Abstract

Background: The relationship between antisense non-coding RNA (ncRNA) in the INK4 locus (ANRIL) polymorphisms and coronary artery disease (CAD) remains inconclusive. Thus, we conducted this meta-analysis to better evaluate the roles of ANRIL polymorphisms in CAD. Methods: Systematic literature search of PubMed, Medline, and Embase was performed to identify potential relevant articles. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the strength of association. Results: Fifteen studies were finally enrolled for analyses. Overall analyses suggested that rs1333040 (dominant model: P<0.0001; recessive model: P<0.0001; allele model: P<0.0001), rs1333049 (dominant model: P=0.02; allele model: P=0.02) and rs2383207 (additive model: P=0.004) polymorphisms were significantly associated with the risk of CAD. Further subgroup analyses showed that rs1333040, rs1333049, and rs2383207 polymorphisms were significantly correlated with the risk of CAD in East Asians, rs2383206 and rs10757274 polymorphisms were significantly correlated with the risk of CAD in West Asians, while rs2383206, rs10757274, and rs10757278 polymorphisms were significantly correlated with the risk of CAD in Caucasians. Conclusion: Our findings indicated that rs1333040, rs1333049, rs2383206, rs2383207, rs10757274, and rs10757278 polymorphisms might serve as genetic biomarkers of CAD in certain ethnicities.

Introduction

Coronary artery disease (CAD), featured by narrowing or even occlusion of coronary arteries, is the primary cause of death and disability worldwide [1,2]. Until now, the exact pathogenic mechanism of CAD remains unclear. Nevertheless, mounting evidence supports that genetic factors are crucial for its occurrence and development. In the first place, family aggregation of CAD was not uncommon, and past twin studies showed that the heredity grade of CHD was over 50% [3,4]. In the second place, numerous genetic variants were found to be associated with an increased risk of CAD by previous genetic association studies, and screening of common causal variants was also shown to be a cost-efficient way to predict the individual risk of developing CAD [5,6]. In summary, these findings jointly indicated that genetic predisposition to CAD played a central part in its pathogenesis.

Non-coding RNAs (ncRNAs) make up the vast majority of the human genome. Although ncRNAs do not encode proteins, it was evident that they play eminent roles in regulating expression levels of neighboring protein-encoding genes [7,8]. Therefore, dysregulation of ncRNAs may cause abnormal gene expression and give rise to the development of multiple diseases. Antisense ncRNA in the INK4 locus (ANRIL) is located on human chromosome 9p21, a region that has been repeated found to be associated with atherosclerosis and its related ischemia cardiovascular diseases like CAD [9]. Although the exact function of ANRIL is still unknown, it was shown that the expression levels of several neighboring protein-encoding genes like cyclin-dependent kinase inhibitors 2A (CDKN2A), CDKN2B, and methylthioadenosine phosphorylase (MTAP) were modulated by ANRIL [10]. Previous investigations demonstrated that the abovementioned proteins were abundantly expressed in atherosclerotic lesions, and they could promote atherosclerosis by impacting vascular remodeling, thrombogenesis, and plaque stability [9,10]. Hence, it is biologically plausible that these ANRIL polymorphisms may also be involved in atherosclerotic-related diseases like CAD. So far, some pilot studies already investigated potential correlations between ANRIL gene polymorphisms and the risk of CAD. But the results of these studies were controversial. Thus, we performed the present meta-analysis to better evaluate the roles of ANRIL polymorphisms in CAD.

Materials and methods

Literature search and inclusion criteria

This meta-analysis complied with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [11]. Potentially related literatures that were published before September 2018 were retrieved from PubMed, Medline, and Embase using the following combination of keywords: (antisense noncoding RNA in the INK4 locus OR CDKN2B antisense RNA OR ANRIL OR CDKN2B-AS long non-coding RNA) AND (polymorphism OR variant OR mutation OR genotype OR allele) AND (coronary heart disease OR coronary artery disease OR angina pectoris OR acute coronary syndrome OR myocardial infarction).

