Meta-analysis of interleukin-10 gene polymorphisms and tuberculosis susceptibility: Insights from recent studies

Abstract

BACKGROUND:

Tuberculosis (TB) remains a universal health problem with significant morbidity and mortality. Understanding the genetic factors affecting TB susceptibility is crucial for effective prevention and treatment. Interleukin-10 (IL-10), a regulatory cytokine, may influence TB pathogenesis through genetic variations.

METHODS:

The PubMed, Embase, and Google Scholar databases were searched to find studies on the relationship between IL-10 gene variants and tuberculosis. Relevant studies from 2016 to 2024 were identified through database searches. The selected case-control studies met the inclusion criteria. Software such as Review Manager was used to analyze quantitative data, with statistical significance set at $p<$ 0.05. We calculated odds ratios and their respective confidence intervals to evaluate the associations.

RESULTS:

Nine studies examined IL-10 gene polymorphisms (rs1800871 and rs1800872) in TB susceptibility. The present study did not show a notable association between IL-10 gene polymorphisms and TB among all genetic models (allelic, homozygote, heterozygote, dominant, and recessive). The obtained $p$ -value $>$ 0.05 indicates an insignificant association between both gene polymorphisms of IL-10. An OR-1.13; 95% CI-0.85, 1.50 was obtained for the SNP rs1800871, whereas an OR-1.02; 95% CI-0.75, 1.40 was obtained for the SNP rs1800872.

CONCLUSION:

Our meta-analysis revealed no significant association between IL-10 gene polymorphisms and TB susceptibility, suggesting that these variations may not significantly contribute to TB susceptibility. Further research with a larger sample size and diverse ethnicities is needed to explore additional genetic variations and their implications in TB pathogenesis.

Keywords

Tuberculosis IL-10 gene polymorphisms meta-analysis susceptibility

1. Introduction

Tuberculosis (TB) persists as the most lethal infectious disease [1], resulting in 1.5 million deaths annually [2]. Each year, an estimated 8.9–9.9 million individuals contract a Mycobacterium tuberculosis (MT) infection. However, only 5–10% of those infected progress to clinical disease. While a minority of these cases are associated with known risk factors like diabetes or HIV infection, for many patients, a combination of genetic disposition and ecological influences may contribute to the onset of active TB [3]. India has a high prevalence of tuberculosis, an endemic disease, and recorded the highest number of new TB cases in 2021 [4]. Tuberculosis (TB) predominantly affects the lungs and is a pulmonary disease. When individuals with lung TB cough, sneeze, or expel saliva, transmission occurs through the air, and infection can occur through inhaling a small number of bacteria [5].

While tuberculosis (TB) typically affects the lungs, it can also manifest in various parts of the body, showcasing diverse symptoms. The primary transmission mode is inhaling aerosolized droplets containing the infectious agent [6]. The body’s ability to control or eradicate the infection depends on the individual’s immune status, genetic factors, and whether it’s the first or subsequent exposure to the bacteria. Mycobacterium tuberculosis has several virulence factors that hinder its elimination by alveolar macrophages. For instance, the high mycolic acid content in its outer capsule makes it challenging for macrophages to engulf the bacteria. Additionally, components like the cord factor can directly harm alveolar macrophages. Studies have demonstrated that M. tuberculosis interferes with forming an effective phagolysosome, impeding the elimination of the bacteria [7]. Upon initial exposure, the manifestation is termed primary tuberculosis, often localized in the middle part of the lungs, forming the Ghon focus. In many cases, the Ghon focus remains dormant, known as latent tuberculosis [8]. Latent tuberculosis can become active again when the host’s immune system is compromised. A small percentage of individuals develop active disease after the initial exposure, termed primary progressive tuberculosis. This form is more common in children, malnourished individuals, those with immunosuppression, and individuals using long-term steroids [9].

