro-inflammatory cytokine, induces the production of other pro-inflammatory cytokines such as IL-1, IL-6, and TNF-α, thereby playing a significant role in the cytokine storm [ 35 ]. The decrease in lymphocytic population observed in COVID-19 patients, along with an increase in the fraction of Th17 lymphocytes and Th17-produced cytokines, supports the involvement of IL-17A in driving the hyperinflammatory immune response [ 36 ]. Several IL-17A SNPs have been examined and found to be associated with asthma and infectious lung diseases, suggesting susceptibility to disease progression [ 37 ]. In line with these findings, a retrospective study investigating IL-17A gene SNPs, which result in low IL-17A production, indicated enhanced 30-day survival rates in patients with ARDS [ 38 ]. Additionally, a meta-analysis revealed that the polymorphism rs2275913, located in the regulatory sequence of the IL-17A gene, was associated with disease prevalence and mortality rates among COVID-19 patients in different countries [ 39 ]. Considering the critical role of IL-6 and IL-17A in modulating the immune response to COVID-19 infection and the cytokine storm, exploring genetic variations within these genes may help elucidate the wide range of symptoms observed in COVID-19 patients. Therefore, we conducted an investigation focusing on two common SNPs, rs1800795 and rs2275913, in Iranian patients with COVID-19, aiming to explore their association with disease severity, mortality, and the cytokine storm. Methodology 2.1 Patient Enrollment This study included 280 adult COVID-19 patients: 140 severe cases and 140 non-severe cases. Patients were referred to the COVID-19 care units in Baqiyatallah Hospital between December 2020 and October 2021. Patients were diagnosed and classified based on their disease severity using World Health Organization (WHO) interim guidelines [ 12 ]. The non-severe group included patients who showed CT features of COVID-19 and peripheral capillary oxygen saturation (SPO 2 ) > 93%, and the severe group included patients with each of the following: respiratory rate ≥ 30 breaths/min or oxygen saturation (SpO 2 ) ≤ 93%, mechanical ventilation upon respiratory failure and/or ICU admission, shock, and organ failure syndrome. A positive RT-PCR result for SARS-CoV-2 RNA in nasopharyngeal swabs confirmed the diagnosis for all patients. The following criteria were used to exclude patients: lack of pneumonia symptoms, missing medical history data, aged < 18 years, and pregnant women. At the time of hospital admission, the demographic and clinical data were recorded for each patient, including age, gender, presence of comorbidities such as chronic renal disease, diabetes, and liver disease. Also, chest Computed Tomography (CT) scan, laboratory parameters, and clinical outcome (ICU admission or not; discharge or death) were recorded. This study was carried out in line with the Helsinki Declaration. The study protocol was approved by the institutional review board (IRB) and research ethics committee of Baqiyatallah University of Medical Sciences, Tehran, Iran (Approval Code: IR.BMSU.REC.1399.466). 2.2 Sample Preparation After 15 min of semi-supine rest, five ml of the study participants' peripheral blood was collected via venipuncture and split into two tubes: a) ACD blood collection tubes that were stored at -30°C until target SNPs were genotyped using tetra primer-amplification refractory mutation system based polymerase chain reaction (tetra-ARMS-PCR), and b) plain blood collection tubes with no anticoagulants were used to separate serum, which was then kept at -30°C until serum concentrations of cytokines were measure by the enzyme-linked immunosorbent assay (ELISA). 2.3 Molecular Template Preparation Following thawing and proper mixing of the frozen blood samples, the genomic DNA was extracted using Rapid Genomic DNA Extraction (RGDE) method [ 40 ] and stored at 4°C until genotyping. The quality, concentration, and purity of the extracted DNA were determined using agarose gel electrophoresis and spectrophotometry. 2.4 Molecular Analysis of Target SNPs Two SNPs (rs1800795 in IL-6 and rs2275913 in IL-17A) were successfully genotyped using tetra-ARMS-PCR. Primers were purchased from Pishgam (Tehran, Iran) (Supplementary Table 1). The genotyping assays were performed in the final volume of 10 µl. The PCR mixture contained five µl of master mix (Amplicon, Denmark), one µl of each Inner (reverse & forward) and Outer (reverse & forward) primers (10 pmol/µl), and one µl of DNA (≈ 20 ng). The PCR machine (Thermal Cycler, BioRad, USA) was set for the following optimized reaction conditions: initial denaturation at 95°C for 5 min, followed by 32 cycles of denaturation at 95°C for 20 s, annealing at 61°C for 35 s, extension at 72°C for 16 s, final extension at 72°C for 10 min, and 10 min cooling at 4°C. Product amplicons were monitored using electrophoresis on agarose gel, and product bands were observed using a UV trans-illuminator (B & L Systems, USA). 2.5 Confirmatory Sequencing Sequencing was performed on 10% of samples to confirm genotypes identified by tetra-ARMS PCR. Amplified PCR products were used for sequencing (3130xl Genetic Analyzer, Applied Biosystems, USA). The sequencing data were examined with the software "GeneRunner version 6.5.50 and Chromas Pro version 2.1.8." By comparing PCR results to sequencing results, the genotyping specificities of the target SNPs were evaluated. 2.6 Statistical Analysis The Statistical Package for Social Sciences (SPSS) was used for statistical analysis (version 26.0 IBM Corp., NY, USA). Quantitative variables were presented as numbers (percent) and median (25th – 75th ) and were analyzed using the independent-samples t‐test or Mann–Whitney U test, wherever applicable. For categorical variables, the Chi-square test was employed to compare groups. In the case of inter-group comparisons (two and three comparison groups), non-normally distributed quantitative data were analyzed by the Mann–Whitney U test and Kruskal-Wallis H-test. Adjusted P values by the Bonferroni method were reported for multiple comparisons in the analysis. To examine the deviation of the observed frequencies in genotypes from the expected values from the Hardy–Weinberg (HW) model, the Chi-square test was used. Several inheritance models were evaluated for genotypic and allelic association with the disease severity or mortality in different groups. Also, odds ratios (OR) and 95% confidence intervals (CI) were calculated to interpret the results. The likelihood of mortality in patients was tested using univariate logistic regression. Receiver operator characteristics (ROC) curves and the highest Youden index (sensitivity + specificity – 1) were employed to estimate the optimal mortality cutoff for each predictor in the regression analysis. P values of < 0.05 were considered significant in any performed test. Results 3.1 Patients' Characteristics In the severe group, patients were considerably older than the non-severe cohort (P < .001). In the non-severe group, there were 69 (49.3%) women and 71 (50.7%) men, while there were 77 (55%) women and 63 (45%) men in the severe group (P = .33). In comparison to the non-severe patients, some comorbidities had significantly higher frequencies in the severe cohort, including diabetes mellitus (DM) (40% Vs. 25%, P = .01), hypertension (HTN) (49.3% Vs. 30%, P = .001), and ischemic heart disease (IHD) (15.7% Vs. 17.1%, P = .02). In comparison to non-severe patients, a significantly lower SPO 2 (P = .007) in the severe cohort were accompanied by significantly higher respiratory rate (P = .03), higher Chest Computed Tomography Severity Scoring (CT-SS) (P = .003), more ICU admission rate (P < .001), and elongated hospital stay duration (P < .001). Furthermore, the in-hospital death rate in severe patients was significantly greater than in non-severe patients. (7(5%) Vs. 45(31.5%), P < .001). In addition, serum concentrations of IL-6 and IL-17A were significantly higher in severe patients than non-severe patients (19.5 pg/mL Vs. 44 pg/mL, P < .001, and 15 pg/mL Vs. 26 pg/mL, P = .004, respectively). Detailed comparisons of symptoms and laboratory findings between the two groups are presented in Table 1 . Table 1 Characteristics of included COVID-19 patients grouped as severe and non-severe. Abbreviations : WBC: white blood cells count; INR: international normalized ratio; CRP: C-reactive protein, AST: aspartate transaminase; ALT: alanine transaminase; LDH: lactate dehydrogenase; IL-6 Interleukin 6; IL-17A: Interleukin 17A. *P-value by Kruskal-Wallis H-test (data as Median (25th percentile – 75th percentile)). For other comparisons P value by Chi-square test (data as count and percentage). Non-Severe Severe P-value Age (years) 41.7 (30.8–47.5) 55 (46-57.7) < .001 Sex n, (%) Female Male 69 (49.3%) 71 (50.7%) 77 (55%) 63 (45%) .33 Comorbidities n, (%) Diabetes Mellitus Hypertension Ischemic Heart Disease Chronic Liver Disease Chronic Kidney Disease 36 (25.7%) 42 (30.0%) 10 (7.1%) 22 (15.7%) 14 (10.0%) 56 (40.0%) 69 (49.3%) 22 (15.7%) 15 (10.7%) 9 (6.4%) . 01 .001 .02 .21 .27 Symptoms n, (%) Cough Diarrhea Dyspnea Fever Fatigue Rhinitis Myalgia Arthralgia Sore throat Loss of taste/smell 101 (72.1%) 5 (3.6%) 103 (73.6%) 97 (69.3%) 124 (88.6%) 21 (15%) 120 (85.7%) 97 (69.3%) 34 (24.3%) 15 (10.7%) 117 (83.6%) 6 (4.3%) 117 (83.6%) 112 (80.0%) 131 (93.6%) 36 (25.7%) 106 (75.7%) 110 (78.6%) 43 (30.7%) 29 (20.7%) .02 .75 .04 .03 .14 .02 .03 .07 .22 ICU Admission n, (%) 12 (8.6%%) 111 (79.3%) < .001 CT-SS * 6 (3–9) 19 (16–21) .003 Hospital stay (days) * 3.5 (2–5) 21 (19–22) < .001 Fate n, (%) Survivor Non-survivor 133 (95%) 7(5%) 95 (67.9%) 45 (31.1%) < .001 Clinical Characteristics * SPO 2 % Respiratory Rate 95 (94–96) 27 (25–29) 82.5 (78–88) 32 (30–33) .007 .03 Laboratory Parameters * Hemoglobin (g/dL) WBC (×10 3 /mm 3 ) Lymphocyte count (×10 3 /mm 3 ) Platelets (×10 3 /mm 3 ) INR D-dimer (ng/mL) CRP (mg/L) AST (U/L) ALT (U/L) LDH (U/L) Creatinine (mg/dL) IL-6 (pg/mL) IL-17A (pg/mL) 11.7 (10.4–13.2) 8.8 (7.5–10.7) 1.3 (1-1.6) 227.3 (185.3–269) 1.12 (1.1–1.2) 431 (366–542) 37.4 (21.7–56.7) 34.5 (23.8–46.8) 26.5 (17.3–35.2) 409 (357.2-454.5) 0.9 (0.8-1) 19.5 (16–25) 15 (12-19.75) 11.5 (10.2–13.5) 9.3 (7.6–11.4) 1.1 (0.84–1.5) 244.9 (225.4-263.9) 1.3 (1.2–1.3) 534 (389.2–659) 56.4 (35.4–77.2) 28.5 (18–42) 38 (26–46) 497.5 (405.7-556.7) 1.12 (0.9–1.4) 44 (36.2–51) 26 (22-29.75) .81 .96 . 001 .003 < .001 < .001 0.01 .052 .01 < .001 .001 < .001 .004 3.2 Tetra-ARMS PCR and Sequencing Results The sizes of specific amplified PCR products of IL-6 rs1800795 were 232 bp (G allele) and 401 bp (C allele) (Fig. 1 A). For IL-17A rs2275913, the sizes of specific amplified PCR products were 193 bp (A allele) and 247 bp (G allele) (Fig. 1 B). The corresponding sequencing data accurately confirmed the genotypes identified by tetra-ARMS PCR (Fig. 2 ). 