Journal of the Neurological Sciences 338 (2014) 3–11

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Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns

Review article

Association between phosphodiesterase 4D polymorphism SNP83 and ischemic stroke Yan Yan a,1, Xiuping Luo b,1, Jinlu Zhang b,1, Li Su c, Wenjie Liang c, Guifeng Huang c, Guangliang Wu a, Guihua Huang d,⁎, Lian Gu a,⁎⁎ a

Department of Neurology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, Guangxi, China School of Basic Medicine, Guangxi Medical University, Nanning, Guangxi, China School of Public Health, Guangxi Medical University, Nanning, Guangxi, China d First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, Guangxi, China b c

a r t i c l e

i n f o

Article history: Received 12 September 2013 Received in revised form 27 November 2013 Accepted 4 December 2013 Available online 12 December 2013 Keywords: PDE4D Polymorphism SNP83 Ischemic stroke Association Meta-analysis

a b s t r a c t Iceland scientists identified the relationship between the PDE4D gene and ischemic stroke (IS) in 2003. Since then, many researches have emerged to estimate this association, but the results are contradictory. In order to confirm this association, we conduct this meta-analysis with a larger sample size. PubMed, Embase and four Chinese databases were searched to identify the relevant studies through January 2013. The odds ratio (OR) with 95% confidence interval (CI) was used to calculate the association between the SNP83 polymorphism and IS risk. Twenty-five publications comprised of 8878 cases and 12 306 controls were included in this metaanalysis. There was a significant association between SNP83 and IS risk, especially in Asian and Chinese populations, but not in Caucasians (dominant model: OR = 0.87, 95% CI = 0.69–1.11; recessive model: OR = 0.95, 95% CI = 0.84–1.07; allele model: OR = 0.95, 95% CI = 0.84–1.08; co-dominant model 1: OR = 0.96, 95% CI = 0.85–1.08; co-dominant model 2: OR = 0.95, 95% CI = 0.83–1.09). The cumulative meta-analysis among the overall population and Chinese population indicated a stable trend of association between SNP83 and IS from 2009 to 2012. In conclusion, we found an association between SNP83 and IS in the overall population and in the Asian and Chinese populations, but not among Caucasians. © 2013 Elsevier B.V. All rights reserved.

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . Materials and methods . . . . . . . . . . . . . 2.1. Literature search . . . . . . . . . . . . 2.2. Selection criteria . . . . . . . . . . . . 2.3. Data extraction . . . . . . . . . . . . . 2.4. Statistical analysis . . . . . . . . . . . . 3. Results . . . . . . . . . . . . . . . . . . . . 3.1. Literature search . . . . . . . . . . . . 3.2. Study characteristics . . . . . . . . . . . 3.3. Meta-analysis results and subgroup analysis 3.4. Heterogeneity exploration . . . . . . . . 3.5. Publication bias . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . Conflict of interest . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .

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⁎ Correspondence to: G. Huang, First Affiliated Hospital, Guangxi University of Chinese Medicine, 89-9 Dongge Road, Nanning, Guangxi, China. Tel.: +86 0771 5848435; fax: +86 0771 5350823. ⁎⁎ Correspondence to: L. Gu, First Affiliated Hospital, Guangxi University of Chinese Medicine, 89-9 Dongge Road, Nanning, Guangxi, China. E-mail addresses: [email protected] (G. Huang), [email protected] (L. Gu). 1 Co-first author. 0022-510X/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jns.2013.12.012

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1. Introduction Stroke is one of the most common central nervous system diseases in the world and may cause death or permanent disability in a few minutes [1]. The World Health Organization (WHO) reported that stroke

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Y. Yan et al. / Journal of the Neurological Sciences 338 (2014) 3–11