IL-10 is a versatile regulatory cytokine known for modulating inflammatory responses. A growing body of research indicates that IL-10 is a broad inhibitor of T helper (Th) 1 and Th2 cell proliferation and cytokine production in controlled laboratory and in vivo settings [10]. Stimuli can influence cytokine production in the local environment, as well as genetic factors. Both in vitro and in vivo studies have shown that polymorphisms in cytokine genes’ coding or noncoding sequences can affect transcription efficiency, thereby altering cytokine production [11]. Its anti-inflammatory effects are achieved by suppressing the synthesis of various cytokines in activated macrophages and interferon-gamma (IFN- $\gamma)$ in T cells. The IL-10 gene resides on chromosome 1, specifically within 1q31–1q32, and consists of 5 exons. The regulatory region of the IL-10 gene displays high polymorphism, with numerous identified variants [10]. The IL-10 protein, generated by macrophages, monocytes, and lymphocytes, also plays a crucial role in suppressing macrophage activity. This suppression promotes the humoral immune response by decreasing the production of various cytokines, which diminishes the host’s defence against pathogens [12].

Recent studies have explored the influence of two common polymorphisms ( $-$ 592A/C and $-$ 819C/T) in the IL-10 gene promoter on tuberculosis (TB) susceptibility. The results suggest that these polymorphisms influence TB occurrence by affecting IL-10 transcription levels [11]. For many years, various studies have explored the association of SNPs 819 C $>$ T and 592 A $>$ C in the IL-10 gene with tuberculosis, yielding controversial results. The relationship between anti-inflammatory cytokines, such as IL-10, and TB has been examined across different populations and ethnic groups, with contradictory and inconsistent findings. However, additional validation is necessary to establish definitive findings. Hence, we performed a comprehensive meta-analysis to investigate the relationship pattern between IL-10 gene polymorphisms and tuberculosis. This analysis aimed to enhance our understanding of the overall association between IL-10 and tuberculosis.

2. Methodology

2.1 The extraction approach and selection of relevant reports for meta-analysis

To identify qualified studies for meta-analysis, we conducted an automated search of published literature utilizing databases like EMBASE, PubMed, Google Scholar, Cochrane Library, and Web of Science. We only searched for articles published in English between 2016 and 2023 and used keywords such as “tuberculosis,” “interleukin 10 gene”, “IL-10 gene”, “SNP,” “association,” “polymorphism,” “rs1800872,” and “rs1800871” to retrieve the relevant full-text articles. We assessed articles according to predefined inclusion and exclusion criteria. Articles were eligible if they fulfilled the following criteria: i) employed a case-control study design and ii) provided genotype frequencies for both cases and controls. Articles were excluded if they met the following criteria: i) lacked control subjects; ii) involved animal models; iii) utilized cell lines or presented case reports; or iv) lacked sufficient genotypic data.

Figure 1.

A Flow diagram shows the overview of the study selection.

2.2 Data retrieval

Data about study characteristics involving the first author’s name, publication year, nation, customs, sample sizes for both cases and controls, genotypic frequencies of each SNP, and the genotyping system employed were extracted from the chosen studies.

2.3 Assessment of methodological quality using HWE and NOS

The investigators evaluated the chosen analysis quality using two criteria: Hardy-Weinberg equilibrium (HWE) and the Newcastle Ottawa Scale (NOS). Control genotype allocation was required to adhere to HWE ( $>$ 0.05). The NOS assessment considers three aspects: selection, equivalence, and revelation, with a maximum possible score of nine points. In this meta-analysis, studies with a score of six or higher were considered for inclusion.

Table 1
Characteristics of study IL-10 rs1800871 included in the meta-analysis

Author and year	Genotypic frequency						Allele frequency				Sample size		NOS Scoring	HWE
	Case			Control			Case		Control
	GG	TG	TT	GG	TG	TT	G	T	G	T	Case	Control
Jinming et al., 2019	30	62	37	40	46	20	122	136	126	86	129	106	7	0.30
Najwa et al., 2013	42	126	23	70	73	63	210	172	213	199	191	206	8	0.00003
Nedio et al., 2013	40	48	12	131	180	56	128	72	442	292	100	367	6	0.65
Shumei et al., 2018	134	333	105	235	268	58	601	543	738	384	572	561	7	0.14
Silva et al., 2020	18	61	56	15	55	45	97	173	85	145	135	115	7	0.77

Table 2

Characteristics of study IL-10 rs1800872 included in the meta-analysis

Author and year	Genotypic frequency						Allele frequency				Sample size		NOS Scoring	HWE
	Case			Control			Case		Control
	GG	TG	TT	GG	TG	TT	G	T	G	T	Case	Control
Atefeh et al., 2020	6	61	0	5	15	0	73	61	25	15	67	20	7	0.007
Najwa et al., 2013	127	47	17	100	68	38	301	81	268	144	191	206	8	0.000084
Shumei et al., 2018	134	333	105	235	267	58	601	543	737	383	572	560	7	0.15
Silva et al., 2020	57	60	18	46	54	16	174	96	146	86	135	116	7	0.98

