Background: Angiotensin-converting enzyme (ACE) gene polymorphisms have recently been shown to be associated with risk of developing left ventricular hypertrophy (LVH). However, the results were controversial. We aimed to conduct this meta-analysis to further confirm the association between ACE rs4646994 polymorphism and hypertrophic cardiomyopathy (HCM)/dilated cardiomyopathy (DCM).

Methods: PubMed, Embase, the Chinese National Knowledge Information, and Wanfang databases were searched for eligible studies. The Newcastle–Ottawa Scale (NOS) was used to evaluate the quality of included studies. Then we evaluated the association between ACE gene mutation and HCM/DCM by calculating odds ratios (ORs) and 95% confidence intervals (95% CIs). Subgroup analysis was further performed to explore situations in specialized subjects. Sensitivity analysis and publication bias was assessed to confirm the study reliability.

Results: There were 13 studies on DCM (2004 cases and 1376 controls) and 16 studies on HCM (2161 controls and 1192 patients). ACE rs4646994 polymorphism was significantly associated with DCM in all genetic models. However, in HCM, four genetic models (allele model, homozygous model, heterozygous model, and dominant model) showed significant association between ACE rs4646994 polymorphism and DCM. In subgroup analysis, we found that ACE rs4646994 polymorphism was significantly associated with DCM/HCM in Asian population. Finally, we also conducted a cumulative meta-analysis, which indicates that the results of our meta-analysis are highly reliable.

Conclusion: ACE rs4646994 polymorphism increases the risk of DCM/HCM in Asians, but not in Caucasians. More case–control studies are needed to strengthen our conclusions and to assess the gene–gene and gene–environment interactions between ACE rs4646994 polymorphism and DCM/HCM.

Cardiomyopathy is a kind of heterogeneous myocardial disease, resulting from pathological changes in the myocardium of different etiologies, manifesting as ventricular hypertrophy or dilation. Myocardial dysfunction due to other cardiovascular diseases is not part of the spectrum of the disease, such as valvular heart disease, hypertensive heart disease, congenital heart disease, coronary heart disease, or congenital heart disease [1,2]. It can eventually lead to progressive heart failure, arrhythmia, thromboembolism and sudden death, and has a poor prognosis. Cardiomyopathy can be generally classified into hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and left ventricular noncompaction (LVNC). Among them, HCM and DCM are the main types of cardiomyopathy [3]. Many previous clinical studies recognized that cardiomyopathy has a familial origin, suggesting that genetic factors may play a crucial role in disease pathogenesis [4,5].

HCM and DCM are caused by mutant sarcomeric genes [6–8]. Mutations in sarcomeric protein genes can cause changes in myofilament tension that determine cardiac hypertrophy and dilation. Polymorphisms including genes encoding components of the renin–angiotensin system (RAS), such as angiotensin-converting enzyme (ACE), have recently been shown to be associated with the risk of developing left ventricular hypertrophy (LVH) [9,10] and thus may influence the clinical phenotype of HCM/DCM. The ACE gene is located on chromosome 17q23 and is characterized by a major insertion/deletion (rs4646994) polymorphism, consisting of a 289 base-pair Alu repeat sequence present or absent from intron 16 [11]. Past studies have shown that LVH is significantly increased in HCM and DCM patients with the ACE-D/D genotype and thus may be a genetic factor in the pathogenesis of HCM/DCM [12,13].

Over the past 20 years, numerous studies have reported the association of insertion/deletion polymorphisms of the angiotensin I-converting enzyme gene (ACE rs4646994) with HCM and DCM. But their results are inconsistent, especially the association with DCM, which is currently controversial [14]. Therefore, we conducted this meta-analysis to further confirm the association between ACE rs4646994 polymorphism and HCM/DCM.

We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (http://prisma-statement.org/) in conducting the systematic review and meta-analysis.

Search strategy

As of March 2021, we have used the terms ‘angiotensin converting enzyme’ or ‘ACE’, ‘polymorphism’ or ‘mutation’ and ‘hypertrophic cardiomyopathy’ and ‘dilated cardiomyopathy’ without language restrictions in PubMed, Embase, the Chinese National Knowledge Information, and Wanfang databases. Retrieved articles were reviewed to select related data of our interest. References included in the literature were also searched and reviewed to find other potentially eligible data.

Inclusion criteria

The studies included in the meta-analysis must meet the following three criteria: (1) evaluating the association between ACE rs4646994 polymorphisms and HCM/DCM; (2) a case–control design was used, and (3) the data had to include the genotypes of II, ID, and DD, as well as comprehensive statistical indexes that were direct or indirect: odds ratio (OR) and 95% confidence interval (95% CI) and fulfilled the Hardy–Weinberg equilibrium (HWE) among the control groups.

