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. 2016 Dec 27;8(4):5692–5702. doi: 10.18632/oncotarget.14249

Significant association between functional microRNA polymorphisms and coronary heart disease susceptibility: a comprehensive meta-analysis involving 16484 subjects

Xu Liu 1, Lianghao You 2, Ruizhi Zhou 2, Jian Zhang 2
PMCID: PMC5351582  PMID: 28035059

Abstract

Molecular epidemiological studies suggest that microRNA polymorphisms may be associated with an increased risk of coronary heart disease (CHD). However, the results of these studies were inconsistent and inconclusive. To derive a more precise evaluation, we performed a meta-analysis focused on the associations between microRNA polymorphisms and CHD risk. PubMed, Embase, CNKI and Wanfang databases were searched. Odds ratios (ORs) with 95% confidence intervals (CIs) were applied to assess the association between microRNA-146a rs2910164, microRNA-196a2 rs11614913, microRNA-499 rs3746444 and microRNA-149 rs71428439 polymorphisms and CHD susceptibility. Heterogeneity, publication bias and sensitivity analysis were conducted to measure the robustness of our findings. A total of thirteen related studies involving 8,120 patients and 8,364 controls were analyzed. Significant associations between microRNA-146a rs2910164 polymorphism and CHD risk were observed in the total population, as well as in subgroup analysis. For microRNA-196a2 rs11614913 and microRNA-499 rs3746444, similarly increased risks were also found. In addition, no significant association was detected between microRNA-149 rs71428439 polymorphism and CHD risk. In conclusion, our meta-analyses suggest that microRNA polymorphisms may be associated with increased risk of CHD development.

Keywords: microRNA, polymorphism, coronary heart disease, meta-analysis, Pathology Section

INTRODUCTION

Coronary heart disease (CHD) has become a main cause of morbidity and mortality worldwide [1]. In 2010, approximately 7,000,000 deaths were reported globally, and in which CHD took up the largest proportion of death causes and years of life lost [2]. Traditional factors, such as hypertension, diabetes and smoking have been proven to contribute to the occurrence and progression of CHD [3–5]. However, more existed risk factors leading to CHD susceptibility need to be explored. Till now, increasing molecular epidemiological studies have revealed the important role of genetic factors in CHD, and the genetic predisposition is attracting more and more attention [6, 7].

MicroRNAs (miRNAs) are small single-stranded non-coding RNA molecules which function in the post-transcriptional regulation of gene expression [8]. Emerging evidence has indicated that the functions of miRNAs appear to be in a variety of fundamental biological processes, involving proliferation, differentiation and stress resistance [9–11]. In addition, recent studies have shown that miRNAs take part in the regulation of glucose and lipid metabolism, the proliferation of smooth muscle cells and vascular inflammation, which play important roles in the pathogenesis of CHD [12–16].

By affecting the miRNA maturation and the binding to target mRNAs, single nucleotide polymorphisms (SNPs) located in pre-microRNA (pre-miR) genes may alter the expression levels of a large number of target genes and cause the complex functional consequences [17]. Therefore, functional SNPs in miRNA genes may affect disease susceptibility. Previous studies have confirmed that four common miRNA polymorphisms (rs2910164 G>C in miR-146a, rs11614913 T>C in miR-196a2, rs3746444 A>G in miR-499 and rs71428439 A>G in miR-149) were associated with several diseases, including various cancers and autoimmune diseases [18–21]. Recently, these four SNPs were under investigation to uncover the possible genetic predisposing to CHD, but the results were inconsistent. Therefore, we conducted a meta-analysis involving all related publications to assess the association between microRNA polymorphisms and CHD risk.

RESULTS

Characteristics of studies

In total, 285 relevant publications were retrieved according to the search strategy. Firstly, we excluded 254 articles after title reviewing and duplicate screening. Then, 19 studies including 6 reviews, 12 studies not for focus polymorphisms, and 1 study without available information [22] were excluded. Finally, 12 eligible articles (13 studies) published from 2012 to 2016 were selected in the meta-analysis, including ten studies on microRNA-146a rs2910164 G>C [23–32], seven studies on microRNA-196a2 rs11614913 T>C [23, 26, 27, 29, 31, 33], six publications on microRNA-499 rs3746444 A>G [23, 25, 26, 29, 31, 33], and two studies on microRNA-149 rs71428439 A>G [29, 34], respectively. The process of study selection was shown in Figure 1. Among the retrieved articles, nine articles [23, 24, 26–30, 33, 34] were written in English and three [25, 31, 32] in Chinese. Moreover, two of the studies involved Caucasians [24, 30], and eleven of them were conducted for Asians. The distribution of genotype was consistent with HWE in all studies but one study for microRNA-146a rs2910164 [32] and two for microRNA-499 rs3746444 polymorphism [25, 31]. Detailed characteristics of included studies were shown in Table 1.

