Imani et al. BMC Medical Genetics(2020) 21:232
https://doi.org/10.1186/s12881-020-01169-w

RESEARCH ARTICLE

Open AccessInterleukin-4 gene polymorphism (C33T)
and the risk of the asthma: a meta-analysis
based on 24 publications
Danyal Imani1, Mohammad Masoud Eslami2, Gholamreza Anani-Sarab3, Mansur Aliyu4, Bahman Razi2 andRamazan Rezaei5*

Abstract
Background: Previous studies evaluated the association of IL-4 C33T polymorphism and risk of bronchial asthma
but failed to establish a consistent conclusive association. In the present meta-analysis, we intend to define a morereliable estimate of the association in the presence of filling published literature.
Methods: An exhaustive search in Web of Science, Scopus, and PubMed databases was performed to identify all
relevant publications before September 2020, and 24 publications (28 studies) with 6587 cases and 8408 controls
were included in final analysis. The association between polymorphism and risk of asthma were measured by Odd
ratios (ORs) and 95% confidence intervals (CIs). Moreover, Cochran’s Q and the I2 statistics were used to evaluate
the degree of heterogeneity between studies.
Results: In the overall study populations, a significant positive association was detected under all genotype models
and announced the IL-4 C33T polymorphism as a potential risk factor in the pathogenesis of asthma. In thesubgroup analysis by age, a significant association between IL-4 C33T polymorphism and risk of asthma in different
age groups was identified in allelic model, which highlighted the predisposing role of the T allele for the asthma
risk in all three age groups. Furthermore, the results of subgroup analysis by continent were heterogenous.
Accordingly, IL-4 C33T polymorphism was a risk factor in Europeans (all models except heterozygote comparison),
Americans (all models except recessive and homozygote comparison) and Asians (just recessive and allelic model).
Finally, the ethnicity-specific analysis disclosed a significant association between IL-4 C33T polymorphism andasthma risk in Caucasians (all genotype models except heterozygote comparison), while this association was not
significant in African-Americans.
Conclusions: This study suggests that IL-4 C33T polymorphism potentially acts as a risk factor for asthma in
different ethnicities and age groups.
Keywords: IL-4, Polymorphism, Interleukin- 4, Meta –analysis, Asthma* Correspondence: ramin.rezaei25@gmail.com
5
Department of Immunology, School of Medicine, Shahid Beheshti Universityof Medical Sciences, Tehran, Iran
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Imani et al. BMC Medical Genetics(2020) 21:232

Background
Asthma is a chronic, complex respiratory disorder inwhich allergen-triggered inflammatory reactions in the
airways contribute to the development of symptoms,
including breathlessness, cough, wheezing, and dyspnea.
It has been estimated that asthma affect about 300
million people in the world [1]. Prognostic markers todetect high-risk individuals are urgently required forearly identification and preventive attention. In thescientific community, genetic vulnerability to asthma is
one of the main research interests [2]. In the recent
decade, many studies have been focused to elucidate thesusceptibility genes of asthma and several single nucleotide polymorphisms (SNPs) in these genes have been described to be related with asthma risk in different
populations [3, 4]. Among different genes, interleukin 4(IL-4) gene has been comprehensively investigated [5, 6].
IL-4 plays a major function in isotype class switching of
B cells to IgE production, type 2 immune responses, and
it is involved in recruitment of mast cell [7, 8]. It has
thus been proposed that IL-4 may have an imperativerole in the development and persistent of asthma [7]. IL4 gene is located on long arm of chromosome 5 (5q31),
a region that has been associated with asthma or relateddisorders such as bronchial hyper responsiveness (BHR)
and atopy [9]. The IL-4 C33T single nucleotide polymorphism (rs2070874) which is located on the untranslated region (UTR) has been represented to be linked
with elevated serum IgE levels and risk of asthma[10–12]. There are several studies in which association between IL-4 C33T polymorphism and asthma
risk have been evaluated [5, 6, 10–31]. Nevertheless,this association remains inconsistent and inconclusive
in several studies. Probably, this could be because of
the small samples size examined in these studies andthe small effect size of the polymorphism that failed
to provide sufficient statistical power to identify staticallysignificant associations. Accordingly, we conducted ameta-analysis to conclude a more exact estimation of therelation between the IL-4 C33T polymorphism and risk ofasthma.

