Association between caspase recruitment domain-containing protein 8 rs2043211 polymorphism and cardiovascular disease susceptibility: A systematic review and meta-analysis

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Address for correspondence: Yan He, MD, Department of Geriatrics Cardiology, First Affiliated Hospital of Guangxi Medical University; 6, Shuangyong Road, Nanning, 530021 Guangxi-China

Phone: 13877109677 E-mail: hyxjwxy@126.com Accepted Date: 18.05.2018 Available Online Date: 24.07.2018

Huijuan Huang, Qi Bi, Heng Wei, Beibei Luo, Yan He

Department of Geriatrics Cardiology, First Affiliated Hospital of Guangxi Medical University; Nanning-China

Association between caspase recruitment domain-containing

protein 8 rs2043211 polymorphism and cardiovascular disease

susceptibility: A systematic review and meta-analysis

Introduction

The term cardiovascular diseases (CVDs) refers to diseases of the heart and/or blood vessels. CVDs are now recognized as the leading cause of death worldwide. In 2013, there were >54 million deaths (95% uncertainty interval=53.6–56.3 million) glob-ally, and 32% of these deaths were attributable to CVDs (1). The three major causes of all CVD deaths are ischemic heart disease, stroke, and hypertensive heart disease (2). CVDs have been asso-ciated with lifestyle and environmental factors, as well as genetic markers that have been identified in large genome-wide associa-tion studies (GWAs). GWAs have been extensively used to study common complex diseases, such as coronary artery disease (CAD). They have revealed 153 CAD suggestive loci, of which at least 46 have been validated as having genome-wide significance (3). However, additional genetic factors for CVDs need to be elu-cidated. In recent years, it has been determined that inflamma-somes are associated with CVDs; therefore, caspase recruitment

domain-containing protein 8 (CARD8) should more seriously con-sidered as an inflammasome constituent (4).

The CARD8 gene, which is also called the tumor-upregulated CARD-containing antagonist of caspase 9 (TUCAN), has been widely discovered in inflammatory diseases, including rheuma-toid arthritis, gout, and aspirin-induced asthma (5-7). To date, CARD8-C10X (rs2043211) is the only CARD8 single nucleotide polymorphism (SNP) that has been discussed as one of the com-ponents of the NLRP3 inflammasome. It is encoded by exon 13 in 19q13, and it changes cysteine to a premature termination codon at codon 10, thus influencing the protein’s function in inflamma-some-mediated processes and nuclear factor (NF)-κB suppres-sion (8, 9). Statistics have shown that CARD8 polymorphisms are related to the CVD risk (10), but several studies have reported different conclusions (8, 11, 12). Here, we present the first meta-analysis verifying the association between SNP rs2043211 in the CARD8 gene and CVDs.

Objective: To verify the association between the caspase recruitment domain-containing protein 8 (CARD8) single nucleotide polymorphism rs2043211 in the CARD8 gene and cardiovascular diseases.

Methods: A comprehensive search was conducted for related literature in the PubMed, Embase, Cochrane Library, Web of Science, Wanfang Data, and China National Knowledge Infrastructure databases. At last, Six eligible case-control studies were included in this meta-analysis. Crude odds ratios (ORs) and 95% confidence intervals (CI) were calculated to estimate the strength of the association between CARD8 rs2043211 polymorphisms and the susceptibility of cardiovascular diseases.

Results: For the homozygous model, OR was 1.21 (1.08-1.36, I2_{=0.0%, P}

heterogeneity=0.542). For the heterozygous model (AT vs. AA), OR was 1.20

(1.04–1.38, I2_{=57.2%, P}

heterogeneity=0.039). For the dominant model, OR was 1.24 (1.14-1.34, I2=38.5%, Pheterogeneity=0.149). For the allele model, OR was

0.96 (0.77-1.20, I2_{=92.7%, P}

heterogeneity=0.000). For the recessive model, OR was 1.00 (0.91-1.10, I2=48.4%, Pheterogeneity=0.085).

