Adu et al. BMC Gastroenterology (2024) 24:374 https://doi.org/10.1186/s12876-024-03456-9 RESEARCH Open Access © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. BMC Gastroenterology Host cytokine genetic polymorphisms in a selected population of persons living with hepatitis B virus infection in the central region of Ghana Faustina Adu1,2†, Ebenezer Aniakwaa-Bonsu3†, Samuel Badu Nyarko1,2, Aikins Sarpong Obeng1, Richmond Owusu Ateko4,5, Akwasi Anyanful1 and Nicholas Ekow Thomford1,2,6* Abstract Background Hepatitis B virus (HBV) infection is a public health concern in resource limited settings like Ghana. Over the past decades, it is noted that the natural course of HBV in persons infected are taking a worse turn leading to liver cirrhosis and cancer. The outcome of HBV infection is influenced by viral and host factors including genetics. Cytokine variations affect virus survival and progression and may even influence associated complications. Cytokines such as tumor necrosis factor alpha (TNF-α), interleukins (IL-4, IL-6, IL-8, IL-10, IL-18), interferon gamma (IFN)-γ, and tumor growth factor-beta (TGF-β) have key roles in HBV infection and modulation. In this study, polymorphisms occurring in five cytokines were analysed to understand how they can influence prognosis of HBV infection. Methods The study is a single centre cross-sectional study involving 227 participants made up of HBV infected participants and HBV-negative controls. Recruitment was from March 2021 to April 2022. Blood samples were taken for full blood count, HBV antigen profile, liver function tests, HBV DNA quantification and cytokine genotyp- ing. FIB score was calculated using available tools. Statistical analysis was undertaken with p < 0.05 set as statistically significant. Results The 20–39-year-old group formed majority of the HBV infected participants with 60% of all participants being classified as healthy HBsAg carriers. IL2 rs1479920 GG carriers ((1258.93; 0.00–5011.87) IU/mL had reduced HBV DNA in comparison to IL2 rs1479920 AA ((5011.87; 2113.49–5956.62) /AG (3548.13; 0.00–6309.57) IU/mL carriers. TNF-α rs1800629 AA carriers (1258.93; 0.00–3981.07) IU/mL had a reduction in HBV DNA levels in comparison to TNF-α rs1800629 GG carriers (1584.89; 0.00–5011.87) IU/mL. The results of univariate (OR = 0.08, 0.00–0.93; p = 0.043) and mul- tivariate (OR = 0.02, 0.00–0.67; p = 0.029) analysis, showed that carrying TNF-α rs1800629 AA genotype reduce suscep- tibility to high FIB score compared with GG genotypes. In univariate analysis, subjects aged 20–39 years (OR = 5.00, 1.13–6.10; p = 0.034) and 40–59 years (OR = 41.99, 3.74–47.21; p = 0.0002) were more susceptible to high FIB score compared to subjects aged 1–19 years. Being female (OR = 2.42, 1.03–5.71; p = 0.043) in the univariate models showed †Faustina Adu and Ebenezer Aniakwaa-Bonsu contributed equally to this work. *Correspondence: Nicholas Ekow Thomford nthomford@ucc.edu.gh Full list of author information is available at the end of the article http://creativecommons.org/licenses/by-nc-nd/4.0/ http://crossmark.crossref.org/dialog/?doi=10.1186/s12876-024-03456-9&domain=pdf Page 2 of 14Adu et al. BMC Gastroenterology (2024) 24:374 higher odds of having high levels of HBV DNA in the multivariate model. There was a reduced likelihood of herbal medicine usage influencing HBV DNA levels significantly (OR = 0.29, 0.10–0.86; p = 0.025). Conclusion In conclusion, variations in IL2 rs1479920 GG and IL2 rs1479921 AA could offer protective effects by reduc- ing HBV DNA. TNF-α rs179924CT may also cause elevation in HBV DNA levels whiles TNF-α -308A/G, showed a potential protective effect on liver scarring in HBV infected participants. It is therefore important to take a further look at such variations for understanding of HBV modulation in the Ghanaian population. Keywords HBV, Cytokines, Hepatitis, Genetics, TNF-α, IL-2, IL-10, IL-18, IFN-γ Introduction The global prevalence of hepatitis B virus (HBV) is soar- ing with a significant associated mortality. It is estimated that the global prevalence of HBV infections is 3.2% which translates into approximately 257 million cases of infections [1]. Approximately 70% of global HBV infec- tions occurs in the African region with 125, 000 associ- ated deaths [2]. Despite being a vaccine-preventable infection, HBV infection is a public health concern in sub-Sahara Africa (SSA). It is estimated that most HBV infections occur in Eastern and other Sub-Saharan Afri- can countries, where persistent liver diseases and liver cancer contributes significantly to mortality [3, 4]. In Ghana, HBV prevalence is relatively high with a reported prevalence of 5–27% [5–8]. This high prevalence is result- ing in reported life-threatening illnesses such as liver cir- rhosis, cancer and other liver diseases [9]. The virus that causes HBV infection has long being considered as non- cytopathic [10, 11] which means, the associated clinical effects of the virus results from host immune mecha- nisms in dealing with the virus [11, 12]. Host immune modulation mechanisms may lead to diverse clinical outcomes ranging from asymptomatic self-limited infec- tion, inactive carrier status, and chronic hepatitis to other complicated phases of liver diseases including fatal liver failure [13–15]. Although viral pathogenesis plays a significant role in the outcome of HBV infections, individual differences in the immune responses to HBV infection are more crucial for these outcomes [16–19]. The functionality and activ- ity of an active immune system which can lead to the suc- cessful elimination of the hepatitis B virus, only occurs in just about 10–30% of infected persons [20]. Several risk factors have been explored to understand and identify persons infected with HBV who may be pre- disposed to less favourable outcomes. One such risk fac- tor includes cytokine modulation and potential genetic influence [21, 22]. Cytokines influence the natural course of HBV infection and play an important role in the ini- tiation and regulation of immune responses [23–25]. It is noted that persons infected with HBV may have a sig- nificant decrease in CD4 + T-helper and CD8 + cytotoxic T-lymphocyte response cascade, which is postulated to be key to HBV clearance [26, 27]. Immuno-modulatory mechanisms involving cytokines such as tumor necro- sis factors alpha (TNF-α), interleukins (IL-4, IL-6, IL-8, IL-10, IL-18), interferon gamma (IFN)-γ, and tumor growth factor-beta (TGF-β) have a key role at the begin- ning of infection and modulatory dynamics of immuno- logical reactions, and hence can influence vulnerability to HBV