Page 2 of 15

risk released before September 2020. We searched(“asthma” [Mesh] OR “asthmatic”) AND (“interleukin-4”
OR “IL-4” OR “interleukin 4”) AND (“single nucleotide
polymorphism” OR “SNP” OR “polymorphisms” OR“mutation” OR “variation”), as main key words. Besides,
cross reference check of review studies were screened for
additional relevant papers.
Inclusion and exclusion criteria

Initial search strategy yield 1873 studies that exported to
Endnote X8. The duplicate studies which were common
among databases were removed and title and abstract ofremain studies were reviewed by two investigators. In
cases that we could not categorize retrieved studies bytitle and abstract, full-text verification was performed.
Eventually, studies considered eligible if met the following criteria: 1) publications that evaluate the association
between IL-4 C33T polymorphism and the risk ofasthma; 2) publications with extractable data to estimate
odds ratios (ORs) and 95% confidence intervals; 3) publications that report genotype or allele distributions ofcase and controls. Duplicate articles, review articles,
editorials, case reports, book chapters, republished data,
comments, and studies with insufficient data after
contacting with authors were all excluded. The usage ofthese criteria results in 24 eligible paper for the metaanalysis.
Data extraction and quality assessment

Two researchers independently and according to a
standard checklist extracted requisite data including: thefirst author family, country of origin, ethnicity, numberof subjects in the case and the control groups for eachgender, mean or range of age, applied genotyping
method, distribution of alleles in cases and controls,
journal and year of publication. The methodologicalquality of included study was scored using the
Newcastle-Ottawa Scale (NOS) criteria [33]. Accordingly, publications with scores 0–3, 4–6 or 7–9 were
low, moderate or high-quality, respectively.
Statistical analysis

Methods
We carried out this meta-analysis by following the Preferred
Reporting Items for Systematic reviews and Meta-Analyses(PRISMA) statement [32]. Since our study did not containany experimental procedure on humans and animals, thus
no ethics committee confirmation was applicable.
Search strategy

A comprehensive systematic search was applied throughthree major databases (MEDLINE, web of science, andScopus) to find all potential publications considering theassociation between IL-4 C33T polymorphism and asthma

All data analysis was accomplished using SPSS (version
23; Chicago, IL, USA) and Stata (version 14; Stata Corporation, College Station, TX) softwares. The strength ofassociation between SNP and the risk of asthma wasmeasured via Odd ratios (ORs) and 95% confidence
intervals (CIs). Moreover, the degree of heterogeneity
between studies was assessed by the Q test (Q-statistic P
value> 0.10, no heterogeneity vs. Q-statistic P value<
0.10, significant heterogeneity) and the I2 test (I2 < 25%,no heterogeneity; I2 = 25–50%, moderate heterogeneity;
I2 = 50–75%, large heterogeneity, I2 > 75%, extreme
heterogeneity) [34]. In the presence of heterogeneity

Imani et al. BMC Medical Genetics(2020) 21:232

random effect model (REM) should be used. Otherwise,
fixed effect model (FEM) should be applied [35]. To
estimate whether the results were substantially changedby any individual study, sensitivity analysis by sequential
omitting of each included study was performed. Furthermore, publication bias was investigated, using Begg’s andEgger’s tests along with visual examination of the funnel plot (p value< 0.05 considered statistically significant) [36].

Results
Study characteristics

A total of 1873 articles were identified through the systematic literature search of databases. After excluding 219
duplicate studies and removing 1496 irrelevant publications based on titles/abstracts, 158 studies went underfull-text screening. Of which, 134 paper were excluded.
Finally, twenty-four studies qualified for quantitative analysis Fig. 1. All included studies were performed between
2000 to 2016 and had good methodological score ranging
5 to 8. Case-control design was common between eligible

Fig. 1 Flow diagram of study selection process

Page 3 of 15

studies and different genotyping method were used byincluded studies. Tables 1 and 2 summarized the characteristics and allele distribution, genotype frequency of theeligible studies.

Meta-analysis of IL-4 C33T polymorphism and the risk ofasthmaTwenty-four studies with 6587 cases and 8408 healthy
controls were included in final meta-analysis of overall
population. Of them, 15 publications were carried out in
Asian countries, 5 publications were in American countries and 4 publications were in Europe. The pooled OR
indicated that IL-4 C33T polymorphism increase risk ofasthma across all genotype models including dominantmodel (OR = 1.15, 95% CI = 1.04–1.26, P = ≤0.001, FEM),
recessive model (OR = 1.16, 95% CI = 1.06–1.28, P =≤0.001, FEM), allelic model (OR = 1.14, 95% CI = 1.07–
1.21, P = ≤0.001, FEM), CC vs. TT model (OR = 1.21, 95%
CI = 1.02–1.43, P = 0.02, FEM) and CT vs. TT model(OR = 1.10, 95% CI = 1–1.22, P = 0.05, FEM) Fig.2. The

Year

2000

2003

2004

2004

2004

2004

2005

2005

2005

2007

2009

2009

2009

2009

2009

2010

2010

2010

2010

2010

Study author

Suzuki et al.

Beghe et al.

