Abstract

Several studies have investigated a potential association between the endothelial lipase gene (LIPG) 584C/T polymorphism and susceptibility to coronary artery disease (CAD), but a uniform conclusion is yet to be reached. To better evaluate the true relationship between the LIPG 584C/T polymorphism and the risk of CAD, a meta-analysis of 14 case–control studies with 9731 subjects was performed. Relevant articles published through August 2020 were searched in the CNKI, PubMed, Embase and Web of Science databases. Thirteen articles, including 14 eligible case–control studies with 4025 cases and 5706 controls, were enrolled in the present meta-analysis. The Newcastle–Ottawa Scale (NOS) scores of the case–control studies ranged from 6 to 8. The pooled results indicated that there is a significant association between the LIPG 584C/T polymorphism and CAD in the homozygote comparison model and the allelic comparison model. Subgroup analyses revealed that the LIPG 584C/T mutation significantly decreased the risk of CAD in the subgroups of African, CAD, hospital-based (HB), and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) populations in some genetic models. No publication bias was found in our meta-analysis, which certifies the robustness of the current meta-analysis. Trial sequential analysis (TSA) also confirmed the stability of our results. The results of our meta-analysis indicate that the LIPG 584C/T polymorphism plays a protective role in the incidence of CAD. More high-quality case–control studies on various ethnicities are needed to confirm our results.

Introduction

Coronary artery disease (CAD), also called coronary heart disease (CHD), characterized by myocardial hypoxia and ischemia is triggered by coronary atherosclerosis [1]. It is a medical problem of human society and one of the leading causes of disability and deaths in every country [2,3]. The precise mechanism of the onset and development of CAD is still obscure. The pathogenesis of CAD involves many risk factors, including hereditary and environmental factors. Recently, increasing evidence has suggested that genetic factors may play an important role in its pathogenesis. Many genetic variants have been identified to be related to the risk of CAD by previous genetic association studies [4]. The single-nucleotide polymorphism (SNP) is one of the most common genetic variants. Some SNPs may be related to the occurrence and development of CAD, while others are not [5,6].

Endothelial lipase (EL), a new member of the triglyceride (TG) lipase family, was first discovered in 1999. EL consists of 483 amino acids, with a molecular weight of approximately 55 kDa and is mainly secreted from endothelial cells and other cells such as hepatocytes and macrophages [7]. Compared with lipoprotein lipase (LPL) and hepatic lipase (HL), which are also members of the TG lipase family, EL has higher phospholipase activity and lower TG lipase. A large number of studies have demonstrated that EL plays a key role in high-density lipoprotein (HDL) metabolism. As a regulator of plasma HDL-C levels, EL can modulate the levels of plasma HDL-C inversely [8]. Elevated plasma HDL levels are associated with a protective effect on CAD and low plasma HDL levels are associated with an elevated risk of CAD [9]. Recently, increasing evidence has indicated that EL plays a pivotal role in the pathogenesis of CAD in part by reducing plasma HDL levels.

The EL gene (LIPG), which encodes the EL protein and is located on chromosome 18q21.1, spans approximately 30 kb with ten exons and nine introns [10]. Several genetic polymorphisms have been confirmed in LIPG. Among these, the 584C/T gene variant (rs2000813) has been the most frequently investigated variant that can lead the amino acid threonine substitution for isoleucine at codons [11]. Several published studies have assessed the association between LIPG 584C/T polymorphism and the risk of CAD; however, the results are still inconclusive and contradictory [12–14]. To testify these results, Cai et al. [15] carried out a meta-analysis to explore the association between the LIPG 584C/T variation and CAD risk in 2014. Unfortunately, the result of their meta-analysis was still inconclusive. Thereafter, several new studies have investigated the correlation between the LIPG 584C/T polymorphism and CAD risk, but the results of these studies remain inconsistent. Therefore, to elucidate the precise correlation of the LIPG 584C/T polymorphism with CAD susceptibility, we performed this updated meta-analysis.

Materials and methods

Search strategy

A systematic literature network search without language limitations was performed in the CNKI, PubMed, Embase and Web of Science databases to acquire all eligible case–control studies published before 15 August 2020. The relevant search terms included the following: (endothelial lipase gene OR EL OR LIPG) AND (polymorphism OR mutation OR variation OR genotype) AND (cardiovascular disease OR coronary heart disease OR coronary artery disease OR angina pectoris OR acute coronary syndrome OR myocardial infarction). Furthermore, to acquire other relevant published articles, the reference lists of the studies and reviews, which were included from the search parameters illuminated above, were searched.

Inclusion and exclusion criteria

CAD was defined as acute myocardial infarction (AMI), CHD, CAD, myocardial infarction (MI), acute coronary syndrome (ACS), cardiovascular disease. The included articles fulfilled the following criteria: (1) studies assessed the correlation between the LIPG 584C/T polymorphism and CAD risk; (2) studies were case–control or cohort; (3) articles reported in Chinese or English; and (4) studies provided precise data for genotype distribution estimation in both groups. Studies were excluded according to the following criteria: (1) not case–control and cohort studies, letters, case reports, reviews or meta-analyses; (2) deficient data; (3) deviating from Hardy–Weinberg equilibrium (HWE) in the control group; and (4) overlapping data or duplicated publications.