Conclusion: The present study showed that CARD8 rs2043211 polymorphism is associated with cardiovascular diseases. (Anatol J Cardiol 2018; 20: 70-6)

Keywords: caspase recruitment domain-containing protein 8, cardiovascular diseases, polymorphism, meta-analysis

A

BSTRACT

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Methods

Search strategy

A comprehensive search was conducted for related litera-ture in the PubMed, Embase, Cochrane Library, Web of Science, Wanfang Data, and China National Knowledge Infrastructure databases until March 2018 with the keywords “CARD8” and “Cardiovascular Diseases.” In addition, the references of the retrieved articles were also reviewed.

Inclusion and exclusion criteria

All the studies included in the meta-analysis were case-control studies that focused on the association between SNPs or haplotypes in the CARD8 gene and CVDs. The studies were excluded if the genotype frequencies in the cases and controls were unavailable, despite contacting the authors via e-mail. In addition, case reports, reviews, and unpublished data were ex-cluded. The inclusion and exclusion criteria were independently screened by two investigators. Flow diagram of studies included in this meta-analysis is shown in Figure 1.

Data extraction

Three reviewers independently extracted the following in-formation from all the identified studies using a standardized data collection form: author name, publication year, ethnicity, location, number of cases and controls, disease type, source of control, the Hardy–Weinberg equilibrium (HWE) in the controls, genotyping method, and odds ratio (OR) with corresponding 95% confidence intervals (CIs). Any disagreements were resolved by a group discussion. The data was merged in cases with the same author in the same experimental conditions but with

differ-ent disease subgroups. The degree of heterogeneity was measured using the

follow-ing criteria: if the heterogeneity analysis results were I2_≥50%, a random-effects or fixed-effects model was used. Therefore, in this paper, heterozygous and alleles models were used with a random-effects model. Additionally, the publication bias was confirmed by a funnel plot and Egger’s test with a p-value of <0.05; otherwise, it was considered to have no publication bias.

Statistical analysis

To evaluate the effect of an individual study on the overall CVD risk, a leave-one-out sensitivity analysis with a recomput-ed poolrecomput-ed OR was adoptrecomput-ed. HWE was calculatrecomput-ed using a chi-squared test for each study in the control groups. We used the random-effects model when I2_{≥50%, which suggested that the} results of this meta-analysis were stable and robust. The entire statistical analysis involved in this meta-analysis was performed using Comprehensive Meta-Analysis version 2 software (Bio-stat, NJ, USA; https://www.meta-analysis.com).

Trial sequential analysis

To reduce the systematic errors (bias) and random errors (chance), to adjust the threshold for statistical significance, and Records identified through

database searching (n=41) Identification Screening Elig ibility Inc luded

Additional records identified through other sources

(n=0)

Records after duplicates removed (n=12)

Records screened (n=11) Full-text articles assessed for eligibility

(n=10) Studies included in qualitative synthesis (n=8) Studies included in quantitative synthesis (meta-analysis) (n=6) Full-text articles excluded without complete data (n=2) Records excluded (n=1)

Figure 1. Flow diagram of studies included in this meta-analysis*

Figure 2. The association of CARD8 rs2043211 (T>A) with CVD risk in different genetic models. a) Dominant model (TT+AT vs. AA). b) Homo-zygous model (TT vs. AA). c) HeteroHomo-zygous model (AT vs. AA)

Study name Statistics for each study

Dominant Odds Lower Upper P- Cases Controls ratio limit limit Value

Garcia-B.M.2013 0.985 0.771 1.259 0.904 177/322 715/1292 Paramel.G.V.2013 1.228 0.990 1.522 0.061 339/539 584/1007 Bai.Y.2014 1.362 1.196 1.550 0.000 1970/2491 1930/2625 Zhou.D.2016 (ASC) 1.038 0.787 1.370 0.790 267/450 222/380 Zhou.D.2016 (CAD) 1.125 0.863 1.465 0.384 305/515 226/401 Zhang.K.2017 1.359 1.088 1.697 0.007 567/758 544/793 1.237 1.139 1.344 0.000 TT+AT/Total 11.44 14.91 40.90 13.95 0.5 1 2 8.95 9.86 Relative weight a