Figure 1. Flow diagram of the study selection process.

Figure 1

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Table 1. Characteristics of case-control studies on microRNA polymorphisms and CHD risk included in the meta-analysis.

First author Year Country/Region Ethnicity Source of controls Case Control Genotype distribution Genotyping methods Age and sex matched P for HWEa
Case Control
microRNA-146a rs2910164 G>C CC GC GG CC GC GG
Sung JH 2016 Korea Asian Hospital 522 535 203 242 77 202 260 73 PCR-RFLP matched 0.460
Bastami M 2016 Iran Caucasian NA 300 300 34 155 111 22 128 150 Taqman matched 0.454
Huang SL 2015 China Asian Hospital 722 721 266 308 143 237 348 132 Taqman matched 0.830
Xiong XD 2014 China Asian Hospital 295 283 113 141 41 97 125 61 PCR-RFLP unmatched 0.086
Prithiksha R 2014 South Africa Asian NA 106 100 13 43 50 9 46 45 PCR-RFLP matched 0.569
Chen CR 2014 China Asian Hospital 919 889 187 463 269 153 435 301 PCR-LDR unmatched 0.846
Hamann L 2014 Germany Caucasian Population 206 200 12 74 120 10 73 117 PCR-HRM unmatched 0.748
Chen L 2013 China Asian Hospital 658 658 172 305 181 134 330 194 Taqman matched 0.769
Yang Y-a 2012 China Asian Population 853 948 272 392 165 271 457 189 Taqman matched 0.885
Li L 2012 China Asian Hospital 415 1010 149 184 82 345 455 210 PCR-RFLP unmatched 0.009
microRNA-196a2 rs11614913 T>C CC TC TT CC TC TT
Sung JH 2016 Korea Korean Hospital 522 535 107 236 179 108 274 153 PCR-RFLP matched 0.465
Huang SL 2015 China Asian Hospital 722 721 147 381 190 156 360 204 Taqman matched 0.905
Xiong XD 2014 China Asian Hospital 295 283 78 131 86 68 132 83 PCR-RFLP unmatched 0.278
Chen CR 2014 China Asian Hospital 919 889 157 450 312 161 406 322 PCR-LDR unmatched 0.097
Zhi H 2012 China Asian Hospital 916 584 155 470 291 98 278 208 PCR-RFLP matched 0.755
Yang Y-a 2012 China Asian Population 853 948 163 463 202 217 463 241 Taqman matched 0.853
Yang Y-b 2012 China Asian Population 1919 1840 433 971 493 389 921 528 Taqman matched 0.734
microRNA-499 rs3746444 G>A GG AG AA GG AG AA
Sung JH 2016 Korea Korean Hospital 522 535 9 155 358 13 168 354 PCR-RFLP matched 0.182
Xiong XD 2014 China Asian Hospital 295 283 3 65 227 4 67 212 PCR-RFLP unmatched 0.616
Chen CR 2014 China Asian Hospital 919 889 70 237 612 37 246 606 PCR-LDR unmatched 0.062
Chen L 2013 China Asian Hospital 658 658 46 149 463 26 158 474 Taqman matched 0.007
Zhi H 2012 China Asian Hospital 916 584 86 201 629 21 167 396 PCR-RFLP matched 0.517
Yang Y-a 2012 China Asian Population 853 948 28 210 589 28 212 683 Taqman matched 0.023
microRNA-149 rs71428439 G>A GG AG AA GG AG AA
Chen CR 2014 China Asian Hospital 919 889 155 389 375 124 381 384 PCR-LDR unmatched 0.062
Ding SL 2013 China Asian NA 289 296 64 130 95 38 126 132 PCR-DNA sequencing matched 0.360
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CHD: coronary heart disease. HWE: Hardy-Weinberg equilibrium. a HWE in control. NA: not available

