Background: Previous studies have explored associations between interleukin-18 (IL-18) promoter polymorphisms and coronary artery disease (CAD). However, the results were controversial. We conducted a meta-analysis to clarify the association between the two polymorphisms and CAD risk. Methods: We searched English and Chinese databases and calculated the odds ratio (OR) and 95% confidence interval (CI) to estimate whether there are genetic associations between IL-18 promoter polymorphisms and the risk of CAD. All relevant studies were screened and meta-analyzed using STATA 15.0. Results: A total of 15 studies, including 12 studies for -137 G/C and 9 studies for -607 C/A, were identified for the meta-analysis. For -137 G/C, the results showed a significantly reduced risk of CAD in the dominant model (OR = 0.85) and heterozygous model (OR = 0.88) in the overall analysis. However, in subgroup analysis, decreased CAD risks were only observed in Asian populations for heterozygous genetic models. For -607 C/A, the overall OR revealed a reduced risk of CAD in all five genetic models (allelic, OR = 0.78; recessive, OR = 0.75; dominant, OR = 0.68; homozygous, OR = 0.61; heterozygous, OR = 0.72). In subgroup analysis, reduced CAD risk was also found in five genetic models of the Asian population. We also found that the IL-18 polymorphisms were correlated with myocardial infarction (MI) and multivessel (MV) disease. Conclusion: Our results suggested that the -137 polymorphism and -607 polymorphism in the IL-18 promoter were negatively associated with CAD, especially in the Asian population. In addition, some genetic models were correlated with the severity of CAD.

Keywords:
coronary artery disease, interleukin-18, meta-analysis, polymorphisms
Subjects:
Cardiovascular System & Vascular Biology

Coronary artery disease (CAD) is an important cause of cardiovascular mortality worldwide [1,2]. CAD includes a group of diseases such as angina, sudden death and myocardial infarction (MI). Atherosclerotic plaques have the main role in the progression of CAD, which are also associated with both innate and adaptive immune responses [3]. Growing evidence indicates that increased levels of circulating pro-inflammatory cytokines could further amplify the CAD risk [4].

The inflammatory response could promote the formation and stability of plaques [5]. Previous studies have indicated that several inflammatory factors contribute to the development of CAD, such as C-reactive protein (CRP) and tumor necrosis factor α (TNF-α) [6,7]. In addition, several new cytokines have been identified to be associated with the development of CAD, such as interleukin-18 (IL-18) [4]. IL-18 was originally identified as an IFN-γ-inducing factor (IGIF). IL-18 mRNA is expressed in a wide range of cells, including Kupffer cells, macrophages, T cells, B cells, osteoblasts, keratinocytes, dendritic cells, astrocytes and microglia [8,9]. Previous studies have shown that the level of plasma IL-18 was significantly elevated in CAD patients [10–12] and that the level of IL-18 could be a biomarker to predict the prognosis of CAD [13].

The gene for human IL-18 is located on chromosome 11q22.2–22.3 and contains six exons. Within the promoter region of the IL-18 gene, substitution of G>C at position 137 changes a histone 4 transcription factor-1 (H4TF-1) nuclear factor-binding site, while a change of C>A at position 607 disrupts a cyclic adenosine monophosphate (cAMP) responsive element protein-binding site. These changes influence the transcriptional activity of the IL-18 gene [14]. Indeed, numerous case–control studies [15–28] have investigated whether polymorphisms at position -137 (rs187238) or -607 (rs1946518) within the IL-18 promoter influence the risk of CAD, but the results were inconclusive and contradictory, prompting us to perform a comprehensive meta-analysis of all available evidence on these potential associations.

Literature search strategy

PubMed, EMBASE, Google Scholar, Cochrane Central Register of Controlled Trials (CENTRAL), the Chinese National Knowledge Infrastructure (CNKI) and Chinese Biomedical Literature Database (CBM), databases were systematically searched for clinical and experimental case–control studies of association between CAD and the -137 polymorphism (rs187238) and/or the -607 polymorphism (rs1946518) in the IL-18 promoter and that were published in English or Chinese up to 10 July 2019. The following search strings were used: interleukin-18 -137; interleukin-18 -607; IL-18 -137; IL-18 -607; rs187238; rs1946518; these six terms in combination with polymorphism, polymorphisms, SNP, variant, variants, variation, genotype, genetic or mutation; and all of the above terms in combination with CAD or CHD or coronary heart disease or coronary artery disease or myocardial infarction or angina or sudden death. Reference lists in identified articles and reviews were also searched manually to identify additional eligible studies.

Inclusion and exclusion criteria

If the obtained studies fulfilled the following criteria, they were identified as eligible: (1) case–control design; (2) research on the association between polymorphisms in the IL-18 promoter and risk of CAD; (3) sufficient published genotype frequencies data to estimate the odds ratio (OR) and 95% confidence interval (CI). The exclusion criteria included the following: (1) the genotype frequency data were unavailable; (2) animal model research; (3) review articles, case reports, meta-analysis; (4) overlapping publications (the studies with more subjects or recently published were included).

Quality assessment

The quality scoring criteria were modified from previous literature, and the score ranged from 0 to 9 points (Table 1) [29]. Two independent investigators (Su and Song) evaluated the quality of articles according to the modified criteria. A study with a score of ≥6 was defined as high quality, while one with a score <6 was low quality.

