Cardiovascular diseases (CVDs) such as coronary artery disease (CAD) and stroke are the leading cause of death worldwide and represent a public health challenge in both industrialized and developing countries.1,2 A number of clinical risk factors for CVD have been identified for decades, involving obesity, dyslipidemia, hypertension, diabetes, and a sedentary lifestyle. Nevertheless, the molecular basis of CVD is complex and linked to a broad range of biological pathways, including lipid and glucose metabolism, vascular repair, and angiogenesis.3 Apart from these, more and more evidence showed that inflammatory molecules might take part in the pathogenesis of CVD as well.4,5
Inflammation has been shown to involve in the manifestation and development of arterial thrombotic diseases.6,7 Interleukins, a group of cytokines, were recognized as crucial agents involved in the host inflammatory response.8 Interleukin 10 (IL-10), secreted by TH2 cells as well as by macrophages, is an important anti-inflammatory cytokine with potent deactivating properties on both macrophages and T cells.9 IL-10 exerts a negative modulator effect on the inflammatory response by inhibiting cytokine synthesis.5,10 Because of its anti-inflammatory function, IL-10 is thought to be involved in arterial thrombotic diseases and further illustrated by epidemiologic studies, which recognized an association between lower levels of plasma IL-10 and increased risk of several end points of CVD such as acute coronary disease (ACS) and ischemic stroke (IS).5,11,12 Previous studies have reported that approximately 75% of individual difference in IL-10 secretion is determined by genetic factors and controlled at transcriptional level.13IL-10 gene is located on chromosome 1, has 5 exons, and has been mapped to the junction between 1q31 and 1q32.14 Three single-nucleotide polymorphisms (SNPs) (G-1082A, C-819T, and C-592A) in the promoter region of IL-10 were found to be associated with transcription activity of IL-10 gene and levels of plasma IL-10.13,15 Owing to their important roles, they were extensively studied and anticipated to be involved in arterial thrombotic diseases. This is supported by several studies that observe an increased risk of CVD in IL-10 -1082 A allele carriers.4,16,17 However, such associations could not be confirmed in other studies.10,14,18,19 The associations between IL-10 -592C/A and IL-10 -819C/T polymorphisms and CVD were not conclusive as well.10,13–17,19–32 Therefore, we performed this systematic review with meta-analysis and trial sequential analysis (TSA) of all the published case–control studies in the hope of providing more precise evidence.
Search Strategy and Identification of Relevant Studies
We carried out a comprehensive search of electronic databases including PubMed, Embase, Web of Science, and Cochrane Library to identify relevant publications reporting on the association between the IL-10 polymorphisms and CVD risk, with the last search update on July 7, 2015. The following keywords and medical subject headings were employed: (“interleukin 10” or “interleukin-10” or “IL-10” or “IL 10”), (“acute coronary syndrome” or “myocardial infarction” or “coronary artery disease” or “coronary heart disease” or “ischemic heart disease” or “cardiovascular disease” or “cardiovascular” or “stroke” or “myocardial ischaemia” or “myocardial ischemia” or “cerebral ischemia” or “cerebral ischaemia” or “cerebral infarction” or “brain infarction”), and (“polymorphism” or “variation” or “variant” or “allele” or “mutation” or “SNP”). Additional relevant publications were identified by a manual search of bibliographies of retrieved studies and recent reviews. Ethical approval and informed consent were not necessary because our analyses were based on data from previously published studies.
Studies were included that met the following criteria: investigation of the association between IL-10 -592C/A, IL-10 -1082G/A, or IL-10 -819C/T polymorphism and CVD among unrelated subjects; case–control design; sufficient information provided to calculate odds ratio (OR) and the corresponding confidence interval (CI). In addition, exclusion criteria were as follows: meeting abstracts, case reports, reviews, or editorials; overlapping data; studies were published in languages other than English and Chinese. The articles with the largest dataset were chosen when there were multiple publications from the same population. Two investigators selected the studies according to the above criteria, and disagreements were resolved by consensus.
Data were extracted from all eligible studies by primary investigators using a standardized extraction form. Extracted forms were reviewed by co-authors and a research assistant to ensure accuracy with dissent settled by consensus. The following information was collected: first author's name, publication year, country and ethnicity of population, outcome, matching status, genotyping methods, number of cases and controls, genotype distributions in cases and controls, and the Hardy-Weinberg Equilibrium (HWE) in controls (P). If these were not possible, the authors of the publications were contacted via E-mail for more detailed data.
Genotype-based mRNA Expression Analysis
The genotypes data for IL-10 -592C/A, IL-10 -1082G/A, and IL-10 -819C/T polymorphisms were available from the HapMap (http://hapmap.ncbi.nlm.nih.gov/) for 270 subjects with 3 different ethnicities and their corresponding mRNA expression levels data were available from SNPexp (http://app3.titan.uio.no/biotools/tool.php?app=snpexp) as described previously.33,34
The methodological quality of the included studies was accessed by 2 authors respectively according to the Newcastle Ottawa Scale (NOS) (www.ohri.ca/programs/clinical_epidemiology/oxford.asp).35 The NOS criteria consist of 3 aspects: selection, comparability, and exposure. Scores ranged from 0 stars (worst) to 9 stars (best) and a score ≥7 indicated that a study was of high quality. Dissent was settled as described above.
