Int J Clin Exp Med 2015;8(11):20796-20804 www.ijcem.com /ISSN:1940-5901/IJCEM0016044
Original Article Association between alpha-1 antichymotrypsin gene A/T polymorphism and primary intracerebral hemorrhage: a meta-analysis Zusen Ye1, Qiang Ye1, Bei Shao1, Jincai He1, Zhenguo Zhu1, Jianhua Cheng1, Yanyan Chen1, Siyan Chen1, Xiaoya Huang2 1 Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, P. R. China; 2Department of Neurology, Wenzhou Central Hospital & Dingli Clinical Institute of Wenzhou Medical University, Wenzhou 325000, P. R. China
Received September 12, 2015; Accepted November 8, 2015; Epub November 15, 2015; Published November 30, 2015 Abstract: The present study is to use meta-analysis to explain the association between alpha-1 antichymotrypsin (ACT) gene A/T polymorphism and the risk of primary intracerebral hemorrhage (PICH). Relevant studies before 1 June 2015 were identified by searching PubMed, Cochrane database and Science Citation Index Expanded (SCIE), and the references of retrieved articles. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were used to assess the strength of the association. Five independent publications, with 774 PICH cases and 940 controls, were included. There was no statistical evidence of association between ACT polymorphism and PICH risk under all genetic models in overall estimates (allele model: OR = 1.01, 95% CI = 0.80-1.28; heterozygote model: OR = 0.93, 95% CI = 0.60-1.45; homozygote model: OR = 1.03, 95% CI = 0.59-1.80; dominant model: OR = 0.97, 95% CI = 0.65-1.46; recessive model: OR = 1.06, 95% CI = 0.72-1.57). No association was found in subgroup analysis based on ethnicity, Hardy-Weinberg equilibrium, location of hematoma and blood pressure. Sensitivity analysis suggested that the combined results were stable and reliable. No significant publication bias was found by Begg’s test and Egger’s regression test. The results of our meta-analysis indicate that ACT polymorphism is unlikely to contribute to PICH susceptibility. Keywords: Alpha-1 antichymotrypsin gene, polymorphism, primary intracerebral hemorrhage, meta-analysis
Introduction Stroke, as the most destructive manifestation of cerebrovascular disease, is the second leading cause of death worldwide [1-3]. Primary intracerebral hemorrhage (PICH) accounts for approximately 10% of all strokes, and leads to a large number of fatal or severe cases [4, 5]. About 35-52% of patients die within the first month after PICH [6]. Although various clinical and imaging predictors of mortality have been observed in previous studies, reasons for the high morbidity are still unclear [7, 8]. A study on twins and families suggests that stroke is a complex disease caused by strong interactions between genetic factors and environment [9]. Little is known about the pathophysiology of PICH, and genetic factors may play an important role.
Alpha-1 antichymotrypsin (ACT, OMIM: 107280) is a member of serine protease inhibitors that regulates the activity of neutrophil cathepsin G [10]. Neutrophil cathepsin G is a proteolytic enzyme that may lead to vascular matrix degradation and coagulation disorders [10, 11]. Human ACT gene is located in the distal region of the long arm of chromosome 14q32.1 and belongs to a cluster of other serine protease inhibitor genes [12]. A variety of epidemiological studies have evaluated the role of ACT gene polymorphism in PICH patients. However, there were apparently conflicting results among these association studies. A study from Spain showed that ACT-TT genotype was associated with normotensive hemorrhagic stroke even after adjustment for age, gender, and vascular risk factors [10]. In contrast, a study from Asian population reported that ACT-TT genotype was
Meta-analysis for PICH risk and ACT Inclusion and exclusion criteria
associated with hypertensive PICH patients [13]. Other studies failed to find an association between ACT polymorphism and PICH [14-16]. It also remains unclear whether ethnicity and the location of hematoma affect the relationship between ACT polymorphism and PICH risk. To clarify the role of ACT polymorphism in PICH, we undertake a meta-analysis based on currently available literatures. Materials and methods Search strategy We performed computerized searches in PubMed, Cochrane database and Science Citation Index Expanded (SCIE) before 1 June 2015. The following terms were used: (“hemorrhage” OR “stroke” OR “hematoma”) AND (“antichymotrypsin” OR “SERPINA3” OR “ACT”) AND (“polymorphism” OR “genotype” OR “gene” OR “mutation” OR “allele” OR “variation” OR “variant”). When possible, we also utilized relevant Medical Subject Headings (MeSH) terms. In addition, the related articles options in PubMed and reference lists of all identified publications were checked to search for other potentially related articles. Our search was performed without language restrictions. Two authors of the present study independently performed entire literature search (S Chen and Y Chen).
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The primary studies included in our study met the following inclusion criteria: i) Casecontrol studies to examine the association of ACT polymorphism and PICH; ii) PICH was confirmed by computer tomography or magnetic resonance imaging; iii) Genotype distributions were available in both cases and controls. Exclusion criteria were as follows: i) The primary Figure 1. Flow diastudy did not report the gengram of study idenotype; ii) The study was not tification, inclusion, and exclusion. relevant to ACT polymorphism or PICH; iii) Studies with patients below 18 years of age; iv) Sibling pairs or family studies, animal studies, reviews or abstracts. For repeated or overlapped publications, we only included the most complete one. Data extraction Relevant information from each eligible primary study was abstracted by 2 independent investigators (S Chen and Y Chen). A standardized form was used for collecting relevant information. For each eligible study, the following data were collected: the first author’s last name, publication year, country, ethnicity, matching conditions, study design, genotype frequencies in cases and controls, and genotypic methods. Ethnicity was classified as Asian and European. The location of hematoma was categorized into lobar and non lobar. PICH patients were categorized into hypertensive and normotensive. Disagreements between investigators regarding data abstraction were resolved by discussion. When necessary, a third reviewer (Z Ye) helped to reach a consensus. In cases of mistaken data in eligible literatures or no detailed data reported, we contacted the original authors to obtain relevant information. Statistical analysis Hardy-Weinberg equilibrium (HWE) was assessed using Chi-square test in control groups. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were used to
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Meta-analysis for PICH risk and ACT Table 1. Main characteristics of selected studies in the meta-analysis First author
Year
Country
Ethnicity
Fu [13] Obach [10] Pera [15] Dardiotis [14] Somarajan [16]
2002 2001 2006 2008 2010
China Spain Poland Greece India
Asian European European European Asian
Study design CC CC CC CC CC
Control source HB HB HB PB HB
Matching criteria NG NG Sex and age Sex and age NG
Genotyping method PCR PCR PCR PCR PCR
NOS score 7 8 8 8 7
Note: CC, case-control; HB, hospital-based; PB, population-based; NG, not given; HWE, Hardy-Weinberg equilibrium; PCR, polymerase chain reaction; NOS, Newcastle-Ottawa Scale.
