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Abstract

The impact of interleukin-10 (IL-10) gene promoter polymorphisms (SNPs) on treatment response in HCV patients was dissimilarly estimated. Hence, the aim of this meta-analysis was to robustly assess the effect of IL-10 SNPs on treatment response in HCV patients. An electronic literature search was carried out through PubMed, EMBASE, Web of science, and Scopus databases. Studies assessing the association between IL-10 polymorphisms and treatment response in HCV patients were included. Studies were excluded if genotype frequencies are not consistent with the Hardy–Weinberg Equilibrium (HWE) or in case of including patients with hepatitis B virus coinfection. Risk of bias in included studies was assessed using the Newcastle–Ottawa Scale. Meta-analyses were performed for the influence of IL-10 gene promoter SNPs (rs1800896 (-1082 A/G), rs1800871 (-819 C/T), and rs1800872 (-592 C/T)) and haplotypes on treatment response in HCV patients. Subgroup analyses, meta-regressions, publication bias assessment, and sensitivity analyses were also conducted. Overall, 32 studies with a total of 5943 HCV cases and 2697 controls were included in the present study. The -1082*G allele was significantly associated with increased risk of non-response (NR) to treatment, OR [95% CI] = 1.29 [1.1–1.51], p = .002. Besides, the rs1800872 -592*C allele was significantly associated with increased NR risk, OR [95% CI] = 1.22 [1.02–1.46], p = .03. Subgroup analysis showed that this association remained significant only in patients treated with PEG-IFN alone, p = .01. The -1082*G/-819*C/-592*C (GCC) haplotype was significantly associated with increased NR risk, OR [95% CI] = 1.62 [1.13–2.23], p = .009. Our results suggest that the IL-10 rs1800896 was associated with NR risk especially in North-African and Asian populations. Moreover, the IL-10 gene promoter -1082*G/-819*C/-592*C (GCC) haplotype which has been associated with higher production of IL-10, was significantly associated with increased NR risk.

Keywords

hepatitis C virus IL-10 polymorphism meta-analysis meta-regression

Introduction

Hepatitis C virus (HCV) infection remains a serious worldwide health problem despite the use of oral direct-acting antivirals (DAA). In fact, the World Health Organization (WHO) estimated its prevalence as high as 58 million persons in 2019 with 290 000 deaths, mostly from cirrhosis and hepatocellular carcinoma (HCC).¹ Acute HCV infections are usually asymptomatic and most do not lead to a life-threatening disease. Around 30% (15–45%) of infected persons spontaneously clear the virus within 6 months of infection without any treatment.¹ The remaining 70% (55–85%) of persons will develop chronic HCV infection. Of those with chronic HCV infection, the risk of cirrhosis ranges from 15% to 30% within 20 years.¹ The remarkable medical breakthrough in hepatitis C treatment using direct acting agents (DAAs) has simplified the care cascades through its high efficacy in achieving sustained virological response (SVR) and tolerable side effects.² Indeed, DAA therapy did benefits beyond SVR, in preventing further worsening of liver status and the occurrence of cirrhosis and HCC.² Although DAA can cure 95% of patients, the problem lies in poor access to diagnosis and treatment in several low- and middle-income countries (LMIC).¹ In fact, universal DAA prescription may pose socioeconomic burdens for underprivileged LMIC.²

Several host factors, including the cytokine network (TNF, IFN-γ, IL-1, IL-6, IL-28, IL-22, IL-27, IL-10, IL-12, IL-35, and IL-17) and the NLRP3 inflammasome, can influence the outcome of HCV infection and treatment response.³ In addition, genetic polymorphisms of some cytokines such as IL-10 have been associated with response to conventional treatment with pegylated-IFN (PEG-IFN),^4,5 or the PEG-IFN/Ribavirin (RIB) combination,^6–8 or even DAAs.⁹

IL-10 is a pleiotropic cytokine that potently inhibits the production of proinflammatory cytokines such as TNF, IL-1, and IL-6 in diverse immune-cell populations and prevents dendritic cell maturation.¹⁰ Furthermore, IL-10 inhibits the expression of MHC and costimulatory molecules which are critical for cell-mediated immunity against pathogens and tumor cells.¹⁰ The IL-10 gene is highly polymorphic, with most notably 3 promoter polymorphisms (SNPs), the rs1800896 (-1082 A/G), the rs1800871 (-819 C/T), and the rs1800872 (-592 C/T).^11,12 The GCC (-1082/-819/-592) haplotype has been associated with high IL-10 production whereas the ATA haplotype has been associated with significantly lower IL-10 level.^11,12

The aim of this meta-analysis was to summarize existing data on the influence of IL-10 promoter SNPs on treatment response in HCV patients by comparing NR risk across IL-10 genotypes and haplotypes.

Material and methods

Search strategy

This study was performed according to the PRISMA guidelines for systematic reviews and meta-analyses (supplemental file 3).¹³ An electronic literature search for eligible studies among all papers published prior to November 30, 2023, was conducted through PubMed, EMBASE, Web of science, and Scopus databases. The following search string was used: (((Interleukin-10) OR (IL-10) OR (IL10) OR (Cytokine)) AND ((Polymorphism) OR (SNP) OR (Variant) OR (Mutation) OR (Allele) OR (Allelic) OR (Genotype) OR (Genotypic) OR (rs1800896) OR (-1082 A/G) OR (rs1800871) OR (-819 C/T) OR (rs1800872) OR (-592 C/T)) AND ((Hepatitis C) OR (HCV))). The literature search was carried out without any language restriction. A detailed search strategy is available within the supplemental file 1.

