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. 2020 May 19;20:232. doi: 10.1186/s12872-020-01510-4

The association between Matrix Metallo-proteinases-9 (MMP-9) gene family polymorphisms and risk of Coronary Artery Disease (CAD): a systematic review and meta-analysis

Reza Hassanzadeh-Makoui 1, Bahman Razi 2, Saeed Aslani 3, Danyal Imani 4, Seyedeh Samaneh Tabaee 5,6,✉
PMCID: PMC7236475  PMID: 32429880

Abstract

Background

We performed a systematic review and meta-analysis of the Matrix metalloproteinases (MMP)-9 (C1562T), MMP-9 (R279Q), MMP-9 (P574R) and MMP-9 (R668Q) polymorphisms and risk of Coronary Artery Disease (CAD).

Methods

After a systematic literature search, pooled odds ratio (OR) and their corresponding 95% confidence interval (CI) were used to evaluate the strength of the association.

Results

We identified 40 studies with 11,792 cases and 8280 controls for C1562T, 7 case-control studies with 5525 cases and 2497 controls for R279Q, 2 studies with 1272 cases and 785 controls for P574R, and 2 studies with 1272 cases and 785 controls for R668Q. MMP-9 (C1562T) polymorphism was associated with increased risk of CAD under dominant model (OR = 1.41, P < 0.001), recessive model (OR = 1.59, P < 0.001), allelic model (OR = 1.38, P < 0.001), TT vs. CC model (OR = 1.70, P < 0.001), and CT vs. CC model (OR = 1.35, P < 0.001). Moreover, the subgroup analysis based on the continent of the study populations in this SNP indicated strong significant association in Asians but not in Europeans. Subgroup analysis was not performed in Africa, America and Oceania, due to lack of sufficient data.

Conclusions

Our meta-analysis revealed that MMP-9 (C1562T) SNP conferred a susceptibility risk for CAD in the overall analysis and Asian population. The overall analysis and subgroup analysis of the other three SNPs reject the association between MMP-9 polymorphisms and the risk of CAD. Although the results should interpret with caution because of small sample size of included studies in these three SNPs.

Keywords: Coronary artery disease, Matrix metalloproteinases, Genetic polymorphism, Meta-analysis

Background

Coronary artery disease (CAD) is a worldwide medical problem that is the leading cause of death in both developed and developing countries, especially in older people [1, 2]. Several studies have shown that the traditional risk factors, such as blood lipid, diabetes, hypertension, obesity play crucial roles in the initiation and perpetuation of CAD. However, it is nowadays accepted that genetic component has an essential role in the development of CAD [3–6]. Researches have suggested that family aggregation of CAD is not unusual, and genetic association investigations revealed that the average heritability of CAD is more than 50% [5, 7]. Epidemiological studies have found many genetic variants especially single-nucleotide polymorphism (SNP) in association with an increased risk of CAD [8]. The exact mechanism underlying the influence of polymorphism on the pathogenesis of CAD is not fully understood. Nevertheless, polymorphisms in numerous genes involved in inflammation, metabolism of lipid and glucose, blood clotting, and homocysteine may affect susceptibility to CAD [9, 10].

. This enzyme is involved in the degradation of extracellular matrix (ECM) components, such as type IV collagen, which is involved in the neovascularization, angiogenesis, inflammatory processes, and development of atherosclerosis.

MMPs are zinc containing enzymes that belong to a neutral protease family. Among the MMP family, MMP-9 is the most important enzyme of this class that is produced by the cells in the vascular wall. Moreover, inflammatory immune cells, such as neutrophils, monocytes as well as endothelial cells and vascular smooth muscle cells (VSMCs) generate MMP-9 [11]. This enzyme is involved in the degradation of extracellular matrix (ECM) components, such as type IV collagen, which is involved in the neovascularization, angiogenesis, inflammatory processes, and development of atherosclerosis [12, 13]. Several studies have shown that the levels of MMPs and their matrix-degrading activity are raised in exposed areas of atherosclerotic plaques, or following acute coronary syndrome [13, 14]. As a result, it is rational to hypothesize that genetic defects resulting in the overexpression of activated MMPs play a crucial role in the pathogenesis of coronary artery disease (CAD).

The MMP family is grouped into gelatinizes (MMP2, 9), collagenases (MMP1, 8, 13, 18), stromelysins (MMP3, 10, 11), and the membrane-type MMPs (MT-MMPs) that are coded by separate genes and have different tissue distribution and bioactive function [15].

Several studies have shown that MMP-9 family polymorphisms might be associated with the risk of CAD [16–18]. However, the results are inconsistent. For example, Mahmoodi et al. conducted a case-control study to investigate the association between -1562C > T genetic polymorphism and susceptibility to CAD. But, genotype and allele frequencies of MMP9 -1562C > T polymorphism were similar between CAD patients and controls (P > 0.05) [19]. However, Rodriguez-Perez et al. demonstrated that MMP9 (1562 C > T) allele and the CT genotype were associated with the risk of developing myocardial infraction (MI) [20]. The causes for these controversial results probably due to small sample sizes, different ethnicity, patient selection, clinical heterogeneity, low statistical power, or a combination of these factors. Therefore, we performed this meta-analysis to evaluate whether MMP-9 gene family polymorphisms play a role in CAD susceptibility.

Methods

We followed a protocol based on observational studies in epidemiology (MOOSE) guidelines [21], and results were reported based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline [22]. This article does not contain any studies with human participants performed by any of the authors.

