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. 2019 Mar 28;18:74. doi: 10.1186/s12944-019-1018-3

Effect of CD14 polymorphisms on the risk of cardiovascular disease: evidence from a meta-analysis

Jin-Jian Xu 1,2,#, Ke-Qi Liu 3,#, Zhi-Min Ying 4,#, Xiao-Wei Zhu 1, Xue-Jin Xu 2, Pian-Pian Zhao 1, Wei-Yang Bai 1, Mo-Chang Qiu 3, Xing-Wei Zhang 2, Hou-Feng Zheng 1,
PMCID: PMC6439994  PMID: 30922395

Abstract

Background

CD14 polymorphisms are associated with an increased risk of cardiovascular events. So far, many studies have been conducted, whereas the results were not always consistent.

Materials and methods

Twenty-six articles involving thirty-seven datasets were recruited to evaluate the association between rs2569190 (9413 patients and 7337 controls), C-159T (4813 patients and 2852 controls) polymorphisms and cardiovascular diseases in a meta-analysis. The random or fixed effect models were used to evaluate the pooled odds ratios (ORs) and their corresponding 95% confidence intervals.

Results

The strongest association was observed between rs2569190 and CVD in overall population (T vs. C, OR = 1.169, 95% CI: 1.087–1.257, p = 2.44 × 10− 5). Analysis after stratification by ethnicity indicated that rs2569190 was related to CVD in East Asian population (T vs. C, OR = 1.370, 95% CI; 1.226–1.531, p = 2.86 × 10− 8) and a potential relationship in European (T vs. C, OR = 1.100, 95% CI: 1.019–1.189, p = 0.015). In the stratification of endpoints, the associations were found in CHD subgroup (T vs. C, OR = 1.357, 95% CI: 1.157–1.592, p = 2.47 × 10− 7) and in AMI subgroup (T vs. C, OR = 1.152, 95% CI: 1.036–1.281, p = 0.009). However, we did not find any association between C-159T polymorphism with cardiovascular disease under any model.

Conclusions

The SNP rs2569190 significantly contribute to susceptibility and development of cardiovascular disease, particularly in the East Asian population and in the subtype CHD group, in addition, a potential association was observed in the AMI group, T allele acts as a risk factor for cardiovascular disease.

Electronic supplementary material

The online version of this article (10.1186/s12944-019-1018-3) contains supplementary material, which is available to authorized users.

Keywords: CD14, Cardiovascular disease, Polymorphism, Meta-analysis

Introduction

Cardiovascular disease (CVD) is a major public health problem owing to associate increased risk of human mortality [1]. By far the most common cause of acute coronary syndrome (ACS) is atherosclerosis and coronary artery stenosis, these lesions are the pathological foundation of CAD [2]. According to the number of coronary artery stenoses and the diverse clinical manifestation, CVD was defined to various clinical phenotypes (like coronary heart disease (CHD), acute myocardial infarction (AMI), myocardial infarction (MI) and so on [3]). Atherosclerosis is a process of progressive thickening and hardening of the walls of medium-sized and large arteries as a result of fat deposits on their inner lining [4]. Atherosclerosis is a pathological condition that underlies several important averse vascular events including coronary artery disease (CAD), stroke, and peripheral arterial disease, responsible for most of the cardiovascular morbidity and mortality [5]. In the 1990s, it was first time to demonstrate a strong association between inflammation and atherosclerosis, and suggested that atherosclerosis was one of chronic inflammatory diseases [5]. Recent studies have suggested that inflammation was an important factor in the initiation and development of atherosclerosis [2, 6, 7], the inflammatory reaction which in the coronary artery atherosclerosis plaque, leads to the intima damage, plaque rupture and acute cardiac ischemia [79]. These points revealed that infection may enhance the inflammatory processes present in atherosclerosis and CAD.

The cluster of differentiation antigen 14 (CD14) is a lipopolysaccharide (LPS) receptor located on the surface of monocytes and macrophages and it is a multiple function inflammation cytokine which is mainly produced by mature mononuclear macrophage [10]. CD14 is known as a surface marker, being glycosylated phosphatidylinositol anchored in the cell membrane (mCD14) [11]. In addition, CD14 could specially combine with LPS, transfer the activation signal to the downstream pathway through the TLR4 and bone marrow differentiation protein-2 [12, 13]. By this way, the monocytes-macrophage system was launched and many pro-inflammation cytokines as TNF-α, IL-1, IL-6 and so on were released [14]. These cytokines have multi-functions in the process of mediating initial immune response and inflammation reaction which causing the endothelia damage, disturbance of immunologic function and vascular smooth muscle cells proliferation [2]. Thus, CD14 was considered to be a key role in the process of atherosclerosis and complications.