Quantitative analysis

Meta-analysis for microRNA-146a rs2910164 G>C polymorphism

Ten eligible studies including 4,996 cases and 5,644 controls were included to assess the association between miR-146a rs2910164 polymorphism and CHD risk. The heterogeneity in all genetic models was not significant statistically (I2<0.5). So we used the fixed effect model to calculate the ORs and 95% CIs. Overall, an increased CHD risk was detected in all five genetic models (C vs. G: OR = 1.12, 95% CI = 1.06–1.18, P<0.01, I2 = 11.2%; CC vs. GG+GC: OR = 1.19, 95% CI = 1.09–1.30, P<0.01, I2 = 0%; GC + CC vs. GG: OR = 1.12, 95% CI = 1.03–1.23, P = 0.012, I2 = 43.6%; CC vs. GG: OR = 1.23, 95% CI = 1.10–1.38, P<0.01, I2 = 9.6%; GC vs. GG: OR = 1.06, 95% CI = 0.97–1.17, P = 0.211, I2 = 46.7%) (Figure 2, Table 2). Subgroup analyses of ethnicity disclosed similar results in Asians. In addition, significant associations were observed in subgroup analyses by source of controls and genotyping method (Table 2). The sensitivity analysis showed that the pooled ORs with corresponding 95%CI were not qualitatively changed by any single study in allelic, recessive, homozygous and heterozygous models, but dominant model (Figure 3). Publication bias was estimated by visual inspection of funnel plot and Egger's test, and the results revealed no asymmetrical evidence (Figure 4). The data of Egger's test supported the above results further (C vs. G: P = 0.682; CC vs. GG + GC: P = 0.283; GC + CC vs. GG: P = 0.911; CC vs. GG: P = 0.379; GC vs. GG: P = 0.877).

Figure 2. Forests for microRNA-146a rs2910164 G>C polymorphism and CHD.

Figure 2

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A. allele model (C vs. G); B. recessive model (CC vs. GG + GC); C. dominant model (GC + CC vs. GG); D. homozygote model (CC vs. GG).

Table 2. Summary ORs and 95% CI of microRNA-146a rs2910164 polymorphisms and CHD risk.

Locus N* Allele Recessive Dominant Homozygote Heterozygote
OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%)
Total 10 1.12 (1.06-1.18) <0.01 11.2 1.19 (1.09-1.30) <0.01 0 1.12 (1.03-1.23) 0.012 43.6 1.23 (1.10-1.38) <0.01 9.6 1.06 (0.97-1.17) 0.211 46.7
Ethnicity
 Asian 8 1.10 (1.04-1.17) <0.01 0 1.18 (1.08-1.29) <0.01 0 1.09 (0.99-1.20) 0.083 22.6 1.20 (1.07-1.35) <0.01 0 1.03(0.93-1.14) 0.631 30.5
 Caucasian 2 1.25 (0.88-1.77) 0.205 66.5 1.47 (0.92-2.35) 0.108 0 1.33 (0.80-2.21) 0.277 75.0 1.74 (1.07-2.84) 0.025 13.4 1.29 (0.79-2.11) 0.312 70.9
Source of controls
 Population 2 1.08 (0.95-1.22) 0.247 0 1.17 (0.96-1.42) 0.130 0 1.04 (0.85-1.27) 0.733 0 1.15 (0.89-1.49) 0.281 0 0.98 (0.80-1.22) 0.883 0
 Hospital 6 1.11 (1.04-1.19) <0.01 0 1.18 (1.07-1.31) <0.01 0 1.11 (0.99-1.24) 0.067 40.9 1.22 (1.07-1.39) <0.01 23.8 1.04 (0.93-1.17) 0.470 47.0
Method
 Taqman 4 1.15 (1.03-1.29) 0.017 50.0 1.24 (1.10-1.41) <0.01 0 1.14 (0.90-1.43) 0.281 67.3 1.23 (1.05-1.44) 0.012 41.9 1.05 (0.82-1.36) 0.698 69.9
 PCR-RFLP 4 1.07 (0.97-1.19) 0.177 0 1.10 (0.94-1.28) 0.227 0 1.09 (0.91-1.31) 0.347 45.0 1.16 (0.94-1.43) 0.165 22.9 1.05 (0.87-1.28) 0.598 44.2
Age and sex matched 6 1.11 (1.03-1.19) <0.01 35.3 1.21 (1.08-1.35) <0.01 0 1.08 (0.90-1.29) 0.420 52.7 1.18 (1.02-1.37) 0.024 25.0 1.00 (0.83-1.22) 0.981 54.1
Controls in HWE 9 1.13 (1.06-1.20) <0.01 17.0 1.21 (1.10-1.33) <0.01 0 1.13 (1.03-1.25) 0.012 49.4 1.25 (1.11-1.41) <0.01 15.7 1.07 (0.92-1.25) 0.396 52.5
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* Numbers of comparisons. PCR-RFLP: polymerase chain reaction-based restriction fragment length polymorphism. HWE: Hardy-Weinberg equilibrium.