assess the strength of the relationship between ACT polymorphism and PICH risk. The significance of pooled ORs was assessed by Z-test. We conducted analysis for five genetic models: allele model (T vs. A), heterozygote model (TA vs. AA), homozygote model (TT vs. AA), dominant model (TT+TA vs. AA), and recessive model (TT vs. TA+AA). In order to control the false positive error rate, we used Bonferroni method to adjust for multiple comparisons. In our meta-analysis, we performed multiple comparisons for 30 times. P value less than 0.05/30 (0.0017) were considered to be statistically significant after Bonferroni correction. Q-test and I2-statistics were used to assess the heterogeneity between studies. If PQ ≤ 0.01 or I2 > 50%, the heterogeneity was considered as significant [17]. In the presence of heterogeneity, we used the random-effects model (Mantel-Haenszel method) to calculate the pooled ORs [18]; otherwise, the fixedeffects model (Mantel-Haenszel method) was applied [19]. To explore the sources of betweenstudy heterogeneity, stratified analyses were performed based on ethnicity, HWE, location of hematoma and PICH patients with or without hypertension. Sensitivity analysis was performed. We omitted one study each time to validate the reliability of the results. Modified Begg’s test and Egger’s linear regression test were performed to estimate publication bias [20]. All analyses were performed by means of STATA 12.0 (StataCorp, College Station, TA, USA). Two-sided values of P < 0.05 were considered to be statistically significant. Results
cations, 424 were irrelevant studies, 3 were abstracts, 1 was a review and 2 were overlapped cases. For overlapped publications, we only used the most complete one [10, 16, 21, 22]. For typographic errors in eligible literatures, we communicated with the authors and editors to obtain relevant information [16]. After all, a total of 5 studies fit the inclusion criteria in our meta-analysis [10, 13-16] (Figure 1). The clinical characteristics of the five eligible studies are not consistent The five eligible studies comprised a total of 744 patients with PICH and 940 controls. Among the five studies, four were hospitalbased case-control studies, and one was population-based case-control study. Two studies were focused on frequency-matched controls to cases by gender and age, while the other three studies did not indicate. In addition, three out the five studies were conducted in Europe and two in Asia (Table 1). Furthermore, four studies reported the distribution of ACT genotypes among hypertensive and normotensive PICH patients. Two studies reported genotype distributions in PICH patients according to the location of hematoma. The genotype distribution of the five selected studies in our metaanalysis is summarized in Table 2. In the study by Fu et al., the genotype distribution in control was out of HWE (P = 0.001), while the other four studies were in HWE. These data suggest that the clinical characteristics of the five eligible studies are not consistent.
A total of 5 studies are included for the metaanalysis after literature search of 512 articles
No statistical evidence of association between ACT polymorphism and PICH risk is observed in overall estimates
To find eligible literatures, we perform computerized searches in multiple databases after 1 June 2015. The initial search identified 512 entries. Among these, 77 were duplicate publi-
To assess the strength of the relationship between ACT polymorphism and PICH risk, pooled ORs with corresponding 95% CIs were used. No significant association between ACT polymor-
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Meta-analysis for PICH risk and ACT Table 2. Genotype distribution of selected studies in the meta-analysis First author
Year
Fu [13] Obach [10]
2002 2001
Pera [15]
2006
Dardiotis [14]
2008
Somarajan [16]
2010
Stratifications Overall Overall Hypertensive Normotensive Overall Hypertensive Normotensive Nonlobar Lobar Overall Hypertensive Normotensive Nonlobar Lobar Overall Hypertensive Normotensive
Sample size Case 220 99 66 33 95 81 14 52 43 147 114 33 98 49 183 126 57
Control 276 80
190
206
188
Genotype distribution (case) AA AT TT 29 103 88 36 37 26 27 24 15 9 13 11 26 44 25 24 36 21 2 8 4 13 28 11 13 16 14 42 68 37 31 57 26 11 11 11 29 45 24 13 23 13 72 97 14 48 69 9 24 28 5
Genotype distribution (control) AA AT TT 67 110 99 26 42 12
0.001 0.457
45
96
49
0.880
48
114
44
0.124
62
96
30
0.478
HWE (P)
Note: HWE, Hardy-Weinberg equilibrium.