Exclusion criteria

All patients were excluded for potential influencing factors such as hypertension, hypertensive heart disease, coronary atherosclerotic heart disease, ischemic heart disease, ischemic cardiomyopathy, severe coronary obstruction for DCM valvular heart disease, valvular heart disease, congenital heart or vascular malformations, and inherent pulmonary disease.

Data extraction

Two authors independently reviewed all the included studies and extracted vital data. Disagreements were resolved by a third researcher, and a common outcome was finally reached. We extracted the following information: first author, year of publication, country from which subjects came, ethnicity, number of cases and controls, allele and genotype frequencies, source of control group, diagnostic criteria, and HWE test. We attempted to contact the original authors if study data were incomplete. Study quality was assessed by the Newcastle–Ottawa Scale (NOS).

Statistical methods

HWE was performed in the control group, and the significance level was set at P<0.05. The association between ACE rs4646994 polymorphisms and HCM/DCM was assessed by fixed- or random-effects models incorporating ORs and 95% CIs. We demonstrated the degree of heterogeneity between studies by using I2 ranging from 0% (complete agreement) to 100% (complete inconsistency). We used a random-effects model (Der Simonian and Laird method) for the pooled analysis, and I2 > 50% indicated heterogeneity among studies. Otherwise, a fixed-effects model (Mantel–Haenszel method) should be used. We also performed subgroup analysis to identify possible heterogeneity and cumulative meta-analysis to determine the reliability of the results. All analyses were performed in five genetic models: allelic model (d vs. I), homozygous model (DD vs. II), heterozygous model (ID vs. II), dominant model (ID + DD vs. II), and recessive model (DD vs. ID + II). Sensitivity analyses assessed the potential impact of individual study datasets on pooling or omitting studies. We also performed Egger’s test and plotted Begg’s funnel plot to determine publication bias, and concluded that there was no statistically significant publication bias when P>0.05. All statistical tests were performed using Stata version 15.0 (Stata Corp, University of Texas).

Research characteristics

Finally we found a total of 368 potential articles related to keywords, of which 48 duplicate studies were excluded. We then initially screened the remaining 320 articles, 263 of which were excluded. From the full-text reading of 57 articles, 28 were excluded because of their disassociation with ACE rs4646994 polymorphisms and HCM/DCM (n=10), review (n=9), insufficient data (n=7), and deviation from the HWE test (n=2). The entire process of exclusion and enrollment is shown in Figure 1. Finally, this meta-analysis included 13 studies on DCM [13,15–26] (2004 controls and 1376 cases, Table 1) and 16 studies on HCM [13,17,20,24,27–38] (2161 controls and 1192 patients, Table 2). The NOS score of each study was more than 6, and therefore, the quality was good. The results are shown in Tables 1 and 2.