caused about 5.7 million deaths in 2005, and half of these deaths occurred in Asian individuals [2]. It has been confirmed that 85% of strokes were ischemic stroke (IS) [3] and their development was a result of the interaction of multiple factors, such as genetic variants, chronic diseases and inflammation [4]. Among these, hypertension, glycuresis, adiposity and heavy smoking have a significant promoting effect on ischemic stroke, while inflammation-related atherosclerosis is one of the key causes of stroke [5,6]. In addition, family and twin studies have shown that genetic factors are the key predisposing factors of IS [7,8]; however, until now, the genetic pathogenesis of stroke remains unclear. The phosphodiesterase 4D (PDE4D) gene is located at chromosomal location 5q12, and has 8 splice variants, 22 exons and hundreds of SNPs [8,9]. It encodes cAMP-specific 3′,5′-cyclic phosphodiesterase 4D, which plays an important role in the degradation of cyclic adenosine monophosphate (cAMP) [10]. The proliferation and migration of vascular smooth muscle cells and macrophages is responsible for atherosclerosis, and cAMP is involved in this process [11–13]. Animal tests have confirmed that increased cAMP levels can inhibit the proliferation of vascular smooth muscle cells after arterial injury [14,15]. The inhibitors of PDE4 could increase cAMP levels [16,17] and decrease the migration of vascular smooth muscle cells [18]. It is possible, therefore, that the PDE4D gene works by accommodating the levels of cAMP in the pathogenesis of atherosclerosis, thus playing a central role in the pathogenesis of IS [8,14]. The relationship between the PDE4D gene and risk of IS was not identified until the Iceland scientists Gretarsdottir et al. [8] implemented a GWAS in 2003. It reported that six SNPs (SNP45, SNP41, SNP83, SNP87, SNP89 and SNP56) in the PDE4D gene were associated with the risk of stroke in Caucasian, and SNP83 (rs966221) was significantly associated with carotid stroke. Since 2003, many studies have emerged that are focused on the SNP83 polymorphism and the risk of IS; however, the results are contradictory. Munshi et al. [10], Li et al. [19] and Saleheen et al. [20] stated that SNP83 was associated with IS, but Milton et al. [21] and Matsushita et al. [22] hold a contrary opinion. To date, 4 meta-analyses have reported the relationship between SNP83 and the risk of IS. Bevan et al. [23] included 16 studies and performed a series of meta-analyses (involving SNP26, SNP45, SNP56, SNP83, SNP87 and SNP89), but none of the meta-analyses could replicate the results of Gretarsdottir et al. [8]. Domingues-Montanari et al. [24] also failed to obtain a positive relationship between SNPs in the PDE4D gene and IS in the Iberian population. Interestingly, Xu et al. [25] and Yoon et al. [26] reported an association between SNP83 and IS in Asian populations. However, we could not rule out the possibility that sample size, genetic background and other factors contributed to these conflicting results. Following the study by Yoon et al., the SNP83 polymorphism and the risk of IS have remained a focus for research. However, the results are still contradictory and there have been no subgroup analyses exploring the risk of IS and SNP83 polymorphism, such as according to ethnicity, sources of controls and Hardy–Weinberg equilibrium (HWE) status. In order to investigate the association between SNP83 and the risk of IS clearly and with a more powerful effect, we conducted this metaanalysis based on a larger sample size. Subgroup analyses according to ethnicity (Asian, Caucasian and Chinese), HWE status and source of controls were subsequently performed in the present meta-analysis. At the same time, we also performed a cumulative meta-analysis to estimate the trend in OR by descending order of publication year. 2. Materials and methods 2.1. Literature search A systematic search was carried out to identify all studies regarding the association of SNP83 within the PDE4D gene and the risk of IS for all years until January 2013 in PubMed, Embase, Chinese Wanfang, Chongqing VIP database, Chinese Biological Medical Literature database (CBM) and Chinese National Knowledge Infrastructure database (CNKI).

The keywords used for the searches were as follows: “ischemic infarction OR cerebrovascular disease OR stroke OR cerebrovascular disorders OR ischemic stroke” in combination with “PDE4D OR phosphodiesterase 4D” in combination with “polymorphism OR variant OR mutation”. Our search was limited to full-text papers published in Chinese or English. In addition, we also performed a manual search of the reference lists for all related articles that were in agreement with the inclusion criteria. In addition, if the genotype data for the SNP83 polymorphism were not offered in the original studies, we contacted the author via email to obtain full data for the meta-analysis.

2.2. Selection criteria The studies were required to be in agreement with the following inclusion criteria: (a) human-based investigations, (b) case–control studies or cohort studies regarding the relationship between SNP83 within the PDE4D gene and the risk of IS, (c) patients clinically diagnosed with IS, and (d) data provided for genotype frequencies or allele frequencies in both cases and controls. The exclusion criteria were as follows: (a) patients younger than 18 years old, (b) patients with transient ischemic attack (TIA) or hemorrhagic stroke diagnosis, (c) evaluation of quantitative or intermediate phenotypes exclusively, (d) studies in which neither genotype frequencies nor allele frequencies were reported, and (e) overlapping publications (where the smaller dataset was discarded).

2.3. Data extraction Two investigators (X.P. Luo and J.L. Zhang) independently extracted the following data from the included publications: name of first author, year of publication, original country, ethnicity, diagnostic criteria for patients, source of controls, matching conditions, sample size of genotyped cases and controls, genotyping methods, genotype distributions and allele frequencies. The extracted data for every item were compared and came to a consensus via discussion. 2.4. Statistical analysis First of all, we estimated whether the distributions of controls were in accordance with HWE with the Pearson test, and it was considered to be statistically significant when the P value was lower than 0.05, in which case the study was considered to not be in HWE. The strength of significant association between the SNP83 polymorphism and susceptibility to IS in the overall population was tested by calculating odds ratios (ORs), together with 95% confidence intervals (CIs). The pooled ORs were calculated for the dominant model (CC + CT vs. TT), recessive model (CC vs. CT + TT), co-dominant model (co-dominant model 1: CC vs. CT, co-dominant model 2: CT vs. TT) and allelic model (C vs. T). Heterogeneity among studies was assessed via Q-test and I2 statistics. If I2 b 50%, this indicated that no statistically significant heterogeneity existed among studies and the fixed effect model (Mantel–Haenszel method) was selected. Alternatively, the random effect model was employed. Potential publication bias was assessed by Begg's funnel plot [27] and the P value of Begg's and Egger's tests (P b 0.05 was considered statistically significant). Cumulative meta-analysis was carried out for five genetic models to estimate the trend in OR by descending order of publication year [28]. In addition, subgroup analyses according to ethnicity (Asian and Caucasian), HWE status, and sources of controls were subsequently performed in the present metaanalysis. Furthermore, an analysis merely including studies of Chinese populations was also carried out to assess the association between SNP83 and IS among Chinese individuals separately. All statistical analyses were performed with software STATA version 11.0 (StataCorp, College Station, TX, USA).