Table 1
The criteria for quality assessment
CriteriaScore
Representativeness of Cases  
Continuous collection and representative cases within clearly defined limits 2 
With potential selection bias 1 
Not described 0 
Source of Controls  
Population-based 2 
Hospital-based 1 
Not described 0 
Hardy–Weinberg Equilibrium in Controls  
Hardy–Weinberg equilibrium 2 
Hardy–Weinberg disequilibrium 1 
Genotyping Examination  
Genotyping done under ‘blinded’ condition 1 
Unblinded done or not mentioned 0 
Statistical Methods  
Appropriate statistics and adjustment for confounders 2 2 
Appropriate statistics but without adjustment for confounders 1 
Inappropriate statistics used 0 
CriteriaScore
Representativeness of Cases  
Continuous collection and representative cases within clearly defined limits 2 
With potential selection bias 1 
Not described 0 
Source of Controls  
Population-based 2 
Hospital-based 1 
Not described 0 
Hardy–Weinberg Equilibrium in Controls  
Hardy–Weinberg equilibrium 2 
Hardy–Weinberg disequilibrium 1 
Genotyping Examination  
Genotyping done under ‘blinded’ condition 1 
Unblinded done or not mentioned 0 
Statistical Methods  
Appropriate statistics and adjustment for confounders 2 2 
Appropriate statistics but without adjustment for confounders 1 
Inappropriate statistics used 0 
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Statistical analysis

To assess the strength of the association between the two polymorphisms in the IL-18 promoter and CAD risks, the ORs with corresponding 95% CIs served as the effect size. For the -137 polymorphism (rs187238), the allelic (C vs. G), recessive (CC vs. GC+GG), dominant (GC+CC vs. GG), homozygous (CC vs. GG) and heterozygous (GC vs. GG) genetic models were used to obtain pooled ORs. For the -607 polymorphism (rs1946518), the allelic (C vs. A), recessive (AA vs. CA+CC), dominant (CA+AA vs. CC), homozygous (AA vs. CC) and heterozygous (CA vs. CC) genetic models were used to obtain pooled ORs. The subgroup analysis was performed according to the ethnicity and Hardy–Weinberg equilibrium (HWE) status of controls. Cochran’s Q statistic and I2 test were used to assess the heterogeneity between different studies [30]. Heterogeneity was acceptable when the P-value was more than 0.10 and I2 was <50%, and a fixed-effects model (the Mantel–Haenszel method) was used. In contrast, ORs were calculated by the random-effects model (DerSimonian and Laird method) [31,32]. Sensitivity analysis was performed to assess the effect of individual studies on pooled results and the stability of the results. The publication bias was detected using Begg’s funnel plot and Egger’s linear regression method [33]. All statistical analyses were performed using STATA 15.0 software with two-sided P-values. A P-value <0.05 was considered significant.

Description of studies

Three hundred and three articles were retrieved from the initial database search, 133 studies were screened for duplicates, case reports, in vitro or in vivo studies, meta-analysis, reviews, and the remaining articles (n=17) for secondary screening. Two articles were excluded in the secondary screening due to insufficient data. Finally, a total of 15 articles were identified, 12 of which considered the -137 polymorphism (rs187238) and 9 considered the -607 polymorphism (rs1946518) (Figure 1). The characteristics of the studies in the meta-analysis are shown in Table 2.

Flow chart of study selection

Figure 1
 
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Flow chart of study selection

 

Figure 1
 
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Flow chart of study selection

 

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Table 2
Characteristics of studies in the meta-analysis
First authorYearEthnicityCountryGenotyping methodType of controlP for HWECases/ ControlsNumber of casesNumber of controlsQuality score
IL-18-137 (rs 187238)GGCGCCGGCGCC
Bazgir, A. [15] 2018 Non-Asian Iran PCR-SSP Healthy 0.183 314/364 168 122 24 160 171 33 7 
Mitrokhin, V. [28] 2018 Non-Asian Russia PCR-Taqman Patients without CAD 0.853 176/116 86 76 14 52 48 12 7 
Fatemeh, H. [23] 2018 Non-Asian Iran PCR Healthy 0.242 100/100 57 39 4 48 46 6 7 
Jabir, N.R. 2017 Non-Asian Saudi Arabia PCR Healthy <0.01 76/74 48 19 9 49 16 9 6 
Buraczynska, M. [24] 2016 Non-Asian Poland PCR-Taqman Healthy <0.01 1103/590 439 562 102 250 306 34 8 
Kumar, R. [18] 2015 Asian India PCR Healthy 0.533 300/300 168 102 30 176 105 19 7 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.439 468/432 352 112 4 308 116 8 6 
Kariž, S. [19] 2011 Non-Asian SLOVENIA PCR- RFLP Patients without CAD 0.301 169/326 90 71 8 162 141 23 8 
Opstad, T.B. [22] 2011 Non-Asian Norway PCR-Taqman Healthy 0.768 1001/204 532 394 69 108 82 14 7 
Shayan, S. [25] 2009 Non-Asian Iran PCR Patients without CAD 0.941 268/140 135 111 22 60 63 17 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.203 234/216 180 53 1 150 63 3 8 
Liu, W. [21] 2009 Asian China PCR Patients without CAD 0.236 241/145 195 46 0 99 44 2 6 
First authorYearEthnicityCountryGenotyping methodType of controlP for HWECases/ ControlsNumber of casesNumber of controlsQuality score
IL-18-137 (rs 187238)GGCGCCGGCGCC
Bazgir, A. [15] 2018 Non-Asian Iran PCR-SSP Healthy 0.183 314/364 168 122 24 160 171 33 7 
Mitrokhin, V. [28] 2018 Non-Asian Russia PCR-Taqman Patients without CAD 0.853 176/116 86 76 14 52 48 12 7 
Fatemeh, H. [23] 2018 Non-Asian Iran PCR Healthy 0.242 100/100 57 39 4 48 46 6 7 
Jabir, N.R. 2017 Non-Asian Saudi Arabia PCR Healthy <0.01 76/74 48 19 9 49 16 9 6 
Buraczynska, M. [24] 2016 Non-Asian Poland PCR-Taqman Healthy <0.01 1103/590 439 562 102 250 306 34 8 
Kumar, R. [18] 2015 Asian India PCR Healthy 0.533 300/300 168 102 30 176 105 19 7 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.439 468/432 352 112 4 308 116 8 6 
Kariž, S. [19] 2011 Non-Asian SLOVENIA PCR- RFLP Patients without CAD 0.301 169/326 90 71 8 162 141 23 8 
Opstad, T.B. [22] 2011 Non-Asian Norway PCR-Taqman Healthy 0.768 1001/204 532 394 69 108 82 14 7 
Shayan, S. [25] 2009 Non-Asian Iran PCR Patients without CAD 0.941 268/140 135 111 22 60 63 17 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.203 234/216 180 53 1 150 63 3 8 
Liu, W. [21] 2009 Asian China PCR Patients without CAD 0.236 241/145 195 46 0 99 44 2 6 
IL-18-607 (rs 1946518)CCCAAACCCAAA
Bazgir, A. [15] 2018 Non-Asian Iran PCR-SSP Healthy 0.494 314/364 109 153 52 136 178 50 7 
Jabir, N.R. 2017 Non-Asian Saudi Arabia PCR Healthy <0.01 74/74 65 0 9 59 2 13 6 
Ma, J.B. [17] 2016 Asian China PCR- RFLP Healthy 0.53 326/326 90 128 108 43 158 125 6 
Kariž, S. [19] 2011 Non-Asian SLOVENIA PCR- RFLP Patients without CAD 0.895 169/326 55 86 28 109 158 59 8 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.616 468/432 170 210 88 90 220 122 6 
Opstad, T.B. [22] 2011 Non-Asian Norway PCR-Taqman Healthy 0.762 1001/204 364 500 132 74 96 34 7 
Shayan, S. [25] 2009 Non-Asian Iran PCR Patients without CAD 0.247 251/127 97 124 30 48 65 14 7 
Zhu, M. [26] 2009 Asian China PCR Healthy 0.653 141/240 47 71 23 51 123 66 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.854 234/216 82 107 45 42 108 66 8 
IL-18-607 (rs 1946518)CCCAAACCCAAA
Bazgir, A. [15] 2018 Non-Asian Iran PCR-SSP Healthy 0.494 314/364 109 153 52 136 178 50 7 
Jabir, N.R. 2017 Non-Asian Saudi Arabia PCR Healthy <0.01 74/74 65 0 9 59 2 13 6 
Ma, J.B. [17] 2016 Asian China PCR- RFLP Healthy 0.53 326/326 90 128 108 43 158 125 6 
Kariž, S. [19] 2011 Non-Asian SLOVENIA PCR- RFLP Patients without CAD 0.895 169/326 55 86 28 109 158 59 8 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.616 468/432 170 210 88 90 220 122 6 
Opstad, T.B. [22] 2011 Non-Asian Norway PCR-Taqman Healthy 0.762 1001/204 364 500 132 74 96 34 7 
Shayan, S. [25] 2009 Non-Asian Iran PCR Patients without CAD 0.247 251/127 97 124 30 48 65 14 7 
Zhu, M. [26] 2009 Asian China PCR Healthy 0.653 141/240 47 71 23 51 123 66 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.854 234/216 82 107 45 42 108 66 8 