phism and risk of PICH was observed in all genetic models (T vs. A: OR = 1.01, 95% CI = 0.80-1.28; TA vs. AA: OR = 0.93, 95% CI = 0.601.45; TT vs. AA: OR = 1.03, 95% CI = 0.59-1.80; TT+TA vs. AA: OR = 0.97, 95% CI = 0.65-1.46; TT vs. TA+AA: OR = 1.06, 95% CI = 0.72-1.57). There was remarkable between-study heterogeneity in all genetic models for overall analysis (Figure 2 and Table 3). The data indicate that no statistical evidence of association between ACT polymorphism and PICH risk is observed in overall estimates. No statistical evidence of association between ACT polymorphism and PICH risk is observed in subgroup analysis To know how ACT polymorphism is related to PICH risk in subgroups, ethnicity, location of hematoma and blood pressure were considered. In respect to ethnicity, the subgroup of Europeans showed no significant betweenstudy heterogeneity, and the heterozygote model (TA vs. AA) was marginally related with PICH susceptibility (OR = 0.70, 95% CI = 0.50-0.98, P = 0.040). However, the association no longer existed after Bonferroni correction. In Asian subgroup, significant heterogeneity still existed, and no significant association between ACT polymorphism and risk of PICH was observed. 20799
In the subgroup analysis according to HWE, we also noted borderline statistical significance in the heterozygote model (TA vs. AA) associated with PICH susceptibility (OR = 0.76, 95% CI = 0.58-0.99, P = 0.045), and no significant result was observed after Bonferroni correction. In addition, further subgroup analyses by location of hematoma and blood pressure were performed. No significant heterogeneity and association were noted between ACT polymorphism and PICH risk according to the location of hematoma. Similarly, no significant heterogeneity and association was observed between hypertensive and normotensive individuals (Table 3). These results suggest that no statistical evidence of association between ACT polymorphism and PICH risk is observed in subgroup analysis. The combined results of our meta-analysis are robust and reliable For sensitivity analysis, one study was removed at each time and the rest studies were analyzed to calculate the pooled ORs. Sensitivity analysis of allele model (T vs. A) showed that no significant alteration existed in the pooled Ors (Figure 3). Data for other models are not shown. This analysis suggests that the combined Int J Clin Exp Med 2015;8(11):20796-20804
Meta-analysis for PICH risk and ACT
Figure 2. Forest plots for relationship between ACT polymorphism and PICH risk in all models. A. Allele model (T vs. A); B. Homozygote model (TT vs. AA); C. Heterozygote model (TA vs. AA); D. Dominant model (TT+TA vs. AA); E. Recessive model (TT vs. TA+AA).
results of our meta-analysis are robust and reliable. No obvious publication bias exists in our metaanalysis To assess publication bias, Begg’s test and Egger’s regression test were utilized. In Begg’s 20800
regression test, P > 0.05 was observed in all genetic models (T vs. A: P = 0.72; TA vs. AA: P = 0.67; TT vs. AA: P = 0.43; TT+TA vs. AA: P = 0.93; TT vs. TA+AA: P = 0.77). In Egger’s regression test, P > 0.05 was also found in all genetic models (Table 4). The results suggest that no obvious publication bias exists in our metaanalysis. Int J Clin Exp Med 2015;8(11):20796-20804
Meta-analysis for PICH risk and ACT Table 3. Summary risk estimates for association between ACT polymorphism and PICH Comparisons
Stratifications
Studies (n)
T vs. A
Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location
5 4 2 3 4 2 5 4 2 3 4 2 5 4 2 3 4 2 5 4 2 3 4 2 5 4 2 3 4 2
TT vs. AA
TA vs. AA
TT+TA vs. AA
TT vs. TA+AA
Pooled estimate OR (95% CI) 1.01 (0.80, 1.28) 0.91 (0.77, 1.07) 1.01 (0.54, 1.87) 1.00 (0.82, 1.22) 0.85 (0.64, 1.13) 1.12 (0.77, 1.62) 1.03 (0.59, 1.80) 0.84 (0.50, 1.40) 0.93 (0.19,4.58) 1.04 (0.70, 1.55) 0.66 (0.37, 1.19) 1.24 (0.60, 2.53) 0.93 (0.60, 1.45) 0.76 (0.58, 0.99) 1.36 (0.56, 3.32) 0.70 (0.50, 0.98) 1.05 (0.67, 1.64) 0.86 (0.56, 1.61) 0.97 (0.65, 1.46) 0.79 (0.61, 1.01) 1.26 (0.46, 3.43) 0.80 (0.58, 1.10) 0.93 (0.61, 1.41) 0.98 (0.55, 1.75) 1.06 (0.72, 1.57) 1.02 (0.59, 1.78) 0.75 (0.28, 2.01) 1.28 (0.92, 1.78) 0.67 (0.41, 1.10) 1.36 (0.75, 2.47)
PZ 0.911 0.251 0.983 0.998 0.265 0.556 0.914 0.501 0.925 0.841 0.166 0.563 0.748 0.045 0.501 0.040 0.840 0.634 0.890 0.061 0.654 0.166 0.720 0.945 0.757 0.938 0.568 0.144 0.113 0.306
Heterogeneity I (%) PQ 2
62.3% 16.1% 89.8% 0.0% 0.0% 0.0% 72.0% 52.4% 92.3% 0.0% 0.0% 0.0% 70.2% 0.0% 85.7% 0.0% 28.2% 10.1% 69.2% 0.0% 89.9% 0.0% 20.5% 0.0% 60.9% 69.5% 85.0% 0.0% 0.0% 0.0%
0.031 0.311 0.002 0.720 0.517 0.955 0.006 0.098 0.000 0.556 0.767 0.944 0.009 0.844 0.008 0.881 0.243 0.292 0.011 0.989 0.002 0.960 0.287 0.494 0.037 0.020 0.010 0.370 0.935 0.437
Note: ACT, Alpha-1 antichymotrypsin; PICH, primary intracerebral hemorrhage; OR, odds ratio; CI, confidence interval; PZ, P value for Z test; PQ, P value for heterogeneity; HWE, Hardy-Weinberg equilibrium.