3.3 rs1800795 and Risk of Severe COVID-19 Associations between IL-6 rs1800795 and IL-17A 2275913 (genotypes and alleles) and the risk of COVID-19 severity are shown in Table 2 . The frequencies of rs1800795 genotypes and alleles were significantly different between severe and non-severe patients (P = .005, and P = .001, respectively). A significant positive association of wild homozygous GG genotype (78/140 (55.7%)), and G allele (205/280 (73.2%)) was observed with severe COVID-19 risk. Patients with GG genotype showed 2.19 times higher odds of showing severe COVID-19 (OR = 2.19, 95%CI: 1.35–3.54, adjusted P value = .006) vs. those with GC and CC genotypes. Whereas heterozygous mutant GC genotype (49/140 (35%)) was associated with a negative risk of severe COVID-19 (OR = 0.58, 95%CI: 0.33–0.94, adjusted P value = .04). Homozygous mutant CC genotype comprised 15.7% (22/140) of the non-severe cohort and 9.3% (13/140) of the severe cohort but represented no significant risk of COVID-19 severity (OR = 0.54, 95%CI: 0.26–1.11, adjusted P value = .10). However, the C allele was significantly linked to reduced risk of COVID-19 (OR = 0.55, 95%CI: 0.38–0.79, adjusted P value = .001). The Chi-square test for Hardy-Weinberg equilibrium (HWE) showed that rs1800795 in both non-severe and severe groups were in HWE (P = .99, and P = .20, respectively). Table 2 Frequency of IL-6 rs1800795 and Il-17A rs2275913 (Genotypes, and Alleles,) and their association with the risk of severe COVID-19 *P-values by Chi-square test; ** P-values are Benforini adjusted Non-severe (n = 140) Severe (n = 140) P-Value* Severe COVID-19 Risk P-Value** Odds Ratio 95% CI Rs1800795 Genotype GG 51 (36.4%) 78 (55.7%) .005 2.19 1.35–3.54 .006 GC 67 (47.9%) 49 (35%) 0.58 0.36–0.94 .04 CC 22 (15.7%) 13 (9.3%) 0.54 0.26–1.11 0.10 Rs2275913 Genotype GG 49 (35%) 64 (45.7%) .012 1.56 0.96–2.52 .09 GA 80 (57.1.%) 56 (40%) 0.5 0.31–0.80 .012 AA 11 (7.9%) 20 (14.3%) 1.95 0.89–4.24 .09 Rs1800795 Allele G 169 (60.4%) 205 (73.2%) .001 1.79 1.25–2.56 .001 C 111 (39.6%) 75 (26.8%) 0.55 0.38–0.79 .001 Rs2275913 Allele G 178 (63.5%) 184 (65.7%) .53 1.09 0.77–1.55 0.59 A 102 (36.5%) 96 (34.3%) 0.91 0.64–1.28 0.59 3.4 rs2275913 and Risk of Severe COVID-19 As regard to IL-17A rs2275913, although the frequency of wild homozygous GG was significantly higher in severe patients in comparison to non-severe cases (49 (35%) Vs. 64 (45.7%)), it was not associated with a significant risk of COVID-19 (OR = 1.56, 95%CI: 0.96–2.52, adjusted P value = .09). On the contrary, heterozygous mutant GA was correlated with a significantly decreased risk of severe COVID-19 (OR = 0.5, 95%CI: 0.31–0.80, adjusted P value = .012). Also, there was no significant link between the homozygous mutant CC genotype and the disease severity (OR = 1.95, 95%CI: 0.89–4.24, adjusted P value = .09). Furthermore, the Chi-square test revealed no difference between either G or A allele frequency within the non-severe and severe cohort (178 (63.5%) vs. 184 (65.7%), and 102 (36.5%) Vs. 96 (34.3%), P = .53, respectively) (Table 2 ). The Chi-square test for HWE showed that rs2275913 in the non-severe group was not in HWE (P = .005), but in the severe group, it was in HWE (P = .18). 3.5 Association Between IL-6 rs1800795 and IL-17A rs2275913 Genotypes with COVID-19 Patients' Characteristics Supplementary Table 2 shows that different genotypes of rs1800795 were significantly associated with IL-6 concentrations in the severe cohort (P < .001), with a higher increase in patients with GG genotype than patients with GC or CC genotype. Also, rs1800795 different genotypes were significantly associated with reduced lymphocyte count (P = .041), elevated C-reactive protein (CRP) (P = .013), and increased serum IL-6 (P = < .03) in severe patients (Fig. 3 ). IL-17A rs2275913 genotypes were only demonstrated a significant association with lactate dehydrogenase (LDH) (P = .048) in no-severe patients and elevated CRP (P = .02) in severe patients (Supplementary Table 3). 3.6 Clinical-Demographic Characteristics of Survivor and Non-Survivor Patients Among 280 included patients, 228 (81%) were discharged, and 52 (19%) did not survive the disease. Non-survivors were older than survivors (52 (43.5–59.8) Vs. 44.6 (37-55.8), P = .004), but no significant difference in the frequency of comorbidities (P = .058) was observed between the two groups. However, non-survivors had a significantly lower SPO 2 and higher respiratory rate than survivors (77 (74.2–81.7) Vs. 93 (88–96), P < .001, and 31 (30–33) Vs. 27 (26–30), P < .001, respectively), and all non-survivors were admitted to ICU, while only 32% of survivors were admitted to ICU (P < .001). Non-survivors also had significantly lower hemoglobin (Hb)(P < .001) and lymphocyte count (P < .001) in comparison to survivors. While, INR (P < .001), D-dimer (P < .001), LDH (P = .04), serum level of IL-6 (P < .001), and serum level of IL-17A (P < .001) in non-survivors were significantly higher than survivors (Table 3 and Fig. 4 ). Table 3 Characteristics of survivor and non-survivor COVID-19 patients. Abbreviations : WBC: white blood cells count; INR: international normalized ratio; CRP: C-reactive protein, AST: aspartate transaminase; ALT: alanine transaminase; LDH: lactate dehydrogenase; IL-6 Interleukin 6; IL-17A: Interleukin 17A. *P-value by Kruskal-Wallis H-test (data as Median (25th percentile – 75th percentile)). For other comparisons P value by Chi-square test (data as count and percentage). Demographic & Clinical Characteristics Survivor (n = 228) Non-survivor (n = 52) P Value Age (years) 44.6 (37-55.8) 52 (43.5–59.8) .004 Sex Female Male 121 (53.1%) 107 (46.9%) 25 (48.1%) 27 (21.9%) .51 Comorbidities n, (%) Diabetes Mellitus Hypertension Ischemic Heart Disease Chronic Liver Disease Chronic Kidney Disease 70 (30.7%) 90 (39.5%) 28 (12.3%) 29 (12.7%) 21 (9.2%) 22 (42.3%) 21 (40.4%) 4 (7.7%) 8 (15.4%) 2 (3.8%) .10 .90 .34 .60 .20 Clinical Characteristics * SPO 2 Respiratory Rate 93 (88–96) 27 (26–30) 77 (74.2–81.7) 31 (30–33) < .001 < .001 IL-6 Genotypes GG GC CC IL-17A Genotypes GG GA AA 98 (43%) 100 (43.9%) 30 (13.2%) 90 (39.5%) 112 (49.1%) 26 (11.4%) 31 (59.6%) 16 (30.8%) 5 (9.6%) 23 (44.2%) 24 (46.2%) 5 (9.6%) .09 .04 .80 Laboratory Parameters * Hemoglobin (g/dL) WBC (×10 3 /mm 3 ) Lymphocyte count (×10 3 /mm 3 ) Platelets (×10 3 /mm 3 ) INR D-dimer (ng/mL) CRP (mg/L) AST (U/L) ALT (U/L) LDH (U/L) Creatinine(mg/dL) IL-6 (pg/mL) IL-17A (pg/mL) 12.1 (10.7–13.7) 8.8 (7.5–10.9) 1.3 (1-1.6) 241.4 (206.6-266.5) 1.18 (1.1–1.3) 443.5 (375-560.2) 45.1 (28.3–63.7) 33.5 (21.4–44.9) 30.4 (21.3–40) 456.5 (375.2-484.3) 1.00 (0.9–1.2) 27 (18–42) 20.1 (14–25) 10.4 (9.4–11.5) 9.9 (7.9–11.3) 0.9 (0.79–1.05) 237.1 (215.3-259.9) 1.3 (1.2–1.3) 632 (498.2-1113.2) 53.4 (32.2–75.8) 28 (18–41) 36 (21.745.7) 503.2 (387-558.2) 1.01 (0.9–1.4) 42.5 (29.5-53.75) 25.7 (22-28.7) < .001 .28 < .001 .64 < .001 < .001 .16 .09 .25 .04 .74 < .001 < .001 3.7 rs1800795 and rs2275913 and Mortality of COVID-19 Although no significant difference in overall genotypes of rs1800795 (P = .009) and rs2275913 (P = .80) was seen between survivor and non-survivor patients, in the recessive model, the GG genotype of rs1800795 revealed a significant association with mortality of COVID-19 (OR = 1.95, 95%CI: 1.06–3.61, adjusted P value = .04). On the contrary, no other significant associations were observed for the remaining genotypes of rs1800795 and rs2275913 (Table 4 ). Table 4 Frequency of IL-6 rs1800795 and Il-17A rs2275913 (Genotypes, and Alleles,) and their association with the risk of COVID-19 mortality. *P-values by Chi-square test; ** P-values are Benforini adjusted Non-Survivor (n = 52) Survivor (n = 228) P-Value* COVID-19 Mortality Risk P-Value** Odds Ratio 95% CI Rs1800795 Genotype GG 31 (36.4%) 98 (43%) .09 1.95 1.06–3.61 .04 GC 16 (47.9%) 100 (43.9%) 0.56 0.29–1.08 .12 CC 5 (15.7%) 30 (13.2%) 0.70 0.25–1.90 0.48 Rs2275913 Genotype GG 23 (44.2%) 90 (39.5%) .80 1.21 0.66–2.23 .52 GA 24 (46.2.%) 112 (49.1%) 0.88 0.48–1.62 .38 AA 5 (9.6%) 26 (11.4%) 0.82 0.30–2.26 .71 Rs1800795 Allele G 78 (75%) 296 (64.9%) .051 1.62 0.99–2.63 .0501 C 26 (25%) 160 (%35.1) 0.61 0.38-1.00 .0501 Rs2275913 Allele G 70 (67.3%) 292 (64%) .52 1.15 0.73–1.81 0.52 A 34 (32.6%) 164 (36%) 0.86 0.55–1.35 0.52 3.8 Mortality Predictor Variables in COVID-19 Patients The results for univariate regression analysis of variables linked to the mortality in COVID-19 patients are summarized in Table 5 . Using the optimal cutoff driven from ROC curves (Fig. 5 ) and maximal Youden index, only respiratory rate ≥ 29 (P = .001), rs1800795 GG genotype (P = .04), IL-6 serum level (P = .013), INR (P = .007), and D-dimer level (P = .019) have been found to be associated with COVID-19 mortality. The accuracy of these indicators is presented in Table 6 . Table 5 Univariate meta-regression Variable OR (96% CI) P value Age 76% ≤ 76% - 1.11 (0.67–1.87) .67 Respiratory Rate 8.42 g/dL ≤ 8.42 g/dL - 1.57 (0.89–1.91) .23 Lymphocyte count > 0.750 /mm3 ≤ 0.750 /mm3 - 3.93 (1.7–9.5) .16 INR < 1.2 ≥ 1.2 - 2.19 (1.61–3.78) .007 D-dimer < 565.5 ng/mL ≥ 565.5 ng/mL - 3.12 (1.27–5.68) .019 LDH < 499 U/L ≥ 499 U/L - 1.005 (0.995–1.016) .31 IL-6 28.5 pg/mL - 1.974(1.942–2.06) .013 IL-17 < 20.5 pg/mL ≥ 20.5 pg/mL - 1.76 (0.91–3.11) .12 Abbreviations : INR: international normalized ratio; LDH: lactate dehydrogenase; IL-6 Interleukin 6; IL-17A: Interleukin 17A Table 6 ROC curve AUC. Variable AUC Cut off 96% CI P value INR .676 ≥ 1.2 .594-.757 < .001 D-dimer .734 ≥ 565.5 ng/mL .649-.819 < .001 IL-6 .744 ≥ 28.5 pg/mL .673-.814 < .001 Respiratory Rate .695 ≥ 29 .620-.770 < .001 Abbreviations : INR: international normalized ratio; IL-6 Interleukin 6; AUC: area under the curve Discussion The involvement of dysregulated immunological signaling in the pathogenesis of SARS-CoV-2 infection is well-documented. Novel findings on pro-inflammatory cytokines, immunological modulation, and signaling pathways related to COVID-19 infection have been used as a platform to develop pharmacological and interventional studies [ 41 ]. One area that can affect such efforts is the genetic variations of different populations, which underlie the susceptibility to and outcome of diseases, especially COVID-19 [ 42 – 45 ]. In the present study, to investigate two genetic variations in IL-6 and IL-17A genes, 280 Iranian COVID-19 patients were evaluated. Our results revealed that the IL-6 rs1800795 GG genotype and G allele were associated with severe COVID-19, while the GC genotype and C allele were associated with decreased risk of COVID-19. Also, the GA genotype of IL-17A rs2275913 was linked to a lower risk of disease severity. As a pleiotropic soluble immunological mediator, IL-6 is produced transiently in response to infectious diseases and tissue damage. By induction of hematopoiesis, regulating acute phase response, and immunological reaction, IL-6 plays a vital role in the host defense. Strict transcriptional and post-transcriptional mechanisms regulate the IL-6 expression; although, its dysregulated production is related to hyper inflammation, chronic inflammation, and autoimmune responses [ 20 ]. Also, elevated serum level of IL-6 has been considered as a hallmark of COVID-19 severity and poor prognosis of patients with ARDS [ 22 , 46 ]. The complications related to COVID-1 9 range from asymptomatic to ARDS development as a life-threatening lower respiratory tract infection [ 47 ]. Several pieces of evidence suggest that the IL-6 genotype might determine the variation in the range of outcomes of COVID-19 infection [ 17 ]. The present study illustrated that the wild GG genotype and the G allele showed association with a greater risk of COVID-19 severity, while GC, CC genotype, and C allele were linked to a lesser risk of disease severity. Also, we found that the rs1800795 GG genotype