and the normal course of infection [21, 28]. As part of immunomodulatory activity towards HBV infection, cytokines may influence liver fibrosis through hepatic stellate cell activation [29, 30]. This therefore means that immune cells and cytokines within the immune system potentially participate in the progression of liver scar- ring collectively exerting significant immunomodulatory effects. The influence of host genetics in HBV progression cannot be over emphasized, as variations in cytokines that contribute to the modulation of immune responses in HBV infection are affected by polymorphisms. Glob- ally, polymorphisms have been linked to host vulner- ability to HBV infection and viral clearance [31, 32]. Several studies have shown that host genetic polymor- phisms influence the transition from acute to chronic status in HBV [33] and increase or reduce the chance of severity of illness [22, 34]. The body of evidence avail- able has shown that host immunity is associated with relevant gene variants, particularly single nucleotide polymorphisms (SNPs) occurring in promoter regions of genes [21, 28]. With the knowledge that immune response is impor- tant to HBV progression and prognosis, it is important to understand and appreciate the genetic profile of cytokines in persons infected with HBV. There is very limited data on genetic polymorphisms in cytokines involved in HBV modulation and their potential rela- tionship to HBV progression in Ghanaians. The aim of this study therefore was to profile for polymorphisms in cytokines including IL-2, IL-10, IL-18, INF-γ, and TNF- α implicated in immunomodulation of HBV infection and understand how these polymorphisms influence prognoses using HBV DNA and the non-invasive fibro- sis-4 (FIB-4) score as indicators. Page 3 of 14Adu et al. BMC Gastroenterology (2024) 24:374 Methods Study design and subjects The study is a single-centre cross-sectional study car- ried out at the hepatitis clinic at the Cape Coast Teaching Hospital, Cape Coast, Ghana. We recruited participants from March 2021 to April 2022. The targeted popula- tion was made up of patients from both genders above 18 years old who have been diagnosed with HBV infec- tion and had available in their medical records viral loads results not less than 6 months old. Both acute and chronic HBV patients were recruited whiles HBV nega- tive participants served as healthy controls. Acute hepa- titis B was defined as acquired hepatitis B virus infection that lasts for less than 6 months whiles chronic hepatitis refers to infection persists for more than 6 months. Sample size The prevalence of HBV in Ghana is estimated to be 5–27% with the central region having a prevalence of 11.1–12.0%. Based on the data at the hepatitis clinic at the Cape Coast Teaching Hospital, with an average patient population of 1000, a confidence interval of 95%, 5% margin of error and a 5% provision for contingency, a response distribution of 84.8%, we calculated a sample size for this exploratory study as 174. The Raosoft soft- ware was used to calculate the sample size. However, to make up for contingencies, the total samples size was 227 made up of HBV infected patients (207) and HBV-nega- tive controls [20]. Ethical considerations The Cape Coast Teaching Hospital Ethical Review Board Committee granted ethical approval for the research, with reference number CCTHERC/EC/2021/005. Partici- pants who met the inclusion criteria were approached at the clinic during their visit and informed of the study and those who were willing were enrolled. In addition, both verbal and/or written consent was sought for. Unique codes were used for participants to ensure confidentiality and anonymity. Laboratory analysis A volume of 5 ml blood using BD vacutainer needles (BD Life Sciences, Plymouth, UK), were drawn from patients into two tubes. A volume of 2.5  ml of the whole blood was collected into ethylenediamine tetraacetic acid (EDTA) vacutainer tubes for full blood count (FBC) and separated at 3000G using the Universal 320R (Hettich Centrifuge, Germany) for the plasma for HBV profile. A 2.5  ml blood from a plain tube was then centrifuged at 3000G for serum for LFT and viral loads. Samples were further processed downstream for DNA extraction and genotyping. HBV antigen profile test The Wondfo One Step HBV Whole Blood/ Serum/ Plasma kits (Wondfo, Guangzhou, China) were used for obtaining the HBV antigen profile from the patients. Three drops of plasma were placed in a labelled cassette and reaction allowed to settle for 15  min before read- ing the results. If a band appears in any of the HBeAg, HBsAg and HBsAb designated zone, then it is considered as reactive while if no band appears in the then it is con- sidered non-reactive. Liver function test Liver function test was performed using the PRO XL chemical analyzer (ElitechGroup, Puteaux, France). Serum samples were used to evaluate liver function markers such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Viral load The HBV DNA quantification was undertaken using a fully automated DNA extraction and RT-PCR amplifi- cation process on the COBAS® AmpliPrep Instrument (Roche Diagnostics, USA) and Cobas AmpliPrep/Cobas TaqMan (CAP/CTM) HBV test kits, v2.0 following man- ufacturer’s protocol [35–37]. FIB 4 index Fibrosis-4 Index (FIB-4) is a simple non-invasive tool developed to determine the presence of advanced hepatic fibrosis, with scores categorized into low (< 1·30), inde- terminate (1·30–2·67), or high (> 2·67) risk of fibrosis. It is calculated using age, AST, ALT, and platelet concentra- tions [38]. FIB-4 calculation formula is shown below: FIB 4 = Age (years) x AST (U/L)/ (platelet count (109/L) x ALT1/2 (U/L). DNA Extraction and cytokine genotyping The samples collected into the EDTA vacutainer tube were stored at 20 °C and subsequently used for the DNA extraction. The omega BIO-TEK E.Z.N.A R Blood DNA Mini Kit (Omega Bio-tek, Inc. Norcross, USA) was used for the extraction process according to the manufac- turer’s instruction. Genotyping was undertaken using the Iplex GOLD SNP genotyping protocol on the Agena MassARRAY ®system (Agena Bioscience™, San Diego, CA, USA) [39] and PCR–RFLP. PCR was performed in a total reaction volume of 25μL made up of 0.5μL of 5X GoTaq poly reaction buffer containing MgCl2, 0.625μL of 5  mM dNTPs, 1μL of forward and reverse primer, 0.2μL of 5U/μL GoTaq Flexi DNA polymerase, 17.175μL of ddH2O and 3μL DNA. The selected SNPS included Page 4 of 14Adu et al. BMC Gastroenterology (2024) 24:374 IL2 (rs1479920 A > G, rs1479921 G > A, rs1479922 T > C), TNF-α (rs1799724 C > T, rs1800629 G > A), IL10 (rs1800782 T > G), IFN-γ (rs2069722 G > A, rs2069723 T > C, rs121913163 T > C), IL18 (rs360722 A > G, rs549908 T > G and IFN-γR1 (rs11914 A > C, rs1887415 A > G). The primer for TNF-α rs1800629 was F: 5 ́-AGG CAA TAG GTT TTG AGG GCCAT-3 ́; R: 5 ́ACA CTC CCC ATC CTC CCT GCT-3 ́ and the enzyme used for restriction diges- tion was NcoI. The expected band patterns were AA: 116 PCR fragment, AG: 116, 96, 20 PCR fragments and GG: 96, 20 PCR fragments. Statistical analysis Kobo toolbox was used to gather data for this investi- gation [40]. Data on age, gender, employment, ethnic- ity, education, smoking status and clinical parameters such as viral loads, full blood count and liver function were collected and entered using a structured ques- tionnaire in the Kobo Toolbox. Baseline features from participants with acute and chronic infections, and HBV- negative controls were compared. Statistical analysis was undertaken using Graph Pad Prism 8 (Prisma, San Diego, California) and STATA, version 18, (StataCorp, Table 1 Demographic data of study participants Others: Moose, Bimbo, Hausa and Dagare Parameter Total population n (%) Acute HBV n (%) Chronic HBV n (%) Controls n (%) p-value Gender < 0.001 Male 102 (44.93) 22 (33.33) 77 (54.61) 3 (15) Female 125 (55.07) 44 (66.67) 64 (45.39) 17 (85) Ethnic group 0.19 Akan 196 (86.34) 61 (92.42) 120 (85.11)) 15 (75.00) Ewe 13 (5.73) 4 (6.06) 8 (5.67) 1 (5.00) Dagbani 3 (1.32) 0(0.00) 2 (1.42) 1 (5.00) Non-Ghanaian 2 (0.88) 0(0.00) 2 (1.42) 0(0.00) Ga 2 (0.88) 0(0.00) 2 (1.42) - Ga-Adangbe 3 (1.32) 0(0.00) 1 (0.71) 2 (10.00) Others 8 (3.52) 1 (1.52) 6(4.26) 1 (5.00) Religion 0.84 Christian 209 (92.07) 60 (90.9) 130 (92.2) 19 (95) Muslim 18 (7.93) 6 (9.1) 11 (7.8) 1 (5) Age 0.18 1–19 8 (3.54) 3 (4.55) 4 (2.84) 1 (5.26) 20–39 138 (61.06) 43 (65.15) 85 (60.28) 10 (52.63) 40–59 64 (28.32) 17 (25.76) 43 (30.5) 4 (21.05) 60–79 15 (6.64) 2 (3.03) 9 (6.38) 4 (21.05) 80 + 1(0.44) 1 (1.52) 0(0.00) 0(0.00) Missing 1 1 Marital status 0.53 Cohabiting 3 (1.33) 0(0.00) 2 (1.42) 1 (5.26) Divorced 7 (3.11) 1 (1.54) 6 (4.26) 0(0.00) Married 118 (52.44) 32 (49.23) 75 (53.19) 11 (57.89) Single 91 (40.44) 29 (44.62) 55 (39.01) 7 (36.84) Widowed 6 (2.67) 3 (4.62) 3 (2.13) 0(0.00) missing 2 1 1 Educational level < 0.001 JHS 42 (19.27) 8 (12.31) 29 (21.64) 5 (26.32) No formal education 15 (6.88) 8 (12.31) 4 (2.99) 3 (15.79) Primary 12 (5.5) - 6 (4.48) 6 (31.58) SHS 51 (23.39) 19 (29.23) 29 (21..64) 3 (15.79) Tertiary 96 (44.04) 30 (46.15) 64 (47.76) 2 (10.53) Other 2 (0.92) - 2 (1.49) - Missing 9 1 7 1 Page 5 of 14Adu et al. BMC Gastroenterology (2024) 24:374 College Station, Texas, USA). Median and Interquartile ranges were computed for the liver enzymes and viral loads between acute, chronic and controls. The Wil- coxon signed and Kruskal Wallis tests were used to test for non-parametric data. Genotype and allele frequen- cies were calculated using ShesisPlus. SNPs were tested for departure from Hardy–Weinberg Equilibrium (HWE) using a chi-square goodness of fit test. The web based tool LDlink [41] and Shesis [42] Univariate and Multivar- iate logistic regression analysis were done using the SNPs of the study and age, duration of diagnosis, treatment, age and gender as covariates. Table 2 Clinical and Anthropometric characteristics of participants TDF Tenofovir disoproxil fumarate, BMI Body mass index *p < 0.05 statistically significant Parameter Total population n (%) Acute HBV n (%) Chronic HBV n (%) Controls n (%) p-value Healthy HbsAg carrier 137 (60.35) 43(65.15) 94(66.67) 0(0.00) < 0.001* Late incubation period 47 (20.7) 17(25.76) 30(21.28) 0(0.00) Persistent carrier state 21 (9.25) 6 (9.09) 15 (10.64) 0(0.00) Resolved 1 (0.44) 0(0.00) 1 (0.71) 0(0.00) Possible resolution of CHBV 1 (0.44) 0(0.00) 1 (0.71) 0(0.00) HBV-negative control 20 (8.81) 0(0.00) 00(0.00) 20 (100.00) Cigarette usage 0.44 Never used 221 (97.36) 66 (100) 135 (95.74) 20 (100) Irregularly used 4 (1.76) _0(0.00) 4 (2.84) 0(0.00) Regularly used 2 (0.88) _0(0.00) 2 (1.42) 0(0.00) Alcohol usage 0.14 Never used 172 (76.79) 46 (71.88) 106 (75.71) 20 (100) Irregularly used 43 (19.20) 14 (21.88) 29 (20.71) 0(0.00) Regularly used 9 (4.02) 4 (6.25) 5 (3.57) 0(0.00) BMI < 0.001* Underweight 15 (11.11) 6 (9.23) 15 (11.11) 1 (5.26) Normal 57 (42.22) 32 (49.23) 57 (42.22) 7 (36.84) Overweight 34 (25.19) 13 (20.0) 34 (25.19) 3 (15.79) Obese 29 (21.48) 14 (21.54) 29 (21.48) 8 (42.11) FIB-4 Score 0–1.30 6 (12.50) 11 (11.22) 6 (42.85) 0.008 1.31–2.67 15 (31.25) 25 (25.51) 2 (14.28) > 2.67 27 (56.25) 62 (63.27) 6 (42.86) Medication 0.01* TDF 23(11.17) 3 (4.55) 20 (14.29) 0(0.00) Levolin 2 (0.97) _0(0.00) 2 (1.43) 0(0.00) Lamivudine 1 (0.49) _0(0.00) 1 (0.71) 0(0.00) No treatment 180 (87.38) 63 (95.45) 117 (83.57) 0(0.00) HBV Herbal groups 0.72 Lev 52 2 (0.99) _0(0.00) 2 (1.46) 0(0.00) COA mixture 6 (2.69) 1 (1.52) 5 (3.65) 0(0.00) Power herbal 2 (0.99) _0(0.00) 2 (1.46) 0(0.00) Others 8 (3.92) 2 (3.04) 5 (3.65) 0(0.00) None 185 (91.13) 63 (95.45) 122 (89.05) 0(0.00) HBV status of partner 0.67 No 47 (22.6) 13 (19.70) 34 (24.11) 0(0.00) Yes 161 (77.4) 53 (80.30) 107 (75.89) 0(0.00) Liver disease 0.06 No 163 (78.74) 54 (81.82) 109 (77.3) 0(0.00) Unknown 28 (13.53) 11 (16.67) 17 (12.06) 20 (100)- Yes 16 (7.73) 1 (1.52) 15 (10.64) 0(0.00) Page 6 of 14Adu et al. BMC Gastroenterology (2024) 24:374 Results This study included a total of 227 participants made up of individuals diagnosed with acute and chronic HBV infec- tion and HBV-negative controls. Of the 227, 125 (55%) were females whiles 102 (45%) were males. Participants from the Akan tribe comprised 88% of the overall num- bers. Approximately 62% of overall participants had been diagnosed with chronic HBV infection with 29% being diagnosed with acute HBV infections whiles 8% were HBV-negative participants. The demographics of the study participants are displayed in Table 1. Table 2 shows the clinical and anthropometric charac- teristics of study participants. Of those with HBV infec- tion, 60% were classified as Healthy HbsAg carriers. The participants were HBV positive but had no symptoms or history of chronic liver disease, normal AST levels and no detectable levels of HbeAg or HBV DNA in serum. Only 12% were on treatment with tenofovir disoproxil fuma- rate (TDF), lamivudine or levolin. Among HBV-positive participants, 9% were on some form of herbal treatment. To ascertain the biochemical characteristics of patients to determine extent of liver damage by the HBV infec- tion, AST, ALT and VL levels were measured. The median AST levels for acute HBV infected individuals were higher than both chronic HBV infected