Homozygous Odds Lower Upper P- Cases Controls ratio limit limit Value

Garcia-B.M.2013 0.928 0.603 1.428 0.732 31/176 133/710 Paramel.G.V.2013 1.236 0.899 1.700 0.192 83/283 142/565 Bai.Y.2014 1.158 0.987 1.357 0.071 578/1099 666/1361 Zhou.D.2016 1.260 0.788 2.015 0.334 54/237 37/195 Zhou.Dong.2016 1.271 0.813 1.986 0.293 61/271 40/215 Zhang.K.2017 1.494 1.131 1.974 0.005 196/387 171/420 1.213 1.082 1.359 0.001 6.97 12.75 51.19 16.70 0.5 1 2 5.88 6.50 Relative weight AA/Total b

Heterozygous Odds Lower Upper P- Cases Controls ratio limit limit Value

Garcia-B.M.2013 0.998 0.772 1.291 0.989 146/291 582/1159 Paramel.G.V.2013 1.225 0.975 1.539 0.081 256/456 442/865 Bai.Y.2014 1.469 1.281 1.684 0.000 1392/1913 1264/1959 Zhou.D.2016 0.994 0.744 1.329 0.968 213/396 185/343 Zhou.Dong.2016 1.093 0.829 1.442 0.528 244/454 186/361 Zhang.K.2017 1.297 1.023 1.644 0.032 371/562 373/622 1.197 1.038 1.381 0.013 15.25 17.05 23.65 16.47 0.5 1 2 13.44 14.13 Relative weight AT/Total c

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Figure 3. The associations of rs2043211 with CVDs in different genetic models by subgroup. a) Homozygous model (TT vs. AA) by ethnicity, b) Homo-zygous model (TT vs. AA) by etiology, c) Dominant model (TT+AT vs. AA) by etiology, (D) Dominant model (TT+AT vs. AA) by ethnicity, e) HeteroHomo-zygous model (AT vs. AA) by ethnicity

b

Etiology Odds Lower Upper P- Cases Controls

ratio limit limit Value

MI Paramel.G.V.2013 1.236 0.899 1.700 0.192 83/283 142/565 MI Bai.Y.2014 1.158 0.987 1.357 0.071 578/1099 666/1361 MI Zhou.D.2016 (CAD) 1.260 0.788 2.015 0.334 54/237 37/195 MI Zhou.D.2016 (ASC) 1.271 0.813 1.986 0.293 61/271 40/215 MI 1.188 1.042 1.353 0.010 Other Garcia-B.M.2013 0.928 0.603 1.428 0.732 31/176 133/710 Other Zhang.K.2017 1.494 1.131 1.974 0.005 196/387 171/420 Other 1.299 1.028 1.641 0.029 Overall 1.213 1.082 1.359 0.001 Study name

Group by Statistics for each study Exposed/Total Odds ratio and 95% CI

16.71 67.07 7.71 8.52 29.44 70.56 Relative weight c

Etiology Odds Lower Upper P- Cases Controls

MI Paramel.G.V.2013 1.228 0.990 1.522 0.061 339/539 584/1007 MI Bai.Y.2014 1.362 1.196 1.550 0.000 1970/2491 1930/2625 MI Zhou.D.2016 (ASC) 1.038 0.787 1.370 0.790 267/450 222/380 MI Zhou.D.2016 (CAD) 1.125 0.863 1.465 0.384 305/515 226/401 MI 1.259 1.144 1.386 0.000 Other Garcia-B.M.2013 0.985 0.771 1.259 0.904 177/322 715/1292 Other Zhang.K.2017 1.359 1.088 1.697 0.007 567/758 544/793 Other 1.176 0.997 1.386 0.054 Overall 1.237 1.139 1.344 0.000 Study name

