Meta-analysis of the association between the insertion/deletion polymorphism in ACE gene and coronary heart disease among the Chinese population

Abstract

Objective: Many studies have suggested that the angiotensin-converting enzyme (ACE) gene might be involved in the development of coronary heart disease (CHD). However, the results have been inconsistent. In this study, the authors performed a meta-analysis to assess the association between ACE I/D polymorphism and CHD susceptibility among the Chinese population.

Methods: Published literature from PubMed, EMBASE, CNKI and Wan Fang Data was searched. Pooled odds ratio (OR) and 95% confidence interval (CI) were calculated using a fixed or random-effects model.

Results: Forty-six studies (5215 cases and 4782 controls) were identified. The results from the meta-analysis indicated statistically significant association between ACE I/D polymorphism and CHD risk under all three genetic models (co-dominant model DD vs. II: OR=2.40, 95% CI 2.02–2.84, DI vs. II: OR=1.19, 95% CI 1.05–1.34; dominant model: OR=1.51, 95% CI 1.33–1.70; recessive model: OR=2.14, 95% CI 1.86–2.45; multiplicative model: OR=1.57, 95% CI 1.43–1.72 ). Further sensitivity analysis confirmed the significant association.

Conclusions: The meta-analysis indicated a significant association between ACE I/D polymorphism and CHD susceptibility among the Chinese population. However, further studies with the consideration of gene–gene and gene–environment interactions should be conducted to investigate the association.

Keywords

polymorphism coronary heart disease meta-analysis Chinese

Introduction

Coronary heart disease (CHD) is a main public health issue around the world.¹ The etiology of CHD involves genetics, environmental factors and their interactions.² Epidemiology studies have suggested that hypertension, hyperlipidemia, diabetes mellitus, obesity and smoking are major risk factors for CHD.³ In addition, genetic factors also play important roles in the pathogenesis of CHD. It has been estimated that approximately 50% of the variability of the major risk factors for CHD is determined by genetics.⁴

During the past decade, the renin–angiotensin system (RAS) in the development of CHD has been generated much interest. The angiotensin-converting enzyme (ACE), the key enzyme in the RAS, is released from the cell membrane. It converts the angiotensin I to angiotensin II and inactivates the bradykinin and tachykinins, thereby regulating its effects on fibrinolysis, platelet activation and aggregation.⁵ The ACE gene is located on chromosome 17, and the I/D polymorphism is characterized by the insertion/deletion (I/D) of a 287-base pair alu repeat sequence resulting within chromosome 16. It has been suggested that the D allele of this polymorphism is associated with higher plasma ACE concentrations.⁶ To date, many studies have investigated the association between ACE I/D polymorphism and CHD risk among different populations. However, the results have been inconsistent. A previous meta-analysis which included the relevant studies published in the English language indicated that the D allele of I/D polymorphism was associated with CHD among Europeans and East Asians.⁷ However, that meta-analysis included only limited studies with Chinese ancestry since most relevant Chinese studies were published in the Chinese language in local journals.^8-53 Therefore, it is very important to clarify the association among the Chinese population.

In this study, we performed a meta-analysis to assess the association between ACE I/D polymorphism and CHD risk among the Chinese.

Materials and methods

Literature and search strategy

PubMed, EMBASE, CNKI and Wan Fang Data were searched for eligible articles. The search strategy to identify all potential studies involved using combinations of the following key words: (‘the renin–angiotensin system gene’ or ‘the angiotensin-1 converting enzyme gene’ or ‘RAS’ or ‘ACE’) and (‘variant’ or ‘polymorphism’) and (‘coronary heart disease’ or ‘coronary artery disease’ or ‘myocardial infarction’ or ‘ischemic heart disease’ or ‘atherosclerosis’ or ‘arteriosclerosis’ or ‘coronary stenosis’). The reference lists of retrieved reviews and articles were hand-searched. The publication language was restricted to English or Chinese. If more than one article was published using the same case series, only the study with the largest sample size was selected. The literature search was updated on 10 September 2011.