is related to COVID-19 mortality. The IL-6 rs1800795 is present in the promoter region of the IL-6 gene, affecting the level of this cytokine which has more prevalent substitutions in Caucasians than East Asian and African populations [ 48 ]. Numerous clinical evidence suggests that the rs1800795 G allele is associated with an increased risk of different diseases, including type 2-DM [ 49 ], cancer [ 50 ], endometriosis [ 51 ], coronary artery disease [ 52 ], Crohn's disease [ 53 ], among others. Also, in most of these studies, an association between the higher levels of IL-6 and the rs1800795 G allele was seen, while the rs1800795 C allele was linked to a low level of IL-6 [ 54 ]. Similarly, in this study, we also showed that IL-6 serum concentration was generally elevated in the severe cohort of COVID-19 patients as well as non-survivor patients. Particularly, IL-6 serum concentration was significantly higher in patients with GG genotype. In contrast, the difference between IL-6 serum levels in survivor and non-survivor patients with CC genotype was not significant. This might partly explain the observed association between GG genotype with disease severity and mortality as higher serum concentration of IL-6 is correlated with poor prognosis in COVID − 19 patients [ 17 ]. In this regard, a recent study on the Turkish population demonstrated that rs1800795 GG genotype and G allele was more frequent in severe COVID-19 patients who developed macrophage activation syndrome (MAS). Furthermore, the G allele has been shown to be a risk factor for increased IL-6 serum concentrations [ 55 ]. Also, HCV-infected patients with low-producing rs1800795 CC genotype were found to have an attenuated adaptive immune response against HCV [ 56 ]. In contrast, higher levels of IL-6 in co-infected HCV/HIV patients with GG genotype demonstrated a higher sustained virologic response (SVR) [ 57 ]. CD4 + T helper 17 (Th17) cells produce the IL-17 family of cytokines. IL-17A and IL-17F are considered inflammatory members of this family [ 58 , 59 ]. IL-17A stimulates the inflammatory response by regulating polymorphonuclear cells (PMNs), regulating the release of granulocyte-colony stimulating factor (G-CSF), differentiation of CD34 + towards neutrophils, and promoting IL-6, IL-1, and TNF-α secretion [ 60 – 64 ]. Therefore, the clinical evidence that highlights the elevated level of IL-17A in COVID-19 patients is of particular importance [ 34 , 65 ]. Rs2275913 is one of the well-studied SNPs of IL-17A located in the promoter region and has shown to be associated with susceptibility to infectious pulmonary diseases [ 66 , 67 ]. We demonstrated that the IL-17A rs2275913 GA genotype is significantly linked to decreased risk of COVID-19 severity. In the European population, the frequency of the rs2275913 G allele is 65%, according to the 1,000 genome database [ 37 ]. The G allele also had the highest frequency among examined groups in our study, but it was not significantly associated with either disease severity or mortality. Also, no significant correlation between rs2275913 genotypes and the A allele with the mortality of COVID-19 was observed. A recent study on post-mortem lung samples from COVID-19 non-survivors and H1N1 non-survivors reported no significant difference in the distribution of genotype frequencies of rs2275913 between the two groups. Also, there was no significant association of rs2275913 with IL-17A tissue expression in lung samples from COVID-19 patients [ 37 ]. Likewise, no significant association between rs2275913 genotypes and the IL-17A serum concentration was identified in the present study, although its overall concentration was significantly higher in severe patients and non-survivors. On the contrary, recently, it has been shown that the rs2275913 G allele significantly increases the risk of the influenza A virus [ 68 ]. Also, Ren et al. found a significant association between rs2275913 GG genotype and G allele with HCV risk in a Chinese Han population [ 69 ]. Since the mortality prediction among COVID-19 patients could aid clinicians in managing clinical care, we aimed to evaluate the potential indicators of COVID-19 mortality. According to univariate analysis, the GG genotype of rs1800795 was linked to COVID-19 mortality. As mentioned earlier, this high IL-6 producing genotype is associated with a higher IL-6 serum level (≥ 28.5 pg/mL) which has shown to be correlated with a poor prognosis in COVID-19 [ 70 ]. Increased respiratory rate (≥ 29) was also a predictor of mortality that represents the hypoxic condition, a known predictor of poor prognosis in infected patients with SARS-CoV-2 [ 71 ]. D-dimer ≥ 565.5 ng/mL and INR ≥ 1.2 were other predictors of death. Previous studies on COVID-19 suggest that a 3 to 4-fold positive change in D-dimer is associated with an unfavorable prognosis [ 72 ]. Furthermore, the dysregulated micro-coagulation and blood coagulation cascades associated with hyperinflation in COVID-19 infection are considered as the main causes of elevated INR [ 73 ]. There are certain limitations to this study. First, this was a single-center study with limited involvement of patients with different ethnicities, representing a small proportion of the population. Second, several comorbidities and non-genetic risk factors were presented in the study sample that might interfere with the final reported outcomes. Third, some laboratory parameters and radiological tests were not performed for a proportion of the patients; thus they were not included in the analysis. Conclusion The present study found that IL-6 rs1800795 GG genotype and G allele were associated with higher risk, and GC genotype was associated with lower risk of COVID-19 severity. Furthermore, the GG allele was associated with higher IL-6 serum concentration in severe and non-survivor patients. 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Life Sciences, 2020. 254 : p. 117788. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3215016","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":222759261,"identity":"f4b0ba8c-7810-450a-84b6-b1887b8012f4","order_by":0,"name":"Mostafa Khafaei","email":"","orcid":"","institution":"Baqiyatallah University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Khafaei","suffix":""},{"id":222759262,"identity":"4fa4d50d-2a34-4d90-84fc-210aa8a359a4","order_by":1,"name":"Reza Asghari","email":"","orcid":"","institution":"Baqiyatallah University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Asghari","suffix":""},{"id":222759263,"identity":"31469a7b-d49b-4da9-a4df-3ee66632f93b","order_by":2,"name":"fariba zafari","email":"","orcid":"","institution":"Qazvin University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"fariba","middleName":"","lastName":"zafari","suffix":""},{"id":222759264,"identity":"59b58c52-0599-4a2b-b23b-477f22609a4e","order_by":3,"name":"morteza sadeghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYNACNgYGA2bmAwyMDaRpYUsgVQsDjwFxWnT7DzB/+FFmY7edneebxM8dNnIM7IePbsCnxexGAptkz7m05J3NvNske8+kGTPwpKXdwK8F6CretsPJBod5t0kAGYkNEjxm+LWcP8D88W/bf6AWnmeSf4nSciCBQZq37YAdUAubNHG23Ehsk5Y5l5xgcJjN2Fq2Lc2YjaBfzh8+/PFNmZ29wfnDD2++bbOR42c/fAyvFgZoXCQCSRYJEIsNv3IEsAdi5g/Eqh4Fo2AUjIKRBQAQIUrfjTApsgAAAABJRU5ErkJggg==","orcid":"","institution":"Baqiyatallah University of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"morteza","middleName":"","lastName":"sadeghi","suffix":""}],"badges":[],"createdAt":"2023-07-29 07:14:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3215016/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3215016/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41103898,"identity":"0779479c-04ca-4605-88f3-13f6f7ef58af","added_by":"auto","created_at":"2023-08-05 00:07:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1611233,"visible":true,"origin":"","legend":"\u003cp\u003eTetra-ARMS PCR results. \u003cstrong\u003eA\u003c/strong\u003e. IL-17A rs2275913 genotyping \u003cstrong\u003eB\u003c/strong\u003e. IL-6 rs1800795 genotyping\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/d1ddd073fb825ef458a5c434.png"},{"id":41101177,"identity":"4ca2da9f-d4b6-4c01-ab65-0d1fe5986eb6","added_by":"auto","created_at":"2023-08-04 23:59:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1249840,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmatory sequencing results for the PCR products of\u003cstrong\u003e \u003c/strong\u003eIL-17A rs2275913 and IL-6 rs1800795\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/5b108f25809d56eef9ac89ea.png"},{"id":41104221,"identity":"1eb103e9-ab60-4b8b-9278-175edd8f4b46","added_by":"auto","created_at":"2023-08-05 00:15:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":412482,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of IL-6 and IL-17A serum levels across multiple genotypes of rs1800795 and rs2275913 in both non-severe and severe groups of COVID-19 patients. \u003cstrong\u003eA. \u003c/strong\u003eIL-6 serum levels across rs1800795 genotypes \u003cstrong\u003eB. \u003c/strong\u003eIL-17A serum levels across rs2275913. P values by t-test.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/67e90b699222a6581f65894c.png"},{"id":41101182,"identity":"a640c433-230f-4821-9993-0b257b89d443","added_by":"auto","created_at":"2023-08-04 23:59:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":457537,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of IL-6 and IL-17A serum levels across multiple genotypes of rs1800795 and rs2275913 in both survivor and non-survivor groups of COVID-19 patients. \u003cstrong\u003eA. \u003c/strong\u003eIL-6 serum levels across rs1800795 genotypes \u003cstrong\u003eB. \u003c/strong\u003eIL-17A serum levels across rs2275913. P values by t-test.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/51b27075cf29d85d08c0d54c.png"},{"id":41103897,"identity":"cfc41ff8-9aaf-4cd3-9142-0e0d43576a43","added_by":"auto","created_at":"2023-08-05 00:07:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":882847,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for quantitative predictors to find a cutoff value that discriminates survivors from non-survivors. \u003cstrong\u003eAbbreviations: \u003c/strong\u003eINR: international normalized ratio; IL-6 Interleukin 6; AUC: area under the curve\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/4360894cc57c83eeb41dc958.png"},{"id":50472718,"identity":"1625f5d0-87ca-4430-85fb-2b9019157f09","added_by":"auto","created_at":"2024-02-01 04:53:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1761175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/8771fc47-1030-4be3-a74b-f7aec8744907.pdf"},{"id":41101178,"identity":"ecb888cf-f841-4660-8591-ac01a02bbba1","added_by":"auto","created_at":"2023-08-04 23:59:40","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":15237,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/c6850049a9b208638ff600b4.docx"},{"id":41101179,"identity":"d255ef60-9940-42bd-8782-064441a84f2f","added_by":"auto","created_at":"2023-08-04 23:59:40","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":21898,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/9c643f8852763224350b328e.docx"},{"id":41101180,"identity":"265f2fbf-8b95-4e66-bdfc-466f0cee9e15","added_by":"auto","created_at":"2023-08-04 23:59:40","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":21519,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3215016/v1/5d6bb9bc57c73cdf4fad0146.