Data extraction

Two researchers (Yue-e Wu and Lan Ma) independently extracted data from the qualified articles based on the inclusion criteria. The following information was extracted from the eligible studies: first author, year of publication, ethnicity and country of participants, type of diseases, genotyping method, sources of controls, number of cases and controls for the 584C/T genotypes of LIPG and the P-value of the HWE test of controls. When we encountered inconsistent evaluations, all researchers were consulted to acquire an agreement regarding the exclusion or inclusion of the study in the present article.

Methodological quality assessment

The Newcastle–Ottawa Scale (NOS) was used to evaluate the quality of each included study [16]. For the selection and exposure categories, each study can be awarded a maximum of one star for each numbered item. A maximum of two stars can be given for the comparability categories. The NOS has a score range of 0–9, and a study with a score higher than 5 could be included in the present meta-analysis.

Statistical analysis

The present meta-analysis was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [17]. Chi-square test was applied to assess the HWE in the control groups with significance set at P less than 0.05. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the strength of the relationship between the LIPG 584C/T polymorphism and the risk of CAD in five genetic models: heterozygote comparisons (CT vs. CC), homozygote comparisons (TT vs. CC), recessive model (TT vs. CT + CC), dominant model (TT + CT vs. CC), and allelic comparisons (T vs. C). Stratified analyses were also performed according to ethnicity (Asian, Caucasian, and African populations), types of diseases (CHD, ACS, and CAD), genotyping method (polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), Taqman, and PCR), and sources of controls (hospital-based (HB) and population-based (PB)). Differences in terms of a Z-test were considered statistically significant if the P-value was less than 0.05. The Cochran’s Q statistical test and the I2 test were used to measure the heterogeneity within the studies, and the significance was set at P less than 0.05 [18]. A fixed-effects model or random-effects model was used to calculate the pooled OR according to the heterogeneity [19]. Influence analysis was carried out to examine the effect of an individual study on the pooled OR by omitting a single study each time to assess the stability of the pooled results. Publication bias was examined by Begg’s funnel plot test and Egger’s test [20,21]. Trial sequential analysis (TSA) was carried out as described in our previous study [22]. STATA 12.0 software (version 12.0; STATA Corp. College Station, TX, U.S.A.) was used to conduct all statistical analyses.

Results

Characteristics of eligible studies

The process of screening eligible articles is shown in Figure 1. One hundred and thirty-seven potential articles were acquired after a systematic literature search. One hundred and twenty-four articles were removed after the comprehensive examining procedures according to the article titles, abstracts, and full-texts. Finally, the remaining 13 articles, including 14 eligible independent case–control studies with 4025 patients and 5706 controls, were identified and included in the meta-analysis based on the inclusion and exclusion criteria [9,12–14,23–31].

The flow diagram of the included and excluded studies

Figure 1
The flow diagram of the included and excluded studies
Figure 1
The flow diagram of the included and excluded studies

Table 1 displays the characteristics of all case–control studies included in our meta-analysis. All studies were reported in English except for two studies that were published in Chinese. Among the 14 studies, 7 studies were conducted in Caucasian populations, 6 in Asian populations, and only 1 in African populations. There were nine HB studies and five PB studies. Seven studies used PCR-RFLP as the genotyping method, four studies used TaqMan, and three studies used PCR. The genotype distributions of controls in all included studies were accordance with HWE. Table 2 shows the distribution of genotypes and allele frequencies of the LIPG 584C/T polymorphism in the cases and controls. Supplementary Table S1 indicated that the studies included in our study were reliable according to the NOS.