The references of retrieved articles were also screened to identify other potentially relevant studies.

To test the research hypothesis of this meta-analysis, included studies must meet all the following criteria: (i) case–control study on correlations between ANRIL polymorphisms and the risk of CAD; (ii) provide genotypic frequencies of investigated ANRIL polymorphisms in cases and controls; and (iii) full text in English available.

Studies were excluded if one of the following criteria was fulfilled: (i) not relevant to ANRIL polymorphisms and CAD; (ii) case reports or case series; and (iii) abstracts, reviews, comments, letters, and conference presentations.

For duplicate publications, we only included the study with the largest sample size for analyses.

Data extraction and quality assessment

The following data were extracted from included studies: (i) name of the first author; (ii) publication time; (iii) country and ethnicity; (iv) sample size; and (v) genotypic distributions of ANRIL polymorphisms in cases and controls. Additionally, the probability value (P-value) of Hardy–Weinberg equilibrium (HWE) was also calculated. When necessary, we wrote to the corresponding authors for raw data. We used the Newcastle–Ottawa scale (NOS) to assess the quality of eligible studies [12]. This scale has a score range of 0–9, and studies with a score of more than 7 were thought to be of high quality. Two reviewers conducted data extraction and quality assessment independently. Any disagreement between two reviewers was solved by discussion until a consensus was reached.

Statistical analyses

All statistical analyses were conducted with Review Manager Version 5.3.3 (The Cochrane Collaboration, Software Update, Oxford, United Kingdom). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate strength of associations between ANRIL polymorphisms and the risk of CAD in all possible genetic models, and P-values ≤0.05 were considered to be statistically significant. Between-study heterogeneities were evaluated with I2 statistic. If I2 was greater than 50%, random-effect models (REMs) would be used to pool the data. Otherwise, fixed-effect models (FEMs) would be employed for synthetic analyses. Subgroup analyses by ethnicity of participants were subsequently performed. Sensitivity analyses were conducted to examine the stability of synthetic results. Funnel plots were used to assess publication bias.

Results

Characteristics of included studies

We found 105 potential relevant articles. Amongst these articles, a total of 15 eligible studies were finally included for synthetic analyses (see Figure 1). The NOS score of eligible articles ranged from 7 to 8, which indicated that all included studies were of high quality. Baseline characteristics of included studies are shown in Table 1.