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of IL-6 rs1800795 and IL-17A rs2275913 Gene Polymorphisms on the COVID-19 Prognosis and Susceptibility in a Sample of Iranian Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of COVID-19 cases has surpassed 390\u0026nbsp;million worldwide, with over 6\u0026nbsp;million reported deaths (source: WHO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covid19.who.int/\u003c/span\u003e\u003cspan address=\"https://covid19.who.int/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). While significant efforts have been devoted to understanding the infection mechanism of Coronavirus Disease-2019 (COVID-19), its physiopathology remains incompletely understood. However, it is widely recognized that the cytokine storm plays a pivotal role in the increased hospitalization rates, intensive care admissions, and mortality observed in a subset of COVID-19 patients who develop severe disease [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cytokine storm, characterized by an acute hyperinflammatory response, is associated with elevated serum levels of various pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α), interleukin (IL)-17A, IL-4, IL-1, IL-6, and interferon-γ (IFN-γ) [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This phenomenon manifests predominantly, though not exclusively, in the lungs, leading to acute respiratory distress syndrome (ARDS) in COVID-19 patients [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While age, body mass index (BMI), and comorbidities are established risk factors for the severity of SARS-CoV-2 infection, disease severity can still occur in patients without evident risk factors. Hence, it is plausible that other factors, such as genetic variations, contribute to disease severity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Exploring the impact of single nucleotide polymorphisms (SNPs) within cytokine and chemokine genes, known to influence the remodeling of the immune response during COVID-19 infection, could shed light on the hyperinflammatory state associated with the cytokine storm in COVID-19 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Notably, numerous polymorphisms in immune-related genes have been implicated in severe COVID-19 infection [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the onset of the pandemic, elevated serum concentrations of IL-6 have consistently been observed in severely ill patients with COVID-19, serving as a prominent indicator [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. IL-6 is a pleiotropic cytokine secreted by various cell types, including macrophages, B and T lymphocytes, and dendritic cells. The increased levels of IL-6 have been closely associated with a hyperinflammatory state and the progression of acute respiratory distress syndrome (ARDS), a leading cause of mortality in coronavirus-related infections such as COVID-19, SARS, and MERS [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Consequently, monitoring IL-6 serum concentrations has been employed as a predictor of COVID-19 severity and mortality [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, recognizing the crucial role of IL-6 in the dysregulated immune response to SARS-CoV-2, numerous clinical trials have emerged to target this cytokine specifically [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Given the significance of IL-6 in regulating CD4\u0026thinsp;+\u0026thinsp;T cells, exploring related genetic polymorphisms can provide insights into the molecular aspects of COVID-19. Among these polymorphisms, rs1800795, located on the IL-6 promoter, has been associated with IL-6 expression levels. The rs1800795 GG genotype has demonstrated protective effects against pneumococcal pneumonia and pneumonia-induced sepsis, while G alleles have been linked to worse outcomes in HCV infection [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A meta-analysis conducted by Ulhaq and Soraya additionally revealed that carrier status of rs1800795 is associated with elevated IL-6 production and increased severity of pneumonia [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, several reports have highlighted the correlation between increased Th17 lymphocytes, elevated serum IL-17A levels, and clinical severity and progression of SARS-CoV-2 infection [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. IL-17A, a non-canonical pro-inflammatory cytokine, induces the production of other pro-inflammatory cytokines such as IL-1, IL-6, and TNF-α, thereby playing a significant role in the cytokine storm [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The decrease in lymphocytic population observed in COVID-19 patients, along with an increase in the fraction of Th17 lymphocytes and Th17-produced cytokines, supports the involvement of IL-17A in driving the hyperinflammatory immune response [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Several IL-17A SNPs have been examined and found to be associated with asthma and infectious lung diseases, suggesting susceptibility to disease progression [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In line with these findings, a retrospective study investigating IL-17A gene SNPs, which result in low IL-17A production, indicated enhanced 30-day survival rates in patients with ARDS [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, a meta-analysis revealed that the polymorphism rs2275913, located in the regulatory sequence of the IL-17A gene, was associated with disease prevalence and mortality rates among COVID-19 patients in different countries [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the critical role of IL-6 and IL-17A in modulating the immune response to COVID-19 infection and the cytokine storm, exploring genetic variations within these genes may help elucidate the wide range of symptoms observed in COVID-19 patients. Therefore, we conducted an investigation focusing on two common SNPs, rs1800795 and rs2275913, in Iranian patients with COVID-19, aiming to explore their association with disease severity, mortality, and the cytokine storm.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient Enrollment\u003c/h2\u003e \u003cp\u003eThis study included 280 adult COVID-19 patients: 140 severe cases and 140 non-severe cases. Patients were referred to the COVID-19 care units in Baqiyatallah Hospital between December 2020 and October 2021. Patients were diagnosed and classified based on their disease severity using World Health Organization (WHO) interim guidelines [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The non-severe group included patients who showed CT features of COVID-19 and peripheral capillary oxygen saturation (SPO\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;93%, and the severe group included patients with each of the following: respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;30 breaths/min or oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;\u0026le;\u0026thinsp;93%, mechanical ventilation upon respiratory failure and/or ICU admission, shock, and organ failure syndrome. A positive RT-PCR result for SARS-CoV-2 RNA in nasopharyngeal swabs confirmed the diagnosis for all patients. The following criteria were used to exclude patients: lack of pneumonia symptoms, missing medical history data, aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years, and pregnant women. At the time of hospital admission, the demographic and clinical data were recorded for each patient, including age, gender, presence of comorbidities such as chronic renal disease, diabetes, and liver disease. Also, chest Computed Tomography (CT) scan, laboratory parameters, and clinical outcome (ICU admission or not; discharge or death) were recorded. This study was carried out in line with the Helsinki Declaration. The study protocol was approved by the institutional review board (IRB) and research ethics committee of Baqiyatallah University of Medical Sciences, Tehran, Iran (Approval Code: IR.BMSU.REC.1399.466).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Preparation\u003c/h2\u003e \u003cp\u003eAfter 15 min of semi-supine rest, five ml of the study participants' peripheral blood was collected via venipuncture and split into two tubes: a) ACD blood collection tubes that were stored at -30\u0026deg;C until target SNPs were genotyped using tetra primer-amplification refractory mutation system based polymerase chain reaction (tetra-ARMS-PCR), and b) plain blood collection tubes with no anticoagulants were used to separate serum, which was then kept at -30\u0026deg;C until serum concentrations of cytokines were measure by the enzyme-linked immunosorbent assay (ELISA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Molecular Template Preparation\u003c/h2\u003e \u003cp\u003eFollowing thawing and proper mixing of the frozen blood samples, the genomic DNA was extracted using Rapid Genomic DNA Extraction (RGDE) method [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and stored at 4\u0026deg;C until genotyping. The quality, concentration, and purity of the extracted DNA were determined using agarose gel electrophoresis and spectrophotometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular Analysis of Target SNPs\u003c/h2\u003e \u003cp\u003eTwo SNPs (rs1800795 in IL-6 and rs2275913 in IL-17A) were successfully genotyped using tetra-ARMS-PCR. Primers were purchased from Pishgam (Tehran, Iran) (Supplementary Table\u0026nbsp;1). The genotyping assays were performed in the final volume of 10 \u0026micro;l. The PCR mixture contained five \u0026micro;l of master mix (Amplicon, Denmark), one \u0026micro;l of each Inner (reverse \u0026amp; forward) and Outer (reverse \u0026amp; forward) primers (10 pmol/\u0026micro;l), and one \u0026micro;l of DNA (\u0026asymp;\u0026thinsp;20 ng). The PCR machine (Thermal Cycler, BioRad, USA) was set for the following optimized reaction conditions: initial denaturation at 95\u0026deg;C for 5 min, followed by 32 cycles of denaturation at 95\u0026deg;C for 20 s, annealing at 61\u0026deg;C for 35 s, extension at 72\u0026deg;C for 16 s, final extension at 72\u0026deg;C for 10 min, and 10 min cooling at 4\u0026deg;C. Product amplicons were monitored using electrophoresis on agarose gel, and product bands were observed using a UV trans-illuminator (B \u0026amp; L Systems, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Confirmatory Sequencing\u003c/h2\u003e \u003cp\u003eSequencing was performed on 10% of samples to confirm genotypes identified by tetra-ARMS PCR. Amplified PCR products were used for sequencing (3130xl Genetic Analyzer, Applied Biosystems, USA). The sequencing data were examined with the software \"GeneRunner version 6.5.50 and Chromas Pro version 2.1.8.