Selection criteria

All studies were independently assessed and evaluated by 2 reviewers (Tarak Dhaouadi, T.D. and Imen Sfar, I.S.) for the inclusion and the exclusion. The following selection criteria were adopted:

Inclusion criteria

• Studies of case-control (retrospective) or cohort (prospective) design.

• Studies assessing the association between IL-10 polymorphisms and response to treatment in HCV patients.

• Full manuscript with genotypes or alleles or haplotypes frequencies.

• Precise definition of the treatment and the treatment response.

Exclusion criteria

• Genotype frequencies are not consistent with the Hardy–Weinberg Equilibrium (HWE) as recommended by Trikalinos et al.¹⁴

• Studies including patients with hepatitis B virus coinfection.

• Case series of subjects, narrative or systematic review, comments, or meta-analysis.

• If many studies have been carried out using duplicate cases, only the study with complete data and the largest sample size was included.

Definition of the treatment response

SVR was defined as an undetectable level of HCV-RNA 24 weeks after the treatment completion. Relapse (REL) was defined as a detectable HCV-RNA during follow-up in patients with undetectable HCV-RNA at the end of treatment. Non-response (NR) was defined as detectable HCV-RNA at the cessation of treatment.

Data extraction

Data were extracted using a predeveloped form and entered in an Excel datasheet. Two investigators (T.D. and I.S.) independently extracted the following information: first author, year of publication, study design, study duration (months), country, continent, mean or median age, and gender ratio, The number of patients, treatment (PEG-IFN or PEG-IFN/RIB or DAA), the numbers of SVR, REL and NR, the number of controls (if applicable), HCV genotypes (if specified), IL-10 SNPs genotyping method, the frequencies of genotypes, alleles, and haplotypes for each of the following SNPs: rs1800896 (-1082 A/G), rs1800871 (-819 C/T), and rs1800872 (-592 C/T). A third investigator (Awatef Riahi) compared the results of the extracted data for potential discrepancies.

Quality assessment

The quality of eligible studies was assessed independently by 2 reviewers (T.D. and I.S.) using the Newcastle–Ottawa Scale (NOS)¹⁵ which is based on the following 3 general categories: selection (4 points), Comparability of the study groups (2 points) and ascertainment of outcome (3 points). Studies with a score ≥7 were classified as high-quality reports. Additionally, risk of bias was assessed for each included study through a generic form (Excel spreadsheet) and visualized via the Cochrane ROBVIS online tool (https://mcguinlu.shinyapps.io/robvis/). Two additional independent reviewers (Taieb Ben Abdallah and Yousr Gorgi) examined the quality-assessment results.

Study endpoints

The primary endpoint of this meta-analysis was the association of the IL-10 promoter SNPs with NR outcome. The secondary endpoint was to evaluate potential confounding factors that might influence the impact of IL-10 SNPs on response to treatment.

Statistical analysis

Statistical analysis was carried out using the Cochrane Review Manager 5.4 software, the OpenMeta-Analyst software and the online available software MetaGenyo (https://metagenyo.genyo.es/). The associations of IL-10 SNPs with NR were assessed using pooled Odds Ratios (ORs) with the 95% confidence interval (95% CI). The statistical significance of pooled ORs was tested by Z-test with a threshold of significance set at 0.05. Random effects models (DerSimonian-Laird) were used as recommended by Borenstein et al.¹⁶ Indeed, as long as the eligible studies were carried out in genetically diverse populations infected with different HCV genotypes, the random-effects model applies.¹⁶ Forest-plots were generated to display the distribution of effect size (OR) across included studies. Sensitivity analyses were carried out to test the results stability by omitting sequentially each individual study. The heterogeneity of between-studies was tested by Q test (significance threshold: 0.1), quantified via I² calculation (proportion of true effects variance) and analyzed through the determination of 95% prediction intervals (PI). PI were obtained through the CMA Prediction Intervals free software. Permission to use the CMA prediction intervals has been obtained since March 21, 2023 (Figure S1). The calculation of the 95% PI was based on the following 4 items: OR, upper bound of 95% CI, Tau² and number of included studies. Subsequently, the heterogeneity was explored for potential sources by subgroup analyses and meta-regressions. Briefly, studies were stratified by continent and type of treatment. Meta-regressions were performed using age, gender ratio (Male/Female), SVR/NR ratio, and the alleles/haplotypes frequencies as independent variables. Both univariate and multivariate models of meta-regression were generated in order to assess the presence of potential confounding factors. Publication bias were assessed by Egger’s test and visualized through the generation of funnel plots. HWE was examined for each study and for every SNP by assessing both univariate-individual and multivariate-adjusted p-values in. For some studies in which genotyping data of controls could not be extracted (absence of a control group or data not shown) the HWE was assessed in patient groups instead of control groups. Genotyping data and HWE p-values are available for each SNP in Tables 1 –3. In this study, the codominant genetic model (allele contrast) was applied. Additionally, results of recessive, dominant and overdominant genetic models were displayed.

Table 1.

rs1800896 genotype frequencies and HWE assessment.