It is generally accepted that genetic predisposition is a major risk factoring for atherosclerosis leading to CAD. In 1999, since Hubacek et al. [15] first reported that CD14 gene single nucleotide polymorphism (SNP) rs2569190, related to the translation start site at upstream of promoter region, was apparently relevant to atherosclerosis in the Czech population, the allele T of rs2569190 was also reported being a risk factoring for MI in European population. Then, a bunch of studies were carried out to verify the causal relationship between rs2569190 and CAD in diverse ethnics. Differently, the negative results that the polymorphism was not associated with CHD and MI were observed in European participants [16]. In addition, the variant of rs2569190 whether effected CAD on Han Chinese has been a research hotpot [17, 18]. Meanwhile, a new promoter polymorphism C-159T in the gene of LPS receptor was also found to be associated with CAD by Unkelbach et al. [19]. However, the inverse finding that there was not interaction between C-159T and CAD was observed by two groups [20, 21]. Besides, the allele T of C-159T was considered to be a risk factor for CAD in a Chinese population [22], then the same conclusion was obtained in Chinese Yanbian population [23]. On the contrary, the T-to-C exchange in C-159T was considered to be risk allele in European populations, especially in older patients with a low atherosclerotic risk profile [19].

Although many studies have been conducted so far to investigate the relationship between CD14 gene polymorphisms and CVD, the results were inconsistent. Population stratification might lead to inconsistent results, especially when allele frequency and incidence rate of the disease vary across ethnic groups. Meanwhile, inclusion of data that didn’t satisfy the requirement of meta-analysis would produce a spurious association. Therefore, in order to reduce the limitations of single study and to overcome the possible random errors, a large-scale meta-analysis involving multifarious ethnics was performed by us.

Materials and methods

Identification of eligible studies

To analyze the association between CD14 gene (SNP rs2569190, C-159T) and CVD, all published literature before December 2017 that researched the relationship between these polymorphisms and CVD risk were concluded. The electronic databases were used including PubMed databases (National Center for Biotechnology, National Library of Medicine), CNKI (China National Knowledge Infrastructure), and Web of Science were retrieved by using the keywords “CD14”, “C-260T”, “C-159T”, “rs2569190”, “polymorphism” connect to “CVD”, “coronary artery disease”, “coronary heart disease”, “myocardial infarction”, or “atherosclerosis” without language restrictions. Finally, we extracted data from the published articles, not included meetings or any conference abstracts. All of the included studies used either case-control or nested case-control design. Appropriate diagnosis criteria (e.g., arteriography confirmed; changes of electrocardiographic and clinical symptoms according to the WHO criteria; a documented history of coronary intervention) and proper genotyping methods were used in most of the studies.

Selection criteria and data extraction

Such major criteria must be followed for included studies (1) Original papers containing complete data, (2) Case–control or cohort studies that assessed the association of CD14 gene -159C/T and rs2569190 polymorphisms with CVD, and (3) Sufficient data to calculate the odds ratio (OR) or P value, (4) Relevant cardiovascular outcomes were angiographically confirmed (generally by WHO criteria) and coronary stenosis was diagnosed as at least one coronary artery stenosis (no less than 50% by a coronary angiography [24, 25]), (5) The genotype distribution in the control group for each individual study should follow Hardy-Weinberg equilibrium (HWE).

The primary reasons for excluded studies: (1) Case-only studies, review or meta-analysis articles, (2) Deviate from the major selection criteria, (3) Overlapping or that supplied inadequate data, (4) Repeated publications or the same authors employed similar data in different papers, the data was only used once.

The study data was extracted based on standard protocol. For studies in which data could not be separated according to type of CVD from published data, cases were classified in the more inclusive category of coronary stenosis for the purposes of subsidiary analyses. Disagreement was settled by a consensus between all authors. Where essential information was not presented in articles, every effort was made to contact the authors. All procedures conformed to the guidelines for meta-analysis of observational studies in epidemiology. The following information were extracted independently by individual in our study: first author, year of publication, ethnicity, study design, types of CVD endpoints, HWE status among control, sample size of case and control, number of genotype and allele frequency.

Statistical analysis

We calculated the allele frequency for each study in allele counting method, the Hardy–Weinberg equilibrium (HWE) was tested by using the Chi square test [26]. We employed pooled ORs and 95% confidence intervals (CIs) to evaluate the strength of association between polymorphisms and cardiovascular disease for every eligible study [27]. The methodology of Cochran’s Q-statistic was used to evaluate the heterogeneity, which similar to the previous study in our lab [28]. If the P value in heterogeneity test was higher than 0.1, the fixed effect model was used. Moreover, the random effect model was used [2931]. We used the following formula to quantified the effect of heterogeneity :I2 = 100 %  × (Q −  )/Q. The proportion of between-study variability attributable to heterogeneity was indicated by I2 value, and I2 values of 25, 50 and 75% were considered to be of low, moderate and high heterogeneity, respectively. If study groups revealed no heterogeneity, the similar results were produced in fixed and random effects models and, otherwise the random effects model usually produced wider CIs than the fixed effects model. In this meta-analysis, P value of less than 0.05 was considered a statistically significant.

In order to get exacting search results, we evaluated possible publication bias by Egger’s linear regression text [32]. If P value < 0.05 the statistical publication bias was considered [33]. Moreover the Begg’s test also used a funnel plot to evaluate the publication bias [32]. For sensitivity analysis, we removed one study orderly from the total and tested residual studies [34]. Statistical analysis was carried out using the software program STATA12.0 (Stata Corporation, College Station, Texas).