Y. Yan et al. / Journal of the Neurological Sciences 338 (2014) 3–11

3. Results

5

there were only allele frequencies and OR values offered in the other 3 independent investigations [21,33,35]; also, we did not obtain responses from the authors within 2 weeks of contacting them via email.

3.1. Literature search A flow chart of the literature search is presented in Fig. 1. A total of 195 studies were identified in the initial search using the keywords and subject terms. After removing duplicates and reading the titles and abstracts, 35 potential articles remained for full-text review. Ten studies were further excluded for the following reasons: not about SNP83 (n = 8); no data offered and author unreachable (n = 1); or overlapping publication (n = 1). As a result, 25 studies were included in the final meta-analysis [5,8,10,19–22,29–46]. 3.2. Study characteristics As shown in Table 1, the studies included 8878 cases and 12306 controls, and were published between 2003 and 2012. Among them, one publication [37] consisted of two independent studies, and another publication [22] contained three independent studies. Therefore, a total of 28 independent studies were included in our meta-analysis. Almost all IS patients among the included studies had been confirmed by clinical features and/or neuroimaging, except for two studies. One underwent a complete neurological examination [20], and the other was diagnosed according to ICD-9 [39]. There were 16 studies of Asian populations and 9 studies of Caucasians. The sources of controls used were as follows: hospital (n = 12); population (n = 12); and hospital or population (n = 1). In 22 independent studies, the controls were matched to cases by age, sex and ethnicity; detailed information was not available in the 6 remaining studies. The genotype distributions among the controls of all studies were in HWE, except for 4 studies. As shown in Table 2, of the eligible studies included, 25 independent studies presented complete data for genotype distributions, whereas

3.3. Meta-analysis results and subgroup analysis We conducted a meta-analysis for 28 independent studies, the results of which are shown in Table 3. In the overall population, the significant association between SNP83 and IS was observed in the dominant model, recessive model, co-dominant model 1, co-dominant model 2 and allele contrast model (OR = 1.15, 95% CI = 1.02–1.30; OR = 1.21, 95% CI = 1.02–1.42; OR = 1.15, 95% CI: = 1.05–1.25; OR = 1.12, 95% CI = 1.04–1.21 and OR = 1.19, 95% CI = 1.06–1.33 (Fig. 2), respectively). There were 19 independent investigations of Asians which were comprised of 5535 cases and 8730 controls, and 9 independent investigations were conducted among Caucasian populations including 3475 cases and 3810 controls. Subgroup analysis based on ethnicity indicated that there was a significant association between SNP83 and IS in Asians (dominant model: OR = 1.28, 95% CI = 1.13–1.44; recessive model: OR = 1.48, 95% CI = 1.22–1.79; co-dominant model 1: OR = 1.43, 95% CI = 1.25–1.64; co-dominant model 2: OR = 1.20, 95% CI = 1.07–1.35; and allele model: OR = 1.35, 95% CI = 1.16–1.57). However, we did not replicate the association in Caucasian populations (dominant model: OR = 0.87, 95% CI = 0.69–1.11; recessive model: OR = 0.95, 95% CI = 0.84–1.07; co-dominant model 1: OR = 0.96, 95% CI = 0.85–1.08; co-dominant model 2: OR = 0.95, 95% CI = 0.83–1.09; and allele model: OR = 0.95, 95% CI = 0.84–1.08). Furthermore, a statistically significant relationship between SNP83 and IS was witnessed only in the hospital-based subgroup when classified according to the sources of controls (Table 3).

Relevant studies identified and screened according to key searching terms (n=195)

Studies after duplicates removed (n=144) Excluded by title or abstract (n=110): Non-human studies (n=28); Not ischemic stroke(n=25); Not about relationship (n = 32); Meta-analysis or review (n=25)

Full-length articles assessed for eligibility (n=34) Excluded (n=10): Not about SNP83 (n=8); Overlapped publish (n=1) Reports finally included in meta-analysis (n=25) Fig. 1. Flow diagram of the literature search.

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Y. Yan et al. / Journal of the Neurological Sciences 338 (2014) 3–11

Table 1 The characteristics of all eligible studies in this meta-analysis. Reference

Year

Country

Sample size

Diagnostic criteria

Cases Controls Cases Gretarsdottir et al.

2003 America

510

349

CT

Saleheen et al.

2005 Pakistan

200

250

Meschia et al.

2005 America

358

256

Complete neurological examination CT or MRI

van Rijn et al.

2005 Netherlands

88

188

CT or MRI

Staton et al. Song et al. Woo et al. Nakayama et al.

2006 2006 2006 2006

Australia America America Japan

151 224 357 208

164 211 303 270

CT ICD-9 Neuroimaging CT or MRI

Kuhlenbaumer et al. 2006 Germany Lin et al. 2007 Taiwan (China) Lin et al. 2007 Taiwan (China) Banerjee et al. 2008 India

1181 96

1569 191

71

20

176

212

CT or MRI Clinical signs and CT or MRI Clinical signs and CT or MRI CT or MRI

Xu et al.

2008 China

232

110

CT or MRI

Xue et al.

2009 China

639

887

CT or MRI

Sun et al.

2009 China

649

761

Munshi et al.

2009 India

250

250

CT or MRI and WHO criteria CT or MRI

Li et al.

2009 China

371

371

CT or MRI

Quarta et al.

2009 Italy

294

235

Matsushita et al.