Study	Non-response			Sustained virological response			Controls			HWE p-value	HWE adjusted p-value
Study	AA	AG	GG	AA	AG	GG	AA	AG	GG	HWE p-value	HWE adjusted p-value
Abdelraheem 2016	8	35	7	17	30	3	38	54	8	0.0617	0.4651
Chuang 2009	42	4	0	90	7	0	124	9	0	0.6863	0.9123
de Souza 2018	26	24	6	13	8	0	52	49	11	0.9123	0.9123
Edwards-Smith 1999	6	5	6	10	9	5	36	52	31	0.1744	0.4651
El Karaksy 2016	6	11	3	9	7	3	NA^a	NA	NA	0.4288†	0.8775‡
Fabricio-Silva 2015	106	110	29	22	16	3	92	77	20	0.5194	0.9123
Grandi 2014	35	18	12	45	49	20	NA	NA	NA	0.3008†	0.8411‡
Helal 2014	9	19	22	7	18	11	NA	NA	NA	0.9402†	0.969‡
Khan 2014	17	21	14	47	46	5	85	55	10	0.7855	0.9123
Knapp 2003	78	124	60	51	57	40	NA	NA	NA	0.5731†	0.8775‡
Kusumoto 2006	316	30	0	103	11	0	NA	NA	NA	0.5883†	0.8775‡
Mangia 2004	91	93	36	28	18	4	56	66	23	0.6307	0.9123
Medhat 2014	4	21	14	4	41	3	6	9	5	0.662	0.9123
Morgan 2008	136	277	124	28	49	23	NA	NA	NA	0.8607†	0.969‡
Naeemi 2017	10	32	28	33	74	25	47	44	19	0.1294	0.4651
Obada 2017	19	21	9	34	42	25	26	57	17	0.1354	0.4651
Paladino 2006	115	104	42	5	19	1	88	100	21	0.336	0672
Par 2011	20	34	37	8	25	26	48	31	10	0.1625	0.4651
Pasha 2018	194	26	0	202	18	0	193	27	0	0.3322	0.672
Sadik 2015	4	19	6	29	21	4	16	19	5	0.8608	0.9123
Sghaier 2017	7	10	4	35	45	19	62	82	46	0.0717	0.4651
Shaker 2013	4	8	2	10	7	3	8	9	3	0.858	0.9123
Swiatek-Koscielna 2016	41	66	19	20	29	5	NA	NA	NA	0.2285†	0.8411‡

†HWE individual p-value in HCV patients; ‡HWE adjusted p-value in HCV patients.

^aNA: not applicable.

Table 2.

rs1800871 genotype frequencies and HWE assessment.

Study	Non-response			Sustained virological response			Controls			HWE p-value	HWE adjusted p-value
Study	CC	CT	TT	CC	CT	TT	CC	CT	TT	HWE p-value	HWE adjusted p-value
Chuang 2009	25	19	2	47	38	12	65	58	11	0.6981	0.9593
de Souza 2018	20	22	14	6	10	5	43	53	16	0.9593	0.9593
Edwards-Smith 1999	18	6	0	7	11	0	74	40	5	0.8892	0.9593
Jing 2020	4	15	27	18	52	50	NA^a	NA	NA	0.4631†	0.8853‡
Khan 2014	12	29	11	36	50	12	57	75	18	0.3746	0.7492
Kusumoto 2006	30	160	156	9	46	59	NA	NA	NA	0.9935†	0.9935‡
Mangia 2004	132	70	18	22	20	8	81	55	9	0.934	0.9593
Medhat 2014	5	34	0	11	33	4	11	9	0	0.1942	0.4661
Naeemi 2017	18	36	16	27	64	41	35	46	29	0.091	0408
Obada 2017	23	19	7	47	37	17	44	39	17	0.1126	0.408
Sghaier 2017	10	9	2	56	35	8	106	80	14	0.8352	0.9593
Shaker 2012	5	15	17	8	23	32	16	32	32	0.136	0408
Shaker 2013	10	2	2	5	4	11	9	9	2	0.9087	0.9593
Shaker 2014	5	15	13	4	21	27	40	40	20	0.0956	0.408
Swiatek-Koscielna 2016	55	49	22	30	21	3	NA	NA	NA	0.7855†	0.9935‡
Villalba 2020	5	6	3	1	4	6	NA	NA	NA	0.7823†	0.9935‡
Yee 2001	72	38	0	24	19	6	NA	NA	NA	0.4687†	0.8853‡

†HWE individual p-value in HCV patients; ‡HWE adjusted p-value in HCV patients.

^aNA: not applicable.

Table 3.

rs1800872 genotype frequencies and HWE assessment.