Flowchart of study selection for the present study

Figure 1
Flowchart of study selection for the present study
Figure 1
Flowchart of study selection for the present study
Table 1
The characteristics of included studies for ANRIL polymorphisms and CAD
First author, year Country Ethnicity Type of disease Sample size Genotype distribution P-value for HWE NOS score 
     Cases Controls   
rs1333040     TT/TC/CC   
Kumar (2011) India West Asian CAD 300/423 147/116/37 175/182/66 0.107 
Lin (2011) Taiwan East Asian MI 411/1352 228/156/27 649/573/130 0.829 
Qi (2012) China East Asian MI 142/192 91/44/7 90/93/9 0.013 
Scheffold (2011) Germany Caucasian MI 976/3532 292/503/181 889/1750/893 0.590 
Zhao (2016) China East Asian CAD 456/683 236/184/36 336/293/54 0.370 
rs1333049     GG/GC/CC   
Ahmed (2013) Pakistan West Asian MI 294/290 65/166/63 87/180/23 <0.001 
Çakmak (2015) Turkey Caucasian CAD 220/240 54/120/46 85/115/40 0.917 
Haslacher (2016) Austria Caucasian MI 493/431 139/236/118 112/222/97 0.514 
Lin (2011) Taiwan East Asian MI 423/1361 100/218/105 395/655/311 0.213 
Qi (2012) China East Asian MI 142/192 42/79/21 50/99/43 0.651 
Scheffold (2011) Germany Caucasian MI 976/999 212/518/246 292/502/205 0.688 
Yang (2018) China East Asian CAD 542/549 162/269/111 176/273/100 0.743 
rs2383206     AA/AG/GG   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 56/124/130 143/279/138 0.934 
Kumar (2011) India West Asian CAD 309/434 112/143/54 116/224/94 0.467 
Scheffold (2011) Germany Caucasian MI 976/9053 183/521/272 2489/4583/1981 0.017 
Shendy (2017) Egypt West Asian CAD 100/50 51/42/7 23/15/12 0.009 
Zhou (2008) China East Asian CAD 1360/1360 373/600/387 382/669/309 0.623 
rs2383207     GG/GA/AA   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 139/121/50 147/277/136 0.807 
Çakmak (2015) Turkey Caucasian CAD 220/240 83/101/36 102/118/20 0.079 
Chen (2009) China East Asian CAD 212/232 107/69/36 71/114/47 0.920 
El-Menyar (2015) Egypt West Asian CAD 236/152 146/77/12 84/58/10 0.998 
Kumar (2011) India West Asian CAD 301/424 137/124/40 174/190/60 0.485 
Lin (2011) Taiwan East Asian MI 415/1349 214/162/39 568/609/172 0.660 
Qi (2012) China East Asian MI 142/192 78/55/9 82/93/17 0.192 
Scheffold (2011) Germany Caucasian MI 976/3532 183/519/274 1016/1770/746 0.628 
Yang (2018) China East Asian CAD 540/548 247/236/57 244/251/53 0.317 
Zhou (2008) China East Asian CAD 1360/1360 702/520/138 592/605/163 0.659 
rs10757274     GG/GA/AA   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 61/119/130 156/283/121 0.728 
El-Menyar (2015) Egypt West Asian CAD 309/434 122/133/54 143/206/85 0.486 
Kumar (2011) India West Asian CAD 310/439 116/135/59 144/210/85 0.591 
Mafi Golchin (2017) Iran West Asian CAD 103/102 40/47/16 22/51/29 0.962 
Scheffold (2011) Germany Caucasian MI 976/9053 208/515/253 2752/4543/1758 0.131 
Zhao (2016) China East Asian CAD 291/385 82/137/72 130/188/67 0.945 
rs10757278     GG/GA/AA   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 59/135/116 159/294/107 0.162 
Chen (2009) China East Asian CAD 212/232 107/69/36 71/114/47 0.920 
El-Menyar (2015) Egypt West Asian CAD 309/427 89/142/78 103/214/110 0.957 