\" By comparing PCR results to sequencing results, the genotyping specificities of the target SNPs were evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe Statistical Package for Social Sciences (SPSS) was used for statistical analysis (version 26.0 IBM Corp., NY, USA). Quantitative variables were presented as numbers (percent) and median (25th \u0026ndash; 75th ) and were analyzed using the independent-samples t‐test or Mann\u0026ndash;Whitney U test, wherever applicable. For categorical variables, the Chi-square test was employed to compare groups. In the case of inter-group comparisons (two and three comparison groups), non-normally distributed quantitative data were analyzed by the Mann\u0026ndash;Whitney U test and Kruskal-Wallis H-test. Adjusted P values by the Bonferroni method were reported for multiple comparisons in the analysis. To examine the deviation of the observed frequencies in genotypes from the expected values from the Hardy\u0026ndash;Weinberg (HW) model, the Chi-square test was used. Several inheritance models were evaluated for genotypic and allelic association with the disease severity or mortality in different groups. Also, odds ratios (OR) and 95% confidence intervals (CI) were calculated to interpret the results. The likelihood of mortality in patients was tested using univariate logistic regression. Receiver operator characteristics (ROC) curves and the highest Youden index (sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026ndash; 1) were employed to estimate the optimal mortality cutoff for each predictor in the regression analysis. P values of \u0026lt;\u0026thinsp;0.05 were considered significant in any performed test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Patients\u0026apos; Characteristics\u003c/h2\u003e\n \u003cp\u003eIn the severe group, patients were considerably older than the non-severe cohort (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). In the non-severe group, there were 69 (49.3%) women and 71 (50.7%) men, while there were 77 (55%) women and 63 (45%) men in the severe group (P\u0026thinsp;=\u0026thinsp;.33). In comparison to the non-severe patients, some comorbidities had significantly higher frequencies in the severe cohort, including diabetes mellitus (DM) (40% Vs. 25%, P\u0026thinsp;=\u0026thinsp;.01), hypertension (HTN) (49.3% Vs. 30%, P\u0026thinsp;=\u0026thinsp;.001), and ischemic heart disease (IHD) (15.7% Vs. 17.1%, P\u0026thinsp;=\u0026thinsp;.02). In comparison to non-severe patients, a significantly lower SPO\u003csub\u003e2\u003c/sub\u003e (P\u0026thinsp;=\u0026thinsp;.007) in the severe cohort were accompanied by significantly higher respiratory rate (P\u0026thinsp;=\u0026thinsp;.03), higher Chest Computed Tomography Severity Scoring (CT-SS) (P\u0026thinsp;=\u0026thinsp;.003), more ICU admission rate (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), and elongated hospital stay duration (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, the in-hospital death rate in severe patients was significantly greater than in non-severe patients. (7(5%) Vs. 45(31.5%), P\u0026thinsp;\u0026lt;\u0026thinsp;.001). In addition, serum concentrations of IL-6 and IL-17A were significantly higher in severe patients than non-severe patients (19.5 pg/mL Vs. 44 pg/mL, P\u0026thinsp;\u0026lt;\u0026thinsp;.001, and 15 pg/mL Vs. 26 pg/mL, P\u0026thinsp;=\u0026thinsp;.004, respectively). Detailed comparisons of symptoms and laboratory findings between the two groups are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" style=\"width: 627px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of included COVID-19 patients grouped as severe and non-severe. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: WBC: white blood cells count; INR: international normalized ratio; CRP: C-reactive protein, AST: aspartate transaminase; ALT: alanine transaminase; LDH: lactate dehydrogenase; IL-6 Interleukin 6; IL-17A: Interleukin 17A. *P-value by Kruskal-Wallis H-test (data as Median (25th percentile \u0026ndash; 75th percentile)). For other comparisons P value by Chi-square test (data as count and percentage).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 231.312px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003eNon-Severe\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e41.7 (30.8\u0026ndash;47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e55 (46-57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eSex n, (%)\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69 (49.3%)\u003c/p\u003e\n \u003cp\u003e71 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77 (55%)\u003c/p\u003e\n \u003cp\u003e63 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eComorbidities n, (%)\u003c/p\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eIschemic Heart Disease\u003c/p\u003e\n \u003cp\u003eChronic Liver Disease\u003c/p\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36 (25.7%)\u003c/p\u003e\n \u003cp\u003e42 (30.0%)\u003c/p\u003e\n \u003cp\u003e10 (7.1%)\u003c/p\u003e\n \u003cp\u003e22 (15.7%)\u003c/p\u003e\n \u003cp\u003e14 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56 (40.0%)\u003c/p\u003e\n \u003cp\u003e69 (49.3%)\u003c/p\u003e\n \u003cp\u003e22 (15.7%)\u003c/p\u003e\n \u003cp\u003e15 (10.7%)\u003c/p\u003e\n \u003cp\u003e9 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.\u003cstrong\u003e01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eSymptoms n, (%)\u003c/p\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003cp\u003eDiarrhea\u003c/p\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003cp\u003eRhinitis\u003c/p\u003e\n \u003cp\u003eMyalgia\u003c/p\u003e\n \u003cp\u003eArthralgia\u003c/p\u003e\n \u003cp\u003eSore throat\u003c/p\u003e\n \u003cp\u003eLoss of taste/smell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e101 (72.1%)\u003c/p\u003e\n \u003cp\u003e5 (3.6%)\u003c/p\u003e\n \u003cp\u003e103 (73.6%)\u003c/p\u003e\n \u003cp\u003e97 (69.3%)\u003c/p\u003e\n \u003cp\u003e124 (88.6%)\u003c/p\u003e\n \u003cp\u003e21 (15%)\u003c/p\u003e\n \u003cp\u003e120 (85.7%)\u003c/p\u003e\n \u003cp\u003e97 (69.3%)\u003c/p\u003e\n \u003cp\u003e34 (24.3%)\u003c/p\u003e\n \u003cp\u003e15 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e117 (83.6%)\u003c/p\u003e\n \u003cp\u003e6 (4.3%)\u003c/p\u003e\n \u003cp\u003e117 (83.6%)\u003c/p\u003e\n \u003cp\u003e112 (80.0%)\u003c/p\u003e\n \u003cp\u003e131 (93.6%)\u003c/p\u003e\n \u003cp\u003e36 (25.7%)\u003c/p\u003e\n \u003cp\u003e106 (75.7%)\u003c/p\u003e\n \u003cp\u003e110 (78.6%)\u003c/p\u003e\n \u003cp\u003e43 (30.7%)\u003c/p\u003e\n \u003cp\u003e29 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.04\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.03\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.03\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003cp\u003e.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eICU Admission n, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e12 (8.6%%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e111 (79.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eCT-SS *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e6 (3\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e19 (16\u0026ndash;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eHospital stay (days) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e3.5 (2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e21 (19\u0026ndash;22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eFate n, (%)\u003c/p\u003e\n \u003cp\u003eSurvivor\u003c/p\u003e\n \u003cp\u003eNon-survivor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e133 (95%)\u003c/p\u003e\n \u003cp\u003e7(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95 (67.9%)\u003c/p\u003e\n \u003cp\u003e45 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eClinical Characteristics *\u003c/p\u003e\n \u003cp\u003eSPO\u003csub\u003e2\u003c/sub\u003e%\u003c/p\u003e\n \u003cp\u003eRespiratory Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95 (94\u0026ndash;96)\u003c/p\u003e\n \u003cp\u003e27 (25\u0026ndash;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82.5 (78\u0026ndash;88)\u003c/p\u003e\n \u003cp\u003e32 (30\u0026ndash;33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.007\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 231.312px;\" align=\"left\"\u003e\n \u003cp\u003eLaboratory Parameters *\u003c/p\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003cp\u003eWBC (\u0026times;10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eLymphocyte count (\u0026times;10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003ePlatelets (\u0026times;10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003cp\u003eD-dimer (ng/mL)\u003c/p\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e\n \u003cp\u003eIL-17A (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140.688px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.7 (10.4\u0026ndash;13.2)\u003c/p\u003e\n \u003cp\u003e8.8 (7.5\u0026ndash;10.7)\u003c/p\u003e\n \u003cp\u003e1.3 (1-1.6)\u003c/p\u003e\n \u003cp\u003e227.3 (185.3\u0026ndash;269)\u003c/p\u003e\n \u003cp\u003e1.12 (1.1\u0026ndash;1.2)\u003c/p\u003e\n \u003cp\u003e431 (366\u0026ndash;542)\u003c/p\u003e\n \u003cp\u003e37.4 (21.7\u0026ndash;56.7)\u003c/p\u003e\n \u003cp\u003e34.5 (23.8\u0026ndash;46.8)\u003c/p\u003e\n \u003cp\u003e26.5 (17.3\u0026ndash;35.2)\u003c/p\u003e\n \u003cp\u003e409 (357.2-454.5)\u003c/p\u003e\n \u003cp\u003e0.9 (0.8-1)\u003c/p\u003e\n \u003cp\u003e19.5 (16\u0026ndash;25)\u003c/p\u003e\n \u003cp\u003e15 (12-19.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.431%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.5 (10.2\u0026ndash;13.5)\u003c/p\u003e\n \u003cp\u003e9.3 (7.6\u0026ndash;11.4)\u003c/p\u003e\n \u003cp\u003e1.1 (0.84\u0026ndash;1.5)\u003c/p\u003e\n \u003cp\u003e244.9 (225.4-263.9)\u003c/p\u003e\n \u003cp\u003e1.3 (1.2\u0026ndash;1.3)\u003c/p\u003e\n \u003cp\u003e534 (389.2\u0026ndash;659)\u003c/p\u003e\n \u003cp\u003e56.4 (35.4\u0026ndash;77.2)\u003c/p\u003e\n \u003cp\u003e28.5 (18\u0026ndash;42)\u003c/p\u003e\n \u003cp\u003e38 (26\u0026ndash;46)\u003c/p\u003e\n \u003cp\u003e497.5 (405.7-556.7)\u003c/p\u003e\n \u003cp\u003e1.12 (0.9\u0026ndash;1.4)\u003c/p\u003e\n \u003cp\u003e44 (36.2\u0026ndash;51)\u003c/p\u003e\n \u003cp\u003e26 (22-29.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8896%;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003cp\u003e.\u003cstrong\u003e001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.003\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Tetra-ARMS PCR and Sequencing Results\u003c/h2\u003e\n \u003cp\u003eThe sizes of specific amplified PCR products of IL-6 rs1800795 were 232 bp (G allele) and 401 bp (C allele) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). For IL-17A rs2275913, the sizes of specific amplified PCR products were 193 bp (A allele) and 247 bp (G allele) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The corresponding sequencing data accurately confirmed the genotypes identified by tetra-ARMS PCR (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 rs1800795 and Risk of Severe COVID-19\u003c/h2\u003e\n \u003cp\u003eAssociations between IL-6 rs1800795 and IL-17A 2275913 (genotypes and alleles) and the risk of COVID-19 severity are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The frequencies of rs1800795 genotypes and alleles were significantly different between severe and non-severe patients (P\u0026thinsp;=\u0026thinsp;.005, and P\u0026thinsp;=\u0026thinsp;.001, respectively). A significant positive association of wild homozygous GG genotype (78/140 (55.7%)), and G allele (205/280 (73.2%)) was observed with severe COVID-19 risk. Patients with GG genotype showed 2.19 times higher odds of showing severe COVID-19 (OR\u0026thinsp;=\u0026thinsp;2.19, 95%CI: 1.35\u0026ndash;3.54, adjusted P value\u0026thinsp;=\u0026thinsp;.006) vs. those with GC and CC genotypes. Whereas heterozygous mutant GC genotype (49/140 (35%)) was associated with a negative risk of severe COVID-19 (OR\u0026thinsp;=\u0026thinsp;0.58, 95%CI: 0.33\u0026ndash;0.94, adjusted P value\u0026thinsp;=\u0026thinsp;.04). Homozygous mutant CC genotype comprised 15.7% (22/140) of the non-severe cohort and 9.3% (13/140) of the severe cohort but represented no significant risk of COVID-19 severity (OR\u0026thinsp;=\u0026thinsp;0.54, 95%CI: 0.26\u0026ndash;1.11, adjusted P value\u0026thinsp;=\u0026thinsp;.10). However, the C allele was significantly linked to reduced risk of COVID-19 (OR\u0026thinsp;=\u0026thinsp;0.55, 95%CI: 0.38\u0026ndash;0.79, adjusted P value\u0026thinsp;=\u0026thinsp;.001). The Chi-square test for Hardy-Weinberg equilibrium (HWE) showed that rs1800795 in both non-severe and severe groups were in HWE (P\u0026thinsp;=\u0026thinsp;.99, and P\u0026thinsp;=\u0026thinsp;.20, respectively).