Table 3

The odds ratio of individual studies associated with the IL-10 rs1800871 and tuberculosis

Study	Sample size	Odds ratio (Random, 95% confidence interval)
	(Case/control)	Allele	Homozygote	Heterozygote	Dominant	Recessive
Jinming et al., 2019	129/106	0.61 [0.42, 0.88]	2.47 [1.20, 5.07]	0.73 [0.37, 1.42]	2.00 [1.13, 3.52]	3.49 [2.18, 5.57]
Najwa et al., 2013	191/206	1.14 [0.86, 1.51]	0.61 [0.33, 1.12]	4.73 [2.71, 8.26]	1.83 [1.17, 2.86]	0.18 [0.10, 0.32]
Nedio et al., 2013	100/367	1.17 [0.85, 1.62]	0.70 [0.34, 1.44]	1.24 [0.62, 2.51]	0.83 [0.53, 1.31]	0.76 [0.39, 1.48]
Shumei et al., 2018	572/561	0.58 [0.49, 0.68]	3.17 [2.16, 4.66]	0.69 [0.48, 0.98]	2.36 [1.82, 3.04]	0.51 [0.35, 0.73]
Silva et al., 2020	135/115	0.96 [0.66, 1.38]	1.04 [0.47, 2.28]	0.89 [0.52, 1.52]	0.97 [0.47, 2.03]	3.05 [1.68, 5.53]

2.4 Statistical analysis

Data analysis was conducted utilizing the Review Manager 5.4 software, with statistical significance determined at $p<$ 0.05 for all genetic variations. These tools and methods are crucial for conducting comprehensive genetic association meta-analyses, assessing the clinical relevance of genetic variants, ensuring statistical power, and conducting significance testing in genetic studies on a large scale. The heterogeneity assumption across earlier research was interpreted employing the Chi-square-based Q statistic test, measured by the I² metric value. A significance threshold of $p<$ 0.1 was deemed noteworthy. The odds ratio and 95% confidence intervals (CI) for preceding studies were evaluated using the random-effects model. The Hardy-Weinberg equilibrium analysis was conducted using the Chi-square test [13]. We plotted the overall odds ratio and 95% confidence interval in a forest plot to evaluate the strength of the gene polymorphism’s association with cancer. A funnel plot was utilized to examine publication bias within the meta-analysis. The Circos plot in Figs 6 and 7 illustrates the chromosomal interactions with the SNPs when the entire data set is visualized with the 3DSNP tool.

3. Results

In particular, this study is intended to assess the link between tuberculosis and peculiarities in the IL-10 gene. We conducted searches on various databases such as Google Scholar, NCBI, and Science Direct and found five studies with a total of 1127 cases and 1355 controls for the SNP rs1800871. Additionally, we found four studies with a total of 965 cases and 902 controls for the SNP rs1800872. Based on the data collected from all nine studies, we found an association between tuberculosis and IL-10 gene polymorphism. The study strategy for examining the IL-10 gene is shown in Fig. 1, and information about all the studies we included, including the characteristics of the cases and controls, can be found in Tables 1 and 2. The six studies included diverse ethnicities [18, 19, 20, 21, 22, 23, 24].

It was found that there is no significant link between the IL-10 rs1800871 gene polymorphism and TB. The results were obtained using the random effects model with an I² value greater than 50%. In the allele model T vs. C (I2 $=$ 86%), the odds ratio (OR) was 0.85 with a 95% CI of [0.60–1.19] and a $p$ -value of 0.34. In the homozygote model TT vs. CC (I2 $=$ 86%), the OR is 1.31 (95% CI [0.62–2.76]), and the $p$ -value is 0.48. The OR in the heterozygote model TC vs. CC (I2 $=$ 89%) is 1.21 (95% CI of [0.59–2.47]), and the $p$ -value is 0.61. In the dominant model TC $+$ CC vs. TT (I2 $=$ 78%), the OR is 1.53 (95% CI of [0.99–2.35]), $p$ -value $=$ 0.06. In the recessive model TT $+$ TC vs. CC (I2 $=$ 95%), the OR is 0.94 (95% CI of [0.32–12.76]), and the and the $p$ -value is 0.91. The odds ratios for the individual studies associated with IL-10 SNP rs1800871 and tuberculosis are presented in Table 3. These results apply to all study subjects, as shown in Fig. 2.

