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. 2018 Jun 7;13(6):e0198693. doi: 10.1371/journal.pone.0198693

Meta-analyses of IL1A polymorphisms and the risk of several autoimmune diseases published in databases

Hang Su 1,*, Na Rei 2, Lei Zhang 1, Jiaxiang Cheng 1
Editor: Sunil K Ahuja3
PMCID: PMC5991676  PMID: 29879187

Abstract

Background

Based on published data, we aimed to quantitatively elucidate the possible genetic influence of rs17561 G/T and rs1800587 C/T polymorphisms of the IL1A (interleukin 1 alpha) gene in the susceptibility to several autoimmune diseases.

Methods

A series of meta-analyses were carried out. After database searching, we utilized our inclusion/exclusion criteria to screen and include the eligible studies. Passociation (P value of association test), Bonferroni-corrected Passociation value; false discovery rate (FDR)-corrected Passociation, ORs (odd ratios), and 95% CI (confidence interval) were generated to assess the magnitudes of genetic relationships.

Results

A total of 35 eligible articles were included. Pooled analysis data of both rs17561 G/T and rs1800587 C/T in the overall population indicated a negative association between cases of autoimmune diseases and negative controls (all Passociation>0.05, Bonferroni-corrected Passociation>0.05, FDR-corrected Passociation>0.05). Similar results were found in most subgroup analyses (all Passociation>0.05, Bonferroni-corrected Passociation>0.05, FDR-corrected Passociation>0.05), apart from the rs1800587 in the Graves’ disease subgroup, which showed an increased risk in some cases, compared with controls, under the models of allele T vs. C, carrier T vs. C, CT+TT vs. CC, and CT vs. CC (all Passociation<0.05, Bonferroni-corrected Passociation<0.05, FDR-corrected Passociation>0.05, OR>1).

Conclusion

Based on the available data, C/T genotype of the rs1800587 polymorphism within IL1A gene may be associated with an increased Graves’ disease risk. We did not see evidence regarding a positive role for rs1800587 or rs17561 in the risk of other autoimmune diseases, such as systemic sclerosis or rheumatoid arthritis. These conclusions still merit further data support and molecular exploration.

Introduction

Human autoimmune diseases are a group of pathologies that cause clinical damage or destruction of body tissue due to an immune response to its own antigens [1, 2]. There are many types of autoimmune diseases, such as SSC (systemic sclerosis), JIA (juvenile idiopathic arthritis), BD (Behcet’s disease), RA (rheumatoid arthritis), MS (multiple sclerosis), GD (Graves’ disease), SLE (systemic lupus erythematosus), and TID (type 1 diabetes) [1, 2]. A few cytokine genes have been reported to be linked to the autoimmune disease [2–4].

Interleukin 1 (IL1), including interleukin 1 alpha (α), beta (β) and receptor antagonist (ra), is a family of cytokines implicated in regulation of the inflammatory response and the incidence of clinical immune disease [5, 6]. The human interleukin 1 alpha (IL1A) gene, located on chromosome 2q13 [7], contains some common single nucleotide polymorphisms (SNPs), including rs1800587 (NM_000575.4:c.-949C>T)and rs17561 (NM_000575.4:c.340G>T), which have been reported to be linked to several autoimmune diseases in some populations [8–11]. However, negative conclusions have also been obtained by some studies [12–15].

Several meta-analyses have reported an association between IL1A rs17561, rs1800587 polymorphisms and the presence of various autoimmune diseases, including systemic lupus erythematosus [16, 17], rheumatoid arthritis [18], multiple sclerosis [19] and Graves’ disease [20]. However, the genetic relationship between IL1A SNPs and the risk of other autoimmune diseases, including systemic sclerosis and type 1 diabetes, has not been reported. In the present study, we probed the genetic role of IL1A gene SNPs rs17561 and rs1800587 in the risk of autoimmune diseases using quantitative synthesis of overall meta-analysis followed by subgroup analyses.

Methods

The meta-analysis was conducted per the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines [21]. S1 File illustrates the meta-analysis on genetic association studies checklist, and S2 File shows the PRISMA 2009 checklist.

Database searching

We obtained potentially suitable articles by systematically searching three databases (up to April 2018): PubMed, WOS (Web of Science), and Embase (Excerpta Medica Database). The search terms were shown in S3 File.

Article screening

The following screening items were used to exclude publications: duplicates, reviews, letters, meta-analysis, abstracts or posters, and studies with unrelated data. Each study should have investigated an association between IL1A gene polymorphisms and autoimmune disease risk. The genotype frequency data could be extracted from both case and control groups. We also performed a chi-square-based Q-test to confirm that the genotype distribution of control group was consistent with HWE (Hardy-Weinberg Equilibrium).