Results

Studies included in the meta-analysis

In this meta-analysis, totally 218 relevant articles (145 for rs2569190 and 73 for C-159T) were searched. After reading titles and abstracts, we excluded irrelevant studies, 89 articles (54 for rs2569190 and 35 for C-159T) for further reading. Then, we excluded 57 articles (33 for rs2569190 and 24 for C-159T), because of no data, insufficient data, repeated date, family-based studies and not referring to cardiovascular disease (CVD). Thus, 32 articles (23 for rs2569190 and 9 for C-159T) met the study inclusion criteria. Lastly, after excluding three groups of data in which the control populations deviated from HWE [17, 35, 36] and three reviews [3739] about rs2569190 and C-159T. After filtering, 26 eligible studies involved 37 data sets were finally included [15, 16, 1823, 4055], in which two articles contained dual data (which articles included two type data about rs2569190 and C-159T) [19, 40]. Eventually, 26 studies providing 14,226 cases and 10,189 controls (rs2569190: 9413 patients and 7337 controls; C-159T: 4813 patients and 2852 controls) were pooled to evaluate the relationship between SNPs of CD14 and CVD in the meta-analysis (Table 1). The flowchart of selecting article process is presented in Fig. 1.

Table 1.

The basic information of every studies included in this meta-analysis

Polymorphisms Study Year Ethnicity design Endpoint P(HWE) Sample size Genotypes Allele frequencies (%)
Cases Controls Cases Controls Cases Controls
CC CT TT CC CT TT C T C T
rs2569190 (C-260T) Wei et al. [40] 2006 East Asian CC CHD 0.22 246 258 58 112 76 83 118 57 46.3 53.7 55.0 45.0
Zhang et al. [18] 2006 East Asian CC CHD 0.05 193 225 20 95 78 39 92 94 35.0 65.0 37.8 62.2
Li et al. [41] 2007 East Asian CC CHD 0.14 193 225 29 95 69 47 124 54 40.0 60.0 48.0 52.0
Xie et al. [42] 2008 East Asian CC CHD 0.24 241 149 49 127 65 56 65 28 46.7 53.3 59.4 40.6
Liu et al. [55] 2010 East Asian CC AMI 0.19 12O 130 23 56 41 43 57 30 42.5 57.5 55.0 45.0
Shimada et al. [43] 2000 East Asian CC MI 0.83 128 83 27 49 52 21 43 19 40.0 60.0 51.0 49.0
Hohda et al. [44] 2003 East Asian CC MI 0.2 502 527 97 242 163 115 278 134 43.4 56.6 48.2 51.8
Nauck et al. [16] 2002 European NCC AMI 0.43 2559 697 675 1262 622 188 358 151 51.0 49.0 53.0 47.0
Nauck et al. [16] 2002 European NCC MI 0.43 1599 697 444 758 397 188 358 151 51.0 49.0 53.0 47.0
Unkelbach et al. [19] 1999 European CC MI 0.99 1053 1175 292 520 241 339 584 252 52.0 48.0 54.0 46.0
Koenig et al. [45] 2002 European CC AMI 0.62 312 476 75 164 73 126 243 107 50.3 49.7 52.0 48.0
Longobardo et al. [46] 2003 European CC AMI 0.38 215 215 44 101 70 55 101 59 44.0 56.0 49.0 51.0
Morange et al. [47] 2005 European CC MI 0.38 194 197 42 98 54 39 104 54 47.0 53.0 46.0 54.0
Giacconi et al. [48] 2007 European CC CHD 0.14 146 148 39 69 38 48 80 20 50.4 49.6 59.0 41.0
Hubacek et al. [15] 1999 European CC MI 0.11 178 135 52 77 49 61 53 21 50.8 49.2 64.8 35.2
Lorenzova et al. [49] 2007 European CC AMI 0.32 230 562 63 116 51 166 268 128 53.0 47.0 53.0 47.0
Arroyo-Espliguro [50] 2005 European CC AMI 0.35 194 94 45 85 64 31 42 21 45.1 54.9 55.3 44.7
Arroyo-Espliguro [50] 2005 European CC CHD 0.35 140 94 34 78 28 31 42 21 52.1 47.9 55.3 44.7
Morange et al. [47] 2005 European CC MI 0.41 54 70 12 28 14 24 31 15 48.0 52.0 56.0 44.0
Morange et al. [47] 2005 European CC MI 0.19 99 121 20 57 22 29 53 39 49.0 51.0 46.0 54.0
Morange et al. [51] 2004 European NCC MI 0.09 128 253 43 59 26 69 113 71 57.0 43.0 50.0 50.0
Morange et al. [51] 2004 European NCC CHD 0.75 123 243 31 58 34 61 124 58 49.0 51.0 51.0 49.0
Morange et al. [47] 2005 European CC MI 0.2 179 176 60 94 25 65 77 34 60.0 40.0 59.0 41.0
Zee et al. [52] 2001 America NCC MI 0.99 387 387 98 215 74 108 193 86 53.0 47.0 53.0 47.0
C-159T Jin et al. [23] 2016 East Asian CC EH-LVH 0.99 116 108 27 58 31 25 54 29 48.3 51.7 48.3 51.7
Jin et al. [23] 2016 East Asian CC EH-NLVH 0.99 107 108 24 46 37 25 54 29 43.8 56.2 48.3 51.7
Jin et al. [23] 2016 East Asian CC EH-LVH 1 103 108 23 52 28 26 54 28 47.6 52.4 49.1 50.9
Jin et al. [23] 2016 East Asian CC EH-NLVH 1 100 108 23 44 33 26 54 28 45.0 55.0 49.1 50.9
Wei et al. [40] 2006 East Asian CC CHD 0.09 246 258 47 128 71 39 139 80 45.1 54.9 42.1 57.9
Li et al. [22] 2005 East Asian CC CHD 0.2 162 196 24 75 63 54 89 53 38.0 62.0 59.2 40.8
Haberbosch et al. [21] 2009 European CC AMI 0.46 54 252 16 24 14 71 131 50 51.9 48.1 54.2 45.8
Haberbosch et al. [21] 2009 European CC MI 0.46 146 252 50 69 27 71 131 50 57.9 42.1 54.2 45.8
Koch et al. [20] 2002 European CC AMI 0.43 998 340 273 498 227 88 177 75 52.0 48.0 52.0 48.0
Koch et al. [20] 2002 European CC MI 0.43 793 340 232 390 171 88 177 75 54.0 46.0 52.0 48.0
Unkelbach et al. [19] 1999 European CC AMI 0.36 1727 501 491 864 372 140 240 121 53.0 47.0 52.0 48.0
Damiano et al. [53] 2012 European CC CHD 0.07 51 49 7 23 21 13 18 18 36.0 64.0 44.0 56.0
Banerjee et al. [54] 2009 Indian CC AMI 0.14 210 232 45 116 49 38 126 68 49.0 51.0 44.0 56.0