Table 4
The odds ratio of individual studies associated with the IL-10 rs1800872 and tuberculosis

Study	Sample size	Odds ratio (Random, 95% confidence interval)
	(Case/control)	Allele	Homozygote	Heterozygote	Dominant	Recessive
Atefeh et al., 2020	67/20	0.72 [0.35, 1.48]	0.30 [0.08, 1.10]	3.39 [0.91, 12.62]	3.39 [0.91, 12.62]	Not estimable
Najwa et al., 2013	191/206	2.00 [1.45, 2.74]	2.10 [1.40, 3.16]	0.66 [0.43, 1.03]	0.48 [0.32, 0.71]	0.43 [0.23, 0.79]
Shumei et al., 2018	572/561	0.58 [0.49, 0.68]	0.42 [0.33, 0.55]	1.53 [1.21, 1.93]	2.36 [1.83, 3.05]	1.95 [1.38, 2.75]
Silva et al., 2020	135/116	1.07 [0.74, 1.54]	1.11 [0.67, 1.84]	0.92 [0.56, 1.51]	0.90 [0.54, 1.49]	0.96 [0.47, 1.98]

Figure 2.

The forest plot shows the heterogeneity in IL-10 rs1800871 in the random effects model.

Figure 3.

The forest plot shows the heterogeneity in IL-10 rs1800872 in the random effects model.

Figure 4.

(a) and (b) The funnel plot shows the publication bias for the SNPs ((a) rs1800871 and (b) rs1800872) in the IL-10 gene.

It was found that there is no substantial link between the IL-10 rs1800872 gene polymorphism and TB. The I² value was greater than 50%, so random effects models were used. In the allele model A vs. C (I2 $=$ 94%), the odds ratio (OR) was 0.98 with a 95% CI of [0.50–1.90] and a $p$ -value of 0.95. In the homozygote model AA vs. CC (I2 $=$ 94%), the OR was 0.79 with a 95% CI of [0.31–2.01] and a $p$ -value of 0.62. In the heterozygote model AC vs. CC (I2 $=$ 79%), the OR was 1.15 with a 95% CI of [0.67–1.95] and a $p$ -value of 0.61. In the dominant model AC $+$ CC vs. AA (I2 $=$ 94%), the OR was 1.26 with a 95% CI of [0.50–3.22] and a $p$ -value of 0.62. In the recessive model AA $+$ AC vs. CC (I2 $=$ 89%), the OR was 0.95 with a 95% CI of [0.36–2.49] and a $p$ -value of 0.92. The odds ratios for the individual studies associated with IL-10 SNP rs1800872 and tuberculosis are presented in Table 4. These results apply to all study subjects, as shown in Fig. 3. Begg’s and Egger’s tests were performed, indicating no evidence of publication bias in this analysis (Fig. 4(a) and (b)).

3.1 Protein-to-protein interaction

Figure 5.

The Protein-Protein Interaction (PPI) network of differentially expressed genes (DEGs) among the selected genes associated with tuberculosis.

Figure 6.

The Circos plot visualizes chromosomal interactions involving the rs1800871 SNP.

Figure 7.

The Circos plot visualizes chromosomal interactions involving the rs1800872 SNP.

The e-search tool STRING database (STRING, V11.0; https://string-db.org/) can forecast functional protein interactions and PPI (protein-protein interaction) scores of $\geqslant$ 0.4 associated with tuberculosis and IL-10 gene polymorphisms (Fig. 5).

4. Discussions

Cytokines are glycoproteins or immunomodulatory proteins generated by a wide range of cells and have intricate functions. They can positively or negatively influence a cell’s ability to divide, develop, differentiate, migrate, die, and produce cytokines and other cell products. The immune response’s equilibrium depends critically on a functioning cytokine network, and disruption of this network might result in an aberrant immunological response. Furthermore, the cytokine network may change in various diseases, complicating the pathogenic processes even more. As a result, genes that regulate the generation of cytokines have been the focus of recent research, particularly gene polymorphisms that might impact the expression levels and, in turn, the overall immune response [14]. IL-10, a significant Th2-regulatory cytokine, is crucial during the latent phase of TB infection and is identified as a cytokine synthesis inhibitory factor. Furthermore, IL-10 suppresses the function of monocytes and macrophages during inflammation by reducing the production of pro-inflammatory cytokines [16, 17]. The IL-10 gene is located on the long arm of chromosome 1, and it has recognized polymorphisms in the IL-10 promoter region, such as -819T/C and -592A/C. Additionally, IL-10 has been linked to TB across various ethnic groups [15].

