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. 2020 Oct 21;21:55. doi: 10.1186/s12865-020-00384-7

Interleukin 4 gene polymorphism (−589C/T) and the risk of asthma: a meta-analysis and met-regression based on 55 studies

Ahmad Kousha 1, Armita Mahdavi Gorabi 2, Mehdi Forouzesh 3, Mojgan Hosseini 4,✉, Markov Alexander 5, Danyal Imani 6, Bahman Razi 7, Mohammad Javad Mousavi 8,9, Saeed Aslani 9, Haleh Mikaeili 10,✉
PMCID: PMC7579954  PMID: 33087044

Abstract

Background

Numerous investigations have previously evaluated the association of interleukin (IL) 4 gene polymorphisms and the risk of asthma, conferring inconsistent results. To resolve the incongruent outcomes yielded from different single studies, we conducted the most up-to-date meta-analysis of IL4 gene −589C/T (rs2243250) polymorphism and susceptibility to asthma.

Methods

A systematic literature search was performed in ISI web of science, Scopus, Medline/PubMed databases prior to September 2020, and the pooled odds ratio (OR) and their corresponding 95% CI were calculated to determine the association strength.

Results

Literature search led to retrieving of 49 publications (55 case-control studies) containing 9572 cases and 9881 controls. It was revealed that IL4 gene −589C/T polymorphism increased the risk of asthma across all genetic models, including dominant model (OR = 1.22), recessive model (OR = 1.17), allelic model (OR = 1.21), and TT vs. CC model (OR = 1.34), but not the CT vs. TT model. The subgroup analysis by age indicated that IL4 gene -589C/T polymorphism was significantly associated with asthma risk in both pediatrics and adults. Additionally, the subgroup analysis by ethnicity revealed significant association in Asian, American, and Europeans. Finally, subgroup analysis by East Asian and non-East Asian populations indicated significant associations.

Conclusions

The current meta-analysis revealed that IL4 gene -589C/T polymorphism was a susceptibility risk in both pediatrics and adults in the whole and different ethnic groups.

Keywords: Asthma, Interleukin 4, Polymorphism, Meta-analysis, Genetic susceptibility

Background

Asthma is one of the most common atopic disorders of the respiratory tract, which results in wheezing, coughing, breathlessness, and bronchial obstruction [1]. The prevalence and incidence of asthma raised regularly and it estimated more than 300 million persons of the world are affected by this disease [2]. The main causes of asthma are not completely clear. However, is has been postulated that asthma is mediated by interactions between specific external stimulating factors, including pollutants, viral and bacterial infections, allergens, tobacco smokes, etc., and genetics of the patients. Additionally, increasing number of studies recommend that genetic factors play a critical role in the etiology of asthma by their interactions with the environmental elements [3, 4]. The heritability of asthma is estimated to be 35 to 95% [5]. Numerous studies have examined the correlation between genetic variations of pro and anti-inflammatory genes and susceptibility to asthma [6, 7]. In recent decades, single nucleotide polymorphisms (SNP) have become one of the frequently studied models of DNA variation in analyses of the association between genetics and susceptibility to disease [8, 9].

The role of immunological factors especially cytokines in modulating and controlling the inflammatory response of the respiratory tracts is essential in the evolution, progression, and exacerbations of asthma [10]. Interleukin (IL)-4 is a key ingredient of the immune system required in the regulation of response to an allergen through controlling the isotype switching of antibody in B lymphocytes to IgG and IgE class [11]. Elevated serum levels of IgE are suggestive of allergic reactions and resemble a high level of IL-4 mRNA assembly [12]. Moreover, it acts as a growth factor to facilitate the differentiation of T helper (Th) 2 cells and mast cells. These characteristics of IL-4 accentuate on the crucial roles of cytokines in the pathogenesis asthma [13, 14]. Additionally, IL4 gene polymorphisms, like promoter region (C + 33 T) SNP [15], and 3017 G/T SNP in intron 2 [16], have been associated with IgE levels, which might be involved in the pathogenesis of asthma.

The IL4 gene is located on chromosome 5q31 [17]. The -589C/T (rs2243250) polymorphism has been recognized on upstream of the transcription initiation site [18]. It has been demonstrated that the binding of a transcription factor is enhanced by the appearance of the polymorphic T allele that may result in an overexpression of the IL4 gene and, thus, raising the power of any immunological response that dependents on IL-4 [19]. To date, many studies have examined the association between IL4 gene -589 C/T polymorphisms and the risk of asthma, but their outcomes have not been consistent. Therefore, we performed this meta-analysis to analyze the relationship between the -589C/T polymorphisms and susceptibility to asthma.

Methods

This study conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement including; literature review, study selection, inclusion and exclusion criteria, data extraction and quality assessment, and statistical analysis [20]. No ethics committee confirmation was necessary for this meta-analysis, which does not contain any studies with human participants or animals performed by any of the authors.

Literature review

A comprehensive search was performed in the ISI web of science, Scopus, Medline/PubMed databases to retrieve published articles prior to September 2020. The following main key words and Medical Subject Headings (Mesh) were searched: (“asthma” [Mesh] OR “asthmatic”) AND (“interleukin-4” OR “IL-4” OR “rs2243250”) AND (“single nucleotide polymorphism” OR “SNP” OR “polymorphisms” OR “mutation” OR “variation”). No restrictions were placed on language, sample size, population or publication date.

Study selection

The retrieved publications by primary literature search were imported into Endnote X8 software. The duplicate studies were removed and title and abstract of remain studies were reviewed by two investigators. Full-text verification was performed if we could not categorize studies based on title and abstract. Any disagreements during study selection was discussed and resolved by consensus.

