PDE4D single nucleotide polymorphism rs918592 is associated with ischemic Stroke risk in Chinese populations: a meta-analysis

We searched the PubMed, Embase, ISI Web of Science, Weipu, China National Knowledge Infrastructure (CNKI), Chinese Biomedical (CBM) and Wanfang databases from inception to November 30, 2023, using the following items: “phosphodiesterase 4D”, “PDE4D”, “rs918592”, “polymorphism”, “stroke”, “cerebral infarction”, “ischemic stroke”, “cerebrovascular disease”, and their synonyms. The references of the identified articles, as well as relevant reviews and meta-analyses, were also manually scanned for other potentially eligible studies.

The selection of studies should be based on the following criteria: (a) case-control, nested case-control, or cohort studies; (b) assessment of the correlation between SNP rs918592 and IS risk in Chinese populations; and (c) using validated techniques to detect SNP rs918592. Articles were excluded if they (a) had no primary findings, (b) were reviews, editorials, case reports, case-only studies, or family-based studies, or (c) were duplicate studies.

The following information was collected from each qualified study: first author’s name, year of publication, ethnicity of participants, number of cases and controls, mean age of cases and controls, methods for detecting SNP rs918592, matching variables of controls, IS subtypes (if mentioned in the article), as well as number of alleles and genotypes.

Literature screening, data collection, and assessment of study quality were conducted independently by two researchers (X.Y. and G. Z.). The divergences that occurred through the process were settled by discussing with the corresponding author (R. L.).

Hardy-Weinberg equilibrium (HWE) was evaluated by a Chi-square test in the controls. Statistical analyses were performed using STATA 11.0 software (Stata Corporation, College Station, TX). The strength of association between SNP rs918592 and IS risk was calculated using pooled odds ratios (ORs) with 95% confidence intervals (CIs) for two comparisons between different genotypes (AG vs. AA, and GG vs. AA), as well as under dominant (GG + AG vs. AA), additive (G vs. A), and recessive (GG vs. AG + AA) genetic models.

The heterogeneity between studies was assessed with chi-square-based Q-test and I² test. When P value was above 0.10, a fixed-effects model using the Mantel-Haenszel method was selected for data analysis; otherwise, a random-effects model using the DerSimonian-Laird method was conducted. I² metric was used to show the degree of heterogeneity, where 0–25%, 25–50%, 50–75% and 75–100% meant no, moderate, large, and extreme heterogeneity, respectively. The underlying factors causing heterogeneity were explored by meta-regression analysis.

Sensitivity analyses by sequentially removing each study at a time were performed to test the stability of the results. Publication bias was estimated by Begg’s test with funnel plot and Egger’s linear regression test with publication bias plot.

To explore the functions of SNP rs918592, two online prediction websites were used for bioinformatics analysis: HaploReg (http://​pubs.​broadinstitute.​org/​mammals/​haploreg/​haploreg.​php) and RegulomeDB (http://​regulomedb.​org/​). HaploReg was applied to discover noncoding genomic annotations for variants and determine their underlying causal correlations with disease pathogenesis. RegulomeDB was utilized for the annotation of variants with regulatory elements by giving ranks. The lower the rank, the more likely it is to have a regulatory function.

After literature search and further screening, 12 articles in total met the inclusive criteria (Fig. 1). Four were subsequently excluded by careful reading of the full text. Xu’s (2008) [14], Bai’s (2011) [15] and Sun’s (2013) [16] studies overlapped with Xu’s (2008) [11], He’s (2012) [12] and Ma’s (2014) [17] studies, respectively, and then were excluded. In Zhang’s (2019) study, SNP87 was incorrectly labeled as rs918592 and actually rs2910829 [18]. Two articles investigated the association in independent populations, so each article was considered as two independent studies [17, 19]. Finally, 10 studies (in 8 articles involving 2,348 IS cases and 2,289 controls) were enrolled in the meta-analysis of the correlation between SNP rs918592 and IS risk (Table 1 and Additional file 2: Table S1) [10‐12, 17, 19‐22]. Each study design was case-control. The genotypic distribution of one study [22] deviated from HWE expectation in controls (Additional file 2: Table S2).

Significant association of SNP rs918592 with IS risk was observed in two comparisons and all the three genetic models (Table 2; Figs. 2, 3, 4, 5 and 6). The G allele was related to reduced risk of IS (G vs. A: OR 0.83, 95% CI 0.74–0.95, P = 0.005). Among the three genetic models, the dominant model had the smallest OR (GG + AG vs. AA: 0.74, 95% CI 0.61–0.90) and P value (0.003), and it might be the best-fitting model. Moderate or large heterogeneity was identified across all studies in the comparison between GG and AA, as well as under the dominant and additive models. Ethnicity, genotyping method, mean age, sample size, and HWE were not the main factors causing heterogeneity, while Song’s (2015) study [21] might be one of the factors causing heterogeneity. After excluding Song’s (2015) study, there was no heterogeneity between studies under the dominant and additive models (Table 2 and Additional file 2: Figs. S1-S5). Notably, all of the patients included in Song’s (2015) study had hypertension, which was markedly different from other studies. All the pooled OR values were not substantially altered after excluding Song’s (2015) study.

When any single study was removed, the pooled OR value was not significantly affected (Additional file 2: Figs. S6-S10), suggesting good stability of the results in the present study. Moreover, the results of Begg’s and Egger’s linear regression tests displayed no significant publication bias (Figs. 7, 8, 9, 10 and 11).

We analyzed the functional roles of SNP rs918592 and variants in strong linkage disequilibrium (LD) with it (defined as r² ≥ 0.8 with rs918592 in the East Asian (CHB, JPT, and CHS) population) using HaploReg v4.1 (Additional file 2: Table S2). The results showed that SNP rs918592 and the correlated 20 variants mapped to PDE4D intronic regions. All of them might affect transcriptional regulatory element activity and be identified as expression quantitative trait loci (eQTL) for prostate androgen-regulated transcript 1 (PART1), whose 5’ end overlaps with the 5’ end of PDE4D and whose transcript is a long non-coding RNA, in thyroid tissue. Among the correlated variants, two might be located within the histone modification regions of enhancers and one in promoters; two were in DNase I-hypersensitive regions; two had the alteration in transcription factor (TF) binding; one (rs918590) was related to ubiquitin conjugating enzyme E2 E1 (UBE2E1) expression in peripheral blood monocytes; and two were located in evolutionarily conserved regions predicted to be functionally constrained according to SiPhy or GERP analysis. As a whole, SNP rs34168777 (r² = 0.99) (with evidence of conserved region, enhancer histone mark, DNase I-hypersensitive region, TF-binding, any TF motif, and eQTL hit) might be worthiest of further functional study. The results of RegulomeDB v2.1 also showed that rs34168777 had a rank of 1b (eQTL + TF binding + any motif + DNase Footprint + DNase peak), which was the best ranking among the 21 SNPs (Additional file 2: Table S3). The rank of SNP rs918592 was 1f.