Inclusion criteria and data extraction

Studies were included if they met the following three inclusion criteria: (1) were using a case-control or cohort design; (2) were evaluating the association of ACE I/D polymorphism and CHD; (3) the number of cases was ≥50; and (4) were providing sufficient data for calculation of an odds ratio (OR) with 95% confidence interval (CI).

The following information was extracted from each study: (1) name of the first author; (2) year of publication; (3) region; (4) mean age and gender frequency of cases and controls; (5) sample size of cases and controls; (6) genotype distributions of cases and controls; (7) endpoint event; and (8) p value for the test of Hardy–Weinberg equilibrium (HWE) in controls. Two authors independently assessed the articles for compliance with the inclusion criteria; disagreement was followed by discussion until consensus was reached.

Statistical analysis

The association between ACE I/D polymorphism and CHD was estimated by calculating a pooled OR and 95% CI under a co-dominant model, a dominant model, a recessive model and a multiplicative model, respectively. The significance of the pooled OR was determined by a Z test (p<0.05 was considered statistically significant). A Q test was performed to evaluate whether the variation was due to heterogeneity or due to chance. A random-effects (DerSimonian–Laird method⁵⁴) or fixed-effects (Mantel–Haenszel method⁵⁵) model was used to calculate the pooled OR in the presence (p≤0.10) or absence (p>0.10) of heterogeneity, respectively. Begg’s funnel plot, a scatter plot of effect against a measure of study size, was generated as a visual aid to detect bias or systematic heterogeneity.⁵⁶ Publication bias was assessed by Egger’s test⁵⁷ (p<0.05 was considered statistically significant). Sensitivity analysis was performed by removing one study at a time to evaluate the stability of the results. Data analyses were performed using STATA version 11 (StataCorp LP, College Station, Texas, USA).

Results

Characteristics of the studies

The literature search identified a total of 189 potentially relevant papers. Of these, 82 papers were excluded after reading the title or abstract because of obvious irrelevance to our study aim. In addition, 26 duplicated publications and six reviews were excluded. Therefore, 75 papers met the primary inclusion criteria. Then, three papers which did not provide sufficient data for calculation of OR and 95% CI, 20 papers which deviated from HWE in controls, and six papers with sample size less than 50 cases were further excluded. A total of 46 studies for the association between ACE I/D polymorphism and CHD were included in the final meta-analysis. A flow chart summarizing the process of study inclusion/exclusion is depicted in Figure 1. The characteristics of the included studies are listed in Table 1.

Table 1.