Systematic search strategy

An exhaustive systematic search was conducted through electronic databases (Scopus, Medline) retrieving all potential publications considered the association between MMP-9 family gene polymorphism and susceptibility to CAD. All publications from inception to February 2020 were included (the search was updated before manuscript submission). The combination of key words and Mesh (Medical Subject Headings) terms were as follow: (“matrix metalloproteinase” [Mesh] OR “MMP” OR “gelatinase”) AND (“coronary heart disease” OR “CAD” OR “coronary syndrome” OR “ischemic heart disease” OR “vascular disease” OR “myocardial infarction” OR “MI” OR “atherosclerosis” OR “arteriosclerosis” OR “coronary stenosis” OR “coronary disease” OR “CHD” OR “angina”) AND (“single nucleotide polymorphism” OR “SNP” OR “polymorphisms” OR “mutation” OR “variation”). The references of review articles were cross-checked to find potential publications. Only human studies and English language publications were considered.

Inclusion and exclusion criteria

We screened retrieval publications according to following inclusion criteria: 1) observational studies (cohort or case-control design); 2) publications considered the association between MMP-9 family gene polymorphism (C1562T, R279Q, P574R and R668Q) and susceptibility to CAD; 3) publications reporting sufficient data to extract or calculate risk estimates with 95% CI; 4) publications reporting numbers or genotype frequencies in cases and healthy controls. Duplicates, book chapters, letters to editor, animal study, case reports, review articles, and studies with repetitive subjects all were excluded. The application of these criteria recognized 40, 7, 2, and 2 eligible studies for C1562T SNP, R279Q SNP, P574R SNP and R668Q SNP, respectively.

Data extraction

Two authors screened the literature and extracted data independently according to the inclusion and exclusion criteria. The following data was extracted: the first author’s name, journal and year of publication, country of origin, ethnicity, number of subjects in the case and control groups, mean or range of age, genotyping method, genotype counts in the case and control group.

Quality assessment

The quality of eligible studies was assessed by using the Newcastle-Ottawa Scale (NOS) [23]. Studies were scored based on three main components: selection, comparability, and ascertainment of outcome. This scale ranges from 1 to 9 stare and studies with scores 0–3, 4–6 or 7–9 were of low, moderate, or high-quality, respectively.

Statistical analysis

In the current meta-analysis, the strengths of association between MMP-9 family gene polymorphism and the risk of CAD was estimated via the OR and 95% CI in five genetic models: dominant model, recessive model, allelic model, homozygote contrast, and heterozygotes contrast. The potential heterogeneity was evaluated by the Q test and the I2 test [23]. According to these two test, if Q had a P value less than 0.1 and I2 exceed 50%, the random effects model (REM) was used; otherwise, the fixed effect model (FEM) was applied [24, 25]. Additionally, risk of publication bias was examined by funnel plot, Egger’s weighted regression test and Begg’s rank correlation test (P < 0.05 was regarded as statistically significant publication bias) [26, 27]. Besides, quality assessment of genotype data in case control studies was evaluated by Hardy–Weinberg equilibrium (HWE). Finally, in order to show the stability of our results, sensitivity analysis was performed. All statistical tests for this meta-analysis were performed with Stata statistical software (version 14.0; Stata Corporation, College Station, TX, USA) and SPSS (version 23.0; SPSS, Inc. Chicago, IL, USA).

Results

Study characteristics

The search and screening process workflow is shown in Fig. 1. Our primary search yielded 1372 records, which 42 of them were included in quantitative analysis [16–20, 28–64]. The studies were published between 2001 to 2019 and all of them had good methodological score ranging between 5 and 8. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) as genotyping method was common between most of studies. The sample size in case and control groups of four SNPs varied between 40 to 2506 and 40 to 689 individual, respectively. The range of mean ages in case and control groups was from 33 to 94, which means studies were conducted among adults. Only one of included studies had cohort design and the other were case-control. Tables 1 and 2 summarized the characteristics and genotype frequency of the included studies.

Fig. 1.

Fig. 1

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Flow diagram of study selection process

Table 1.