The PRISMA flow diagram of the study selection and exclusion

Figure 1
The PRISMA flow diagram of the study selection and exclusion
Figure 1
The PRISMA flow diagram of the study selection and exclusion
Close modal
Table 1
The characteristics of included studies and ACE rs4646994 polymorphism genotype distribution and allele frequency of DCM in case and control groups
AuthorYearCountryEthnicitySample sizeGenotype (n)Allele frequency (n, %)NOS scoreHWE test
CasesControlsCasesControls
IIIDDDTotalIIIDDDTotalIDRAFIDRAF
Montgomery et al. 1995 U.K. Caucasian 463 18 50 31 99 84 168 112 364 86 112 0.57 336 392 0.54 0.173 
Sanderson et al. 1996 China Asian 200 39 49 12 100 39 48 13 100 127 73 0.37 126 74 0.37 0.767 
Yamada et al. 1997 Japan Asian 210 36 35 17 88 50 55 17 122 107 69 0.39 155 89 0.36 0.764 
Tiret et al. 2000 France Caucasian 809 94 200 128 422 71 190 126 387 388 456 0.54 332 442 0.57 0.966 
Shan et al. 2001 China Asian 238 27 25 31 83 50 80 25 155 79 87 0.52 180 130 0.42 0.456 
Wu et al. 2002 China Asian 106 14 22 43 23 28 12 63 50 36 0.42 74 52 0.41 0.509 
Zou et al. 2005 China Asian 96 12 18 13 43 28 20 53 42 44 0.51 76 30 0.28 0.609 
Rai et al. 2008 India Asian 215 33 10 51 47 87 30 164 49 53 0.52 181 147 0.45 0.353 
Kucukarabaci et al. 2008 Turkey Caucasian 49 18 29 20 28 30 0.52 23 17 0.43 0.722 
Mahjoub et al. 2010 Tunisia Caucasian 227 12 38 26 76 46 83 22 151 62 90 0.59 175 127 0.42 0.116 
Kong et al. 2012 China Asian 206 20 49 32 101 30 53 22 105 89 113 0.56 113 97 0.46 0.874 
Rani et al. 2017 India Asian 377 15 120 42 177 72 86 42 200 150 204 0.58 230 170 0.43 0.089 
Chen et al. 2017 China Asian 184 17 29 18 64 51 57 12 120 63 65 0.51 159 81 0.34 0.496 
AuthorYearCountryEthnicitySample sizeGenotype (n)Allele frequency (n, %)NOS scoreHWE test
CasesControlsCasesControls
IIIDDDTotalIIIDDDTotalIDRAFIDRAF
Montgomery et al. 1995 U.K. Caucasian 463 18 50 31 99 84 168 112 364 86 112 0.57 336 392 0.54 0.173 
Sanderson et al. 1996 China Asian 200 39 49 12 100 39 48 13 100 127 73 0.37 126 74 0.37 0.767 
Yamada et al. 1997 Japan Asian 210 36 35 17 88 50 55 17 122 107 69 0.39 155 89 0.36 0.764 
Tiret et al. 2000 France Caucasian 809 94 200 128 422 71 190 126 387 388 456 0.54 332 442 0.57 0.966 
Shan et al. 2001 China Asian 238 27 25 31 83 50 80 25 155 79 87 0.52 180 130 0.42 0.456 
Wu et al. 2002 China Asian 106 14 22 43 23 28 12 63 50 36 0.42 74 52 0.41 0.509 
Zou et al. 2005 China Asian 96 12 18 13 43 28 20 53 42 44 0.51 76 30 0.28 0.609 
Rai et al. 2008 India Asian 215 33 10 51 47 87 30 164 49 53 0.52 181 147 0.45 0.353 
Kucukarabaci et al. 2008 Turkey Caucasian 49 18 29 20 28 30 0.52 23 17 0.43 0.722 
Mahjoub et al. 2010 Tunisia Caucasian 227 12 38 26 76 46 83 22 151 62 90 0.59 175 127 0.42 0.116 
Kong et al. 2012 China Asian 206 20 49 32 101 30 53 22 105 89 113 0.56 113 97 0.46 0.874 
Rani et al. 2017 India Asian 377 15 120 42 177 72 86 42 200 150 204 0.58 230 170 0.43 0.089 
Chen et al. 2017 China Asian 184 17 29 18 64 51 57 12 120 63 65 0.51 159 81 0.34 0.496 

Abbreviations: D, mutant type; I, wildtype; n, number; RAF, risk allele frequency; risk allele, D allele.