CHD coronary heart disease, AMI acute myocardial infarction, MI myocardial infarction, EH –LVH/ EH –LVH essential hypertension with left/not left ventricular hypertrophy, CC case–control, NCC nested case–control, Endpoint diseases of cases, HWE Hardy-Weinberg Equilibrium

Fig. 1.

Fig. 1

The process of the articles selected in this meta-analysis

Meta-analysis results

SNP rs2569190 and cardiovascular disease risk

The test of heterogeneity indicated that there was potential heterogeneity in the overall population (p = 0.001, I2 = 52.5%), but after stratified by ethnicity, the heterogeneity was resolved in any subgroup (p = 0.308, I2 = 15.9%). An outstanding association was found in the overall population, under the random effect model (T vs. C, OR = 1.169, 95% CI: 1.087–1.257, P = 2.44 × 10− 5, Table 2, Fig. 2a1). Accordingly, dominant and recessive models were also tested to estimate the relationship between rs2569190 and CVD risk, the significant associations were observed in overall population (TT + CT vs. CC, OR = 1.233, 95% CI: 1.110–1.370, p = 3.79 × 10− 5, Table 2, Additional file 1: Figure S1a; TT vs. CC + CT, OR = 1.195, 95% CI: 1.062–1.345, p = 0.003, Table 2, Additional file 1: Figure S1b). In the subgroup’s analysis by ethnicity, we obtained the most significant relationship between rs2569190 with CVD, particularly in East Asian population under allele model (T vs. C, OR = 1.370, 95% CI: 1.226–1.531, p = 2.86 × 10− 8, Table 2, Fig. 2a1), dominant model (TT + TC vs. CC, OR = 1.574, 95% CI: 1.278–1.938, p = 9.11 × 10− 7, Table 2, Additional file 1: Figure S1a) and recessive model (TT vs. TC + CC, OR = 1.492, 95% CI: 1.236–1.801, p = 3.05 × 10− 5, Table 2, Additional file 1: Figure S1b). Besides, the results revealed that rs2569190 was associated with CVD in European under allele and dominant models (T vs. C, OR = 1.100, 95% CI: 1.019–1.189, p = 0.015, Table 2, Additional file 1: Figure S1a; TT + TC vs. CC, OR = 1.113, 95% CI: 1.006–1.232, p = 0.039, Table 2, Additional file 1: Figure S1b). Meanwhile, no relationship under any model was found in American population (Table 2, Fig. 2a1).

Table 2.

