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Erschienen in: BMC Cardiovascular Disorders 1/2020

Open Access 01.12.2020 | Research article

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

verfasst von: Reza Hassanzadeh-Makoui, Bahman Razi, Saeed Aslani, Danyal Imani, Seyedeh Samaneh Tabaee

Erschienen in: BMC Cardiovascular Disorders | Ausgabe 1/2020

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.
Hinweise

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Abkürzungen
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

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 [36]. 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 [1618]. 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 [1620, 2864]. 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.
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
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
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 [1620, 2849, 5161, 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.
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
*
*
*
*
*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.

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).

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).

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.
Not applicable.
Not applicable.

Competing interests

The authors declare that they have no competing interests.
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Metadaten
Titel
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
verfasst von
Reza Hassanzadeh-Makoui
Bahman Razi
Saeed Aslani
Danyal Imani
Seyedeh Samaneh Tabaee
Publikationsdatum
01.12.2020
Verlag
BioMed Central
Erschienen in
BMC Cardiovascular Disorders / Ausgabe 1/2020
Elektronische ISSN: 1471-2261
DOI
https://doi.org/10.1186/s12872-020-01510-4

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