Basehore et al. (i)Basehore et al.(ii)Basehore et al.(iii)Park et al.

Donfack et al. (i)Donfack et al.(ii)

Garcia et al.

Battle et al.

Amirzargar et al.

Jiang et al.

Daley et al.

Haller et al.

Wang et al.

Berce et al.

Undarmaa et al. (i)Undarmaa et al.(ii)Wu et al.

Michel et al.

Germany

European

Asian

Asian

Asian

European

Asian

AmericanAustralian

Asian

Asian

AmericanEuropean

AmericanAmericanAsian

American(USA Hispanic)

AmericanAmerican(USA white)

EuropeanAsiancontinent

Caucasian

Caucasian

Caucasian

Caucasian

Caucasian

Caucasian

American- African

Caucasian

Caucasian

Caucasian

American- African

Caucasian

American- African

Caucasian

Caucasian

Caucasian(Hispanic)

American- African

Caucasian

Caucasian

Caucasian

Ethnicity

M = NRM = 138/114
F = 118/109

M = NRF=NR

M = NRF=NR

M = NR
F=NR

M = 299/147
F = 245/265

M = NRF=NR

M = NRF = NRM = NRF = NRM = NR
F=NR

M = 105/156
F = 67/109

M = NRF=NR

M = NRF=NR

M = NR
F=NR

M = 248/302
F = 85/86

M = 54/62
F = 41/89

M = 77/91
F = 121/148

M = 93/140
F = 98/147

M = 88/99
F = 93/89

M = 50/60
F = 100/150

Sex
cases/controls

703 / 658

252 / 227

367 / 676

324 / 336

106 / 89446 / 510

72 / 70643 / 751

24 / 24

59 / 139

261 / 176

133 / 79

205 / 183

126 / 205

532 / 170

116 / 130

168 / 269

233 / 245

187 / 182

120 / 120

Total cases/control

ChildrenChildren

Adult

ChildrenChildrenChildren

Adult

Mixed

Adult

Mixed

Mixed

Mixed

Mixed

Mixed

Mixed

Adult

Adult

Adult

Adult

Adult

Age case/control

Illumina Sentrix

PCR-RFLP

TaqMan-ASA

TaqMan-ASA

PCR-RFLP

TaqManPCR-RFLP

Illumina Beadarray system

PCR-RFLP

PCR-SSPPCR-RFLP

TaqManLAS

LASSnaP shot

PCR

PCR

PCR

PCR-RFLP

PCR-RFLP

Genotyping
method

8

7

8

7

6

7

6

8

5

5

6

6

6

6

8

6

6

7

7

6

Qualityscore(2020) 21:232

China

Japan

Japan

Slovenia

TaiwanUSA

Australia

China

Iran

USA

Spain

USA

USA

Korea

USA

USA

USA

UK

Japan

Country

Table 1 Characteristics of studies included in meta-analysis of overall asthma

Imani et al. BMC Medical Genetics
Page 4 of 15

2011

2011

2011

2013

2013

2013

2015

2016

Huang et al.

Yang et al.

Chen et al.

Micheal et al.

Miyake et al.

Davoodi et al.

Wang et al.

Li et al.

NR not reported, M male, F female

Year

Study author

China

China

India

Japan

Pakistan

China

China

China

Country

Asian

Asian

Asian

Asian

Asian

Asian

Asian

Asian

continent

Caucasian

Caucasian

Caucasian

Caucasian

Caucasian

Caucasian

Caucasian

Caucasian

Ethnicity

M = 134/183
F = 151/200

M = NR
F=NR

M = 45/55
F = 21/29

M = 0/89
F = 0/1281

M = NRF=NR

M = NR
F=NR

M = 101/101
F = 155/50

M = 51/49
F = 70/52

F=NR

Sex
cases/controls

Table 1 Characteristics of studies included in meta-analysis of overall asthma (Continued)

317 / 351

392 / 849

100 / 5089 / 1281

108 / 120202 / 191

202 / 205

100 / 122

Total cases/control

ChildrenChildren

Adult

Adult

Mixed

Children

Adult

ChildrenAge case/control

PCR-RFLP

Mass Array

Mass Array

TaqManPCR-RFLP

MALDI-TOF

MALDI-TOF

PCR-RFLP

Bead chip

Genotyping
method

7

8

5

6

6

7

6

6

Qualityscore

Imani et al. BMC Medical Genetics(2020) 21:232
Page 5 of 15

48

19

83

68

Basehore et al.(iii)Park et al.

Donfack et al. (i)Donfack et al.(ii)77Micheal et al.