Table 2
The characteristics of included studies and ACE rs4646994 polymorphism genotype distribution and allele frequency of HCM in case and control groups
AuthorYearCountryEthnicitySample sizeGenotype (n)Allele frequency (n, %)NOS scoreHWE test
CasesControlsCasesControls
IIIDDDTotalIIIDDDTotalIDRAFIDRAF
Marian et al. 1993 U.S.A. Caucasian 206 49 44 100 22 46 38 106 63 137 0.69 90 122 0.58 0.778 
Yamada et al. 1997 Japan Asian 193 31 32 71 50 55 17 122 94 48 0.34 155 89 0.36 0.667 
Moiseev et al. 1997 Russia Caucasian 181 13 33 55 80 168 17 0.65 121 215 0.64 0.315 
Lopez-Haldon et al. 1999 Spain Caucasian 309 13 25 40 33 125 111 269 17 63 0.79 191 347 0.64 0.952 
Cai et al. 2000 China Asian 101 16 16 13 45 26 23 56 48 42 0.47 75 37 0.33 0.528 
Gao et al. 2000 China Asian 101 12 15 13 40 31 18 12 61 39 41 0.51 80 42 0.34 0.185 
Yang et al. 2000 China Asian 149 13 35 15 63 37 36 13 86 61 65 0.52 110 62 0.36 0.86 
Li et al. 2001 China Asian 96 13 19 33 28 23 12 63 45 21 0.32 79 47 0.37 0.001 
Ogimoto et al. 2002 Turkey Caucasian 343 53 64 21 138 83 95 27 205 170 106 0.38 261 149 0.36 0.653 
Zou et al. 2003 China Asian 66 13 28 20 53 17 0.35 76 30 0.28 0.052 
Kawaguchi et al. 2003 Japan Asian 168 26 41 13 80 43 28 17 88 93 67 0.42 114 62 0.35 0.661 
Doolan et al. 2004 Australia Caucasian 236 10 14 12 36 48 94 58 200 34 38 0.53 190 210 0.52 0.147 
Rai et al. 2008 India Asian 282 11 63 44 118 47 87 30 164 85 151 0.64 181 147 0.45 0.048 
Kaya et al. 2010 Turkey Asian 83 34 21 63 20 50 76 0.6 19 21 0.52 0.661 
Coto et al. 2010 Spain Caucasian 507 35 100 72 207 46 135 119 300 170 244 0.59 227 373 0.62 0.147 
Rani et al. 2017 India Asian 332 16 89 27 132 72 86 42 200 121 143 0.54 230 170 0.43 0.048 
AuthorYearCountryEthnicitySample sizeGenotype (n)Allele frequency (n, %)NOS scoreHWE test
CasesControlsCasesControls
IIIDDDTotalIIIDDDTotalIDRAFIDRAF
Marian et al. 1993 U.S.A. Caucasian 206 49 44 100 22 46 38 106 63 137 0.69 90 122 0.58 0.778 
Yamada et al. 1997 Japan Asian 193 31 32 71 50 55 17 122 94 48 0.34 155 89 0.36 0.667 
Moiseev et al. 1997 Russia Caucasian 181 13 33 55 80 168 17 0.65 121 215 0.64 0.315 
Lopez-Haldon et al. 1999 Spain Caucasian 309 13 25 40 33 125 111 269 17 63 0.79 191 347 0.64 0.952 
Cai et al. 2000 China Asian 101 16 16 13 45 26 23 56 48 42 0.47 75 37 0.33 0.528 
Gao et al. 2000 China Asian 101 12 15 13 40 31 18 12 61 39 41 0.51 80 42 0.34 0.185 
Yang et al. 2000 China Asian 149 13 35 15 63 37 36 13 86 61 65 0.52 110 62 0.36 0.86 
Li et al. 2001 China Asian 96 13 19 33 28 23 12 63 45 21 0.32 79 47 0.37 0.001 
Ogimoto et al. 2002 Turkey Caucasian 343 53 64 21 138 83 95 27 205 170 106 0.38 261 149 0.36 0.653 
Zou et al. 2003 China Asian 66 13 28 20 53 17 0.35 76 30 0.28 0.052 
Kawaguchi et al. 2003 Japan Asian 168 26 41 13 80 43 28 17 88 93 67 0.42 114 62 0.35 0.661 
Doolan et al. 2004 Australia Caucasian 236 10 14 12 36 48 94 58 200 34 38 0.53 190 210 0.52 0.147 
Rai et al. 2008 India Asian 282 11 63 44 118 47 87 30 164 85 151 0.64 181 147 0.45 0.048 
Kaya et al. 2010 Turkey Asian 83 34 21 63 20 50 76 0.6 19 21 0.52 0.661 
Coto et al. 2010 Spain Caucasian 507 35 100 72 207 46 135 119 300 170 244 0.59 227 373 0.62 0.147 
Rani et al. 2017 India Asian 332 16 89 27 132 72 86 42 200 121 143 0.54 230 170 0.43 0.048 

Abbreviations: D, mutant type; I, wildtype; n, number; RAF, risk allele frequency; risk allele, D allele.

Association between ACE rs4646994 polymorphism and susceptibility to DCM

Our meta-analysis showed that potential heterogeneity was found in all five genetic models (allele model: I2: 69.6%; homozygous gene model: I2: 71.7%; heterozygous gene model: I2: 74.3%; dominant gene model: I2: 74%; recessive gene model: I2: 64.3%). Therefore, a random-effects model was used in the meta-analysis (Figure 2). The results of the study on the association between ACE rs4646994 polymorphism and the pathogenesis of DCM showed that allele gene model (D vs. I): OR = 1.39, 95% CI = 1.14–1.69, P=0.001; homozygote gene model (DD vs. II): OR = 2.02, 95% CI = 1.32–3.09, P=0.001; heterozygote gene model (ID vs. II): OR = 1.46, 95% CI = 1.01–2.12, P=0.045; dominance gene model (ID+DD vs. II): OR = 1.62, 95% CI = 1.14–2.29, P=0.006; recessive gene model (DD vs. ID and II): OR = 1.53, 95% CI = 1.12–2.08, P=0.007. In summary, our meta-analysis showed that there was a significant association between ACE rs4646994 polymorphism and DCM in the five gene models. It can be concluded that the D allele and DD genotype of ACE rs4646994 polymorphism may be the genetic risk factors of DCM.

Forest plot from the meta-analysis on the association of ACE rs4646994 gene polymorphism and DCM risk

Figure 2
Forest plot from the meta-analysis on the association of ACE rs4646994 gene polymorphism and DCM risk

(A) Allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Figure 2
Forest plot from the meta-analysis on the association of ACE rs4646994 gene polymorphism and DCM risk

