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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Aug 15;8(8):11958–11984.

The role of gene variants of the inflammatory markers CRP and TNF-α in cardiovascular heart disease: systematic review and meta-analysis

Yazmín Hernández-Díaz 1, Carlos Alfonso Tovilla-Zárate 2, Isela Juárez-Rojop 3, Manuel Alfonso Baños-González 4, Manuel Eduardo Torres-Hernández 4, María Lilia López-Narváez 5, Teresa Guadalupe Yañez-Rivera 6, Thelma Beatriz González-Castro 1
PMCID: PMC4612795  PMID: 26550110

Abstract

It is widely acknowledged that cardiovascular heart disease (CHD) has a genetic influence. Several studies have investigated the role of inflammatory markers like C-reactive protein (CRP) and tumor necrosis factor α (TNF-α) in the causation of cardiovascular diseases. Although there have been several positive studies associating CRP and TNF-α genes with CHD, the evidence is not entirely consistent. Therefore, we performed a meta-analysis to gain a better understanding into this issue. The meta-analysis was conducted with 22 articles of genetic association studies of CRP (G1059C rs1800947, C1444T rs1130864, C717T rs2794521 and G3872A rs1205) and TNF-α (C857T rs1799724, C863A rs1800630 and T1031C rs1799964) genes. To analyze the association of these variants with CHD we used the following models: allelic, additive, dominant and recessive. In addition, we performed a sub-group analysis by Caucasian population using the same four models. CRP and TNF-α gene polymorphisms showed a positive significant association with CHD. This study provides evidence that rs2794521 of the CRP gene and rs1799724, rs1800630 and rs1799964 of the TNF-α gene polymorphisms may be risk factors to manifest CHD. The analysis of rs1800947 and rs1205 of the CRP gene yielded a protective effect in the pathogenesis of this disease. Only the analysis of the rs1130864 polymorphism showed a lack of association with CHD. To have conclusive outcomes it is necessary to integrate more studies to confirm our findings.

Keywords: Cardiovascular disease, polymorphisms, C-reactive protein, TNF-α, meta-analysis and systematic review

Introduction

The global burden of cardiovascular heart disease (CHD) represents the highest cause of mortality and one of the highest causes of morbidity around the world [1]. Several reports have explored a contribution in the pathogenesis of CHD considering environmental and intrinsic variables such as smoking, infections, age, obesity, lipid levels, blood pressure, among other causes, but nowadays there is growing evidence indicating a substantial genetic component [2,3]. Several studies have investigated the role of inflammatory markers like C-reactive protein (CRP) and tumor necrosis factor α (TNF-α) in the causation of cardiovascular diseases [4,5]. The human CRP gene is located on chromosome 1q21-1q23. It spans approximately 1,900 base pairs and contains 2 exons [6,7]. There are more than 30 single nucleotide polymorphisms (SNPs) of the human CRP gene listed (accessed on September 2014) in the National Center for Biotechnology Information (NCBI) SNP Database (http://www.ncbi.nlm.nih.gov/SNP). The most studied variant of the CRP gene is rs1800947 in which a change of guanine to cytosine at position 1059 has occurred. CRP polymorphisms have been related to several diseases [8-11]. The other CRP variant associated with CHD is rs1130864, where the change of cytosine to thymine at position 1444 has taken place [12-14]. With regard to polymorphisms rs2794521 and rs1205, these have been less studied in association studies with CHD, but similarly to the above mentioned polymorphisms they have yielded interesting outcomes [14-16]. The genetic change of rs2794521 occurs at position 717, where a cytosine is replaced by thymine; as for rs1205 a guanine is substituted by adenine at position 3872 [11,17]. With regard to the gene encoding TNF-α, it is located on chromosome 6p21 [18,19]. Some studies have reported that rs1799964, a specific TNF-α gene polymorphism, is associated with increased TNF-α secretion (Bennet et al., 2006; Cui et al., 2012; Ghazouani et al., 2009); in this variant T is substituted by C in the promoter region at position 1031. Other studies have shown that the TNF-α gene promoter region bears other polymorphisms, including C857T rs1799724 and C863A rs1800630, which are associated with CAD. Several studies have investigated whether the TNF-α gene polymorphisms that affect TNF-α expression or activity influences the susceptibility of cardiovascular disorders (Asifa et al., 2013; Bennet et al., 2006; Cho et al., 2013; Koch et al., 2001).

Although numerous studies have been conducted, this issue has not been settled conclusively. Some of these studies did not find an association between CRP and TNF-α with CHD, whereas other reports presented some evidence in favor of this hypothesis [20,21]. It is necessary to conduct larger and more detailed analyses to get more reliable assessment for the possibility of any moderate causal role. For this reason our aim was to perform a meta-analysis and updated systematic review with the following polymorphisms: G1059C rs1800947, C1444T rs1130864, C717T rs2794521 and G3872A rs1205 of the CRP gene, and C857T rs1799724, C863A rs1800630 and T1031C rs1799964 of the TNF-α gene to get a better understanding of the role these polymorphisms play in the pathogenesis of cardiovascular disease.

Materials and methods

The meta-analysis and systematic review were performed by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria [22,23].