We initially assessed HWE among control subjects by χ2 test and P < 0.05 was considered as significant disequilibrium. The pooled ORs with their 95% CIs were calculated to evaluate the strength of the association between the IL-10 gene polymorphisms and CVD risk based on 5 genetic comparison models: allele model, homozygous model, heterozygous model, dominant model, and recessive model. Statistical heterogeneity between eligible studies was evaluated by using the Cochran's Q statistic and I2 test.36P < 0.1 and I2 exceeding 50% indicated substantial heterogeneity across studies, then a random-effects model was chosen to perform meta-analysis; otherwise, the fixed-effects model was selected. Predefined subgroup analyses were conducted a priori according to ethnicity (Asian, white, or mixed), disease subtype (CAD or stroke), and quality score (low quality: score <7; high quality: score ≥7). A power calculation was performed using Power and Sample Size Calculation version 3.1.2 (http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize). Sensitivity analyses were performed to look at more narrowly drawn subsets of the studies by removing an individual study or by removing studies with similar feature to assess their influence separately. Begg funnel plot and Egger regression test were used to search for publication bias and a P value >0.05 suggests no significant publication bias has been detected.37 The fail-safe number (Nfs) set at a significance of 0.05 was also calculated to inspect publication bias, according to the formula Nfs0.05 = (∑Z/1.64)2-k, where k is the number of studies included. If the Nfs was less than the number of observed studies for a polymorphism, we deemed that there exists a significant publication bias.
For each statistically significant association identified, we estimated the false-positive reporting probability (FPRP).38 The FPRP value is determined by the P value, the prior probability for the association, and statistical power. We set 0.2 as an FPRP threshold and assigned a prior probability of 0.1 to detect an OR of 1.50/0.67 for alleles with a risk/protective effect. Only the results with FPRP values <0.2 were referred as noteworthy.
All P values were 2-sided. All above statistical analyses were performed using STATA software version 12.0 (STATA Corporation, College Station, TX).
Meta-analyses may result in type I errors owing to an increased risk of random error when sparse data are analyzed and to repeated significance testing when a cumulative meta-analysis is updated with new trials. TSA has been introduced to control the risk of type I error by estimating the required information size and an adjusted threshold for statistical significance. 39–41 A required information size was calculated with the adjustment by diversity (D2) between trials. We performed TSA with a desire to maintain an overall 5% risk of a type I error and 20% of the type II error (a power of 80%).42 As for IL-10 -592C/A, IL-10 -1082G/A, and IL-10 -819C/T polymorphisms, the required information size was calculated based on a relative risk increase of 6%, a relative risk reduction of 2%, a relative risk reduction of 10%, respectively, with low-risk bias (taking the data of dominant model for example). For IL-10 -819C/T polymorphism and CAD, we observed an 11% relative risk reduction. The control event proportion was calculated from the actual meta-analyses.
When the cumulative Z-curve crosses the trial sequential monitoring boundary, a sufficient level of evidence may have been reached and further trials are unnecessary. If the Z curve does not cross any of the boundaries and the required information size has not been reached, evidence to reach a conclusion is insufficient.43 We used software Trial Sequential Analysis (version 0.9, http://www.ctu.dk/tsa/) and provided the 95% CIs adjusted for sparse data or repetitive testing, which we describe as the TSA-adjusted 95% CIs.
The Main Characteristics of Included Studies
The process of literature retrieval and exclusion was shown in Figure 1. The initial comprehensive search identified a total of 1087 potentially relevant articles, 227 articles were excluded for duplication, and 789 additional articles were excluded for their unmatched titles or abstracts. After reading the full text of the remaining 71 articles, 44 articles were removed due to reviews, meeting abstract, studies with insufficient data, and so on. Since 1 article included 2 populations, both of them were considered as an independent study.27 Finally, 27 articles including 28 case–control studies, involving a total sample size of 20875, were included in our meta-analysis.4,10,13–32,44–48 Detailed characteristics and genotype distributions of included studies were summarized in Tables 1 and 2, respectively.4,10,13–32,44–48 The 28 studies concerned IL-10 -592C/A polymorphism, IL-10 -819C/T polymorphism, IL-10 -1082G/A polymorphism, respectively. These 3 polymorphisms were found to occur in frequencies consistent with HWE in the control populations of the vast majority of the published studies. There were 10 studies based on Asian population,4,13,16,17,20,23,25,28,32,48 16 studies conducted in white population,10,14,18,19,21,22,26,27,29–31,44–47 and 2 studies from mixed population.15,24 Among these included studies, cases were generally recruited in referral centers with documented CAD or stroke, and the controls were without any direct evidence of overt disease. The number of cases among all selected studies varied from 86 to 1791, whereas the numbers of controls varied from 48 to 2089. All the studies included met quality criteria ranging from 4 to 9 (Supplemental Table 1, https://links.lww.com/MD/A696).