(A) Allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Close modal

We try to determine more reliable results and explore the sources of heterogeneity by analyzing different subgroups. First of all, a subgroup analysis was carried out on the ethnicity (Asian race and White race). As shown in Table 3, the results show that four gene models of Asian race suggest that there is a significant association between ACE rs4646994 polymorphism and DCM (allele gene model: OR = 1.47, 95% CI = 1.21–1.78, P<0.001; homozygous gene model: OR = 2.28, 95% CI = 1.49–3.47, P<0.001; dominant gene model: OR = 1.72, 95% CI = 1.12–2.64, P=0.01; recessive gene model: OR = 1.67, 95% CI = 1.16–2.39, P=0.05). However, no association was shown between the Asian heterozygous gene model (heterozygous gene model: OR = 1.51, 95% CI = 0.91–2.50, P=0.11) and the white subgroup (allele gene model: OR = 1.25, 95% CI = 0.85–1.84, P=0.27; homozygous gene model: OR = 1.61, 95% CI = 0.70–3.67, P=0.26; Heterozygous gene model: OR = 1.27, 95% CI = 0.77–2.10, P=0.35; dominant gene model: OR = 1.40, 95% CI = 0.78–2.51, P=0.26; recessive gene model: OR = 1.29, 95% CI = 0.74–2.24, P=0.37). The results showed that the mutation of ACE gene significantly increased the risk of DCM in Asian population. Although there was no statistical significance between the mutation of ACE gene and the incidence of DCM in white population, it had a tendency to increase the risk of DCM. We conducted a subgroup analysis of the sample size, and the subgroup analysis of the sample size > 200 showed that there was an association between ACE rs4646994 polymorphism and the DCM risk of the three gene models (allele gene model: OR = 1.35, 95% CI = 1.07–1.70, P=0.01; homozygous gene model: OR = 1.97, 95% CI = 1.17–3.30, P=0.01; recessive gene model: OR = 1.47, 95% CI = 1.04–2.08, P=0.03). In the subgroup with sample size ≤ 200, this relationship disappeared (allele gene model: OR = 1.49, 95% CI = 1.00–2.23, P=0.05; homozygous gene model: OR = 2.16, 95% CI = 0.95–4.90, P=0.06; heterozygous gene model: OR = 1.40, 95% CI = 0.98–2.00, P=0.07; recessive gene model: OR = 1.66, 95% CI = 0.81–3.40, P=0.16).

Table 3
Subgroup analysis of association between ACE I/D gene polymorphism and DCM
Number of studiesAllele comparison D vs. IHomozygous DD vs. IIHeterozygous ID vs. IIDominant ID + DD vs. IIRecessive DD vs. ID + II
OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2
Total 13       
Ethnicity        
Asian 1.47 1.21–1.78 <0.001 48.5 0.05 2.28 1.49–3.47 <0.001 51.1 0.04 1.51 0.91–2.50 0.11 78.2 <0.001 1.72 1.12–2.64 0.01 73.7 <0.001 1.67 1.12–2.39 0.05 52.2 0.03 
Caucasian 1.25 0.85–1.84 0.27 78.4 0.003 1.61 0.70–3.67 0.26 79.4 0.002 1.27 0.77–2.10 0.35 55.7 0.08 1.4 0.87–2.51 0.26 70.4 0.018 1.29 0.74–2.24 0.37 73 0.01 
Sample size                           
>200 1.35 1.07–1.70 0.01 74 <0.001 1.97 1.17–3.30 0.01 77.2 <0.001 1.43 0.84–2.46 0.19 83.9 <0.001 1.6 0.99–2.59 0.06 82.4 <0.001 1.47 1.04–2.08 0.03 68.4 0.002 
≤200 1.49 1.00–2.23 0.05 64.5 0.02 2.16 0.95–4.90 0.06 62 <0.001 1.4 0.98–2.00 0.07 0.584 1.62 1.06–2.49 0.03 32.8 <0.001 1.66 0.81–3.40 0.16 60 0.04 
Number of studiesAllele comparison D vs. IHomozygous DD vs. IIHeterozygous ID vs. IIDominant ID + DD vs. IIRecessive DD vs. ID + II
OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2
Total 13       
Ethnicity        
Asian 1.47 1.21–1.78 <0.001 48.5 0.05 2.28 1.49–3.47 <0.001 51.1 0.04 1.51 0.91–2.50 0.11 78.2 <0.001 1.72 1.12–2.64 0.01 73.7 <0.001 1.67 1.12–2.39 0.05 52.2 0.03 
Caucasian 1.25 0.85–1.84 0.27 78.4 0.003 1.61 0.70–3.67 0.26 79.4 0.002 1.27 0.77–2.10 0.35 55.7 0.08 1.4 0.87–2.51 0.26 70.4 0.018 1.29 0.74–2.24 0.37 73 0.01 
Sample size                           
>200 1.35 1.07–1.70 0.01 74 <0.001 1.97 1.17–3.30 0.01 77.2 <0.001 1.43 0.84–2.46 0.19 83.9 <0.001 1.6 0.99–2.59 0.06 82.4 <0.001 1.47 1.04–2.08 0.03 68.4 0.002 
≤200 1.49 1.00–2.23 0.05 64.5 0.02 2.16 0.95–4.90 0.06 62 <0.001 1.4 0.98–2.00 0.07 0.584 1.62 1.06–2.49 0.03 32.8 <0.001 1.66 0.81–3.40 0.16 60 0.04 

Abbreviation: I/D, insertion/deletion.