Study	Non-response			Sustained virological response			Controls			HWE p-value	HWE adjusted p-value
Study	CC	CA	AA	CC	CA	AA	CC	CA	AA	HWE p-value	HWE adjusted p-value
Barkhash 2017	13	3	0	25	13	1	123	74	6	0.1913	0.6986
Chuang 2009	2	19	25	12	37	48	10	59	65	0.4946	0.8882
Corchado 2014	76	78	0	55	58	0	NA^a	NA	NA	0.7862†	0.9935‡
de Souza 2018	20	22	14	7	10	4	41	56	15	0.5466	0.8882
Edwards-Smith 1999	18	6	0	7	12	0	75	38	6	0.6795	0.9295
El Karaksy 2016	2	10	8	1	10	8	NA	NA	NA	0.3421†	0.8292‡
Fabricio Silva 2015	106	110	29	18	19	4	85	78	26	0.2399	0.6986
Jing 2020	27	15	4	50	52	18	NA	NA	NA	0.4631†	0.8292‡
Junaid 2021	3	6	4	155	122	25	NA	NA	NA	0.8854†	0.9935‡
Khan 2014	38	10	4	49	26	24	63	55	22	0.0962	0.6986
Knapp 2003	150	97	17	77	63	14	NA	NA	NA	0.8293†	0.9935‡
Kusumoto 2006	30	160	156	9	46	59	NA	NA	NA	0.9935†	0.9935‡
Mangia 2004	132	70	18	22	20	8	81	55	9	0.934	0934
Medhat 2014	11	17	11	5	26	17	2	8	10	0.8314	0.9295
Morgan 2008	301	201	35	62	35	3	NA	NA	NA	0.4606†	0.8292‡
Naeemi 2017	24	32	14	35	55	42	34	49	27	0.2687	0.6986
Obada	19	21	9	41	42	18	49	40	11	0.5158	0.8882
Sghaier 2017	10	9	2	56	34	9	106	80	14	0.8352	0.9295
Shaker 2013	4	6	4	2	11	7	8	9	3	0.858	0.9295
Shaker 2014	6	13	14	4	21	27	15	40	45	0.2267	0.6986
Swiatek-Koscielna 2016	56	48	22	27	21	6	NA	NA	NA	0.5388†	0.8852‡
Villalba 2020	5	6	3	3	4	4	NA	NA	NA	0.3765†	0.8292‡
Yee 2001	72	38	0	24	19	6	NA	NA	NA	0.4687†	0.8292‡

†HWE individual p-value in HCV patients; ‡HWE adjusted p-value in HCV patients.

^aNA: not applicable.

A supplementary file with additional tables and figures is available with the full-manuscript.

Systematic review registration

This review has been registered on PROSPERO: CRD42024495977, Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024495977.

Results

Search results and study characteristics

A PRISMA flow diagram was generated to depict the study selection process (Figure 1). Overall, 32 studies with a total of 5943 HCV cases (NR: 3380; SVR: 2563) and 2697 controls were included in the present study.^{4–8,17–43} Included studies characteristics are summarized in Table 4. Twenty-three studies were included for the rs1800896 (-1082 A/G) SNP,^{4–8,17,19,22–26,29–37,39,41} 17 for the rs1800871 (-819 C/T) SNP,^{4–7,19,22,27,30,31,33,37–43} 23 for the rs1800872 (C/T -592) SNP,^{4–7,18–20,22–24,27–33,37,39–43} and 9 for the IL-10 haplotype analysis .^{4,5,7,19,21,29,30,34,41} The NOS quality score results for each included study are shown in Table 4. Risk of bias is summarized in Figure 2.

Figure 1.

PRISMA flow diagram for study selection.

Table 4.

Characteristics of included studies.