individuals and HBV-negative controls (p < 0.001) (Table 3) although they were still within the acceptable reference ranges. However, the median viral load for chronic HBV infected individuals was 6620 (3466 -30111) IU/mL in comparison to acute HBV infected individuals 4386 (2674.50–6482) IU/mL, although it was not statistically significant. Using a selection criteria of HBV DNA levels, gender, age and availability of clinicodemographic data, a total of 175 samples were selected for further genotyping. Geno- type frequency distribution of the cytokines is presented in Table 4. All polymorphisms were in HWE equilibrium except rs1479922 T > C and rs11914 A > C that exhibited departure (p < 0.05). Variant allele frequencies (VAF) were compared between this study, other Africans, East Asians, South Asians, Americans and Europeans as reported in dbSNP (https:// www. ncbi. nlm. nih. gov/ snp/) (supplementary Table  1). This implies that the studied variations were adequately represented in our population. The effect of cytokine polymorphisms on HBV DNA levels is shown in Fig.  1. Variations in IL2 rs1479920 A > G saw a reduction in HBV DNA levels. The median HBV DNA for GG genotype (1258.93; 0.00–5011.87) IU/ mL was lower than AG (3548.13; 0.00–6309.57) IU/mL and AA (5011.87; 2113.49–5956.62) IU/mL genotypes. The was a significant difference in HBV DNA among IL2 rs1479921 A > G variant carriers (p = 0.0424). Carriers of the AA genotype (1258.93;0.00–4731.51) IU/mL, had lower HBV DNA than AG (3548.13; (0.00–6309.57) IU/ mL and GG (5011.87;2818.38–39810.72) IU/mL carriers. However, IL2 rs1479922 C > T carriers of the homozy- gous variant allele TT (5011.87; 2113.49–5956.62) IU/mL had higher HBV DNA than CT (3548.13;0.00–5308.84) IU/mL and CC (1258.93; 0.00- 5011.87) IU/mL carriers. TNF-α rs1800629 AA carriers (1258.93; 0.00–3981.07) IU/mL had a reduction in HBV DNA levels in compari- son to TNF-α rs1800629 GG carriers (1584.89; 0.00– 5011.87) IU/mL. Significance was again observed in HBV DNA levels among TNF-α rs179924C > T variant carriers (p = 0.0156) with lower HBV DNA levels for CC carri- ers (1258.9; 0.00–5011.99) and elevated for CT carriers (5011.99;22.39–35481.00). Similar patterns are observed in the other cytokine variations where carriers of het- erozygous and homozygous variations have alternating patterns of HBV DNA levels. Univariate and multivariate analysis of factors including variations in cytokines on HBV DNA levels and higher FIB-4 score is shown in Table 5. In the univariate model there was an association for a carrier of TNF-α rs1800629 AA genotype to FIB score (OR = 0.08, 0.01–0.93; p = 0.043) with reduced odds. The multivariate model Table 3 Biochemical characteristics of participants AST Aspartate aminotransferase, ALT Alanine transaminase, VL Viral load n/a = not applicable *p < 0.05 statistically significant Parameter n (%) Acute Median (IQR) Chronic Median (IQR) Controls Median (IQR) p-value AST U/L male 33.4 (25.6—38.9) 27.3 (22.5—36) 13.5 (12.2—21.6) 0.06 female 27.3 (16.3—35.8) 26.4 (20.7—38) 21.8 (17.6—23.9) 0.33 ALT U/L male 8.65 (6.5—13.9) 9.3 (5.85—15) 10.2 (7.1—12.9) < 0.001* female 5.9 (2.8—10.3) 7.45 (4.2—11.6) 11.2 (9—14.6) < 0.001* VL IU/mL ≤ 2000 186 (89.86) 29.3 (0–708) 0 (0—480) n/a 0.44 > 2000 21 (10.14) 4386 (2674.50–6482) 6620 (3466 -30111) n/a 0.24 https://www.ncbi.nlm.nih.gov/snp/ https://www.ncbi.nlm.nih.gov/snp/ Page 7 of 14Adu et al. BMC Gastroenterology (2024) 24:374 Table 4 Genotypic distribution of cytokine polymorphisms Acute HBV (n = 52) Chronic HBV (n = 104) HBV-negative Control (n = 20) p-value SNP ID Genotype, n (freq) IL2 rs1479920 A > G GG 36 (0.69) 89 (0.86) 17 (0.85) 0.089 AG 13 (0.25) 14 (0.13) 3 (0.15) AA 3 (0.06) 1 (0.01) 0 (0.00) HWE p∇ 0.127 rs1479921A > G AA 36 (0.69) 89 (0.86) 17 (0.85) 0.089 AG 13 (0.25) 14 (0.13) 3 (0.15) GG 3 (0.06) 1 (0.01) 0 (0.00) HWE p∇ 0.127 rs1479922 T > C CC 37 (0.71) 93 (0.89) 17 (0.85) 0.044* TC 12 (0.23) 10 (0.10) 3 (0.15) TT 3 (0.06) 1 (0.01) 0 (0.00) HWE p∇ 0.030* TNF-α rs1799724 C > T CC 50 (0.96) 100 (0.96) 20 (1.00) 1.000 TC 2 (0.04) 4 (0.04) 0 (0.00) TT 0(0.00) 0(0.00) 0(0.00) HWE p∇ 0.818 rs1800629 G > A GG 34 (0.65) 74 (0.71) 13 (0.65) 0.862 AG 17 (0.33) 28 (0.27) 7 (0.35) AA 1 (0.02) 2 (0.02) 0 (0.00) HWE p∇ 0.330 IL10 rs1800872 T > G GG 23 (0.45) 28 (0.27) 5 (0.25) 0.133 GT 22 (0.43) 49 (0.47) 11 (0.55) TT 6 (0.12) 27 (0.26) 4 (0.20) HWE p∇ 0.544 IFN-γ rs2069722 G > A GG 37 (0.71) 91 (0.88) 17 (0.85) 0.048* AG 15 (0.29) 13 (0.12) 3 (0.15) AA 0(0.00) 0(0.00) 0(0.00) HWE p∇ 0.200 rs2069723 T > C TT 46 (0.88) 97 (0.93) 19 (0.95) 0.596 TC 6 (0.12) 7 (0.07) 1 (0.05) CC 0(0.00) 0(0.00) 0(0.00) HWE p∇ 0.583 rs121913163 T > C TT 52 (1.00) 104 (1.00) 20 (1.00) N/A TC 0(0.00) 0(0.00) 0(0.00) CC 0(0.00) 0(0.00) 0(0.00) Page 8 of 14Adu et al. BMC Gastroenterology (2024) 24:374 further confirmed the association with reduced odds (OR = 0.02, 0.00–0.67; p = 0.029). There was an associa- tion and higher odds of age ranges of 20–39 (OR = 5.00, 1.13–6.10; p = 0.034) and 40–59 years (OR = 41.99, 3.74– 47.21; p = 0.0002) of having higher FIB-4 scores in the univariate model. Being female (OR = . 2.42, 1.03–5.71; p = 0.043) in the univariate models showed higher odds of having high levels of HBV DNA in the multivariate model. There was a reduced likelihood of herbal medi- cine usage influencing HBV DNA levels significantly (OR = 0.29, 0.10–0.86; p = 0.025). Analysis of LD on the IFN-γR1 rs11914, IFN-γ rs121913163, IFN-γR1 rs1887415, IFN-γ rs2069722 and IFN-γ rs2069723 was undertaken as show in Fig. 2. There was strong LD between rs11914, rs2069723, rs1887415 and rs2069723 (D’ = 0.99- The LD for IL2 rs1479920, rs1479921 and rs1479922 is shown in Fig. 3. Strong LD was again observed for all three variants with D’ values at 0.99 and R’ values from 0.88–0.99. These means the variations are linked and could be inherited together and have some associated functional effects. Discussion The objective of this work was to study the association of polymorphisms of genes coding for cytokines IL-2, IL-10, IL-18, INF-γ, and TNF-α with plasma HBV DNA level and FIB score in patients with HBV infection in Ghana. Irrespective of drug treatment, HBV infection can lead to a range of clinical outcomes, from viral clearance to infection persistence resulting in chronicity and complex liver disorders. Factors that influence hepatitis outcomes include viral, environmental, co-infections, lifestyle and host genetics. Due to the important role of cytokines in immunomodulation, some studies have looked at the role of SNPs in influencing the activities of interleukins (IL2, IL4, IL10, 