In order to further determine the reliability of the results, through cumulative meta-analysis, we find that the more stable the association between ACE rs4646994 polymorphism and the incidence of DCM is as the year of publication approaches (Figure 3). It indicates that the results of this meta-analysis are very reliable.

The cumulative meta-analysis of the association of ACE rs4646994 gene polymorphism and DCM risk

Figure 3
The cumulative meta-analysis of the association of ACE rs4646994 gene polymorphism and DCM risk

(A) Allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Figure 3
The cumulative meta-analysis of the association of ACE rs4646994 gene polymorphism and DCM risk

(A) Allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Close modal

Association between ACE rs4646994 polymorphism and susceptibility to HCM

Our meta-analysis showed that there was a significant association between ACE rs4646994 polymorphism and HCM in four genetic models: allele gene model (D vs. I): OR = 1.36, 95% CI = 1.13–1.63, P=0.001; homozygous gene model (DD vs. II): OR = 1.80, 95% CI = 1.21–2.67, P=0.003; heterozygous gene model (ID vs. II): OR = 1.76, 95% CI = 1.29–2.40, P<0.001; dominant gene model (ID+DD vs. II): OR = 1.77, 95% CI = 1.30–2.41, P<0.001. The difference is that the recessive gene model (DD vs. ID and II: OR = 1.28, 95% CI = 0.99-1.67, P=0.064) shows that ACE gene mutation has nothing to do with HCM. However, the trend of increasing risk can still be seen. The results of the forest plot are shown in Figure 4.

Forest plot from the meta-analysis on the association of ACE rs4646994 gene polymorphism and HCM risk

Figure 4
Forest plot from the meta-analysis on the association of ACE rs4646994 gene polymorphism and HCM risk

(A) allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Figure 4
Forest plot from the meta-analysis on the association of ACE rs4646994 gene polymorphism and HCM risk

(A) allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Close modal

In order to determine more reliable results and explore the source of heterogeneity, we conducted a subgroup analysis. First of all, we conducted a subgroup analysis of ethnicity, and Table 4 showed that the mutation of ACE gene was not associated with the incidence of HCM in White population (allele gene model: OR = 1.19, 95% CI = 0.91–1.54, P=0.02; homozygous gene model: OR = 1.40, 95% CI = 0.83–2.35, P=0.212; heterozygous gene model: OR = 1.18, 95% CI = 0.81–1.74, P=0.39; dominant gene model: OR = 1.25, 95% CI = 0.82–1.91, P=0.29; recessive gene model: OR = 1.21, 95% CI = 0.87–1.68, P=0.26). Although the recessive gene model (OR = 1.31, 95% CI = 0.87–1.97, P=0.20) analysis in Asian population showed that there was no association between ACE rs4646994 polymorphism and the incidence of HCM, there was a significant association among the other four models (allele gene model: OR = 1.49, 95% CI = 1.20–1.85, P<0.001; homozygous gene model: OR = 2.09, 95% CI = 1.25–3.50, P=0.005; heterozygous gene model: OR = 2.15, 95% CI = 1.51–3.06, P<0.001; dominant gene model: OR = 2.11, 95% CI = 1.48–3.00, P<0.001). Therefore, the mutation of ACE gene is associated with the increased incidence of HCM in Asian population. Then we made a subgroup analysis of the sample size. The results of subgroup analysis with sample size > 200 showed that there were three gene models suggesting that the mutation of ACE gene was associated with the pathogenesis of HCM (allele gene model: OR = 1.39, 95% CI = 1.05–1.84, P=0.02; homozygous gene model: OR = 2.06, 95% CI = 1.10–3.87, P=0.02; dominant gene model: OR = 1.89, 95% CI = 1.06–3.36, P=0.03), while the other two showed nothing to do with it (heterozygous gene model: OR = 1.78, 95% CI = 1.00–3.15, P=0.05; recessive gene model: OR = 1.36, 95% CI = 0.95–1.94, P=0.10). Similarly, the results in the subgroup with sample size ≤ 200 also showed that there was an association among the three gene models (allele gene model: OR = 1.33, 95% CI = 1.06–1.68, P=0.02; heterozygous gene model: OR = 1.71, 95% CI = 1.27–2.30, P<0.001; dominant gene model: OR = 1.71, 95% CI = 1.27–2.30, P<0.001), while there was no such phenomenon in the other two gene models (homozygous gene model: OR = 1.61, 95% CI = 0.99–2.64, P=0.06; recessive gene model: OR = 1.18, 95% CI = 0.78–1.79, P=0.44).