Ref	First author	Year	Study design	Country	Age	Gender ratio (M/F)	HCV genotypes	Treatment^b	NR^c	SVR^d	Controls	Genotyping method	Quality score
¹⁷	Abdelraheem	2016	Retrospective	Egypt	46.4 ± 7.4	13.28 (93/7)	NS	IFN+RBV	50	50	100	Real-time PCR	7
¹⁸	Barkhash	2017	Retrospective	Russia	18–70	≈1	1b, 3a, 2a & 2c	IFN+RBV	16	39	221	PCR RFLP	6
¹⁹	Chuang	2009	Prospective	Taiwan	53.8 ± 9.7	1.75 (91/52)	1 & 2	IFN+RBV	46	97	134	PCR RFLP	8
²⁰	Corchado	2014	Retrospective	Spain	41	4.93 (222/245)	1, 2, 3 & 4	IFN+RBV	154	113	NA^e	Real-time PCR	7
²¹	Costantini	2002	Retrospective	UK+Poland	44.4 (19–72)	1.35	1b	IFN	52	60	152	PCR SSO	8
²²	de Souza	2018	Retrospective	Brazil	54	1.16 (67/58)	1a, 1b & 1c	IFN+RBV	64	26	112	Sequencing	6
⁴	Edwards-Smith	1999	Prospective	Australia	23–53	2.3 (30/13)	1a, 1b & 3a	IFN	19	24	119	PCR RFLP	7
²³	El-Karaksy	2016	Retrospective	Egypt	11.5 – 9.98	2 (26/13)	NS	IFN+RBV	20	19	NA	Real-time PCR	7
²⁴	Fabricio-Silva	2015	Retrospective	Brazil	57.3 ± 11.1	0.98 (121/124)	1, 2 & 3	IFN+RBV	69	37	189	Real-time PCR	6
²⁵	Grandi	2014	Retrospective	Brazil	50.4 ± 10.7	1.41 (154/109)	NS	IFN+RBV	85	114	NA	Sequencing	6
²⁶	Helal	2014	Prospective	Egypt	30–65	6.14 (86/14)	4	IFN+RBV	50	36	NA	Real-time PCR	6
²⁷	Jing	2020	Prospective	China	52 (21–71)	0.52 (68/130)	1b	IFN+RBV	46	120	142	MALDI-TOF	7
²⁸	Junaid	2021	Prospective	Pakistan	41.6 ± 10.6	1.18	3 (95%), 1, 2 & 4	DAA	13	302	NA	Real-time PCR	6
⁶	Khan	2015	Prospective	India	48.3 ± 9.2	1238 (83/67)	3	IFN+RBV	52	98	150	PCR RFLP	7
²⁹	Knapp	2003	Prospective	UK	NS^a	1.18 (357/302)	NS	IFN	554	105	NA	Sequencing	8
³⁰	Kusumoto	2006	Retrospective	Japan	63.4 – 67.9	0.54 (162/298)	1 & 2	IFN	346	114	NA	Real-time PCR	7
⁵	Mangia	2004	Retrospective	Italy	47–58	0.94 (131/140)	1	IFN	220	50	145	Sequencing	7
³¹	Medhat	2014	Prospective	Egypt	43.12	5.21 (73/14)	NS	IFN+RBV	39	48	20	PCR SSP	6
³²	Morgan	2008	Prospective	USA	50.1 ± 7.04	2.67 (563/211)	1	IFN+RBV	537	100	NA	NS	8
⁷	Naeemi	2017	Prospective	Pakistan	41.3 ± 9.8	1.49 (121/81)	3a	IFN+RBV	70	132	110	PCR RFLP	6
³³	Obada	2017	Prospective	Egypt	41.6 ± 9.42	1.94 (99/51)	NS	IFN+RBV	49	101	100	PCR-RFLP	8
³⁴	Paladino	2006	Retrospective	Argentina	55 (22–77)	1.29 (161/125)	1	IFN	261	25	209	PCR SSO	6
³⁵	Pár	2011	Retrospective	Hungary	48.3 ± 9.9	0.87	1	IFN+RBV	91	59	91	PCR RFLP	8
³⁶	Pasha	2012	Prospective	Egypt	57.1 ± 7.5	1.95 (291/149)	NS	IFN+RBV	220	220	220	PCR RFLP	7
⁸	Sadik	2015	Prospective	Egypt	43.55 ± 7.3	5.38 (70/13)	NS	IFN+RBV	29	54	40	PCR RFLP	8
³⁷	Sghaier	2017	Unclear	Tunisia	41.33 ± 13.1	0.9 (57/63)	2a & 2b	IFN+RBV	21	99	200	Real-time PCR	8
³⁸	Shaker	2012	Prospective	Egypt	39.18 ± 7.8	1.27 (56/44)	4	IFN+RBV	37	63	80	PCR RFLP	7
³⁹	Shaker	2013	Prospective	Egypt	13.8 ± 1.7	2.1 (23/11)	4	IFN+RBV	14	20	20	PCR RFLP	7
⁴⁰	Shaker	2014	Prospective	Egypt	39.01 ± 7.49	1.36 (49/36)	4	IFN+RBV	33	52	100	PCR RFLP	8
⁴¹	Swiatek-Koscielna	2016	Prospective	Poland	39 (20– 64)	1.39 (114/82)	1a & 1b	IFN+RBV	54	126	NA	PCR RFLP	8
⁴²	Villalaba	2020	Prospective	Cuba	44.9	5.25 (21/4)	1b	IFN+RBV	14	11	NA	Real-time PCR	6
⁴³	Yee	2001	Prospective	USA	NS	NS	NS	IFN+RBV	55	49	NA	PCR SSP	7

^aNS: Not Specified.

^bIFN: PEG-IFN, IFN + RBV: PEG-IFN + Ribavirin, DAA: direct acting agent.

NR: Non-response.

^dSVR: Sustained virological response.

^eNA: Not applicable.

Figure 2.

Summary of study risk of bias.

IL-10 rs1800896 meta-analysis

The -1082*G mutated allele was significantly associated with increased risk of NR in HCV patients, OR [95% CI] = 1.29 [1.1–1.51], p = .002 (Figure 3). This association with the codominant model was also significant when recessive and dominant models were applied (Table 5). However, there was a significant amount of between-studies heterogeneity, I² = 47%, Tau² = 0.0646, 95% PI = [0.74–2.24] and p = .007. Subgroup analysis by continent revealed that the increase of NR risk conferred by the G allele was significant only in North-African and Asian populations; OR [95% CI] = 1.44 [1.1–1.9], p = .008 and OR [95% CI] = 1.64 [1.11–2.44], p = .013, respectively (Figure 4) (Table S1). Besides, the association between the G allele and the risk of NR remained significant only in patients treated with PEG-IFN/RBV combination (OR [95% CI] = 1.35 [1.11–1.63]) while no significant overall effect was observed in groups treated with PEG-IFN alone (Figure 5) (Table S2). Meta-regression revealed no significant association of the overall effect size with age, the gender ratio (M/F), the SVR/NR ratio, or the frequency of G allele in HCV patients, Omnibus p-value = .308 (Table S3).

Figure 3.

Forest plot for the association between the IL-10 rs1800896 (-1082 A/G) SNP and NR risk.

Table 5.

Main results of the 3 IL-10 SNPs in NR risk.

SNP	Genetic model	Contrast	NR risk		Heterogeneity
SNP	Genetic model	Contrast	Or [95% CI]	p-value	I²	p-value
rs1800896	Codominant	G vs. A	1.29 [1.1–1.51]	0.00197	47%	0.007
	Recessive	GG vs. GA+AA	1.63 [1.18–2.25]	0.0029	51%	0.0049
	Dominant	GG+GA vs. AA	1.26 [1.01–1.57]	0.04	41%	0.02
	Overdominant	GA vs. GG+AA	0.97 [0.77–1.21]	0.7607	50%	0.0035
rs1800871	Codominant	C vs. T	1.23 [0.96–1.57]	0.09	67%	<0.0001
	Recessive	CC vs. CT+TT	1.2 [0.89–1.63]	0.2387	48%	0.01
	Dominant	CC+CT vs. TT	1.29 [0.89–1.89]	0.1803	53%	0.01
	Overdominant	CT vs. CC+TT	1.03 [0.86–1.23]	0.7785	0	0.49
rs1800872	Codominant	C vs. T	1.22 [1.02–1.46]	0.03	57%	0.0004
	Recessive	CC vs. CT+TT	1.31 [1.04–1.63]	0.0204	43%	0.02
	Dominant	CC+CT vs. TT	1.19 [0.91–1.56]	0.1941	31%	0.08
	Overdominant	CT vs. CC+TT	0.93 [0.81–1.08]	0.3368	0	0.84

Figure 4.