Identification and selection of publications

The literature search was conducted using PubMed and EBSCO databases, using the following keywords: “C-reactive protein AND polymorphisms AND cardiovascular heart disease”, “C-reactive protein AND polymorphisms AND coronary heart disease”, “CRP AND polymorphisms AND CVD”, “CRP AND polymorphisms AND CHD (PubMed: 474; EBSCO: 486) and “TNF-α AND polymorphisms AND cardiovascular heart disease”, “TNF-α AND polymorphisms AND coronary heart disease”, “TNF-α AND polymorphisms AND CVD”, “TNF-α AND polymorphisms AND CHD (PubMed: 316; EBSCO: 310). These words were combined to retrieve the summaries. The search also implicated the review of the bibliography cited at the end of the various research articles.

Inclusion criteria

Two researchers (González-Castro and Juárez-Rojop) working independently screened each of the titles, abstracts and full texts to determine inclusion. When the researchers were in disagreement a third researcher (Tovilla-Zárate) was consulted. Eligible studies fulfilled the following criteria: (1) to be published in peer-reviewed journals, (2) to contain independent data, (3) to be association studies in which the frequencies of three genotypes were clearly stated or could be calculated, (3) diagnosis of cardiovascular disease in the patient study group, and (4) the articles had to be written in English. The following polymorphisms were studied: G1059C rs1800947, C1444T rs1130864, C717T rs2794521 and G3872A rs1205 of the CRP gene, and C857T rs1799724, C863A rs1800630 and T1031C rs1799964 of the TNF-α gene

Data extraction

The same authors mentioned previously extracted the information from all the included reports. These researchers worked independently and in agreement with the inclusion criteria listed above. The following data were obtained from each of the studies: authors, year of publication, location, ethnic group, number of cases and/or controls, age, gender, cardiovascular diagnosis of the participants and CRP serum levels. If these data were not available in the studies, we contacted the respective authors to ask for the lacking information. The outcomes of the meta-analysis were built by taking into consideration the categories reported in previously published studies [25-28].

Statistical analysis

For the meta-analysis the odds ratio (OR) and 95% confidence interval (CI) were estimated and used to evaluate the strength of the association of CRP and TNF-α variants with CHD risk. Pooled ORs were calculated respectively for each of the models used, viz.: allelic (Example: C vs. A), additive (e.g. C/C vs A/A), dominant (e.g. C/C+C/A vs A/A) and recessive (e.g. C/C vs C/A+A/A); OR and 95% CI values were estimated for each study. Also, we analyzed these CRP variants by populations. For all these procedures we used the EPIDAT 3.1 program (http://dxsp.sergas.es). This software is freely available for epidemiologic analysis of tabulated data. Data were analyzed with the random-effects model following the reports in the literature [29-31]. On the other hand, to explore the problem of publication bias, the Egger’s test and funnel plots were calculated with the same software. This last approach standardizes the effect of each of the published studies on the vertical axis and its correspondent precision on the horizontal axis. Sample heterogeneity was analyzed with the Dersimonian and Laird’s Q test. Q test results were complemented with graphs to help the visualization of those studies favoring heterogeneity. Next, a chi-squared (χ2) analysis was used to calculate the Hardy-Weinberg equilibrium to evaluate genotype distribution.

In order to strengthen the analysis we evaluated publication bias using the GRADE approach and assessed the risk of bias. The Newcastle-Ottawa Assessment Scale (NOS) was used for inclusion in the systematic review by scoring the methodological quality. We established a score of six or more as a cut-off point to distinguish high from low quality studies (see Additional file).

Results

Figure 1 shows the stages of the meta-analysis and systematic review. The combined search included 1586 potentially relevant articles (790 in PubMed and 796 in EBSCO). In the end, 22 articles were obtained after discarding overlapping references that did not agree with the inclusion criteria [8-17,32-41,44,47]. The total data were gathered from 17 521 CHD patients and 13 089 controls with respect to CRP gene variants, and 12 268 CHD patients and 12 162 controls for TNF-α gene variants. We divided the included studies into Caucasian populations and the sample sizes were: 15 077 CHD patients and 10 853 controls for CRP gene variants and 5844 CHD patients and 5824 controls for TNF-α gene variants. Also, we measured the Hardy-Weinberg equilibrium (HWE) in both groups (cases and controls) for all the samples of CRP and TNF-α gene variants and additionally we explored all populations in a combined way (Table 1). To develop the analysis further we present descriptive characteristics of the studies in Table 2.

Figure 1.

Figure 1

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Flow-chart showing the search strategy and inclusion/exclusion criteria used in the meta-analysis and systematic review.

Table 1.

Genotype and allele distribution in association studies on CRP (rs1800947, rs1130864, rs2794521 and rs1205) and TNF-α (rs1799724, rs1800630 and rs1799964) gene variants with CHD