2009 Japan

367

367

Matsushita et al.

2009 Japan

355

1786

Clinical symptoms and CT or MRI Clinical features and CT or MRI CT or MRI

Matsushita et al.

2009 Japan

17

1576

CT or MRI

Zhang et al.

2009 China

122

44

Kalita et al.

2011 India

145

188

Clinical features, CT or MRI MRI

Milton et al.

2011 Australia

180

301

CT

Cheng et al.

2011 China

280

258

CT or MRI

Wang et al.

2012 China

235

105

DSA or CTA or MRA

Li et al.

2012 China

440

486

Zhao et al.

2012 China

682

598

WHO criteria and CT or MRI CT or MRI

Controls The stroke-free participants were recruited from the relatives of the patients. All controls randomly selected from local population free of stroke.a A total of 256 controls randomly selected from spouses and unrelated friends of the patients. Individuals who were living in the same isolated population without a history of stroke or TIA were selected as controls.a 164 controls were free of neurological diseases.a 211 control subjects were free of history of stroke.a Controls are identified by random digit dialing methods.a Control subjects were selected from among the outpatients at the same hospital.a 1569 subjects without a history of stroke served as controls.a Stroke-free participants were recruited from the general population. Stroke-free participants were recruited from the general population. Healthy controls were without any history or occurrence of cerebrovascular or cardiovascular diseases.a A total of 110 controls from the hospitalized patients without cerebral embolism and history of cerebral hemorrhage.a All controls were free of neurological diseases.a

The individuals who have a health check-up in the hospital during the same period were selected as controls.a 250 healthy individuals were recruited from the same demographic area.a 371 control subjects were free of cerebrovascular or cardiovascular diseases.a 235 unrelated control subjects were hospitalized patients without cerebrovascular and cardiovascular disease. A total of 367 control subjects were selected from the participants of the Hisayama screening survey.a 1786 control subjects were selected from the subjects who were registered with BioBank Japan for other diseases.a A cohort population which free of neurological diseases were followed up for 14 years until the occurrence of cardiovascular disease or death. All controls were hospitalized patients without cerebrovascular disease.a All controls randomly selected from unrelated individuals free of neurological diseases.a All controls randomly selected from unrelated individuals free of neurological diseases.a All unrelated controls free of cerebrovascular disease or ischemic attack were selected from the same hospital.a The controls were free of stroke which had performed a physical examination in hospital.a All controls had performed a physical examination in hospital and without stroke.a All control subjects were free of neurological diseases.a

Genotyping methods

Source of controls

PCR

Hospital

PCR

Population

PCR

Hospital

PCR

Population

No mention PCR TaqMan TaqMan

Population Population Population Hospital

TaqMan TaqMan

Population Population

TaqMan

Population

PCR

Population

PCR

Hospital

PCR

PCR

Hospital and population Hospital

PCR

Population

PCR

Hospital

PCR

Hospital

TaqMan

Population

TaqMan

Population

TaqMan

Population

PCR–LDR

Hospital

PCR

Population

PCR

Population

PCR

Hospital

PCR

Hospital

PCR

Hospital

PCR

Hospital

CT, Computed Tomography; MRI, Magnetic Resonance Imaging; TOAST-3, Trial of Org10172 in Acute Stroke Treatment-3th; DSA, Digital Subtraction Angiography; CTA, Clinical Trial Agreement; MRA, Magnetic Resonance Angiography; PCR, Polymerase Chain Reaction; ICD-9, International Classification of Diseases—9th. a Cases and controls were age-matched or sex-matched and ethnically matched.

Among all of the included studies, 21 independent studies were in HWE and another 4 independent studies were not, with 17 847 and 3703 participants included, respectively. In the stratified analysis by HWE status, it was suggested that SNP83 was obviously associated with IS risk in populations within HWE for four genetic models (recessive model: OR = 1.22, 95% CI = 1.03–1.44; co-dominant model 1: OR = 1.20, 95% CI = 1.05–1.39; co-dominant model 2: OR = 1.09, 95% CI = 1.01–1.18; and allele model: OR = 1.12, 95% CI = 1.02–1.23). The same result was found in the group which was not in HWE for codominant model 2 (OR = 1.30, 95% CI = 1.08–1.56). A subsequent meta-analysis that only consisted of Chinese populations and contained 7648 subjects from 11 independent studies was

performed; this showed that the association between SNP83 and IS was obvious in all models (dominant model: OR = 1.32, 95% CI = 1.19–1.45; recessive model: OR = 1.47, 95% CI = 1.26–1.70; codominant model 1: OR = 1.34, 95% CI = 1.15–1.58; co-dominant model 2: OR = 1.26, 95% CI = 1.14–1.39; and allele model: OR = 1.43, 95% CI = 1.17–1.75). As shown in Fig. 3, the cumulative meta-analysis for the allele model for the overall population indicated a stable trend of association between SNP83 and the risk of IS from 2009 to 2012, with a pooled OR ranging from 1.16 to 1.19 and with 95% CI ranging from 1.01 to 1.35. In Chinese populations, a significant association between SNP83 variants and the risk of IS was also observed from 2009 to 2012 via the

Y. Yan et al. / Journal of the Neurological Sciences 338 (2014) 3–11

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Table 2 Distribution of SNP83 (rs966221) genotype and allele between cases and controls. Study

Gretarsdottir et al. Saleheen et al. Meschia et al. van Rijn et al. Nakayama et al. Woo et al. Song et al. Staton et al. Lin et al. Lin et al. Banerjee et al. Xu et al. Sun et al. Matsushita et al. Matsushita et al. Matsushita et al. Quarta et al. Munshi et al. Xue et al. Li et al. Zhang et al. Cheng et al. Kalita et al. Milton et al. Wang et al. Li et al. Zhao et al. Kuhlenbaumer et al.