Scheffold (2011) Germany Caucasian MI 976/718 211/522/243 224/366/128 0.308 
Shendy (2017) Egypt West Asian CAD 100/50 37/45/18 13/18/19 0.057 
First author, year Country Ethnicity Type of disease Sample size Genotype distribution P-value for HWE NOS score 
     Cases Controls   
rs1333040     TT/TC/CC   
Kumar (2011) India West Asian CAD 300/423 147/116/37 175/182/66 0.107 
Lin (2011) Taiwan East Asian MI 411/1352 228/156/27 649/573/130 0.829 
Qi (2012) China East Asian MI 142/192 91/44/7 90/93/9 0.013 
Scheffold (2011) Germany Caucasian MI 976/3532 292/503/181 889/1750/893 0.590 
Zhao (2016) China East Asian CAD 456/683 236/184/36 336/293/54 0.370 
rs1333049     GG/GC/CC   
Ahmed (2013) Pakistan West Asian MI 294/290 65/166/63 87/180/23 <0.001 
Çakmak (2015) Turkey Caucasian CAD 220/240 54/120/46 85/115/40 0.917 
Haslacher (2016) Austria Caucasian MI 493/431 139/236/118 112/222/97 0.514 
Lin (2011) Taiwan East Asian MI 423/1361 100/218/105 395/655/311 0.213 
Qi (2012) China East Asian MI 142/192 42/79/21 50/99/43 0.651 
Scheffold (2011) Germany Caucasian MI 976/999 212/518/246 292/502/205 0.688 
Yang (2018) China East Asian CAD 542/549 162/269/111 176/273/100 0.743 
rs2383206     AA/AG/GG   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 56/124/130 143/279/138 0.934 
Kumar (2011) India West Asian CAD 309/434 112/143/54 116/224/94 0.467 
Scheffold (2011) Germany Caucasian MI 976/9053 183/521/272 2489/4583/1981 0.017 
Shendy (2017) Egypt West Asian CAD 100/50 51/42/7 23/15/12 0.009 
Zhou (2008) China East Asian CAD 1360/1360 373/600/387 382/669/309 0.623 
rs2383207     GG/GA/AA   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 139/121/50 147/277/136 0.807 
Çakmak (2015) Turkey Caucasian CAD 220/240 83/101/36 102/118/20 0.079 
Chen (2009) China East Asian CAD 212/232 107/69/36 71/114/47 0.920 
El-Menyar (2015) Egypt West Asian CAD 236/152 146/77/12 84/58/10 0.998 
Kumar (2011) India West Asian CAD 301/424 137/124/40 174/190/60 0.485 
Lin (2011) Taiwan East Asian MI 415/1349 214/162/39 568/609/172 0.660 
Qi (2012) China East Asian MI 142/192 78/55/9 82/93/17 0.192 
Scheffold (2011) Germany Caucasian MI 976/3532 183/519/274 1016/1770/746 0.628 
Yang (2018) China East Asian CAD 540/548 247/236/57 244/251/53 0.317 
Zhou (2008) China East Asian CAD 1360/1360 702/520/138 592/605/163 0.659 
rs10757274     GG/GA/AA   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 61/119/130 156/283/121 0.728 
El-Menyar (2015) Egypt West Asian CAD 309/434 122/133/54 143/206/85 0.486 
Kumar (2011) India West Asian CAD 310/439 116/135/59 144/210/85 0.591 
Mafi Golchin (2017) Iran West Asian CAD 103/102 40/47/16 22/51/29 0.962 
Scheffold (2011) Germany Caucasian MI 976/9053 208/515/253 2752/4543/1758 0.131 
Zhao (2016) China East Asian CAD 291/385 82/137/72 130/188/67 0.945 
rs10757278     GG/GA/AA   
Abdullah (2008) U.S.A. Caucasian CAD 310/560 59/135/116 159/294/107 0.162 
Chen (2009) China East Asian CAD 212/232 107/69/36 71/114/47 0.920 
El-Menyar (2015) Egypt West Asian CAD 309/427 89/142/78 103/214/110 0.957 
Scheffold (2011) Germany Caucasian MI 976/718 211/522/243 224/366/128 0.308 
Shendy (2017) Egypt West Asian CAD 100/50 37/45/18 13/18/19 0.057 