Characteristics of studies included in meta-analysis of overall CAD

Study author Year Country Ethnicity Study design Type of CAD Total cases/controls Age
Case / Control (Mean)		 Genotyping method Quality
score
MMP-9 (C1562T)
 Pollanen et al. 2001 Finland European Case-control ACS 109 / 167 33–69 / 33–69 PCR-RFLP 6
 Wang et al. 2001 Australia Oceania Case-control Stable 619 / 169 57.7 ± 0.5 / NR PCR-RFLP 7
 Cho et al. 2002 Korea Asian Case-control Stable 63 / 134 NR / NR PCR-RFLP 5
 Kim et al. 2002 Korea Asian Case-control Stable 131 / 117 61.3 ± 7.9 / 59.3 ± 8.5 PCR-RFLP 6
 Jones et al. 2002 New Zealand European Case-control Stable 414 / 203 71.7 ± 7.6 / 70.8 ± 8.0 PCR-RFLP 8
 Tang et al. 2005 China Asian Case-control ACS 101 / 105 NR / NR PCR-RFLP 5
 Chen et al. 2005 China Asian Case-control ACS 78 / 81 NR / NR PCR-RFLP 5
 Meng et al. 2006 China Asian Case-control Stable 117 / 99 NR / NR PCR-RFLP 5
 Nuzzo et al. 2006 Italy European Case-control ACS 49 / 123 NR / NR PCR-RFLP 5
 Chen et al. 2007 China Asian Case-control Stable 150 / 70 NR / NR PCR-RFLP 5
 Nanni et al. 2007 Italy European Case-control ACS 200 / 201 47.8 ± 6.2 / 47.0 ± 5.5 PCR-RFLP 7
 Wang et al. 2007 China Asian Case-control ACS 245 / 204 NR / NR PCR-RFLP 8
 Zhang et al. 2008 China Asian Case-control ACS 92 / 95 NR / NR PCR-RFLP 5
 Koh et al. 2008 Korea Asian Case-control ACS 206 / 173 61.1 ± 11.8 / 58.3 ± 11.8 PCR-RFLP 6
 Alp et al. 2009 Turkey European Case-control Stable 146 / 122 59.30 ± 9.1 / 57.30 ± 9.7 PCR-RFLP 6
 Wu et al. 2009 China Asian Case-control ACS 2517 / 689 NR / 60.42 ± 9.07 PCR-RFLP 8
 Gao et al. 2010 China Asian Case-control Stable 96 / 78 NR / NR PCR-RFLP 5
 Fallah et al. 2010 Iran Asian Case-control Stable 145 / 157 58.49 ± 9.12 / 55.35 ± 9.43 PCR-RFLP 6
 Yong et al. 2010 China Asian Case-control ACS 128 / 106 NR / NR PCR-RFLP 5
 Ghaderian et al. 2010 Iran Asian Case-control ACS 400 / 200 NR / 65.8 ± 5.9 TaqMan 8
 Zhi et al. 2010 China Asian Case-control Stable 762 / 555 67.46 ± 9.61 / 69.90 ± 11.48 PCR-RFLP 8
 Wang et al. 2011 China Asian Case-control ACS 384 / 451 55.6 ± 10.9 / 54.1 ± 10.3 PCR-RFLP 8
 Opstad et al. 2012 Norway European Case-control Stable 996 / 204 62 / NR TaqMan 8
 Han et al. 2012 China Asian Case-control Stable 91 / 101 NR / NR PCR-RFLP 5
 Saracini et al. 2012 Italy European Case-control Stable 423 / 423 40–94 / 41–94 Nano gene electronic microchip technology 8
 Spurthi et al. 2012 India Asian Case-control Stable 100 / 100 56.73 ± 12.2 / 54.55 ± 14.38 PCR-RFLP 5
 Sewelam et al. 2013 Egypt African Case-control ACS 40 / 40 NR / NR PCR-RFLP 5
 Wu et al. 2013 China Asian Case-control ACS 258 / 153 63.97 ± 12.32 / 63.61 ± 11.8 PCR-RFLP 7
 Xu et al. 2013 China Asian Case-control Stable 382 / 466 62 ± 12 / 62 ± 10 PCR-RFLP 8
 Rodriguez et al. 2016 Mexico American Case-control ACS 236 / 285 59 / 58 PCR-RFLP 8
 Yin et al. 2016 China Asian Case-control Stable 194 / 251 55.60 ± 10.42 / 56.21 ± 9.83 PCR-RFLP 7
 Beton et al. 2016 Turkey European Case-control Stable 200 / 200 60.2 ± 7.4 / 58.3 ± 7.7 PCR-RFLP 7
 Daraei et al. 2016 Iran Asian Case-control ACS 117 / 120 62.96 ± 12.80 / 52.55 ± 9.80 PCR-RFLP 6
 El-Aziz et al. 2016 Egypt African Case-control ACS 184 / 180 57.2 ± 10.9 / 58.8 ± 8.3 PCR-RFLP 7
 Qin et al. 2016 China Asian case-control Stable 261 / 261 58.75 ± 9.36 / 59.21 ± 10.10 PCR-RFLP 7
 Peksiene et al. 2017 Lithuania European Case-control ACS 518 / 645 61.9 ± 11.1 / 60.6 ± 11.9 TaqMan 8
 Mahmoodi et al. 2017 Iran Asian case–control Stable 100 / 100 59.4 ± 23.5 / 56.7 ± 29.5 PCR-RFLP 5
 Xu et al. 2017 China Asian Case-control Stable 264 / 186 59 ± 11.67 / 58 ± 10.72 PCR-RFLP 7
 Makrygiannis et al. 2018 Greece European Case-control Stable 175 / 166 72.7 ± 7.6 / 71.5 ± 7.1 PCR-RFLP 7
 Malkani et al. 2019 Iran Asian Case-control Stable 101 / 100 59.2 ± 10.2 / 47.3 ± 13.1 PCR-RFLP 5
MMP-9 (R279Q)
 Nanni et al. 2007 Italy European Case-control ACS 200 / 201 47.8 ± 6.2 / 47.0 ± 5.5 PCR-RFLP 7
 Wu et al. 2009 China Asian Case-control ACS 2506 / 687 NR / 60.42 ± 9.07 PCR-RFLP 8
 Zhi et al. 2010 China Asian Case-control Stable 762 / 555 67.46 ± 9.61 / 69.90 ± 11.48 PCR-RFLP 8
 Wang et al. 2011 China Asian Case-control ASC 384 / 451 55.6 ± 10.9 / 54.1 ± 10.3 PCR-RFLP 8
 Mishra et al. 2012 India Asian Cohort Stable 510 / 230 NR/ 54.2 ± 8.5 PCR-RFLP 8
 Opstad et al. 2012 Norway European Case-control Stable 994 / 204 62 / NR TaqMan 8
 Fiotti et al. 2017 Italy European Case-control Stable 169 / 169 69–78 / 67–80 Sequencing 7
MMP-9 (P574R)
 Zhi et al. 2010 China Asian Case-control Stable 762 / 555 67.46 ± 9.61 / 69.90 ± 11.48 PCR-RFLP 8
 Mishra et al. 2012 India Asian Cohort Stable 510 / 230 NR / 54.2 ± 8.5 PCR-RFLP 8
MMP-9 (R668Q)
 Zhi et al. 2010 China Asian Case-control Stable 762 / 555 67.46 ± 9.61 / 69.90 ± 11.48 PCR–RFLP 8
 Mishra et al. 2012 India Asian Cohort Stable 510 / 230 NR / 54.2 ± 8.5 PCR–RFLP 8
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NR, not reported; ACS, Acute coronary syndrome

Table 2.