Abbreviations: PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism; SSP, sequence-specific primer.

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Quantitative data synthesis

The results of the meta-analysis for the associations between the IL-18 promoter polymorphism -137 (rs187238), -607 (rs1946518) and CAD risks are shown in Table 3, andFigures 2 and 3. There were 12 eligible studies with 4450 cases and 3007 controls that focused on the association between the -137 polymorphism (rs187238) and CAD risk. Overall, significant associations were only revealed in the results under the genetic models of dominant and heterozygous. A decreased risk of CAD was observed in dominant and heterozygous genetic models. (GC+CC vs. GG, OR = 0.85, 95% CI = 0.74–0.98, P=0.024, I2 = 42.5%; GC vs. GG, OR = 0.88, 95% CI = 0.79–0.97, P=0.012, I2 = 17.3%) (Figure 2).

Forest plot describing the association between the -137 polymorphism (rs187238) and risk of across all study participants according to different genetic models

Figure 2
(A) Allelic (C-allele vs. G-allele), (B) recessive (CC vs. GC + GG), (C) dominant (GC + CC vs. GG), (D) homozygous (CC vs. GG) and (E) heterozygous (GC vs. GG).
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Forest plot describing the association between the -137 polymorphism (rs187238) and risk of across all study participants according to different genetic models

(A) Allelic (C-allele vs. G-allele), (B) recessive (CC vs. GC + GG), (C) dominant (GC + CC vs. GG), (D) homozygous (CC vs. GG) and (E) heterozygous (GC vs. GG).

Figure 2
(A) Allelic (C-allele vs. G-allele), (B) recessive (CC vs. GC + GG), (C) dominant (GC + CC vs. GG), (D) homozygous (CC vs. GG) and (E) heterozygous (GC vs. GG).
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Forest plot describing the association between the -137 polymorphism (rs187238) and risk of across all study participants according to different genetic models

(A) Allelic (C-allele vs. G-allele), (B) recessive (CC vs. GC + GG), (C) dominant (GC + CC vs. GG), (D) homozygous (CC vs. GG) and (E) heterozygous (GC vs. GG).

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Forest plot describing the association between the -607 (rs 1946518) and risk of across all study participants according to different genetic models

Figure 3
(A) Allelic (A-allele vs. C-allele), (B) recessive (AA vs. CA+ CC), (C) dominant (AA+CA vs. CC), (D) homozygous (AA vs. CC) and (E) heterozygous (CA vs. CC).
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Forest plot describing the association between the -607 (rs 1946518) and risk of across all study participants according to different genetic models

(A) Allelic (A-allele vs. C-allele), (B) recessive (AA vs. CA+ CC), (C) dominant (AA+CA vs. CC), (D) homozygous (AA vs. CC) and (E) heterozygous (CA vs. CC).

Figure 3
(A) Allelic (A-allele vs. C-allele), (B) recessive (AA vs. CA+ CC), (C) dominant (AA+CA vs. CC), (D) homozygous (AA vs. CC) and (E) heterozygous (CA vs. CC).
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Forest plot describing the association between the -607 (rs 1946518) and risk of across all study participants according to different genetic models

(A) Allelic (A-allele vs. C-allele), (B) recessive (AA vs. CA+ CC), (C) dominant (AA+CA vs. CC), (D) homozygous (AA vs. CC) and (E) heterozygous (CA vs. CC).

