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12 March 2026

13 Pages

Interleukin-6 Gene Polymorphisms (rs1800796, rs1800797, and rs2069845) and Susceptibility to Severe COVID-19 in Southern Brazil

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1
Graduate Program in Pharmaceutical Sciences, Laboratory of the Research Group on Metabolic Diseases, Federal University of Paraná, Curitiba 80210-170, Brazil
2
Department of Computer Science, Federal Institute of Paraná, Colombo 83403-515, Brazil
3
Department of Clinical Analysis, Federal University of Paraná, Curitiba 80210-170, Brazil
4
Clinical Hospital Complex, Federal University of Paraná, Curitiba 80060-900, Brazil
COVID2026, 6(3), 46;https://doi.org/10.3390/covid6030046
This article belongs to the Section Host Genetics and Susceptibility/Resistance
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Abstract

Infection with SARS-CoV-2, the virus responsible for COVID-19, is associated with cytokine storm, an excessive immune response. Interleukin-6, a multifunctional cytokine, is involved in the COVID-19 immune response. Functional polymorphisms in the interleukin-6 gene promoter, namely rs1800796, rs1800797, and rs2069845, may contribute to individual susceptibility to or severity of COVID-19. In this study, 106 healthy SARS-CoV-2-negative individuals (controls) and 106 patients with severe COVID-19 (COVID-19 group), confirmed by qPCR or rapid antigen tests, were genotyped using fluorescent probes for polymorphisms. All participants were from southern Brazil. Groups were matched for sex and body mass index, with a median age of 56–57 years. The COVID-19 group exhibited blood biomarker concentrations consistent with severe disease. No significant differences were detected in genotypic or allelic frequencies between groups, and all polymorphisms conformed to the Hardy–Weinberg equilibrium. The control group minor allelic frequencies for rs1800796 (allele C, 11.3%; 95% CI, 7–16), rs1800797 (allele A, 28.3%; 95% CI, 22–34), and rs2069845 (allele G, 36.8%; 95% CI, 30–50) were similar to those of African, American, and European populations. The polymorphisms investigated were not associated with severe COVID-19 in this cohort.

1. Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of coronavirus disease 2019 (COVID-19), presenting across a broad clinical spectrum, from asymptomatic to severe outcomes such as acute respiratory distress syndrome. Although infection is often asymptomatic or associated with mild to moderate self-limiting symptoms, including fever, dry cough, myalgia, and fatigue [1,2,3,4,5], a subset of patients develops a clinically severe hyperinflammatory state, or cytokine storm, for which acute respiratory distress syndrome is a cardinal feature [1,2,3,4,5,6]. Furthermore, a distinct multisystem inflammatory syndrome has been observed in a subgroup of previously healthy children with acute SARS-CoV-2 infection, differing from the adult cytokine storm [7].
To understand disease progression from asymptomatic to extremely severe is necessary to elucidate the mechanisms underlying cell damage and immune responses. The literature demonstrates that while some infected subjects have mild or no symptoms, in others the disease is severe and even fatal [8]. The severity of COVID-19 is determined by multiple factors, such as diabetes mellitus, arterial hypertension, and obesity; age and comorbidities are also severity predictors [9,10]. Severe outcomes are frequently characterized by hyperinflammation and elevated levels of proinflammatory cytokines (e.g., IFN-α, IFN-γ, and IL-6) and chemokines, namely monocyte chemotactic protein-3 (MCP-1/CCL2) [11,12,13]. Additionally, various studies have reported that high levels of IL-6 are predictive of mortality and associated with respiratory failure, shock, and severe symptoms in hospitalized patients [14,15,16].
Genetic variations may affect innate and adaptive immune responses [17,18,19]. Polymorphisms in genes encoding proinflammatory cytokines may play a role in their production and contribute to the pathogenesis of severe COVID-19. Single-nucleotide polymorphisms (SNPs) in the TNFA, IL6, IL8, IL10, CCL5, and CXCL6 genes have been associated with COVID-19 severity, suggesting that SNPs could help explain COVID-19 outcomes [12,20,21,22,23,24,25,26]. Interleukin-6 (IL-6) was targeted in this study because it is a primary inflammatory agent associated with the response to viral infection. Variability in the IL-6 gene, particularly in the promoter region, may be associated with COVID-19 and/or the severity of viremia.
Polymorphic variation affecting IL6 expression could therefore influence individual susceptibility. In humans, the IL6 gene (OMIN 147620) is located on chromosome 7p21 [27] and contains four introns and five exons [27,28]. The IL6 gene alone has 3569 SNPs in coding, non-coding, and regulatory regions [29]. Several common polymorphisms have been identified in the 5′ flanking region of IL6, including rs1800797 (−597 A>G), rs1800796 (−572 C>G), and rs1800795 (−174 G>C) [30,31,32], and rs2069845 at an intronic position. The −597A and −174C alleles show almost complete allelic association in Caucasian subjects [31,32]. In contrast, the −572C allele generally occurs with the −174G [32]. These polymorphisms are positioned near cis-acting regulatory elements involved in controlling IL-6 expression at the transcription level, suggesting that they may influence protein–DNA interactions at these sites [33] (Figure 1).
Figure 1. Schematic representation of the IL6 gene intron–exon structure and 5′-flanking regions. Arrows indicate the positions of single-nucleotide polymorphisms. Light blue boxes represent known and putative regulatory elements and dark blue boxes exons. The IL6 gene, located on chromosome 7p15.3, comprises 5 exons and 4 introns. Exons, introns, untranslated regions (UTRs), the evaluated polymorphisms (rs1800796 [−572 C/G][−572 C/G], rs1800796 [−597 A/G], and rs2069845 [+3.331 G/A], and rs1800795 [−174G/C]) and the putative transcription factor binding sites as well as the position and orientation of the oligonucleotide pairs used in single-strand conformation polymorphism (SSCP), restriction fragment length polymorphism (RFLP), and sequencing analyses, are depicted above. Binding sites are relative to the main transcription initiation site: TATA box −27 to −24; NF-kB −73 to −64; NF-IL6) −158 to −145; CRE: cAMP responsive element, −163 to −158; NRD: negative regulatory domain, −225 to −164; AP-1 −283 to −277; AnTn replication polymorphism −392 to −373; GRE1 and GRE2: glucocorticoid-responsive elements 1 and 2, −466 to −461 and −557 to −552, respectively. Shaded blocks: coding exons; white blocks: 5′ and 3′ UTRs; +1: first base of the transcription start site; GRE: glucocorticoid-responsive element; AP-1: activator protein-1; MRE: metal-responsive element; GRE: cAMP-responsive element; NF-IL6: nuclear factor for IL-6 expression; NFkB: nuclear factor kappa B; TATA box: promoter sequence; INT: transcription start. D’ values between SNPs are also presented. Adapted from Fishman et al. [30]; Chung et al. [34]; and Yee et al. [35].
The rs1800795 and rs1800796 polymorphisms were linked to COVID-19 severity in a western Sicilian [36], Turkish [37], Mexican [38], and Ukrainian [39] populations. The rs2069845 variant in intron 4 of IL6, which is in moderate linkage disequilibrium with rs1800795 (D’ = 1.0; R2 = 0.63), is associated with overweight/obesity and measures of adiposity in children [40,41,42].
Therefore, this study aimed to evaluate the associations between rs1800796, rs1800797, and rs2069845 polymorphisms located in the promoter or enhancer region of IL6 genes that were chosen based on evidence from the HaploReg [43] and ENCODE [44] databases, indicating functional regulatory activity, with COVID-19 outcomes and serum levels of biochemical biomarkers.