Data extraction

Detailed data, including the first author name, publication year, SNP, disease type, genotype frequency, genotyping assay, and ethnicity, were extracted and summarized independently. Conflicting data were discussed with all authors, and missing data were requested by e-mail. We also used the Newcastle-Ottawa Scale (NOS) system to assess the study quality and generate an NOS score. An NOS score < 5 means the study was poor quality, and such studies were excluded.

Statistical association analysis

Stata/SE 12.0 software (StataCorp, USA) was used. To evaluate the strength of genetic relationships, Passociation, pooled ORs (odd ratios), and 95% CI (confidence interval) were generated referring to relevant publications [22–26]. The Passociation value was then adjusted by the Bonferroni and false discovery rate (FDR) correction method [27], using R software version 3.4.3. Bonferroni and FDR-corrected Passociation <0.05 from the association test was considered statistically significant. Six comparison models were utilized: allele T vs. G for rs17561, allele T vs. C for rs1800587 (allele); carrier T vs. G for rs17561, carrier T vs. C for rs1800587 (carrier); TT vs. GG for rs17561, TT vs. CC for rs1800587 (homozygote); GT vs. GG for rs17561, CT vs. CC for rs1800587 (heterozygote); GT+TT vs. GG for rs17561, CT+TT vs.CC for rs1800587 (dominant); TT vs. GG+GT for rs17561, and TT vs. CC+CT for rs1800587 (recessive). We also performed the subgroup analyses according to the characteristics of ethnicity, disease type, and control source.

Q statistics with Pheterogeneity (P value of heterogeneity) and I2 tests with I2 values were conducted to assess heterogeneity among the studies. When Pheterogeneity was >0.05 and the I2 value was <50%, the absence of high heterogeneity was inferred, and a fixed-effects model (Mantel-Haenszel method) was applied. Otherwise, a random-effects model (DerSimonian and Laird method) was utilized.

Sensitivity analysis and bias evaluation

We performed sensitivity analysis to test whether the pooled results were stable. In sensitivity analysis, the effect of each study on the pooled ORs was evaluated as each included study was excluded one-by-one. We also performed Begg’s test and Egger’s test to evaluate publication bias. P values of Begg’s test and Egger’s test, namely PBegg and PEgger, below 0.05 indicate the absence of publication bias.

Results

Study characteristics

As shown in Fig 1, we searched three databases, identified a total of 240 articles [PubMed (n = 53), WOS (n = 81), Embase (n = 106)], and subsequently removed 45 duplicate articles. Then, 150 articles were excluded by our screening criteria. Assessing the eligibility of the remaining 45 articles, ten articles were removed, because seven did not contain complete genotype data and three were not consistent with HWE. Eventually, a total of 35 articles [8–15, 20, 28–53] were included, and none exhibited poor quality (all NOS score > 5). We list the characteristics of these studies in Table 1.

Fig 1. Flow diagram of database searching and article screening.

Fig 1

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Table 1. Characteristics of eligible studies in meta-analysis.