Distribution of genotype and allele among CAD patients and controls

Study author CAD cases Healthy control P-HWE MAF
CC CT TT C T CC CT TT C T
MMP-9 (C1562T)
 Pollanen et al. 78 21 10 177 41 124 30 13 278 56 0 0/168
 Wang et al. 479 128 12 1086 152 128 41 0 297 41 0/072 0/121
 Cho et al. 48 15 0 111 15 67 63 4 197 71 0/016 0/265
 Kim et al. 99 32 0 230 32 85 32 0 202 32 0/086 0/137
 Jones et al. 257 145 12 659 169 145 57 1 347 59 0/063 0/145
 Tang et al. 73 27 1 173 29 91 13 1 195 15 0/494 0/071
 Chen et al. 57 21 0 135 21 73 8 0 154 8 0/640 0/049
 Meng et al. 91 26 0 208 26 80 18 1 178 20 0/991 0/101
 Nuzzo et al. 7 39 3 53 45 86 36 1 208 38 0/181 0/154
 Chen et al. 97 48 5 242 58 61 6 3 128 12 0 0/086
 Nanni et al. 136 62 2 334 66 135 63 3 333 69 0/147 0/172
 Wang et al. 191 52 2 434 56 178 25 1 381 27 0/903 0/066
 Zhang et al. 67 22 3 156 28 83 12 0 178 12 0/511 0/063
 Koh et al. 151 52 3 354 58 142 31 0 315 31 0/195 0/090
 Alp et al. 99 42 5 240 52 90 29 3 209 35 0/718 0/143
 Wu et al. 1995 495 27 4485 549 545 143 1 1233 145 0 0/105
 Gao et al. 49 38 9 136 56 59 18 1 136 20 0/775 0/128
 Fallah et al. 11 57 77 79 211 19 76 62 114 200 0/558 0/637
 Yong et al. 97 30 1 224 32 92 14 0 198 14 0/466 0/066
 Ghaderian et al. 296 88 16 680 120 141 53 6 335 65 0/708 0/163
 Zhi et al. 585 174 3 1344 180 442 110 3 994 116 0/164 0/105
 Wang et al. 286 87 11 659 109 373 72 6 818 84 0/244 0/093
 Opstad et al. 756 225 15 1737 255 154 46 4 354 54 0/794 0/132
 Han et al. 65 25 1 155 27 75 25 1 175 27 0/489 0/134
 Saracini et al. 313 98 12 724 122 307 101 15 715 131 0/071 0/155
 Spurthi et al. 40 47 13 127 73 48 46 6 142 58 0/241 0/290
 Sewelam et al. 32 7 1 71 9 40 0 0 80 0 0 0
 Wu et al. 193 56 9 442 74 131 22 0 284 22 0/337 0/072
 Xu et al. 268 109 5 645 119 361 103 2 825 107 0/059 0/115
 Rodriguez et al. 210 26 0 446 26 271 14 0 556 14 0/670 0/025
 Yin et al. 98 73 23 269 119 157 84 10 398 104 0/766 0/207
 Beton et al. 158 38 4 354 46 154 43 3 351 49 0/999 0/123
 Daraei et al. 66 50 1 182 52 79 38 3 196 44 0/528 0/183
 El-Aziz et al. 125 52 7 302 66 141 36 3 318 42 0/690 0/117
 Qin et al. 134 100 27 368 154 171 85 5 427 95 0/129 0/182
 Peksiene et al. 340 156 22 836 200 431 185 29 1047 243 0/115 0/188
 Mahmoodi et al. 68 27 5 163 37 72 26 2 170 30 0/844 0/150
 Xu et al. 188 69 7 445 83 151 31 4 333 39 0/126 0/105
 Makrygiannis et al. 133 40 2 306 44 133 31 2 297 35 0/898 0/105
 Malkani et al. 79 3 19 161 41 100 0 0 200 0 0 0
Study author CAD cases Healthy control P-HWE MAF
AA AG GG A G AA AG GG A G
MMP-9 (R279Q)
 Nanni et al. 85 94 21 264 136 94 87 20 275 127 0/984 0/316
 Wu et al. 1177 1102 227 3456 1556 297 312 78 906 468 0/772 0/341
 Zhi et al. 398 296 68 1092 432 267 226 62 760 350 0/179 0/315
 Wang et al. 185 150 49 520 248 239 167 45 645 257 0/052 0/285
 Mishra et al. 114 253 143 481 539 53 103 74 209 251 0/142 0/546
 Opstad et al. 405 472 117 1282 706 79 98 27 256 152 0/693 0/373
 Fiotti et al. 75 69 25 219 119 57 88 24 202 136 0/282 0/402
Study author CAD cases Healthy control P-HWE MAF
PP PR RR P R PP PR RR P R
MMP-9 (P574R)
 Zhi et al. 406 296 60 1108 416 279 231 45 789 321 0/770 0/155
 Mishra et al. 346 150 14 842 178 169 57 4 395 65 0/747 0/276
Study author CAD cases Healthy control P-HWE MAF
RR RQ QQ R Q RR RQ QQ R Q
MMP-9 (R668Q)
 Zhi et al. 564 179 19 1307 217 398 141 16 937 173 0/416 0/289
 Mishra et al. 191 286 33 668 352 113 107 10 333 127 0/012 0/141
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P-HWE p-value for Hardy–Weinberg equilibrium, MAF minor allele frequency of control group