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFrequency of IL-6 rs1800795 and Il-17A rs2275913 (Genotypes, and Alleles,) and their association with the risk of severe COVID-19 *P-values by Chi-square test; ** P-values are Benforini adjusted\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon-severe (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP-Value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSevere COVID-19 Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP-Value**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eRs1800795\u003c/p\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (55.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u0026ndash;3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026ndash;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u0026ndash;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eRs2275913\u003c/p\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u0026ndash;2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (57.1.%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026ndash;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026ndash;4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRs1800795\u003c/p\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169 (60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205 (73.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026ndash;2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026ndash;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRs2275913\u003c/p\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (63.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u0026ndash;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u0026ndash;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 rs2275913 and Risk of Severe COVID-19\u003c/h2\u003e\n \u003cp\u003eAs regard to IL-17A rs2275913, although the frequency of wild homozygous GG was significantly higher in severe patients in comparison to non-severe cases (49 (35%) Vs. 64 (45.7%)), it was not associated with a significant risk of COVID-19 (OR\u0026thinsp;=\u0026thinsp;1.56, 95%CI: 0.96\u0026ndash;2.52, adjusted P value\u0026thinsp;=\u0026thinsp;.09). On the contrary, heterozygous mutant GA was correlated with a significantly decreased risk of severe COVID-19 (OR\u0026thinsp;=\u0026thinsp;0.5, 95%CI: 0.31\u0026ndash;0.80, adjusted P value\u0026thinsp;=\u0026thinsp;.012). Also, there was no significant link between the homozygous mutant CC genotype and the disease severity (OR\u0026thinsp;=\u0026thinsp;1.95, 95%CI: 0.89\u0026ndash;4.24, adjusted P value\u0026thinsp;=\u0026thinsp;.09). Furthermore, the Chi-square test revealed no difference between either G or A allele frequency within the non-severe and severe cohort (178 (63.5%) vs. 184 (65.7%), and 102 (36.5%) Vs. 96 (34.3%), P\u0026thinsp;=\u0026thinsp;.53, respectively) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The Chi-square test for HWE showed that rs2275913 in the non-severe group was not in HWE (P\u0026thinsp;=\u0026thinsp;.005), but in the severe group, it was in HWE (P\u0026thinsp;=\u0026thinsp;.18).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Association Between IL-6 rs1800795 and IL-17A rs2275913 Genotypes with COVID-19 Patients\u0026apos; Characteristics\u003c/h2\u003e\n \u003cp\u003eSupplementary Table\u0026nbsp;2 shows that different genotypes of rs1800795 were significantly associated with IL-6 concentrations in the severe cohort (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), with a higher increase in patients with GG genotype than patients with GC or CC genotype. Also, rs1800795 different genotypes were significantly associated with reduced lymphocyte count (P\u0026thinsp;=\u0026thinsp;.041), elevated C-reactive protein (CRP) (P\u0026thinsp;=\u0026thinsp;.013), and increased serum IL-6 (P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;.03) in severe patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIL-17A rs2275913 genotypes were only demonstrated a significant association with lactate dehydrogenase (LDH) (P\u0026thinsp;=\u0026thinsp;.048) in no-severe patients and elevated CRP (P\u0026thinsp;=\u0026thinsp;.02) in severe patients (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Clinical-Demographic Characteristics of Survivor and Non-Survivor Patients\u003c/h2\u003e\n \u003cp\u003eAmong 280 included patients, 228 (81%) were discharged, and 52 (19%) did not survive the disease. Non-survivors were older than survivors (52 (43.5\u0026ndash;59.8) Vs. 44.6 (37-55.8), P\u0026thinsp;=\u0026thinsp;.004), but no significant difference in the frequency of comorbidities (P\u0026thinsp;=\u0026thinsp;.058) was observed between the two groups. However, non-survivors had a significantly lower SPO\u003csub\u003e2\u003c/sub\u003e and higher respiratory rate than survivors (77 (74.2\u0026ndash;81.7) Vs. 93 (88\u0026ndash;96), P\u0026thinsp;\u0026lt;\u0026thinsp;.001, and 31 (30\u0026ndash;33) Vs. 27 (26\u0026ndash;30), P\u0026thinsp;\u0026lt;\u0026thinsp;.001, respectively), and all non-survivors were admitted to ICU, while only 32% of survivors were admitted to ICU (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). Non-survivors also had significantly lower hemoglobin (Hb)(P\u0026thinsp;\u0026lt;\u0026thinsp;.001) and lymphocyte count (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) in comparison to survivors. While, INR (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), D-dimer (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), LDH (P\u0026thinsp;=\u0026thinsp;.04), serum level of IL-6 (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), and serum level of IL-17A (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) in non-survivors were significantly higher than survivors (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" style=\"width: 678px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of survivor and non-survivor COVID-19 patients. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: WBC: white blood cells count; INR: international normalized ratio; CRP: C-reactive protein, AST: aspartate transaminase; ALT: alanine transaminase; LDH: lactate dehydrogenase; IL-6 Interleukin 6; IL-17A: Interleukin 17A. *P-value by Kruskal-Wallis H-test (data as Median (25th percentile \u0026ndash; 75th percentile)). For other comparisons P value by Chi-square test (data as count and percentage).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 263.719px;\" align=\"left\"\u003e\n \u003cp\u003eDemographic \u0026amp; Clinical Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 161.281px;\" align=\"left\"\u003e\n \u003cp\u003eSurvivor\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;228)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 145px;\" align=\"left\"\u003e\n \u003cp\u003eNon-survivor\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 77px;\" align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263.719px;\" align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161.281px;\" align=\"char\"\u003e\n \u003cp\u003e44.6 (37-55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\" align=\"char\"\u003e\n \u003cp\u003e52 (43.5\u0026ndash;59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263.719px;\" align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161.281px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e121 (53.1%)\u003c/p\u003e\n \u003cp\u003e107 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25 (48.1%)\u003c/p\u003e\n \u003cp\u003e27 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n \u003cp\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263.719px;\" align=\"left\"\u003e\n \u003cp\u003eComorbidities n, (%)\u003c/p\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eIschemic Heart Disease\u003c/p\u003e\n \u003cp\u003eChronic Liver Disease\u003c/p\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161.281px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70 (30.7%)\u003c/p\u003e\n \u003cp\u003e90 (39.5%)\u003c/p\u003e\n \u003cp\u003e28 (12.3%)\u003c/p\u003e\n \u003cp\u003e29 (12.7%)\u003c/p\u003e\n \u003cp\u003e21 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (42.3%)\u003c/p\u003e\n \u003cp\u003e21 (40.4%)\u003c/p\u003e\n \u003cp\u003e4 (7.7%)\u003c/p\u003e\n \u003cp\u003e8 (15.4%)\u003c/p\u003e\n \u003cp\u003e2 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003cp\u003e.34\u003c/p\u003e\n \u003cp\u003e.60\u003c/p\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263.719px;\" align=\"left\"\u003e\n \u003cp\u003eClinical Characteristics *\u003c/p\u003e\n \u003cp\u003eSPO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eRespiratory Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161.281px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93 (88\u0026ndash;96)\u003c/p\u003e\n \u003cp\u003e27 (26\u0026ndash;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77 (74.2\u0026ndash;81.7)\u003c/p\u003e\n \u003cp\u003e31 (30\u0026ndash;33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263.719px;\" align=\"left\"\u003e\n \u003cp\u003eIL-6 Genotypes\u003c/p\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003cp\u003eIL-17A Genotypes\u003c/p\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161.281px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98 (43%)\u003c/p\u003e\n \u003cp\u003e100 (43.9%)\u003c/p\u003e\n \u003cp\u003e30 (13.