170

176

31

33

31

72

56

23

210

83

154

142

31

147

36

150

9

56

128

39

107

37

164

53

83

72

41

56

0

167

4

44

0

124

132

76

35

163

185

155

8

277

15

12

15

1

48

1

30

6

349

15

34

8

6

53

464

274

161

57

185

84

84

25

1126

95

210

196

165

191

78

1112

9

60

298

225

243

203

202

149

185

378

321

78

170

510

39

121

31

320

320

175

280

409

524

452

47

701

66

174

39

58

224

41

167

49

862

83

151

88

53

162

T

170

102

36

160

93

6

7

3

474

11

64

37

51

16

27

555

2

61

57

64

70

150

7

60

87

185

132

10

181

410

14

604

27

62

67

49

173

87

286

144

35

186

33

181

10

78

87

15

86

50

57

57

132

56

48

59

CT

0

337

0

517

0

123

131

70

11

129

326

155

3

308

10

15

12

0

32

0

27

5

106

13

50

4

2

51

TT

521

614

86

924

213

74

81

55

1121

109

414

218

137

218

87

1291

14

200

201

143

226

350

71

177

306

426

312

79

C

181

1084

14

1638

27

308

329

189

195

345

938

454

41

802

53

211

34

78

151

15

140

60

269

83

232

64

52

161

T

< 0.001

0/18

0/24

0/42

0/16

0/58

0/65

0/09

0/28

0/44

0/91

0/68

0/3

0/05

0/98

0/95

0/96

< 0.001

0/9

0/35

0/94

0/73

0/84

0/92

0/99

0/91

0/29

0/21

P-HWE

0/257

0/638

0/14

0/639

0/112

0/806

0/802

0/774

0/148

0/759

0/693

0/675

0/23

0/786

0/378

0/14

0/708

0/28

0/428

0/094

0/382

0/146

0/791

0/319

0/431

0/13

0/142

0/67

MAF

(2020) 21:232

P-HWE, p-value for Hardy–Weinberg equilibrium; MAF, minor allele frequency of control group49

6

Chen et al.

147

14

Yang et al.

Li et al.

1

Huang et al.

Wang et al.

458

Michel et al.

126

Wu et al.

65

28

Undarmaa et al.(ii)

Davoodi et al.

27

Undarmaa et al. (i)Miyake et al.

22

Haller et al.

67

21

Daley et al.

Berce et al.

481

Jiang et al.

Wang et al.

20

Amirzargar et al.

93

51

Basehore et al.(ii)

85

153

Basehore et al. (i)Battle et al.

140

Garcia et al.

11

Beghe et al.

C

Healthy control
TT

CC

CT

Asthma cases

CC

Suzuki et al.

Study author

Table 2 Distribution of genotype and allele among asthma patients and controls

Imani et al. BMC Medical Genetics
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Imani et al. BMC Medical Genetics(2020) 21:232

detailed findings for different analysis models are shownin Table 3.
Subgroup analysis

We categorized studies into different subgroups on thebasis of age, continent and ethnicity. The results of pooled
ORs, heterogeneity tests and publication bias tests fordifferent analysis models are reported in Table 3.
Subgroup analysis by age

In this group, we stratified included publications into
three groups including: adult (8 articles), children (7 articles) and mixed (cover both ranges; 9 articles). Overall,
the results rejected significant association between IL-4
C33T polymorphism and risk of asthma in different age
group except for allelic model [adults (OR = 1.14, 95%
CI = 1.02–1.26, P = 0.02, FEM), mixed (OR = 1.14, 95%
CI = 1.01–1.29, P = 0.03, REM), children (OR = 1.13, 95%
CI = 1.04–1.24, P = ≤0.001, FEM)] and recessive model(just in children (OR = 1.18, 95% CI = 1.03–1.35, P =0.01, REM)) Fig. 3.
Subgroup analysis by continent

Our included studies performed in Asia (15 articles),Europe (4 articles), America (4 articles), and Oceania (1article). Since there was only one study for Oceania, we
exclude it. The final findings indicated strong significant
association between IL-4 C33T polymorphism and asthma
risk in European population across dominant model(OR = 1.23, 95% CI = 1.01–1.50, P = 0.03, FEM), recessivemodel (OR = 2.94, 95% CI = 1.54–5.62, P = ≤0.001, FEM),
allelic model (OR = 1.30, 95% CI = 1.10–1.54, P = ≤0.001,
FEM) and CC vs. TT (OR = 3, 95% CI = 1.56–5.76, P =≤0.001, FEM). Moreover, there was a significant association between IL-4 C33T polymorphism and risk ofasthma in American population under dominant model(OR = 1.26, 95% CI = 1.05–1.51, P = ≤0.001, FEM), allelicmodel (OR = 1.17, 95% CI = 1.03–1.33, P = 0.01, FEM),
and CT vs. TT model (OR = 1.23, 95% CI = 1.02–1.49, P =0.03, FEM). Eventually, Significant positive association was
revealed in Asians just in recessive model (OR = 1.14, 95%
CI = 1.02–1.26, P = 0.01, FEM), and allelic model (OR =1.12, 95% CI = 1.03–1.21, P = ≤0.001, FEM) Fig. 3.
Subgroup analysis by ethnicity