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Table 3
Overall meta-analysis of the association between the -137 polymorphism (rs187238), IL-18-607 (rs1946518) and risk of CAD
VariablesnC vs. GCC vs. GC+GGCC+GC vs. GGCC vs. GGGC vs. GG
IL-18-137 (rs187238)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 12 0.88 [0.77, 1.00] 0.050 57.4 0.92 [0.69, 1.23] 0.594 37.1 0.85 [0.74, 0.98] 0.024 42.5 0.86 [0.62, 1.19] 0.360 46.6 0.88 [0.79, 0.97] 0.012 17.3 
Ethnicity 
Asian 4 0.80 [0.58, 1.10] 0.169 74.4 0.64 [0.21, 1.95] 0.433 57.6 0.78 [0.57, 1.05] 0.103 63.0 0.59 [0.18, 1.92] 0.382 61.2 0.80 [0.67, 0.97] 0.020 42.2 
Non-Asian 8 0.91 [0.80, 1.05] 0.190 45.5 0.94 [0.70, 1.26] 0.670 32.3 0.89 [0.77, 1.04] 0.144 28.8 0.87 [0.62, 1.23] 0.427 45.2 0.91 [0.80, 1.03] 0.146 0 
HWE 
HWE-Yes 10 0.84 [0.74. 0.95] 0.005 38.8 0.84 [0.62, 1.14] 0.272 16.4 0.81 [0.71, 0.92] 0.002 23 0.77 [0.57, 1.04] 0.089 20 0.82 [0.73, 0.92] 0.001 0 
HWE-No 2 1.14 [0.99, 1.32] 0.074 0 1.54 [1.06,1.28] 0.022 0 1.11 [0.92, 1.35] 0.273 0 1.58 [1.08, 2.33] 0.020 0 1.06 [0.86, 1.29] 0.593 0 
VariablesnC vs. GCC vs. GC+GGCC+GC vs. GGCC vs. GGGC vs. GG
IL-18-137 (rs187238)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 12 0.88 [0.77, 1.00] 0.050 57.4 0.92 [0.69, 1.23] 0.594 37.1 0.85 [0.74, 0.98] 0.024 42.5 0.86 [0.62, 1.19] 0.360 46.6 0.88 [0.79, 0.97] 0.012 17.3 
Ethnicity 
Asian 4 0.80 [0.58, 1.10] 0.169 74.4 0.64 [0.21, 1.95] 0.433 57.6 0.78 [0.57, 1.05] 0.103 63.0 0.59 [0.18, 1.92] 0.382 61.2 0.80 [0.67, 0.97] 0.020 42.2 
Non-Asian 8 0.91 [0.80, 1.05] 0.190 45.5 0.94 [0.70, 1.26] 0.670 32.3 0.89 [0.77, 1.04] 0.144 28.8 0.87 [0.62, 1.23] 0.427 45.2 0.91 [0.80, 1.03] 0.146 0 
HWE 
HWE-Yes 10 0.84 [0.74. 0.95] 0.005 38.8 0.84 [0.62, 1.14] 0.272 16.4 0.81 [0.71, 0.92] 0.002 23 0.77 [0.57, 1.04] 0.089 20 0.82 [0.73, 0.92] 0.001 0 
HWE-No 2 1.14 [0.99, 1.32] 0.074 0 1.54 [1.06,1.28] 0.022 0 1.11 [0.92, 1.35] 0.273 0 1.58 [1.08, 2.33] 0.020 0 1.06 [0.86, 1.29] 0.593 0 
VariablesnA vs. CAA vs. CA+CCAA+CA vs. CCAA vs. CCCA vs. CC
IL-18-607 (rs1946518)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 9 0.78 [0.65, 0.93] 0.006 77.2 0.75 [0.61, 0.91] 0.005 42.7 0.68 [0.51, 0.92] 0.011 79.9 0.61 [0.43, 0.87] 0.006 74.5 0.72 [0.54, 0.96] 0.025 74.6 
Ethnicity 
Asian 4 0.62 [0.55, 0.70] 0.000 0 0.63 [0.52, 0.77] 0.000 8.4 0.46 [0.38, 0.55] 0.000 0 0.38 [0.30,0.48] 0.000 0 0.49 [0.40, 0.61] 0.000 0 
Non-Asian 5 0.99 [0.88, 1.12] 0.872 1.6 0.94 [0.75, 1.19] 0.621 0 1.01 [0.85, 1.20] 0.903 0 0.96 [0.75, 1.24] 0.777 0 1.04 [0.86, 1.25] 0.627 0 
HWE 
HWE-Yes 8 0.79 [0.65, 0.95] 0.014 79.6 0.75 [0.61, 0.93] 0.01 49.6 0.69 [0.51, 0.95] 0.021 82.3 0.61 [0.42, 0.89] 0.01 77.7 0.73 [0.54, 0.97] 0.032 77.2 
HWE-No 1 0.59 [0.31, 1.13] 0.1111 / 0.65 [0.26, 1.63] 0.358 / 0.68 [0.51, 0.92] 0.185 / 0.63 [0.25, 1.58] 0.332 / 0.18 [0.01, 3.86] 0.274 
VariablesnA vs. CAA vs. CA+CCAA+CA vs. CCAA vs. CCCA vs. CC
IL-18-607 (rs1946518)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 9 0.78 [0.65, 0.93] 0.006 77.2 0.75 [0.61, 0.91] 0.005 42.7 0.68 [0.51, 0.92] 0.011 79.9 0.61 [0.43, 0.87] 0.006 74.5 0.72 [0.54, 0.96] 0.025 74.6 
Ethnicity 
Asian 4 0.62 [0.55, 0.70] 0.000 0 0.63 [0.52, 0.77] 0.000 8.4 0.46 [0.38, 0.55] 0.000 0 0.38 [0.30,0.48] 0.000 0 0.49 [0.40, 0.61] 0.000 0 
Non-Asian 5 0.99 [0.88, 1.12] 0.872 1.6 0.94 [0.75, 1.19] 0.621 0 1.01 [0.85, 1.20] 0.903 0 0.96 [0.75, 1.24] 0.777 0 1.04 [0.86, 1.25] 0.627 0 
HWE 
HWE-Yes 8 0.79 [0.65, 0.95] 0.014 79.6 0.75 [0.61, 0.93] 0.01 49.6 0.69 [0.51, 0.95] 0.021 82.3 0.61 [0.42, 0.89] 0.01 77.7 0.73 [0.54, 0.97] 0.032 77.2 
HWE-No 1 0.59 [0.31, 1.13] 0.1111 / 0.65 [0.26, 1.63] 0.358 / 0.68 [0.51, 0.92] 0.185 / 0.63 [0.25, 1.58] 0.332 / 0.18 [0.01, 3.86] 0.274 
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However, in ethnicity subgroup analysis, significantly decreased CAD risks were only observed in Asian populations for heterozygous genetic models (GC vs. GG, OR = 0.80, 95% CI = 0.67–0.97, P=0.020, I2 = 42.2%). When we restricted the analysis to HWE, a significant association with decreased CAD risk was not only observed in the dominant and heterozygous genetic models but also in the allele genetic model (GC+CC vs. GG, OR = 0.81, 95% CI = 0.71–0.92, P=0.002, I2 = 23%; GC vs. GG, OR = 0.82, 95% CI = 0.73–0.92, P=0.001, I2 = 0%, C vs. G, OR = 0.84, 95% CI = 0.74–0.95, P=0.005, I2 = 38.8%).