Characteristics of the studies included in the meta-analysis

Study	Region	Gender (M/F)		Age, mean±SD, years		Sample size		Genotype frequency in cases			Genotype frequency in controls			Endpoint event	p _HWE
Study	Region	Cases	Controls	Cases	Controls	Cases	Controls	II	ID	DD	II	ID	DD	Endpoint event	p _HWE
Saha et al., 1996⁸	Singapore	40/0	40/0	53.0±0.5	44.0±1.0	276	147	97	132	47	55	63	29	CAD	0.163
Chuang et al., 1997⁹	Taiwan	NA	NA	58±5	56±10	70	197	32	27	11	83	87	27	CHD	0.583
Ko et al., 1997¹⁰	Taiwan	206/62	181/157	61.5±0.6	56.0±0.6	268	338	107	119	42	145	156	37	CAD	0.606
Lu et al., 1997¹¹	Shanghai	77/39	67/46	67.8±10.9	59.4±12.9	116	103	41	44	31	47	44	12	MI	0.729
Tan et al., 1997¹²	Shanghai	50/22	50/22	66.1±10.9	54.3±8.5	72	72	13	27	32	24	33	15	MI	0.559
Yuan et al., 1997¹³	Shenyang	39/12	20/10	59.9	55.6	51	30	16	26	5	9	12	9	MI	0.273
Zhang et al., 1997¹⁴	Tianjin	74/23	57/47	63.3±9.8	51.5±6.5	97	104	20	33	44	25	53	26	MI	0.844
Zheng et al., 1997¹⁵	Beijing	66/37	56/40	64.5±8.5	48.1±6.5	103	96	17	46	40	30	40	26	MI	0.105
Gu et al., 1998¹⁶	Beijing	54/41	62/38	58.2±12.9	56.8±13.3	95	100	25	37	33	43	44	13	CHD	0.742
Dai et al., 1998¹⁷	Nanjing	59/21	108/42	64.2±6.4	51.7±14.2	80	150	31	38	11	82	59	9	MI	0.706
Lv et al., 1998¹⁸	Shandong	32/26	22/19	NA	NA	58	41	16	18	24	15	17	9	MI	0.328
Lai et al., 1999¹⁹	Guangdong	46/7	16/17	55.6±10.5	58.6±11.2	53	33	20	11	22	19	9	5	CAD	0.054
Lai et al., 1999²⁰	Guangdong	48/15	80/32	65.8	63.5	63	112	13	24	26	37	50	25	MI	0.306
Liu et al., 1999²¹	Hebei	51/29	48/32	61.4±7.8	54.0±10.2	80	80	23	33	23	39	33	8	MI	0.794
Qiu et al., 1999²²	Zhejiang, Hubei	83/35	66/36	63.1±7.8	63.2±9.5	118	102	24	49	45	42	43	17	CHD	0.298
Tan et al., 1999²³	Shanghai	96/41	36/27	61.4±10.2	52.5±6.4	137	63	24	51	62	21	29	13	CAD	0.610
Shan et al., 2000²⁴	Zhejiang	105/43	100/59	63±9	61±7	148	159	55	54	39	49	85	25	CHD	0.235
Lv et al., 2000²⁵	Shandong	33/31	22/19	56.3±7.2	NA	64	41	17	20	27	15	17	9	MI	0.328
Tian et al., 2000²⁶	Guangdong	37/13	39/11	61.8±10.2	33.6±9.4	50	50	11	20	19	17	25	8	MI	0.813
Yang et al., 2000²⁷	Shandong	32/26	22/19	NA	NA	58	41	16	18	24	5	17	9	MI	0.521
You et al., 2000²⁸	Liaoning	44/21	58/32	NA	NA	65	90	16	21	28	44	32	14	MI	0.058
Chen et al., 2001²⁹	Hubei	43/8	25/5	58.4±9.9	51.4±8.0	51	30	21	17	13	14	14	2	CHD	0.543
Xie et al., 2001³⁰	Jiangsu	82/24	54/32	61.4±9.5	52.8±8.7	106	86	32	55	19	37	36	13	CAD	0.393
Deng et al., 2002³¹	Sichuan	76/29	74/28	71.8±9.3	70.5±11.1	105	102	45	53	7	40	50	12	CHD	0.542
Liu et al., 2002³²	Jilin	36/15	43/40	54.8	52.5	51	83	7	26	18	22	48	13	CHD	0.121
Su et al., 2002³³	Jiangxi	128/29	69/43	63.7±7.2	61.5±9.0	157	112	30	77	50	45	47	20	CAD	0.217
Dai et al., 2003³⁴	Beijing	219/31	50/45	NA	51.7±10.1	250	95	93	124	33	43	43	9	CHD	0.709
Huang et al., 2003³⁵	Jiangxi	70/19	42/33	63.4±8.4	61.6±7.7	89	75	18	41	30	27	33	15	CHD	0.401
Zhang et al., 2003³⁶	Guangdong	71/31	91/57	69.3±8.4	65.5±16.1	102	148	38	40	24	61	75	12	CAD	0.092
Li et al., 2004³⁷	Jiangxi	81/48	54/36	65.8±9.2	65.3±9.8	129	90	17	59	53	36	39	15	CAD	0.429
Zhang et al., 2004³⁸	Guangdong	57/32	91/57	67.8±9.2	65.5±16.1	89	148	32	35	22	61	75	12	CAD	0.092
Zhu et al., 2004³⁹	Beijing	NA	NA	NA	NA	192	98	79	88	25	43	43	12	CAD	0.805
Ai et al., 2005⁴⁰	Xinjiang	NA	NA	57.0±10.4	54.1±10.7	71	81	12	35	24	21	39	21	CHD	0.739
Liang et al., 2006⁴¹	Guangdong	100/33	116/38	64±8	63±8	133	154	40	55	38	71	59	24	CAD	0.054
Shi et al., 2006⁴²	Zhejiang	139/30	124/44	62.0±9.9	61.0±7.7	169	168	53	66	50	55	86	27	CHD	0.492
Wang et al., 2006⁴³	Guangdong	74/31	41/9	NA	53.10±7.03	105	50	37	44	24	22	24	4	CAD	0.467
Zhang et al., 2006⁴⁴	Hubei	63/30	56/31	NA	NA	93	87	22	41	30	33	43	11	MI	0.601
Qiu et al., 2007⁴⁵	Hubei	83/47	47/43	61.5±11.8	56±10	130	90	39	41	50	30	47	13	CHD	0.430
Li et al., 2008⁴⁶	Hebei	80/0	100/0	64.2±4.5	61.1±2.4	80	100	10	40	30	13	50	37	CAD	0.541
Shi et al., 2008⁴⁷	Sichuan	57/23	50/30	54.2±8.0	51.9±9.0	80	80	14	39	27	26	42	12	CHD	0.457
Yu et al., 2009⁴⁸	Xinjiang	63/61	25/25	52.2±6.7	48.2±6.7	124	50	44	55	25	22	20	8	CHD	0.351
Wang et al., 2009⁴⁹	Hannan	110/40	115/35	57.0±10.7	57.4±9.0	150	150	64	49	37	63	66	21	CAD	0.580
Yun et al., 2009⁵⁰	Hainan	106/44	83/67	57.3±13.3	57.3±8.0	150	150	47	68	35	67	63	20	CHD	0.401
Wang et al., 2010⁵¹	Jiangxi	109/52	71/38	63.2±10.6	62.4±9.5	161	109	48	49	64	33	49	27	CAD	0.305
Zhu et al., 2010⁵²	Hunan, Guizhou	96/55	71/46	59.7±11.3	58.1±10.8	151	127	31	67	53	45	63	19	CHD	0.689
Yi et al., 2011⁵³	Guangdong	128/52	99/81	57.3±12.3	57.3±8.1	180	180	76	59	45	72	83	25	CHD	0.890