19.98 54.81 11.99 13.22 45.05 54.95 Relative weight a

Ethnicity Odds Lower Upper P- Cases Controls

Asian Bai.Y.2014 1.158 0.987 1.357 0.071 578/1099 666/1361

Asian Zhou.D.2016 (CAD) 1.260 0.788 2.015 0.334 54/237 37/195 Asian Zhou.D.2016 (ASC) 1.271 0.813 1.986 0.293 61/271 40/215

Asian Zhang.K.2017 1.494 1.131 1.974 0.005 196/387 171/420 Asian 1.238 1.090 1.405 0.001 Caucasian Garcia-B.M.2013 0.928 0.603 1.428 0.732 31/176 133/710 Caucasian Paramel.G.V.2013 1.236 0.899 1.700 0.192 83/283 142/565 Caucasian 1.117 0.864 1.443 0.398 Overall 1.213 1.082 1.359 0.001 Study name

63.77 7.33 8.10 20.80 35.34 64.66 Relative weight

d Group by Study name Statistics for each study Exposed/Total Odds ratio and 95% CI

Asian Bai.Y.2014 1.362 1.196 1.550 0.000 1970/2491 1930/2625

Asian Zhou.D.2016 (ASC) 1.038 0.787 1.370 0.790 267/450 222/380 Asian Zhou.D.2016 (CAD) 1.125 0.863 1.465 0.384 305/515 226/401

Asian Zhang.K.2017 1.359 1.088 1.697 0.007 567/758 544/793 Asian 1.284 1.165 1.414 0.000 Caucasian Garcia-B.M.2013 0.985 0.771 1.259 0.904 177/322 715/1292 Caucasian Paramel.G.V.2013 1.228 0.990 1.522 0.061 339/539 584/1007 Caucasian 1.116 0.949 1.312 0.184 Overall 1.237 1.139 1.344 0.000 1 0.5 2 55.53 12.14 13.39 18.94 43.41 56.59 Relative weight

e Group by Study name Statistics for each study Exposed/Total Odds ratio and 95% CI

Asian Bai.Y.2014 1.469 1.281 1.684 0.000 1392/1913 1264/1959

Asian Zhou.D.2016 (CAD) 0.994 0.744 1.329 0.968 213/396 185/343 Asian Zhou.D.2016 (ASC) 1.093 0.829 1.442 0.528 244/454 186/361

Asian Zhang.K.2017 1.297 1.023 1.644 0.032 371/562 373/622 Asian 1.314 1.187 1.455 0.000 Caucasian Garcia-B.M.2013 0.998 0.772 1.291 0.989 146/291 582/1159 Caucasian Paramel.G.V.2013 1.225 0.975 1.539 0.081 256/456 442/865 Caucasian 1.119 0.944 1.328 0.195 Overall 1.260 1.154 1.375 0.000 1

0.5 Decreased risk Increased risk 2

55.58 12.36 13.56 18.50 44.04 55.96 Relative weight

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to estimate the power of the current conclusion (13, 14) the Trial Sequential Analysis (TSA) tool (Copenhagen Trial Unit, Centre for Clinical Intervention Research, Denmark) was used. TSA version

3.0 (http://www.ctu.dk/tsa/) was used while performing the pres-ent investigation.

Results

Literature search characteristics

Six case-control articles were included in this meta-analysis, consisting of 11,573 subjects, 5,075 cases, and 6,498 controls (8, 10-12, 15, 16) after removing the duplicate articles, unrelated articles, and articles lacking a complete genotype distribution. The characteristics of all the included articles are summarized in Table 1. Among these included studies, two were performed in Caucasian populations and four in Asian populations. These studies included many CVDs, such as myocardial infarctions, CADs, acute coronary syndrome, and arteriosclerosis obliterans.