Reference Genotype cases Genotype controls Allele cases Allele controls HWE cases HWE controls
CRP gene variants
    G1059C rs1800947 GG GC CC GG GC CC G C G C p value p value
        Zee R.Y. [8] 142 81 3 638 86 2 1365 87 1362 90 0.614 0.02*
        Brull D.J. [9] 163 231 1 209 23 1 349 25 441 25 0.670 0.00*
        Miller D.T. [14] 568 75 2 615 79 2 1209 77 1309 83 0.748 0.774
        Balistreri C.R. [32] 84 18 4 112 7 1 186 26 231 9 0.03* 0.02*
        Dai D.F. [10] 303 55 7 138 29 4 661 69 305 37 0.113 0.02*
        Pai J.K. [16] 414 80 5 89 100 4 908 90 1882 108 0.00* 0.607
        Grammer T.B. [12] 2238 305 12 620 72 1 4781 329 1312 74 0.463 0.643
        Pasalic D. [33] 184 20 2 101 23 1 388 24 225 25 0.803 0.098
        Akbarzadeh-Najar R. [11] 845 92 13 834 105 11 1782 118 1773 127 0.00* 0.00*
        Ghattas M.H. [34] 128 21 1 121 27 2 277 123 269 3 0.725 0.891
        Singh P. [13] 140 37 3 142 31 2 317 43 315 35 0.833 0.759
        Total 5209 1015 53 3619 582 31 12223 1011 9424 616 0.155 0.646
    C144T rs1130864 CC CT TT CC CT TT C T C T p value p value
        Brull D.J. [9] 86 78 22 122 92 13 250 122 336 118 0.42 0.50
        Miller D.T. [14] 315 270 58 331 298 67 900 386 960 432 0.99 0.98
        Grammer T.B. [12] 1150 1124 281 305 316 76 3424 1686 926 468 0.66 0.78
        Singh P. [13] 90 75 15 113 55 7 255 105 281 69 0.92 0.91
        Total 1892 1739 418 1344 1144 266 5523 2575 3832 1676 0.32 0.53
    C717T rs2794521 CC CT TT CC CT TT C T C T p value p value
        Brull D.J. [9] 98 73 14 117 88 17 269 101 322 122 0.93 0.93
        Chen J. [17] 452 163 4 439 158 18 1067 179 1067 179 0.41 0.00*
        Miller D.T. [14] 352 247 44 361 281 54 951 335 1003 389 0.94 0.94
        Grammer T.B. [12] 1395 981 179 351 294 52 3771 1339 996 398 0.37 0.71
        Akbarzadeh-Najar R. [11] 683 252 15 648 249 53 1618 282 1545 355 0.00* 0.12
        Singh P. [13] 112 60 8 137 35 3 284 76 309 41 0.66 0.99
        Total 3092 1776 264 2053 1105 197 7960 2312 5242 1484 0.06 0.66
    G3872A rs1205 CC CT TT CC CT TT C T C T p value p value
        Miller D.T. [14] 248 303 92 313 308 75 799 487 934 458 0.95 0.97
        Mathew J.P. [15] 153 132 10 61 73 14 438 152 195 101 0.23 0.00*
        Pai J.K. [16] 218 221 56 447 447 89 657 1333 1341 625 0.12 0.99
        Total 619 656 158 821 828 178 1894 1972 2470 1184 0.13 0.42
TNF-α gene variants
    C857T rs1799724 CC CT TT CC CT TT C T C T p value p value
        Herrmann, S.M.[35] 153 41 1 125 45 1 348 43 295 47 0.47 0.20
        Herrmann, S.M.[35] 302 122 10 340 155 10 726 142 835 175 0.72 0.12
        Bennet, A.M. [36] 959 161 16 1265 210 21 2079 193 2740 252 0.00* 0.00*
        Cui, G. [37] 907 459 22 700 299 28 2273 503 1699 355 0.00* 0.66
        Cui, G. [37] 642 278 41 529 262 30 1562 360 1320 322 0.13 0.82
        Cho, H.C. [38] 133 54 10 145 30 53 320 74 672 136 0.16 0.00*
        Total 3096 1115 100 3104 1001 143 7308 1315 7561 1287 0.97 0.30
    C863A rs1800630 CC CA AA CC CA AA C A C A p value p value
        Koch, W. [39] 716 261 21 242 90 8 1693 303 574 106 0.71 1.00
        Koch, W. [39] 546 227 20 242 90 8 1319 267 574 106 0.61 1.00
        Bennet, A.M. [36] 792 314 37 1011 438 43 1898 388 2460 524 0.40 0.65
        Asifa, G.Z. [40] 92 215 3 201 101 8 399 221 503 117 0.00* 0.35
        Cho, H.C. [38] 141 54 2 313 51 40 336 58 677 131 0.26 0.00*
        Total 2287 1071 83 2009 770 107 5645 1237 4788 984 0.07 0.06
    T1031C rs1799964 TT TC CC TT TC CC T C T C p value p value
        Bennet, A.M. [36] 726 364 55 889 523 68 1816 474 2301 659 0.00* 0.24
        Ghazouani, L. [41] 270 134 14 284 111 11 674 162 679 133 0.00* 1.00
        Cui, G. [37] 650 281 30 557 239 25 1581 341 1353 289 0.89 1.00
        Asifa, G.Z. [40] 178 106 26 182 210 18 462 158 474 146 0.97 0.98
        Total 2696 1360 166 2511 1464 169 6752 1692 6386 1702 0.88 0.99
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*

p value: statistical significance.

Table 2.

Descriptive characteristics of the studies about variants of the CRP and TNF-α genes included in the meta-analysis and systematic review