2. Materials and Methods

2.1. Sample

Sampling was performed with patients hospitalized for COVID-19 and control individuals from October 2021 to March 2023. In this case–control study, individuals testing positive for COVID-19 (n = 106) and the asymptomatic controls testing negative for COVID-19 (n = 106) were evaluated. The population under study, residing in Curitiba, Paraná State (southern Brazil), is composed of 65% Euro-Brazilians, 34% Afro-Brazilians, and 1% Asians and Indigenous people, according to the 2022 census, which uses predominantly (>98%) in-person data collection [45].
The inclusion criteria were as follows: individuals of either sex, aged between 18 and 80 years, with clinical features of COVID-19 and a positive COVID-19 test. The COVID-19 patients were admitted to the intensive care unit (ICU) of the University Hospital of the Federal University of Paraná or the Worker’s Hospital of the Paraná State Health Department and classified as severe COVID-19 cases [46]. Exclusion criteria included pregnancy, incomplete clinical records, and individuals related to the participants. The control group was recruited from the University Hospital of the Federal University of Paraná’s blood bank. All controls tested negative for COVID-19 and reported no signs or symptoms of viremia.
The study was approved by the human research ethics committee of the Federal University of Paraná, the University Hospital of the Federal University of Paraná, and the Worker’s Hospital of the Federal University of Paraná (CAAE nos. 43948621.7.3001.0096, 43948621.7.3003.5225, and 43948621.7.0000.0102, respectively). Informed consent was obtained from all participants. All samples were collected from public health institutions providing free care.

2.2. Genotyping

Genomic DNA was isolated by the salting-out method described by Lahiri and Nuerberger [47]. The DNA quality was assessed by the 260/280 nm absorbance ratio; only ratios between 1.6 and 1.8 were accepted. The DNA integrity was also verified via 1% agarose gel electrophoresis stained with ethidium bromide (Êxodo Científica, São Paulo, Brazil). The DNA concentration was quantified using spectrophotometry (UV-Vis BioDrop Duo+, Biochrom, Harvard Bioscience, Holliston, MA, USA) and adjusted to 20 ng/mL. Genotyping for rs1800796 (C__11326893_10), rs1800797 (C___1839695_20), and rs2069845 (C___1839699_10) were performed through real-time polymerase chain reaction (PCR) with fluorescent probes on the TaqMan® system (Applied Biosystems/ThermoFisher, Waltham, MA, USA), employing the 7500 Fast real-time PCR thermocycler (Applied Biosystems/ThermoFisher, MA, USA) following the reagent manufacturer’s instructions.

2.3. Biochemical Biomarkers

Whole blood (EDTAK2 tube), serum (SST tube), and plasma (EDTAK2 and NaF-EDTAK2 tubes) samples were collected from all participants using BD Vacutainer tubes (Becton, Dickinson and Company, Franklin Lakes, NJ, USA). Biomarkers of glycemic, lipid, and nutritional profiles, as well as renal and hepatic function, were quantified using automated analyzers Architect Ci8200 (Abbott Diagnostics, Chicago, IL, USA); or Labmax 400 (Labtest, Minas Gerais, Brazil) with appropriate calibrators, controls, and reagents provided by their manufacturers. The 1,5-anhydroglucitol was determined by enzymatic assay GlycoMark (Diazyme Laboratories, Inc., Gregg Ct., Poway, CA, USA) on the Labmax 400 (Labtest, Lagoa Santa, Minas Gerais, Brazil).

2.4. COVID-19 Detection

Reverse-transcription PCR was performed by using Reagent BIOMOL One-Step/COVID-19 (Instituto de Biologia Molecular do Paraná—IBMP, Paraná, Brazil) in nasopharyngeal swab. This assay includes two viral genes ORF 1ab gene and N-region gene SARS-CoV-2 detection according to the manufacturer’s instructions, a human endogenous gene was used as target for internal control. Serological tests were carried out using lateral flow immunochromatographic assays (LFAs), based on colloidal gold-labeled immunochromatography (GICA) principle and one-step method with results obtained within 15 min. The reagents detect IgM/IgG antibodies for SARS-CoV-2 were COVID-19 IgG/Ig MECO Test (Eco Diagnóstica, Minas Gerais, Brazil), COVID-AG (Instituto de Biologia Molecular do Parana, Paraná—IBMP, Paraná, Brazil), COVID-19 IgG/IgM ECO Teste (Fundação Oswaldo Cruz-Fiocruz, Rio de Janeiro, Brazil) and PANBIO™ COVID-19 (Abbott Diagnostics, IL, USA). All the tests were performed at room temperature according to the manufacturer’s instructions.