Meta-analysis results

All the studies described the genotype distributions for the AA, AT, and TT allele combinations and were divided into five models. For the homozygous model, OR was 1.21 (1.08–1.36, I2_{=0.0%, Pheterogeneity=0.542). For the heterozygous model (AT} vs. AA), OR was 1.20 (1.04–1.38, I2_{=57.2%, Pheterogeneity=0.039).} For the dominant model, OR was 1.24 (1.14–1.34, I2_=38.5%, Pheterogeneity=0.149). For the allele model, OR was 0.96 (0.77–1.20, I2_{=92.7%, Pheterogeneity=0.00). For the recessive model, OR was 1.00} (0.91–1.10, I2_{=48.4%, P}

heterogeneity=0.085) (Fig. 2).

Regarding the ethnicity subgroup, in the homozygous, het-erozygous, and dominant models, rs2043211 indicated a CVD risk in the Asian population (Fig. 3). As stratified by etiology for rs2043211, significant associations were found in the myocardial infarction group, a increased risk of CVDs was observed in the homozygous and dominant models.

Sensitivity analysis

To detect the influence of each study and the stability of the results, a sensitivity analysis was conducted by omitting one in-dividual study. Heterogeneity was not found in any of the gene models (Fig. 4).

Figure 4. Sensitivity analysis for the models of CARD8 rs2043211 with CVD risk in different genetic models. a) Dominant model (TT+AT vs. AA). b) Homozygous model (TT vs. AA). c) Heterozygous model (AT vs. AA)

Study name Odds ratio (95% CI)

with study removed Dominant Lower Upper

Point limit limit P-Value Garcia-B, M.2013 1.274 1.167 1.392 0.000 Paramel, G.V.2013 1.239 1.132 1.355 0.000 Bai, Y.2014 1.158 1.039 1.290 0.008 Zhou, D.2016 (ASC) 1.259 1.154 1.373 0.000 Zhou, D.2016 (CAD) 1.250 1.146 1.364 0.000 Zhang, K.2017 1.219 1.114 1.333 0.000 1.237 1.139 1.344 0.000 0.5 1 2 a

Study name Statistics with study removed Odds ratio (95% CI) with study removed Homozygous Lower Upper

Point limit limit Z-Value P-Value Garcia-B, M.2013 1.238 1.100 1.393 3.539 0.000 Paramel, G.V.2013 1.210 1.070 1.369 3.032 0.002 Bai, Y.2014 1.274 1.082 1.499 2.909 0.004 Zhou, D.2016 (CAD) 1.210 1.076 1.362 3.168 0.002 Zhou, D.2016 (ASC) 1.209 1.075 1.360 3.155 0.002 Zhang, K.2017 1.163 1.027 1.318 2.376 0.017 1.213 1.082 1.359 3.323 0.001 0.5 1 2 b

Study name Odds ratio (95% CI)

with study removed Heterozygous Lower Upper

Point limit limit P-Value Garcia-B, M.2013 1.243 1.078 1.434 0.003 Paramel, G.V.2013 1.183 0.993 1.411 0.060 Bai, Y.2014 1.132 1.011 1.269 0.032 Zhou, D.2016 (CAD) 1.235 1.067 1.429 0.005 Zhou, D.2016 (ASC) 1.212 1.032 1.424 0.019 Zhang, K.2017 1.171 0.984 1.393 0.076 1.197 1.038 1.381 0.013 0.5 1 2 c

Table 1. Characteristics of the studies included in the meta-analysis

Trial Country Ethnicity Disease Control Methods AA/AT/TT AA/AT/TT Case Control NOS Scale Score source

Garcia-B, M. 2013 Spain Caucasian CVDs HB RT-PCR†_{145 146 31 577 582 133 582 133 0.44 *** * *** 7}