First author, year SNP Disease case control Source Assay NOS Ethnicity
AA AB BB AA AB BB
Abtahi, 2015 rs1800587 SSc 82 72 16 98 98 21 PB PCR-SSP seven Asian
Aggarwal, 2012 rs1800587 JIA 42 47 5 93 78 14 PB PCR-RFLP seven Asian
Akman, 2008 rs1800587 BD 32 17 4 19 22 7 PB PCR-SSP Tray/Minitray and String Kits seven Caucasian
Beretta, 2007 rs1800587 SSc 117 70 17 112 76 16 PB PCR-SSP eight Caucasian
Crilly, 2000 rs1800587 RA 45 47 7 33 22 5 PB PCR-RFLP six Caucasian
Dominguez, 2017 rs1800587 RA 53 22 5 36 39 5 PB PCR eight Caucasian
rs17561 RA 55 21 4 47 29 4 PB PCR eight Caucasian
Donn, 2001 rs1800587 JIA 183 125 22 105 113 18 PB PCR-RFLP six Caucasian
Ferri, 2000 rs1800587 MS 189 177 33 198 203 38 PB PCR-RFLP eight Caucasian
Genevay, 2002 rs17561 RA 105 101 24 76 60 8 PB PCR six Caucasian
Harrison, 2008 rs1800587 RA 355 321 63 286 269 49 PB PCR eight Caucasian
Havemose, 2007 rs1800587 JIA 5 3 2 14 10 1 PB PCR-RFLP seven Caucasian
Havemose, 2007 rs1800587 RA 10 7 6 14 10 1 PB PCR-RFLP seven Caucasian
Havemose, 2007 rs17561 JIA 5 3 2 14 10 1 PB PCR-RFLP seven Caucasian
Havemose, 2007 rs17561 RA 10 7 6 14 10 1 PB PCR-RFLP seven Caucasian
Hooper, 2003 rs1800587 MS 189 239 64 102 105 21 PB PCR-RFLP seven Caucasian
Hutyrova, 2004 rs1800587 SSc 17 23 6 87 49 14 PB PCR-SSP seven Caucasian
Johnsen, 2008 rs1800587 RA 687 507 89 546 445 105 PB primer extension of multiplex products eight Caucasian
rs17561 RA 686 513 87 545 443 104 PB primer extension of multiplex products eight Caucasian
Kaijzel, 2002 rs17561 RA 194 171 31 117 79 22 PB PCR-RFLP seven Caucasian
Kammoun, 2007 rs1800587 GD 89 42 0 188 37 0 PB PCR-RFLP six African
Karasneh, 2003 rs1800587 BD 76 44 8 45 49 11 PB gene sequencing six Caucasian
Kawaguchi, 2003 rs1800587 SSc 54 6 0 38 24 8 PB gene sequencing seven Asian
rs17561 SSc 54 6 0 30 30 10 PB gene sequencing seven Asian
Khalilzadeh, 2009 rs1800587 GD 23 57 27 62 62 12 PB PCR-SSP seven Asian
Kobayashi, 2007a rs17561 RA 66 19 1 84 15 1 PB PCR-RFLP seven Asian
Kobayashi, 2007b rs17561 SLE 24 1 0 37 7 0 PB PCR-RFLP nine Asian
Kobayashi, 2009 rs17561 RA 116 20 1 91 16 1 PB PCR-RFLP eight Asian
Liu, 2010 rs1800587 GD 617 137 5 638 92 3 PB GenomeLab SNPstream 12-plex Genotyping System seven Asian
Mann, 2002 rs1800587 MS 169 152 39 68 64 11 HB PCR-RFLP five Caucasian
Mattuzzi, 2007 rs1800587 SSc 43 28 7 364 275 50 PB Taqman MGB probes seven Caucasian
McDowell, 1995 rs1800587 RA 108 127 34 51 37 11 PB gene sequencing eight Caucasian
Mirowska, 2011 rs17561 MS 106 107 15 87 90 16 PB PCR-RFLP six Caucasian
Parks, 2004 rs1800587 SLE 62 57 25 18 43 12 PB PCR-RFLP seven African
43 32 11 68 109 25 PB PCR-RFLP seven Caucasian
Pehlivan, 2011 rs1800587 ITP 53 18 0 67 4 0 PB PCR-RFLP eight Caucasian
Sánchez, 2006 rs1800587 SLE 220 164 33 209 166 45 PB gene sequencing seven Caucasian
Sarial, 2008 rs1800587 MS 33 66 1 62 62 12 PB PCR-SSP six Asian
Tahmasebi, 2013 rs1800587 SLE 87 103 16 95 93 21 PB PCR-SSP seven Asian
Zhou, 2016 rs1800587 TID 171 140 21 209 112 11 PB TaqMan allelic discrimination assay seven Asian
JIA 23 27 3 62 62 12 PB PCR-SSP six Asian
Ziaee, 2014 rs1800587 SLE 26 25 7 62 62 12 PB PCR-SSP six Asian
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Note: SNP, single nucleotide polymorphisms; SSC, systemic sclerosis; JIA, juvenile idiopathic arthritis; BD, Behcet’s disease; RA, rheumatoid arthritis; MS, multiple sclerosis; GD, Graves’ disease; SLE, systemic lupus erythematosus; ITP, immune thrombocytopenic purpura; TID, type 1 diabetes; AA, major allele/major allele; AB, major allele/minor allele; BB, minor allele/minor allele; PB, population-based; HB, hospital-based; PCR-SSP, polymerase chain reaction with sequence-specific primers; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; NOS, Newcastle-Ottawa Scale.

Meta-analysis of rs17561

Eleven case-control studies with 2,561 cases and 2,099 controls were enrolled for the meta-analysis of the IL1A rs17561 G/T polymorphism. As shown in Table 2, compared with controls, no increased risk was detected in any of the cases under six comparison models, including allele T vs. G [Passociation (P value in test of association) = 0.576, Bonferroni-corrected Passociation = 1.000, FDR-corrected Passociation = 0.703]; carrier T vs. G (Passociation = 0.586, Bonferroni-corrected Passociation = 1.000, FDR-corrected Passociation = 0.703); TT vs. GG (Passociation = 0.909, Bonferroni-corrected Passociation = 1.000, FDR-corrected Passociation = 0.909); GT vs. GG (Passociation = 0.419, Bonferroni-corrected Passociation = 1.000, FDR-corrected Passociation = 0.703); GT+TT vs. GG (Passociation = 0.438, Bonferroni-corrected Passociation = 1.000, FDR-corrected Passociation = 0.703); TT vs. GG+GT (Passociation = 0.043, Bonferroni-corrected Passociation = 1.000, FDR-corrected Passociation = 0.258). Forest plot data of the meta-analyses under different models are provided in Fig 2 and S1–S5 Figs. We also performed subgroup analyses by ethnicity and disease types. Similar negative results were obtained under different comparison models (all Passociation>0.05, Bonferroni-corrected Passociation >0.05, FDR-corrected Passociation>0.05, Table 3), except for the Asian (Passociation = 0.024, Bonferroni-corrected Passociation = 0.144, FDR-corrected Passociation = 0.048) and PB (Passociation = 0.043, Bonferroni-corrected Passociation = 0.258, FDR-corrected Passociation = 0.043) subgroups under the TT vs. GG+GT model. These data suggested that the IL1A rs17561 G/T polymorphism seems not be related to a risk for autoimmune disease overall.