Table 4
Subgroup analysis of association between ACE I/D gene polymorphism and HCM
Number of studiesAllele comparison D vs. IIHomozygous DD vs. IHeterozygous ID vs. IIDominant ID + DD vs. IIRecessive DD vs. ID + II
OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2
Total 16       
Ethnicity        
Asian 10 1.49 1.20–1.85 <0.001 47 0.05 2.09 1.25–3.50 0.005 55.3 0.02 2.15 1.51–3.06 <0.001 45.3 0.06 2.11 1.48–3.00 <0.001 51.7 0.03 1.31 0.87–1.97 0.20 51.4 0.03 
Caucasian 1.19 0.91–1.54 0.20 55.3 0.05 1.4 0.83–2.35 0.212 49.8 0.08 1.18 0.81–1.74 0.39 28.3 0.22 1.25 0.82–1.91 0.29 44.3 0.11 1.21 0.87–1.68 0.26 40.3 0.14 
Sample size                           
>200 1.39 1.05–1.84 0.02 76.4 <0.001 2.06 1.10–3.87 0.02 76.9 <0.001 1.78 1.00–3.15 0.05 77.8 <0.001 1.89 1.06–3.36 0.03 80.3 <0.001 1.36 0.95-1.94 0.10 64.9 0.01 
≤200 1.33 1.06–1.68 0.02 29.5 0.18 1.61 0.99–2.64 0.06 30.7 0.17 1.71 1.27–2.30 <0.001 0.50 1.65 1.25–2.19 <0.001 2.8 0.41 1.18 0.78-1.79 0.44 28.3 0.19 
Number of studiesAllele comparison D vs. IIHomozygous DD vs. IHeterozygous ID vs. IIDominant ID + DD vs. IIRecessive DD vs. ID + II
OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2OR95% CIPI2 (%)P for I2
Total 16       
Ethnicity        
Asian 10 1.49 1.20–1.85 <0.001 47 0.05 2.09 1.25–3.50 0.005 55.3 0.02 2.15 1.51–3.06 <0.001 45.3 0.06 2.11 1.48–3.00 <0.001 51.7 0.03 1.31 0.87–1.97 0.20 51.4 0.03 
Caucasian 1.19 0.91–1.54 0.20 55.3 0.05 1.4 0.83–2.35 0.212 49.8 0.08 1.18 0.81–1.74 0.39 28.3 0.22 1.25 0.82–1.91 0.29 44.3 0.11 1.21 0.87–1.68 0.26 40.3 0.14 
Sample size                           
>200 1.39 1.05–1.84 0.02 76.4 <0.001 2.06 1.10–3.87 0.02 76.9 <0.001 1.78 1.00–3.15 0.05 77.8 <0.001 1.89 1.06–3.36 0.03 80.3 <0.001 1.36 0.95-1.94 0.10 64.9 0.01 
≤200 1.33 1.06–1.68 0.02 29.5 0.18 1.61 0.99–2.64 0.06 30.7 0.17 1.71 1.27–2.30 <0.001 0.50 1.65 1.25–2.19 <0.001 2.8 0.41 1.18 0.78-1.79 0.44 28.3 0.19 

Abbreviation: I/D, insertion/deletion.

As shown in Figure 5, through cumulative meta-analysis, we found that with the passage of time of the five gene models, the more stable the association between ACE rs4646994 polymorphism and the risk factors of HCM.

The cumulative meta-analysis on the association of ACE rs4646994 gene polymorphism and HCM risk

Figure 5
The cumulative meta-analysis on the association of ACE rs4646994 gene polymorphism and HCM risk

(A) Allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Figure 5
The cumulative meta-analysis on the association of ACE rs4646994 gene polymorphism and HCM risk

(A) Allele; (B) homozygote; (C) heterozygote; (D) dominant; and (E) recessive.

Close modal

Sensitivity analysis

We conducted the sensitivity analysis to assess whether omitting each study would change the overall ORs. As shown in Supplementary Figures S1 and S2, none of the studies would change the results of our meta-analysis, which showed that our results were reliable.

Publication bias

The Begg’s funnel plots associated with the above analyses are presented in Supplementary Figure S3 and S4. From the Begg’s funnel plot, it can be seen that there was no obvious asymmetry in each meta-analysis, thus indicating that there was no publication bias in our study. We performed Egger’s test to further validate the above conclusion (DCM: allele model: P=0.068; homozygote model: P=0.051; heterozygote model: P=0.121; dominant model: P=0.040; and recessive model: P=0.079. HCM: allele model: P=0.472; homozygote model: P= 0.678; heterozygote model: P=0.343; dominant model: P=0.087; and recessive model: P=0.897).

In this meta-analysis, we critically reviewed all eligible published studies that met the inclusion and exclusion criteria to evaluate the association between ACE rs4646994 polymorphisms and the risk of DCM/HCM. There were 13 studies regarding DCM and 16 on HCM. Our findings suggest that ACE rs4646994 polymorphisms may be associated with both HCM and DCM.