Table 1
Characteristics of eligible case–control studies included in this meta-analysis
First authorYearCountryEthnicityType of diseaseSource of controlsGenotyping methodNumber of casesNumber of controlsHWE (Control)NOS score
Zhu et al. [122007 China Asian CHD HB PCR-RFLP 242 196 0.063 
Ji et al. [232015 China Asian ACS HB PCR-RFLP 195 159 0.077 
Jensen et al. [242009 Denmark Caucasian ACS PB TaqMan 998 1643 0.888 
Rimm et al. [251992 America Caucasian CAD PB PCR 262 519 0.063 
Colditz et al. [261997 America Caucasian CAD PB PCR 241 477 0.220 
Tjonneland et al.(1) [272007 Denmark Caucasian CHD PB TaqMan 235 763 0.838 
Tjonneland et al.(2) [272007 Denmark Caucasian CHD PB TaqMan 763 880 0.997 
Tang et al. [92008 China Asian CAD HB PCR-RFLP 265 265 0.103 
Xie et al. [132015 China Asian CAD HB PCR 287 367 0.065 
Solim et al. [282018 Turkey Caucasian CAD HB TaqMan 74 73 0.545 
Elnaggar et al. [292019 Egypt African CAD HB PCR-RFLP 84 42 0.492 
Dalan et al. [302013 Turkey Caucasian CAD HB PCR-RFLP 104 76 0.286 
Cai et al. [142014 China Asian CAD HB PCR-RFLP 135 166 0.146 
Toosi et al. [312015 Iran Asian CAD HB PCR-RFLP 140 80 0.092 
First authorYearCountryEthnicityType of diseaseSource of controlsGenotyping methodNumber of casesNumber of controlsHWE (Control)NOS score
Zhu et al. [122007 China Asian CHD HB PCR-RFLP 242 196 0.063 
Ji et al. [232015 China Asian ACS HB PCR-RFLP 195 159 0.077 
Jensen et al. [242009 Denmark Caucasian ACS PB TaqMan 998 1643 0.888 
Rimm et al. [251992 America Caucasian CAD PB PCR 262 519 0.063 
Colditz et al. [261997 America Caucasian CAD PB PCR 241 477 0.220 
Tjonneland et al.(1) [272007 Denmark Caucasian CHD PB TaqMan 235 763 0.838 
Tjonneland et al.(2) [272007 Denmark Caucasian CHD PB TaqMan 763 880 0.997 
Tang et al. [92008 China Asian CAD HB PCR-RFLP 265 265 0.103 
Xie et al. [132015 China Asian CAD HB PCR 287 367 0.065 
Solim et al. [282018 Turkey Caucasian CAD HB TaqMan 74 73 0.545 
Elnaggar et al. [292019 Egypt African CAD HB PCR-RFLP 84 42 0.492 
Dalan et al. [302013 Turkey Caucasian CAD HB PCR-RFLP 104 76 0.286 
Cai et al. [142014 China Asian CAD HB PCR-RFLP 135 166 0.146 
Toosi et al. [312015 Iran Asian CAD HB PCR-RFLP 140 80 0.092 
Table 2
LIPG 584C/T polymorphism genotype distribution and allele frequency in cases and controls
First authorYearGenotype (n)Allele frequency (n)HWE (Control)
CaseControlCaseControl
TotalCCCTTTTotalCCCTTTCTCT
Zhu et al. [122007 242 186 56 196 150 46 428 56 346 46 0.063 
Ji et al. [232015 195 112 76 159 81 71 300 90 233 85 0.077 
Jensen et al. [242009 998 509 406 83 1643 837 673 133 1424 572 2347 939 0.888 
Rimm et al. [251992 262 129 117 16 519 240 239 40 375 149 719 319 0.063 
Colditz et al. [261997 241 115 110 16 477 224 214 39 340 142 662 292 0.220 
Tjonneland et al.(1) [272007 235 116 102 17 763 391 312 60 334 136 1094 432 0.838 
Tjonneland et al.(2) [272007 763 393 304 66 880 446 361 73 1090 436 1253 507 0.997 
Tang et al. [92008 265 174 85 265 125 122 18 433 97 372 158 0.103 
Xie et al. [132015 287 160 116 11 367 216 139 12 436 138 571 163 0.065 
Solim et al. [282018 74 40 27 73 26 33 14 107 39 85 61 0.545 
Elnaggar et al. [292019 84 58 23 42 17 21 139 27 55 29 0.492 
Dalan et al. [302013 104 44 59 76 45 29 147 61 119 33 0.286 
Cai et al. [142014 135 84 45 166 97 64 213 57 258 74 0.146 
Toosi et al. [312015 140 67 70 80 28 46 204 76 102 58 0.092 
First authorYearGenotype (n)Allele frequency (n)HWE (Control)
CaseControlCaseControl
TotalCCCTTTTotalCCCTTTCTCT
Zhu et al. [122007 242 186 56 196 150 46 428 56 346 46 0.063 
Ji et al. [232015 195 112 76 159 81 71 300 90 233 85 0.077 
Jensen et al. [242009 998 509 406 83 1643 837 673 133 1424 572 2347 939 0.888 
Rimm et al. [251992 262 129 117 16 519 240 239 40 375 149 719 319 0.063 
Colditz et al. [261997 241 115 110 16 477 224 214 39 340 142 662 292 0.220 
Tjonneland et al.(1) [272007 235 116 102 17 763 391 312 60 334 136 1094 432 0.838 
Tjonneland et al.(2) [272007 763 393 304 66 880 446 361 73 1090 436 1253 507 0.997 
Tang et al. [92008 265 174 85 265 125 122 18 433 97 372 158 0.103 
Xie et al. [132015 287 160 116 11 367 216 139 12 436 138 571 163 0.065 
Solim et al. [282018 74 40 27 73 26 33 14 107 39 85 61 0.545 
Elnaggar et al. [292019 84 58 23 42 17 21 139 27 55 29 0.492 
Dalan et al. [302013 104 44 59 76 45 29 147 61 119 33 0.286 
Cai et al. [142014 135 84 45 166 97 64 213 57 258 74 0.146 
Toosi et al. [312015 140 67 70 80 28 46 204 76 102 58 0.092 

Test of heterogeneity

To assess the heterogeneity among the included studies, Q-test and I2 statistics were applied. High heterogeneity was found across studies in all genetic models except for the recessive comparisons. Thus, random-effects analysis was applied to synthesize the data. Moreover, we explored the heterogeneity of all genetic models based on different ethnicities, type of diseases, genotyping method, and sources of controls. However, the significant heterogeneity could not be entirely explained by diverse ethnicities (Table 3).