Table 2. Meta-analysis of IL1A rs17561 G/T and rs1800587 C/T polymorphism.

SNP Genetic models N Case/Control Passociation Passociation& Passociation# ORs (95% CIs) I2 (%) Pheterogeneity F/R PBegg PEgger
rs17561 allele T vs. G 11 2,561/2,099 0.576 1.000 0.703 0.93 (0.71, 1.21) 76.1 <0.001 R 1.000 0.950
carrier T vs. G 11 2,561/2,099 0.586 1.000 0.703 0.94 (0.75, 1.18) 56.2 0.011 R 0.876 0.724
TT vs. GGxs 10 2,536/2,055 0.909 1.000 0.909 0.97 (0.59, 1.59) 51.4 0.029 R 1.000 0.368
GT vs. GG 11 2,561/2099 0.419 1.000 0.703 0.89 (0.67, 1.18) 64.4 0.002 R 0.161 0.393
GT+TT vs. GG 11 2,561/2,099 0.438 1.000 0.703 0.88 (0.65, 1.21) 72.2% <0.001 R 0.640 0.668
TT vs. GG+GT 10 2,536/2,055 0.043 0.258 0.258 0.79 (0.64, 0.99) 44.9 0.060 F 0.858 0.289
rs1800587 allele T vs. C 31 7,381/4,049 0.548 1.000 0.860 1.04 (0.92, 1.18) 76.5 <0.001 R 0.634 0.396
carrier T vs. C 31 7,381/4,049 0.546 1.000 0.860 1.03 (0.93, 1.14) 54.7 <0.001 R 0.683 0.502
TT vs. CC 29 7.179/3,794 0.860 1.000 0.860 1.02 (0.81, 1.28) 54.7 <0.001 R 1.000 0.499
CT vs. CC 31 7,381/4,049 0.747 1.000 0.860 1.03 (0.87, 1.21) 74.7 <0.001 R 0.919 0.915
CT+TT vs. CC 31 7,381/4,049 0.672 1.000 0.860 1.04(0.88, 1.22) 77.4 <0.001 R 0.734 0.662
TT vs. CC+CT 29 7.179/3794 0.698 1.000 0.860 1.04(0.86, 1.25) 38.2 0.020 R 0.866 0.481
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Note: SNP, single nucleotide polymorphisms; N, number of case-control study; ORs, odd ratios; CIs, confidence intervals; Passociation, P value of association test; &, Bonferroni-corrected Passociation value; #, FDR-corrected Passociation value; F, fixed; R, random.

Fig 2. Meta-analysis of the IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under allele T vs. G model.

Fig 2

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Table 3. Subgroup analysis of IL1A rs17561 G/T polymorphism.