Meta-analysis of MMP-9 (C1562T) and risk of CAD

A total of 40 studies with 11,792 cases and 8280 controls were included in quantitative synthesis of the association between MMP-9 (C1562T) polymorphism and CAD susceptibility [16–20, 28–49, 51–61, 63, 64]. Among included studies, 26 studies were carried out in Asian countries, 10 studies were in European countries, 2 studies were in African countries, one study in America, and one in Oceania. Since there were only two studies for Africans and one study for American and Oceania, we excluded them from subgroup analysis. The pooled OR divulged a strong positive association between MMP-9 (C1562T) polymorphism and risk of CAD and announced this SNP as a risk factor for CAD. In details, dominant model (OR = 1.41, 95% CI = 1.23–1.61, P < 0.001), recessive model (OR = 1.59, 95% CI = 1.29–1.96, P < 0.001), allelic model (OR = 1.38, 95% CI = 1.23–1.55, P < 0.001), TT vs. CC model (OR = 1.70, 95% CI = 1.35–2.13, P < 0.001), and CT vs. CC model (OR = 1.35, 95% CI = 1.18–1.54, P < 0.001). FEM was used for recessive and homozygote compressions and REM was applied for dominant, heterozygote, and allelic models. Furthermore, the results of subgroup analysis by ethnicity remarkably showed that MMP-9 (C1562T) polymorphism increase the susceptibility of CAD in the Asian under all genotyping models; dominant model (OR = 1.47, 95% CI = 1.25–1.74, P < 0.001), recessive model (OR = 2.06, 95% CI = 1.57–2.71, P < 0.001), allelic model (OR = 1.45, 95% CI = 1.26–1.67, P < 0.001), TT vs. CC model (OR = 2.42, 95% CI = 1.77–3.32, P < 0.001), and CT vs. CC model (OR = 1.39, 95% CI = 1.19–1.64, P < 0.001) (Fig. 2). No statistically significant association was observed in Europeans.

Fig. 2.

Fig. 2

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Pooled odds OR and 95% confidence interval of individual studies and pooled data for the association between MMP-9 (C1562T) polymorphism and the risk of CAD in overall populations. a Dominat model, b Allelic model

The stratification of studies also performed based on type of CAD, including acute coronary syndrome (ACS) and stable angina. The findings demonstrated a statistically significant association between MMP9 (C1562T) polymorphism and stable angina susceptibility across all genotype model. However, the positive association between MMP-9 (C1562T) polymorphism and ACS susceptibility was observed in dominant model (OR = 1.66, 95% CI = 1.32–2.10, P < 0.001, REM), allelic model (OR = 1.57, 95% CI = 1.29–1.92, P < 0.001, REM), CT vs. CC model (OR = 1.62, 95% CI = 1.28–2.04, P < 0.001, REM), but not recessive model (OR = 1.32, 95% CI = 0.93–1.86, P = 0.12, FEM) and TT vs. CC model (OR = 1.40, 95% CI = 0.99–1.98, P = 0.06. The results of pooled ORs, heterogeneity tests and publication bias tests in different analysis models are shown in Table 3.

Table 3.

Main results of pooled ORs in meta-analysis of MMP9 gene polymorphisms and CAD risk