Reference Date of publication Cases Controls Mean age cases Mean age controls Location Origin Diagnosis or procedure CRP serum levels in cases/controls
CRP gene variants
    G1059C rs1800947
        Zee R.Y. [8] May 2002 726 726 60 60 USA Caucasians Arterial thrombosis 1.43 mg/L/1.23 mg/L
        Brull D.J. [9] July 2003 187 233 63 63 UK Caucasians Patients with coronary artery bypass 1.39 mg/L/1.19 mg/L
        Miller D.T. [14] Jun 2005 643 696 60 60 USA Caucasians Myocardial infarction and ischemic stroke 1.6 mg/L/1.2 mg/L
        Balistreri C.R. [32] May 2006 106 120 41 39 Italy Caucasians Acute myocardial infarction -
        Mathew J.P. [15] Jan 2007 295 148 61 60 USA Caucasians Patients with coronary artery bypass -
        Dai D.F. [10] May 2007 365 171 60 62 China Asians Coronary angiography 3.3 mg/L/-
        Pai J.K. [16] Jan 2008 499 995 60 60 USA Caucasians Acute myocardial infarction 1.02 mg/L/-
        Grammer T.B. [12] Jan 2009 2555 693 64 58 Germany Caucasians Coronary angiography 4.2 mg/L/2.8 mg/L
        Pasalic D. [33] April 2009 206 125 61 58 Croatia Caucasians Coronary angiography 2.8 mg/L/2.8 mg/L
        Akbarzadeh-Najar R. [11] April 2012 950 950 53 50 Iran Asians Acute myocardial infarction 2.5 mg/L/2.5 mg/L
        Ghattas M.H. [34] Nov 2012 150 150 - - Egypt Africans Acute myocardial infarction -
        Singh P. [13] Jun 2014 180 175 55 54 Punyab Indians Coronary angiography 2.3 mg/L/1.4 mg/L
    C144T rs1130864
        Brull D.J. [9] July 2003 227 186 63 63 UK Caucasians Myocardial infarction and ischemic stroke 1.6 mg/L/1.2 mg/L
        Miller D.T. [14] Jun 2005 643 696 60 60 USA Caucasians Myocardial infarction and ischemic stroke 1.6 mg/L/1.2 mg/L
        Pai J.K. [16] Jan 2008 485 959 60 60 USA Caucasians Acute myocardial infarction 1.02 mg/L/-
        Grammer T.B. [12] Jan 2009 2555 697 64 58 Germany Caucasians Coronary angiography 4.2 mg/L/2.8 mg/L
    C717T rs2794521
        Brull D.J. [9] July 2003 185 222 63 63 UK Caucasians Patients with coronary artery bypass 1.39 mg/L/1.19 mg/L
        Chen J. [17] Jan 2005 619 615 53 53 China Asians Acute mycocardial infarction 2.38 mg/L-1.82 mg/L
        Miller D.T. [14] Jun 2005 643 696 60 60 USA Caucasians Myocardial infarction and ischemic stroke 1.6 mg/L/1.2 mg/L
        Grammer T.B. [12] Jan 2009 2555 697 64 58 Germany Caucasians Coronary angiography 4.2 mg/L/2.8 mg/L
        Akbarzadeh-Najar R. [11] April 2012 950 950 53 50 Iran Asians Acute myocardial infarction 2.5 mg/L/2.5 mg/L
        Singh P. [13] Jun 2014 180 175 55 54 Punyab Indians Coronary angiography 2.3 mg/L/1.4 mg/L
    G3872A rs1205
        Miller D.T. [14] Jun 2005 643 696 60 60 USA Caucasians Myocardial infarction and ischemic stroke 1.6 mg/L/1.2 mg/L
        Mathew J.P. [15] Jan 2007 295 148 61 60 USA Caucasians Patients with coronary artery bypass -
Reference Date of publication Cases Controls Mean age cases Mean age controls Location Origin Diagnosis CRP serum levels in cases/controls
TNF-α gene variants
    C857T rs1799724
        Herrmann, S.M. [35] Jan 1998 195 171 - - UK Caucasians Myocardial infarction or angina pectoris -
        Herrmann, S.M. [35] Jan 1998 434 505 - - France Caucasians Myocardial infarction or angina pectoris -
        Bennet, A.M. [36] Jan 2006 1136 1496 61 61 Sweden Caucasians Myocardial infarction 1.8 mg/L/1.2 mg/L
        Cui, G. [37] Oct 2012 1338 1027 - - China Asians Ischemic stroke -
        Cui, G. [37] Oct 2012 961 821 - - China Asians Ischemic stroke -
        Cho, H.C. [38] March 2013 394 808 61 62 Korea Asians Myocardial infarction 1.9 mg/L/-
    C863A rs1800630
        Koch, W. [39] Jan 2001 998 340 64 63 Germany Caucasians Coronary stenosis -
        Koch, W. [39] Jan 2001 793 340 63 63 Germany Caucasians Myocardial infarction -
        Bennet, A.M. [36] Jan 2006 1143 1492 61 61 Sweden Caucasians Myocardial infarction 1.8 mg/L/1.2 mg/L
        Asifa, G.Z. [40] Sep 2013 310 310 54 53 Pakistan Oriental Coronary angiography -
        Cho, H. C. [38] March 2013 394 808 61 62 Korea Asians Myocardial infarction 1.3 mg/L/-
    T1031C rs1799964
        Bennet, A.M. [36] Jan 2006 1145 1480 61 61 Sweden Caucasians Myocardial infarction 1.8 mg/L/1.2 mg/L
        Ghazouani, L. [41] July 2009 418 406 58 57 Tunisia African Myocardial infarction -
        Cui, G. [37] Oct 2012 1338 1027 0 0 China Asians Ischemic stroke -
        Cui, G. [37] Oct 2012 961 821 0 0 China Asians Ischemic stroke -
        Asifa, G.Z. [40] Sep 2013 310 310 54 53 Pakistan Oriental Ischemic stroke -
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PCR gene variants

G1059C rs1800947 polymorphism

In the analysis of rs1800947, we included eleven association studies with CHD. When we explored the role of this variant in all populations in the allelic and recessive models with heterogeneity we found a slight association [fixed: OR 0.86; CI 95%: (0.77-0.96); Q test: <0.0001; Egger’s test: 0.12; and fixed: OR 0.85; (CI 95%: 0.75-0.96); Q test: <0.0001; Egger’s test: 0.74, respectively (Figures 2 and 3)]. With regard to the rest of the models, the analysis of all populations in the study yielded a lack of association. Moreover, we performed a sub-analysis in Caucasian populations to get a more comprehensive understanding of this common variant and we could confirm the association in the same allelic and recessive models with heterogeneity [fixed: OR 0.83; CI 95%: (0.72-0.95); Q test: 0.0007; Egger’s test: 0.87 and fixed: OR 0.75; (CI 95%: 0.65-0.87); Q test: <0.0001; Egger’s test: 0.72] (see Additional file). The rest of the outcomes as well as the results excluding heterogeneity and bias in the publication are shown in Table 3.