Genetic models subgroup N Case/Control Passociation Passociation& Passociation# ORs (95% CIs) I2(%) Pheterogeneity
Allele T vs. G Caucasian 7 2,253/1,777 0.811 1.000 0.811 1.02 (0.85, 1.24) 53.4 0.045
Asian 4 308/322 0.258 1.000 0.516 0.94 (0.75, 1.18) 88.9 <0.001
RA 7 2,238/1,767 0.593 1.000 0.625 1.06 (0.86, 1.32) 55.9 0.035
PB 11 2,561/2,099 0.576 1.000 0.576 0.93 (0.71, 1.21) 76.1 <0.001
carrier T vs. G Caucasian 7 2,253/1,777 0.493 1.000 0.493 0.96 (0.86, 1.07) 0.0 0.570
Asian 4 308/322 0.279 1.000 0.493 0.54 (0.18, 1.64) 81.9 0.001
RA 7 2,238/1,767 0.612 1.000 0.790 0.97 (0.87, 1.09) 0.0 0.448
PB 11 2,561/2,099 0.586 1.000 0.586 0.94 (0.75, 1.18) 56.2 0.011
TT vs. GG Caucasian 7 2,253/1,777 0.851 1.000 0.851 1.05 (0.65, 1.69) 54.0 0.043
Asian 3 283.278 0.355 1.000 0.710 0.30 (0.02, 3.79) 58.4 0.090
RA 7 2,238/1,767 0.882 1.000 0.882 1.04 (0.63, 1.72) 45.5 0.088
PB 10 2,536/2,055 0.909 1.000 0.909 0.97 (0.59, 1.59) 51.4 0.029
GT vs. GG Caucasian 7 2,253/1,777 0.805 1.000 0.805 0.98 (0.86, 1.12) 0.0 0.444
Asian 4 308/322 0.280 1.000 0.560 0.50 (0.14, 1.77) 85.3 0.000
RA 7 2,238/1,767 0.694 1.000 0.904 1.04 (0.86, 1.25) 18.7 0.287
PB 11 2,561/2,099 0.419 1.000 0.419 0.89 (0.67, 1.18) 64.4 0.002
GT+TT vs. GG Caucasian 7 2,253/1,777 0.984 1.000 0.984 1.00 (0.84, 1.19) 24.9 0.239
Asian 4 308/322 0.262 1.000 0.524 0.45 (0.11, 1.82) 88.3 0.000
RA 7 2,238/1,767 0.657 1.000 0.772 0.45 (0.11, 1.82) 37.8 0.141
PB 11 2,561/2,099 0.438 1.000 0.438 0.88 (0.65, 1.21) 72.2 0.000
TT vs. GG+GT Caucasian 7 2,253/1,777 0.125 0.750 0.125 0.84 (0.67, 1.05) 52.2 0.051
Asian 3 283.278 0.024 0.144 0.048 0.21 (0.05, 0.81) 40.9 0.184
RA 7 2,238/1,767 0.123 0.738 0.220 0.83 (0.65, 1.05) 41.2 0.116
PB 10 2,536/2,055 0.043 0.258 0.043 0.79 (0.64, 0.99) 44.9 0.060
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Note: RA, rheumatoid arthritis; PB, population-based control; ORs, odd ratios; CIs, confidence intervals; Passociation, P value of association test; &, Bonferroni-corrected Passociation value; #, FDR-corrected Passociation value; Pheterogeneity, P value of heterogeneity.

Meta-analysis of rs1800587

A total of 31 case-control studies with 7,381 cases and 4,049 controls were used for meta-analysis of the IL1A rs1800587 C/T Polymorphisms. Pooled data from the overall population (Table 2) presented the negative results under all comparison models (all Passociation >0.05, Bonferroni-corrected Passociation >0.05, FDR-corrected Passociation>0.05). Nevertheless, the data from the GD (Graves’ disease) subgroup analysis (Table 4), comprising three studies, showed an increased risk in cases of autoimmune diseases compared with controls under the genetic models of allele T vs. C (Passociation<0.001, Bonferroni-corrected Passociation <0.006, FDR-corrected Passociation <0.006, OR = 1.89, 95% CIs = 1.40, 2.55), carrier T vs. C (Passociation<0.001, Bonferroni-corrected Passociation <0.006, FDR-corrected Passociation <0.006, OR = 1.60, 95% CIs = 1.30, 1.98), CT vs. CC (Passociation<0.001, Bonferroni-corrected Passociation <0.006, FDR-corrected Passociation <0.006, OR = 1.94, 95% CIs = 1.38, 2.72), CT+TT vs.CC (Passociation = 0.001, Bonferroni-corrected Passociation = 0.006, FDR-corrected Passociation = 0.006, OR = 2.12, 95% CIs = 1.38, 3.25). We did not observe a positive association between case and control groups in other subgroup analyses (Table 4, all Passociation>0.05). Fig 3 and S6–S8 Figs show the forest plots of the subgroup analysis by disease type under the models of allele T vs. C, carrier T vs. C, CT+TT vs.CC and CT vs. CC, respectively. S9 and S10 Figs show the forest plot data of subgroup analysis by ethnicity and control source under allele models. Based on the above data, the C/T genotype of IL1A rs1800587 C/T polymorphism is more likely to be statistically associated with an increased risk of Graves’ disease, but not other autoimmune diseases, such as systemic sclerosis, juvenile idiopathic arthritis, rheumatoid arthritis, multiple sclerosis and systemic lupus erythematosus.

Table 4. Subgroup analysis of IL1A rs1800587 C/T polymorphism.