1L18), TNF-α and IFNγ [43–46]. This study focused on thirteen [13] SNPs in cytokines including IL2, IL10, IL18, TNF-α, IFN-γ and IFN-γR1. The effect of the cytokines polymorphisms was compared in terms of HBV DNA where it was found out that some car- riers of the variant alleles in for example IL2 rs1479920 GG had lower median HBV DNA than their homozygous Table 4 (continued) Acute HBV (n = 52) Chronic HBV (n = 104) HBV-negative Control (n = 20) p-value HWE p∇ IFN-γR1 rs11914 A > C AA 44 (0.85) 97 (0.93) 19 (0.95) 0.388 CA 7 (0.14) 6 (0.06)_ 1 (0.05) CC 1 (0.02) 1(0.01) HWE p∇ 0.017* rs1887415 A > G AA 49 (0.94) 102 (0.98) 19 (0.95) 0.355 AG 3 (0.06) 2 (0.02) 1 (0.05) GG 0(0.00) 0(0.00) 0(0.00) HWE p∇ 0.818 IL18 rs360722 A > G GG 17 (0.33) 47 (0.46) 6 (0.30) 0.201 GA 30 (0.58) 41 (0.39) 11 (0.55) AA 5 (0.01) 16 (0.15) 3 (0.15) HWE p∇ 0.998 rs549908 T > G TT 42 (0.81) 76 (0.73) 14 (0.70) 0.663 GT 10 (0.19) 26 (0.25) 6 (0.30) GG 0 (0.00) 2 (0.02) 0 (0.00) HWE p∇ 0.505 HWE p∇ < 0.05 = Deviation from Hardy-Weinberg *p < 0.05 statistically significant Page 9 of 14Adu et al. BMC Gastroenterology (2024) 24:374 wildtype AA and AG allele carriers respectively. IL2 con- tributes to HBV infection by boosting host immunologi- cal activity [47, 48] so this reduction in HBV DNA due to the IL2 rs1479920 GG variation may augur well for carri- ers of this genotype. The variation is an intronic variant which may affect function and therefore carriers of this variant may be offered a certain protective effect. This is also observed in reduced median HBV DNA of AG carri- ers compared to AA. There was variable HBV DNA levels in relation to being a carrier of IL10, IL18, TNF-α, IFN-γ and IFN-γR1 variation. Although, there was an observed significance in HBV DNA levels (p = 0.0424) among car- riers IL2 rs1479921A > G variants, those with AA geno- types still had lower HBV DNA levels in comparison with their GA and GG genotypes. Although no studies have reported this level of significance, it has been previ- ously reported that IL2 may have a heterogenous effect in immunomodulation and genetic variation may fur- ther influence its effect and function [24]. On another hand, there was a significant difference in the HBV DNA levels (p = 0.0156) of TNF-α rs1799724 C > T (-857C/T) carriers. The promoter polymorphisms at -857C/T regu- late TNF-α production thus affecting immune regulation. There is also a relationship between TNF-α and HLA II expression which facilitates viral antigen presentation [49]. This could account for the differences in the HBV DNA levels as this promoter polymorphism could influ- ence the levels TNF-α subsequently HLA. Previous stud- ies and meta-analysis have shown that TNF-α rs1799724 C > T variations were markedly related and associated with HBV infection and persistence [50–52]. Variations that exists in cytokines may offer protective or deleteri- ous effects and for this study, the differences in HBV DNA observed when comparing the various studied cytokines supports the body of evidence that variations in cytokines can influence viral DNA levels and persistence and pos- sibly cirrhosis [53–58]. TNF-α is produced by macrophages, monocytes, neu- trophils, T-lymphocytes and NK cells. It is an impor- tant stimulatory factor that promotes the secretion of other cytokines and expression of adhesion molecules on endothelial cells [59, 60]. The study observed an Fig. 1 Effect of cytokine variations on HBV DNA levels in HBV-positive participants. There was a significant difference in HBV DNA levels among IL2 rs1479921 G > A (p = 0.0424) and TNF-α rs179924C > T (p = 0.0156) variant carriers Page 10 of 14Adu et al. BMC Gastroenterology (2024) 24:374 Table 5 Association of Genetic Polymorphisms and Clinical Factors with HBV DNA Levels and FIB Score in Patients Abbreviations: COR Crude odds ratio, AOR Adjusted odds ratio, 95% CI 95% confidence interval, Crude and adjusted odds ratios (OR) and 95% confidence intervals (variables found to be associated through a univariate analysis were entered into the multivariate model) * Significant association (P < 0.05) HBV DNA FIB Score Factor COR (95% CI) p-value AOR (95% CI) p-value COR (95% CI) p-value AOR (95% CI) p-value IFNGR1 rs11914 A > C AA (ref ) 1 CA 0.92 (0.28–2.95) 0.884 2.32 (0.46–11.76) 0.309 0.76 (0.15–3.81) 0.742 1.54 (0.07–33.87) 0.787 IL2 rs1479920 A > G GG (ref ) 1 AG 1.23 (0.51–2.99) 0.641 1.05 (0.13–8.64) 0.967 2.07 (0.45–9.61) 0.349 0.14 (0.01–2.69) 0.191 IL2 rs1479921 A > G AA 1 1 - - AG 1.23 (0.51–2.99) 0.641 2.07 (0.45–9.61) 0.349 - - IL2 rs1479922 T > C TT 1 1 1 1 TC 1.40 (0.53–3.67) 0.493 1.26 (0.17–13.56) 0.851 3.8 (0.49–30.34) 0.202 1.3 (0.39–4.58) 0.152 TNF-α rs1799724 C > T CC (ref ) 1 1 - - - TC 1.24 (0.22–7.02) 0.804 0.45 (0.02–7.71) 0.585 1 - - - TNF-α rs1800629 G > A GG (ref ) 1 1 AG 1.07 (0.52–2.22) 0.853 0.74 (0.28–1.95) 0.548 0.97 (0.34–2.74) 0.948 1.36 (0.23–8.03) 0.734 AA 1.22 (0.10–3.93) 0.871 0.93 (0.5–1.72) 0.960 0.08 (0.01–0.93) 0.043* 0.02 (0.00–0.67) 0.029* IL10 rs1800872 T > G GG (ref ) 1 1 GT 0.70 (0.33–1.50) 0.361 1.04 (0.41–2.66) 0.939 1.48 (0.48–4.53) 0.497 3.05 (0.46–20.03) 0.245 TT 0.59 (0.24–1.47) 0.255 0.66 (0.19–2.36) 0.535 0.53 (0.16–1.70) 0.282 0.27 (0.03–1.84) 0.180 IFN-γR1 rs1887415 AA 1 1 1 AG 0.14 (0.02–1.29) 0.083 - 0.32 (0.056–1.90) 0.212 0.12 (0.00–3.79) 0.231 IFN-γ rs2069722 G > A GG 1 1 1 1 AG 1.49 (0.60–3.67) 0.388 1.75 (0.54–5.66) 0.348 1.49 (0.60–3.67) 0.388 3.24 (0.27–3.94) 0.356 IFN-γ rs2069723 T > C TT (ref ) 1 1 1 TC 0.66 (0.21–2.07) 0.478 0.36 (0.09–1.42) 0.146 1.80 (0.22–14.86) 0.585 0.81 (0.04–14.81) 0.890 IL18 rs360722 A > G GG (ref ) 1 1 GA 0.95 (0.47–1.92) 0.885 0.77 (0.30–1.96) 0.589 0.62 (0.23–1.74) 0.366 2.70 (0.53 -13.87) 0.233 AA 0.75 (0.27–2.04) 0.571 0.43 (0.11–1.65) 0.219 0.70 (0.22–1.74) 0.639 1.21 (0.13–11.55) 0.866 IL18 rs549908 T > G TT (ref ) 1 1 GT 1.12 (0.51–2.40) 0.799 1.64 (0.55–4.90) 0.373 1.25 (0.39–4.02) 0.711 2.21 (0.28–2.76) 0.453 Treatment TDF (ref.) 1 1 1 No treatment 1.07 (0.45–2.55) 0.877 0.48 (0.10–2.16) 0.335 1.07 (0.45–2.55) 0.877 - - Age (years) 1–19 (ref.) 1 1 20–39 0.83 (0.24–2.87) 0.773 0.83 (0.12–5.65) 0.847 5.00 (1.13–6.10) 0.034* 19.83 (0.89–23.54) 0.060 40–59 1.17 (0.32–4.25) 0.815 1.26 (0.16–9.73) 0.826 41.99 (3.74–47.21) 0.002* 2.47 (0.50–4.77) 0.11* Gender Male (ref.) 1 1 1 Female 1.55 (0.89–2.68) 0.121 2.42 (1.03–5.71) 0.043* 0.80 (0.32–2.00) 0.633 0.56 (0.14–2.28) 0.45 Herbal Medicine Usage No 1 1 1 - - Yes 0.29 (0.10–0.86) 0.025* 0.22 (0.01–3.92) 0.303 0.26 (0.02–3.01) 0.280 - - Page 11 of 14Adu et al. BMC Gastroenterology (2024) 24:374 Fig. 2 Linkage disequilibrium (L.D) plot