Year

2003 2005 2005 2005 2006 2006 2006 2006 2007 2007 2008 2008 2009 2009 2009 2009 2009 2009 2009 2009 2009 2011 2011 2011 2012 2012 2012 2006

Country

Cases

America Pakistan America Netherlands Japan America America Australia Taiwan (China) Taiwan (China) India China China Japan Japan Japan Italy India China China China China India Australia China China China Germany

Controls

P value for HWE

CC

CT

TT

C

T

CC

CT

TT

C

T

167 55 55 34 / 108 40 43 2 4 46 8 40 0 7 4 / 26 40 117 7 12 54 / 16 48 210 434

249 96 139 37 / 170 93 68 22 29 81 92 223 4 79 84 / 124 213 173 46 102 64 / 82 182 320 546

94 47 164 17 / 68 59 39 47 63 49 132 385 13 269 279 / 100 386 81 69 166 27 / 137 210 152 179

583 206 249 105 62 386 173 154 26 37 173 108 303 4 93 92 317 176 293 407 184 126 172 202 114 278 740 1414

437 190 467 71 354 306 211 146 116 155 179 356 993 30 617 642 217 324 985 335 60 434 118 158 356 602 624 904

98 49 46 70 / 97 45 61 2 11 38 6 35 36 42 7 / 5 29 76 4 12 38 / 4 39 138 595

167 139 120 86 / 134 93 75 4 47 110 29 230 383 436 91 / 100 255 197 10 94 92 / 25 170 310 700

84 69 90 32 / 46 48 28 14 133 64 75 496 1157 1308 269 / 145 603 98 30 152 58 / 76 277 150 254

363 237 212 226 62 328 183 197 8 69 186 41 300 455 520 105 252 110 313 364 18 118 168 346 33 248 586 1890

335 277 300 150 478 226 189 131 32 313 238 179 1222 2697 3052 629 218 390 1461 378 70 398 208 256 177 724 610 1208

0.4389 0.1569 0.5869 0.5270 / 0.9807 0.9972 0.5492 0.0935 0.0197 0.4353 0.1705 0.2133 0.5193 0.4298 0.828 / 0.0089 0.7485 0.2054 0.0454 0.5984 0.8901 / 0.2998 0.0789 0.3629 0.0484

cumulative meta-analysis for the recessive model (data not shown). The results of the study by Zhang et al. [42] in 2009 increased the extent of the 95% CI. However, the extent of the 95% CI was gradually narrowed with the increased number of studies and sample size, and was higher than 1.00 from 2009, which suggests a stable trend of association between SNP83 and the risk of IS in the Chinese population.

the Caucasian population, heterogeneity existed in the dominant model (I2 = 62.9%) and the allele model (I2 = 65.6%). A further analysis of heterogeneity in the Chinese population showed that there was no heterogeneity in most genetic models (I2 b 50%), and heterogeneity was found in the allele model (I2 = 83.8%). The above data are shown in Table 3.

3.4. Heterogeneity exploration

3.5. Publication bias

Significant heterogeneity between studies in the overall population was found in four genetic models (I2 N 50%), but it was not observed in co-dominant model 2 (I2 = 43.0%). Analysis of Asian populations indicated that there was heterogeneity in the allele model (I2 = 80.4%), but no heterogeneity was observed in the other models (I2 b 50%). In

Publication bias was qualitatively estimated by funnel plots and quantitatively examined by Begg's and Egger's tests. Visual inspection of the Begg's funnel plot did not reveal any evidence of obvious asymmetry for any of the models, both in the overall population (Fig. 4) and in the Chinese population. Also, no significant bias was shown by

Table 3 Summary of comparative results. Variables

Total Subgroup by HWE Yes No Subgroup by ethnicity Asian HWE—Yes HWE—No Caucasian HWE—Yes HWE—No China HWE—Yes HWE—No Subgroup by controls Hospital Population Mix

CC + CT vs. TT

CC vs. CT + TT

CC vs. CT

CT vs. TT

C vs. T

OR (95% CI)

I2 (%)

OR (95% CI)

I2 (%)

OR (95% CI)

I2 (%)

OR (95% CI)

I2 (%)

OR (95% CI)

I2 (%)

1.15 (1.02–1.30)

61.6

1.21 (1.02–1.42)

62.9

1.15 (1.05–1.25)

50.0

1.12 (1.04–1.21)

48.4

1.19 (1.06–1.33)

81.7

1.23 (1.09–1.39) 1.45 (0.95–2.24)

60.5 70.6

1.22 (1.03–1.44) 1.14 (0.40–3.29)

53.7 79.8

1.20 (1.05–1.39) 0.93 (0.33–2.60)

31.1 75.8

1.09 (1.01–1.18) 1.30 (1.08–1.56)

46.5 52.1

1.12 (1.02–1.23) 2.13 (0.91–4.98)