For the -607 polymorphism (rs1946518), we included nine studies to analyze the association with CAD risk. These studies involving 2978 cases and 2309 controls were pooled into the meta-analysis. The overall OR with its 95% CI revealed a significantly reduced risk of CAD in all five genetic models (A vs. C, OR = 0.78, 95% CI = 0.65–0.93, P=0.006, I2 = 77.2%; AA vs. CA+CC, OR = 0.75, 95% CI = 0.61–0.91, P=0.005, I2 = 42.7%; AA+CA vs. CC, OR = 0.68, 95% CI = 0.51–0.92, P=0.011, I2 = 79.9%; AA vs. CC, OR = 0.61, 95% CI = 0.43–0.87, P=0.006, I2 = 74.5%, CA vs. CC, OR = 0.72, 95% CI = 0.54–0.96, P=0.025, I2 = 74.6%) (Figure 3).

In subgroup analysis by ethnicity, reduced CAD risk was also found in five genetic models of the Asian population (A vs. C, OR = 0.62, 95% CI = 0.55–0.70, P=0.000, I2 = 0%; AA vs. CA+CC, OR = 0.63, 95% CI = 0.52–0.77, P=0.000, I2 = 8.4%; AA+CA vs. CC, OR = 0.46, 95% CI = 0.38–0.55, P=0.000, I2 = 0%; AA vs. CC, OR = 0.38, 95% CI = 0.30–0.48, P=0.000, I2 = 0%, CA vs. CC, OR = 0.49, 95% CI = 0.40–0.61, P=0.000, I2 = 0%). When restricted to HWE, a significant association with decreased CAD risk was also observed in five genetic models (A vs. C, OR = 0.79, 95% CI = 0.65–0.95, P=0.014, I2 = 79.6%; AA vs. CA+CC, OR = 0.75, 95% CI = 0.61–0.93, P=0.010, I2 = 49.6%; AA+CA vs. CC, OR = 0.69, 95% CI = 0.51–0.95, P=0.021, I2 = 82.3%; AA vs. CC, OR = 0.61, 95% CI = 0.42–0.89, P=0.010, I2 = 77.7%, CA vs. CC, OR = 0.73, 95% CI = 0.54–0.97, P=0.032, I2 = 77.2%).

CAD concludes many subtypes, such as myocardial infarction (MI), sudden death, angina and so on. MI causes more human deaths worldwide than any other disease. Therefore, we conclude the data that referred to the MI population and analyzed the relationship between MI risk factors and IL-18 promoter polymorphism (Tables 4 and 6, and Figure 4). For different genetic models of the -137 polymorphism (rs187238), we observed significantly reduced risk in four genetic models: allelic, homozygous, dominant and recessive (C vs. G, OR = 0.81, 95% CI = 0.69–0.95, P=0.009, I2 = 0%; CC vs. GC+GG, OR = 0.58, 95% CI = 0.35–0.97, P=0.036, I2 = 0%; CC+GC vs. GG, OR = 0.80, 95% CI = 0.67–0.97, P=0.022, I2 = 0%; CC vs. GG, OR = 0.55, 95% CI = 0.33–0.93, P=0.025, I2 = 0%). However, the reduced risk of MI can only be observed in the recessive genetic model of the -607 polymorphism (rs1946518) (AA vs. CA+CC, OR = 0.67, 95% CI = 0.54–0.83, P=0.000, I2 = 49.7%).

Forest plot describing the association between the IL-18 promoter polymorphism and risk of MI in recessive genetic model

Figure 4
(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).
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Forest plot describing the association between the IL-18 promoter polymorphism and risk of MI in recessive genetic model

(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).

Figure 4
(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).
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Forest plot describing the association between the IL-18 promoter polymorphism and risk of MI in recessive genetic model

(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).