Finally, we stratified eligible articles according ethnicity including Caucasians (20 articles), and African-Americans(4 articles). The results showed significant association between IL-4 SNP (C33T) and asthma risk in Caucasians
under dominant model (OR = 1.15, 95% CI = 1.04–1.28,
P = 0.008, FEM), recessive model (OR = 1.17, 95% CI =1.06–1.30, P = 0.002, FEM), allelic model (OR = 1.14, 95%
CI = 1.07–1.22, P = ≤0.001, FEM),and CC vs. TT model(OR = 1.23, 95% CI = 1.01–1.49, P = 0.03, FEM) but not

Page 7 of 15

CT vs. TT model (OR = 1.1, 95% CI = 0.98–1.22, P = 0.09,
FEM). However, there was no significant association between IL-4 C33T polymorphism and risk of asthma in
American-African population across all genotype models
Fig. 3.
Evaluation of heterogeneity

No significant heterogeneity was detected for IL-4 C33T
polymorphism neither in overall population nor subgroup
analysis, therefore we did not perform mete-regression
analysis for possible parameters (Table 3).
Sensitivity analysis and publication bias

Begg’s and Egger’s tests were performed to estimate thepublication biases of studies. As showed in Table 3 noevidence of publication bias was detected in overall populations and subgroup analysis. Also, symmetric shapeof Begg’s funnel plot confirm this finding Fig. 4. Moreover, the impact of individual study on pooled OR was
evaluated by sensitivity analysis, which confirmed stability of our results Fig. 5.

Discussion
The cytokine IL-4 act as a key player in the development
and pathogenesis of allergic inflammation [37] and atopy[38] through the induction of the heavy chain isotype
switching, secretion of IgE antibody (IgE synthesis) by B
cells, functioning as a growth factor for Th2 cells [37].
The IL-4 promotes IgE-dependent immune responses as
it induces overexpression of IgE receptors on the surfaceof various immune cells: FcεRI on basophils and mast
cells; and FcεRII (CD23) on mononuclear phagocytic
cells and B lymphocytes [39]. The IL-4 tilts the immune
response to anti-inflammatory, inhibiting macrophages
pro-inflammatory effect and downregulating secretion of
pro-inflammatory cytokines [40]. The IL-4 critically,
initiate immediate allergic responses by triggering IgEmediated mast cell activation [41]. The IL-4 plays a pivotal
role in the priming of naïve T cell towards Th2 differentiation as well as exacerbate allergic inflammation throughinduction of vascular adhesion molecule 1 (VCAM-1) that
recruit leukocytes and promote their survival [39]. The IL4 induce airway remodeling encountered in asthma by itsrole in the proliferation of bronchial fibroblasts, myofibroblasts, and airway smooth muscles [38].
At the turn of the millennium genetic polymorphismsof the IL-4 gene in the development and maintenance ofasthma have drawn increasing consideration. Modulation of the immune system is the common denominatorin IL-4 polymorphisms [40]. Suzuki and coworkers
found a single nucleotide polymorphism of C replacement of T at position 33 bp of exon 1 (C33T) of the IL-4
proximal promoter region [42]. Asthmatic patients with
C33T have higher serum level of IL-4 and IgE [43].

Imani et al. BMC Medical Genetics(2020) 21:232

Page 8 of 15

Fig. 2 Pooled odds ratio (OR) and 95% confidence interval of individual studies and pooled data for the association between IL-4 C33T gene
polymorphism and asthma risk in overall populations for a dominant model, b recessive model

6587 / 8404

CC vs. TT

CT vs. TT

CT vs. TT

2842 / 3333

2842 / 3333

2842 / 3333

2842 / 3333

Recessive model

Allelic modelCC vs. TT

CT vs. TT

2842 / 3333

2067 / 1823

CC vs. TT

Dominant model

2067 / 1823

2067 / 1823

Allelic model

2067 / 1823

2067 / 1823

CC vs. TT

CT vs. TT

Dominant model

1678 / 3252

1678 / 3252

Allelic model

Recessive model

1678 / 3252

1678 / 3252

Recessive model

1678 / 3252

6587 / 8404

Allelic model

Dominant model

6587 / 8404

6587 / 8404

Recessive model

6587 / 8404

EuropeAsia

Case/Control

Sample size

Dominant model

Genetic model

1129 / 1008

1129 / 1008

1129 / 1008

CC vs. TT

CT vs. TT

1129 / 1008

1.13

3

1.30

2.94

1.23

1.02

1.08

1.12

1.14

1.10

1.08

1.27

1.13

1.18

1.15

1.13

1.10

1.14

1.08

1.14

1.09

1.22

1.14

1.17

1.16

1.10

1.21

1.14

1.16

1.15

OR

0.92–1.38 (0.24)