Inclusion and exclusion criteria

The following inclusion criteria were used to distinguish eligible studies: i) studies with distinct case and control group evaluating the association between IL-4 C589T polymorphism and susceptibility to asthma; ii) studies with calculable or extractable data for odds ratio (OR) and 95% confidence intervals (CIs); iii) studies with sufficient data for alleles and genotypes in case and control group. The duplicates, reviews, book chapters, and meta-analysis were excluded. The application of these criteria results in 49 qualified studies for the meta-analysis.

Data extraction and quality assessment

Two of our authors independently and according to an extraction checklist extracted the following data: the first author, journal and year of publication, country of origin, ethnicity, number of subjects in the case and the control groups for each gender, mean or range of age, genotyping method, genotype counts in the case and the control group. The quality of each study was assessed using the Newcastle-Ottawa Scale (NOS) criteria [21]. Studies with scores 0–3, 4–6 or 7–9 were low, moderate or high-quality, respectively.

Statistical analysis

Statistical analyses were carried out using STATA (version 14.0; Stata Corporation, College Station, TX) and SPSS (version 23.0; SPSS, Inc. Chicago, IL). The strength of association between polymorphism and asthma susceptibility was estimated by odd ratios (ORs) and 95% confidence intervals (CIs) for the dominant model, recessive model, allele contrasts, and additive comparison. Heterogeneity among included studies was measured via Q statistics (P value< 0.1 considered statistically significant) and I2-test (I2 values of 25, 50 and 75% were described as low, moderate, and high heterogeneity, respectively). In the presence of heterogeneity random effect model (REM) was used, however fixed effect model (FEM) was applied in homogeneous condition [22, 23]. In order to assessed the predefined sources of heterogeneity among included studies, subgroup analysis and meta-regression analysis based on year of population, the continent of the study population, and genotyping method were performed. The genotypic frequency distribution in the controls was checked for consistency of the Hardy– Weinberg equilibrium (HWE). Furthermore, publication bias was computed by the Begg’s and Egger’s test and visual examination of the funnel plot (P value< 0.05 considered statistically significant) [24, 25]. Additionally, to calculate overall effect size in absence of each study, a sensitivity analysis was conducted.

Results

Search results and characteristics of the selected studies

Our primary search retrieved 2121 potential articles. After removing of duplicate articles (n = 301), 1820 articles remain for abstract and full-text screening. Of 1820 articles, 1612 were excluded base on title and abstract and 159 articles based on full-text reading. Ultimately 49 publications with 9579 cases and 9881 controls met the inclusion criteria and their data were extracted for meta-analysis. Among these 49 publications, four of them, including Basehore et al. [16], Donfack et al. [26], Zhang et al. [27], and Baye et al. [28] examined two or three different populations with separate case and control; therefore, we assumed them as 9 case-control studies collectively (55 studies). The detailed information on study selection process is illustrated in Fig. 1, Tables 1, and 2.

Fig. 1.

Fig. 1

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Flow diagram of study selection process

Table 1.