2.5. Statistical Analysis

The continuous variables’ normality was verified using the Kolmogorov–Smirnov test. Since these variables were not normally distributed in either group, values were reported as medians and interquartile ranges (25–75%). Comparisons were performed using the Mann–Whitney U test. Categorical variables were analyzed using the Chi-square test, while the continuous variables were compared utilizing the Mann–Whitney U test.
Hardy–Weinberg equilibrium, genotype and allele frequencies, and 95% confidence intervals (95% CI) for minor allele frequencies were obtained using the https://sites.google.com/view/generiskcalc/home/gene-association-calculator (accessed on 22 August 2025). Genotypes were coded as 1 (most frequent; homozygous), 2 (heterozygous), and 3 (least frequent; homozygous).
For comparison, allele frequencies from other populations were considered similar when they fell within the 95% CI. Statistical analyses were conducted using MedCalc software (v. 23.0.2, MedCalc Software Ltd., Ostend, Belgium), Statistica (v. 14, Cloud Software Group, Santa Clara, CA, USA), and Jamovi project (2025) v. 2.7. Statistical significance was defined as p < 0.05.

3. Results

3.1. Study Population

The participant’s characteristics and clinical information are shown in Table 1. Groups were matched by sex and body mass index, with similar median ages (56–57 years).
Table 1. Anthropometric and laboratory characteristics of the study groups.
The glycated hemoglobin A1c did not differ between groups, with concentrations consistent with the absence of chronic hyperglycemia. The median blood glucose was elevated in the COVID-19 group (reference interval < 100 mg/dL), approximately 20% higher than in the control group (117/96 = 1.22). This group also exhibits a reduction in 1,5-anhydroglucitol. When HbA1c, a marker of chronic hyperglycemia, shows no difference between groups, the increase in blood glucose may be attributed to stress-induced hyperglycemia, a transient increase in blood glucose during acute physiological stress such as COVID-19. All other parameters showed significant differences between groups (p ≤ 0.001).
For renal function markers, urea and creatinine were significantly higher in the COVID-19 group. The median urea concentration exceeded the reference interval (15–45 mg/dL), roughly twofold higher than in the control group, while creatinine remained within the reference range (0.4–1.2 mg/dL). The COVID-19 group had significantly lower lipid parameters compared to the control group, except for triglycerides, which were not significantly different. The HDL-C was notably reduced by 50% in the COVID-19 group.
The median serum albumin in the COVID-19 group was around 20% lower than in the control group and below the reference range (3.5–5.5 g/dL), suggesting hypoalbuminemia. The aminotransferases (aspartate aminotransferase and alanine aminotransferase) associated with liver function were elevated in the COVID-19 group, albeit within the reference range (<40 U/L).
The nonspecific marker of inflammatory processes, median high-sensitivity C-reactive protein, was approximately 40 times higher (65/1.6) in the COVID-19 group, representing the greatest difference among all biomarkers assessed.

3.2. Allelic and Genotypic Frequencies and Linkage Disequilibrium

To determine the relationship between IL6 promoter polymorphisms and susceptibility to COVID-19, three distinct polymorphic regions, rs1800796 (−572 G>C), rs180097 (−597 G>A), and rs2069845, were investigated. The genotypes and allele frequencies’ distribution in the control and severe COVID-19 groups are summarized in Table 2. No significant deviation from the Hardy–Weinberg equilibrium was detected in any group (p > 0.05).
Table 2. Allelic and genotypic frequencies of polymorphisms in the IL6 gene in the study groups.
The polymorphisms under study were also evaluated using binomial logistic regression with sex and age as confounders, confirming the absence of association. Therefore, no statistically significant differences were observed in allele and genotype distributions between the control and severe COVID-19 groups.