Genetic models subgroup N Case/Control Passociation Passociation& Passociation# ORs (95% CIs) I2(%) Pheterogeneity
allele T vs. C Asian 10 1,939/1,419 0.465 1.000 0.733 1.10 (0.85, 1.43) 80.5 <0.001
Caucasian 19 5,167/2,424 0.612 1.000 0.733 0.97 (0.85, 1.10) 65.5 <0.001
SSc 5 558/699 0.441 1.000 0.593 0.83 (0.52, 1.33) 84.0 <0.001
JIA 4 487/274 0.494 1.000 0.593 0.92 (0.71, 1.18) 28.1 0.244
RA 6 2,493/966 0.980 1.000 0.980 1.00 (0.81, 1.24) 66.7 0.010
MS 4 1,351/430 0.284 1.000 0.568 1.11 (0.76, 1.64) 7.4 0.356
GD 3 997/888 <0.001 <0.006 <0.006 1.89 (1.40, 2.55) 52.5 0.122
SLE 5 911/452 0.071 0.426 0.213 0.87 (0.75, 1.01) 10.5 0.346
PB 30 7,021/3,981 0.582 1.000 0.582 1.04 (0.91, 1.18) 77.2 <0.001
carrier T vs. C Asian 10 1,939/1,419 0.316 1.000 0.677 1.11 (0.90, 1.37) 61.8 0.005
Caucasian 19 5,167/2,424 0.451 1.000 0.677 0.96 (0.87, 1.06) 30.6 0.101
SSc 5 558/699 0.512 1.000 0.614 0.88 (0.59, 1.30) 71.5 0.007
JIA 4 487/274 0.385 1.000 0.614 0.91 (0.73, 1.13) 0.0 0.576
RA 6 2,493/966 0.680 1.000 0.680 0.97 (0.84, 1.12) 24.3 0.252
MS 4 1,351/430 0.494 1.000 0.614 1.05 (0.91, 1.22) 0.0 0.721
GD 3 997/888 <0.001 <0.006 <0.006 1.60 (1.30, 1.98) 0.0 0.518
SLE 5 911/452 0.236 1.000 0.614 0.91 (0.78, 1.06) 0.0 0.718
PB 30 7,021/3,981 0.568 1.000 0.787 1.03 (0.93, 1.15) 56.2 <0.001
TT vs. CC Asian 10 1,939/1,419 0.746 1.000 0.746 1.11 (0.60, 2.04) 69.5 0.001
Caucasian 18 5,096/2,357 0.651 1.000 0.746 0.95 (0.77, 1.18) 36.5 0.061
SSc 5 558/699 0.893 1.000 0.893 1.04 (0.58, 1.86) 44.6 0.125
JIA 4 487/274 0.334 1.000 0.668 0.78 (0.46, 1.30) 0.0 0.505
RA 6 2,493/966 0.876 1.000 0.893 0.97 (0.66, 1.43) 46.0 0.099
MS 4 1,351/430 0.660 1.000 0.893 1.13 (0.66, 1.91) 52.6 0.097
GD 2 866/700 0.032 0.192 0.192 3.72 (1.12, 12.39) 54.8 0.137
SLE 5 911/452 0.082 0.492 0.246 0.75 (0.55, 1.04) 0.0 0.773
PB 28 6,819/3,726 0.963 1.000 0.963 1.01 (0.80, 1.27) 36.5 0.061
CT vs. CC Asian 10 1,939/1,419 0.120 0.720 0.360 1.24 (0.94, 1.64) 69.2 0.001
Caucasian 19 5,167/2,424 0.327 1.000 0.491 0.92 (0.78, 1.09) 64.4 <0.001
SSc 5 558/699 0.513 1.000 0.770 0.84 (0.50, 1.42) 77.5 0.001
JIA 4 487/274 0.795 1.000 0.883 0.94 (0.60, 1.48) 54.3 0.087
RA 6 2,493/966 0.883 1.000 0.883 0.98 (0.74, 1.29) 64.0 0.016
MS 4 1,351/430 0.351 1.000 0.702 1.15 (0.86, 1.53) 57.9 0.068
GD 3 997/888 <0.001 <0.006 <0.006 1.94 (1.38, 2.72) 42.1 0.178
SLE 5 911/452 0.166 1.000 0.498 0.76 (0.51, 1.12) 70.9 0.008
PB 30 7,021/3,981 0.733 1.000 0.826 1.03 (0.87, 1.22) 75.5 <0.001
CT+TT vs. CC Asian 10 1,939/1,419 0.237 1.000 0.650 1.20 (0.89, 1.63) 77.0 <0.001
Caucasian 19 5,167/2,424 0.433 1.000 0.650 0.94 (0.79, 1.11) 67.3 <0.001
SSc 5 558/699 0.473 1.000 0.710 0.81 (0.46, 1.43) 82.6 <0.001
JIA 4 487/274 0.730 1.000 0.876 0.93 (0.62, 1.40) 48.4 0.121
RA 6 2,493/966 0.963 1.000 0.963 0.99 (0.75, 1.31) 66.8 0.010
MS 4 1,351/430 0.294 1.000 0.588 1.14 (0.89, 1.45) 45.5 0.138
GD 3 997/888 0.001 0.006 0.006 2.12 (1.38, 3.25) 63.7 0.063
SLE 5 911/452 0.139 1.000 0.417 0.78 (0.55, 1.09) 63.5 0.027
PB 30 7,021/3,981 0.680 1.000 0.902 1.04 (0.88, 1.23) 78.1 <0.001
TT vs. CC+CT Asian 10 1,939/1,419 0.948 1.000 0.948 1.02 (0.60, 1.73) 62.9 0.004
Caucasian 18 5,096/2,357 0.684 1.000 0.948 0.97 (0.82, 1.14) 13.0 0.299
SSc 5 558/699 0.786 1.000 0.794 1.06 (0.69, 1.63) 14.0 0.325
JIA 4 487/274 0.503 1.000 0.794 0.84 (0.51, 1.39) 0.0 0.444
RA 6 2,493/966 0.723 1.000 0.794 0.94 (0.67, 1.31) 35.0 0.174
MS 4 1,351/430 0.794 1.000 0.794 1.08 (0.62, 1.86) 58.6 0.065
GD 2 866/700 0.001 0.006 0.006 2.97 (1.54, 5.72) 0.0 0.349
SLE 5 911/452 0.356 1.000 0.794 0.87 (0.64, 1.17) 0.0 0.688
PB 28 6,819/3,726 0.823 1.000 0.794 1.02 (0.85, 1.24) 38.8 0.020
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Note: SSC, systemic sclerosis; JIA, juvenile idiopathic arthritis; RA, rheumatoid arthritis; MS, multiple sclerosis