Characteristics of studies included in meta-analysis of overall asthma

Study author Year Country Ethnicity 1 (Continent) Ethnicity 2 Ethnicity 3 Age group Total cases/control Genotyping method Quality Score
Walley et al. [29] 1996 UK Europe non East-Asian Caucasian Pediatric 124 / 59 PCR-RFLP 6
Hijazi et al. [30] 2000 Kuwait Asia non East-Asian Arab Mixed 84 / 100 PCR-RFLP 6
Sandford et al. [31] 2000 New Zealand Europe non East-Asian Caucasian Adult 233 / 143 PCR-RFLP 7
Takabayashi et al. [32] 2000 Japan Asia East-Asian Caucasian Pediatric 100 / 100 PCR-RFLP 6
Hakonarson et al. [33] 2001 Iceland Europe non East-Asian Caucasian Mixed 94 / 94 PCR 6
Cui et al. [34] 2003 China Asia East-Asian Caucasian Mixed 241 / 175 PCR-RFLP 7
Basehore et al. (i) [16] 2004 USA America non East-Asian African American Adult 233 / 245 PCR 7
Basehore et al. (ii) [16] 2004 USA America non East-Asian African American Adult 168 / 269 PCR 7
Basehore et al. (iii) [16] 2004 USA America non East-Asian African American Adult 116 / 130 PCR 6
Lee et al. [35] 2004 Korea Asia East-Asian Caucasian Pediatric 254 / 100 PCR-RFLP 6
Park et al. [36] 2004 Korea Asia East-Asian Caucasian Mixed 532 / 170 SNaPshot 8
Wang et al. [37] 2004 China Asia East-Asian Caucasian Adult 93 / 62 PCR-RFLP 6
Adjers et al. [38] 2004 Finland Europe non East-Asian Caucasian Adult 243 / 401 PCR-RFLP 7
Donfack et al. (i) [26] 2005 USA America non East-Asian African American Mixed 126/ 205 LAS 6
Donfack et al. (ii) [26] 2005 USA America non East-Asian African American Mixed 205 / 183 LAS 7
Zhang et al. (i) [27] 2005 China Asia East-Asian Caucasian Adult 152 / 157 PCR-RFLP 6
Zhang et al. (ii) [27] 2005 Malaysia Asia East-Asian Caucasian Adult 76 / 100 PCR-RFLP 6
Zhang et al. (iii) [27] 2005 India Asia non East-Asian Caucasian Adult 87 / 103 PCR-RFLP 6
Gervaziev et al. [39] 2006 Russia Europe non East-Asian Caucasian Adult 109 / 68 PCR-RFLP 6
Schubert et al. [40] 2006 Germany Europe non East-Asian Caucasian Pediatric 231 / 270 PCR-RFLP 7
Kabesch et al. [41] 2006 Germany Europe non East-Asian Caucasian Pediatric 73 / 773 PCR-RFLP 6
Battle et al. [42] 2007 USA America non East-Asian African American Mixed 255 / 175 PCR-RFLP 6
Hosseini-Farahabadi et al. [43] 2007 Iran Asia non East-Asian Caucasian Adult 30 / 50 PCR-RFLP 5
Kamali-Sarvestani et al. [44] 2007 Iran Asia non East-Asian Caucasian Adult 149 / 112 PCR-RFLP 6
Chiang et al. [45] 2007 China Asia East-Asian Caucasian Adult 167 / 111 PCR-RFLP 6
Mak et al. [46] 2007 China Asia East-Asian Caucasian Adult 289 / 292 PCR-RFLP 7
Attab et al. [47] 2008 Jordan Asia non East-Asian Arab Pediatric 40 / 40 PCR-RFLP 5
De Faria et al. [48] 2008 Brazil America non East-Asian Caucasian Pediatric 88 / 202 PCR-RFLP 6
Jiang et al. [49] 2009 China Asia East-Asian Caucasian Adult 13 / 13 PCR-RFLP 5
Amirzargar et al. [50] 2009 Iran Asia non East-Asian Caucasian Mixed 59 / 139 PCR-RFLP 6
Daley et al. [51] 2009 Australia Oceania non East-Asian Caucasian Mixed 644 / 751 Illumina Bead array system 8
Haller et al. [52] 2009 USA America non East-Asian African American Adult 72 / 70 PCR-RFLP 6
Rad et al. [53] 2010 Iran Asia non East-Asian Caucasian Adult 64 / 65 PCR-RFLP 6
Wu et al. [54] 2010 China Asia East-Asian Caucasian Pediatric 252 / 227 PCR-RFLP 7
Beghe et al. [55] 2010 UK and Italy Europe non East-Asian Caucasian Mixed 299 / 176 PCR-RFLP 7
Bijanzadeh et al. [56] 2010 India Asia non East-Asian Caucasian Mixed 100 / 50 PCR-RFLP 6
Fance et al. [57] 2010 China Asia East-Asian Caucasian Adult 62 / 30 PCR-RFLP 6
Baye et al. (i) [28] 2011 USA America non East-Asian African American Pediatric 413 / 298 Illumina GoldenGate Assay system 7
Baye et al. (ii) [28] 2011 USA America non East-Asian African American Pediatric 315 / 51 Illumina GoldenGate Assay system 6
Daneshmandi et al. [58] 2011 Iran Asia non East-Asian Caucasian Adult 81 / 124 PCR-RFLP 7
Huang et al. [59] 2011 China Asia East-Asian Caucasian Pediatric 100 / 122 PCR-RFLP 6
Hwang et al. [60] 2012 China Asia East-Asian Caucasian Pediatric 188 / 376 PCR-RFLP 7
Chiang et al. [61] 2012 China Asia East-Asian Caucasian Adult 452 / 106 PCR-RFLP 6
Micheal et al. [62] 2013 Pakistan Asia non East-Asian Caucasian Mixed 108 / 120 PCR-RFLP 6
Ricciardolo et al. [63] 2013 Italy Europe non East-Asian Caucasian Mixed 57 / 124 PCR-SSP 6
Smolnikova et al. [64] 2013 Russia Europe non East-Asian Caucasian Mixed 64 / 50 PCR-RFLP 6
Li et al. [65] 2014 China Asia East-Asian Caucasian Pediatric 491 / 503 PCR-LDR 7
Wang et al. [66] 2015 China Asia East-Asian Caucasian Mixed 392 / 849 Mass array 7
Dahmani et al. [67] 2016 Algeria Africa non East-Asian Arab Adult 44 / 19 PCR-RFLP 6
Li et al. [68] 2016 China Asia East-Asian Caucasian Pediatric 317 /351 PCR and Sequencing 7
Narozna et al. [69] 2016 Poland Europe non East-Asian Caucasian Mixed 177 / 189 Taq Man 7
Zhang et al. [68] 2016 China Asia East-Asian Caucasian Pediatric 38 / 35 PCR and Sequencing 6
Hussein et al. [70] 2017 Iraq Asia non East-Asian Arab Mixed 48 / 25 ARMS-PCR 6
Abood et al. [71] 2018 Iraq Asia non East-Asian Arab Mixed 100 / 100 AS-PCR 6
Zhang et al. [72] 2019 China Asia East-Asian Caucasian Pediatric 37 / 29 PCR and Sequencing 5
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Table 2.