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Table 4
Characteristics of studies which refer to MI
First authorYearEthnicityCountryGenotyping methodType of controlP for HWECases/ ControlsNumber of casesNumber of controlsQuality score
IL-18-137 (rs 187238)GGCGCCGGCGCC
Kariž, S. [19] 2011 White Slovenia PCR- RFLP Patients without CAD 0.439 169/326 90 71 8 162 141 23 8 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.301 468/432 352 112 4 308 116 8 6 
Shayan, S. [25] 2009 Asian Iran PCR Patients without CAD 0.768 136/140 64 61 11 60 63 17 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.203 234/216 180 53 1 150 63 3 8 
First authorYearEthnicityCountryGenotyping methodType of controlP for HWECases/ ControlsNumber of casesNumber of controlsQuality score
IL-18-137 (rs 187238)GGCGCCGGCGCC
Kariž, S. [19] 2011 White Slovenia PCR- RFLP Patients without CAD 0.439 169/326 90 71 8 162 141 23 8 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.301 468/432 352 112 4 308 116 8 6 
Shayan, S. [25] 2009 Asian Iran PCR Patients without CAD 0.768 136/140 64 61 11 60 63 17 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.203 234/216 180 53 1 150 63 3 8 
IL-18-607 (rs 1946518)CCCAAACCCAAA
Kariž, S. [19] 2011 Non-Asian Slovenia PCR- RFLP Patients without CAD 0.895 169/326 55 86 28 109 158 59 8 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.616 468/432 170 210 88 90 220 122 6 
Shayan, S. [25] 2009 Asian Iran PCR Patients without CAD 0.247 130/127 53 59 18 48 65 14 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.854 234/216 82 107 45 42 108 66 8 
IL-18-607 (rs 1946518)CCCAAACCCAAA
Kariž, S. [19] 2011 Non-Asian Slovenia PCR- RFLP Patients without CAD 0.895 169/326 55 86 28 109 158 59 8 
Zhang, X. [27] 2011 Asian China PCR-SSP Patients without CAD 0.616 468/432 170 210 88 90 220 122 6 
Shayan, S. [25] 2009 Asian Iran PCR Patients without CAD 0.247 130/127 53 59 18 48 65 14 7 
Pei, F. [20] 2009 Asian China PCR-SSP Patients without CAD 0.854 234/216 82 107 45 42 108 66 8 

Abbreviations: PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism; SSP, sequence-specific primer.

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The number of stenotic coronary arteries is one of the indicators of the severity of coronary heart disease. We conclude data from three studies to analyze the correlation between the number of stenotic coronary artery and IL-18 promoter polymorphisms (Tables 5 and 7 and Figure 5). We defined only one coronary artery stenosis as a single-vessel group (SV), and two or more coronary stenosis was a multivessel group (MV). Table 7 shows that there was no significant correlation between the -137 polymorphism (rs187238) in the SV group, but in the MV group, we observed a reduced risk in the allelic, dominant and heterozygous models (C vs. G, OR = 0.49, 95% CI = 0.28–0.84, P=0.009, I2 = 75.3%; CC+GC vs. GG, OR = 0.40, 95% CI = 0.23–0.70, P=0.001, I2 = 68.7%; GC vs. GG, OR = 0.4, 95% CI = 0.24–0.68, P=0.001, I2 = 62.9%).

Forest plot describing the association between the -137 polymorphism (rs187238) and the number of stenotic coronaries in allelic genetic model

Figure 5
(A) For SV group; (B) for MV group.
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Forest plot describing the association between the -137 polymorphism (rs187238) and the number of stenotic coronaries in allelic genetic model

(A) For SV group; (B) for MV group.

Figure 5
(A) For SV group; (B) for MV group.
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Forest plot describing the association between the -137 polymorphism (rs187238) and the number of stenotic coronaries in allelic genetic model

(A) For SV group; (B) for MV group.

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Table 5
Characteristics of studies which refers to the number of stenotic coronaries
First authorYearEthnicityCountryGenotyping methodType of controlP for HWECases/ ControlsNumber of casesNumber of controlsQuality score
IL-18-137 (rs 187238)-SVGGCGCCGGCGCC
Bazgir, A. [15] 2018 Asian Iran PCR-SSP Healthy 0.183 198/364 112 68 18 160 171 33 7 
Fatemeh, H. [23] 2018 Asian Iran PCR Healthy 0.242 63/100 51 10 2 48 46 6 7 
Liu, W. [21] 2009 Asian China PCR Patients without CAD 0.236 152/145 128 24 0 99 44 2 6 
First authorYearEthnicityCountryGenotyping methodType of controlP for HWECases/ ControlsNumber of casesNumber of controlsQuality score
IL-18-137 (rs 187238)-SVGGCGCCGGCGCC
Bazgir, A. [15] 2018 Asian Iran PCR-SSP Healthy 0.183 198/364 112 68 18 160 171 33 7 
Fatemeh, H. [23] 2018 Asian Iran PCR Healthy 0.242 63/100 51 10 2 48 46 6 7 
Liu, W. [21] 2009 Asian China PCR Patients without CAD 0.236 152/145 128 24 0 99 44 2 6 
IL-18-137 (rs 187238)-MVCCCAAACCCAAA
Bazgir, A. [15] 2018 Asian Iran PCR-SSP Healthy 0.183 198/364 112 68 18 160 171 33 7 
Fatemeh, H. [23] 2018 Asian Iran PCR Healthy 0.242 63/100 51 10 2 48 46 6 7 
Liu, W. [21] 2009 Asian China PCR Patients without CAD 0.236 152/145 128 24 0 99 44 2 6 
IL-18-137 (rs 187238)-MVCCCAAACCCAAA
Bazgir, A. [15] 2018 Asian Iran PCR-SSP Healthy 0.183 198/364 112 68 18 160 171 33 7 
Fatemeh, H. [23] 2018 Asian Iran PCR Healthy 0.242 63/100 51 10 2 48 46 6 7 
Liu, W. [21] 2009 Asian China PCR Patients without CAD 0.236 152/145 128 24 0 99 44 2 6 

Abbreviations: PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism; SSP, sequence-specific primer.