Paramel, G. V. 2013 China Caucasian MI HB RT-PCR 200 256 83 423 442 142 442 142 0.09 **** ** *** 9 Bai, Y. 2014 Sweden Asian CAD+IS HB RT-PCR 521 1392 578 695 1264 666 1264 666 0.06 *** ** *** 8 Zhou, D. 2016 China Asian ASC PB PCR 183 213 54 158 185 37 185 37 0.1 ** *** 5 Zhou, D. 2016 China Asian CAD PB PCR 210 244 61 175 186 40 186 40 0.35 ** *** 5 Zhang, K. 2017 China Asian AO PB RT-PCR 191 371 196 249 373 171 373 171 0.16 ** * *** 6

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Publication bias

A bias analysis was performed by generating funnel plots for each polymorphism of the dominant and heterozygous models (Fig. 5). Obviously, the allelic (p=0.60), recessive (p=0.14), and codominant homozygous (p=0.88) genetic models exhibited no publication bias. We observed that the codominant heterozy-gous (p=0.007) and allelic (p=0.04) models may have exhibited bias, so the trim and fill method was used (17). By removing and supplementing part of the study, the results showed no statisti-cal difference in the conclusion, so we believe that the results were stable.

Trial sequential analysis

We performed TSAs for the allelic and heterozygous mod-els (AT vs. AA) of SNP rs2043211 (Fig. 6). The results of both showed that the blue line of the cumulative z-curve crossed the TSA monitoring boundary. Moreover, the heterozygous model touched the required sample size. Therefore, based on the het-erozygous model, the results suggested that no further studies were necessary to confirm the association. However, the allelic

model did not reach either the required or the cumulative sample size, intimating that there may be a false positive.

Discussion

Subclinical chronic inflammation due to endothelial vessel wall damage has long been hypothesized as part of the patho-physiology of CVDs (18). Extensive clinical and pathophysiologi-cal research has confirmed that therapeutic intervention targeted against inflammatory mediators is effective for the treatment of myocardial infarction (19). For example, Luo reported that NLRP3 gene silencing therapy ameliorated cardiac inflammation, pyrop-tosis, fibrosis, and cardiac function (20). Genetic variations lead-ing to the altered production of inflammatory cytokines or altered inflammasome function have been linked to various inflamma-tory disease; the most frequently studied genetic variations are NLRP3 and CARD. The NLRP3 inflammasome is composed of the NLRP3 scaffold protein, CARD-containing adaptor protein, and caspase-1 (21).

CARD8 contains a homotypic interaction motif called the cas-pase recruitment domain, and its polymorphism can introduce a translation stop codon at codon 10 (known as Cys10Stop or C10X), thereby expressing a premature CARD8 protein with almost no function. Regarding the C10X polymorphism in the CARD8 gene, associations were seen between rs2043211 and different

dis-Figure 5. Funnel plots for the models of CARD8 rs2043211with CVD risk in different genetic models. a) Dominant model (TT+AT vs. AA). b) Homo-zygous model (TT vs. AA). c) HeteroHomo-zygous model (AT vs. AA)

Funnel Plot of Standard Error by Log odds ratio

Funnel Plot of Standard Error by Log odds ratio Funnel Plot of Standard Error by Log odds ratio

Log odds ratio

Log odds ratio Log odds ratio

Standard Error Standard Error Standard Error 0.00 0.00 0.0 0.05 0.05 0.1 0.10 0.10 0.2 0.15 0.15 0.3 0.20 0.20 0.4 –2.0 –2.0 –2.0 –1.5 –1.5 –1.5 –1.0 –1.0 –1.0 –0.5 –0.5 –0.5 –0.0 –0.0 –0.0 0.5 0.5 0.5 1.0 1.0 1.0 1.5 1.5 1.5 2.0 2.0 2.0 a b c