66.3 96.2

1.20 (1.13–1.44) 1.22 (0.98–1.26) 1.90 (1.40–2.58) 0.87 (0.69–1.11) 0.86 (0.69–1.07) 0.79 (0.64–0.98) 1.32 (1.19–1.45) 1.31 (1.19–1.45) 1.49 (0.81–2.72)

46.2 43.7 0.0 63.3 35.1 / 0.0 0.0 0.0

1.48 (1.22–1.79) 1.52 (1.32–1.74) 1.88 (0.91–3.87) 0.95 (0.84–1.07) 0.94 (0.80–1.11) 0.96 (0.82–1.12) 1.47 (1.26–1.70) 1.50 (1.28–1.74) 0.49 (0.16–1.41)

39.0 17.9 82.7 8.2 23.1 / 22.1 2.0 0.0

1.43 (1.25–1.64) 1.41 (1.22–1.63) 0.77 (0.10–5.74) 0.96 (0.85–1.08) 0.98 (0.83–1.17) 0.96 (0.85–1.08) 1.34 (1.15–1.58) 1.38 (1.17–1.62) 0.31 (0.09–1.01)

36.9 15.4 82.1 0.0 0.0 / 39.9 22.9 0.0

1.20 (1.10–1.30) 1.16 (1.06–1.27) 1.82 (1.32–2.51) 0.95 (0.83–1.09) 0.86 (0.72–1.03) 1.11 (0.89–1.38) 1.26 (1.14–1.39) 1.24 (1.12–1.38) 1.88 (0.96–3.69)

35.5 31.3 0.0 52.7 48.1 / 8.1 14.9 0.0

1.35 (1.16–1.57) 1.22 (1.10–1.35) 2.8 (0.74–10.66) 0.95 (0.84–1.08) 0.89 (0.74–1.07) 1.00 (0.90–1.12) 1.43 (1.17–1.75) 1.27 (1.18–1.37) 3.34 (0.27–42.1)

80.4 52.5 94.3 61.3 66.6 / 83.8 0.0 95.6

1.24 (1.04–1.48) 1.05 (0.88–1.26) 1.39 (1.13–1.72)

62.0 58.3 /

1.29 (1.08–1.54) 1.13 (0.87–1.45) 1.98 (1.21–3.22)

31.4 67.0 /

1.27 (1.10–1.47) 1.05 (0.93–1.18) 1.65 (0.99–2.75)

30.1 54.5 /

1.17 (1.05–1.31) 1.03 (0.93–1.15) 1.30 (1.04–1.63)

61.3 30.6 /

1.37 (1.13–1.66) 1.05 (0.91–1.21) 1.39 (1.16–1.66)

86.1 71.7 /

OR, odds ratio; CI, confidence interval; I2, inconsistency index; HWE, Hardy–Weinberg equilibrium.

8

Y. Yan et al. / Journal of the Neurological Sciences 338 (2014) 3–11

Study ID Caucasian Gretarsdottir et al. (2003) Meschia et al. (2005) van Rijn et al. (2005) Woo et al. (2006) Kuhlenbaumer et al. (2006) Song et al. (2006) Staton et al. (2006) Quarta et al. (2009) Milton et al. (2011) Subtotal (I-squared = 61.3%, p = 0.008) . Asian Saleheen et al. (2005) Nakayama et al. (2006) Lin et al. (2007) Lin et al. (2007) Banerjee et al. (2008) Xu et al. (2008) Sun et al. (2009) Matsushita et al. (2009) Matsushita et al. (2009) Matsushita et al. (2009) Munshi et al. (2009) Xue et al. (2009) Li et al. (2009) Zhang et al. (2009) Cheng et al. (2011) Kalita et al. (2011) Wang et al. (2012) Li et al. (2012) Zhao et al. (2012) Subtotal (I-squared = 80.4%, p = 0.000) . Overall (I-squared = 81.7%, p = 0.000)

OR (95% CI)

% Weight

1.23 (1.01, 1.49) 0.75 (0.60, 0.95) 0.98 (0.68, 1.41) 0.87 (0.69, 1.09) 1.00 (0.90, 1.12) 0.85 (0.64, 1.13) 0.70 (0.51, 0.96) 1.26 (0.98, 1.62) 0.95 (0.73, 1.23) 0.95 (0.84, 1.08)

4.29 4.06 3.27 4.10 4.67 3.75 3.56 3.96 3.88 35.54

1.27 (0.97, 1.65) 1.35 (0.93, 1.97) 0.90 (0.37, 2.17) 1.08 (0.70, 1.69) 1.24 (0.93, 1.64) 1.32 (0.89, 1.98) 1.24 (1.04, 1.49) 0.79 (0.28, 2.25) 0.88 (0.70, 1.12) 0.86 (0.64, 1.16) 1.93 (1.46, 2.55) 1.39 (1.16, 1.66) 1.26 (1.03, 1.55) 11.93 (6.58, 21.61) 0.98 (0.74, 1.30) 1.80 (1.32, 2.46) 1.72 (1.12, 2.63) 1.35 (1.10, 1.65) 1.23 (1.06, 1.44) 1.35 (1.16, 1.57)

3.89 3.19 1.25 2.82 3.76 3.05 4.36 0.96 4.04 3.66 3.78 4.37 4.23 2.11 3.75 3.60 2.91 4.24 4.48 64.46

1.19 (1.06, 1.33)

100.00

NOTE: Weights are from random effects analysis

.0463

1

21.6

Fig. 2. Forest plot of IS risk associated with the SNP83 polymorphism (C vs. T).