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Table 6
Overall meta-analysis of the association between the -137 polymorphism (rs187238), IL-18-607 (rs1946518) and risk of MI
VariablesnC vs. GCC vs. GC+GGCC+GC vs. GGCC vs. GGGC vs. GG
IL-18-137 (rs 187238)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 4 0.81 [0.69, 0.95] 0.009 0 0.58 [0.35, 0.97] 0.036 0 0.80 [0.67, 0.97] 0.022 0 0.55 [0.33, 0.93] 0.025 0 0.84 [0.69, 1.01] 0.067 0 
VariablesnC vs. GCC vs. GC+GGCC+GC vs. GGCC vs. GGGC vs. GG
IL-18-137 (rs 187238)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 4 0.81 [0.69, 0.95] 0.009 0 0.58 [0.35, 0.97] 0.036 0 0.80 [0.67, 0.97] 0.022 0 0.55 [0.33, 0.93] 0.025 0 0.84 [0.69, 1.01] 0.067 0 
VariablesnA vs. CAA vs. CA+CCAA+CA vs. CCAA vs. CCCA vs. CC
IL-18-607 (rs 1946518)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 4 0.75 [0.57, 1] 0.051 79.1 0.67 [0.54, 0.83] 0.000 49.7 0.65 [0.42, 1.01] 0.055 79.2 0.59 [0.33, 1.03] 0.062 76.9 0.68 [0.46, 1] 0.050 69.9 
VariablesnA vs. CAA vs. CA+CCAA+CA vs. CCAA vs. CCCA vs. CC
IL-18-607 (rs 1946518)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Total 4 0.75 [0.57, 1] 0.051 79.1 0.67 [0.54, 0.83] 0.000 49.7 0.65 [0.42, 1.01] 0.055 79.2 0.59 [0.33, 1.03] 0.062 76.9 0.68 [0.46, 1] 0.050 69.9 
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Table 7
Overall meta-analysis of the association between the -137 polymorphism (rs187238) and the number of stenotic coronaries
VariablesnC vs. GCC vs. GC+GGCC+GC vs. GGCC vs. GGGC vs. GG
IL-18-137 (rs 187238)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
SV 3 1.03 [0.59, 1.8] 0.920 76.7 0.58 [0.27, 1.24] 0.158 0 1.28 [0.53, 3.13] 0.584 83.7 0.64 [0.29, 1.38] 0.253 29.5 1.36 [0.56, 3.32] 0.503 83.3 
MV 3 0.49 [0.28, 0.84] 0.009 25.3 0.85 [0.49, 1.47] 0.566 0 0.40 [0.23, 0.70] 0.001 68.7 0.64 [0.36, 1.12] 0.12 0 0.40 [0.24, 0.68] 0.001 62.9 
VariablesnC vs. GCC vs. GC+GGCC+GC vs. GGCC vs. GGGC vs. GG
IL-18-137 (rs 187238)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
SV 3 1.03 [0.59, 1.8] 0.920 76.7 0.58 [0.27, 1.24] 0.158 0 1.28 [0.53, 3.13] 0.584 83.7 0.64 [0.29, 1.38] 0.253 29.5 1.36 [0.56, 3.32] 0.503 83.3 
MV 3 0.49 [0.28, 0.84] 0.009 25.3 0.85 [0.49, 1.47] 0.566 0 0.40 [0.23, 0.70] 0.001 68.7 0.64 [0.36, 1.12] 0.12 0 0.40 [0.24, 0.68] 0.001 62.9 
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Sensitivity analysis

For the -137 polymorphism (rs187238), the sensitivity analysis showed that no single individual study significantly affected the pooled OR in all genetic models. Additionally, for the -607 polymorphism (rs1946518), sensitivity analysis showed that none of the studies led to changes in the global ORs, indicating the robustness and stability of the results in this meta-analysis (Figure 6).

The influence of each study by removal of individual studies for allelic genetic model

Figure 6
(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).
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The influence of each study by removal of individual studies for allelic genetic model

(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).

Figure 6
(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).
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The influence of each study by removal of individual studies for allelic genetic model

(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).

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Publication bias

To evaluate the publication bias, Begg’s funnel plot and Egger’s test were performed. The P-values for Begg’s and Egger’s tests are shown in Table 8. Obvious publication bias was observed for the -137 polymorphism (rs187238) in allelic, homozygous and recessive models in Egger’s test. For the -607 polymorphism (rs1946518), there was no publication bias in all models. These results were also demonstrated by the shape of the funnel plot (Figure 7).

Begg’s funnel plot to assess publication bias in the meta-analysis of a potential association between IL-18 promoter polymorphism and risk of CAD in allelic genetic model

Figure 7
(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).
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Begg’s funnel plot to assess publication bias in the meta-analysis of a potential association between IL-18 promoter polymorphism and risk of CAD in allelic genetic model

(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).

Figure 7
(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).
View largeDownload slide
Begg’s funnel plot to assess publication bias in the meta-analysis of a potential association between IL-18 promoter polymorphism and risk of CAD in allelic genetic model

(A) For -137 polymorphism (rs187238); (B) for -607 polymorphism (rs 1946518).

Close modal
Table 8
Egger’s and Begg’s tests for the publication bias of IL-18-137 (rs 187238) and IL-18-607 (rs 1946518)
Begg’s test P-valueEgger’s test P-value
IL-18-137 (rs 187238)   
C vs G 0.373 0.026 
CC vs. GC+GG 0.064 0.004 
CC+GC vs. GG 0.373 0.096 
CC vs. GG 0.115 0.008 
GC vs. GG 0.451 0.211 
IL-18-607 (rs 1946518)   
A vs. C 0.917 0.972 
AA vs. CA+CC 0.602 0.729 
AA+CA vs. CC 0.917 0.599 
AA vs. CC 0.348 0.563 
AC vs. CC 0.466 0.513 
Begg’s test P-valueEgger’s test P-value
IL-18-137 (rs 187238)   
C vs G 0.373 0.026 
CC vs. GC+GG 0.064 0.004 
CC+GC vs. GG 0.373 0.096 
CC vs. GG 0.115 0.008 
GC vs. GG 0.451 0.211 
IL-18-607 (rs 1946518)   
A vs. C 0.917 0.972 
AA vs. CA+CC 0.602 0.729 
AA+CA vs. CC 0.917 0.599 
AA vs. CC 0.348 0.563 
AC vs. CC 0.466 0.513 
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IL-18 is a pleiotropic pro-inflammatory cytokine that affects both innate and acquired inflammatory responses [34,35], and it has been associated with the development of atherosclerosis by stimulating the production of atherogenic IFN-γ [36]. Previous studies have demonstrated that the concentration of IL-18 is higher in CAD patients, and the level of IL-18 could be an indicator to evaluate the risk of CAD [37,38]. A meta-analysis supported that circulating IL-18 was prospectively and independently associated with cardiovascular disease risk [39]. As the upstream of gene expression, the IL-18 promoter plays an important role in influencing the expression of IL-18. However, the relationship between IL-18 promoter polymorphisms and CAD is controversial.