Table 3
Meta-analysis results
Genetic modelCategorynModelOR (95% CI)PHeterogeneityBegg’s testEgger’s test
I2PPP
Homozygote (TT vs. CCOverall 14 0.740 [0.555; 0.987] 0.041 47.5% 0.029 0.059 0.121 
 Asian 0.599 [0.275; 1.303] 0.196 61.2% 0.035 0.806 0.747 
 Caucasian 0.920 [0.764; 1.110] 0.384 7.7% 0.370 0.072 0.115 
 African 0.147 [0.025; 0.870] 0.035 
 CHD 1.006 [0.741; 1.364] 0.972 0.0% 0.836 1.000 
 ACS 1.002 [0.753; 1.333] 0.990 0.0% 0.542 1.000 
 CAD 0.545 [0.336; 0.884] 0.014 49.6% 0.044 0.602 0.651 
 HB 0.474 [0.255; 0.880] 0.018 50.6% 0.048 0.711 0.814 
 PB 0.963 [0.796; 1.165] 0.696 0.0% 0.860 0.086 0.080 
 PCR-RFLP 0.420 [0.253; 0.698] 0.001 39.1% 0.145 1.000 0.709 
 Taqman 0.963 [0.783; 1.186] 0.725 44.4% 0.145 0.089 0.151 
 PCR 0.850 [0.577; 1.252] 0.411 0.0% 0.613 0.296 0.077 
Heterozygote (CT vs. CCOverall 14 0.883 [0.765; 1.031] 0.115 61.1% 0.001 0.063 0.441 
 Asian 0.786 [0.599; 1.031] 0.082 59.5% 0.030 0.707 0.856 
 Caucasian 0.994 [0.898; 1.101] 0.910 37.2% 0.145 1.000 0.790 
 African 0.321 [0.144; 0.715] 0.005 
 CHD 0.997 [0.850; 1.168] 0.966 0.0% 0.747 1.000 0.707 
 ACS 0.961 [0.824; 0.715] 0.614 9.5% 0.293 1.000 
 CAD 0.804 [0.605; 1.121] 0.131 72.6% 0.000 0.251 0.675 
 HB 0.779 [0.574; 1.056] 0.108 71.5% 0.000 0.466 0.848 
 PB 0.986 [0.888; 1.094] 0.788 0.0% 0.929 1.000 0.725 
 PCR-RFLP 0.760 [0.529; 1.092] 0.138 72.5% 0.001 0.764 0.541 
 Taqman 0.978 [0.871; 1.099] 0.712 14.4% 0.320 0.734 0.444 
 PCR 1.006 [0.838; 1.208] 0.947 0.0% 0.643 1.000 0.421 
Dominant (TT + CT vs. CCOverall 14 0.856 [0.725; 1.010] 0.066 68.3% 0.000 0.051 0.361 
 Asian 0.772 [0.570; 1.044] 0.093 68.6% 0.007 0.707 0.898 
 Caucasian 0.984 [0.893;1.085] 0.752 46.7% 0.081 0.764 0.996 
 African 0.293 [0.135; 0.636] 0.002   
 CHD 0.998 [0.857; 1.162] 0.979 0.0% 0.830 1.000 0.739 
 ACS 0.966 [0.834; 1.120] 0.649 22.0% 0.258 1.000 
 CAD 0.764 [0.566; 1.032] 0.079 77.1% 0.000 0.348 0.649 
 HB 0.745 [0.538; 1.031] 0.076 76.4% 0.000 0.466 0.802 
 PB 0.983 [0.889; 1.086] 0.730 0.0% 0.923 0.806 0.922 
 PCR-RFLP 0.734 [0.503; 1.072] 0.109 75.9% 0.000 0.764 0.529 
 Taqman 0.977 [0.874; 1.091] 0.677 46.0% 0.136 0.734 0.326 
 PCR 0.988 [0.828; 1.180] 0.898 0.0% 0.527 0.296 0.346 
Recessive (TT vs. CT + CCOverall 14 0.881 [0.745; 1.041] 0.136 26.0% 0.182 0.100 0.103 
 Asian 0.682 [0.435; 1.068] 0.094 47.1% 0.109 0.806 0.781 
 Caucasian 0.932 [0.778; 1.117] 0.448 0.0% 0.529 0.016 0.031 
 African 0.235 [0.041; 1.337] 0.103 
 CHD 1.007 [0.750; 1.352] 0.962 0.0% 0.686 1.000 
 ACS 1.013 [0.768; 1.336] 0.925 0.0% 0.668 1.000 
 CAD 0.658 [0.487; 0.889] 0.006 26.7% 0.207 0.602 0.659 
 HB 0.578 [0.390; 0.856] 0.006 30.6% 0.184 0.902 0.726 
 PB 0.969 [0.806; 1.165] 0.136 0.0% 0.860 0.086 0.035 
 PCR-RFLP 0.499 [0.302; 0.825] 0.007 21.3% 0.273 0.707 0.881 
 Taqman 0.976 [0.798; 1.193] 0.814 18.7% 0.297 0.089 0.104 
 PCR 0.855 [0.586; 1.246] 0.414 0.0% 0.700 0.296 0.028 
Allelic (T vs. COverall 14 0.869 [0.761; 0.992] 0.037 68.5% 0.000 0.080 0.288 
 Asian 0.813 [0.630; 1.048] 0.110 69.7% 0.006 1.000 0.912 
 Caucasian 0.978 [0.907; 1.055] 0.571 44.2% 0.096 0.174 0.484 
 African 0.368 [0.200; 0.678] 0.001 
 CHD 1.000 [0.886; 1.128] 0.999 0.0% 0.953 1.000 0.871 
 ACS 0.981 [0.874; 1.102] 0.750 13.5% 0.282 1.000 
 CAD 0.785 [0.622; 0.992] 0.043 76.3% 0.000 0.348 0.620 
 HB 0.772 [0.595; 1.001] 0.051 75.3% 0.000 0.466 0.830 
 PB 0.984 [0.910; 1.064] 0.682 0.0% 0.909 0.462 0.471 
 PCR-RFLP 0.765 [0.571; 1.026] 0.074 73.0% 0.001 0.548 0.434 
 Taqman 0.981 [0.900; 1.070] 0.669 58.0% 0.066 0.308 0.231 
 PCR 0.971 [0.845; 1.117] 0.682 0.0% 0.467 0.296 0.028 