of IFN-γR1 rs11914, IFN-γ rs121913163, IFN-γR1 rs1887415, IFN-γ rs2069722 and IFN-γ rs2069723. Legend: For the pair-wise LD association between five SNPs and the corresponding D’ and R’ values, the colour gradient from red to white reveals higher to lower LD (D’ 1–0; R’ 1–0) Fig. 3 Linkage disequilibrium (LD) plot of IL2 rs1479920, rs1479921 and rs1479922 and observed D’ and R’ values (Fig. 4.6). The pair-wise LD association between three SNPs and the corresponding D’ and R’ values Page 12 of 14Adu et al. BMC Gastroenterology (2024) 24:374 association between TNF-α -308A/G (rs1800629) and FIB-4 score which is a non-invasive indicator of liver scarring. Carriers of AA genotype had a reduced likeli- hood of getting a higher FIB-4 score in both univariate (OR 0.08; 0.01–0.93; p = 0.043) and multivariate (OR 0.02; 0.03–1.84; p = 0.029) models. This could mean that the presence of the -308A allele is associated with a better prognosis of liver scaring and potentially other complications. This finding though showing an associa- tion between TNF-α -308A/G (rs1800629) and the non- invasive FIB-4 score is similar to a study that showed that TNF-α -308A/G is an independent risk factor for hepatocellular carcinoma [61]. TNF-α plays a role in the inflammatory process in the liver and in HBV infec- tion, excessive TNF-α production triggers inflammation and subsequent death of liver cells which increases the development of liver connective tissue. If this process continues, even healthy liver cells may be damaged and replaced by connective tissue. What this means for this study therefore is that for carriers of A allele, the rate of liver inflammation in such HBV positive persons may be reduced reducing the risk of liver scarring. Though other findings have showed an increased likelihood of liver related complication such as HCC, the findings from this study showed a reduced likelihood. This further under- scores the need to consider ethnic background of popu- lations under study in such analysis. It will, however, be important to undertake further studies on this relation- ship in future studies among Ghanaians. Other variations in the univariate and multivariate analysis such as IL2 rs1479920 AG, IL2 rs1479921 AG, IL2 rs1479922 TC, TNF-α -308A/G and TNF-α -857C/T showed increased likelihood of HBV-positive persons having high HBV DNA although the association was not significant. Such patterns of increased likelihood of having high FIB-4 scores were found in a non-signifi- cant association with IL2 rs1479920 AG, IL2rs1479921 AG, IL2 rs1479922 TC, IL10 rs1800872GT, and IFN-γ rs2069722AG. Previous studies had shown that some of these variations may offer protective effects in HBV infec- tion [43, 62, 63]. These cytokines may also contribute to liver scarring and HBV DNA progression through immune dysfunction where liver inflammation can cause HBV asso- ciated complications. The lack of comparable numbers for acute, chronic, and HBV-negative controls is one of the limitations of this study. This was because we were undertaking ran- dom recruitment without targeting specific patients. This led to different numbers although all analyses undertaken arestill valid. Another limitation is that not all recruited persons were genotyped due to availability of resources. However, selection of genotyped samples was done to represent all aspect of available data. Conclusion Variations in some cytokines such as IL2 rs1479920 GG and IL2 rs1479921 AA could offer protective effects by reducing HBV DNA. TNF-α rs179924CT may also cause an elevated HBV DNA level in HBV infected persons leading to persistence. TNF-α -308A/G showed a protec- tive effect on liver scarring in HBV infected patients. The observation from the study showing that TNF-α -308A/G could offer a potential protective effect against liver dam- age in HBV-positive patients advances the overarching sustainable development goals (SDG) objective of hepa- titis prevention. Through the identification of genetic markers that impact the course of the disease and the effectiveness of therapy, focused medical treatments and public health measures may be developed, ultimately lowering the prevalence of HBV and enhancing health outcomes as stated in SDG3.3. What this means for this study therefore is that for carriers of A allele, the rate of liver inflammation in such HBV positive persons may be reduced reducing the risk of liver scarring. There- fore, it will be important to undertake further studies on this relationship in future studies within the Ghanaian population. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12876- 024- 03456-9. Supplementary Material 1. Supplementary Material 2. Acknowledgements Authors would like to acknowledge the nurses and laboratory staff of the Cape Coast Teaching Hospital who assisted with recruitment and laboratory analysis of samples. Authors’ contributions NET conceived the idea, supervised and secured funding for the project. NET, FA,ASO and SBN were involved in recruitment, data retrieval, laboratory analysis. NET, EAB, CK, AA and ROA performed analysis. All authors contributed to the writing of the manuscript. Funding This work is supported with funds from the National Research Foundation (NRF) of South Africa awarded to Dr Nicholas Ekow Thomford through a rated research incentive award (UID127492) and Sam and Brew Butler postgraduate funding from the School of Graduate Studies, University of Cape Coast, Ghana awarded to Faustina Adu. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Raw data has been depos- ited in GEO submission with accession number GSE274827. Declarations Ethics approval and consent to participate The Cape Coast Teaching Hospital Ethical Review Board Committee granted ethical approval for the research, with reference number CCTHERC/ EC/2021/005. Participants who met the inclusion criteria were approached at https://doi.org/10.1186/s12876-024-03456-9 https://doi.org/10.1186/s12876-024-03456-9 Page 13 of 14Adu et al. BMC Gastroenterology (2024) 24:374 the clinic during their visit and informed of the study and those that willing were enrolled. Informed consent was obtained from all participants. In addi- tion, both verbal and/or written consent was sought for. Unique codes were used for participants to ensure confidentiality and anonymity. The study was undertaken in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Competing interest The authors declare no competing interests. Author details 1 Department of Medical Biochemistry, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana. 2 Pharmacogenomics and Genomic Medicine Group & Lab, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana. 3 Department of Microbiology & Immunology, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana. 4 Department of Chemical Pathology, University of Ghana Medi- cal School University of Ghana, Legon, Accra, Ghana. 