1.56–5.76 (≤0.001)

1.10–1.54 (≤0.001)

1.54–5.62 (≤0.001)

1.01–1.50 (0.03)

0.89–1.18 (0.76)

0.87–1.33 (0.49)

1.03–1.21 (≤0.001)

1.02–1.26 (0.01)

0.93–1.130 (0.25)

0.92–1.26 (0.33)

0.97–1.65 (0.08)

1.04–1.24 (≤0.001)

1.03–1.35 (0.01)

0.99–1.34 (0.07)

0.95–1.35 (0.15)

0.77–1.57 (0.60)

1.01–1.29 (0.03)

0.84–1.40 (0.53)

0.96–1.34 (0.13)

0.90–1.32 (0.91)

0.93–1.61 (0.15)

1.02–1.26 (0.02)

0.99–1.39 (0.06)

0.97–1.40 (0.10)

1–1.22 (0.05)

1.02–1.43 (0.02)

1.07–1.21 (≤0.001)

1.06–1.28 ((≤0.001)

1.04–1.26 (≤0.001)

95% CI (p-value)

Test of association

52.8

0

31.2

0

48.3

29.6

0

34.9

0

29.3

4.5

42.3

44.6

54

12.4

53.7

0

56.9

0

55.8

0.2

0

0

0

0

20.5

0

27.8

0

20.9

0.09

0.87

0.22

0.95

0.12

0.12

0.77

0.08

0.56

0.13

0.39

0.09

0.07

0.03

0.33

0.03

0.88

0.02

0.89

0.02

0.43

0.72

0.75

0.93

0.54

0.17

0.58

0.08

0.56

0.16

P

Test of heterogeneity
I2 (%)

1
0.60
0.49

−0.68

0.60

−0.52
−0.52

0.49

−0.68
0

0.65

1

0.02

0.39

0.45

0

2.31

0.85

0.41

0.67

0.81

0.32
−0.42

0.67

0.99

0.42

0.21

0.67

1.24

0.01

−0.42

0.14

2.47

1.47

0.007

0.62

2.72

0.02

−0.49

0.24

2.23

0.40

−1.16

0.31

0.01

0.32

0.81

0.07

0.01

0.001

0.44

0.83

1.01

2.41

−0.98

0.23

1.78

2.53

2.68

0.77

P

Test of publication bias (Begg’s test)
z

P

−0.59

0.61

0.44

0.56

−0.68
−1.20

0.55

0.58

0.36

0.93

0.01

0.24

0.17

0.54

0.36

0.65

0.05

0.64

0.01

0.13

0.02

0.67

0.01

0.12

0.54

0.18

0.007

0.19

0.58

0.13

0.02

0.001

0.37

−0.85

−0.64

0.93

0.09

2.70

1.24

1.44

−0.64

0.97

0.47

2.34

−0.48

3.38

2.01

3.11

0.47

3.47

−1.72

0.64

1.44

3.57

−1.43

0.56

1.57

2.39

2.88

0.90

t

Test of publication bias (Egger’s test)(2020) 21:232

Allelic model

1129 / 1008

CC vs. TT

CT vs. TT

Recessive model

3634 / 5371

3634 / 5371

Allelic model

Dominant model

3634 / 5371

3634 / 5371

Recessive model

3634 / 5371

Dominant modelContinent subgroups

Children

Mixed

AdultsAge subgroups

Overall

Subgroup

Table 3 Main results of pooled ORs in meta-analysis of IL-4 (C33T) gene polymorphisms

Imani et al. BMC Medical Genetics
Page 9 of 15

1181 / 1278

1181 / 1278

1181 / 1278

1181 / 1278

Recessive model

Allelic modelCC vs. TT

CT vs. TT

1181 / 1278

African-American

CaucasianCase/Control

Sample size

Dominant model

Genetic model

706 / 698

706 / 698

706 / 698

Allelic modelCC vs. TT

CT vs. TT

706 / 698

CT vs. TT

706 / 698

5881 / 7710

CC vs. TT

Dominant model

5881 / 7710

Allelic model

Recessive model

5881 / 7710

5881 / 7710

Recessive model

5881 / 7710

Dominant model

Ethnicity subgroups

America

Subgroup

1.13

1.16

1.08

1.08

1.14

1.1

1.23

1.14

1.17

1.15

1.23

1.26

1.17

1.16

1.26

OR

0.87–1.45 (0.35)