Genetic modelCategorynModelOR (95% CI)PHeterogeneityBegg’s testEgger’s test
I2PPP
Homozygote (TT vs. CCOverall 14 0.740 [0.555; 0.987] 0.041 47.5% 0.029 0.059 0.121 
 Asian 0.599 [0.275; 1.303] 0.196 61.2% 0.035 0.806 0.747 
 Caucasian 0.920 [0.764; 1.110] 0.384 7.7% 0.370 0.072 0.115 
 African 0.147 [0.025; 0.870] 0.035 
 CHD 1.006 [0.741; 1.364] 0.972 0.0% 0.836 1.000 
 ACS 1.002 [0.753; 1.333] 0.990 0.0% 0.542 1.000 
 CAD 0.545 [0.336; 0.884] 0.014 49.6% 0.044 0.602 0.651 
 HB 0.474 [0.255; 0.880] 0.018 50.6% 0.048 0.711 0.814 
 PB 0.963 [0.796; 1.165] 0.696 0.0% 0.860 0.086 0.080 
 PCR-RFLP 0.420 [0.253; 0.698] 0.001 39.1% 0.145 1.000 0.709 
 Taqman 0.963 [0.783; 1.186] 0.725 44.4% 0.145 0.089 0.151 
 PCR 0.850 [0.577; 1.252] 0.411 0.0% 0.613 0.296 0.077 
Heterozygote (CT vs. CCOverall 14 0.883 [0.765; 1.031] 0.115 61.1% 0.001 0.063 0.441 
 Asian 0.786 [0.599; 1.031] 0.082 59.5% 0.030 0.707 0.856 
 Caucasian 0.994 [0.898; 1.101] 0.910 37.2% 0.145 1.000 0.790 
 African 0.321 [0.144; 0.715] 0.005 
 CHD 0.997 [0.850; 1.168] 0.966 0.0% 0.747 1.000 0.707 
 ACS 0.961 [0.824; 0.715] 0.614 9.5% 0.293 1.000 
 CAD 0.804 [0.605; 1.121] 0.131 72.6% 0.000 0.251 0.675 
 HB 0.779 [0.574; 1.056] 0.108 71.5% 0.000 0.466 0.848 
 PB 0.986 [0.888; 1.094] 0.788 0.0% 0.929 1.000 0.725 
 PCR-RFLP 0.760 [0.529; 1.092] 0.138 72.5% 0.001 0.764 0.541 
 Taqman 0.978 [0.871; 1.099] 0.712 14.4% 0.320 0.734 0.444 
 PCR 1.006 [0.838; 1.208] 0.947 0.0% 0.643 1.000 0.421 
Dominant (TT + CT vs. CCOverall 14 0.856 [0.725; 1.010] 0.066 68.3% 0.000 0.051 0.361 
 Asian 0.772 [0.570; 1.044] 0.093 68.6% 0.007 0.707 0.898 
 Caucasian 0.984 [0.893;1.085] 0.752 46.7% 0.081 0.764 0.996 
 African 0.293 [0.135; 0.636] 0.002   
 CHD 0.998 [0.857; 1.162] 0.979 0.0% 0.830 1.000 0.739 
 ACS 0.966 [0.834; 1.120] 0.649 22.0% 0.258 1.000 
 CAD 0.764 [0.566; 1.032] 0.079 77.1% 0.000 0.348 0.649 
 HB 0.745 [0.538; 1.031] 0.076 76.4% 0.000 0.466 0.802 
 PB 0.983 [0.889; 1.086] 0.730 0.0% 0.923 0.806 0.922 
 PCR-RFLP 0.734 [0.503; 1.072] 0.109 75.9% 0.000 0.764 0.529 
 Taqman 0.977 [0.874; 1.091] 0.677 46.0% 0.136 0.734 0.326 
 PCR 0.988 [0.828; 1.180] 0.898 0.0% 0.527 0.296 0.346 
Recessive (TT vs. CT + CCOverall 14 0.881 [0.745; 1.041] 0.136 26.0% 0.182 0.100 0.103 
 Asian 0.682 [0.435; 1.068] 0.094 47.1% 0.109 0.806 0.781 
 Caucasian 0.932 [0.778; 1.117] 0.448 0.0% 0.529 0.016 0.031 
 African 0.235 [0.041; 1.337] 0.103 
 CHD 1.007 [0.750; 1.352] 0.962 0.0% 0.686 1.000 
 ACS 1.013 [0.768; 1.336] 0.925 0.0% 0.668 1.000 
 CAD 0.658 [0.487; 0.889] 0.006 26.7% 0.207 0.602 0.659 
 HB 0.578 [0.390; 0.856] 0.006 30.6% 0.184 0.902 0.726 
 PB 0.969 [0.806; 1.165] 0.136 0.0% 0.860 0.086 0.035 
 PCR-RFLP 0.499 [0.302; 0.825] 0.007 21.3% 0.273 0.707 0.881 
 Taqman 0.976 [0.798; 1.193] 0.814 18.7% 0.297 0.089 0.104 
 PCR 0.855 [0.586; 1.246] 0.414 0.0% 0.700 0.296 0.028 
Allelic (T vs. COverall 14 0.869 [0.761; 0.992] 0.037 68.5% 0.000 0.080 0.288 
 Asian 0.813 [0.630; 1.048] 0.110 69.7% 0.006 1.000 0.912 
 Caucasian 0.978 [0.907; 1.055] 0.571 44.2% 0.096 0.174 0.484 
 African 0.368 [0.200; 0.678] 0.001 
 CHD 1.000 [0.886; 1.128] 0.999 0.0% 0.953 1.000 0.871 
 ACS 0.981 [0.874; 1.102] 0.750 13.5% 0.282 1.000 
 CAD 0.785 [0.622; 0.992] 0.043 76.3% 0.000 0.348 0.620 
 HB 0.772 [0.595; 1.001] 0.051 75.3% 0.000 0.466 0.830 
 PB 0.984 [0.910; 1.064] 0.682 0.0% 0.909 0.462 0.471 
 PCR-RFLP 0.765 [0.571; 1.026] 0.074 73.0% 0.001 0.548 0.434 
 Taqman 0.981 [0.900; 1.070] 0.669 58.0% 0.066 0.308 0.231 
 PCR 0.971 [0.845; 1.117] 0.682 0.0% 0.467 0.296 0.028 