Discussion PICH is known to be a multifactorial disease. Accumulating evidence shows that genetic elements contribute to the pathogenesis of PICH [23]. Recently, a variety of studies have been performed to evaluate the association between ACT polymorphism and the risk of PICH. However, the results remain controversial. Since a single study has a relatively small number of participants with low power to detect the effect of gene polymorphism on PICH, a metaanalysis may be an appropriate approach to obtain a more definitive conclusion [24]. There-
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fore, we designed this meta-analysis to derive a more convinced conclusion for the association between ACT polymorphism and PICH risk. In this meta-analysis, a total of five eligible studies involving 744 PICH cases and 940 controls were included. No statistically significant evidence of an association between ACT polymorphism and PICH risk was found in the overall study population for all genetic models. Despite negative results of our study, we still cannot exclude the possible role of ACT polymorphism in PICH. As an inhibitor of proteases, ACT gene might prevent the degradation of ex-
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Meta-analysis for PICH risk and ACT
Figure 3. Sensitivity analysis for allele model (T vs. A). Only one study was removed at each time, and the rest studies were analyzed to calculate the pooled ORs.
performed stratification analysis. However, we failed to effectively eliminate heterogeneity in all subgroups. After excluding the study deviating from HWE in controls [13], moderate heterogeneity was still found in homozygote and recessive models, but not in allele, heterozygote or dominant models. The study deviating from HWE in controls [13] was the main source of between-study heterogeneity in the three models. Significant heterogeneity was noted in all genetic models in Asian subgroup, but not in European subgroup, suggesting that environment and ethnic differences may play a role [30].
In subgroup analysis, no statistically significant association between ACT polymorphism and PICH risk was Egger’s test Begg’s test Comparisons found in all genetic models. The negat value P value 95% CI P value Z value tive result may be due to the limited T vs. A 0.40 0.72 (-18.37, 14.28) 1.00 -0.24 number of eligible studies with availTA vs. AA 0.47 0.67 (-23.56, 17.63) 0.81 0.24 able data. In our study, only two eligiTT vs. AA 0.92 0.43 (-21.73, 11.98) 0.46 0.73 ble studies reported genotype distriTT+TA vs. AA 0.10 0.93 (-23.37, 21.98) 0.46 0.73 butions in PICH patients according to TT vs. TA+AA 0.32 0.77 (-11.08, 9.03) 0.81 0.24 the location of hematoma. Limited Note: ACT, Alpha-1 antichymotrypsin; PICH, primary intracerebral hemornumber of eligible studies had low rhage; CI, confidence interval. power to detect slight effects or might generate fluctuating risk estimates. Therefore, more studies with large sample size tracellular matrix in vessel wall [15]. ACT polyare required. morphism might be in linkage disequilibrium with other functional mutations of ACT gene, In this meta-analysis, search strategy and and there are various frequencies of these study criteria were conducted strictly. In order mutations among different races, where sigto reduce false positive error rate, we used nificant ethnic differences in the distribution Bonferroni method to adjust for multiple comof genotypes exist [25, 26]. In addition, high parisons. In addition, we performed ‘leave-onebetween-study heterogeneity is found in the out’ sensitive analysis to calculate the pooled overall study population for all genetic models. ORs. Sensitivity analysis demonstrated that the In meta-analysis for genetic association studcombined results of our meta-analysis were ies, between-study heterogeneity is common stable and reliable. In addition, no significant [27]. Therefore, stratification analysis was perpublication bias for ACT polymorphism in the formed to explore the sources of heterogeneifive hereditary models was found according to ty. Begg’s test and Egger’s regression test. Considering that differences in ethnicity, diversity in design, characteristics of the subjects To better interpret the results, some limitations and clinical heterogeneity may contribute to of our present meta-analysis should be noted. common sources of heterogeneity [28, 29], we First, the sample size in individual included Table 4. Publication bias tests for association between ACT polymorphism and PICH
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Meta-analysis for PICH risk and ACT studies and the number of included studies in our meta-analysis were relatively small. Second, PICH is a heterogeneous disease, and potential interactions between environmental factors and other genes should be evaluated. In the present meta-analysis, we only focused on ACT gene. It is possible that the potential roles of ACT gene are enhanced by other gene-environment or gene-gene interactions. In conclusion, the present meta-analysis suggests that ACT polymorphism is unlikely to be associated with genetic susceptibility of PICH based on currently published studies. Considering the limitations of the present meta-analysis, more welldesigned studies with large sample sizes are still needed in the future. Acknowledgements We thank Dr. Niange Xia and Dr. Jia Li for their helpful suggestions. This work was supported by funding from the Wenzhou Science & Technology Bureau (No. Y20140531). Disclosure of conflict of interest None. Address correspondence to: Dr. Xiaoya Huang, Department of Neurology, Wenzhou Central Hospital & Dingli Clinical Institute of Wenzhou Medical University, No. 32 Dajian Lane, West Riverside Road, Wenzhou 325000, Zhejiang Province, P. R. China. Tel: 86-15057557216; Fax: 86-577-55579365; E-mail: [email protected]
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[22] Vila N, Obach V, Revilla M, Oliva R, Chamorro A. Alpha(1)-antichymotrypsin gene polymorphism in patients with stroke. Stroke 2000; 31: 21032105. [23] Woo D, Sauerbeck LR, Kissela BM, Khoury JC, Szaflarski JP, Gebel J, Shukla R, Pancioli AM, Jauch EC, Menon AG, Deka R, Carrozzella JA, Moomaw CJ, Fontaine RN, Broderick JP. Genetic and environmental risk factors for intracerebral hemorrhage: preliminary results of a population-based study. Stroke 2002; 33: 1190-1195. [24] Wang M, Jiang X, Wu W, Zhang D. Endothelial NO synthase gene polymorphisms and risk of ischemic stroke in Asian population: a metaanalysis. PLoS One 2013; 8: e60472. [25] Wang X, DeKosky ST, Luedecking-Zimmer E, Ganguli M, Kamboh MI. Genetic variation in alpha(1)-antichymotrypsin and its association with Alzheimer’s disease. Hum Genet 2002; 110: 356-365. [26] Grasbon-Frodl EM, Egensperger R, Kösel S, Krüger R, Riess O, Mehraein P, Graeber MB. The alpha1-antichymotrypsin A-allele in German Parkinson disease patients. J Neural Transm 1999; 106: 729-736. [27] Munafo MR, Flint J. Meta-analysis of genetic association studies. Trends 2004; 20: 439444. [28] Thompson SG. Why sources of heterogeneity in meta-analysis should be investigated. BMJ 1994; 309: 1351-1355. [29] Jin J, Li W, Peng L, Chen J, Li R, Wu P, Tan S. Relationship between interleukin-10 -1082A/ G polymorphism and risk of ischemic stroke: a meta-analysis. PLoS One 2014; 9: e94631. [30] Zhang Z, Xu G, Liu D, Fan X, Zhu W, Liu X. Angiotensin-converting enzyme insertion/deletion polymorphism contributes to ischemic stroke risk: a meta-analysis of 50 case-control studies. PLoS One 2012; 7: e46495.