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90 (39.5%)\u003c/p\u003e\n \u003cp\u003e112 (49.1%)\u003c/p\u003e\n \u003cp\u003e26 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31 (59.6%)\u003c/p\u003e\n \u003cp\u003e16 (30.8%)\u003c/p\u003e\n \u003cp\u003e5 (9.6%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23 (44.2%)\u003c/p\u003e\n \u003cp\u003e24 (46.2%)\u003c/p\u003e\n \u003cp\u003e5 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.04\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263.719px;\" align=\"left\"\u003e\n \u003cp\u003eLaboratory Parameters *\u003c/p\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003cp\u003eWBC (\u0026times;10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eLymphocyte count (\u0026times;10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003ePlatelets (\u0026times;10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003cp\u003eD-dimer (ng/mL)\u003c/p\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003cp\u003eCreatinine(mg/dL)\u003c/p\u003e\n \u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e\n \u003cp\u003eIL-17A (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161.281px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.1 (10.7\u0026ndash;13.7)\u003c/p\u003e\n \u003cp\u003e8.8 (7.5\u0026ndash;10.9)\u003c/p\u003e\n \u003cp\u003e1.3 (1-1.6)\u003c/p\u003e\n \u003cp\u003e241.4 (206.6-266.5)\u003c/p\u003e\n \u003cp\u003e1.18 (1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003cp\u003e443.5 (375-560.2)\u003c/p\u003e\n \u003cp\u003e45.1 (28.3\u0026ndash;63.7)\u003c/p\u003e\n \u003cp\u003e33.5 (21.4\u0026ndash;44.9)\u003c/p\u003e\n \u003cp\u003e30.4 (21.3\u0026ndash;40)\u003c/p\u003e\n \u003cp\u003e456.5 (375.2-484.3)\u003c/p\u003e\n \u003cp\u003e1.00 (0.9\u0026ndash;1.2)\u003c/p\u003e\n \u003cp\u003e27 (18\u0026ndash;42)\u003c/p\u003e\n \u003cp\u003e20.1 (14\u0026ndash;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.4 (9.4\u0026ndash;11.5)\u003c/p\u003e\n \u003cp\u003e9.9 (7.9\u0026ndash;11.3)\u003c/p\u003e\n \u003cp\u003e0.9 (0.79\u0026ndash;1.05)\u003c/p\u003e\n \u003cp\u003e237.1 (215.3-259.9)\u003c/p\u003e\n \u003cp\u003e1.3 (1.2\u0026ndash;1.3)\u003c/p\u003e\n \u003cp\u003e632 (498.2-1113.2)\u003c/p\u003e\n \u003cp\u003e53.4 (32.2\u0026ndash;75.8)\u003c/p\u003e\n \u003cp\u003e28 (18\u0026ndash;41)\u003c/p\u003e\n \u003cp\u003e36 (21.745.7)\u003c/p\u003e\n \u003cp\u003e503.2 (387-558.2)\u003c/p\u003e\n \u003cp\u003e1.01 (0.9\u0026ndash;1.4)\u003c/p\u003e\n \u003cp\u003e42.5 (29.5-53.75)\u003c/p\u003e\n \u003cp\u003e25.7 (22-28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.28\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.64\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e.04\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e.74\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 rs1800795 and rs2275913 and Mortality of COVID-19\u003c/h2\u003e\n \u003cp\u003eAlthough no significant difference in overall genotypes of rs1800795 (P\u0026thinsp;=\u0026thinsp;.009) and rs2275913 (P\u0026thinsp;=\u0026thinsp;.80) was seen between survivor and non-survivor patients, in the recessive model, the GG genotype of rs1800795 revealed a significant association with mortality of COVID-19 (OR\u0026thinsp;=\u0026thinsp;1.95, 95%CI: 1.06\u0026ndash;3.61, adjusted P value\u0026thinsp;=\u0026thinsp;.04). On the contrary, no other significant associations were observed for the remaining genotypes of rs1800795 and rs2275913 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFrequency of IL-6 rs1800795 and Il-17A rs2275913 (Genotypes, and Alleles,) and their association with the risk of COVID-19 mortality. *P-values by Chi-square test; ** P-values are Benforini adjusted\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon-Survivor (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSurvivor\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP-Value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCOVID-19 Mortality Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP-Value**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eRs1800795\u003c/p\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026ndash;3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u0026ndash;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u0026ndash;1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eRs2275913\u003c/p\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u0026ndash;2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (46.2.%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u0026ndash;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u0026ndash;2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRs1800795\u003c/p\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e296 (64.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u0026ndash;2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.0501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (%35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.0501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRs2275913\u003c/p\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (67.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u0026ndash;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u0026ndash;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Mortality Predictor Variables in COVID-19 Patients\u003c/h2\u003e\n \u003cp\u003eThe results for univariate regression analysis of variables linked to the mortality in COVID-19 patients are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Using the optimal cutoff driven from ROC curves (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) and maximal Youden index, only respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;29 (P\u0026thinsp;=\u0026thinsp;.001), rs1800795 GG genotype (P\u0026thinsp;=\u0026thinsp;.04), IL-6 serum level (P\u0026thinsp;=\u0026thinsp;.013), INR (P\u0026thinsp;=\u0026thinsp;.007), and D-dimer level (P\u0026thinsp;=\u0026thinsp;.019) have been found to be associated with COVID-19 mortality. The accuracy of these indicators is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate meta-regression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (96% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.15 (0.99\u0026ndash;1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPO2\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;76%\u003c/p\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.11 (0.67\u0026ndash;1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory Rate\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;29\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.19 (1.12\u0026ndash;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-6 Genotype\u003c/p\u003e\n \u003cp\u003eGC\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.95 (1.06\u0026ndash;3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;8.42 g/dL\u003c/p\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;8.42 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.57 (0.89\u0026ndash;1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.750 /mm3\u003c/p\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.750 /mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e3.93 (1.7\u0026ndash;9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.2\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e2.19 (1.61\u0026ndash;3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD-dimer\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;565.5 ng/mL\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;565.5 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e3.12 (1.27\u0026ndash;5.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;499 U/L\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;499 U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.005 (0.995\u0026ndash;1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;28.5 pg/mL\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;28.5 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.974(1.942\u0026ndash;2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-17\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;20.5 pg/mL\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;20.5 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.76 (0.91\u0026ndash;3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: INR: international normalized ratio; LDH: lactate dehydrogenase; IL-6 Interleukin 6; IL-17A: Interleukin 17A\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eROC curve AUC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCut off\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e96% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.594-.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD-dimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;565.5 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.649-.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;28.5 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.673-.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.620-.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: INR: international normalized ratio; IL-6 Interleukin 6; AUC: area under the curve\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe involvement of dysregulated immunological signaling in the pathogenesis of SARS-CoV-2 infection is well-documented. Novel findings on pro-inflammatory cytokines, immunological modulation, and signaling pathways related to COVID-19 infection have been used as a platform to develop pharmacological and interventional studies [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. One area that can affect such efforts is the genetic variations of different populations, which underlie the susceptibility to and outcome of diseases, especially COVID-19 [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. In the present study, to investigate two genetic variations in IL-6 and IL-17A genes, 280 Iranian COVID-19 patients were evaluated. Our results revealed that the IL-6 rs1800795 GG genotype and G allele were associated with severe COVID-19, while the GC genotype and C allele were associated with decreased risk of COVID-19. Also, the GA genotype of IL-17A rs2275913 was linked to a lower risk of disease severity.\u003c/p\u003e\n\u003cp\u003eAs a pleiotropic soluble immunological mediator, IL-6 is produced transiently in response to infectious diseases and tissue damage. By induction of hematopoiesis, regulating acute phase response, and immunological reaction, IL-6 plays a vital role in the host defense. Strict transcriptional and post-transcriptional mechanisms regulate the IL-6 expression; although, its dysregulated production is related to hyper inflammation, chronic inflammation, and autoimmune responses [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Also, elevated serum level of IL-6 has been considered as a hallmark of COVID-19 severity and poor prognosis of patients with ARDS [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. The complications related to COVID-1 9 range from asymptomatic to ARDS development as a life-threatening lower respiratory tract infection [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. Several pieces of evidence suggest that the IL-6 genotype might determine the variation in the range of outcomes of COVID-19 infection [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe present study illustrated that the wild GG genotype and the G allele showed association with a greater risk of COVID-19 severity, while GC, CC genotype, and C allele were linked to a lesser risk of disease severity. Also, we found that the rs1800795 GG genotype is related to COVID-19 mortality. The IL-6 rs1800795 is present in the promoter region of the IL-6 gene, affecting the level of this cytokine which has more prevalent substitutions in Caucasians than East Asian and African populations [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Numerous clinical evidence suggests that the rs1800795 G allele is associated with an increased risk of different diseases, including type 2-DM [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], cancer [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e], endometriosis [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e], coronary artery disease [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e], Crohn's disease [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], among