Figure 3. Sensitivity analyses for microRNA-146a rs2910164 G>C polymorphism and CHD.

Figure 3

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A. allele model (C vs. G); B. recessive model (CC vs. GG + GC); C. dominant model (GC + CC vs. GG); D. homozygote model (CC vs. GG).

Figure 4. Funnel plots for microRNA-146a rs2910164 G>C polymorphism and CHD.

Figure 4

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A. allele model (C vs. G); B. recessive model (CC vs. GG + GC); C. dominant model (GC + CC vs. GG); D. homozygote model (CC vs. GG).

Meta-analysis for microRNA-196a2 rs11614913 T>C polymorphism

Seven original studies involving 6,668 cases and 6,335 controls were analyzed for miRNA-196a2 rs11614913 T>C polymorphism and CHD susceptibility. In the overall analysis, significant associations were found in the dominant model (TC + CC vs. TT: OR = 1.08, 95%CI = 1.00–1.17, P = 0.046, I2= 27.3%) and heterozygous model (TC vs. TT: OR = 1.10, 95%CI = 1.01–1.19, P = 0.029, I2= 40%) (Figure 5, Table 3). In the stratified analysis, significant results were observed in group with population-based controls as well as genotyping method of Taqman (Table 3). Publication bias analyses were performed, and the shapes of funnel plots (Supplementary Figure 1) were consistent with the Egger's test approved (C vs. T: P = 0.262; CC vs. TT + TC: P = 0.650; TC + CC vs. TT: P = 0.226; CC vs. TT: P = 0.220; TC vs. TT: P = 0.292). However, when sensitivity analysis was performed, some changes of the pooled ORs were detected under both dominant and heterozygous models (Supplementary Figure 2).

Figure 5. Forests for microRNA-196a2 rs11614913 T>C polymorphism and CHD.

Figure 5

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A. dominant model (TC + CC vs. TT); B. heterozygote model (TC vs. TT).

Table 3. Summary ORs and 95% CI of microRNA-196a2 rs11614913 polymorphisms and CHD risk.

Locus N* Allele Recessive Dominant Homozygote Heterozygote
OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%)
Total 7 1.03 (0.98-1.09) 0.252 0 0.99 (0.90-1.08) 0.801 6.0 1.08 (1.00-1.17) 0.046 27.3 1.05 (0.95-1.16) 0.370 0 1.10 (1.01-1.19) 0.029 40.0
Source of controls
 Population 2 1.03 (0.91-1.17) 0.615 60.3 0.95 (0.69-1.30) 0.736 81.3 1.13 (1.00-1.28) 0.042 0 1.05 (0.80-1.39) 0.711 64.8 1.15 (1.01-1.30) 0.032 0
 Hospital 5 1.02 (0.95-1.09) 0.671 0 0.98 (0.87-1.11) 0.793 0 1.05 (0.94-1.17) 0.389 44.8 1.01 (0.88-1.17) 0.863 0 1.04 (0.87-1.24) 0.666 55.4
Method
 Taqman 3 1.04 (0.97-1.11) 0.242 25.3 0.95 (0.78-1.16) 0.604 63.9 1.13 (1.01-1.25) 0.030 0 1.08 (0.94-1.23) 0.299 34.2 1.15 (1.02-1.28) 0.017 0
 PCR-RFLP 3 1.01 (0.91-1.12) 0.847 30.4 1.04 (0.87-1.25) 0.661 0 0.98 (0.74-1.29) 0.867 68.5 1.02 (0.83-1.25) 0.874 0 0.95 (0.69-1.32) 0.775 72.4
Age and sex matched 5 1.03 (0.97-1.09) 0.315 30.4 0.99 (0.90-1.09) 0.825 28.9 1.07 (0.93-1.22) 0.342 50.4 1.05 (0.94-1.18) 0.387 18.0 1.08 (0.93-1.26) 0.316 57.3
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* Numbers of comparisons. PCR-RFLP: polymerase chain reaction-based restriction fragment length polymorphism.