Figure 2.

Figure 2

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Odds ratios and forest plots of the PCR rs1800947 variant in overall studies, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Figure 3.

Figure 3

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Egger’s funnel plots in overall studies indicating publication bias in studies on CHD and PCR rs1800947 variant, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Table 3.

Analysis of association studies on CRP (rs1800947, rs1130864, rs2794521 and rs1205) and TNF-α (rs1799724, rs1800630 and rs1799964) gene variants with CHD in Caucasians and all populations in the study

Model analysis Caucasians P value of Q test P value of Egger’s test All populations P value of Q test P value of Egger’s test
Random effects OR (CI 95%) Fixed effects OR (CI 95%) Fixed effects OR (CI 95%) Fixed effects OR (CI 95%)
PCR gene variants
    G1059C rs1800947
        Allelic With heterogeneity 0.82 (0.61-1.10) 0.83 (0.72-0.95) 0.0007 0.87 0.74 (0.55-1.01) 0.86 (0.77-0.96) <0.0001 0.12
Without heterogeneity 0.91 (0.78-1.07) 0.91 (0.78-1.07) 0.60 0.90 0.99 (0.86-1.14) 0.99 (0.87-1.12) 0.30 0.50
        Additive With heterogeneity
Without heterogeneity 0.62 (0.22-1.74) 0.74 (0.35-1.54) 0.08 0.20 0.83 (0.48-1.45) 0.86 (0.54-1.38) 0.27 0.42
        Dominant With heterogeneity
Without heterogeneity 0.68 (0.31-1.48) 0.70 (0.34-1.46) 0.35 0.39 0.84 (0.52-1.34) 0.84 (0.52-1.34) 0.53 0.65
        Recessive With heterogeneity 0.65 (0.23-1.78) 0.75 (0.65-0.87) <0.0001 0.72 0.79 (0.41-1.50) 0.85 (0.75-0.96) <0.0001 0.74
Without heterogeneity 1.06 (0.72-1.57) 0.95 (0.78-1.16) 0.06 0.08 1.05 (0.87-1.26) 1.02 (0.88-1.18) 0.16 0.15
    C1444T rs1130864
        Allelic With heterogeneity 0.92 (0.77-1.09) 0.99 (0.91-1.07) 0.004 0.06
Without heterogeneity 1.00 (0.88-1.14) 1.02 (0.94-1.11) 0.09 0.25 1.00 (0.88-1.14) 1.02 (0.94-1.11) 0.09 0.25
        Additive With heterogeneity 0.87 (0.61-1.25) 0.99 (0.82-1.20) 0.02 0.08
Without heterogeneity 0.98 (0.71-1.35) 1.03 (0.85-1.25) 0.06 0.30 1.02 (0.76-1.37) 1.05 (0.87-1.28) 0.11 0.28
        Dominant With heterogeneity 0.91 (0.67-1.23) 0.98 (0.82-1.18) 0.05 0.12
Without heterogeneity 0.98 (0.73-1.30) 1.01 (0.84-1.22) 0.09 0.35 1.03 (0.81-1.30) 1.04 (0.86-1.25) 0.23 0.38
        Recessive With heterogeneity 0.93 (0.76-1.13) 0.99 (0.89-1.10) 0.02 0.06
Without heterogeneity 1.03 (0.92-1.16) 1.03 (0.93-1.15) 0.35 0.15 1.03 (0.92-1.16) 1.03 (0.93-1.15) 0.35 0.15
    C717T rs2794521
        Allelic With heterogeneity 1.02 (0.86-1.21) 1.10 (1.01-1.19) 0.001 0.08
Without heterogeneity 1.10 (1.00-1.21) 1.10 (1.00-1.21) 0.81 0.29 1.13 (1.03-1.23) 1.13 (1.04-1.22) 0.30 0.47
        Additive With heterogeneity 1.47 (0.86-2.54) 1.39 (1.11-1.73) <0.0001 0.79
Without heterogeneity 1.15 (0.90-1.47) 1.15 (0.90-1.47) 0.93 0.45 1.08 (0.80-1.45) 1.10 (0.86-1.40) 0.29 0.12
        Dominant With heterogeneity 3.60 (1.83-7.09) 2.79 (2.24-3.47) <0.0001 0.37
Without heterogeneity 2.05 (1.61-2.62) 2.05 (1.61-2.62) 0.93 0.98 2.04 (1.60-2.60) 2.04 (1.60-2.60) 0.97 0.53
        Recessive With heterogeneity 1.03 (0.86-1.24) 1.10 (1.00-1.21) 0.009 0.37
Without heterogeneity 1.14 (1.01-1.29) 1.14 (1.01-1.29) 0.74 0.12 1.14 (1.04-1.26) 1.14 (1.04-1.26) 0.92 0.28
    G3872A rs1205
        Allelic With heterogeneity 0.64 (0.22-1.88) 0.44 (0.40-0.48) <0.0001 0.45 0.64 (0.22-1.88) 0.44 (0.40-0.48) <0.0001 0.45
Without heterogeneity
        Additive With heterogeneity 1.05 (0.53-2.09) 0.79 (0.62-1.01) 0.001 0.07 1.05 (0.53-2.09) 0.79 (0.62-1.01) 0.001 0.07
Without heterogeneity 0.70 (0.54-0.90) 0.70 (0.54-0.90) 0.48 1.00 0.70 (0.54-0.90) 0.70 (0.54-0.90) 0.48 1.00