others. Also, in most of these studies, an association between the higher levels of IL-6 and the rs1800795 G allele was seen, while the rs1800795 C allele was linked to a low level of IL-6 [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. Similarly, in this study, we also showed that IL-6 serum concentration was generally elevated in the severe cohort of COVID-19 patients as well as non-survivor patients. Particularly, IL-6 serum concentration was significantly higher in patients with GG genotype. In contrast, the difference between IL-6 serum levels in survivor and non-survivor patients with CC genotype was not significant. This might partly explain the observed association between GG genotype with disease severity and mortality as higher serum concentration of IL-6 is correlated with poor prognosis in COVID \u0026minus;\u0026thinsp;19 patients [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn this regard, a recent study on the Turkish population demonstrated that rs1800795 GG genotype and G allele was more frequent in severe COVID-19 patients who developed macrophage activation syndrome (MAS). Furthermore, the G allele has been shown to be a risk factor for increased IL-6 serum concentrations [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. Also, HCV-infected patients with low-producing rs1800795 CC genotype were found to have an attenuated adaptive immune response against HCV [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. In contrast, higher levels of IL-6 in co-infected HCV/HIV patients with GG genotype demonstrated a higher sustained virologic response (SVR) [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T helper 17 (Th17) cells produce the IL-17 family of cytokines. IL-17A and IL-17F are considered inflammatory members of this family [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]. IL-17A stimulates the inflammatory response by regulating polymorphonuclear cells (PMNs), regulating the release of granulocyte-colony stimulating factor (G-CSF), differentiation of CD34\u003csup\u003e+\u003c/sup\u003e towards neutrophils, and promoting IL-6, IL-1, and TNF-\u0026alpha; secretion [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]. Therefore, the clinical evidence that highlights the elevated level of IL-17A in COVID-19 patients is of particular importance [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. Rs2275913 is one of the well-studied SNPs of IL-17A located in the promoter region and has shown to be associated with susceptibility to infectious pulmonary diseases [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWe demonstrated that the IL-17A rs2275913 GA genotype is significantly linked to decreased risk of COVID-19 severity. In the European population, the frequency of the rs2275913 G allele is 65%, according to the 1,000 genome database [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. The G allele also had the highest frequency among examined groups in our study, but it was not significantly associated with either disease severity or mortality. Also, no significant correlation between rs2275913 genotypes and the A allele with the mortality of COVID-19 was observed. A recent study on post-mortem lung samples from COVID-19 non-survivors and H1N1 non-survivors reported no significant difference in the distribution of genotype frequencies of rs2275913 between the two groups. Also, there was no significant association of rs2275913 with IL-17A tissue expression in lung samples from COVID-19 patients [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Likewise, no significant association between rs2275913 genotypes and the IL-17A serum concentration was identified in the present study, although its overall concentration was significantly higher in severe patients and non-survivors. On the contrary, recently, it has been shown that the rs2275913 G allele significantly increases the risk of the influenza A virus [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. Also, Ren et al. found a significant association between rs2275913 GG genotype and G allele with HCV risk in a Chinese Han population [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSince the mortality prediction among COVID-19 patients could aid clinicians in managing clinical care, we aimed to evaluate the potential indicators of COVID-19 mortality. According to univariate analysis, the GG genotype of rs1800795 was linked to COVID-19 mortality. As mentioned earlier, this high IL-6 producing genotype is associated with a higher IL-6 serum level (\u0026ge;\u0026thinsp;28.5 pg/mL) which has shown to be correlated with a poor prognosis in COVID-19 [\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]. Increased respiratory rate (\u0026ge;\u0026thinsp;29) was also a predictor of mortality that represents the hypoxic condition, a known predictor of poor prognosis in infected patients with SARS-CoV-2 [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. D-dimer\u0026thinsp;\u0026ge;\u0026thinsp;565.5 ng/mL and INR\u0026thinsp;\u0026ge;\u0026thinsp;1.2 were other predictors of death. Previous studies on COVID-19 suggest that a 3 to 4-fold positive change in D-dimer is associated with an unfavorable prognosis [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]. Furthermore, the dysregulated micro-coagulation and blood coagulation cascades associated with hyperinflation in COVID-19 infection are considered as the main causes of elevated INR [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThere are certain limitations to this study. First, this was a single-center study with limited involvement of patients with different ethnicities, representing a small proportion of the population. Second, several comorbidities and non-genetic risk factors were presented in the study sample that might interfere with the final reported outcomes. Third, some laboratory parameters and radiological tests were not performed for a proportion of the patients; thus they were not included in the analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study found that IL-6 rs1800795 GG genotype and G allele were associated with higher risk, and GC genotype was associated with lower risk of COVID-19 severity. Furthermore, the GG allele was associated with higher IL-6 serum concentration in severe and non-survivor patients. Moreover, the IL-17A rs2275913 GA genotype was linked to decreased vulnerability to severe COVID-19. In addition, INR, D-dimer, respiratory rate, IL-6 serum concentration, and IL-6 rs1800795 GG genotype were predictive of COVID-19 mortality, which their monitoring can help reduce mortality and conduct more favorable clinical care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the institutional review board (IRB) and research ethics committee of Baqiyatallah University of Medical Sciences, Tehran, Iran (Approval Code: IR.BMSU.REC.1399.466).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHalpert, G. and Y. 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Mansouritorghabeh, \u003cem\u003eD-dimer level in COVID-19 infection: a systematic review.\u003c/em\u003e Expert Rev Hematol, 2020. \u003cstrong\u003e13\u003c/strong\u003e(11): p. 1265-1275.\u003c/li\u003e\n\u003cli\u003eKermali, M., et al., \u003cem\u003eThe role of biomarkers in diagnosis of COVID-19 \u0026ndash; A systematic review.\u003c/em\u003e Life Sciences, 2020. \u003cstrong\u003e254\u003c/strong\u003e: p. 117788.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polymorphism, COVID-19, Interleukin-17A, SARS-CoV-2, Interleukin-6","lastPublishedDoi":"10.21203/rs.3.rs-3215016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3215016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eElevated levels of interleukin (IL)-6 and IL-17A have been linked to hyper inflammation in COVID-19 patients, and their levels are indicative of the progression of the disease. This study aimed to investigate whether single-nucleotide polymorphisms (SNPs) in IL-6 and IL-17A are linked to COVID-19 susceptibility and prognosis in Iranian patients.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThe study enrolled 280 COVID-19 patients, divided into 140 non-severe and 140 severe cases. Genotyping for IL-6 rs1800795 and IL-17A rs2275913 was performed using tetra primer-amplification refractory mutation system-polymerase chain reaction (tetra-ARMS-PCR). IL-6 and IL-17A circulating levels were measured using enzyme-linked immunosorbent assay (ELISA). The study also investigated predictors of COVID-19 mortality.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe rs1800795 GG genotype (78/140 (55.7%)) and G allele (205/280 (73.2%)) were significantly associated with a higher risk of severe COVID-19 infection (OR\u0026thinsp;=\u0026thinsp;2.19, 95%CI: 1.35\u0026ndash;3.54, P\u0026thinsp;=\u0026thinsp;.006 and OR\u0026thinsp;=\u0026thinsp;1.79, 95%CI: 1.25\u0026ndash;2.56, P\u0026thinsp;\u0026lt;\u0026thinsp;.001, respectively). The rs1800795 GG genotype was also significantly linked to disease mortality (OR\u0026thinsp;=\u0026thinsp;1.95, 95%CI: 1.06\u0026ndash;3.61, P\u0026thinsp;=\u0026thinsp;.04). In contrast, the rs2275913 GA genotype was found to be protective against severe COVID-19 (OR\u0026thinsp;=\u0026thinsp;0.5, 95%CI: 0.31\u0026ndash;0.80, P\u0026thinsp;=\u0026thinsp;.012), but no significant association was observed with disease mortality. Several predictors of COVID-19 mortality were identified, including INR\u0026thinsp;\u0026ge;\u0026thinsp;1.2 (OR\u0026thinsp;=\u0026thinsp;2.19, 95%CI: 1.61\u0026ndash;3.78, P\u0026thinsp;=\u0026thinsp;.007), D-dimer\u0026thinsp;\u0026ge;\u0026thinsp;565.5 ng/mL (OR\u0026thinsp;=\u0026thinsp;3.12, 95%CI: 1.27\u0026ndash;5.68, P\u0026thinsp;=\u0026thinsp;.019), respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;29 (OR\u0026thinsp;=\u0026thinsp;1.19, 95%CI: 1.12\u0026ndash;1.28, P\u0026thinsp;=\u0026thinsp;.001), IL-6 serum concentration\u0026thinsp;\u0026ge;\u0026thinsp;28.5 pg/mL (OR\u0026thinsp;=\u0026thinsp;1.97, 95%CI: 1.942\u0026ndash;2.06, P\u0026thinsp;=\u0026thinsp;.013), and IL-6 rs1800795 GG genotype (OR\u0026thinsp;=\u0026thinsp;1.95, 95%CI: 1.06\u0026ndash;3.61, P\u0026thinsp;=\u0026thinsp;.04).\u003c/p\u003e\u003ch2\u003econclusion:\u003c/h2\u003e \u003cp\u003eThe results of this study suggest that the rs1800795 GG genotype and G allele are associated with greater disease severity in COVID-19 patients, while the rs2275913 GA genotype is protective. These findings provide insights into the genetic and molecular mechanisms underlying COVID-19 and may inform the development of more effective therapies and prognostic tools for this disease.\u003c/p\u003e","manuscriptTitle":"Impact of IL-6 rs1800795 and IL-17A rs2275913 Gene Polymorphisms on the COVID-19 Prognosis and Susceptibility in a Sample of Iranian Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-04 23:59:35","doi":"10.21203/rs.3.rs-3215016/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e899055-3ba3-49e7-a208-95f9e281e0a8","owner":[],"postedDate":"August 4th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-01T04:45:19+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-04 23:59:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3215016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3215016","identity":"rs-3215016","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}