the Begg's test (dominant model: P = 1.00; recessive model: P = 0.73; co-dominant model 1: P = 0.20; co-dominant model 2: P = 0.76; and allele model: P = 1.00) or the Egger's test (dominant model: P = 0.76; recessive model: P = 0.85; co-dominant model 1: P = 0.83; codominant model 2: P = 0.95; and allele model: P = 0.30) in the overall population. In the Chinese population, no obvious statistical evidence of publication bias was observed using the Begg's test (dominant model: P = 0.755; recessive model: P = 0.013; co-dominant model 1: P = 0.020; co-dominant model 2: P = 0.276; and allele model: P = 0.640) or the Egger's test (dominant model: P = 0.604; recessive model: P = 0.013; co-dominant model 1: P = 0.001; co-dominant model 2: P = 0.198; and allele model: P = 0.276). 4. Discussion With the inclusion of 8878 cases and 12 306 controls from 25 publications in this meta-analysis, we systematically estimated the relationship between SNP83 and IS. In this meta-analysis, SNP83 was associated with a statistically significant increased risk of IS among the overall population. Furthermore, we came to the conclusion that SNP83 was a marker of susceptibility for IS among Asian and Chinese individuals. Yoon et al. [26], in a 2011 review comprised of 15 publications with 19 318 subjects, failed to find an association between SNP83 and IS in the overall population. The study by Liu et al. [47], published in Chinese in 2011, comprised of 16 publications with 5118 cases and 5770 controls, and demonstrated that there was no association between the SNP83 polymorphism and IS in the overall population. Compared with the study by Yoon et al. [26], we included an additional 10 independent publications (Li et al. [19], Quarta et al. [35], Kalita et al. [34], Milton et al. [21], Xu et al. [45], Zhang et al. [42], Wang et al. [44], Cong et al. [40], Cheng et al. [41], and Zhao et al. [30]), with 2232 additional participants,

meaning that we were likely to make a more precise estimation with a larger sample size. SNP83 showed a significant association with ischemic stroke in the present meta-analysis composed of 25 publications among the overall population. In addition, the results of the cumulative meta-analysis presented a stable trend of association between SNP83 and ischemic stroke from 2009 to 2012 among the overall population, and the precision of the risk estimates was enhanced. Thus, the present study was sufficiently powered to raise the opinion that SNP83 was a genetic risk factor for IS. To date, the significant association between SNP83 and IS risk among Asian individuals has been observed by a number of investigations, with the exception of Matsushita et al., who failed to replicate the results [22]. Meanwhile, two meta-analyses [25,26] have confirmed the association in recent years. In this study, we were able to draw the conclusion that SNP83 was associated with the increased risk of IS among Asians. In Caucasians, Staton et al. [43] and Gretarsdottir et al. [8] found that SNP83 was associated with an increased risk of IS. However, Quarta et al. [35] and Milton et al. [21] observed no association of SNP83 with ischemic stroke among Caucasian individuals. Nevertheless, a lack of statistically significant association among Caucasians was confirmed in this meta-analysis. We should be cautious when interpreting the positive result observed in previous reports among Caucasians, since the sample size of these studies was much smaller than that of this metaanalysis, which contained a total of 21550 participants. With a relatively large sample size, we offered a reliable result that SNP83 variation did not significantly influence IS susceptibility in Caucasians. As described above, we obtained a contrary result between Asians and Caucasians, possibly due to the differences in inherited background and the environment in which the individuals live. As the international HapMap project shows, the allele frequencies of SNP83 in Caucasians are significantly different from those in Asians. The C allele frequency is 0.571,

Y. Yan et al. / Journal of the Neurological Sciences 338 (2014) 3–11

9

Study ID

OR (95% CI)

Gretarsdottir et al. (2003) Saleheen et al. (2005) Meschia et al. (2005) van Rijn et al. (2005) Nakayama et al. (2006) Woo et al. (2006) Kuhlenbaumer et al. (2006) Song et al. (2006) Staton et al. (2006) Lin et al. (2007) Lin et al. (2007) Banerjee et al. (2008) Xu et al. (2008) Sun et al. (2009) Matsushita et al. (2009) Matsushita et al. (2009) Matsushita et al. (2009) Munshi et al. (2009) Quarta et al. (2009) Xue et al. (2009) Li et al. (2009) Zhang et al. (2009) Cheng et al. (2011) Kalita et al. (2011) Milton et al. (2011) Wang et al. (2012) Li et al. (2012) Zhao et al. (2012)

1.23 (1.01, 1.49) 1.24 (1.06, 1.45) 1.06 (0.76, 1.46) 1.04 (0.80, 1.35) 1.09 (0.87, 1.36) 1.04 (0.86, 1.27) 1.03 (0.89, 1.19) 1.01 (0.88, 1.15) 0.98 (0.85, 1.12) 0.97 (0.85, 1.11) 0.98 (0.87, 1.11) 1.00 (0.89, 1.12) 1.01 (0.90, 1.14) 1.04 (0.93, 1.16) 1.03 (0.93, 1.15) 1.02 (0.92, 1.13) 1.01 (0.92, 1.12) 1.05 (0.94, 1.18) 1.06 (0.95, 1.19) 1.08 (0.97, 1.21) 1.09 (0.98, 1.21) 1.17 (1.01, 1.34) 1.16 (1.01, 1.32) 1.18 (1.03, 1.35) 1.17 (1.03, 1.33) 1.18 (1.04, 1.34) 1.19 (1.05, 1.34) 1.19 (1.06, 1.33)

1

.669

1.49

Fig. 3. Results of the cumulative meta-analysis. The random effects pooled OR with 95% CI at the end of each information step is shown.