        Dominant With heterogeneity 1.02 (0.58-1.81) 0.83 (0.66-1.04) 0.007 0.07 1.02 (0.58-1.81) 0.83 (0.66-1.04) 0.007 0.07
Without heterogeneity 0.74 (0.58-0.95) 0.74 (0.58-0.95) 0.7567 1.00 0.74 (0.58-0.95) 0.74 (0.58-0.95) 0.75 1.00
        Recessive With heterogeneity 0.99 (0.71-1.37) 0.91 (0.79-1.06) 0.01 0.29 0.99 (0.71-1.37) 0.91 (0.79-1.06) 0.01 0.29
Without heterogeneity 0.85 (0.69-1.04) 0.85 (0.73-0.99) 0.19 1.00 0.85 (0.69-1.04) 0.85 (0.73-0.99) 0.19 1.00
TNF-α gene variants
    C857T rs1799724
        Allelic With heterogeneity
Without heterogeneity 1.04 (0.90-1.20) 1.04 (0.90-1.20) 0.65 0.10 1.00 (0.92-1.09) 1.00 (0.92-1.09) 0.65 0.45
        Additive With heterogeneity 1.42 (0.08-2.51) 1.35 (1.03-1.79) 0.003 0.80
Without heterogeneity 0.96 (0.57-1.61) 0.96 (0.57-1.61) 0.96 0.73 1.08 (0.80-1.46) 1.08 (0.80-1.46) 0.65 0.99
        Dominant With heterogeneity 1.46 (1.07-2.75) 1.38 (1.05-1.82) 0.0006 0.82
Without heterogeneity 0.95 (0.56-1.59) 0.95 (0.56-1.59) 0.95 0.89 1.07 (0.79-1.45) 1.07 (0.80-1.45) 0.40 0.98
        Recessive With heterogeneity
Without heterogeneity 1.06 (1.00-1.24) 1.06 (0.90-1.24) 0.48 0.13 1.02 (1.00-1.14) 1.01 (0.92-1.12) 0.32 0.08
    C863A rs1800630
        Allelic With heterogeneity 1.16 (0.84-1.61) 1.10 (1.00-1.22) <0.0001 0.58
Without heterogeneity 0.98 (0.88-1.10) 0.98 (0.88-1.10) 0.64 0.52 0.97 (0.88-1.08) 0.97 (0.88-1.08) 0.75 0.94
        Additive With heterogeneity
Without heterogeneity 1.05 (1.04-1.51) 1.05 (1.04-1.51) 0.89 0.60 0.77 (0.42-1.39) 0.92 (0.65-1.28) 0.06 0.19
        Dominant With heterogeneity 1.41 (0.86-2.34) 1.18 (1.06-1.33) <0.0001 0.26
Without heterogeneity 0.97 (0.86-1.10) 0.97 (0.86-1.10) 0.54 0.41 1.02 (0.88-1.18) 1.00 (0.89-1.13) 0.28 0.09
        Recessive With heterogeneity 0.64 (0.32-1.28) 0.87 (0.62-1.22) 0.01 0.08
Without heterogeneity 1.06 (0.75-1.52) 1.06 (0.75-1.52) 0.88 0.44 0.99 (0.70-1.40) 0.99 (0.70-1.40) 0.47 0.14
    T1031C rs1799964
        Allelic With heterogeneity 0.99 (0.70-1.40) 0.99 (0.70-1.40) 0.47 0.14
Without heterogeneity 0.97 (0.86-1.10) 0.94 (0.87-1.02) 0.06 0.06
        Additive With heterogeneity
Without heterogeneity 0.97 (0.71-1.32) 0.93 (0.74-1.17) 0.13 0.32
        Dominant With heterogeneity 0.88 (0.73-1.06) 0.88 (0.80-0.96) 0.003 0.92
Without heterogeneity 0.94 (0.81-1.11) 0.92 (0.83-1.01) 0.06 0.06
        Recessive With heterogeneity
Without heterogeneity 1.05 (1.00-1.51) 1.00 (0.80-1.25) 0.06 0.39
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C1444T rs1130864 polymorphism

When the whole sample was explored with respect to this variant gene, the analysis revealed no significant association of this polymorphism in relation to CHD using the following models: allelic [random: OR 1.00; CI 95% (0.88-1.14); Q test: 0.09; Egger’s test: 0.25], additive [random: OR 1.02; CI 95% (0.76-1.37); Q test: 0.11; Egger’s test: 0.28], dominant [random: OR 1.03; CI 95% (0.81-1.30); Q test: 0.23; Egger’s test: 0.38], and recessive [random: OR 1.03; CI 95% (0.92-1.16); Q test: 0.35; Egger’s test: 0.15] (Figures 4 and 5). Due to these results, we explored the role of this variant gene in the Caucasian population only (see Additional file), and even though the results did not reveal heterogeneity we confirmed the same results of the first analysis, using the same models: allelic [random: OR 1.00; CI 95% (0.88-1.14); Q test: 0.09; Egger’s test: 0.25], additive [random: OR 0.98; CI 95% (0.71-1.35); Q test: 0.06; Egger’s test: 0.30], dominant [random: OR 0.98; CI 95% (0.73-1.30); Q test: 0.09; Egger’s test: 0.35], and recessive [random: OR 1.03; CI 95% (0.92-1.16); Q test: 0.35; Egger’s test: 0.15]. Additional data can be found in Table 3.