Meta-analysis for microRNA-499 rs3746444 A>G polymorphism

Six relevant studies comprising 4,163 patients and 3,897 controls were included in the meta-analysis for miRNA-499 rs3746444 A>G polymorphism and CHD risk. The pooled analyses indicated that this polymorphism was associated with an increased risk of CHD in three genetic models (G vs. A: OR = 1.11, 95% CI = 1.02–1.20, P = 0.015, I2 = 17.8%; GG vs. AA + AG: OR = 1.55, 95% CI = 1.07–2.27, P = 0.022, I2= 58.1%; GG vs. AA: OR = 1.54, 95% CI = 1.08–2.20, P = 0.017, I2= 52.6%) (Figure 6, Table 4). Subsequent subgroup analyses revealed similar results in the hospital-based control group, genotyping method of Taqman group as well as age and sex matched group (Table 4). No significant publication bias was found, indicating that the meta-analysis results are reliable (G vs. A: P = 0.092; GG vs. AA + AG: P = 0.156; AG + GG vs. AA: P = 0.182; GG vs. AA: P = 0.198; AG vs. AA: P = 0.821) (Supplementary Figure 3). However, further sensitivity analysis revealed that omission of each study made some significant differences on the findings (Supplementary Figure 4).

Figure 6. Forests for microRNA-499 rs3746444 A>G polymorphism and CHD.

Figure 6

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A. allele model (G vs. A); B. recessive model (GG vs. AA + AG); C. homozygote model (GG vs. AA).

Table 4. Summary ORs and 95% CI of microRNA-499 rs3746444 polymorphisms and CHD risk.

Locus N* Allele Recessive Dominant Homozygote Heterozygote
OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%) OR (95%CI) P I2(%)
Total 6 1.11 (1.02-1.20) 0.015 17.8 1.55 (1.07-2.27) 0.022 58.1 1.03 (0.94-1.13) 0.545 0 1.54 (1.08-2.20) 0.017 52.6 0.95 (0.85-1.05) 0.275 21.8
Source of controls
 Population 1 1.13 (0.94-1.36) 0.199 NA 1.12 (0.66-1.91) 0.676 NA 1.15 (0.93-1.42) 0.193 NA 1.16 (0.68-1.98) 0.588 NA 1.15 (0.92-1.43) 0.218 NA
 Hospital 5 1.10 (1.01-1.21) 0.039 33.7 1.68 (1.11-2.55) 0.014 55.9 1.00 (0.90-1.12) 0.990 0 1.65 (1.10-2.46) 0.015 52.4 0.90 (0.80-1.01) 0.061 0
Method
 Taqman 2 1.15 (1.01-1.32) 0.042 0 1.46 (1.02-2.10) 0.038 42.7 1.12 (0.96-1.31) 0.156 0 1.48 (1.03-2.12) 0.034 30.1 1.07 (0.90-1.26) 0.446 0.8
 PCR-RFLP 3 1.03 (0.90-1.17) 0.693 48.8 1.24 (0.42-3.66) 0.693 77.9 0.93 (0.79-1.08) 0.330 0 1.20 (0.42-3.39) 0.737 76.1 0.84 (0.71-0.99) 0.033 0
Age and sex matched 4 1.10 (1.00-1.22) 0.052 27.5 1.51 (0.89-2.57) 0.127 71.1 1.03 (0.92-1.15) 0.631 0 1.49 (0.91-2.45) 0.113 66.5 0.94 (0.79-1.12) 0.493 52.7
Controls in HWE 4 1.08 (0.98-1.20) 0.132 45.2 1.55 (0.86-2.79) 0.144 66.9 0.98 (0.87-1.11) 0.744 0 1.51 (0.85-2.66) 0.159 64.3 0.88 (0.77-1.00) 0.050 0
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* Numbers of comparisons. PCR-RFLP: polymerase chain reaction-based restriction fragment length polymorphism. HWE: Hardy-Weinberg equilibrium. NA: not available.