Distribution of genotype and allele among asthma patients and controls

Study author Asthma cases Healthy control P-HWE MAF
CC CT TT C T CC CT TT C T
Walley et al. [29] 56 55 13 167 81 31 23 5 85 33 0/8 0/72
Hijazi et al. [30] 5 25 54 35 133 9 31 60 49 151 0/1 0/245
Sandford et al. [31] 146 78 9 370 96 100 41 2 241 45 0/33 0/842
Takabayashi et al. [32] 6 43 51 55 145 10 39 51 59 141 0/53 0/295
Hakonarson et al. [33] 73 20 1 166 22 67 25 2 159 29 0/85 0/845
Cui et al. [34] 11 89 141 111 371 9 52 114 70 280 0/34 0/2
Basehore et al. (i) [16] 153 72 8 378 88 181 59 5 421 69 0/94 0/859
Basehore et al. (ii) [16] 22 77 69 121 215 29 119 121 177 361 0/97 0/329
Basehore et al. (iii) [16] 43 55 18 141 91 55 59 16 169 91 0/97 0/65
Lee et al. [35] 9 77 168 95 413 3 29 68 35 165 0/96 0/175
Park et al. [36] 19 164 349 202 862 7 54 109 68 272 0/92 0/2
Wang et al. [37] 29 42 22 100 86 21 26 15 68 56 0/22 0/548
Adjers et al. [38] 106 103 34 315 171 189 164 48 542 260 0/18 0/675
Donfack et al. (i) [26] 85 34 7 204 48 144 55 6 343 67 0/78 0/836
Donfack et al. (ii) [26] 25 82 98 132 278 24 82 77 130 236 0/76 0/355
Zhang et al. (i) [27] 4 47 101 55 249 3 45 109 51 263 0/5 0/162
Zhang et al. (ii) [27] 11 35 30 57 95 16 43 41 75 125 0/4 0/375
Zhang et al. (iii) [27] 50 31 6 131 43 66 30 7 162 44 0/17 0/786
Gervaziev et al. [39] 16 75 18 107 111 18 43 7 79 57 0/01 0/58
Schubert et al. [40] 143 78 10 364 98 189 74 7 452 88 0/93 0/837
Kabesch et al. [41] 42 29 2 113 33 564 188 21 1316 230 0/26 0/851
Battle et al. [42] 28 113 114 169 341 19 77 79 115 235 0/97 0/328
Hosseini-Farahabadi et al. [43] 17 8 5 42 18 38 12 0 88 12 0/33 0/88
Kamali-Sarvestani et al. [44] 139 6 4 284 14 93 18 1 204 20 0/9 0/91
Chiang et al. [45] 1 19 147 21 313 7 34 70 48 174 0/31 0/216
Mak et al. [46] 15 95 179 125 453 19 87 186 125 459 0/05 0/214
Attab et al. [47] 31 9 0 71 9 33 7 0 73 7 0/54 0/912
De Faria et al. [48] 38 41 9 117 59 67 108 27 242 162 0/1 0/599
Jiang et al. [49] 0 8 5 8 18 1 9 3 11 15 0/13 0/423
Amirzargar et al. [50] 0 59 0 59 59 10 129 0 149 129 < 0.001 0/535
Daley et al. [51] 476 155 13 1107 181 549 186 16 1284 218 0/95 0/854
Haller et al. [52] 6 30 36 42 102 7 31 32 45 95 0/89 0/321
Rad et al. [53] 46 18 0 110 18 42 23 0 107 23 0/08 0/823
Wu et al. [54] 6 83 163 95 409 11 84 132 106 348 0/61 0/233
Beghe et al. [55] 232 63 4 527 71 136 37 3 309 43 0/79 0/877
Bijanzadeh et al. [56] 92 4 4 188 12 48 1 1 97 3 < 0.001 0/97
Fance et al. [57] 38 13 11 89 35 27 1 2 55 5 < 0.001 0/916
Baye et al. (i) [28] 267 130 16 664 162 233 61 4 527 69 0/99 0/884
Baye et al. (ii) [28] 35 140 140 210 420 12 25 14 49 53 0/89 0/48
Daneshmandi et al. [58] 63 15 3 141 21 94 26 4 214 34 0/2 0/862
Huang et al. [59] 1 19 80 21 179 4 43 75 51 193 0/46 0/209
Hwang et al. [60] 1 51 136 53 323 12 89 275 113 639 0/15 0/15
Chiang et al. [61] 13 110 329 136 768 7 34 65 48 164 0/38 0/226
Micheal et al. [62] 26 63 19 115 101 31 84 5 146 94 < 0.001 0/608
Ricciardolo et al. [63] 35 19 3 89 25 109 12 3 230 18 < 0.001 0/927
Smolnikova et al. [64] 36 28 0 100 28 39 11 0 89 11 0/38 0/89
Li et al. [65] 17 150 324 184 798 21 144 338 186 820 0/26 0/184
Wang et al. [66] 50 177 165 277 507 104 412 333 620 1078 0/17 0/365
Dahmani et al. [67] 13 19 12 45 43 6 11 2 23 15 0/35 0/605
Li et al. [68] 112 0 205 224 410 138 0 213 276 426 < 0.001 0/393
Narozna et al. [69] 117 55 5 289 65 133 53 3 319 59 0/37 0/843
Zhang et al. [68] 8 11 19 27 49 17 13 5 47 23 0/34 0/671
Hussein et al. [70] 42 5 1 89 7 8 13 4 29 21 0/73 0/58
Abood et al. [71] 66 17 17 149 51 7 90 3 104 96 < 0.001 0/52
Zhang et al. [72] 7 13 17 27 47 11 15 3 37 21 0/51 0/637
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P-HWE p-value for Hardy–Weinberg equilibrium, MAF minor allele frequency of control group

Meta-analysis of IL-4 SNP (C-589 T) and the risk of asthma

Overall, 55 studies with 9572 cases and 9881 controls included in quantitative analysis of the association between IL-4 gene -589C/T polymorphism and the risk of asthma. Of those, 11 articles were conducted in European countries [29, 31, 33, 38–41, 55, 63, 64, 69], 32 articles were in Asian countries [27, 30, 32, 34–37, 43–46, 49, 50, 53, 54, 56–62, 65, 66, 68, 70–73], 10 articles in American countries [16, 26, 28, 42, 48, 52] and one article in each Algeria [67] and Australia country [51]. The analysis of overall population revealed the significant positive association between IL4 gene -589C/T polymorphism and the risk of asthma across all genetic models; including dominant model (OR = 1.22, 95% CI = 1.04–1.44, P = 0.01, REM), recessive model (OR = 1.17, 95% CI = 1.08–1.27, P < 0.001, FEM), allelic model (OR = 1.21, 95% CI = 1.09–1.33, P < 0.001, REM), and TT vs. CC model (OR = 1.34, 95% CI = 1.18–1.52, P < 0.001, FEM), except CT vs. TT model (OR = 1.13, 95% CI = 0.95–1.34, P = 0.17, REM) (Fig. 2). Additionally, along with subgroup analysis based on age we stratified the analysis by ethnicity in three conditions.