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Original Article Association between alpha-1 antichymotrypsin gene A/T polymorphism and primary intracerebral hemorrhage: a meta-analysis Zusen Ye1, Qiang Ye1, Bei Shao1, Jincai He1, Zhenguo Zhu1, Jianhua Cheng1, Yanyan Chen1, Siyan Chen1, Xiaoya Huang2 1 Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, P. R. China; 2Department of Neurology, Wenzhou Central Hospital & Dingli Clinical Institute of Wenzhou Medical University, Wenzhou 325000, P. R. China
Received September 12, 2015; Accepted November 8, 2015; Epub November 15, 2015; Published November 30, 2015 Abstract: The present study is to use meta-analysis to explain the association between alpha-1 antichymotrypsin (ACT) gene A/T polymorphism and the risk of primary intracerebral hemorrhage (PICH). Relevant studies before 1 June 2015 were identified by searching PubMed, Cochrane database and Science Citation Index Expanded (SCIE), and the references of retrieved articles. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were used to assess the strength of the association. Five independent publications, with 774 PICH cases and 940 controls, were included. There was no statistical evidence of association between ACT polymorphism and PICH risk under all genetic models in overall estimates (allele model: OR = 1.01, 95% CI = 0.80-1.28; heterozygote model: OR = 0.93, 95% CI = 0.60-1.45; homozygote model: OR = 1.03, 95% CI = 0.59-1.80; dominant model: OR = 0.97, 95% CI = 0.65-1.46; recessive model: OR = 1.06, 95% CI = 0.72-1.57). No association was found in subgroup analysis based on ethnicity, Hardy-Weinberg equilibrium, location of hematoma and blood pressure. Sensitivity analysis suggested that the combined results were stable and reliable. No significant publication bias was found by Begg’s test and Egger’s regression test. The results of our meta-analysis indicate that ACT polymorphism is unlikely to contribute to PICH susceptibility. Keywords: Alpha-1 antichymotrypsin gene, polymorphism, primary intracerebral hemorrhage, meta-analysis
Introduction Stroke, as the most destructive manifestation of cerebrovascular disease, is the second leading cause of death worldwide [1-3]. Primary intracerebral hemorrhage (PICH) accounts for approximately 10% of all strokes, and leads to a large number of fatal or severe cases [4, 5]. About 35-52% of patients die within the first month after PICH [6]. Although various clinical and imaging predictors of mortality have been observed in previous studies, reasons for the high morbidity are still unclear [7, 8]. A study on twins and families suggests that stroke is a complex disease caused by strong interactions between genetic factors and environment [9]. Little is known about the pathophysiology of PICH, and genetic factors may play an important role.
Alpha-1 antichymotrypsin (ACT, OMIM: 107280) is a member of serine protease inhibitors that regulates the activity of neutrophil cathepsin G [10]. Neutrophil cathepsin G is a proteolytic enzyme that may lead to vascular matrix degradation and coagulation disorders [10, 11]. Human ACT gene is located in the distal region of the long arm of chromosome 14q32.1 and belongs to a cluster of other serine protease inhibitor genes [12]. A variety of epidemiological studies have evaluated the role of ACT gene polymorphism in PICH patients. However, there were apparently conflicting results among these association studies. A study from Spain showed that ACT-TT genotype was associated with normotensive hemorrhagic stroke even after adjustment for age, gender, and vascular risk factors [10]. In contrast, a study from Asian population reported that ACT-TT genotype was
Meta-analysis for PICH risk and ACT Inclusion and exclusion criteria
associated with hypertensive PICH patients [13]. Other studies failed to find an association between ACT polymorphism and PICH [14-16]. It also remains unclear whether ethnicity and the location of hematoma affect the relationship between ACT polymorphism and PICH risk. To clarify the role of ACT polymorphism in PICH, we undertake a meta-analysis based on currently available literatures. Materials and methods Search strategy We performed computerized searches in PubMed, Cochrane database and Science Citation Index Expanded (SCIE) before 1 June 2015. The following terms were used: (“hemorrhage” OR “stroke” OR “hematoma”) AND (“antichymotrypsin” OR “SERPINA3” OR “ACT”) AND (“polymorphism” OR “genotype” OR “gene” OR “mutation” OR “allele” OR “variation” OR “variant”). When possible, we also utilized relevant Medical Subject Headings (MeSH) terms. In addition, the related articles options in PubMed and reference lists of all identified publications were checked to search for other potentially related articles. Our search was performed without language restrictions. Two authors of the present study independently performed entire literature search (S Chen and Y Chen).