Figure 6. Trial sequential analysis for the models of CARD8 rs2043211with CVD risk in different genetic models. a) Dominant model (TT+AT vs. AA). b) Homozygous model (TT vs. AA)

8 7 6 5 4 3 2 1 –1 –2 –3 –4 –5 –6 –7 –8 Fa vours control Fa vours case

AST is a Two-sided graph

AST=5293 Z-curve Cumulative Z-Score (2013) Gar cia-B , M AST (2013) P aramel, G .V

(2016) Zhou, D (ASC) (2016) Zhou, D (CAD)

(2017) Zhang , K (2014) Bai, Y 8 7 6 5 4 3 2 1 –1 –2 –3 –4 –5 –6 –7 –8 Fa vours control Fa vours case

TSA is a Two-sided graph TSA=5611 Z-curve Cumulative Z-Score (2013) Gar cia-B TSA (2013) P aramel, G V

(2016) Zhou, D (ASC) (2016) Zhou, D (CAD) (2017) Zhang

, K (2014) Bai, Y

a

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ease susceptibilities. It has been reported that a CARD8 muta-tion causes Crohn’s disease (22) and that CARD8 polymorphisms influence higher disease activity in aspirin-induced asthma and rheumatoid arthritis (22). In addition, ankylosing spondylitis, gout, and Alzheimer’s disease have been found to have relationships with the CARD8 variant rs2043211, as well as CVDs (6, 7, 23).

CARD8 rs2043211 showed no association with the risks of myocardial infarction or CAD or the risk of developing cardio-vascular events in patients with rheumatoid arthritis. However, it was found to be associated with a lower expression of CARD8 in the plaque as well as with lower C-reactive protein and mono-cyte chemoattractant protein-1 levels in the plasma (8, 15, 16). However, in one Chinese cohort, rs2043211 was associated with ischemic stroke (10), and it is probably associated with the de-velopment of arteriosclerosis obliterans in the Chinese Han male population (11). Moreover, it has shown a modest protective ef-fect against abdominal aortic aneurysms (24). Therefore, the CVDs risk of CARD8 rs2043211 is worth assessing.

To our knowledge, this is the first meta-analysis involving the relationship between CARD8 and the CVD risk. We found that rs2043211 indicated a CVD risk in the homozygous, heterozygous, and dominant models, particularly in the Asian population. In the etiology subgroup, in the homozygous and dominant models, there was also a risk of myocardial ischemia. Moreover, the TSA results showed that based on the heterozygous model, no fur-ther studies were necessary to confirm the association, and that ischemic heart disease is one of the leading causes of premature death worldwide (2).

This meta-analysis has several limitations. First, we were un-able to analyze the potential gene–environment and gene–gene interactions. Second, significant between-study heterogeneity was detected in some of the comparisons, which might have af-fected the results. Finally, different genotype methods and dis-ease statuses may have influenced the data explanation of the included studies.

Conclusion

In conclusion, this meta-analysis showed that the dominant model, heterozygous model and homozygote model of the CARD8 rs2043211 polymorphism may be associated with CVDs and that the association seems to be population-dependent. However, the exact mechanism by which the CARD8 rs2043211 gene polymor-phism influences CVDs susceptibility remains to be elucidated. Further studies based on a larger sample size and case-control design in different populations are needed to clarify this associa-tion.

Acknowledgment: This work was supported by the National Natu-ral Science Foundation of China (Grant number: 81760061, 81560061) and Guangxi Nature Science Foundation (Grant number: 2016GXNS-FAA380167).

Conflict of interest: None declared. Peer-review: Externally peer-reviewed.

Authorship contributions: Concept – H.H., Y.H.; Design – H.H., Y.H.; Supervision – Q.B., H.W.; Fundings – Y.H.; Materials – Q.B., H.W.; Data collection &/or processing – Q.B., B.L.; Analysis &/or interpretation – H.H., H.W.; Literature search – H.H., Q.B.; Writing – H.H.; Critical review – H.H., Y.H.

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