Meta-analysis for microRNA-149 rs71428439 A>G polymorphism

A total of 2 studies with 1,208 cases and 1,185 controls were selected in the meta-analysis. This polymorphism was not found to be significantly associated with CHD risk in all five models (G vs. A: OR = 1.30, 95% CI = 0.94–1.79, P = 0.107, I2 = 82.1%; GG vs. AA+AG: OR = 1.50, 95% CI = 0.98–2.27, P = 0.059, I2 = 64.2%; AG + GG vs. AA: OR = 1.31, 95% CI = 0.89–1.93, P = 0.169, I2 = 75.8%; GG vs. AA: OR = 1.67, 95% CI = 0.93–3.01, P = 0.086, I2 = 78.1%; AG vs. AA: OR = 1.18, 95% CI = 0.87–1.59, P = 0.281, I2 = 55.6%).

DISCUSSION

Coronary heart disease is the most common cause of morbidity and mortality in most regions worldwide. Although we have conducted some major advances in the understanding of cardiovascular disease in more recent decades, detailed pathogenesis of CHD remain to be explored. Nowadays, the association between polymorphisms of microRNAs and CHD risk is drawing more and more attention.

In the current meta-analysis, we comprehensively investigated the associations between microRNA-146a rs2910164 G>C, microRNA-196a2 rs11614913 T>C, microRNA-499 rs3746444 A>G and microRNA-149 rs71428439 A>G polymorphisms and CHD risk according to thirteen included case-control studies, consisting of 8,120 patients and 8,364 controls. Overall, significant increased risks of CHD were observed for microRNA-146a rs2910164, microRNA-196a2 rs11614913 and microRNA-499 rs3746444, but not miRNA-149 rs71428439.

As for miRNA-146a rs2910164 G>C, this is the latest and largest meta-analysis investigated the association with CHD risk. Compared with the previous meta-analysis with four studies including 2506 subjects [35], we found that the significant association existed in recessive model, as well as no association in heterozygous model. The advantages of our analysis are as follows. First, our meta-analysis had much larger sample size: we added another six recent studies involving 8,134 subjects which were not part of the previous meta-analysis [23, 24, 27, 29, 31, 32]. Second, sensitivity analyses showed that our results were statistically robust in four genetic models. Also, no significant publication bias was detected in our meta-analysis. Third, we performed a more comprehensive subgroup analyses. Stratification by ethnicity showed an increased CHD risk for microRNA-146a rs2910164 G>C polymorphism in Asians. Furthermore, similar increased results were observed in the group with genotyping method of Taqman, rather than PCR-RFLP. It revealed that Taqman was a more useful genotyping method to improve the accuracy of experiment.

To the best of our knowledge, this is the first meta-analysis assessing the association of miRNA-196a2 rs11614913 T>C, miRNA-499 rs3746444 A>G, and miRNA-149 rs71428439 A>G polymorphisms with CHD susceptibility. Interestingly, by increasing the sample size, the results of the combined analysis revealed a significant association with CHD risk for microRNA-196a2 rs11614913, even though no association was found in each single original study. How can we explain the association of miRNA-196a2 rs11614913 with CHD susceptibility? First, the miRNA-196a2 rs11614913 polymorphism involved a T to C nucleotide substitution and situated in the 3p strand of mature miRNA regions, which might affect both pre-miRNA maturation of 5p and 3p miRNAs and the interacting of target mRNAs to 3p mature miRNAs [36]. Second, it has been reported that miR-196a2 was closely associated with the regulation of annexin A1 (ANXA1) [37]. As an important modulator in atherosclerosis, ANXA1 can inhibit not only the monocyte adhesion to endothelium but also the expression of inflammatory enzymes, such as inducible cyclooxygenase 2 (COX-2) and phospholipase A2 [38, 39]. Additionally, the predicted targets of miR-196a2 included hundreds of genes ( http://www.targetscan.org). There also existed the possibility that other targets of miR-196a2 might play some roles in the development of CHD, despite it was unknown by far.