0.290 and 0.140 in Caucasian, Chinese and Japanese populations, respectively, while for the T allele, the results are 0.429, 0.791 and 0.860, respectively. The above data support the conclusion that it is genetic

Begg's funnel plot with pseudo 95% confidence limits 3

logor

2

1

0

-1 0

.2

.4

.6

s.e. of: logor Fig. 4. Begg's funnel plot for publication bias in selection of studies on the SNP83 polymorphism (C vs. T).

background that accounts for the distinct susceptibility for IS. Furthermore, it has been widely agreed that IS is a complex neurological disease which can be mediated by both genetic and environmental factors [22], and the interaction of genetic factors and environmental factors may also play a major role in the pathogenesis of IS [8,48,49]. Hence, we speculate that environmental factors are also responsible for the inconsistency in ethnicity. When classified according to the sources of controls, a statistically significant relationship was not witnessed in the population-based subgroup for all genetic models, but was witnessed in the hospital-based subgroup. Generally speaking, controls from hospitals are less desirable because of the existence of unavoidable selection bias [50], and a total of 11 studies with hospital-based controls were included in our metaanalysis. These control subjects were mainly patients who had undergone a physical examination in hospital and relatives of the patients, although a few of them were hospitalized patients or outpatients that were free of neurological disease. As a result, our results might be influenced by some confounding factors; however, we could not further analyze these possible confounding factors. We subsequently performed a sub-type analysis based on the HWE status of the controls. The association was mostly unchanged (shown in Table 3), suggesting that the results did not obviously change when the 4 non-HWE studies were included in our analysis. One metaanalysis by Ma et al. [51], which contained 1899 cases and 2431 controls

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Y. Yan et al. / Journal of the Neurological Sciences 338 (2014) 3–11

and was published in Chinese, found a relationship between SNP83 and IS among Chinese populations. The other meta-analysis by Liu et al. [52], which had a sample size of 1818 cases and 2409 controls and was also written in Chinese, indicated the same result. In this analysis, the association was also observed among Chinese individuals, which replicated the results of Ma et al. [51] and Liu et al. [52]. Furthermore, the result of the cumulative meta-analysis offered further evidence that SNP83 was associated with IS in the Chinese population. As shown in Fig. 2, we identified one study [42] with an extreme odds ratio (OR = 11.93) which was an obvious outlier. To assess its effect on the stability of our result, we implemented some additional analysis. The association between SNP83 and IS was also significant in the overall population (OR = 1.13, 95% CI: 1.03–1.24, I2 = 70.2%), Asian individuals (OR = 1.26, 95% CI: 1.14–1.39, I2 = 54.6%) and Chinese populations (OR = 1.27, 95% CI: 1.18–1.37, I2 = 0.0%), and the heterogeneity was decreased after removing the study by Zhang et al. [42]. There was no statistical evidence of publication bias in the present study. The heterogeneity between studies was detected for partial models in this meta-analysis. Several factors were considered to have led to the occurrence of heterogeneity. First, studies with different diagnostic criteria or different matching criteria were included, which might have led to significant inconsistency among research findings. Second, the differences between studies in sample size were prominent, especially that of Zhang et al. [42], whose sample size was much smaller than the others, leading to obvious heterogeneity when combining the results. Third, we did not exclude any studies that were not in HWE, and the combinations of data from studies in the HWE subgroup and the subgroup not in HWE may have led to heterogeneity. Fourth, studies with different genotyping methods were included in this meta-analysis, resulting in heterogeneity between studies. Fifth, heterogeneity is visible in the additive model (C vs. T) and we thought that this may be associated with the additional studies which only provided allele distribution frequencies [21,31,35]. However, the heterogeneity was not decreased when we removed these three studies and recalculated the statistics. Also, it was interesting that when we excluded those studies which were not in HWE, the heterogeneity was significantly decreased or disappeared (as shown in Table 3). Thus, it may be that the HWE status was the important factor that affected the heterogeneity in our study. Apart from the presence of obvious heterogeneity, there still existed several inevitable limitations that need to be addressed in this meta-analysis. Firstly, due to the limited number of studies for stroke sub-types or various classification methods for ischemic stroke, stroke subtype analysis was not conducted in this metaanalysis. Secondly, the potential publication bias could not be ruled out because the databases that we searched were limited and we only included studies published in English and Chinese. In addition, there was a lack of sufficient data regarding confounding factors such as age, sex and hypertension, which may modulate the relationship when discussing those confounding factors. Finally, we did not have access to the original data of the included publications, so further estimation of potential gene– gene interactions and gene–environment interactions was limited in this study. In summary, the current meta-analysis demonstrated the association between SNP83 within the PDE4D gene and the increased susceptibility of IS among the overall, Asian and Chinese populations, but not among Caucasians, offering evidence that PDE4D may be involved in the pathogenesis of IS. The estimation of potential gene–gene interactions and gene–environment interactions should be taken into consideration in future studies and stroke subtype analysis is suggested for future high quality studies.

Conflict of interest The authors declare no conflict of interest.

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