4. Discussion

The COVID-19 pandemic has exposed geographical disparities in infection and mortality rates, driven by public health interventions, healthcare infrastructure, socioeconomic factors, and individual behaviors [48,49]. Additionally, genetic variants may influence individual susceptibility to the disease. Infectious diseases are a major selective pressure [50,51,52], and genes involved in immune responses are among the most numerous and diverse in the human genome [53], supporting the evolutionary advantage of a varied immunological response across pathogens [54].
Genome-wide association studies and candidate gene studies have identified SNPs associated with susceptibility to SARS-CoV-2 infection and severe COVID-19 [17,18,55]. The IL-6 has been implicated in regulating genes and proteins involved in angiogenesis and immune cell recruitment [20], also exerting pleiotropic effects and contributing to various physiological and pathological processes, including inflammation, tumorigenesis, and immune modulation [22].
The IL6 polymorphisms have been associated with viral infection outcomes through their effects on IL-6 production, an early indicator of tissue injury and systemic inflammation. Thus, this study investigated the relationship between IL6 rs1800796, rs1800797, and rs2069845 polymorphisms and COVID-19, as well as serum biomarkers.
The SARS-CoV-2 virus, the agent of COVID-19, affects multiple organs beyond the respiratory system [56]. Our case–control analysis identified severe viremia in the COVID-19 group, underscoring the severity of the ongoing process.
Although respiratory symptoms predominate in COVID-19, metabolic disturbances, including hyperglycemia and new-onset diabetes, have been reported soon after the pandemic’s onset [57,58]. Hyperglycemia was most frequently associated with an increased risk of adverse outcomes (e.g., disease severity, physiological stress, and intubation) [59,60], especially in patients with pre-existing diabetes [61]. Although elevated blood glucose levels are common in critical illness, the prevalence of hyperglycemia and new-onset diabetes among COVID-19 patients appears to be unexpectedly high [62]. The observed hyperglycemia is associated with stress-induced insulin resistance and increased glucose synthesis (Table 1) [63]. The reference-range glycated hemoglobin fraction A1c and 1,5-anhydroglucitol values suggest that no patients with diabetes were present in the study groups [64].
The increase in urea and creatinine concentrations in early COVID-19 has been documented and is considered a severity marker [65,66]. Reduced albumin is reported in many chronic and acute inflammatory processes [67]. In COVID-19, hypoalbuminemia (serum albumin < 3.4 g/dL) has been proposed as a predictor of in-hospital mortality, independent of age, comorbidities, and inflammation [68].
The relationship between COVID-19 and lipid profile is complex [69]. Lower high-density lipoprotein cholesterol concentrations are associated with a higher likelihood of severe COVID-19 [70]. These studies are consistent with the present study findings of the COVID-19 group, which showed a 50% lower median high-density lipoprotein cholesterol than the control group (Table 1).
Markedly increased high-sensitivity C-reactive protein in the COVID-19 group is a severity marker associated with ICU admission, acute kidney injury, and poor outcomes [71]. Given the symptoms displayed, the COVID-19 group had severe viremia. The results revealed that the IL6 rs1800796, rs1800797, and rs2069845 were not associated with an increased risk of COVID-19 (Table 2). Although the rs1800796 (−572 C>G) variant has been related to COVID-19 severity in a western Sicilian [36], Turkish [37], Mexican [38,72], and Ukrainian [39] populations, no correlation was found between rs1800796 and COVID-19 in Iranian individuals [73] and in a systematic review across 23 countries [74]. Similarly, studies in Iranian [73], Moroccan [75], Chinese [76], and Indian [77] populations reported no association between rs1800797 (−597 A>G) and COVID-19. Conversely, Chinese homozygous carriers of the C allele at rs1800796 presented a lower risk of severe symptoms [76], while in American and European populations, it increased the risk of severe disease, likely reflecting differences in immunogenetic backgrounds among populations.
Indeed, rs1800796, highly prevalent in Asia, has been proposed to explain ethnic-specific susceptibility to certain infectious diseases. Individuals of Hmong ancestry (China) harboring rs1800796, rs1524107, and rs2066992 had a greater risk of severe fungal infection than individuals of European ancestry harboring the wild-type haplotype [78]. For hepatitis B virus (HBV) infection that is endemic to Asian countries, the rs1800796 C/C genotype was detrimental to spontaneous HBV clearance in Chinese [79] and increased risk of chronic HBV infection in Malaysians [80]. The IL-6 promoter SNPs rs1800795 and rs1800797 are exclusively polymorphic in Caucasians. The G/G genotype of rs1800795 was protective against pneumococcal pneumonia in white Spanish patients [31,81]. In contrast, the G-G alleles at rs1800795 and rs1800797 were linked to worse chronic HBV in Italians [82] and susceptibility to human papillomavirus HPV-associated cervical cancer in North Indians [83]. Thus, the impact of IL-6 polymorphisms in infectious disease susceptibility varies with both pathogen and ethnicity.
Notably, the two promoter SNPs exhibited striking ethnic disparities in global allele frequencies (Table 3). The rs1800796 variant was most frequent in East Asians (79%), followed by South Asians (39%) and Americans (30%), but rare in Africans (10%) and Europeans (5%). By contrast, rs1800797 was highly polymorphic in Caucasians, albeit nearly monomorphic in Asians and Africans. The rs1800797 and rs2069845 were most and least prevalent, respectively, in Africans (32 and 2%), whereas distribution in other populations was more similar (Table 3). The allele frequency of the Brazilian population (this study) of rs1800796 (11.3%, 95% CI 7–16) was similar only to Africans (10%), and rs1800797 (28.3%, 95% CI 22–34) was not similar to any other population. The rs2069845 (36.8%, 95% CI 30–43) was similar to Africans (32%), Europeans (43%), Americans (29%), and other South Brazilians (Table 3) [42,84].
Table 3. Comparisons between allele frequencies for the studied polymorphisms in different populations based on Ensembl data.
Considering the ethnic disparities in IL-6 functional SNPs, Chen et al. [76] compared IL-6 expression levels in European, African, and East Asian populations, noting significantly higher levels in Europeans, intermediate levels in Africans, and the lowest in East Asians. Therefore, genetic variability among populations may partially explain differences in COVID-19 course and susceptibility to its complications. Other unexamined variants in linear dichroism could likely influence cytokine levels.
Our data showed that these three SNPs are not associated with COVID-19 in the southern Brazilian population. Despite our promising findings, this study has some limitations that should be acknowledged. First, RNA expression or IL-6 protein levels were not measured to assess the effects of three SNP genotypes on IL-6 expression. Second, the sample size was relatively small, and the patients were selected from only one region of Brazil. Third, additional SNPs in other IL-6 regions were not examined. Fourth, several factors that may affect the validity and generalizability of the results, specifically regarding potential population stratification, lack of adjustment for comorbidities, and the absence of multivariable modeling. Lastly, to confirm these results, more extensive studies across diverse ethnic populations are required to investigate the association between IL-6 genetic polymorphisms and COVID-19 pathogenesis. Further investigations of other SNPs in these and related genes implicated in COVID-19 severity are necessary to develop prevention strategies and precision medical therapies. In summary, in the study population, the COVID-19 group did not differ from healthy controls in the allelic or genotypic frequencies of the studied IL6 variants. Further studies with larger sample sizes are needed to confirm the results presented.