Fig. 2.

Fig. 2

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Pooled OR and 95% CI of individual studies and pooled data for the association between Il-4 C589T polymorphism and asthma risk in; a allelic model, b recessive Model

Subgroup analysis by age

We stratified eligible articles into three groups including: pediatrics (16 articles), adults (21 articles) and mixed (cover both range;18 articles). The results highlighted a predisposing role of IL4 gene -589C/T polymorphism for the asthma risk in pediatrics and adults under all genotype models. However, no significant association was detected in mixed group (Table 3, Fig. 3).

Table 3.

Main results of pooled ORs in meta-analysis of IL-4 gene polymorphisms in asthmatic patients

Subgroup Sample size Test of association Test of heterogeneity Test of publication bias (Begg’s test) Test of publication bias (Egger’s test)
Genetic model Case/Control OR 95% CI (p-value) I2 (%) P z P t P
Overall Dominant model 9579 / 9881 1.22 1.04–1.44 (0.01) 69.7 < 0.001 - 1.33 0.24 - 1.17 0.39
Recessive model 9579 / 9881 1.17 1.08–1.27 (< 0.001) 48.5 < 0.001 −1.38 0.16 −0.60 0.55
Allelic model 9579 / 9881 1.21 1.09–1.33 (< 0.001) 71.1 < 0.001 − 1.05 0.41 −1.82 0.07
TT vs. CC 9579 / 9881 1.34 1.18–1.52 (< 0.001) 30.5 0.02 −1.25 0.24 −1.90 0.65
CT vs. CC 9579 / 9881 1.13 0.95–1.34 (0.17) 68.7 < 0.001 −2.06 0.33 −1.73 0.09
Age groups
 Pediatrics Dominant model 3061 / 3536 1.54 1.24–1.92 (< 0.001) 41 0.04 − 1.93 0.05 −1.63 0.23
Recessive model 3061 / 3536 1.20 1.05–1.37 (< 0.001) 58.3 < 0.001 −0.36 0.71 −1.14 0.27
Allelic model 3061 / 3536 1.37 1.16–1.63 (< 0.001) 68 < 0.001 −1.53 0.12 − 1.99 0.06
TT vs. CC 3061 / 3536 1.51 1.22–1.87 (< 0.001) 51.6 0.01 −1.44 0.15 −1.47 0.24
CT vs. CC 3061 / 3536 1.49 1.23–1.81 (< 0.001) 10.6 0.33 −1.92 0.05 −1.22 0.42
 Adults Dominant model 2933 / 2670 1.23 1.01–1.51 (0.04) 35.2 0.066 −2.10 0.03 −1.86 0.08
Recessive model 2933 / 2670 1.21 1.04–1.40 (0.01) 46 0.01 −0.91 0.36 −0.71 0.48
Allelic model 2933 / 2670 1.24 1.05–1.47 (< 0.001) 63.8 < 0.001 −0.97 0.33 −1.45 0.16
TT vs. CC 2933 / 2670 1.37 1.09–1.72 (< 0.001) 5 0.39 −1.01 0.47 −1.77 0.19
CT vs. CC 2933 / 2670 1.15 0.96–1.39 (0.13) 23 0.17 −2.13 0.03 −1.56 0.13
 Mixed Dominant model 3585 / 3675 0.92 0.65–1.32 (0.65) 83.6 < 0.001 −0.09 0.92 − 1.05 0.31
Recessive model 3585 / 3675 1.12 0.97–1.28 (0.11) 45.4 0.02 −0.41 0.68 0.39 0.70
Allelic model 3585 / 3675 1.03 0.85–1.24 (0.78) 76.3 < 0.001 −0.72 0.47 0.02 0.98
TT vs. CC 3585 / 3675 1.14 0.91–1.42 (0.24) 20.8 0.21 −0.18 0.85 −0.28 0.87
CT vs. CC 3585 / 3675 0.87 0.59–1.28 (0.48) 84.9 < 0.001 0 1 −1.11 0.28
Ethnicity-1 (Continent)
 Asia Dominant model 5196 / 4936 1.15 0.84–1.56 (0.39) 75.6 < 0.001 −1.86 0.06 −1.44 0.20
Recessive model 5196 / 4936 1.16 1.06–1.28 (< 0.001) 65 < 0.001 −1.62 0.10 −0.60 0.55
Allelic model 5196 / 4936 1.17 1–1.37 (0.04) 76.7 < 0.001 −1.72 0.08 −1.04 0.30
TT vs. CC 5196 / 4936 1.34 1.13–1.58 (< 0.001) 42.7 0.01 −1.48 0.13 −1.15 0.40
CT vs. CC 5196 / 4936 1 0.70–1.42 (0.97) 75.1 < 0.001 −2 0.04 −1.42 0.20
 Europe Dominant model 1704 / 2347 1.46 1.15–1.85 (< 0.001) 56.9 0.01 0 1 −0.70 0.49
Recessive model 1704 / 2347 1.35 0.98–1.86 (0.06) 0 0.94 − 1.58 0.11 −1.91 0.08
Allelic model 1704 / 2347 1.34 1.12–1.61 (< 0.001) 51 0.02 −1.03 0.30 −1.50 0.16
TT vs. CC 1704 / 2347 1.53 1.10–2.14 (0.01) 0 0.80 0.16 0.87 −0.87 0.40
CT vs. CC 1704 / 2347 1.44 1.13–1.83 (< 0.001) 55.6 0.01 0.78 0.43 0.33 0.74
 America Dominant model 1991 / 1828 1.22 0.95–1.58 (0.11) 54.5 0.01 −1.33 0.27 −2.05 0.07
Recessive model 1991 / 1828 1.15 0.96–1.39 (0.12) 24.3 0.22 −1.34 0.18 0.99 0.35
Allelic model 1991 / 1828 1.19 0.99–1.44 (0.06) 64.8 < 0.001 − 0.98 0.32 −0.48 0.64