Meta-analysis of the association between rs2569190 (C-260T) polymorphism and CVD risk

Sub-group analysis No of data sets No of cases/controls Allele model (T VS.C) Dominant model (CC VS.TT + CT) Recessive model (TT VS.CC + CT)
Cases Controls OR (95% CI) POR PH OR (95% CI) POR PH OR (95% CI) POR PH
Overall 24 9413 7337 1.169(1.087–1.257) 2.44 × 10−5* 0.001 1.233(1.110–1.370) 3.79 × 10−5* 0.02 1.195(1.062–1.345) 0.003* 0.002
Ethnicity
 East Asian 7 1503 1597 1.370(1.226–1.531) 2.86 × 10−8* 0.308 1.574(1.278–1.938) 9.11 × 10−7* 0.217 1.492(1.236–1.801) 3.05 × 10−5* 0.22
 European 16 7403 5353 1.100(1.019–1.189) 0.015 0.047 1.113(1.006–1.232) 0.039 0.237 1.112(0.975–1.269) 0.113 0.04
 America 1 387 387 1.000(0.819–1.221) 1 1.142(0.830–1.571) 0.416 0.827(0.584–1.173) 0.287
Endpoint
 CHD 7 1019 1005 1.357(1.157–1.592) 2.47 × 10−7* 0.074 1.669(1.388–2.007) 2.48 × 10−6* 0.433 1.342(1.011–1.781) 0.042 0.035
 AMI 7 762 1244 1.152(1.036–1.281) 0.009 0.143 1.186(0.997–1.412) 0.054 0.131 1.162(1.030–1.310) 0.015 0.483
 MI 10 4641 3915 1.077(0.976–1.188) 0.139 0.072 1.061(0.949–1.186) 0.297 0.799 1.118(0.907–1.378) 0.297 0.002

OR odd ratio, 95%CI 95% confidence interval, PORP value for the test of association, PHP value for heterogeneity analysis

Significant P-value

Fig. 2.

Fig. 2

Forest plot for the meta-analysis of the association between CD14 gene polymorphisms and CVD. a1 rs2569190 and CVD (T VS.C), stratification by ethnicity. a2 rs2569190 and CVD (T VS.C), stratification by endpoint. b1 C-159T and CVD (T VS.C), stratification by ethnicity. b2 C-159T and CVD (T VS.C), stratification by endpoint

Similarly, we carried out subgroup analysis by endpoints to estimate the relationship between rs2569190 and the specific isoforms of CVD, a very significant association was identified between rs2569190 and CHD under allelic model (T vs. C, OR = 1.357, 95% CI: 1.157–1.592, p = 2.47 × 10− 7, Table 2, Fig. 2a2), dominant model (TT + TC vs. CC, OR = 1.669, 95% CI: 1.388–2.007, p = 2.48 × 10− 6, Table 2, Additional file 1: Figure S2a) and a potential association in recessive model (TT vs. CC + CT, OR = 1.342, 95% CI: 1.011–1.781, p = 0.042, Table 2, Additional file 1: Figure S2b). Besides, the association between rs2569190 and AMI was also observed under allelic model (T vs. C, OR = 1.152, 95% CI: 1.036–1.281, p = 0.009, Table 2, Fig. 2a2), and recessive model (TT vs. CC + CT, OR = 1.162, 95% CI: 1.030–1.310, p = 0.015, Table 2, Additional file 1: Figure S2b).

C (− 159) T and cardiovascular disease risk

The association between C-159T and CVD was conducted in 13 independent studies with 7665 participants in this meta-analysis and all of datasets followed the inclusion criteria. The test of heterogeneity in the overall population was not significant (p = 0.324, I2 = 12.1%), suggesting that the fixed effect model could be used.

We didn’t observe association in overall population under allele model (T vs. C, OR = 1.009, 95% CI: 0.941–1.082, p = 0.854, Table 3, Fig. 2b1). We also tested the dominant and recessive genetic models in overall population, but no associations were found in the two models (Table 3, Additional file 1: Figure S1a, d). In the subgroup analysis by ethnicity, no potential association was found between C-159T and the risk of CVD in any populations under all genetic models (Table 3, Fig. 2b1, Additional file 1: Figure S1c, d). Furthermore, we also performed subgroup analysis by endpoints in three models, respectively, which indicated that no statistically significant association was discovered in these isoforms of CVD (Table 3, Fig. 2b2, Additional file 1: Figure S2c, d). In essence, the results did not reveal any allele-specific or genotype-specific relation of the C-159T with CVD (Table 3).

Table 3.

Meta-analysis of the association between C-159T polymorphism and CVD risk

Sub-group analysis No of data sets No of cases/controls Allele model (T VS.C) Dominant model (CC VS.TT + CT) Recessive model (TT VS.CC + CT)
Cases Controls OR (b95% CI) POR PH OR (95% CI) POR PH OR (95% CI) POR PH
Overall 13 4813 2852 1.009(0.941–1.082) 0.854 0.078 1.049(0.936–1.176) 0.41 0.164 1.009(0.900–1.132) 0.878 0.324
Ethnicity
 East Asian 6 834 886 0.881(0.723–1.075) 0.212 0.066 1.120(0.888–1.413) 0.34 0.109 1.198(0.974–1.474) 0.087 0.327
 European 6 3769 1734 1.039(0.953–1.132) 0.387 0.717 0.923(0.806–1.058) 0.249 0.488 0.962(0.832–1.113) 0.602 0.751
 Indian 1 210 232 1.249(0.958–1.628) 0.101 0.718(0.445–1.160) 0.176 0.734(0.479–1.125) 0.156
Endpoint
 EH-LVH 2 219 216 0.974(0.746–1.271) 0.845 0.81 1.045(0.669–1.631) 0.847 0.817 1.028(0.672–1.573) 0.898 0.87
 EH-NLVH 2 207 216 0.846(0.646–1.109) 0.227 0.981 1.051(0.669–1.653) 0.828 0.967 1.424(0.938–2.162) 0.097 0.957
 CHD 3 459 503 0.804(0.514–1.257) 0.338 0.007 1.292(0.953–1.785) 0.121 0.007 1.186(0.904–1.556) 0.217 0.1
 AMI 4 2989 1325 1.048(0.950–1.156) 0.351 0.512 0.926(0.790–1.085) 0.342 0.729 0.917(0.778–1.080) 0.3 0.314
 MI 2 939 592 1.109(0.952–1.293) 0.184 0.664 0.817(0.642–1.038) 0.098 0.669 0.957(0.735–1.246) 0.743 0.851