5 Division of Chemical Pathology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa. 6 Division of Human Genetics, Depart- ment of Pathology, Faculty of Health Sciences, University of Cape Town, Anzio Road, Observatory, Cape Town 7925, South Africa. Received: 1 May 2024 Accepted: 9 October 2024 References 1. Collaborators PO. Global prevalence, cascade of care, and prophylaxis coverage of hepatitis B in 2022: a modelling study. Lancet Gastroenterol Hepatol. 2023;8(10):879–907. 2. World Health Organization. Regional Office for A. 91 million Africans infected with Hepatitis B or C 2022. Available from: https:// www. afro. who. int/ news/ 91- milli on- afric ans- infec ted- hepat itis-b- or-c. 3. Patmore LA, van Eekhout KMA, Buti M, Koc ÖM, Agarwal K, de Knegt RJ, et al. Hepatocellular carcinoma risk in sub-Saharan African and Afro-Surinamese individuals with chronic hepatitis B living in Europe. J Hepatol. 2024;80(2):243–50. 4. Ndububa DA. Liver cancer surveillance in people with hepatitis B in Africa. The Lancet Gastroenterology & Hepatology. 2024. 5. Ofori-Asenso R, Agyeman AA. Hepatitis B in Ghana: a systematic review and meta-analysis of prevalence studies (1995–2015). BMC Infect Dis. 2016;16:130. 6. Abesig J, Chen Y, Wang H, Sompo FM, Wu IXY. Prevalence of viral hepatitis B in Ghana between 2015 and 2019: a systematic review and meta-analy- sis. PLoS ONE. 2020;15(6):e0234348. 7. Adanusa M, Adjei G, Eliason S, Amoah S, Benson C, Sirikyi I, et al. Sero- prevalence of Hepatitis B and C at a Primary Healthcare centres in Ghana AfricArXiv. 2023. 8. Luuse A, Dassah S, Lokpo S, Ameke L, Noagbe M, Adatara P, et al. Sero- Prevalence of Hepatitis B Surface Antigen Amongst Pregnant Women Attending an Antenatal Clinic, Volta Region, Ghana. J Public Health Afr. 2016;7(2):584. 9. Nartey YA, Antwi SO, Bockarie AS, Hiebert L, Njuguna H, Ward JW, et al. Mortality burden due to liver cirrhosis and hepatocellular carcinoma in Ghana; prevalence of risk factors and predictors of poor in-hospital survival. PLoS ONE. 2022;17(9):e0274544. 10. Seeger C, Mason WS. Molecular biology of hepatitis B virus infection. Virology. 2015;479–480:672–86. 11. Wang J, Wu X, Kuniya T. Analysis of a diffusive HBV model with logistic proliferation and non-cytopathic antiviral mechanisms. Commun Nonlin- ear Sci Numer Simul. 2022;106:106110. 12. Zai W, Hu K, Ye J, Ding J, Huang C, Li Y, et al. Long-Term Hepatitis B Virus Infection Induces Cytopathic Effects in Primary Human Hepatocytes, and Can Be Partially Reversed by Antiviral Therapy. Microbiology Spectrum. 2022;10(1):e01328–e1421. 13. Kaufmann SHE, Dorhoi A, Hotchkiss RS, Bartenschlager R. Host-directed therapies for bacterial and viral infections. Nat Rev Drug Discovery. 2018;17(1):35–56. 14. Busca A, Kumar A. Innate immune responses in hepatitis B virus (HBV) infection. Virology Journal. 2014;11(1):22. 15. Cui D, Jiang D, Yan C, Liu X, Lv Y, Xie J, et al. Immune checkpoint mol- ecules expressed on CD4+ T cell subsets in chronic asymptomatic hepa- titis B virus carriers with hepatitis B e antigen-negative. Front Microbiol. 2022;13:887408. 16. Zheng P, Dou Y, Wang Q. Immune response and treatment targets of chronic hepatitis B virus infection: innate and adaptive immunity. Front Cellular Infect Microbiol. 2023;13:1206720. 17. Chuang Y-C, Tsai K-N, Ou J-HJ. Pathogenicity and virulence of Hepatitis B virus. Virulence. 2022;13(1):258–96. 18. Shin E-C, Sung PS, Park S-H. Immune responses and immunopathology in acute and chronic viral hepatitis. Nat Rev Immunol. 2016;16(8):509–23. 19. Chang M-L, Liaw Y-F. Hepatitis B flares in chronic hepatitis B: Pathogen- esis, natural course, and management. J Hepatol. 2014;61(6):1407–17. 20. Ferreira SC, Chachá SG, Souza FF, Teixeira AC, Santana RC, Villanova MG, et al. Factors associated with spontaneous HBsAg clearance in chronic hepatitis B patients followed at a university hospital. Ann Hepatol. 2014;13(6):762–70. 21. Tunçbilek S. Relationship between cytokine gene polymorphisms and chronic hepatitis B virus infection. World J Gastroenterol. 2014;20(20):6226–35. 22. Xu J, Zhan Q, Fan Y, Yu Y, Zeng Z. Human genetic susceptibility to hepati- tis B virus infection. Infect Genet Evol. 2021;87:104663. 23. Ribeiro CRdA, de Almeida NAA, Martinelli KG, Pires MA, Mello CEB, Bar- ros JJ, et al. Cytokine profile during occult hepatitis B virus infection in chronic hepatitis C patients Virol J. 2021;18(1):15. 24. Zhong S, Zhang T, Tang L, Li Y. Cytokines and Chemokines in HBV Infec- tion. Frontiers in Molecular Biosciences. 2021;8:805625. 25. Dimitriadis K, Katelani S, Pappa M, Fragkoulis GE, Androutsakos T. The Role of Interleukins in HBV Infection: A Narrative Review. Journal of Personal- ized Medicine. 2023;13(12):1675. 26. Buschow SI, Jansen DTSL. CD4+ T Cells in Chronic Hepatitis B and T Cell- Directed Immunotherapy. Cells. 2021;10(5):1114. 27. Heim K, Neumann-Haefelin C, Thimme R, Hofmann M. Heterogeneity of HBV-Specific CD8+ T-Cell failure: implications for immunotherapy. Front Immunol. 2019;10:2240. 28. Ben-Ari Z, Mor E, Papo O, Kfir B, Sulkes J, Tambur AR, et al. Cytokine gene polymorphisms in patients infected with hepatitis B virus. Am J Gastroen- terol. 2003;98(1):144–50. 29. Guo Z, Wu Q, Xie P, Wang J, Lv W. Immunomodulation in non-alcoholic fatty liver disease: exploring mechanisms and applications. Front Immu- nol. 2024;15:1336493. 30. Nati M, Chung K-J, Chavakis T. The Role of Innate Immune Cells in Nonal- coholic Fatty Liver Disease. J Innate Immun. 2021;14(1):31–41. 31. Bourdon M, Manet C, Montagutelli X. Host genetic susceptibility to viral infections: the role of type I interferon induction. Genes Immun. 2020;21(6–8):365–79. 32. Frodsham AJ. Host genetics and the outcome of hepatitis B viral infec- tion. Transpl Immunol. 2005;14(3):183–6. 33. Wungu CDK, Ariyanto FC, Prabowo GI, Soetjipto S, Handajani R. Meta- analysis: Association between hepatitis B virus preS mutation and hepatocellular carcinoma risk. J Viral Hepatitis. 2021;28(1):61–71. 34. Rybicka M, Woziwodzka A, Romanowski T, Sznarkowska A, Stalke P, Dręczewski M, et al. Host genetic background affects the course of infec- tion and treatment response in patients with chronic hepatitis B. J Clin Virol. 2019;120:1–5. 35. Ortonne V, Wlassow M, Bouvier-Alias M, Melica G, Poveda JD, Laperche S, et al. Diagnosis and Monitoring of Hepatitis B Virus Infection Using the Cobas(®) HBV Test for Use on the Cobas(®) 4800 System. Microorganisms. 