This study specifically investigates the polymorphisms of the IL-10 gene, examining the relationship between IL-10 gene polymorphisms and susceptibility to TB. Recently, studies have explored a significant link between tuberculosis and IL-10 gene polymorphisms [18]. The study conducted by Shumei et al. (2018 in the Tibetan Chinese population showed an association of SNPs rs1800871 and rs1800872 of the IL-10 gene with tuberculosis based on the $p$ -value showing less than 0.05 [19]. Based on the study conducted in 2013 by Nedio et al., there was no association of SNP rs1800871 of the IL-10 gene with tuberculosis in the Mozambican population [20], whereas a significant association was shown in the Caucasian sub-group based on the $p$ -value ( $p$ -value $=$ 0.01) [21]. The investigation done by Jinming et al. in 2019 showed the association of SNP rs1800871 (A/G) polymorphism in the IL-10 gene with spinal tuberculosis [22].

The study conducted in 2013 showed an increased risk of developing tuberculosis due to the association of SNP rs1800872 with the IL-10 gene polymorphism in the Sudanese population [23]. The study conducted by Silva et al. in 2020 showed the high susceptibility risk of tuberculosis in association with two SNPs, rs1800871 and rs1800872, in the IL-10 gene polymorphism observed in the Brazilian population [24].

The IL-10 -819 T/C polymorphisms are associated with the Asian population, while the IL-10 -592 A/C polymorphisms show a significant association with the European population, which was confirmed in the study conducted by Liang et al. (2014) [25]. The meta-analysis study conducted by Gao et al. (2015) suggests that the IL-10 -819T/C polymorphism is linked to an increased risk of TB in Asians, while the IL-10 -592A/C polymorphism may be a risk factor for TB in Europeans [14].

The previous results do not align with the findings of the current meta-analysis. In the present study, neither of the IL-10 gene’s SNPs (rs1800871 and rs1800872) significantly correlated with tuberculosis. The study was conducted using NOS scoring to ensure the selection of high-quality studies with reliable methodologies, thereby minimizing the risk of bias. The study concluded that the IL-10 gene polymorphism was consistent with the principle of HWE value. To evaluate publication bias, both a funnel plot and Egger’s test were employed, neither of which showed any signs of bias. According to the PPI network, the proteins exhibit higher interactions than expected from a randomly selected group of proteins of similar size and distribution in the genome. This enrichment indicates a degree of biological interconnection among the proteins and the interconnected network of other genes and proteins with the IL-10 gene. Consequently, the statistical data strongly supports the conclusion. A rigorous protocol was followed to extract and analyze the data, ensuring reliable and satisfactory results for the study. Thus, our data provides solid evidence to support the conclusion. Our research enhances our understanding of the genetic factors involved in tuberculosis, potentially leading to improved detection and therapy. Our study emphasizes the ongoing need to understand the mechanisms of IL-10 gene variation despite conflicting results regarding the insignificant association between IL-10 gene polymorphisms and tuberculosis risk due to the insufficient and low sample size. Identifying genetic markers could aid in risk assessment, early detection, and personalized therapy. Ultimately, our study provides valuable insights into the relationship between interleukin gene polymorphisms and TB risk associations, enhancing our understanding and emphasizing the growing importance of unravelling these complexities.

5. Conclusion

In conclusion, our meta-analysis investigated the association between tuberculosis (TB) susceptibility and polymorphisms in the IL-10 gene. Despite previous studies suggesting a potential link between IL-10 gene polymorphisms and TB, our analysis did not identify an association between the IL-10 rs1800871 or rs1800872 gene polymorphisms and TB susceptibility across different genetic models. These results were consistent across various ethnicities and were not influenced by publication bias. These findings indicate that while IL-10 is a crucial cytokine involved in modulating inflammatory responses and has been implicated in TB pathogenesis, the specific polymorphisms examined in this study may not significantly contribute to TB susceptibility. Further research exploring additional genetic variations and their functional implications in TB pathogenesis is warranted to understand better the complex interplay between host genetics and TB susceptibility.