OR odd ratio, 95%CI 95% confidence interval, PORP value for the test of association, PHP value for heterogeneity analysis

Allele frequency of the rs2569190 and comparing to the 1000 genome phase 3 population

We displayed the alleles frequencies of different ethnicities in our meta-analysis and 1000 genomes alleles frequencies of rs2569190 in Table 4. In view of the sample size and population, the allelic frequencies of rs2569190 in this meta-analysis were consistent with the allelic frequencies in the 1000 Genome Project EAS (East Asian ancestry), EUR (European ancestry), respectively, however, there was distinction between the allele frequencies in AMR (Admixed American) and 1000 Genomes Project. Although such inconsistent allele frequencies in AMR were observed in the comparison to 1000 Genomes Project, the approximate allele frequency was obtained in overall population to 1000 Genomes Project. Because of the deficient data, we fail to make a comparison for C-159T to1000 Genomes Project (Table 4).

Table 4.

The allele frequency comparison between the meta-analysis and 1000 Genomes Project

Polymorphism Populations Meta-analysis (alleles frequencies) 1000 genomes (alleles frequencies)
Case Control
C T C T C T
rs2569190 (C-260T) East Asian 0.43 0.57 0.5 0.5 0.43(EAS) 0.57(EAS)
European 0.51 0.49 0.53 0.47 0.51(EUR) 0.49(EUR)
America 0.53 0.47 0.53 0.47 0.47(AMR) 0.53(AMR)
All 0.51 0.49 0.52 0.48 0.53 0.47
C-159T East Asian 0.44 0.56 0.47 0.53 NA NA
European 0.53 0.47 0.52 0.48 NA NA
Indian 0.49 0.51 0.44 0.46 NA NA
All 0.49 0.51 0.5 0.5 NA NA

NA Not Available, EAS East Asian ancestry, EUR European ancestry, AMR Admixed American, All overall individuals from Phase 3 of the 1000 Genomes Project

Publication bias and sensitivity analysis

Begg’s funnel plot and Egger’s test were performed to estimate publication bias (Fig. 3a-b). There were no evidence of publication bias for SNP rs2569190 (p = 0.118) and C-159T (p = 0.077) under allele genetic model (Additional file 1: Table S1; Fig. 3a-b; Additional file 1: Figure S2 a-d). In addition, no significant difference in the Egger’s test neither, suggesting no obvious bias of publication in the present meta-analysis. We also conducted sensitivity analysis to assess the influence of individual studies on the pooled ORs. We found the pooled OR was not substantially altered, when a single study involved in the meta-analysis was deleted each time (Fig. 4a-b; Additional file 1: Figure S3 a-d).

Fig. 3.

Fig. 3

Begg’s funnel plot of publication bias in the meta-analysis of the association of CD14 polymorphisms with CVD risk. a rs2569190 and CVD (T vs. C). b C-159T and CVD (T vs. C)

Fig. 4.

Fig. 4

Sensitivity analysis to assess the stability of the meta-analysis. a rs2569190 and CVD (T vs. C). b C-159T and CVD (T vs. C)

Discussion

In the present study, we conducted a meta-analysis to evaluate the association between rs2569190 and C-159T with the susceptibility of cardiovascular disease (CVD). It was verified that the T allele of rs2569190 apparently increased the risk of CVD, particularly in East Asian population. In stratified analysis by endpoints, rs2569190 was significantly associated with CHD, whereas, no any allele-specific or genotype-specific relation of the C-159T with cardiovascular disease was observed in diverse ethnicities.

The SNP rs2569190, locating in the promoter region of the CD14 gene was considered modulating the capacity to stimulate inflammation through the regulation of CD14 gene expression and plasma soluble CD14 (sCD14) levels [56]. A potential functional role of rs2569190 on CD14 has been suggested as it alters a Sp1 transcription factor binding site and modulates the activity of promoter, the T allele was associated with higher transcription. This SNP was identified to be associated with multiple inflammatory diseases [57, 58]. In previous studies, rs2569190 was indicated to be associated with inflammatory bowel disease [59] and eczema [60]; afterward, studies showed the relevance between rs2569190 and diverse vascular events, such as atherosclerosis [61]. In 1999, rs2569190 was first shown to influence the interaction between CD14 receptor and human coronary artery atherosclerosis and complications [5].