P-values ≤0.05 were considered to be statistically significant. Abbreviation: MI, myocardial infarction.

Overall and subgroup analyses

To investigate potential correlations between ANRIL polymorphisms and the risk of CAD, five studies about rs1333040 polymorphism, seven studies about rs1333049 polymorphism, five studies about rs2383206 polymorphism, ten studies about rs2383207 polymorphism, six studies about rs10757274 polymorphism, and five studies about rs10757278 polymorphism were enrolled for analyses.

Significant associations with the risk of CAD were detected for rs1333040 (dominant model: P<0.0001, OR = 1.30, 95% CI: 1.17–1.44; recessive model: P<0.0001, OR = 0.71, 95% CI: 0.62–0.82; allele model: P<0.0001, OR = 1.25, 95% CI: 1.16–1.34), rs1333049 (dominant model: P=0.02, OR = 0.81, 95% CI: 0.67–0.96; allele model: P=0.02, OR = 0.86, 95% CI: 0.76–0.98), and rs2383207 (additive model: P=0.004, OR = 0.80, 95% CI: 0.69–0.93) polymorphisms in overall analyses.

Further subgroup analyses according to ethnicity of participants revealed that rs1333040 (dominant model and allele model), rs1333049 (recessive model), and rs2383207 (all genetic models) polymorphisms were significantly correlated with the risk of CAD in East Asians, rs2383206 (dominant model and allele model) and rs10757274 (dominant model and allele model) polymorphisms were significantly correlated with the risk of CAD in West Asians, while rs2383206 (dominant model, recessive model, and allele model), rs10757274 (dominant model, recessive model, and allele model) and rs10757278 (dominant model, recessive model, and allele model) polymorphisms were significantly correlated with the risk of CAD in Caucasians (see Table 2).