Author Contributions

Conceptualization, R.S.G.d.S., F.G.d.M.R. and G.P.; methodology, N.M.P.S., C.M.G.d.L. and P.R.S.; validation, M.H.M.S. and A.L.d.P.; formal analysis, G.P. and R.S.G.d.S.; investigation, I.R., G.C. and M.H.M.S.; data curation, A.L.d.P.; writing—original draft preparation, F.G.d.M.R. and R.S.G.d.S.; writing—review and editing, G.P. and M.B.N.; project administration, F.G.d.M.R.; funding acquisition, F.G.d.M.R. and G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by Araucaria Foundation (grant no. SUS2020131000069), the National Council for Scientific and Technological Development—CNPq (projects nos. 407034/2023-4 and 405828/2025-0), and the Federal University of Paraná.

Institutional Review Board Statement

The human research ethics committee approved this study (CAAE nos. 43948621.7.3001.0096 on 4 June 2021, 43948621.7.3003.5225 on 10 June 2021, and 43948621.7.0000.0102 on 5 May 2021).

Data Availability Statement

The data supporting the findings of this study are available within the article.

Acknowledgments

The authors kindly acknowledge the Araucaria Foundation, the National Council for Scientific and Technological Development—CNPq, the Coordination for the Improvement of Higher Education Personnel—CAPES and its Institutional Internationalization Program, and the Federal University of Paraná for the research grants and scholarships (Finance Code 001).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SARS-CoV-2Severe acute respiratory syndrome coronavirus 2
COVID-19Coronavirus disease 2019
SNPsSingle-nucleotide polymorphisms
ICUIntensive care unit
HBVHepatitis B virus

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