2021;9(3):573. 36. Allice T, Cerutti F, Pittaluga F, Varetto S, Gabella S, Marzano A, et al. COBAS AmpliPrep-COBAS TaqMan hepatitis B virus (HBV) test: a novel automated real-time PCR assay for quantification of HBV DNA in plasma. J Clin Micro- biol. 2007;45(3):828–34. 37. Chevaliez S, Bouvier-Alias M, Laperche S, Hézode C, Pawlotsky JM. Perfor- mance of version 2.0 of the Cobas AmpliPrep/Cobas TaqMan real-time https://www.afro.who.int/news/91-million-africans-infected-hepatitis-b-or-c https://www.afro.who.int/news/91-million-africans-infected-hepatitis-b-or-c Page 14 of 14Adu et al. BMC Gastroenterology (2024) 24:374 PCR assay for hepatitis B virus DNA quantification. J Clin Microbiol. 2010;48(10):3641–7. 38. Kim BK, Kim DY, Park JY, Ahn SH, Chon CY, Kim JK, et al. Validation of FIB-4 and comparison with other simple noninvasive indices for predicting liver fibrosis and cirrhosis in hepatitis B virus-infected patients. Liver Int. 2010;30(4):546–53. 39. Millis MP. Medium-throughput SNP genotyping using mass spectrom- etry: multiplex SNP genotyping using the iPLEX® Gold assay. Methods Mol Biol. 2011;700:61–76. 40. Lakshminarasimhappa MC. Web-Based and Smart Mobile App for Data Collection: Kobo Toolbox / Kobo Collect. 2022. 2022;57(2):8. 41. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31(21):3555–7. 42. Shen J, Li Z, Chen J, Song Z, Zhou Z, Shi Y. SHEsisPlus, a toolset for genetic studies on polyploid species. Sci Rep. 2016;6:24095. 43. Gusatti CdS, Costi C, de Medeiros RM, Halon ML, Grandi T, Medeiros AFR, et al. Association between cytokine gene polymorphisms and outcome of hepatitis B virus infection in southern Brazil. J Med Virol. 2016;88(10):1759–66. 44. Fabricio-Silva GM, Poschetzky BS, de Mello PR, Dos Santos RC, Cavalini LT, Porto LC. Association of cytokine gene polymorphisms with hepatitis C virus infection in a population from Rio de Janeiro. Brazil Hepat Med. 2015;7:71–9. 45. Li W, Jiang Y, Jin Q, Shi X, Jin J, Gao Y, et al. Expression and gene polymor- phisms of interleukin 28B and hepatitis B virus infection in a Chinese Han population. Liver Int. 2011;31(8):1118–26. 46. Karimi MH, Yaghobi R, Arjmandi K, Geramizadeh B, Nikeghbalian S, Male- khosseini SA. Cytokine Gene Polymorphisms and Viral Hepatitis Infections in Kidney Transplant Recipients. Laboratory Medicine. 2013;44(2):114–20. 47. Pol JG, Caudana P, Paillet J, Piaggio E, Kroemer G. Effects of interleu- kin-2 in immunostimulation and immunosuppression. J Exp Med. 2019;217(1):e20191247. 48. Bendickova K, Fric J. Roles of IL-2 in bridging adaptive and innate immunity, and as a tool for cellular immunotherapy. J Leukoc Biol. 2020;108(1):427–37. 49. Horie Y, Chiba M, Suzuki T, Kudo T, Kamata A, Iizuka M, et al. Induction of major histocompatibility complex class II antigens on human colonic epithelium by interferon-gamma, tumor necrosis factor-alpha, and inter- leukin-2. J Gastroenterol. 1998;33(1):39–47. 50. Wei Y, Zhao Z, Wang Z, Zhang K, Tang Z, Tao C. Relationships between IL-1β, TNF-α genetic polymorphisms and HBV infection: A meta-analytical study. Gene. 2021;791:145617. 51. Li Y, Zhou H, Wu W, Zhang W, Ye Y, Jia W, et al. Associations between single nucleotide polymorphisms of cytokines and hepatitis B virus-related liver cirrhosis: A case-control study. Immun Inflamm Dis. 2024;12(9):e70017. 52. Wungu CDK, Ariyanto FC, Prabowo GI, Soetjipto, Handajani R. Associa- tion between five types of Tumor Necrosis Factor-α gene polymor- phism and hepatocellular carcinoma risk: a meta-analysis. BMC Cancer. 2020;20(1):1134. 53. Zhu QR, Ge YL, Gu SQ, Yu H, Wang JS, Gu XH, et al. Relationship between cytokines gene polymorphism and susceptibility to hepatitis B virus intrauterine infection. Chin Med J (Engl). 2005;118(19):1604–9. 54. Basturk B, Karasu Z, Kilic M, Ulukaya S, Boyacioglu S, Oral B. Association of TNF-alpha -308 polymorphism with the outcome of hepatitis B virus infection in Turkey. Infect Genet Evol. 2008;8(1):20–5. 55. Heidari Z, Moudi B, Mahmoudzadeh Sagheb H, Moudi M. Association of TNF-α Gene Polymorphisms with Production of Protein and Susceptibil- ity to Chronic Hepatitis B Infection in the South East Iranian Population. Hepat Mon. 2016;16(11):e41984. 56. Wu JF, Ni YH, Lin YT, Lee TJ, Hsu SH, Chen HL, et al. Human interleukin-10 genotypes are associated with different precore/core gene mutation patterns in children with chronic hepatitis B virus infection. J Pediatr. 2011;158(5):808–13. 57. Gao L, Chen X, Zhang L, Wu D, Zhao H, Niu J. Association of IL-10 poly- morphisms with hepatitis B virus infection and outcome in Han popula- tion. Eur J Med Res. 2016;21(1):23. 58. Moudi B, Heidari Z, Mahmoudzadeh-Sagheb H, Hashemi M, Metanat M, Khosravi S, et al. Association Between IL-10 Gene Promoter Polymor- phisms (-592 A/C, -819 T/C, -1082 A/G) and Susceptibility to HBV Infection in an Iranian Population. Hepat Mon. 2016;16(2):e32427. 59. Terrando N, Monaco C, Ma D, Foxwell BMJ, Feldmann M, Maze M. Tumor necrosis factor-α triggers a cytokine cascade yielding postoperative cognitive decline. Proc Natl Acad Sci. 2010;107(47):20518–22. 60. Liu C, Chu D, Kalantar-Zadeh K, George J, Young HA, Liu G. Cytokines: From Clinical Significance to Quantification. Advanced Science. 2021;8(15):2004433. 61. Jeng JE, Tsai JF, Chuang LY, Ho MS, Lin ZY, Hsieh MY, et al. Tumor necrosis factor-alpha 308.2 polymorphism is associated with advanced hepatic fibrosis and higher risk for hepatocellular carcinoma. Neoplasia. 2007;9(11):987–92. 62. Elbrolosy AM, Elabd NS, ElGedawy GA, Abozeid M, Abdelkreem M, Montaser B, et al. Toll- like receptor 2 polymorphism and IL-6 profile in relation to disease progression in chronic HBV infection: a case control study in Egyptian patients. Clin Exp Med. 2023;23(1):117–29. 63. Temel EN, Akcam FZ, Caner V, Bagci G, Tepebasi MY. Relationship between IL-17, TNF-alpha, IL-10, IFN-gamma, and IL-18 polymorphisms with the outcome of hepatitis B virus infection in the Turkish population. Rev Assoc Med Bras (1992). 2023;69(8):e20230355. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations. Host cytokine genetic polymorphisms in a selected population of persons living with hepatitis B virus infection in the central region of Ghana Abstract Background Methods Results Conclusion Introduction Methods Study design and subjects Sample size Ethical considerations Laboratory analysis HBV antigen profile test Liver function test Viral load FIB 4 index DNA Extraction and cytokine genotyping Statistical analysis Results Discussion Conclusion Acknowledgements References