Table 2
Overall and subgroup analyses for ANRIL polymorphisms and CAD
Polymorphisms Population Sample size Dominant comparison Recessive comparison Additive comparison Allele comparison 
   P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) 
rs1333040 Overall 2285/6182 <0.0001 1.30 (1.17–1.44) <0.0001 0.71 (0.62–0.82) 0.12 0.85 (0.691.04) <0.0001 1.25 (1.16–1.34) 
 East Asian 1009/2227 0.03 1.37 (1.04–1.80) 0.18 0.82 (0.611.09) 0.06 0.76 (0.571.01) 0.001 1.22 (1.08–1.38) 
rs1333049 Overall 3090/4062 0.02 0.81 (0.67–0.96) 0.08 1.24 (0.981.57) 0.35 1.05 (0.951.15) 0.02 0.86 (0.76–0.98) 
 Caucasian 1689/1670 0.37 0.85 (0.601.20) 0.93 1.02 (0.691.51) 0.32 1.10 (0.911.31) 0.59 0.93 (0.721.20) 
 East Asian 1107/2102 0.16 0.82 (0.611.09) 0.02 1.19 (1.03–1.38) 0.31 1.07 (0.941.20) 0.06 0.87 (0.751.01) 
rs2383206 Overall 3055/11457 0.60 0.91 (0.641.29) 0.33 1.18 (0.841.65) 0.36 0.90 (0.721.13) 0.45 0.91 (0.721.16) 
 Caucasian 1286/9613 <0.0001 0.61 (0.53–0.71) 0.02 1.71 (1.08–2.71) 0.61 0.88 (0.531.45) 0.0002 0.68 (0.56–0.84) 
 West Asian 409/484 0.006 1.49 (1.12–1.99) 0.19 0.47 (0.151.46) 0.83 1.08 (0.532.19) 0.002 1.36 (1.12–1.65) 
rs2383207 Overall 4712/8589 0.09 1.29 (0.961.72) 0.66 0.94 (0.731.22) 0.004 0.80 (0.69–0.93) 0.15 1.17 (0.951.45) 
 Caucasian 1506/4332 0.97 1.02 (0.422.47) 0.58 1.21 (0.622.35) 0.48 0.88 (0.611.26) 0.94 0.98 (0.551.75) 
 East Asian 2669/3681 0.0003 1.45 (1.18–1.79) 0.03 0.83 (0.71–0.98) <0.0001 0.77 (0.69–0.85) 0.0005 1.28 (1.11–1.48) 
 West Asian 537/576 0.08 1.24 (0.971.58) 0.57 0.89 (0.611.32) 0.15 0.84 (0.651.07) 0.12 1.16 (0.961.39) 
rs10757274 Overall 2299/10973 0.89 0.98 (0.691.39) 0.27 1.23 (0.851.76) 0.15 0.86 (0.701.06) 0.89 0.98 (0.691.39) 
 Caucasian 1286/9613 <0.0001 0.62 (0.54–0.72) 0.03 1.92 (1.08–3.42) 0.54 0.83 (0.461.50) 0.001 0.65 (0.50–0.84) 
 West Asian 722/975 0.003 1.36 (1.11–1.67) 0.17 0.84 (0.661.08) 0.08 0.84 (0.691.02) 0.04 1.28 (1.01–1.62) 
rs10757278 Overall 1907/1987 0.81 1.09 (0.631.87) 0.72 1.10 (0.671.81) 0.24 0.83 (0.611.13) 0.87 1.03 (0.701.52) 
 Caucasian 1286/1278 <0.0001 0.60 (0.50–0.73) 0.008 1.95 (1.19–3.19) 0.61 0.89 (0.571.39) <0.0001 0.65 (0.52–0.81) 
 West Asian 409/477 0.06 1.33 (0.981.80) 0.35 0.63 (0.241.66) 0.54 0.92 (0.701.20) 0.22 1.37 (0.832.27) 
Polymorphisms Population Sample size Dominant comparison Recessive comparison Additive comparison Allele comparison 
   P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) 
rs1333040 Overall 2285/6182 <0.0001 1.30 (1.17–1.44) <0.0001 0.71 (0.62–0.82) 0.12 0.85 (0.691.04) <0.0001 1.25 (1.16–1.34) 
 East Asian 1009/2227 0.03 1.37 (1.04–1.80) 0.18 0.82 (0.611.09) 0.06 0.76 (0.571.01) 0.001 1.22 (1.08–1.38) 
rs1333049 Overall 3090/4062 0.02 0.81 (0.67–0.96) 0.08 1.24 (0.981.57) 0.35 1.05 (0.951.15) 0.02 0.86 (0.76–0.98) 
 Caucasian 1689/1670 0.37 0.85 (0.601.20) 0.93 1.02 (0.691.51) 0.32 1.10 (0.911.31) 0.59 0.93 (0.721.20) 
 East Asian 1107/2102 0.16 0.82 (0.611.09) 0.02 1.19 (1.03–1.38) 0.31 1.07 (0.941.20) 0.06 0.87 (0.751.01) 
rs2383206 Overall 3055/11457 0.60 0.91 (0.641.29) 0.33 1.18 (0.841.65) 0.36 0.90 (0.721.13) 0.45 0.91 (0.721.16) 
 Caucasian 1286/9613 <0.0001 0.61 (0.53–0.71) 0.02 1.71 (1.08–2.71) 0.61 0.88 (0.531.45) 0.0002 0.68 (0.56–0.84) 
 West Asian 409/484 0.006 1.49 (1.12–1.99) 0.19 0.47 (0.151.46) 0.83 1.08 (0.532.19) 0.002 1.36 (1.12–1.65) 
rs2383207 Overall 4712/8589 0.09 1.29 (0.961.72) 0.66 0.94 (0.731.22) 0.004 0.80 (0.69–0.93) 0.15 1.17 (0.951.45) 
 Caucasian 1506/4332 0.97 1.02 (0.422.47) 0.58 1.21 (0.622.35) 0.48 0.88 (0.611.26) 0.94 0.98 (0.551.75) 
 East Asian 2669/3681 0.0003 1.45 (1.18–1.79) 0.03 0.83 (0.71–0.98) <0.0001 0.77 (0.69–0.85) 0.0005 1.28 (1.11–1.48) 
 West Asian 537/576 0.08 1.24 (0.971.58) 0.57 0.89 (0.611.32) 0.15 0.84 (0.651.07) 0.12 1.16 (0.961.39) 
rs10757274 Overall 2299/10973 0.89 0.98 (0.691.39) 0.27 1.23 (0.851.76) 0.15 0.86 (0.701.06) 0.89 0.98 (0.691.39) 
 Caucasian 1286/9613 <0.0001 0.62 (0.54–0.72) 0.03 1.92 (1.08–3.42) 0.54 0.83 (0.461.50) 0.001 0.65 (0.50–0.84) 
 West Asian 722/975 0.003 1.36 (1.11–1.67) 0.17 0.84 (0.661.08) 0.08 0.84 (0.691.02) 0.04 1.28 (1.01–1.62) 
rs10757278 Overall 1907/1987 0.81 1.09 (0.631.87) 0.72 1.10 (0.671.81) 0.24 0.83 (0.611.13) 0.87 1.03 (0.701.52) 
 Caucasian 1286/1278 <0.0001 0.60 (0.50–0.73) 0.008 1.95 (1.19–3.19) 0.61 0.89 (0.571.39) <0.0001 0.65 (0.52–0.81) 
 West Asian 409/477 0.06 1.33 (0.981.80) 0.35 0.63 (0.241.66) 0.54 0.92 (0.701.20) 0.22 1.37 (0.832.27) 

The values in bold represent that there are statistically significant differences between cases and controls. P-values ≤0.05 were considered to be statistically significant. Abbreviation: MI, myocardial infarction.