SD: standard deviation; NA: not available; CAD: coronary artery disease; CHD: coronary heart disease; MI: myocardial infarction; p_HWE, p value for the test of Hardy–Weinberg equilibrium (HWE) in controls.

Meta-analysis results

A total of 5215 cases and 4782 controls were identified. There was evidence of between-study heterogeneity under a co-dominant model (DD vs. II: I²=48.5%, p for heterogeneity <0.001; DI vs. II: I²=34.9%, p for heterogeneity =0.012), a dominant model (I²=45.5%, p for heterogeneity =0.001), and a recessive model (I²=42.3%, p for heterogeneity =0.002), respectively. Therefore, a random-effects model was used for all three genetic models. The results from the meta-analysis indicated statistically significant association between ACE I/D polymorphism and CHD risk under all three genetic models (DD vs. II: OR=2.40, 95% CI 2.02–2.84; DI vs. II: OR=1.19, 95% CI 1.05–1.34; dominant model: OR=1.51, 95% CI 1.33–1.70; recessive model: OR=2.14, 95% CI 1.86–2.45; multiplicative model: OR=1.57, 95% CI 1.43–1.72) (Table 2 and Figures 2 and 3). In the subgroup analysis, the effect sizes were similar among studies with CHD (DD vs. II: OR=2.27, 95% CI 1.86–2.78; DI vs. II: OR=1.16, 95% 1.00–1.34; dominant model: OR=1.44, 95% CI 1.25–1.67; recessive model: OR=2.04, 95% CI 1.73–2.40; multiplicative model: OR=1.50, 95% CI 1.35–1.67) and among those with myocardial infarction (MI) (DD vs. II: OR=2.79, 95% CI 2.06–3.79; DI vs. II: OR=1.32, 95% CI 1.07–1.62; dominant model: OR=1.76, 95% CI 1.45–2.13; recessive model: OR=2.45, 95% CI 1.91–3.15; multiplicative model: OR=1.78, 95% CI 1.52–2.08).