Overall, our study contributes to the growing body of literature on the genetic basis of TB susceptibility and underscores the importance of robust meta-analyses in elucidating the role of genetic factors in infectious diseases. Further investigations are needed to unravel the intricate mechanisms underlying TB pathogenesis and identify potential targets for therapeutic interventions and preventive strategies.

Limitations

The current study has limitations, as it does not consider the genetic environment or other demographic characteristics. Additionally, due to the limited sample size and lack of ethnic diversity, subgroup analysis was not performed. Consequently, future research should include a larger and more diverse sample size across various ethnicities. Further studies involving diverse populations are recommended to increase the relevance and applicability of the findings.

Author’s contributions

As authors of this manuscript, SM and JM have written the contents and edited the figures and tables; RV has designed the study, corrected and approved the final manuscript.

Disclosure statement

The authors report no conflicts of interest.

Ethics and consent to participate

This article is based on published data; hence, ethical approval is not required.

Funding

Not applicable.

Consent for publication

All authors have read and approved the manuscript.

Footnotes

Acknowledgments

The authors would like to acknowledge the unwavering encouragement and support of the Chettinad Academy of Research Education.

References

Manoharan

Chellaiyan

V.G.

Maruthappapandian

and Liaquathali

, Impact of educational intervention on tuberculosis knowledge among medical students, Chennai, International Journal Of Community Medicine And Public Health 6(12) (2019), 5317–5320.

Murray

, Challenges of tuberculosis control, Canadian Medical Association Journal 174(1) (2006), 33–34.

Ben-Selma

Harizi

and Boukadida

, Association of TNF-α and IL-10 polymorphisms with tuberculosis in Tunisian populations, Microbes and Infection 13(10) (2011), 837–843.

Pauline

Devaraj

D.V.

Sivasubramanian

Velmurugan

Stephen

S.B.

Yasam

S.K.

et al., Systemic assessment of solute carrier family 11-member A1 (rs17235409) gene polymorphism and Mycobacterium tuberculosis risk in Asian and Caucasian populations: A comprehensive updated meta-analysis, The International Journal of Mycobacteriology 12(4) (2023), 467–477.

Patterson

Wood

, Is cough really necessary for TB transmission? Tuberculosis 117 (2019), 31–35.

Turner

R.D.

and Bothamley

G.H.

, Cough and the transmission of tuberculosis, The Journal of Infectious Diseases 211(9) (2015), 1367–1372.

Sia

J.K.

and Rengarajan

, Immunology of Mycobacterium tuberculosis Infections, Microbiology Spectrum 7(4) (2019), 10–1128.

Sholeye

A.R.

Williams

A.A.

Loots

D.T.

Tutu van Furth

A.M.

van der Kuip

and Mason

, Tuberculous Granuloma: Emerging Insights From Proteomics and Metabolomics, Frontiers in Neurology 13 (2022), 804838.

Kiazyk

and Ball

T.B.

, Latent tuberculosis infection: An overview, Canada Communicable Disease Report 43(3–4) (2017), 62.

10.

Howell

W.M.

and Rose-Zerilli

M.J.

, Cytokine gene polymorphisms, cancer susceptibility, and prognosis, The Journal of Nutrition 137(1) (2007), 194S–199S.

11.

Areeshi

M.Y.

Mandal

R.K.

Dar

S.A.

Jawed

Wahid

Lohani

Panda

A.K.

Mishra

B.N.

Akhter

and Haque

, IL-10-1082 A > G (rs1800896) polymorphism confers susceptibility to pulmonary tuberculosis in Caucasians but not in Asians and Africans: a meta-analysis, Bioscience Reports 37(5) (2017), BSR20170240.

12.

Harishankar

Selvaraj

and Bethunaickan

, Influence of genetic polymorphism towards pulmonary tuberculosis susceptibility, Frontiers in Medicine 5 (2018), 213.

13.

Rodriguez

Gaunt

T.R.

and Day

I.N.

, Hardy-Weinberg equilibrium testing of biological ascertainment for Mendelian randomization studies, American Journal of Epidemiology 169(4) (2009), 505–514.

14.

Henao

M.I.