Figure 4.

Figure 4

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Odds ratios and forest plots of PCR rs1130864 variant in overall studies, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Figure 5.

Figure 5

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Egger’s funnel plots in overall studies indicating publication bias in studies on CHD and PCR rs1130864 variant, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

C717T rs2794521 polymorphism

The analysis of all the populations as a whole showed that the rs2794521 polymorphism in the four models analyzed yielded a significant relation to present CHD clinically in the following models: allelic [random: OR 1.13; CI 95% (1.04-1.22); Q test: 0.30; Egger’s test: 0.47; without heterogeneity], dominant [random: OR 2.04; CI 95% (1.60-2.60); Q test 0.97; Egger’s test: 0.53; without heterogeneity] and recessive [random: OR 1.14; CI 95% (1.04-1.26); Q test: 0.92; Egger’s test: 0.28; without heterogeneity] (Figures 6 and 7). Furthermore, a more careful analysis of this gene variant was performed reporting Caucasian population only and we still observed the same results showing a strong relationship of this polymorphism and CHD: allelic [random: OR 1.10; CI 95% (1.00-1.21); Q test: 0.81; Egger’s test: 0.29], dominant [random: OR 2.05; CI 95% (1.61-2.62); Q test: 0.93; Egger’s test: 0.98] and recessive [random: OR 1.14; CI 95% (1.01-1.29); Q test: 0.74; Egger’s test: 0.12]. These data did not reveal heterogeneity (Table 3).

Figure 6.

Figure 6

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Odds ratios and forest plots of the PCR rs2794521 variant in overall studies, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Figure 7.

Figure 7

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Egger’s funnel plots in overall studies indicating publication bias in studies on CHD and PCR rs2794521 variant, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

G3872A rs1205 polymorphism

In this gene variant we only included reports with Caucasian populations (see Additional file). We conducted only one analysis taking the whole population as sample and found a significant association of this polymorphism with CHD in the following models: additive [random: OR 0.70; CI 95% (0.54-0.90); Q test: 0.48; Egger’s test: 1.00] and dominant [random: OR 0.74; CI 95% (0.58-0.95); Egger’s test: 0.75; Egger’s test: 1.00] (Figures 8 and 9). In the analysis with the allelic model we could not exclude heterogeneity (Table 3).

Figure 8.

Figure 8

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Odds ratios and forest plots of the PCR rs1205 variant in overall studies, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Figure 9.

Figure 9

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Egger’s funnel plots in overall studies indicating publication bias in studies on CHD and PCR rs1205 variant, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

TNF-α gene variants

C857T rs1799724 polymorphism

The analysis of this variant included the same models proposed in the CRP polymorphisms: allelic, additive, dominant and recessive with and without heterogeneity. In this way we could observe in the recessive model without heterogeneity a significant association of allele T of the rs1799724 polymorphism in whole CHD population [random: OR 1.02; CI 95% (1.00-1.14); Q test: 0.32; Egger’s test: 0.08] (Figures 10 and 11). This result was confirmed when we explored this same variant but only in Caucasians, using the recessive model without heterogeneity [random: OR 1.06; CI 95% (1.00-1.24); Q test: 0.48; Egger’s test: 0.13] (see Additional file). In addition, the findings in the others models did not show C857T polymorphisms as a predisposing factor in CHD (Table 3).

Figure 10.

Figure 10

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Odds ratios and forest plots of the TNF-α rs1799724 variant in overall studies, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Figure 11.

Figure 11

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Egger’s funnel plots in overall studies indicating publication bias in studies on CHD and TNF-α rs1799724 variant, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

C863A rs1800630 polymorphism

First, the interaction of this variant in CHD was analyzed; the outcomes did not show a significant association in the population when heterogeneity was discarded. Subsequently, we explored this association only in Caucasians (see Additional file) and we could observe in the additive model without heterogeneity a slight 5% increase in the risk to present CHD [random: OR 1.05; CI 95% (1.04-1.51); Q test: 0.89; Egger’s test: 0.60] (Figures 12 and 13).

Figure 12.

Figure 12

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Odds ratios and forest plots of the TNF-α rs1800630 variant in overall studies, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Figure 13.

Figure 13

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Egger’s funnel plots in overall studies indicating publication bias in studies on CHD and TNF-α rs1800630 variant, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

T1031C rs1799964 polymorphism

The findings of this variant in the dominant model with heterogeneity revealed a probability that this SNP could have a protective effect in the CHD for allele T [random: OR 0.88; CI 95% (0.80-0.96); Q test: 0.003; Egger’s test: 0.92]. Although the outcome in the recessive model without heterogeneity [random: OR 1.05; CI 95% (1.00-1.51); Q test: 0.06; Egger’s test: 0.39] showed that allele C of the variant T1031C could be considered a risk factor (Figures 14 and 15). An analysis by ethnicity was not performed.

Figure 14.