Subgroup analysis by continent for the IL-10 rs1800896 (-1082 A/G) SNP.

Figure 5.

Subgroup analysis by treatment for the IL-10 rs1800896 (-1082 A/G) SNP.

IL-10 rs1800871 meta-analysis

The integrated analysis did not show any association between the IL-10 -819 C/T SNP and the risk of NR, OR [95% CI] = 1.23 [0.96–1.57], p = .09 (Figure 6). All the genetic models did not show any association with the NR risk (Table 5). Nevertheless, the heterogeneity between included studies was substantial, I² = 67%, Tau² = 0.1635, 95% PI = [0.50–3.03] and p < .0001. Subgroup analysis by continent revealed a significant higher NR risk conferred by the C allele in populations from America (OR [95% CI] = 1.89 [1.06–3.36], p = .03) but the 95% PI was [0.01–509.4] attributable to a significant amount of heterogeneity (I² = 41%) and the small number (n = 3) of studies from America (Figure S2) (Table S4). Besides, stratified analysis by type of treatment showed a significant association between C allele and NR risk in patients treated with PEG-IFN alone (OR [95% CI] = 1.56 [1.02–2.37], p = .039) with, however, a large 95% PI of [0.02–103.71] as a consequence of both heterogeneity (I² = 47%) and small number (n = 3) of studies (Figure S2) (Table S5). Subsequent meta-regression (Table S6) revealed a positive correlation of the effect size with the gender ratio (Male/Female), p = .039 (Figure 7(A)). This result indicates that the C allele was associated with a greater risk of non-response mainly in men. Inversely, a negative correlation was observed between the effect size and the SVR/NR ratio, p = .013 (Figure 7(B)). Thus, the association between the C allele and the NR risk could be missed in some studies when the SVR rate is high.

Figure 6.

Forest plot for the association between the IL-10 rs1800871 (-819 C/T) SNP and NR risk.

Figure 7.

A: Meta-regression for the rs1800871 with gender ratio (M/F). B: Meta-regression for the rs1800871 with the SVR/NR ratio.

IL-10 rs1800872 meta-analysis

The -592*C wild allele was significantly associated with increased risk of NR in HCV patients, OR [95% CI] = 1.22 [1.02–1.46], p = .03 (Figure 8). This association was also noted when the recessive model was applied while no association was observed in the dominant and overdominant models (Table 5). Nonetheless, there was a significant amount of between-studies heterogeneity, I² = 57%, Tau² = 0.0979, 95% PI = [0.62–2.4] and p = .0004. Subgroup analysis by continent revealed no significant associations (Figure S4) (Table S7). In contrast, subgroup analysis by treatment showed that the C allele conferred an increased risk of NR in patients treated with PEG-IFN alone (OR [95% CI] = 1.41 [1.09–1.83], p = .01, 95% PI = [0.61–3.25]), whereas the only study in which patients were treated with DAA²⁸ showed that the C allele favored SVR (Figure 9) (Table S8). Besides, meta-regression (Table S9) revealed that the SVR/NR ratio was negatively correlated with the effect size (p = .007) which indicates that a high SVR rate could hide the effect of the C allele on response to treatment (Figure 10).

Figure 8.

Forest plot for the association between the IL-10 rs1800872 (-592 C/T) SNP and NR risk.

Figure 9.

Subgroup analysis by treatment for the IL-10 rs1800872 (-592 C/T) SNP.

Figure 10.

Meta-regression for the rs1800872 with the SVR/NR ratio.

IL-10 haplotype meta-analysis

As the IL-10 gene promoter -1082*G/-819*C/-592*C (GCC) haplotype has been associated with higher production of IL-10,^11,12 a meta-analysis was performed to investigate its association with NR risk. The pooling estimate showed a significant association with NR, OR [95% CI] = 1.62 [1.13–2.33], p = .009 (Figure 11). However, there was a significant amount of between-studies heterogeneity, I² = 68%, Tau² = 0.1841, 95% PI = [0.54–4.89] and p = .001. Subgroup analysis by continent showed no significant association for studies carried out in Asia and Europe, while a single study carried out in America and another in Australia showed that the association between the GCC haplotype and NR was significant, p = .01 and p = .0017, respectively (Figure S5) (Table S10). Conversely, the subgroup analysis by treatment revealed a significant association of the GCC haplotype with NR risk in patients treated with PEG-IFN alone, p = .035 (Figure 12) (Table S11). Subsequent meta-regression (Table S12) revealed a significant positive correlation between the gender ratio (M/F) and the overall effect size, p = .014 (Figure 13). Hence, the effect of the GCC haplotype on NR outcome seems to be greater in men than in women.

Figure 11.

Forest plot for the association between the IL-10 promoter GCC haplotype and NR risk.

Figure 12.

Subgroup analysis by treatment for the IL-10 GCC haplotype.

Figure 13.

Meta-regression for the IL-10 GCC haplotype with gender ratio (M/F).