Abbreviations: F, fixed-effects model; n, number of studies; R, random-effects model.

Meta-analysis results

The association between the LIPG 584C/T polymorphism and the risk of CAD was assessed. The integrated results indicated that the LIPG 584C/T variation was significantly associated with CAD risk in the homozygote comparison model (TT vs. CC: OR = 0.74, 95% CI = 0.56–0.99, P=0.041, Pheterogeneity=0.029, Table 3 and Figure 2) and allelic comparison model (T vs. C: OR = 0.87, 95% CI = 0.76–0.99, P=0.037, Pheterogeneity=0.000, Table 3 and Figure 3). There was no significant association with CAD risk in the other three models, including CT vs. CC: OR = 0.89, 95% CI = 0.77–1.03, P=0.115, Pheterogeneity=0.001; TT + CT vs. CC: OR = 0.86, 95% CI = 0.73–1.01, P=0.066, Pheterogeneity=0.000; and TT vs. CT + CC: OR = 0.88, 95% CI = 0.75–1.04, P=0.136, Pheterogeneity=0.182 (Table 3).

Forest plots of the LIPG 584C/T polymorphism and CAD risk (homozygote comparisons: TT vs. CC)

Figure 2
Forest plots of the LIPG 584C/T polymorphism and CAD risk (homozygote comparisons: TT vs. CC)
Figure 2
Forest plots of the LIPG 584C/T polymorphism and CAD risk (homozygote comparisons: TT vs. CC)

Forest plots of the LIPG 584C/T polymorphism and CAD risk (allelic comparisons: T vs. C)

Figure 3
Forest plots of the LIPG 584C/T polymorphism and CAD risk (allelic comparisons: T vs. C)
Figure 3
Forest plots of the LIPG 584C/T polymorphism and CAD risk (allelic comparisons: T vs. C)

In the subgroup analysis according to ethnicity, the results showed a decreased CAD risk in African populations under four genetic models (TT vs. CC: OR = 0.15, 95% CI = 0.03–0.87, P=0.035; CT vs. CC: OR = 0.32, 95% CI = 0.14–0.72, P=0.005; TT + CT vs. CC: OR = 0.29, 95% CI = 0.14–0.64, P=0.002; and T vs. C: OR = 0.37, 95% CI = 0.20–0.68, P=0.001). However, there was no significant association between the LIPG 584C/T polymorphism and the risk of CAD that was identified in Asian and Caucasian populations in all genetic models. In the subgroup analysis stratified by type of diseases, the results indicated that the LIPG 584C/T polymorphism was significantly associated with decreased CAD risk in the subgroup of CAD in three genetic models (TT vs. CC: OR = 0.55, 95% CI = 0.34–0.88, P=0.014; TT vs. CT + CC: OR = 0.66, 95% CI = 0.49–0.89, P=0.006; and T vs. C: OR = 0.79, 95% CI = 0.62–0.99, P=0.043), and the same results were shown in the HB group (TT vs. CC: OR = 0.48, 95% CI = 0.26–0.88, P=0.018) and PCR-RFLP (TT vs. CC: OR = 0.42, 95% CI = 0.25–0.70, P=0.001). Detailed results are shown in Table 3.