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The primary studies included in our study met the following inclusion criteria: i) Casecontrol studies to examine the association of ACT polymorphism and PICH; ii) PICH was confirmed by computer tomography or magnetic resonance imaging; iii) Genotype distributions were available in both cases and controls. Exclusion criteria were as follows: i) The primary Figure 1. Flow diastudy did not report the gengram of study idenotype; ii) The study was not tification, inclusion, and exclusion. relevant to ACT polymorphism or PICH; iii) Studies with patients below 18 years of age; iv) Sibling pairs or family studies, animal studies, reviews or abstracts. For repeated or overlapped publications, we only included the most complete one. Data extraction Relevant information from each eligible primary study was abstracted by 2 independent investigators (S Chen and Y Chen). A standardized form was used for collecting relevant information. For each eligible study, the following data were collected: the first author’s last name, publication year, country, ethnicity, matching conditions, study design, genotype frequencies in cases and controls, and genotypic methods. Ethnicity was classified as Asian and European. The location of hematoma was categorized into lobar and non lobar. PICH patients were categorized into hypertensive and normotensive. Disagreements between investigators regarding data abstraction were resolved by discussion. When necessary, a third reviewer (Z Ye) helped to reach a consensus. In cases of mistaken data in eligible literatures or no detailed data reported, we contacted the original authors to obtain relevant information. Statistical analysis Hardy-Weinberg equilibrium (HWE) was assessed using Chi-square test in control groups. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were used to
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Meta-analysis for PICH risk and ACT Table 1. Main characteristics of selected studies in the meta-analysis First author
Year
Country
Ethnicity
Fu [13] Obach [10] Pera [15] Dardiotis [14] Somarajan [16]
2002 2001 2006 2008 2010
China Spain Poland Greece India
Asian European European European Asian
Study design CC CC CC CC CC
Control source HB HB HB PB HB
Matching criteria NG NG Sex and age Sex and age NG
Genotyping method PCR PCR PCR PCR PCR
NOS score 7 8 8 8 7
Note: CC, case-control; HB, hospital-based; PB, population-based; NG, not given; HWE, Hardy-Weinberg equilibrium; PCR, polymerase chain reaction; NOS, Newcastle-Ottawa Scale.
assess the strength of the relationship between ACT polymorphism and PICH risk. The significance of pooled ORs was assessed by Z-test. We conducted analysis for five genetic models: allele model (T vs. A), heterozygote model (TA vs. AA), homozygote model (TT vs. AA), dominant model (TT+TA vs. AA), and recessive model (TT vs. TA+AA). In order to control the false positive error rate, we used Bonferroni method to adjust for multiple comparisons. In our meta-analysis, we performed multiple comparisons for 30 times. P value less than 0.05/30 (0.0017) were considered to be statistically significant after Bonferroni correction. Q-test and I2-statistics were used to assess the heterogeneity between studies. If PQ ≤ 0.01 or I2 > 50%, the heterogeneity was considered as significant [17]. In the presence of heterogeneity, we used the random-effects model (Mantel-Haenszel method) to calculate the pooled ORs [18]; otherwise, the fixedeffects model (Mantel-Haenszel method) was applied [19]. To explore the sources of betweenstudy heterogeneity, stratified analyses were performed based on ethnicity, HWE, location of hematoma and PICH patients with or without hypertension. Sensitivity analysis was performed. We omitted one study each time to validate the reliability of the results. Modified Begg’s test and Egger’s linear regression test were performed to estimate publication bias [20]. All analyses were performed by means of STATA 12.0 (StataCorp, College Station, TA, USA). Two-sided values of P < 0.05 were considered to be statistically significant. Results
cations, 424 were irrelevant studies, 3 were abstracts, 1 was a review and 2 were overlapped cases. For overlapped publications, we only used the most complete one [10, 16, 21, 22]. For typographic errors in eligible literatures, we communicated with the authors and editors to obtain relevant information [16]. After all, a total of 5 studies fit the inclusion criteria in our meta-analysis [10, 13-16] (Figure 1). The clinical characteristics of the five eligible studies are not consistent The five eligible studies comprised a total of 744 patients with PICH and 940 controls. Among the five studies, four were hospitalbased case-control studies, and one was population-based case-control study. Two studies were focused on frequency-matched controls to cases by gender and age, while the other three studies did not indicate. In addition, three out the five studies were conducted in Europe and two in Asia (Table 1). Furthermore, four studies reported the distribution of ACT genotypes among hypertensive and normotensive PICH patients. Two studies reported genotype distributions in PICH patients according to the location of hematoma. The genotype distribution of the five selected studies in our metaanalysis is summarized in Table 2. In the study by Fu et al., the genotype distribution in control was out of HWE (P = 0.001), while the other four studies were in HWE. These data suggest that the clinical characteristics of the five eligible studies are not consistent.
A total of 5 studies are included for the metaanalysis after literature search of 512 articles
No statistical evidence of association between ACT polymorphism and PICH risk is observed in overall estimates
To find eligible literatures, we perform computerized searches in multiple databases after 1 June 2015. The initial search identified 512 entries. Among these, 77 were duplicate publi-
To assess the strength of the relationship between ACT polymorphism and PICH risk, pooled ORs with corresponding 95% CIs were used. No significant association between ACT polymor-
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Meta-analysis for PICH risk and ACT Table 2. Genotype distribution of selected studies in the meta-analysis First author
Year
Fu [13] Obach [10]
2002 2001
Pera [15]
2006
Dardiotis [14]
2008
Somarajan [16]
2010
Stratifications Overall Overall Hypertensive Normotensive Overall Hypertensive Normotensive Nonlobar Lobar Overall Hypertensive Normotensive Nonlobar Lobar Overall Hypertensive Normotensive
Sample size Case 220 99 66 33 95 81 14 52 43 147 114 33 98 49 183 126 57
Control 276 80
190
206
188
Genotype distribution (case) AA AT TT 29 103 88 36 37 26 27 24 15 9 13 11 26 44 25 24 36 21 2 8 4 13 28 11 13 16 14 42 68 37 31 57 26 11 11 11 29 45 24 13 23 13 72 97 14 48 69 9 24 28 5
Genotype distribution (control) AA AT TT 67 110 99 26 42 12
0.001 0.457
45
96
49
0.880
48
114
44
0.124
62
96
30
0.478
HWE (P)
Note: HWE, Hardy-Weinberg equilibrium.