GD, Graves’ disease; SLE, systemic lupus erythematosus; PB, population-based control; ORs, odd ratios; CIs, confidence intervals.

Passociation, P value of association test; &, Bonferroni-corrected Passociation value; #, FDR-corrected Passociation value; Pheterogeneity, P value of heterogeneity.

Fig 3. Subgroup analysis by disease type of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under allele T vs. C model.

Fig 3

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Heterogeneity, bias and sensitivity

Apart from the TT vs. GG+GT comparison of rs17561, larger heterogeneity was detected (Table 2, I2 >50.0% or Pheterogeneity >0.05), and random effect models were utilized. In addition, as shown in Table 2, P value of Begg’s test and Egger’s test were >0.05 in all genetic models, indicating the absence of large publication bias. The plot data are shown in Fig 4 and S11 Fig. Furthermore, we believe our data are stable, because we did not observe any remarkable change of pooled ORs under any genetic models. The data for the allele T vs. C model of rs1800587 are shown in Fig 5, and other data are not shown.

Fig 4. Begg’s test and Egger’s test for the allele T vs. C model of IL1A rs1800587 C/T polymorphism.

Fig 4

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(A) Begg’s test; (B) Egger’s test.

Fig 5. Sensitivity analysis for the allele T vs. C model of IL1A rs1800587 C/T polymorphism.

Fig 5

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Discussion

Previously, the rs1800587 C/T SNP of IL1A gene was reported to not be linked to the risk or severity of systemic lupus erythematosus in a Spanish population [12], juvenile idiopathic arthritis in an Iranian population [15], and juvenile idiopathic arthritis in the UK [13]. IL1A rs17561 SNP was not associated with rheumatoid arthritis susceptibility in a Mexican population [14]. However, the IL1A rs1800587 and rs17561 SNPs were also reported to be associated with the risk of systemic sclerosis in a Japanese population [8]. The rs1800587 C/T SNP of IL1A gene has been related to susceptibility to systemic sclerosis in a Slovak Caucasian population [9], Graves’ ophthalmopathy in an Iranian population [10], and Graves’ disease in a Tunisian population [11]. Therefore, we first comprehensively explored the association between IL1A rs17561 and rs1800587 SNPs and the risk of overall autoimmune diseases using meta-analysis and subgroup analyses by characteristics of ethnicity, disease type and source of control.

In 2013, a meta-analysis was reported [17], investigating the genetic relationship between IL1A rs1800587 and rs17561 SNPs and the risk of systemic lupus erythematosus based on four case-control studies from three articles [12, 42, 48], which did not provide strong evidence for an association. In 2014, data from another meta-analysis containing four studies from three articles [12, 48, 51] supported a potential association for rs1800587 in Europeans [16]. In this study, we added another case-control study [53] to the subgroup meta-analysis of systemic lupus erythematosus for rs1800587, and observed a negative association.

In one meta-analysis of rheumatoid arthritis susceptibility[18], there were four case-control studies [32, 35, 38, 54] for rs1800587 and three case-control studies [34, 39, 43] for rs17561. No positive association between IL1A rs1800587 and rs17561 SNPs and the risk of rheumatoid arthritis was observed [18]. Here, we included more data for our updated meta-analysis and removed one case-control study [54], in which the genotype distribution of control group did not fulfill Hardy-Weinberg equilibrium. Seven case-control studies [14, 34, 36, 38, 39, 41, 43] were enrolled for the subgroup analysis of rs17561, and six case-control studies [14, 32, 35, 36, 38, 46] were used for rs1800587. Our pooled data with enhanced statistical power also indicated that the IL1A rs1800587 and rs17561 SNPs were not linked to the risk of rheumatoid arthritis, which was consistent with previous data [18].