Figure 14

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Odds ratios and forest plots of the TNF-α rs1799964 variant in overall studies, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Figure 15.

Figure 15

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Egger’s funnel plots in overall studies indicating publication bias in studies on CHD and TNF-α rs1799964 variant, without heterogeneity, using the following models: A. Allelic; B. Additive; C. Dominant, and D. Recessive.

Discussion

It is well known that CHD is a complex disease where multiple genetic markers interact and inflammatory markers play a vital role in its pathogenesis. Due to this, the present study aimed to explore the involvement of TNF-α and CRP genetic variants with CHD in a meta-analysis and systematic review to get a more comprehensive understanding of this pathology. To start, we chose the TNF-α and CRP genes because there is strong evidence that they are powerful predictors of incident cardiovascular events. As a result, the most common variants studied in association with CHD were the following polymorphisms: G1059C rs1800947, C1444T rs1130864, C717T rs2794521 and G3872A rs1205 of the CRP gene, and C857T rs1799724, C863A rs1800630 and T1031C rs1799964 of the TNF-α gene with interesting results.

First, we explored the rs1800947 polymorphism and found that in all the studies concerning this variant, the pooled ORs suggested a possible protective role to present CHD clinically. The allelic model was used to analyze all the populations as a whole [fixed: OR 0.86; CI 95% (0.77-0.96)], and also Caucasian populations only [fixed: OR 0.83; CI 95% (0.72-0.95); with heterogeneity] (Table 3). This polymorphism is the most studied CRP gene in relation to CHD. We found that our results are in agreement with other reports on the subject [33,34]. However, there are also reports not showing such association [9,10,14]. We encourage researchers to take into consideration that the fixed model is not as conservative as the random model, because the latter assumes the existence of normal distribution effects and parameterizes inter-studies. Therefore, the outcomes from the random effects model are more reliable when heterogeneity is present.

Next, we analyzed the rs1130864 variant in association studies with CHD, but we could not find any significant association in all the analyses performed (Table 3); these outcomes are in agreement with other studies [9,13]. The lack of association could be explained by the scarce number of studies, fewer than for rs1800947. Hence, it is necessary to increase the number of studies to have a definitive outcome. In the next analysis we studied the role of rs2794521 in CHD and we encountered that pooled ORs in almost all the models used in this variant showed a tendency from 13-260% to be a risk factor to present CHD (Table 3). Even though this variant has not been as studied as rs1800947, our data could be helpful in the search for genetic markers in this pathology. Researchers should take this information with caution because the sample size limits the outcomes obtained. Finally, the last CRP variant addressed was rs1205. Even though this polymorphism has been even less studied than the rest, the outcomes showed the same results as for the rs180097 polymorphism (Table 3), i.e., this variant may have a protective effect in CHD. However, we cannot take this result as conclusive -even with the agreement encountered with other studies [15]- given that when the meta-analysis was performed we could not exclude heterogeneity in all the models and this may have an effect on population variation and stratification. Moreover, we could not perform an analysis by ethnic group, since we thought useful to explore the interaction of the gene depending on the origin of the subjects.

With regard to the TNF-α gene, we selected polymorphisms C857T rs1799724, C863A rs1800630 and T1031C rs1799964 for this study. The first approach was with the rs1799724 variant where we obtained a probability range of 2-46% for this variant to be a marker of risk in CHD in the general population. But when we analyzed this role in Caucasians we only observed a 6% risk. This same tendency was seen in the analysis of rs1800630 where in the case of Caucasians we got a 5% risk to manifest CHD. Moreover, in the analysis of all populations with the random effects model, no percentage of risk was obtained. This may be so because in some association studies the genotype distribution may depend on the ethnicity of the subjects studied and probably in other populations the interaction of a hypothetical gene is necessary to manifest CHD [36,40]. In the final variant, rs1799964, the statistical analysis yielded a slight association with CHD in the recessive model (CC+TC vs TT) (Table 3). Therefore, there is a remote possibility of a protective effect with allele T when using the dominant form with heterogeneity in the fixed effects model. But as was explained previously, we did not take into consideration these results given the limitations present with the fixed effects model when heterogeneity is present.

To our knowledge the present study is the first meta-analysis and systematic review with several gene variants of inflammatory markers in the analysis. We also used different models for the analyses (allelic, additive, dominant and recessive; Caucasian population only) to gain a better comprehension of CHD. On the other hand, there are some limitations in our study that must be taken into consideration. First, our findings can be ambiguous because the case-control design of the studies can be affected by population stratification, where systematic differences in the ancestry can lead to spurious findings, even though we tried to address this problem by conducting an analysis which only included Caucasians. Second, although we analyzed several TNF-α and CRP gene variants, some of the studies used a small sample size, which may not provide sufficient power to detect small effects of the gene. Alternatively, we did not conduct an analysis of all TNF-α and CRP gene variants in the Asian population or an analysis by gender, because there were not enough studies with available data. In addition, although we evaluated heterogeneity in the studies, we could not exclude it from the sample as a whole.

In conclusion the present investigation demonstrates that polymorphisms rs2794521 of CRP gene, and rs1799724, rs1800630 and rs1799964 of TNF-α gene are possible risk markers to present CHD. Conversely, the analysis of rs1800947 and rs1205 of the CRP gene could exert a protective effect on the pathogenesis of this disease. Only the analysis of polymorphism rs1130864 shows a lack association with CHD, but it is necessary to integrate more studies to obtain more robust conclusions.

Disclosure of conflict of interest

None.

References

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