Montes

París

S.C.

and García

L.F.

, Cytokine gene polymorphisms in Colombian patients with different clinical presentations of tuberculosis, Tuberculosis 86(1) (2006), 11–19.

15.

Gao

Chen

Tong

Yang

Yao

and Zhou

, Interleukin-10 promoter gene polymorphisms and susceptibility to tuberculosis: a meta-analysis, PloS One 10(6) (2015), e0127496.

16.

Yuan

Zhang

Guo

and Xiong

, IL-10 Polymorphisms and Tuberculosis Susceptibility: An Updated Meta-Analysis, Yonsei Medical Journal 56(5) (2015), 1274.

17.

Oral

H.B.

Budak

Uzaslan

E.K.

Baştürk

Bekar

Akalin

Ege

Ener

and Göral

, Interleukin-10 (IL-10) gene polymorphism as a potential host susceptibility factor in tuberculosis, Cytokine 35(3–4) (2006), 143–147.

18.

Sadeghi Shermeh

Khoshmirsafa

Delbandi

A.A.

Tabarsi

Mortaz

Bolouri

M.R.

Alipour Fayez

Seif

and Shekarabi

, IL-10-592 A/C gene polymorphism and cytokine levels are associated with susceptibility to drug resistance in tuberculosis, Turkish Journal of Immunology 8(3) (2020).

19.

Yang

Zhao

Wang

Liu

Sheng

Yuan

and Jin

, Association of IL4, IL6, and IL10 polymorphisms with pulmonary tuberculosis in a Tibetan Chinese population, Oncotarget 9(23) (2018), 16418.

20.

Mabunda

Alvarado-Arnez

L.E.

Vubil

Mariamo

Pacheco

A.G.

Jani

I.V.

and Moraes

M.O.

, Gene polymorphisms in patients with pulmonary tuberculosis from Mozambique, Molecular Biology Reports 42 (2015), 71–76.

21.

Zhang

Chen

Nie

X.B.

W.H.

Zhang

X.M.

and Lu

J.X.

, Interleukin-10 polymorphisms and tuberculosis susceptibility: a meta-analysis, The International Journal of Tuberculosis and Lung Disease 15(5) (2011), 594–601.

22.

Wang

and Lu

, Relationship between IL-10 gene polymorphism and spinal tuberculosis, Medical Science Monitor: International Medical Journal of Experimental and Clinical Research 25 (2019), 4901.

23.

Mhmoud

Fahal

and Wendy van de Sande

W.J.

, Association of IL-10 and CCL5 single nucleotide polymorphisms with tuberculosis in the Sudanese population, Tropical Medicine and International Health 18(9) (2013), 1119–1127.

24.

Silva

C.A.

Fernandes

D.C.R.O.

Braga

A.C.O.

Cavalcante

G.C.

Sortica

V.A.

Hutz

M.H.

Leal

D.F.V.B.

Fernandes

M.R.

Santana-da-Silva

M.N.

Lopes Valente

S.E.

Pastana

L.F.

Pinto

P.D.C.

Costa

G.E.

Ribeiro-Dos-Santos

Santos

and Santos

N.P.C.

, Investigation of genetic susceptibility to Mycobacterium tuberculosis (VDR and IL10 genes) in a population with a high level of substructure in the Brazilian Amazon region, International Journal of Infectious Diseases 98 (2020), 447–453.

25.

Liang

Guo

and Kong

, Association between IL-10 gene polymorphisms and susceptibility of tuberculosis: evidence based on a meta-analysis, PLoS One 9(2) (2014), e88448.

Meta-analysis of interleukin-10 gene polymorphisms and tuberculosis susceptibility: Insights from recent studies

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methodology

2.1 The extraction approach and selection of relevant reports for meta-analysis

2.3 Assessment of methodological quality using HWE and NOS

Table 1 Characteristics of study IL-10 rs1800871 included in the meta-analysis

3. Results

Table 4 The odds ratio of individual studies associated with the IL-10 rs1800872 and tuberculosis

5. Conclusion

Limitations

Author’s contributions

Disclosure statement

Ethics and consent to participate

Funding

Consent for publication

Footnotes

Acknowledgments

References

Table 1
Characteristics of study IL-10 rs1800871 included in the meta-analysis

Table 4
The odds ratio of individual studies associated with the IL-10 rs1800872 and tuberculosis