phism and risk of PICH was observed in all genetic models (T vs. A: OR = 1.01, 95% CI = 0.80-1.28; TA vs. AA: OR = 0.93, 95% CI = 0.601.45; TT vs. AA: OR = 1.03, 95% CI = 0.59-1.80; TT+TA vs. AA: OR = 0.97, 95% CI = 0.65-1.46; TT vs. TA+AA: OR = 1.06, 95% CI = 0.72-1.57). There was remarkable between-study heterogeneity in all genetic models for overall analysis (Figure 2 and Table 3). The data indicate that no statistical evidence of association between ACT polymorphism and PICH risk is observed in overall estimates. No statistical evidence of association between ACT polymorphism and PICH risk is observed in subgroup analysis To know how ACT polymorphism is related to PICH risk in subgroups, ethnicity, location of hematoma and blood pressure were considered. In respect to ethnicity, the subgroup of Europeans showed no significant betweenstudy heterogeneity, and the heterozygote model (TA vs. AA) was marginally related with PICH susceptibility (OR = 0.70, 95% CI = 0.50-0.98, P = 0.040). However, the association no longer existed after Bonferroni correction. In Asian subgroup, significant heterogeneity still existed, and no significant association between ACT polymorphism and risk of PICH was observed. 20799
In the subgroup analysis according to HWE, we also noted borderline statistical significance in the heterozygote model (TA vs. AA) associated with PICH susceptibility (OR = 0.76, 95% CI = 0.58-0.99, P = 0.045), and no significant result was observed after Bonferroni correction. In addition, further subgroup analyses by location of hematoma and blood pressure were performed. No significant heterogeneity and association were noted between ACT polymorphism and PICH risk according to the location of hematoma. Similarly, no significant heterogeneity and association was observed between hypertensive and normotensive individuals (Table 3). These results suggest that no statistical evidence of association between ACT polymorphism and PICH risk is observed in subgroup analysis. The combined results of our meta-analysis are robust and reliable For sensitivity analysis, one study was removed at each time and the rest studies were analyzed to calculate the pooled ORs. Sensitivity analysis of allele model (T vs. A) showed that no significant alteration existed in the pooled Ors (Figure 3). Data for other models are not shown. This analysis suggests that the combined Int J Clin Exp Med 2015;8(11):20796-20804
Meta-analysis for PICH risk and ACT
Figure 2. Forest plots for relationship between ACT polymorphism and PICH risk in all models. A. Allele model (T vs. A); B. Homozygote model (TT vs. AA); C. Heterozygote model (TA vs. AA); D. Dominant model (TT+TA vs. AA); E. Recessive model (TT vs. TA+AA).
results of our meta-analysis are robust and reliable. No obvious publication bias exists in our metaanalysis To assess publication bias, Begg’s test and Egger’s regression test were utilized. In Begg’s 20800
regression test, P > 0.05 was observed in all genetic models (T vs. A: P = 0.72; TA vs. AA: P = 0.67; TT vs. AA: P = 0.43; TT+TA vs. AA: P = 0.93; TT vs. TA+AA: P = 0.77). In Egger’s regression test, P > 0.05 was also found in all genetic models (Table 4). The results suggest that no obvious publication bias exists in our metaanalysis. Int J Clin Exp Med 2015;8(11):20796-20804
Meta-analysis for PICH risk and ACT Table 3. Summary risk estimates for association between ACT polymorphism and PICH Comparisons
Stratifications
Studies (n)
T vs. A
Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location Overall In HWE Asians Europeans Hypertensive Lobar location
5 4 2 3 4 2 5 4 2 3 4 2 5 4 2 3 4 2 5 4 2 3 4 2 5 4 2 3 4 2
TT vs. AA
TA vs. AA
TT+TA vs. AA
TT vs. TA+AA
Pooled estimate OR (95% CI) 1.01 (0.80, 1.28) 0.91 (0.77, 1.07) 1.01 (0.54, 1.87) 1.00 (0.82, 1.22) 0.85 (0.64, 1.13) 1.12 (0.77, 1.62) 1.03 (0.59, 1.80) 0.84 (0.50, 1.40) 0.93 (0.19,4.58) 1.04 (0.70, 1.55) 0.66 (0.37, 1.19) 1.24 (0.60, 2.53) 0.93 (0.60, 1.45) 0.76 (0.58, 0.99) 1.36 (0.56, 3.32) 0.70 (0.50, 0.98) 1.05 (0.67, 1.64) 0.86 (0.56, 1.61) 0.97 (0.65, 1.46) 0.79 (0.61, 1.01) 1.26 (0.46, 3.43) 0.80 (0.58, 1.10) 0.93 (0.61, 1.41) 0.98 (0.55, 1.75) 1.06 (0.72, 1.57) 1.02 (0.59, 1.78) 0.75 (0.28, 2.01) 1.28 (0.92, 1.78) 0.67 (0.41, 1.10) 1.36 (0.75, 2.47)
PZ 0.911 0.251 0.983 0.998 0.265 0.556 0.914 0.501 0.925 0.841 0.166 0.563 0.748 0.045 0.501 0.040 0.840 0.634 0.890 0.061 0.654 0.166 0.720 0.945 0.757 0.938 0.568 0.144 0.113 0.306
Heterogeneity I (%) PQ 2
62.3% 16.1% 89.8% 0.0% 0.0% 0.0% 72.0% 52.4% 92.3% 0.0% 0.0% 0.0% 70.2% 0.0% 85.7% 0.0% 28.2% 10.1% 69.2% 0.0% 89.9% 0.0% 20.5% 0.0% 60.9% 69.5% 85.0% 0.0% 0.0% 0.0%
0.031 0.311 0.002 0.720 0.517 0.955 0.006 0.098 0.000 0.556 0.767 0.944 0.009 0.844 0.008 0.881 0.243 0.292 0.011 0.989 0.002 0.960 0.287 0.494 0.037 0.020 0.010 0.370 0.935 0.437
Note: ACT, Alpha-1 antichymotrypsin; PICH, primary intracerebral hemorrhage; OR, odds ratio; CI, confidence interval; PZ, P value for Z test; PQ, P value for heterogeneity; HWE, Hardy-Weinberg equilibrium.