Recently, in the genome-wide association studies (GWAS) of Alex P et al. [62], a robust result was discovered that rs2569190 was significantly associated with CAD in older European-American adults and a potential association in black (European Americans P = 6.15e-08, Blacks P = 0.04), meanwhile the same findings were also obtained by other investigators in another European population [50]. To check this association in different ethnicities, we carried out a comprehensive ethnicity-specific meta-analysis involving 9413 cases and 7337 controls with 24 separate comparisons and used subgroup analysis as well as the random effect model to deal with the heterogeneity. We found that the association between rs2569190 and CAD achieved significant level in overall population and reached genome-wide significance in East Asian population (T vs. C, OR = 1.370, 95% CI: 1.226–1.531, P = 2.86 × 10− 8) as well as a certain relevance in the European, but no association in America population. Similarly, in the previous meta-analysis with 28 case–control articles testified a significant association between rs2569190 with CHD in East Asian, but they acquired a negative result in European populations under any genetic models [37], which was associated result in our study. After carefully read, we found that the data in their study were not convinced. Some studies about C-159T were included by researchers [20, 21, 54], and three studies with data deviated from HWE [17, 35, 36]. Papers of this kind were not supposed to pass the inclusion criteria of meta-analysis may deviate from the true results. Therefore, we excluded these studies to carry out a correct meta-analysis.

In our meta-analysis with rs2569190, 16 studies for the European were included. After analyzed, the marginal significance was discovered in European subgroup (P = 0.015), which was negative result in another meta-analysis study [37]. It was easy for us to see that the frequencies of the T allele in our European population approximate to that in the overall population. Meanwhile, the T allele of control was discordant with C allele in European and C allele frequency was distinctly larger than T. Besides, we could notice that the alleles frequencies are concordant in the comparison of European subgroup to another meta-analysis study [37]. In addition, we made a comparison for rs2569190 to1000 Genomes Project and found the allele frequencies of rs2569190 in this meta-analysis were consistent with the allele frequencies in the 1000 Genome Project EUR (European ancestry), EAS (East Asian ancestry), respectively, however, there was distinction between the allele frequencies in AMR (Admixed American) and 1000 Genomes Project due to the weak number of studies about American population. More studies with statistical enough power for American race are needed for deeply evaluation.

Of course, so far some research had reported that there was no association was found between rs2569190 and CVD in European population [16, 46], which was contradictory with our findings. It’s normal that such distinct consequences were obtained in separated studies. CVD is considered to be a common multifactor syndrome due to its complicated pathogenesis [63]. A part of previous studies verified that the rs2569190 was associated with CVD in European population [50] and the diametrical results were also carried out in other participants [46]. It was validated that the BMI and smoking will significantly contribute to susceptibility and development of CVD [64]. In addition, the gender difference was the key role in CVD morbidity [65]. However, the basic data which lack of BMI level in participants might lead to inconsistent results. These phenomena and discrepancy needed further investigation on the basis of large sample size.

C-159T base at − 159 lies 49 bp adjacent to an experimentally detected binding site for transcription factor Sp1 at − 110 and 1 bp adjacent to a putative Ap2 site at − 158, it was considered to be an important CD14-activating mediator of inflammatory responses that may result in atherosclerosis, coronary heart disease (CAD), thrombus formation and myocardial infarction (MI) [54]. Previous studies had confirmed that C-159T polymorphism was associated with CAD and indicated that C-159T expression was obviously higher in CAD cases than in controls [19, 23], but there were distinct conflictions among different researchers in the different ethnics [53].

In 2015, Li et al. [38] performed a meta-analysis included 2798 cases and 1669 controls from 7 articles. The results indicated that C-159T could increase the risk of CAD under allelic model (p = 0.05), recessive (p = 0.01) in the whole population with marginal significance, no significant association was found between them under dominant (p = 0.10). A significant association was detected in Chinese population (p < 0.05), while there was no significant association in the European subgroup (p > 0.05). To precisely explore the association between C-159T and CAD, we analyzed the data that were consistent with HWE. Ultimately, we carried out a comprehensive meta-analysis with 7665 participants. In contrast, we obtained the ineffective relationship between C-159T with any phenotypes of CVD in all subgroups from our study. Then, we compared to previous meta-analysis, and we found that they only included 5 articles about Chinese and 2 studies about European, such deficient data sets may lead to imprecise results. Simultaneously, we realized that the marginal significance under the allelic model and recessive model will be leaded by limited data sizes for C-159T, whereas our study included 13 researches to perform a more comprehensive study which will be more persuasive. In addition, the significant heterogeneity was easily found under all models in their study, of which the heterogeneity reached threshold (I2 = 83%) in the most relative recessive model. So many limitations were discovered, which did not emerge in our study, will produce contradictory conclusion.

Although no significant association between C-159T and CAD was found in our study, the positive results were discovered by previous studies [22]. We found that the participants which were included by the positive study, were stratified in the gender and the males were much higher than females [19]. Besides, the age was inconsistent in diverse studies, the youngest cohort is 47.9 ± 5.8 and the oldest is 65.30 ± 10.53 in cases of CAD [21, 36]. It’s likely that such differences may, at less in part, attributed to the conclusion difference.