Our meta-analysis had several limitations. First of all, the ethnicity of most subjects was Asian in the current study and this restricted the general application of the results to other populations. Second, only articles published in English or Chinese were selected, potentially causing a language bias. Third, in the sensitivity analysis for miRNA-196a2 rs11614913 T>C and miRNA-499 rs3746444 A>G, we found that omission of each study made some significant differences on the results. Although it may be explained by the small number of studies included, the caution should be indicated when interpreting the association of these two miRNA polymorphisms with CHD. Third, the heterogeneity existed in our meta-analysis for miRNA-499 rs3746444 A>G and microRNA-149 rs71428439 A>G. For rs3746444, although subgroup and sensitivity analyses were performed, unfortunately, we have not found the sources of heterogeneity. Also, as for rs71428439, only two included studies were too small to analyze the sources of heterogeneity. Fourth, CHD is both multi-factorial disease influenced by genetic and environmental factors. However, in our current meta-analysis, the inter-gene and gene-environment interactions could not be conducted owing to the data deficiency. Last but not the least, genetic epidemiological studies show different genetic variants can predispose to different subtypes of CHD [40–42]. So subtypes of CHD, such as myocardial infarction, acute coronary syndrome and stable angina should be further analyzed. Unfortunately, we could not assess the difference among these subtypes of CHD due to insufficient statistical data in the literature.

In conclusion, the current meta-analysis demonstrated that three functional polymorphisms of microRNA-146a rs2910164 G>C, microRNA-196a2 rs11614913 T>C and microRNA-499 rs3746444 A>G might take important part in the development of CHD. Considering the limitations in the current meta-analysis, our results should be interpreted with caution. More eligible studies with rigorous design are needed to confirm the association of above polymorphisms in miRNA and CHD risk in the future.

MATERIALS AND METHODS

Search strategy

We searched four electronic databases (Pubmed, Embase, CNKI and Wanfang) for articles written in English or Chinese published prior to August 31, 2016. The following medical subject heading terms were used: (microRNA OR miRNA) AND (myocardial infarction OR ischemic heart disease OR ischaemic heart disease OR coronary heart disease OR coronary artery disease OR coronary syndrome OR coronary stenosis OR coronary disease OR cardiovascular disease OR CAD OR CHD OR MI) AND (genotype OR gene OR allele OR polymorphism OR variant OR SNP).

Study selection

All selected studies had to meet the following criteria: (1) published studies based on case-control design assessing the association of rs2910164 G>C in miR-146a, rs11614913 T>C in miR-196a2, rs3746444 A>G in miR-499 and rs71428439 A>G in miR-149 with CHD risk; (2) availability of allele or genotype frequency for calculating odds radio (OR) and their 95% confidence interval (CI). Studies were excluded if they investigated the progression, severity, phenotype modification, response to treatment, survival or family based studies. Moreover, meeting abstracts, case reports, editorials, review articles and non-English and non-Chinese articles were also excluded. For duplicate publications, the one with more complete design or larger sample size was finally selected.

Data extraction

The two of the authors independently extracted the data from each relevant study including the first author, publication year, study country/region, ethnicity of participants (such as Asian or Caucasian), sources of controls, genotyping method, case-control matched status, HWE status of controls and number of genotypes in CHD cases and controls. Disagreements were reconciled through group discussion. The Hardy-Weinberg equilibrium (HWE) was calculated based on the genotypes of the controls.

Statistical analysis

Heterogeneity among studies was examined with the I2 statistic and I2>50% indicates significant heterogeneity between the studies. Based on the test of heterogeneity, a pooled OR was calculated by using fixed or random effect model, along with the 95% CI to measure the strength of the genetic association. For the microRNA-146a rs2910164 G>C polymorphism, the pooled ORs were obtained for the allele contrast (C vs. G), recessive model (CC vs. GG+GC), dominant model (GC+CC vs. GG), homozygous (co-dominant) model (CC vs. GG) and heterozygous (co-dominant) model (GC vs. GG). Similar genetic models were also assessed for microRNA-196a2 rs11614913 T>C, microRNA-499 rs3746444 A>G and microRNA-149 rs71428439 A>G variants. Subgroup analyses of ethnicity, source of controls, genotyping methods, case-control matched status and HWE status of controls were also submitted to statistical testing. In order to evaluate the stability of the results, sensitivity analysis was used, which meant omitting one study at a time, and then compared to show whether a significant difference existed between the former and the latter results. Publication bias was examined by the visual inspection of funnel plot, and Egger's regression test. Data were analyzed and processed using Stata 12.0 (Stata Corporation, College Station, TX, USA). P<0.05 was considered statistically significant.

SUPPLEMENTARY MATERIALS FIGURES AND TABLES

Footnotes

CONFLICTS OF INTEREST

No conflicts of interest were disclosed.

GRANT SUPPORT

This work was supported by the National Natural Science Foundation of China (Grant No. 81302127, 81400950) and the Joint Specialized Research Fund for the Doctoral Program of Higher Education in China (Grant No. 20132104120018).

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