Sensitivity analysis

Sensitivity analysis was carried out to examine the influence of each eligible study on the pooled ORs by the sequential removal of each individual study form the analysis. The individual removal procedure affected the pooled ORs in the homozygote and allelic comparisons model, indicating the instability and unreliability of our findings. Sensitivity analyses of the other three genetic models showed similar results (Figure 4).

The results of sensitivity analysis between the LIPG 584C/T polymorphism and susceptibility to CAD

Figure 4
The results of sensitivity analysis between the LIPG 584C/T polymorphism and susceptibility to CAD

(A) homozygous model; (B) heterozygous model; (C) dominant model; (D) recessive model; (E) allele model.

Figure 4
The results of sensitivity analysis between the LIPG 584C/T polymorphism and susceptibility to CAD

(A) homozygous model; (B) heterozygous model; (C) dominant model; (D) recessive model; (E) allele model.

Publication bias

To assess the potential for publication bias among the studies on the the LIPG 584C/T polymorphism included in the present meta-analysis, Begg’s and Egger’s tests were performed for the five genetic models. No evidence of asymmetry was observed by the appearance of the shape in the Begg’s funnel plots, and neither Egger’s regression nor Begg’s rank correlation indicated publication bias among the studies in the five genetic models. The publication bias tests of subgroups were also performed, and no significant publication bias were found, except in few subgroups (Table 3 and Figure 5).

Funnel plot assessing evidence of publication bias (homozygote comparisons: TT vs. CC)

Figure 5
Funnel plot assessing evidence of publication bias (homozygote comparisons: TT vs. CC)
Figure 5
Funnel plot assessing evidence of publication bias (homozygote comparisons: TT vs. CC)

TSA

The results of the TSA using the homozygote and allelic comparisons model are shown in Figures 6 and 7. The cumulative z-curve obviously crossed the traditional boundary, and it further confirmed the results of our meta-analysis that the LIPG 584C/T polymorphism was significantly associated with decreased CAD risk. Nevertheless, the cumulative z-curve had crossed the TSA monitoring boundary without success before reaching the required information size and demonstrated that the cumulative sample size is insufficient and the more eligible case–control studies are necessary.

TSA for the LIPG 584C/T polymorphism and CAD risk in the homozygote comparisons

Figure 6
TSA for the LIPG 584C/T polymorphism and CAD risk in the homozygote comparisons
Figure 6
TSA for the LIPG 584C/T polymorphism and CAD risk in the homozygote comparisons

TSA for the LIPG 584C/T polymorphism and CAD risk in the allelic comparisons

Figure 7
TSA for the LIPG 584C/T polymorphism and CAD risk in the allelic comparisons
Figure 7
TSA for the LIPG 584C/T polymorphism and CAD risk in the allelic comparisons

Discussion

The present meta-analysis indicated that the LIPG 584C/T variation was significantly associated with CAD risk in the homozygote comparison model (TT vs. CC: OR = 0.74, 95% CI = 0.56–0.99, P=0.041) and allelic comparison model (T vs. C: OR = 0.87, 95% CI = 0.76–0.99, P=0.037). According to the integrated analyses of 14 eligible case–control studies, our study revealed that the LIPG 584C/T polymorphism was significantly associated with a decreased risk of CAD, which suggested that the LIPG 584C/T polymorphism plays a protective role in the incidence of CAD in individuals and that the T allele may be a protective factor against CAD.

It has been proven that elevated plasma HDL levels are associated with a protective effect on CAD, and low plasma HDL levels are associated with an elevated risk of CAD [9]. The role of EL in the pathogenesis of CAD is regulated by its effect on the metabolism of HDL by its increasing clearance. Therefore, it is speculated that LIPG is closely related to the incidence of CAD by affecting the expression of EL. In recent years, more and more attention has been paid to genetic research on CAD, which is not surprising considering the impact of CAD on the health of people around the world [13]. To date, many molecular epidemiological studies have been performed to investigate the association between the LIPG 584C/T variant and CAD risk. However, the results of these studies are inconsistent or even contradictory. The results found by Ji et al. [23], Zhu et al. [12], and Jensen et al. [24] revealed that there was no significant association between the LIPG 584C/T polymorphism and risk of CAD; in contrast, Tang et al. [9], Solim et al. [28], and Elnaggar et al. [29] found similar results as ours that the LIPG 584C/T polymorphism was significantly associated with a decreased risk of CAD. Cai et al. conducted a meta-analysis to investigate the true association between the LIPG 584C/T variant and CAD risk in 2014. However, their analysis included only 9 case–control studies, including 3036 cases and 4777 controls, and concluded that there was no significant association between the LIPG 584C/T polymorphism and the susceptibility to CAD. Their results were inconsistent with our study, but three case–control studies of their meta-analysis did not conform to HWE [32–34]. A latest meta-analysis of 13 published case–control studies was performed by Zhao et al. (2020) [35], and also significant results were found. However, in addition to also including three case–control studies that did not conform to HWE, some qualified studies were not included in their meta-analysis. Compared with their meta-analyses, the results of our meta-analysis, which included 14 case–control studies that all were in line with HWE, had more statistical power and reliability.