Table 2.

Meta-analysis of association between ACE I/D polymorphism and coronary heart disease risk

Contrasts	Pooled OR (95% CI)	p_Z _-test	I² (%)	p _H
DD vs. II	2.40 (2.02–2.84)	<0.001	48.5	<0.001
DI vs. II	1.19 (1.05–1.34)	0.005	34.9	0.012
DD+DI vs. II (Dominant)	1.51 (1.33–1.70)	<0.001	45.5	0.001
DD vs. DI+II (Recessive)	2.14 (1.86–2.45)	<0.001	42.3	0.002
D vs. I	1.57 (1.43–1.72)	<0.001	57.1	0.054

OR: odds ratio; CI: confidence interval; Pz-test, P value for Z test; p_H, p value for between-study heterogeneity based on Q test.

Sensitivity analysis

Sensitivity analysis was performed by excluding each study at a time (data not shown). The results confirmed the significant association between ACE I/D polymorphism and CHD risk.

Potential publication bias

No publication bias was detected for the association between ACE I/D polymorphism and CHD risk (all p>0.05).

Discussion

The strength of our meta-analysis of the association between ACE I/D polymorphism and CHD risk is based on a great number of published studies which achieved sufficient statistical power to detect the modest effect estimate. Further sensitivity analysis confirmed the significant association between ACE I/D polymorphism and CHD risk among the Chinese population. The finding was consistent with those from the previous meta-analysis, which indicated that the association was significant among Europeans and East Asians.⁷

Heterogeneity between studies is very common in the meta-analysis of genetic association studies. The between-study heterogeneity was also observed in our meta-analysis. It might be due to many reasons, such as differences in recruitment procedures of the study population and environmental backgrounds. In addition, evidences have suggested that deviation from HWE might reflect the presence of genotyping errors, population stratification and selection bias in the controls (or the non-representation of the studied sample).⁵⁸ Therefore, individual studies not in HWE were excluded from our meta-analysis, which guaranteed the study quality.

The mechanism of how ACE I/D polymorphism relates to CHD risk is still unclear. The I/D polymorphism has been recognized to be a major determinant of plasma ACE activity, with the highest values found in subjects homozygous for the D allele and the lowest in subjects homozygous for the I allele; those with ID heterozygote show intermediate values.⁵⁹ ACE is a key factor in the production of angiotensin II and in the degradation of bradykinin, which are important peptides involved in cardiovascular physiology,⁶⁰ such as the simulation of vascular smooth muscle cell proliferation, intimal fibrosis, inflammatory reactions, thrombotic processes and plaque calcification.⁶¹ ACE is also involved in many pathological conditions including vasoconstriction, coronary thrombosis, heart failure and ventricular remodeling.⁶² Those evidences suggested that ACE I/D polymorphism might play an important role in the development of CHD.

Several limitations should be noted. First, the present meta-analysis was based primarily on unadjusted effect estimates and the confounding factors were not controlled for. Second, the effect of gene–gene and gene–environment interactions was not addressed in this meta-analysis. Third, only ACE I/D polymorphism was considered in this meta-analysis. Further meta-analysis should clarify the association of other polymorphisms in the RAS genes, such as AGT M235T and AT1-receptor A1166C polymorphisms etc. with CHD in the Chinese population.

In conclusion, the results indicated significant association of ACE I/D polymorphism with CHD susceptibility in the Chinese population. However, further studies with consideration of gene–gene and gene–environment interactions should be conducted to investigate the association.

Footnotes

This work was supported by Key Project of Chinese Ministry of Education (grant number 2010148).

We declare that we have no conflict of interest.

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