0.82–1.62 (0.4)

0.93–1.27 (0.32)

0.80–1.46 (0.6)

0.89–1.45 (0.29)

0.98–1.22 (0.09)

1.01–1.49 (0.03)

1.07–1.22 (≤0.001)

1.06–1.30 (0.002)

1.04–1.28 (0.008)

1.02–1.49 (0.03)

0.93–1.71 (0.14)

1.03–1.33 (0.01)

0.88–1.52 (0.29)

1.05–1.51(≤0.001)

95% CI (p-value)

Test of association

0

0

0

0

0

30.6

4.6

35.7

6.7

30.7

0

0

0

0

0

I2 (%)

0.8

0.79

0.74

0.87

0.76

0.08

0.4

0.04

0.3

0.08

0.85

0.84

0.55

0.89

0.76

P

Test of heterogeneity

Table 3 Main results of pooled ORs in meta-analysis of IL-4 (C33T) gene polymorphisms (Continued)

0.45

2.25

1.65

2.55

0.65

0.02

0.09

0.01

0.65

0.49

0.45

0.25

−0.68

0.06

0.04

0.43

0.65

0.02

0.09

0.01

0.65

1.14

1.87

1.98

0.78

0.45

2.25

1.65

2.55

0.45

P

Test of publication bias (Begg’s test)
z

0.18

3.17

2.71

6.60

0.54

−0.59

0.72

1.79

2.44

0.69

0.18

3.17

2.71

6.60

0.54

t

0.86

0.05

0.04

0.06

0.61

0.61

0.48

0.08

0.02

0.50

0.86

0.05

0.04

0.06

0.61

P

Test of publication bias (Egger’s test)

Imani et al. BMC Medical Genetics(2020) 21:232
Page 10 of 15

Imani et al. BMC Medical Genetics(2020) 21:232

Page 11 of 15

Fig. 3 Pooled odds ratio (OR) and 95% confidence interval of individual studies and pooled data for the association between IL-4 C33T gene
polymorphism and asthma risk in different subgroup analysis: a allelic model, b recessive model, c dominant model

Anovazzi and colleagues studied IL-4 haplotypes and reported that the studied haplotypes induce an opposing
immune response, as well they recorded minimal functional activity in polymorphisms involving the promoter
region [40]. An increasing body of evidence has demonstrated that C33T of the IL-4 gene untranslated region(UTR) of chromosome 5q was associated with elevatedserum IgE levels and the risk of asthma [44, 45]. However, this association remains inconclusive. If this isindeed the case, a meta-analysis with big sample size,
sufficient statistical power, and subgroup analysis wasneeded.

Our current meta-analysis composed of 24 publications (28 studies) involving 6587 cases and 8408 controls, we systematically assessed the relationship between
IL-4 C33T polymorphism and asthma susceptibility.
Cumulatively, the result illustrated IL-4 C33T polymorphism as a risk factor in the pathogenesis of asthma.
The result indicated that the presence of T allele acrossdifferent genetic models increased asthma risk by 10 to
21%. In the subgroup analysis by age, the results rejected
the significant association between IL-4 C33T polymorphism and risk of asthma in different age groups except forallelic model, which highlighted the predisposing role ofImani et al. BMC Medical Genetics(2020) 21:232

Page 12 of 15

Fig. 4 Begg’s funnel plot for publication bias test. Dominant model IL-4 C33T. Each point represents a separate study for the indicated association

the T allele for the asthma risk in all three age groups.
Subgroup analysis by continent revealed a significant association between IL-4 C33T polymorphism and asthma riskin the European population. In the Asian population,
there was a significant association between IL-4 C33T
polymorphism and the risk of asthma under recessive andallelic models. In contrast, the American population

showed a significant association under dominant andallelic models.
Additionally, subgroup analysis was conducted according
to ethnicity; the results showed a significant association between IL-4 C33T polymorphism and asthma risk in
Caucasians under all models except CT vs TT model.
While, no significant association between IL-4 C33T