Polymorphisms in the ACE gene, encoding one of the components of the renin–angiotensin–aldosterone system (RAAS), have been found to be associated with a variety of cardiovascular diseases, such as hypertension, myocardial infarction, and cardiomyopathy [39,40]. Increased synthesis of angiotensin II induces cell proliferation, migration, and hypertrophy and can enhance proinflammatory cytokine and matrix metalloproteinase production. Studies have shown that the anatomical features of HCM are characterized by asymmetric hypertrophy of the ventricles, whereas DCM is a class of cardiomyopathies characterized by systolic dysfunction of the left ventricle or biventricular enlargement class. Meanwhile, aldosterone production is regulated by the renin–angiotensin system, and studies have shown that it has a direct effect on the heart, including recurrent cardiac hypertrophy and fibrosis, ultimately leading to cardiac remodeling [41,42]. Therefore, ACE rs4646994 polymorphisms provisionally play an important role in the pathogenesis of HCM/DCM cardiomyopathy. While the results of our meta-analysis revealed that ACE rs4646994 polymorphism was associated with the risk of DCM/HCM incidence, providing a rationale of genetic aspects for the treatment of DCM/HCM.

Our subgroup analysis showed that ACE gene mutations can increase the risk of DCM and HCM in Asian population, while no such results were obtained for Caucasian population. The above results suggest an association between the risk of incident DCM and HCM and the race. In addition, the population may be the source of heterogeneity because the heterogeneity was reduced in the subgroup analysis of the population. In the subgroup analysis with a dividing line of sample size 200, we found that the association of ACE gene mutations with the risk of DCM incidence was shown in the subgroup analysis with a sample size greater than 200 in DCM but not in the subgroup analysis with less than 200. Therefore, we believe that a larger sample size is needed for the association of DCM with ACE gene mutations to confirm the reliability of the results. Whereas in HCM the analysis of a subsample size, both showed an association of ACE gene mutations with the onset of HCM. The results of our time-series analyses all showed a stable relationship between ACE gene mutations and the risk of incident DCM/HCM. None of the studies could change the meta-analysis results in the sensitivity analysis (Supplementary Figures S1 and S2). No publication bias was found in our study (Supplementary Figures S3 and S4).

There are certain limitations to our study. First, we failed to group familial DCM/HCM and sporadic DCM/HCM due to limited data. Second, most of our reference studies had small sample sizes, which may affect the results of the meta-analysis. Third, the ethnic distribution of our included studies was relatively single, only Caucasian and Asian ethnicities were included, and subgroup analysis could not be performed for all ethnic populations. Besides, heterogeneity due to differences in the regression models of the included studies could not be avoided due to unavailability of specific information. The review protocol of the present study was not pre-registered with PROSPERO.

This meta-analysis showed an association between the onset of HCM/DCM and ACE rs4646994 polymorphism. The findings of the current study may contribute to stratification strategies for patients with HCM/DCM. In addition, these results also show the potential possibility to treat HCM/DCM by modulating the RAAS system function in patients.

The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.

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

This work was supported by grants from National Natural Science Foundation of China[grant number 81873486]; Suzhou "Promoting Health through Science and Education" Youth Science and Technology Project [grant number KJXW2020001]; Medical research project of Jiangsu Provincial Health Commission [grant number Z2021011]; Biomedical translational medicine innovation and applied research project [grant number SZM2021020]; Project of Suzhou Science and Technology Development Program-Applied Basic Research [grant number SKJYD2021045]; Suzhou Medical Health Science and technology innovation project [grant number SKY2021002].

The funders had no roles in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Jinsheng Shen: Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft. Xuesong Qian: Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Writing—original draft. Xiaofei Mei: Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Writing—original draft. Jialu Yao: Resources, Formal analysis, Validation, Investigation. Hezi Jiang: Conceptualization, Resources, Data curation, Software, Investigation, Visualization. Kexin Li: Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization. Tan Chen: Conceptualization, Resources, Data curation, Software, Investigation, Writing—review & editing. Yufeng Jiang: Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing—review & editing. Yafeng Zhou: Conceptualization, Supervision, Funding acquisition, Validation, Methodology, Project administration, Writing—review & editing.

Ethics approval was not needed because this is a meta-analysis.

     
  • ACE

    angiotensin-converting enzyme

  •  
  • DCM

    dilated cardiomyopathy

  •  
  • HCM

    hypertrophic cardiomyopathy

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • LVH

    left ventricular hypertrophy

  •  
  • NOS

    Newcastle–Ottawa Scale

  •  
  • OR

    odds ratio

  •  
  • RAAS

    renin–angiotensin–aldosterone system

  •  
  • 95% CI

    95% confidence interval

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Author notes

*

These authors contributed equally to this work and should be considered co-first authors.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data