In the present meta-analysis, for the -137 polymorphism (rs187238), we found that heterozygous and dominant models had a negative correlation with CAD. In addition, decreased CAD risks were only observed in Asian populations for heterozygous genetic models. This result is different from a previous meta-analysis by Dong et al. [40], which included only six studies. A recent study published by Mitrokhin et al. [28] did not find any relationship between the -137 polymorphism and CAD [28], and the same result was also reported by Kumar et al. [18], Kariž et al. [19], Pei et al. [20] etc. However, another study published an opposite conclusion: a significant increase in the G allele or GG genotype was observed in CAD patients. The present study also found that G-allele carriers in MV disease patients had a higher occurrence rate when compared with SV disease patients [21]. Thus, the -137 polymorphism (rs187238) not only correlated with CAD prevalence but also correlated with the severity of CAD. Therefore, we screened 12 studies and reanalyzed the relationship between the number of stenotic coronaries and the -137 polymorphism (rs187238). We also found that the -137 polymorphism (rs187238) correlated with MV disease. The allelic, heterozygous and dominant genetic models showed a reduced risk for MV disease. Another indicator to evaluate the severity of CAD is the type of CAD, MI is the most severe type of CAD, and we observed a significant correlation between MI and the -137 polymorphism (rs187238). Except for the heterozygous genetic model, the remaining four genetic models had a negative correlation with MI.

Previous studies have also shown that the genotype carrying the IL-18 promoter—607 C/A gene locus C was related to the high expression of IL-18, which leads to the up-regulation of cytokines, chemokines, adhesion molecules and matrix metalloproteinases [41,42]. Some studies have discussed the relationship between the -607 polymorphism (rs1946518) and CAD; however, the results are controversial. Shayan et al. [25] evaluated the role of two IL-18 gene polymorphisms at the -607(C/A) position in patients with CAD and healthy controls. They reported no significant association between genotypes and alleles and CAD. However, contrary conclusions were reported by other studies [17,10,26,27]. In our meta-analysis, we observed a significant association between the -607 polymorphism (rs1946518) and CAD, in which the -607 polymorphism (rs1946518) was negatively correlated with CAD. After subgroup analysis, this influence was only observed in the Asian population. It is worth noting that we observed a significant heterogeneity in overall analysis, but the heterogeneity was disappeared in ethnicity subgroup analysis. This results further indicate the -607 SNP is correlated with CAD risk for Asian population. Additionally, -607 SNP was also correlated with MI. However, unlike the -137 polymorphism (rs187238), only the recessive genetic model had a negative correlation with MI. Unfortunately, no study has revealed the -607 polymorphism (rs1946518) related to the number of stenotic coronaries.

To our knowledge, this is an update study focused on the association between IL-18 promoter polymorphisms and CAD risk, but it was the first meta-analysis evaluating the potential association of these two IL-18-related polymorphisms and the risk of MI and the number of stenotic coronaries. The strengths of our study are listed as follows: first, most of the genotype distributions in controls were consistent with ethnicity and HWE. Second, the relationship was analyzed using five types of genetic models, and the results were statistically significant. Third, the methodological issues for meta-analysis, such as Egger’s test, Begg’s funnel plots and subgroup analysis, were performed to ensure the stability of the results.

However, we also pay attention to the limitations in our meta-analysis. First, the small sample size of studies included was still inadequate, so the statistical power was reduced. Second, two studies did not conform to HWE expectations. Third, an obvious asymmetry in funnel plots and significant P-values for the -137 polymorphism (rs187238) through Egger’s test were found in the present study. In the present study, the small sample size may be an important reason for publication bias. Furthermore, studies only in English or Chinese have been searched. There might be studies in other languages that are not included, which might be another reason for the asymmetry. Last, in the study of the -137 polymorphism (rs187238), there are a few studies on Asian populations, and we expect more data on Asian populations.

In conclusion, our results suggested that the -137 polymorphism (rs187238) and -607 polymorphism (rs1946518) of the IL-18 promoter were negatively associated with CAD, especially in the Asian population. In addition, some genetic models were correlated with the severity of CAD. However, the association between CAD and the -137 polymorphism (rs187238) should be interpreted with caution because of publication bias. Further detailed investigations involving larger, multiethnic samples are needed to clarify the role of these polymorphisms in CAD risk.

Designed the study: Zheng Lian and Hong Chen. Searched databases and collected full-text papers: Li-Na Su and Yu-Xia Cui. Extracted and analyzed the data: Wei-Jue Xiong, Su-Fang Li and Man-Yan Wu. Statistical analyses: Jun-Xian Song, Chong-You Lee and Dan Hu. Wrote the manuscript: Zheng Lian and Shi-Ran Yu. All authors reviewed the manuscript.

This work was supported by the National Natural Science Foundation of China [grant numbers 81770356 and 81970301]; and the Peking University People’s Hospital Research and Development Funds [grant number RDY2018-26].

The authors declare that there are no competing interests associated with the manuscript.

CAD

coronary artery disease

CHD

coronary heart disease

CI

confidence interval

HWE

Hardy–Weinberg equilibrium

IFN

interferon

IL-18

interleukin-18

MI

myocardial infarction

MV

multivessel

OR

odds ratio

SV

single-vessel

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2019
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).