Sensitivity analysis

The sensitivity analysis revealed that association results were stable for all performed meta-analyses (Figures S6, S7, S8, and S9), signifying a high level of integrity with reliable results.

Publication bias

Generated funnel plots (Figure 14) were found to be overall symmetrical and Egger’s tests confirmed these findings with non-significant p-values for the 3 IL-10 SNPs (0.1557, 0.1366, and 0.4579) and the GCC haplotype (0.3429), which indicated that results were not weakened by publication biases.

Figure 14.

Funnel plots assessing publication bias.

Discussion

In untreated⁴⁴ HCV-infected patients, the high frequency of chronicity outcome is striking as HCV is an RNA virus with no DNA intermediate in its cycle and no ability to integrate into the host genome. Hence, it is obvious that HCV has developed extraordinary mechanisms to insure its persistence despite innate and adaptive immune anti-HCV responses.⁴⁵ In treated patients, there are many factors including viral, host, and treatment-related factors that determine treatment response.⁴⁶ Viral factors include HCV genotypes, subtypes, and baseline viral load.⁴⁶ Indeed, SVR rates are higher in case of genotypes 2, 3, 5, and 6 than in 1 and 4.⁴⁶ Furthermore, in patients under triple therapy including DAAs, 1a subtype has been associated with higher frequencies of viral breakthrough and drug resistance comparatively to 1b subtype.⁴⁶ Besides, several host factors including age, obesity, HIV coinfection, insulin resistance, vitamin D levels, the extent of liver fibrosis, and the genetic compound impact negatively SVR rate.⁴⁶ Regarding the host genetic compound, IL-28B SNPs were found to be the strongest pre-treatment SVR predictor.⁴⁶ However, IL-28B variants account for roughly 50% of the host-genetic influence on treatment response. Indeed, numerous other genetic factors including IL-10 SNPs were found to influence treatment response.⁴⁷ It is of note that HCV has been shown to induce IL-10 production, and increased IL-10 levels were associated with persistent HCV infection, higher inflammation grade, and an increased risk of hepatocellular carcinoma occurrence.⁴⁷

Over the past two decades, several studies have focused on the relationship between 3 IL-10 SNPs, namely rs1800896, rs1800871, and rs1800872, and treatment response. However, published data were mostly inconclusive. Although a previous meta-analysis⁴⁸ was published in 2016, the literature search cut-off time in our study was fresher. In addition, while the study of Guo et al.⁴⁸ involved only 14 studies, our meta-analysis included 32 reports which could provide a more comprehensive and accurate estimation of IL-10 SNPs influence on NR risk.

The present study revealed that the IL-10 -1082*G allele was associated with approximately 29% increase in risk of NR. However, heterogeneity between studies was substantial and the true effect in 95% comparable populations could vary from 26% decreased risk to 124% increased risk. Subgroup analysis by continent revealed that NR risk conferred by the -1082*G allele was significant only in North-African and Asians populations, but again there was a large amount of heterogeneity between studies. Regarding the treatment, the association between the -1082*G allele and NR risk remained significant only in patients treated with PEG-IFN/RBV combination whereas no significant influence was noted in case of PEG-IFN alone use. A subgroup analysis by HCV genotype could not be performed, as many studies reported a varied mixture of genotypes, and many did not even provide information on the involved HCV genotypes. Besides, univariate and multivariate models of meta-regression did not show any influence of age, gender ratio, SVR/NR ratio, and the G allele frequency on the relationship between IL-10 rs1800896 SNP and NR risk.

In this study, the rs1800871 (-819 C/T) was not found to be associated with NR risk. As there was a large amount of between-studies heterogeneity subgroup analysis by continent and treatment together with a meta-regression were performed. Subgroup analysis by continent revealed a significant increased NR risk conferred by the -819*C allele in patients from the American continent, though the 95% PI was too large. Similarly, the significant increased NR risk in patients treated with PEG-IFN alone was hampered by the considerable amount of heterogeneity between studies and the wide 95% PI. As for the rs1800896 SNP, a subgroup analysis by HCV genotype for the rs1800871 SNP was unfeasible. Interestingly, meta-regression revealed a significant positive correlation of NR risk with the gender ratio (M/F) indicating that the -819*C allele conferred a higher risk of NR in male patients. This peculiar finding corroborates those of previous reports in which female patients had significantly higher SVR rates^49,50 and a more frequent spontaneous HCV clearance [51, 52]. Besides, -819*C allele conferred NR risk was negatively correlated to the SVR/NR ratio. In this regard, the SVR/NR ratio varied considerably between studies from 0.095 to 23.23. Hence, the relationship between the -819*C allele and NR risk could have been missed in studies with high SVR rate.

IL-10 -592*C allele was found to be associated with 22% increase in NR risk in the present study. Nevertheless, heterogeneity of between-studies was important and the true effect in 95% comparable populations could diverge from 38% decreased risk to 140% increased risk. Subsequent subgroup analysis by continent showed that the association between the rs1800872 SNP and NR risk remained significant only in studies from Asia and America, though with high levels of heterogeneity and wide 95% PI. Regarding treatment, the -592*C allele was significantly associated with increased NR risk only in patients treated with PEG-IFN alone, but with decreased NR risk when DAA were used. It is of note that only one study investigated the role of IL-10 rs1800872 SNP in patients treated with DAAs.²⁸ Hence, this result needs to be replicated in independent large cohorts. Besides, the 592*C allele effect on NR risk was negatively correlated with the SVR/NR ratio. Thus, a low NR rate in some studies could have hidden the association with rs1800872 SNP.