Subgroup Sample size Test of association Test of heterogeneity Test of publication bias (Begg’s test) Test of publication bias (Egger’s test)
Genetic model Case/Control OR 95% CI (p-value) I2 (%) P Z P T P
MMP-9 (C1562T)
Overall Dominant model 11,792 / 8280 1.41 1.23–161 (≤0.001) 68.8 ≤0.001 1.48 0.13 1.62 0.11
Recessive model 11,792 / 8280 1.59 1.29–1.96 (≤0.001) 18.3 0.19 1.34 0.17 1.49 0.14
Allelic model 11,792 / 8280 1.38 1.23–1.55(≤0.001) 68.7 ≤0.001 1.39 0.16 1.54 0.13
TT vs. CC 11,792 / 8280 1.70 1.35–2.13 (≤0.001) 34 0.42 1.56 0.11 2.05 0.04
CT vs. CC 11,792 / 8280 1.35 1.18–1.54 (≤0.001) 65.4 ≤0.001 1.48 0.13 1.62 0.11
Subgroup
Asian Dominant model 7483 / 5152 1.47 1.25–1.74 (≤0.001) 64.5 ≤0.001 −0.72 0.47 −0.14 0.88
Recessive model 7483 / 5152 2.06 1.57–2.71 (≤0.001) 0 0.45 1.94 0.05 1.39 0.18
Allelic model 7483 / 5152 1.45 1.26–1.67 (≤0.001) 64.3 ≤0.001 0.99 0.32 1.64 0.15
TT vs. CC 7483 / 5152 2.42 1.77–3.32 (≤0.001) 0 0.45 0 1 −0.18 0.86
CT vs. CC 7483 / 5152 1.39 1.19–1.64(≤0.001) 60.8 ≤0.001 −0.25 0.80 0.15 0.88
European Dominant model 3230 / 2331 1.26 0.97–1.66 (0.08) 76.3 ≤0.001 0.78 0.45 0.84 0.43
Recessive model 3230 / 2331 1.05 0.75–1.47 (0.77) 0 0.59 1.04 0.29 0.62 0.55
Allelic model 3230 / 2331 1.22 0.97–1.53(0.08) 75.5 ≤0.001 −1.73 0.08 −0.69 0.51
TT vs. CC 3230 / 2331 1.10 0.78–1.54 (0.59) 32.1 0.15 −0.21 0.83 0.2 0.82
CT vs. CC 3230 / 2331 1.25 0.96–1.64 (0.09) 74.1 ≤0.001 −0.25 0.80 −0.68 0.52
ACS Dominant model 5862 / 4018 1.66 1.32–2.10 (≤0.001) 76.1 ≤0.001 −0.25 0.80 0.89 0.39
Recessive model 5862 / 4018 1.32 0.93–1.86 (0.12) 2.8 0.416 −0.25 0.80 −0.63 0.54
Allelic model 5862 / 4018 1.57 1.29–1.92 (≤0.001) 74.5 ≤0.001 −0.35 0.72 −0.75 0.46
TT vs. CC 5862 / 4018 1.40 0.99–1.98 (0.06) 35.1 0.11 0.05 0.96 −0.57 0.57
CT vs. CC 5862 / 4018 1.62 1.28–2.04 (≤0.001) 75 ≤0.001 −0.45 0.65 −0.99 0.34
Stable Dominant model 5930 / 4262 1.26 1.07–1.48 (≤0.001) 60.9 ≤0.001 0.38 0.70 0.24 0.81
Recessive model 5930 / 4262 1.77 1.37–2.30 (≤0.001) 23.6 0.18 −0.12 0.90 −0.42 0.68
Allelic model 5930 / 4262 1.26 1.09–1.46 (≤0.001) 63.9 ≤0.001 0.12 0.90 −0.49 0.63
TT vs. CC 5930 / 4262 1.95 1.45–2.64 (≤0.001) 31.9 0.10 1.57 0.11 14.14 0.04
CT vs. CC 5930 / 4262 1.20 1.03–1.39 (0.01) 52.2 ≤0.001 0.52 0.60 0.38 0.76
MMP-9 (R279Q)
Overall Dominant model 5525 / 2497 0.92 0.83–1.02 (0.12) 38.7 0.13 0.05 0.96 −0.23 0.83
Recessive model 5525 / 2497 0.88 0.76–1.02 (0.08) 0 0.48 −0.18 0.85 −0.21 0. 83
Allelic model 5525 / 2497 0.93 0.86–1(0.05) 38.1 0.13 0.05 0.96 −0.06 0.95
GG vs. AA 5525 / 2497 0.86 0.73–1.01(0.07) 17.9 0.29 0.45 0.65 0.33 0.74
AG vs. AA 5525 / 2497 0.94 0.85–1.05 (0.26) 29.7 0.20 0.19 0.85 −0.19 0.85
Subgroup
Asian Dominant model 4162/ 1923 0.93 0.83–1.04 (0.19) 45.4 0.13 −0.98 0.32 −1.70 0.18
Recessive model 4162/ 1923 0.86 0.72–1.01 (0.06) 36 0.19 0.56 0.57 0.37 0.73
Allelic model 4162/ 1923 0.92 0.85–1 (0.06) 59.6 0.06 0.09 0.92 −0.03 0.97
GG vs. AA 4162/ 1923 0.85 0.71–1.02 (0.08) 53.7 0.09 1.16 0.24 0.92 0.38
AG vs. AA 4162/ 1923 0.95 0.84–1.07 (0.41) 15.3 0.31 1.34 0.18 1.58 0.15
European Dominant model 1363 / 574 0.91 0.74–1.13 (0.38) 53.2 0.11 0.27 0.78 0.46 0.65
Recessive model 1363 / 574 0.96 0.70–1.32 (0.80) 0 0.84 0.55 0.58 0.74 0.47
Allelic model 1363 / 574 0.94 0.81–1.10 (0.45) 10.1 0.32 0.27 0.78 0.10 0.92
GG vs. AA 1363 / 574 0.90 0.64–1.26 (0.53) 0 0.68 0 1 0.38 0.71
AG vs. AA 1363 / 574 0.91 0.73–1.14 (0.39) 58.9 0.08 0.52 0.60 −0.47 0.72
MMP-9 (P574R)
Overall Dominant model 1272 / 785 1.05 0.72–1.53 (0.81) 0.69 0.07 * * * *
Recessive model 1272 / 785 1.01 0.69–1.49 (0.95) 0 0.47 * * * *
Allelic model 1272 / 785 0.93 0.79–1.10 (0.41) 0 0.41 * * * *
RR vs. PP 1272 / 785 0.97 0.55–1.44 (0.87) 0 0.38 * * * *
PR vs. PP 1272 / 785 1.03 0.72–1.48 (0.87) 63.7 0.09 * * * *
MMP-9 (R668Q)
Overall Dominant model 1272 / 785 1.19 0.66–2.13 (0.56) 88.3 ≤0.001 * * * *
Recessive model 1272 / 785 1.12 0.68–1.84 (0.64) 21.2 0.26 * * * *
Allelic model 1272 / 785 1.11 0.73–1.69 (0.62) 85.1 0.01 * * * *
QQ vs. RR 1272 / 785 1.26 0.55–2.89 (0.58) 63.1 0.01 * * * *
RQ vs. RR 1272 / 785 1.18 0.68–2.06 (0.43) 86.4 ≤0.001 * * * *
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*Begg’s and Egger’s test were not calculable

ACS acute coronary syndrome, OR odds ratio, CI confidence interval, MMP matrix metalloproteinase

Meta-analysis of MMP-9 (R279Q) and risk of CAD

There were 7 case-control studies with 5525 cases and 2497 controls concerning MMP-9 (R279Q) polymorphism and risk of CAD [18, 38, 42, 47, 48, 50, 62]. Of those, 4 studies were performed in Asians and 3 studies were in Europeans. The pooled results indicated a negative, but not significant, association between MMP-9 (R279Q) gene polymorphism and CAD risk under all genotype models for the overall population and subgroup analysis (Fig. 3). The results of pooled ORs, heterogeneity tests and publication bias tests in different analysis models are shown in Table 3.