Discussion PICH is known to be a multifactorial disease. Accumulating evidence shows that genetic elements contribute to the pathogenesis of PICH [23]. Recently, a variety of studies have been performed to evaluate the association between ACT polymorphism and the risk of PICH. However, the results remain controversial. Since a single study has a relatively small number of participants with low power to detect the effect of gene polymorphism on PICH, a metaanalysis may be an appropriate approach to obtain a more definitive conclusion [24]. There-
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fore, we designed this meta-analysis to derive a more convinced conclusion for the association between ACT polymorphism and PICH risk. In this meta-analysis, a total of five eligible studies involving 744 PICH cases and 940 controls were included. No statistically significant evidence of an association between ACT polymorphism and PICH risk was found in the overall study population for all genetic models. Despite negative results of our study, we still cannot exclude the possible role of ACT polymorphism in PICH. As an inhibitor of proteases, ACT gene might prevent the degradation of ex-
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Meta-analysis for PICH risk and ACT
Figure 3. Sensitivity analysis for allele model (T vs. A). Only one study was removed at each time, and the rest studies were analyzed to calculate the pooled ORs.
performed stratification analysis. However, we failed to effectively eliminate heterogeneity in all subgroups. After excluding the study deviating from HWE in controls [13], moderate heterogeneity was still found in homozygote and recessive models, but not in allele, heterozygote or dominant models. The study deviating from HWE in controls [13] was the main source of between-study heterogeneity in the three models. Significant heterogeneity was noted in all genetic models in Asian subgroup, but not in European subgroup, suggesting that environment and ethnic differences may play a role [30].
In subgroup analysis, no statistically significant association between ACT polymorphism and PICH risk was Egger’s test Begg’s test Comparisons found in all genetic models. The negat value P value 95% CI P value Z value tive result may be due to the limited T vs. A 0.40 0.72 (-18.37, 14.28) 1.00 -0.24 number of eligible studies with availTA vs. AA 0.47 0.67 (-23.56, 17.63) 0.81 0.24 able data. In our study, only two eligiTT vs. AA 0.92 0.43 (-21.73, 11.98) 0.46 0.73 ble studies reported genotype distriTT+TA vs. AA 0.10 0.93 (-23.37, 21.98) 0.46 0.73 butions in PICH patients according to TT vs. TA+AA 0.32 0.77 (-11.08, 9.03) 0.81 0.24 the location of hematoma. Limited Note: ACT, Alpha-1 antichymotrypsin; PICH, primary intracerebral hemornumber of eligible studies had low rhage; CI, confidence interval. power to detect slight effects or might generate fluctuating risk estimates. Therefore, more studies with large sample size tracellular matrix in vessel wall [15]. ACT polyare required. morphism might be in linkage disequilibrium with other functional mutations of ACT gene, In this meta-analysis, search strategy and and there are various frequencies of these study criteria were conducted strictly. In order mutations among different races, where sigto reduce false positive error rate, we used nificant ethnic differences in the distribution Bonferroni method to adjust for multiple comof genotypes exist [25, 26]. In addition, high parisons. In addition, we performed ‘leave-onebetween-study heterogeneity is found in the out’ sensitive analysis to calculate the pooled overall study population for all genetic models. ORs. Sensitivity analysis demonstrated that the In meta-analysis for genetic association studcombined results of our meta-analysis were ies, between-study heterogeneity is common stable and reliable. In addition, no significant [27]. Therefore, stratification analysis was perpublication bias for ACT polymorphism in the formed to explore the sources of heterogeneifive hereditary models was found according to ty. Begg’s test and Egger’s regression test. Considering that differences in ethnicity, diversity in design, characteristics of the subjects To better interpret the results, some limitations and clinical heterogeneity may contribute to of our present meta-analysis should be noted. common sources of heterogeneity [28, 29], we First, the sample size in individual included Table 4. Publication bias tests for association between ACT polymorphism and PICH
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Meta-analysis for PICH risk and ACT studies and the number of included studies in our meta-analysis were relatively small. Second, PICH is a heterogeneous disease, and potential interactions between environmental factors and other genes should be evaluated. In the present meta-analysis, we only focused on ACT gene. It is possible that the potential roles of ACT gene are enhanced by other gene-environment or gene-gene interactions. In conclusion, the present meta-analysis suggests that ACT polymorphism is unlikely to be associated with genetic susceptibility of PICH based on currently published studies. Considering the limitations of the present meta-analysis, more welldesigned studies with large sample sizes are still needed in the future. Acknowledgements We thank Dr. Niange Xia and Dr. Jia Li for their helpful suggestions. This work was supported by funding from the Wenzhou Science & Technology Bureau (No. Y20140531). Disclosure of conflict of interest None. Address correspondence to: Dr. Xiaoya Huang, Department of Neurology, Wenzhou Central Hospital & Dingli Clinical Institute of Wenzhou Medical University, No. 32 Dajian Lane, West Riverside Road, Wenzhou 325000, Zhejiang Province, P. R. China. Tel: 86-15057557216; Fax: 86-577-55579365; E-mail: [email protected]
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