Regarding multiple sclerosis susceptibility, in 2013, Huang et al. enrolled five case-control studies [33, 37, 44, 50, 55] for a meta-analysis of rs1800587 SNP and two case-control studies [47, 56] for meta-analysis of rs17561 SNP. However, negative association was reported for both s1800587 and rs17561 [19]. Here, due to the limitation of Hardy-Weinberg equilibrium, one case-control study [55] was excluded from our subgroup meta-analysis of rs1800587. We also found that the rs1800587 SNP was not linked to the risk of multiple sclerosis.

In 2010, Liu et al. investigated the genetic relationship between IL1A rs1800587 SNP and risk of Graves’ disease via a meta-analysis and found a positive association in an Asian population [20]. Here, our data in the subgroup meta-analysis of Graves’ disease showed similar results. It is possible that the rs1800587 SNP within the 5'-flanking regulatory region of IL1A gene affects the normal production, secretion or function of interleukin-1.

Some limitations exist in our meta-analysis. First, we did not obtain strong evidence regarding the effect of rs1800587 and rs17561 SNPs for the risk of different types of autoimmune diseases, due to the limited number of included independent case-control studies. Only two case-control studies [30, 40] were included in the subgroup of Graves’ disease under the homozygote and recessive models. Second, even though no remarkable publication bias was detected by our Begg’s test and Egger’s test, larger heterogeneity existed in the majority of comparisons. We observed a decreased level of heterogeneity in some subgroup analyses by disease type, such as the “rheumatoid arthritis, RA” subgroup of rs17561 and “multiple sclerosis, MS” subgroup of rs1800587. The factor of specific disease type may be involved in the source of heterogeneity. Further relevant researches with larger sample sizes were required. Third, we only acquired suitable case-control studies published in English. The outcome may be affected by the inclusion of unpublished articles, or articles published in another language. Fourth, it is worth analyzing the combined influence of different SNPs or cytokine genes, when more case-control studies become available.

Taken together, based on published articles in databases, our meta-analysis suggested that the rs1800587 polymorphism, rather than rs17561, within the IL1A gene, may be a genetic risk factor for Graves’ disease. However, IL1A rs17561 or rs1800587 polymorphism seems not to be statistically linked to the risk of other analyzed autoimmune diseases, such as systemic sclerosis, juvenile idiopathic arthritis, rheumatoid arthritis, multiple sclerosis and systemic lupus erythematosus.

Supporting information

S1 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under carrier T vs. G model.

(TIF)

Click here for additional data file. (7.8MB, tif)
S2 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under TT vs. GG model.

(TIF)

Click here for additional data file. (7.6MB, tif)
S3 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under GT vs. GG model.

(TIF)

Click here for additional data file. (7.5MB, tif)
S4 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under GT+TT vs. GG model.

(TIF)

Click here for additional data file. (7.3MB, tif)
S5 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under TT vs. GG+GT model.

(TIF)

Click here for additional data file. (7.4MB, tif)
S6 Fig. Subgroup analysis by disease type of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under carrier T vs. C model.

(TIF)

Click here for additional data file. (9.1MB, tif)
S7 Fig. Subgroup analysis by disease type of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under CT+TT vs. CC model.

(TIF)

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S8 Fig. Subgroup analysis by disease type of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under CT vs. CC model.

(TIF)

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S9 Fig. Subgroup analysis by ethnicity of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under allele T vs. C model.

(TIF)

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S10 Fig. Subgroup analysis by control source of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under allele T vs. C model.

(TIF)

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S11 Fig. Begg’s test and Egger’s test for the allele T vs. G model of IL1A rs17561 G/T polymorphism.

(A) Begg’s test; (B) Egger’s test.

(TIF)

Click here for additional data file. (2.6MB, tif)
S1 File. Meta-analysis of genetic association studies checklist.

(DOCX)

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S2 File. PRISMA 2009 checklist.

(DOC)

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S3 File. The search terms of database searching.

(DOC)

Click here for additional data file. (43.5KB, doc)

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under carrier T vs. G model.

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S2 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under TT vs. GG model.

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S3 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under GT vs. GG model.

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S4 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under GT+TT vs. GG model.

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S5 Fig. Meta-analysis of IL1A rs17561 G/T polymorphism and the risk of autoimmune diseases under TT vs. GG+GT model.

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S6 Fig. Subgroup analysis by disease type of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under carrier T vs. C model.

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S7 Fig. Subgroup analysis by disease type of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under CT+TT vs. CC model.

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S8 Fig. Subgroup analysis by disease type of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under CT vs. CC model.

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S9 Fig. Subgroup analysis by ethnicity of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under allele T vs. C model.

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S10 Fig. Subgroup analysis by control source of the association between IL1A rs1800587 C/T polymorphism and the risk of autoimmune diseases under allele T vs. C model.

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S11 Fig. Begg’s test and Egger’s test for the allele T vs. G model of IL1A rs17561 G/T polymorphism.

(A) Begg’s test; (B) Egger’s test.

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S1 File. Meta-analysis of genetic association studies checklist.

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S2 File. PRISMA 2009 checklist.

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S3 File. The search terms of database searching.

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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