Fig. 5 Sensitivity analysis in present meta-analysis investigates the single nucleotide polymorphisms of IL-4 C33T contribute to risk for asthmaImani et al. BMC Medical Genetics(2020) 21:232

polymorphism and the risk of asthma in the AmericanAfrican population were detected.
It should be noted that our results are not in agreement with those of Liu et al. [46] meta-analysis on therole of IL-4 C33T polymorphism and asthma. Theysuggested a significant association between whites andAsians. While they reported a significant association
between the IL-4 C33T polymorphism and asthma riskin the overall population, they did not find a significant
association among atopic and non-atopic asthma
patients in subgroup analysis. Furthermore, in contrast
to our meta-analysis, in the subgroup analysis by age,they reported an increased risk of asthma among children but not in the adult. Finally, while they reported
evidence of publication bias, we identified no evidenceof publication bias for the overall population and subgroup analysis under all genetic models.
The main reason for these discrepancies raised could
be from the fact that Liu and colleagues included 18
studies with 5523 cases and 5618 controls. However, ourmeta-analysis encompasses 28 studies including 6587
cases and 8408 controls from different ethnicities andcontinents.
The C33T single nucleotide polymorphism is detectedon the 5′ untranslated regions (UTR) of the IL-4 gene[42]. The 5′ UTRs region of mRNA may contain many
gene regulatory elements (GRE) that regulate thelocalization, translation and degradation of transcripts[47]. In the eukaryotic mRNAs, the 5′ UTRs regulate
both cap-dependent and cap-independent translation
initiation of mRNA [48]. Researchers revealed a relationship between IL-4 C33T polymorphism and elevatedserum IgE levels in a group of the Japanese population[49]. While the exact mechanisms by which the IL-4
C33T allele modulates the gene expression of the IL-4
remain elusive, it has been suggested that this variation
may influence the stability of mRNA, as well as transcriptional or translational efficiency of the IL-4 gene,highlighting that the 5′ UTR may involve many cisacting elements [47, 50–52].
Heterogeneity and publication bias, which may affectthe results of meta-analyses, should always be considered.
The result of this study did not show significant heterogeneity. Moreover, there was no significant publication
bias in the overall population and subgroup analysis under
all genetic models. Consequently, heterogeneity and publication bias did not appear to have inclined the results.
Sensitivity analyses were also performed. There was a little
variation of the estimates after exclusion of a single study
and the significance of the pooled ORs was not affected
proposing the consistency of this result.
The current study had some limitations. First, mostincluded articles were from the Asia continent with
Caucasian race and there was no study from Africans;

Page 13 of 15

accordingly, the results of this meta-analysis may not beappropriate to Africans. Second, in some studies, thediagnostic criteria and asthma phenotype were notclearly determined; while the asthma diagnostic criteria
were primarily based on physical examination, clinicalhistory, and pulmonary function tests (PFT), there did
exist a little dissimilarity among studies. Third, theoverall results were based on unadjusted estimates; amore precise evaluation should be accompanied when
all singular raw data are accessible, which would facilitate the adjustment by other potential co-variants suchas; age, gender, obesity, environmental factors, smoking
status, and other lifestyles. Fourth, due to a lack of
extractable data, we failed to address gene-environmentand gene-gene interactions. In contrast to these limitations, two main strengths of our meta-analysis include;
Firstly, a large number of patients and the healthy individuals were pooled from various studies, which considerably
augment the statistical power of the meta-analysis. Secondly,
no evidence of publication biases was identified, representing that the whole collected data may be unbiased.

Conclusion
Taken together, this study suggests that IL-4 C33T polymorphism potentially acts as a risk factor for asthma in
different ethnicities and age groups. Nevertheless, large
sample studies from different continents and races withhomogeneous asthmatic patients and well-matched healthy
subjects are still needed. Furthermore, gene-environmentand gene-gene interactions should also be regarded infuture studies. With taking these factors into account in future studies, it would ultimately lead to our comprehensive
and better understanding of the association between the
IL-4 C33T polymorphism and asthma susceptibility.
Abbreviations
IL-4: interleukin 4; SNP: Single nucleotide polymorphisms; PRISMA: Preferred
Reporting Items for Systematic reviews and Meta-Analyses; NOS: NewcastleOttawa Scale
Acknowledgements
None.
Authors’ contributions
RR generated the idea. DI and MME analyzed and interpreted the data. GAS
and BR prepared the original draft. MA, DI, and GAS critically revised thepaper. RR supervised the project. All authors read and approved the final
manuscript.
Funding
This research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors.
Availability of data and materials
All data generated or analyzed during this study are included in thispublished article.
Ethics approval and consent to participate
Not applicable.

Imani et al. BMC Medical Genetics(2020) 21:232

Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Immunology, School of Public Health, Tehran University of
Medical Sciences (TUMS), Tehran, Iran. 2Department of Hematology, Schoolof Medicine, Tarbiat Modares University (TMU), Tehran, Iran. 3Cellular andMolecular Research Center, Birjand University of Medical Sciences, Birjnad,
Iran. 4Department of Medical Microbiology and Parasitology, Faculty ofClinical Sciences, College of Health Science, Bayero University, Kano, Nigeria.
5Department of Immunology, School of Medicine, Shahid Beheshti Universityof Medical Sciences, Tehran, Iran.
Received: 19 December 2019 Accepted: 10 November 2020

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