TT vs. CC 1991 / 1828 1.27 0.98–1.64 (0.07) 43.7 0.06 − 1.52 0.12 −1.91 0.09
CT vs. CC 1991 / 1828 1.18 0.94–1.48 (0.15) 39.3 0.09 −1.52 0.12 −1.94 0.08
Ethnicity-2
 East-Asian Dominant model 4246 / 3908 1.43 1.14–1.79 (< 0.001) 26.3 0.14 −1.08 0.28 1.53 0.29
Recessive model 4246 / 3908 1.14 1.03–1.26 (< 0.001) 66.6 < 0.001 −1.02 0.27 −1.51 0.36
Allelic model 4246 / 3908 1.29 1.10–1.52 (< 0.001) 72 < 0.001 −1.79 0. 58 −3.10 0.06
TT vs. CC 4246 / 3908 1.33 1.11–1.59 (< 0.001) 41.8 0.02 −1.27 0.29 −1.39 0.31
CT vs. CC 4246 / 3908 1.24 1.00–1.53 (0.04) 0 0.74 −1.89 0.68 −1.71 0.10
 Non-East-Asian Dominant model 5333 / 5973 1.10 0.90–1.36 (0.35) 77.4 < 0.001 −0.80 0.42 −1.18 0.35
Recessive model 5333 / 5973 1.25 1.08–1.45 (< 0.001) 21.9 0.14 0.59 0.55 0.73 0.47
Allelic model 5333 / 5973 1.15 1–1.32 (0.04) 71.5 < 0.001 −1.05 0.48 −1.82 0.07
TT vs. CC 5333 / 5973 1.34 1.12–1.61 (< 0.001) 24 0.11 −0.37 0.70 −1.04 0.30
CT vs. CC 5333 / 5973 1.03 0.83–1.28 (0.78) 77.9 < 0.001 −1.16 0.24 −1.93 0.06
Ethnicity 3
 Caucasian Dominant model 7360 / 7971 1.30 1.12–1.51 (< 0.001) 49.2 < 0.001 −1.04 0.48 −1.51 0.18
Recessive model 7360 / 7971 1.16 1.06–1.27 (< 0.001) 49.7 < 0.001 −1.31 0.24 −2.77 0.09
Allelic model 7360 / 7971 1.25 1.12–1.39 (< 0.001) 65 < 0.001 1.40 0.17 −1.12 0.38
TT vs. CC 7360 / 7971 1.34 1.16–1.56 (< 0.001) 24.9 0.09 −1.52 0.16 −1.34 0.29
CT vs. CC 7360 / 7971 1.22 1.05–1.42 (< 0.001) 39.6 < 0.001 −1.54 0.12 −1.80 0.08
 Arab Dominant model 316 / 284 0.36 0.07–1.88 (0.22) 91.5 < 0.001 0.68 0.49 −0.17 0.83
Recessive model 316 / 284 1.53 0.27–1.48 (0.09) 87.4 < 0.001 0 1 −1.67 0.19
Allelic model 316 / 284 0.63 0.67–3.68 (0.29) 85.4 < 0.001 0.49 0.62 −0.11 0.92
TT vs. CC 316 / 284 0.93 0.43–1.99 (0.85) 66.6 0.02 0.68 0.49 1.25 0.33
CT vs. CC 316 / 284 0.29 0.05–1.84 (0.19) 92.3 < 0.001 0 1 −0.71 0.55
 African-American Dominant model 1903 / 1626 1.34 1.07–1.67 (0.01) 35.3 0.13 −1.67 0.09 1.97 0.27
Recessive model 1903 / 1626 1.18 0.98–1.43 (0.07) 24.7 0.22 0.63 0.53 1.11 0.30
Allelic model 1903 / 1626 1.25 1.04–1.50 (0.01) 58.9 0.01 −1.46 0.14 −0.81 0.44
TT vs. CC 1903 / 1626 1.37 1.04–1.80 (0.02) 36.2 0.12 −1.67 0.09 −1.44 0.40
CT vs. CC 1903 / 1626 1.30 1.06–1.58 (0.01) 13.9 0.31 −1.67 0.09 −1.46 0.41
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Fig. 3.

Fig. 3

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Pooled odds ratio and 95% confidence interval of individual studies and pooled data for the association between IL-4 C589T polymorphism and asthma risk in different subgroups for; a dominant model [age subgroup], b dominant model [continent]

Subgroup analysis by ethnicity 1 (continent)

In this subgroup we categorized studies by their continent: including Asia (32 articles), Europe (11 articles), America (10 articles), Africa (1 article), and Oceania (1 article). Since there was only one study for each one of the African and Australian population, these studies were excluded from the analysis. The results indicated that presence of IL4 gene -589C/T SNP in Asian population increased susceptibility of asthma across all genotype models except dominant model (OR = 1.15, 95% CI = 0.84–1.56, P = 0. 39, REM) and CT vs. CC model (OR = 1, 95% CI = 0.70–1.42, P = 0. 97, REM). Moreover, in contrast with effect of IL4 gene -589C/T SNP on the risk of asthma in American populations, a significant positive association was detected in European population thorough dominant model (OR = 1.46, 95% CI = 1.15–1.85, P < 0.001, REM), allelic model (OR = 1.34, 95% CI = 1.12–1.61, P < 0.001, REM), TT vs. CC model (OR = 1.53, 95% CI = 1.10–2.14, P = 0.01, FEM), and CT vs.CC model (OR = 1.44, 95% CI = 1.13–1.83, P < 0.001, REM) (Table 3, Fig. 3).