Sensitivity analyses

We performed sensitivity analyses by excluding studies that deviated from HWE. No alterations of results were detected in sensitivity analyses, which suggested that our findings were statistically reliable.

Publication biases

Publication biases were evaluated with funnel plots. We did not find obvious asymmetry of funnel plots in any comparisons, which indicated that our findings were unlikely to be impacted by severe publication biases.

Discussion

To the best of our knowledge, this is so far the most comprehensive meta-analysis on correlations between ANRIL polymorphisms and the risk of CAD. The overall analyses suggested that rs1333040, rs1333049, and rs2383207 polymorphisms were significantly associated with CAD risk in overall population. Further subgroup analyses revealed that rs1333040, rs1333049, and rs2383207 polymorphisms were significantly correlated with the risk of CAD in East Asians, rs2383206 and rs10757274 polymorphisms were significantly correlated with the risk of CAD in West Asians, while rs2383206, rs10757274, and rs10757278 polymorphisms were significantly correlated with the risk of CAD in Caucasians.

There are several points that need to be addressed about this meta-analysis. First, the exact function of ANRIL is still unclear, and therefore the underlying mechanisms of our positive findings need to be elucidated by future investigations. Second, the pathogenic mechanism of CAD is highly complex, and therefore it is unlikely that a single gene polymorphism can significantly contribute to its development. As a result, to better illustrate potential correlations of certain gene polymorphisms with CAD, we strongly recommend further studies to perform haplotype analyses and explore potential gene–gene interactions. Thirdly, according to a previous investigation, subjects carrying mutant allele of rs1333049 polymorphism had elevated TC, TG and LDL-C levels, and this may partially explain the observed significant correlation between this polymorphism and the risk of CAD. For other investigated polymorphisms, however, future studies are needed to investigate whether these polymorphisms are correlated with clinical and biochemical parameters of CAD [13].

As with all meta-analysis, the present study certainly has some limitations. First, our results were derived from unadjusted analyses, and lack of further adjusted analyses for age, gender, and co-morbidity conditions may impact the reliability of our findings [14]. Second, obvious heterogeneities were found in several subgroups, which indicated that the controversial results of included studies could not be fully explained by differences in ethnic background and type of disease, and other baseline characteristics of participants may also contribute to between-study heterogeneities [15]. Third, associations between ANRIL polymorphisms and CAD may also be modified by gene-gene and gene-environmental interactions. However, most eligible studies did not consider these potential interactions, which impeded us to perform relevant analyses accordingly [16]. On account of abovementioned limitations, our findings should be cautiously interpreted.

Conclusion

In conclusion, our meta-analysis suggested that rs1333040, rs1333049, rs2383206, rs2383207, rs10757274, and rs10757278 polymorphisms might serve as genetic biomarkers of CAD in certain ethnicities. However, further well-designed studies are still warranted to confirm our findings.

Author contribution

L.H. conceived the study and participated in its design. L.H. and G.S. conducted the systematic literature review. X.W. performed data analyses. L.H. drafted the manuscript. All authors gave final approval and agreed to be accountable for all aspects of work ensuring integrity and accuracy.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Competing interests

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

Abbreviations

     
  • ANRIL

    antisense non-coding RNA in the INK4 locus

  •  
  • CAD

    coronary artery disease

  •  
  • CI

    confidence interval

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • ncRNA

    non-coding RNA

  •  
  • NOS

    Newcastle–Ottawa scale

  •  
  • OR

    odds ratio

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