We noticed significant heterogeneities among the studies and carried out subgroup analyses according to ethnicity. According to previous studies, the LIPG 584C/T variant had different effects on CAD in different ethnicities. Even results from studies on the same ethnicity were inconsistent. For example, Tang et al. conducted a study including 530 Chinese subjects to assess the relationship between the common variant and CAD risk [9]. They demonstrated that the T allele could significantly reduce the CAD risk. The same results were found in studies conducted by Solim et al. [28] on the Turkish population, by Shimizu et al. [32] on the Japanese population, and by Toosi et al. [31] on the Iranian population. However, Jensen et al. [24] and Zhu et al. [12] showed no relationship between the LIPG 584C/T variant and CAD susceptibility in Caucasian and Asian populations. Our present study showed that the LIPG 584C/T variant be connected with a decreased CAD risk only in African populations. However, there was no significant association between the LIPG 584C/T polymorphism and the risk of CAD that was identified in Asian and Caucasian populations. This result was not convincing enough, because only one case–control study was included in the African subgroup; therefore, this result needs to be confirmed by more high-quality case–control studies in various ethnicities. When carrying out subgroup analysis by type of diseases, we found that the LIPG 584C/T polymorphism was significantly associated with a reduced risk in the CAD group, but we failed to find a significant risk association in other types. This result may be explained by the inherent heterogeneity of development in diverse diseases types.

Some inherent drawbacks of our meta-analysis should be illustrated when interpreting our results. First, only unadjusted estimates were applied to evaluate the strength of the relationship between the LIPG 584C/T variant and CAD risk. Due to a lack of raw data, such as exposing factors, life habits, gene–environment interactions, interactions between gene–gene and even diverse mutation loci in the same gene factors, a further exact adjustment analysis adjusting for confounding factors could not be carried out. Second, there is still high heterogeneity in our meta-analysis, although we used strict inclusion criteria, accurate data extraction, and rigorous data analysis to perform the present meta-analysis. The persuasiveness of the significant association between the LIPG 584C/T polymorphism and CAD susceptibility was affected. Third, the sample sizes of our study were still small, especially in subgroup analysis. Only one eligible study was included in the analysis of African populations. TSA identified that the cumulative sample size was insufficient. There were not enough eligible case–control studies, weakening the statistical power to detect the real correlation between the LIPG 584C/T polymorphism and susceptibility to CAD. Fourth, the result of our meta-analysis should be interpreted with caution and needs to be confirmed by more case–control studies, because the sensitivity analyses indicated that deletion of certain individual study had an impact on the reliability of our results. Fifth, although Begg’s and Egger’s tests proved that there was no publication bias, it may inevitably exist in our meta-analysis. Only published articles reported in English or Chinese were included in the present study. Some candidate studies may not be included because they were not published, discovered, or because they were not published in English or Chinese.

In conclusion, our meta-analysis suggested that the LIPG 584C/T polymorphism plays a protective role in the incidence of CAD in individuals. The T allele may be a protective factor against CAD. Because of the limitations mention above, more high-quality case–control studies in various ethnicities are needed to confirm our results.

Competing Interests

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

Funding

This work was supported by the Anhui Provincial Postdoctoral Science Foundation [grant number 2016B097]; and the Major Project of Natural Science Research of Colleges and Universities in Anhui Province [grant number KJ2019ZD65].

Author Contribution

Y.W. and Z.H. conceived the study. Y.W., L.M. and H.Z. searched the databases and extracted the data. X.C. and X.X. analyzed the data. Y.W. wrote the draft of the paper. Z.H. and L.M. reviewed the manuscript.

Acknowledgements

We thank those who helped us in the present study.

Abbreviations

     
  • ACS

    acute coronary syndrome

  •  
  • CAD

    coronary artery disease

  •  
  • CHD

    coronary heart disease

  •  
  • CI

    confidence interval

  •  
  • CNKI

    China National Knowledge Infrastructure

  •  
  • EL

    endothelial lipase

  •  
  • HB

    hospital-based

  •  
  • HDL

    high-density lipoprotein

  •  
  • HDL-C

    high-density lipoprotein cholesterol

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • LIPG

    endothelial lipase gene

  •  
  • NOS

    Newcastle–Ottawa Scale

  •  
  • OR

    odds ratio

  •  
  • PB

    population-based

  •  
  • PCR-RFLP

    polymerase chain reaction-restriction fragment length polymorphism

  •  
  • SNP

    single-nucleotide polymorphism

  •  
  • TG

    triglyceride

  •  
  • TSA

    trial sequential analysis

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Supplementary data