Subgroup analysis by ethnicity 2 (east and non-east Asian)

The subgroup analysis according to East Asian (20 articles) and non-East Asian (35 articles) title revealed the significant association between IL4 gene -589C/T polymorphism and the risk of asthma across in all genotype models of East Asians and three genotype models of non-East Asian including; recessive model (OR = 1.25, 95% CI = 1.08–1.45, P < 0.001, FEM), allelic model (OR = 1.15, 95% CI = 1–1.32, P = 0.04, REM), TT vs. CC model (OR = 1.34, 95% CI = 1.12–1.61, P < 0.001, FEM) (Table 3, Fig. 4).

Fig. 4.

Fig. 4

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Pooled odds ratio and 95% confidence interval of individual studies and pooled data for the association between IL-4 C589T polymorphism and asthma risk in different subgroups for; a dominant model [East and non-East Asian], b dominant model [ethnicity]

Subgroup analysis by ethnicity 3

Finally, subgroup analysis of eligible articles according ethnicity including Caucasians (41 articles), African-Americans (9 articles), and Arabs (5 articles) showed that there was no significant association between IL4 gene -589C/T SNP and asthma risk in Arab population. Also, except recessive model (OR = 1.18, 95% CI = 0.98–1.43, P = 0.07, FEM) other genotype models in African-American population were significant including dominant model (OR = 1.34, 95% CI = 1.07–1.67, P = 0.01, FEM), allelic model (OR = 1.25, 95% CI = 1.04–1.50, P = 0.01, REM), TT vs. CC model (OR = 1.37, 95% CI = 1.04–1.80, P = 0.02, FEM), and CT vs. CC model (OR = 1.30, 95% CI = 1.06–1.58, P = 0.01, FEM). Conversely, all genotype models were significant in Caucasians and presence of IL4 gene -589C/T SNP increase risk of asthma (Table 3, Fig. 4).

Meta-regression analyses

Meta-regression analyses were performed to explore potential sources of heterogeneity among included studies (Table 4). The findings indicated that none of the expected heterogeneity parameter were the source of heterogeneity (Fig. 5).

Table 4.

Meta-regression analyses of potential source of heterogeneity

Heterogeneity Factors Coefficient SE T P-value 95% CI
UL LL
Publication Year Dominant model 0.035 0.041 0.85 0.40 −0.048 1.119
Recessive model 0.140 0.036 3.81 0.07 −0.066 0.213
Allelic model 0.035 0.022 1.58 0.11 −0.009 0.080
TT vs. CC 0.123 0.064 1.91 0.06 −0.006 0.254
CT vs. CC 0.020 0.035 0.58 0.56 −0.050 0.090
continent Dominant model −0.238 0.265 −0.90 0.37 −0.772 0.294
Recessive model 0.022 0.274 0.08 0.93 −0.530 0.574
Allelic model −0.116 0.146 −0.79 0.43 −0.410 0.177
AA vs. CC −0.096 0.435 −0.22 0.82 −0.973 0.780
CA vs. CC −0.265 0.209 −1.27 0.21 −0.685 0.154
Genotyping methods Dominant model −0.137 0.241 −0.57 0.57 −0.621 0.346
Recessive model 0.382 0.232 1.65 0.10 −0.084 0.849
Allelic model 0.039 0.130 0.30 0.76 −0.221 0.300
TT vs. CC 0.056 0.388 0.14 0.88 −0.726 0.838
CT vs. CC −0.114 0.199 −0.57 0.57 −0.515 0.287
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Fig. 5.

Fig. 5

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Meta-regression plots of the association between IL-4 C589T polymorphism and risk of asthma based on; a Continent (dominant), b Genotyping methods (recessive), c Publication year (Allelic)

Publication bias

To check existence of publication, Egger’s linear regression and Begg’s funnel plot test were used. The shape of funnel plot did not disclose obvious asymmetry under all genotype model of the IL4 gene -589C/T polymorphism (Fig. 6).

Fig. 6.

Fig. 6

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Begg’s funnel plot for publication bias test. Dominant model C598T. Each point represents a separate study for the indicated association

Sensitivity analysis

The impact of individual study on pooled OR was evaluated by sequential omission of each studies. The result showed that no individual study significantly affected the pooled ORs under all genotype models of the IL4 gene -589C/T polymorphism (Fig. 7).

Fig. 7.

Fig. 7

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Sensitivity analysis in present meta-analysis investigates the single nucleotide polymorphisms of IL-4 C589T contribute to risk for asthma

Discussion

To date, several individual case-control replication studies have attempted to divulge the association of IL4 gene -589C/T polymorphism and risk of asthma. Due to some differences, however, these disperse investigation demonstrated incongruous reports. The differences in the race of study subjects, diversity in the diagnostic criteria of the patients, limited sample sizes may be the cause of such inconsistent results [74]. On the other hand, meta-analysis is a tool that has the potential to solve the problem of inconsistency by removing the confining issues of insufficient statistical power in the individual studies. Therefore, to resolve the mentioned confining factors about the IL4 gene -589C/T polymorphism, the present most up-to-date meta-analysis was conducted to determine a bona fide estimation of the association between IL4 gene -589C/T polymorphism and susceptibility to asthma. Our analysis indicated that this SNP was associated with increased risk of asthma in the overall population as well as during subgroup analysis by age groups and ethnicity/continent.