In addition, CD14 gene, located in 5q23–31, spans 3.9 kb, which encodes the glycoprotein with 375 amino acids [66]. The two identified SNPs, rs2569190, in upstream of the CD14 promoter at base pair − 260 from the major transcription start site and C-159T, in the 5′ flanking region of the CD14 gene at position − 159 [6769]. Many previous studies had suggested that the two polymorphisms will increase sCD14 levels in homozygous carriers of T allele [19, 50] and the base cytosine (C) is replaced by thymine (T) in CD14 gene polymorphisms rs2569190 and C-159T has been reported being associated with a higher risk of CAD [22]. Thus, the relationship that C-159T whether dependent on rs2569190 to alter the activity of CD14 gene promoter affect CD14 gene expression and lead to atherosclerosis, increase the risk of CAD, was not to be researched.

Although we revealed some new discoveries in this study, there were still several limitations should be taken into consideration. In our study, the overall sample size is large, but the size of each study is relatively small, the smallest sample is 54 cases and 70 controls [47], and we need numerous data to validate the relationship between rs2569190 and CVD for further study in American and other populations. For C-159T, the included data in current meta-analysis for ethnicity more from population with European and East Asian origin, and the findings are applicable to only these populations, more studies are required in Indian and other populations. Secondly, we had to indicate that significant between-study heterogeneity that was detected. We used the random effect model to deal with the heterogeneity, but this might induce an imprecise statistic because fixed effect model and random effect model address different research questions. Additionally, we are unable to analyze the actual impact of immanent factors on cardiovascular disease because of the incomplete data. Age is a powerful predictor of cardiovascular adverse events. It is vague why older patients continue to have poor outcomes after ACS despite improved access to contemporary treatment [70]. Meanwhile, the cardiovascular morbidity will also vary due to gender differences [71]. In this study, we did not investigate the contribution of age and gender to the onset of cardiovascular disease due to the insufficient data. Furthermore, the mechanism of CVD is considered to be comprehensive, including gene-gene and gene-environment interactions. To sum up, more studies with enough statistical power are needed for deeply evaluation.

Conclusions

We conducted a meta-analysis to evaluate the effects of CD14 polymorphisms (rs2569190 and C-159T) on the risk of cardiovascular disease. This meta-analysis indicated that SNP rs2569190 significantly contribute to susceptibility and development of CVD, particularly in the East Asian population and in the subtype CHD group, in addithon, a potential association was observed in the AMI group, T allele acts as a risk factor for cardiovascular disease. However, we failed to acquire the positive association between rs2569190 and other subtypes of CVD. Meanwhile, the associations between C-159T polymorphism with CVD were not observed under any model. Further efforts should be put on investigating the association between the functional mutations within CD14 gene and CVD, and the interactions in potential gene-gene and gene-environment should be comprehensively analyzed.

Additional file

Additional file 1: (582.3KB, pdf)

Table S1. The information of publication bias in all Polymorphisms. Figure S1. Forest plot for the meta-analysis of the association between CD14 gene polymorphisms and CVD under the Dominant and Recessive models. Figure S2. Begg’s funnel plot of publication bias in the meta-analysis of the association of CD14 polymorphisms with CVD risk. Figure S3. Sensitivity analysis to assess the stability of the meta-analysis. (PDF 576 kb)

Acknowledgments

The funding agencies had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors wish to thank all colleagues and friends who helped us in writing this article. The kind and helpful advice from Dr. Peikuan Cong is gratefully acknowledged. We thank the peer reviewers for their thorough and helpful review of this manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (81871831), the Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars of China (LR17H070001).

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its Additional information files].

Abbreviations

95% CI

95% confidence interval

AMI

Acute myocardial infarction

AMR

Admixed American

CC

Case–control

CHD

Coronary heart disease

CVD

Cardiovascular disease

EAS

East Asian ancestry

EH –LVH/EH –LVH

Essential hypertension with left/not left ventricular hypertrophy

EUR

European ancestry

HWE

Hardy-Weinberg Equilibrium

MI

Myocardial infarction

NA

Not available

NCC

Nested case–control

OR

Odd ratio

Authors’ contributions

HFZ and JJX conceived of the study, participated in the design, and drafted the manuscript. KQL, ZMY and XWZ carried out the study searches, XJX, PPZ and WYB collected the data. MCQ, PKC and XWZ performed the statistical analyses. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional.

Competing interests

The authors declare that they have no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1: (582.3KB, pdf)

Table S1. The information of publication bias in all Polymorphisms. Figure S1. Forest plot for the meta-analysis of the association between CD14 gene polymorphisms and CVD under the Dominant and Recessive models. Figure S2. Begg’s funnel plot of publication bias in the meta-analysis of the association of CD14 polymorphisms with CVD risk. Figure S3. Sensitivity analysis to assess the stability of the meta-analysis. (PDF 576 kb)

Data Availability Statement

All data generated or analysed during this study are included in this published article [and its Additional information files].


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