In this study, only 9 eligible reports were included in the IL-10 haplotypes meta-analysis. The GCC haplotype was associated with 62% increase of NR risk. Nonetheless, due to a high level of heterogeneity, the true effect of the GCC haplotype in 95% comparable populations could fluctuate from 46% decreased risk to 389% increased risk. The GCC haplotype association with NR risk was only significant in patients treated with PEG-IFN alone. Interestingly, meta-regression showed a significant negative correlation of the GCC effect on NR risk with the gender ratio (M/F). This finding is in line with literature data which indicated a better HCV outcome in female patients.^49–51.

In summary, the present meta-analysis revealed that IL-10 promoter SNPs could play a significant role in treatment response in HCV infected patients. However, it is noteworthy to admit the substantial between studies heterogeneity. To our knowledge, this study was the first to perform several meta-regressions in order to explore the aforementioned heterogeneity. Meta-regression provided the study with some interesting findings such as a positive correlation of the effect size with the gender ratio (M/F) and a negative correlation with the SVR/NR ratio. However, there are some limitations that need to be acknowledged. Firstly, the treatment response depends on several confounding factors such as the HCV genotype, the extent of liver fibrosis, the body mass index, etc., and as the present study analyses derived from pooling IL-10 SNPs aggregate findings without any access to raw data, there was a lack of further adjustment for baseline characteristics. Secondly, there was a diverse mixture of HCV genotypes in many studies without any raw data on the interaction between IL-10 SNPs and HCV genotypes, whereas several other studies did not even specify the HCV genotype. This issue prevented us from performing a robust subgroup analysis by HCV genotype. Thirdly, there was no published study from sub-Saharan Africa. Hence, the results of the present meta-analysis could not be generalized to sub-Saharan African populations. Fourthly, there was only one included study that investigated IL-10 SNPs influence on response to DAAs. Further studies are therefore required to accurately estimate the impact of IL-10 SNPs on treatment response in the DAA era.

In spite of the above-mentioned limitations, our results suggest that the IL-10 rs1800896 SNP was associated with NR risk especially in North-African and Asian populations. Moreover, the IL-10 gene promoter -1082*G/-819*C/-592*C (GCC) haplotype which has been associated with higher production of IL-10 was significantly associated with increased NR risk. Of note, some associations with NR risk were influenced by study-gender-ratios, SVR/NR ratios, and the type of treatment given to HCV patients.

Conclusions

This study demonstrated that IL-10 promoter SNPs could play a significant role in HCV treated infection outcome even though there was a substantial heterogeneity between studies. Further studies investigating their influence on response to DAA are needed.

Supplemental Material

Supplemental Material - Impact of IL-10 gene promoter polymorphisms on treatment response in HCV patients: A systematic review, a meta-analysis, and a meta-regression

Supplemental Material for Impact of IL-10 gene promoter polymorphisms on treatment response in HCV patients: A systematic review, a meta-analysis, and a meta-regression by Tarak Dhaouadi, Awatef Riahi, Taïeb Ben Abdallah, Yousr Gorgi and Imen Sfar in International Journal of Immunopathology and Pharmacology.

Footnotes

Author contributions

Conceptualization: Tarak Dhaouadi, Awatef Riahi, Taieb Ben Abdallah, Yousr Gorgi, Imen Sfar. Data curation: Tarak Dhaouadi, Imen Sfa.Formal analysis: Tarak Dhaouadi, Awatef Riahi, Imen Sfar. Investigation: Tarak Dhaouadi, Imen Sfar. Methodology: Tarak Dhaouadi, Awatef Riahi, Taieb Ben Abdallah, Yousr Gorgi, Imen Sfar. Supervision: Awatef Riahi, Taieb Ben Abdallah, Yousr Gorgi, Imen Sfar. Writing - original draft: Tarak Dhaouadi. Writing - review & editing: Tarak Dhaouadi.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was granted and supported by the Research Laboratory in Immunology of Renal Transplantation and Immunopathology (LR03SP01), Charles Nicolle Hospital, Tunis El Manar University, Tunisia.

Ethical statement

ORCID iD

Tarak Dhaouadi

Data Availability Statement

The data that support the findings of this study are available within the article [and/or] its supplementary materials.

Supplemental Material

Supplemental material for this article is available online.

Appendix

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Supplementary Material

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Impact of IL-10 gene promoter polymorphisms on treatment response in HCV patients: A systematic review,a meta-analysis,and a meta-regression

Abstract

Keywords

Introduction

Material and methods

Search strategy

Selection criteria

Inclusion criteria

Exclusion criteria

Definition of the treatment response

Data extraction

Quality assessment

Study endpoints

Statistical analysis

Systematic review registration

Results

Search results and study characteristics

IL-10 rs1800896 meta-analysis

IL-10 rs1800871 meta-analysis

IL-10 rs1800872 meta-analysis

IL-10 haplotype meta-analysis

Sensitivity analysis

Publication bias

Discussion

Conclusions

Supplemental Material

Supplemental Material - Impact of IL-10 gene promoter polymorphisms on treatment response in HCV patients: A systematic review, a meta-analysis, and a meta-regression

Footnotes

Author contributions

Declaration of conflicting interests

Funding

Ethical statement

ORCID iD

Data Availability Statement

Supplemental Material

Appendix

References

Supplementary Material