Asthma is a complicated pulmonary disease, characterized by airway hyperresponsiveness, airway inflammation, and airway remodeling [75, 76]. During asthma, there is a hyperactivity of Th2 responses, in which the cytokines of the type 2 immunity, such as IL-4, IL-5, and IL-13 promote the harmful inflammatory events in the airways. Studies have reported that local administration of IL-4 gene plasmids prior to antigen challenge could stimulate the airway hyperresponsiveness and accumulation of eosinophils in mice [77]. This phenotype of asthma is commonly referred to “eosinophilic” asthma. On the other side, “noneosinophilic” asthma is characterized by low frequency of eosinophils in the involved sites, but other inflammatory cells are dominant in the effector phase, such as neutrophils, mixed granulocyte inflammatory cells, or even little number of inflammatory cells, called paucigranulocytic inflammation. Th17 mediated IL-17 axis and lack of significant Th2/Th17 inflammation have been attributed to the noneosinophilic asthma [78]. Among the SNPs in the IL4 gene, the -589C/T (rs2243250) polymorphism has been widely investigated in susceptibility to asthma. It has been shown that the T allele of this SNP leads to increased affinity of the binding of transcription factors in comparison to the C allele, leading to overexpression of IL4 mRNA [79, 80]. As a consequence, it is a biological justification that IL4 gene −589C/T SNP impresses the IL-4 expression and, hence, could affect the asthma susceptibility.

Previously, three meta-analysis studies have attempted to disclose the association of IL4 gene −589C/T SNP with the risk of asthma. Wang et al. in 2012 indicated that the T allele of IL4 gene −589C/T SNP increased the risk of asthma (OR = 1.12). Basically, individuals carrying the T allele had a 24% increased risk of asthma in comparison to the CC homozygote model. Subgroup analysis revealed the association of this polymorphism in the Caucasians [81]. In addition, Nie et al. in 2013 included 40 studies involving 7345 cases and 7819 controls in their meta-analysis [18]. This meta-analysis indicated that TT vs. CC (OR = 1.40) and CT vs. CC (OR = 1.22) models were significantly associated with increased risk of asthma. In the subgroup analysis by ethnicity, significant associations were found among Asians and Caucasians, but not in the African-Americans. In addition, the subgroup analysis by atopic status revealed no significant association among atopic asthma patients and non-atopic asthma patients. On the other side, Zhang et al. [75] by evaluating pediatric asthma risk by evolving 17 case-control studies (15 publications) containing 3427 cases and 4247 controls revealed that IL4 -589C/T polymorphism was associated with increased risk of asthma in pediatrics. Furthermore, the subgroup analyses by ethnicity, indicated significant association in Caucasians and Asians.

Our analysis was performed on 55 case-control studies containing 9572 cases and 9881 controls. It was observed that IL4 gene -589C/T polymorphism increased the risk of asthma across all genetic models, including dominant model (OR = 1.22), recessive model (OR = 1.17), allelic model (OR = 1.21), and TT vs. CC model (OR = 1.34), but not the CT vs. TT model. Furthermore, subgroup analysis by age indicated that IL4 gene -589C/T polymorphism was significantly associated with asthma risk in both pediatrics and adults. The subgroup analysis by ethnicity revealed significant association in Asian, American, and Europeans. Finally, subgroup analysis by East Asian and non-East Asian populations indicated significant associations.

This meta-analysis bears some limitations and caveats. First, the analysis was according to crude estimation of IL4 gene -589C/T polymorphism association with asthma susceptibility, regardless of the effect of confounding factors, like age, sex, environmental factors, and contribution of other genes in LD with IL4 gene. Second, we did not analyze other genes that could be contributing in understanding of cytokine involvement in the susceptibility to asthma.

Conclusion

All in all, here we carried out the most up-to-date analysis of the IL4 gene 589C/T polymorphism and asthma risk prior to September 2020. Our meta-analysis further confirmed some results of the previously performed meta-analysis, while rejected some of them. In a whole, IL4 gene -589C/T polymorphism increased the risk of asthma across all genetic models. Moreover, the subgroup analysis by age indicated that IL4 gene -589C/T polymorphism was significantly associated with asthma risk in both pediatrics and adults. Also, the subgroup analysis by ethnicity revealed significant association in Asian, American, and Europeans. Ultimately, subgroup analysis by East Asian and non-East Asian populations indicated significant associations.

Acknowledgements

Not applicable.

Abbreviations

IL

Interleukin

Th

T helper

CI

Confidence interval

OR

Odds ratio

SNP

Single-nucleotide polymorphism

PRISMA

Preferred Reporting Items for Systematic reviews and Meta-Analyses

NOS

Newcastle–Ottawa scale

HWE

Hardy–Weinberg equilibrium

Authors’ contributions

BR and DI originated the study, acquired data. AK, AMG, and AMF analyzed and interpreted the data. MH, MA, and DI prepared the original draft. BR, DI, and MJM critically revised the paper. SA and HM supervised the project. All authors read and approved the final manuscript.

Funding

Not applicable.

Availability of data and materials

All data that support the conclusions of this manuscript are included within the article.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Mojgan Hosseini, Email: mojgan-hosseini@iiau.ac.ir.

Haleh Mikaeili, Email: mikaeilihale@gmail.com.

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