Fig. 3.

Fig. 3

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Pooled odds OR and 95% confidence interval of individual studies and pooled data for the association between MMP-9 (R279Q) polymorphism and the risk of CAD in overall populations. a Dominant model, b Allelic model

Meta-analysis of MMP-9 (P574R) and risk of CAD

For MMP-9 (P574R) SNP, two studies with 1272 case and 785 controls were included for quantitative analysis [47, 50]. Studies were carried out in China and India. The results of overall population reject any association between MMP-9 (P574R) SNP and risk of CAD across all genotype models. The results of pooled ORs, heterogeneity tests and publication bias tests in different analysis models are shown in Table 3.

Meta-analysis of MMP-9 (R668Q) and risk of CAD

Two studies with 1272 case and 785 controls were included for quantitative analysis for MMP-9 (R668Q) SNP [47, 50]. Studies were carried out in China and India. There was no evidence of significant association between MMP-9 (R668Q) SNP and risk of CAD under all genotype models. The results of pooled ORs, heterogeneity tests and publication bias tests in different analysis models are shown in Table 3.

Publication bias and heterogeneity

In this study, we used Egger’s regression test, Begg’s adjusted rank correlation test and visual examination of the funnel plot (just for C1562T and R279Q) to measure publication bias (Fig. 4). Overall, no significant publication bias was detected. Besides, the estimation of heterogeneity by I2 and Q test was significant in some models (Table 3).

Fig. 4.

Fig. 4

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Begg’s funnel plot for publication bias test. Each point represents a separate study for the indicated association. a Dominant model (C1562T), b Dominant model (R279Q)

Sensitivity analysis

The leave-one-out method was used in the sensitivity analysis to explore the effect of individual data on the pooled ORs (just for C1562T and R279Q). The significance of ORs was not altered through omitting any single study, indicating that our results were statistically robust (Fig. 5).

Fig. 5.

Fig. 5

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Sensitivity analysis in present meta-analysis estimates the individual influence of studies on pooled results. a Dominant model (rs C1562T), b Dominant model (R279Q)

Discussion

Up until now, numerous investigations have been performed to address the association between SNPs of the MMP gene family, including MMP-9 (C1562T), MMP-9 (R279Q), MMP-9 (P574R), and MMP-9 (R668Q) polymorphisms and risk of CAD. The findings of these investigations have sometimes been in accordance with each other, but sometimes conflicting. By meeting the limitations of the individual studies, such as little statistical strength and small sample size, meta-analysis studies provide a beneficial tool to settle these limitations and confer a conclusive outcome. In order to solve this issue in respective of MMP-9 gene family polymorphisms and risk of CAD, here we carried out this meta-analysis by including the most comprehensive and up-to-date original studies worldwide to come up with valid approximation of this association. In the current meta-analysis, we included 40 studies with 11,792 cases and 8280 controls for MMP-9 (C1562T) SNP, 7 case-control studies with 5525 cases and 2497 controls for MMP-9 (R279Q) SNP, 2 studies with 1272 case and 785 controls for MMP-9 (P574R) SNP, and 2 studies with 1272 case and 785 controls for MMP-9 (R668Q) SNP. The analysis indicated that MMP-9 (C1562T) SNP was significantly associated with increased risk of CAD susceptibility in the overall analysis and Asian population particularly.

Different human and animal experiments have suggested that there is an elevated level of MMP-9 in the atherosclerotic arteries in comparison to health controls. It was also shown that MMP-9 is predominantly active in the lipid core margin of the atherosclerotic plaques, the shoulder regions of plaque, and in regions with active formation of microvessels. As a consequence, it appears that MMP-9 plays a critical role in the stability/instability of the coronary artery plaques and development of myocardial infarction during CAD [65]. In addition, researchers have observed that in mice knock-out for the MMP9 gene, migration potency of the VSMCs as well as atherosclerosis lesions were reduced in comparison to the wild-type animals [66]. According to clinical observations, upregulation of MMP-9 exhibited a correlation with instability of the atherosclerosis plaque and premature CAD development [67]. Based on the prospective studies, serum levels of MMP-9 could confer a tool to estimate the mortality risk during the cardiovascular diseases [68].

Zhang et al. in the 214 meta-analysis, by including 26 studies containing 12,776 cases and 6371 controls, indicated that MMP-9 (C1562T) polymorphism was not associated with the risk of CAD in the overall results [69]. However, they reported that MMP-9 (C1562T) SNP is involved in the decrease susceptibility to CAD in Asian population. In 2016, a meta-analysis was conducted on 10 case-control studies to assess the possible relationship between the MMP-9 (C1562T) SNP and CAD in the Chinese Han population. This study indicated that all genetic comparisons of the MMP-9 (C1562T) SNP increased the risk of CAD in the Chinese Han population [70]. In the current meta-analysis, association between SNPs of the MMP-9 gene family, including MMP-9 (C1562T), MMP-9 (R279Q), MMP-9 (P574R), and MMP-9 (R668Q) polymorphisms and risk of CAD was evaluated. Our literature search led to identification and inclusion of 40 studies with 11,792 cases and 8280 controls for MMP-9 (C1562T) SNP, 7 case-control studies with 5525 cases and 2497 controls for MMP-9 (R279Q) SNP, 2 studies with 1272 case and 785 controls for MMP-9 (P574R) SNP, and 2 studies with 1272 case and 785 controls for MMP-9 (R668Q) SNP. Therefore, this is the most comprehensive meta-analysis of MMP-9 gene family polymorphisms and risk of CAD to date (March 2020). Our analysis revealed that MMP-9 (C1562T) polymorphism increased the risk of CAD in the overall analysis under dominant (OR = 1.41), recessive (OR = 1.59), allelic (OR = 1.38), homozygous TT vs. CC (OR = 1.70), and heterozygous CT vs. CC (OR = 1.35) models. In contrast to Zhang et al. [69] study, we noticed that MMP-9 (C1562T) polymorphism increased the susceptibility of CAD risk in the Asian population under all genotyping models; dominant (OR = 1.47), recessive (OR = 2.06), allelic (OR = 1.45), homozygous TT vs. CC (OR = 2.42), and heterozygous CT vs. CC (OR = 1.39) models. However, other three polymorphisms of the MM9 gene, including MMP-9 (R279Q), MMP-9 (P574R), and MMP-9 (R668Q) polymorphisms, were not associated with CAD risk.

Regulatory mechanisms at the transcriptional level is involved in the modulation of MMP-9 expression. The MMP-9 (C1562T) SNP is harbored within the 9 bp sequence GCGCAC/TGCC (− 1567 → − 1559), which is considered as a regulatory element of the gene and confers a site for binding of molecules involved in the inhibition of transcription [71]. It was found that an alteration in the binding site structure by substitution of the MMP-9-1562 C allele with − 1562 T allele led to decreased binding potential of the proteins involved in the inhibition of transcription to the DNA sequence [30]. As a result, MMP-9 (C1562T) SNP plays a role in orchestrating the transcription activity of MMP-9 and, hence, modulate the susceptibility risk to several diseases. Therefore, we analyzed the available data to gain a wide understanding of this SNP in case of CAD. We noticed that T allele representation was increased in all models of MMP-9 (C1562T) SNP comparison in the overall analysis, including dominant model (OR = 1.41, 95% CI = 1.23–1.61, P < 0.001), recessive model (OR = 1.59, 95% CI = 1.29–1.96, P < 0.001), allelic model (OR = 1.38, 95% CI = 1.23–1.55, P < 0.001), TT vs. CC model (OR = 1.70, 95% CI = 1.35–2.13, P < 0.001), and CT vs. CC model (OR = 1.35, 95% CI = 1.18–1.54, P < 0.001), which was associated with an increased risk of CAD significantly. Upregulation of MMP-9 may be involved in the CAD development by multiple approaches, including increased proliferation and migration of VSMCs, remodeling of the injured vascular cells, and enhancing the plaque instability and rupture (that leads to the development of thrombosis), eventuating in myocardial infarction and CAD [72].

In spite of an attempt to perform the most comprehensive meta-analysis of the MMP9 gene SNPs and the risk of CAD, a number of limitations and caveats of this meta-analysis study should be taken into consideration. First, the number of studies and sample size for MMP-9 (R279Q), MMP-9 (P574R), and MMP-9 (R668Q) polymorphisms in this meta-analysis was relatively small to conclude a valid report of the association of these SNPs and CAD risk. Second, we searched for the articles published in only the English language and a number of potential studies might be omitted. Third, this meta-analysis was based on a crude analysis of the genetic polymorphisms, and the adjusting the analysis by gender, age, and other environmental factors were not implemented. Fourth, we detected some degrees of heterogeneity for the analyzed SNPs, that might stem from difference in genetic stratification and ethnicity, diversity in the environmental factors in different populations, and the detection methods.

Conclusion

Taken all the evidence into conclusion, this was the most comprehensive evaluation of the four MMP9 gene SNPs in association with CAD. We reported that MMP-9 (C1562T) SNP conferred a susceptibility risk for CAD in the overall analysis and Asian population. That notwithstanding, other three polymorphisms were not associated with disease risk, probably due to little sample size. Hence, we warrant further studies with respect to evaluation of other MMP9 gene SNPs in association with CAD. Furthermore, the role of other factors, such as age, gender, environmental contributing factors as well as other MMP9 gene variations in the analyses ahead will hopefully shed further light on the bona fide association of MMP9 gene polymorphisms and risk of CAD susceptibility.

Acknowledgements

The authors are grateful of Deputy of Research from Neyshabur University of Medical Science.

Abbreviations

MMPs

Matrix metallo proteinases

CAD

Coronary artery disease

VSMCs

Vascular smooth muscle cells

ECM

Extracellular matrix

CI

Confidence interval

OR

Odds ratio

SNP

Single-nucleotide polymorphism

PRISMA

Preferred reporting items for systematic reviews and meta-analyses

NOS

Newcastle–Ottawa scale

HWE

Hardy–Weinberg equilibrium

Authors’ contributions

RH and DI originated the study, acquired data. BR and SA performed statistical analysis, interpreted data, drafted the manuscript. ST revised the manuscript. All authors read and approved the final manuscript.

Funding

Not applicable.

Availability of data and materials

All data that support the conclusions of this manuscript are included within the article.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

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

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