Association Between the IL-10 Polymorphisms and CVD Risk
Data on IL-10 -592C/A polymorphism were obtained from 19 studies including 7284 CVD patients and 7469 controls.10,13–17,19–29,32 In overall comparison, there was no obvious evidence of an association between IL-10 -592C/A polymorphism and the incidence of CVD under all genetic models (A vs C: OR = 1.03, 95% CI = 0.90–1.17; AA vs CC: OR = 1.08, 95% CI = 0.82–1.41; AC vs CC: OR = 1.04, 95% CI = 0.88–1.22; AA vs AC + CC: OR = 1.00, 95% CI = 0.83–1.20; AA +AC vs CC: OR = 1.06, 95% CI = 0.88–1.26) (Figure 2A and Table 3). Similar results were identified in subgroup analysis in light of ethnicity, disease subtype, and quality score. Significant between-study heterogeneity was observed under all 5 genetic models. In the subgroup analysis, heterogeneity vanished in stroke studies as well as dramatically decreased in white subgroup (Table 3).
TSA showed that 39.6% (14753) of the required information size of 37,263 subjects were accrued. The cumulative Z-curve has not crossed the conventional boundary before reaching the required information size, suggesting that there was insufficient evidence to show a 6% relative risk increase, and further studies are necessary (Supplemental Figure 1, https://links.lww.com/MD/A696). The TSA-adjusted 95% CI was 0.78 to 1.44.
Twenty-one studies had data on IL-10 -1082G/A polymorphism, with 8263 CVD patients and 5765 controls.4,10,14–20,22,24,26,30–32,44–48 Likewise, we failed to confirm the association between IL-10 -1082G/A polymorphism and CVD risk under all genetic models (A vs G: OR = 1.03, 95% CI = 0.91–1.16; AA vs GG: OR = 1.02, 95% CI = 0.81–1.29; AG vs GG: OR = 0.94, 95% CI = 0.78–1.15; AA vs AG + GG: OR = 1.08, 95% CI = 0.90–1.31; AA + AG vs GG: OR = 0.98, 95% CI = 0.82–1.19) (Figure 2B and Table 3). In the subgroup according to ethnicity, disease subtype and quality score, similar trends with overall results were observed. Substantial heterogeneities were noticed under all 5 genetic models. Following subgroup analysis, heterogeneities almost disappeared in mixed population subgroup (Table 3).
TSA showed that 13,693 of the required information size of 89,658 subjects were accrued. The required information size is far from reached and the conventional boundary has not been crossed, leaving the meta-analysis inconclusive of a 2% relative risk reduction (Supplemental Figure 2, https://links.lww.com/MD/A696). The TSA-adjusted 95% CI was 0.46 to 2.11.
Twelve studies provided data on IL-10 −819C/T polymorphism consisting of 4502 CVD cases and 3190 controls.10,14–16,20,22,24,26,30–32 Overall, the pooled results revealed a significant association between IL-10 -819C/T polymorphism and decreased CVD risk under allelic comparison and dominant model (T vs C: OR = 0.91, 95% CI = 0.84–0.98; TT + TC vs CC: OR = 0.90, 95% CI = 0.81–1.00) (Figure 2C and Table 3). If we set α = 0.05, based on the data set for -819 T allele, we have a 77.2% power to detect an OR of 0.91. Similar results were found when the meta-analysis was restricted to studies whose controls were in agreement with HWE. In the subgroup analysis stratified by disease subtype, the statistically significant association were found for CAD under all genetic models except heterozygote comparison (T vs C: OR = 0.90, 95% CI = 0.83–0.97; TT vs CC: OR = 0.81, 95% CI = 0.66–1.00; TT vs TC + CC: OR = 0.82, 95% CI = 0.69–0.98; TT + TC vs CC: OR = 0.89, 95% CI = 0.80–0.99) (Table 3). No significant heterogeneity was detected under any genetic models. Meanwhile, no obvious heterogeneity was observed in the vast majority of subgroups.
Using the TSA, the required information size is 4495 subjects to demonstrate the issue. Until now, the cumulative Z-curve has crossed the conventional boundary and the required information size has been reached, confirming that IL-10 -819 C/T polymorphism is associated with decreased risk of CVD and further relevant trials are unnecessary (Figure 3). The TSA adjusted 95% CI was 0.80 to 1.00. Additionally, the TSA of 10 studies reporting CAD showed that sufficient evidence was established to show a relative risk reduction of 11%, the cumulative Z-curve has crossed the conventional boundary and the required information size has been reached (Figure 4). The TSA adjusted 95% CI was 0.78 to 1.01. The FPRP values for all significant findings are shown in Supplemental Table 2, https://links.lww.com/MD/A696. For a prior probability of 0.1 and OR of 0.67, the FPRP analyses suggested that all significant associations were deserving of attention.
The Correlation Between the mRNA Expression and Genotypes
The correlation between IL10 mRNA expressions levels by the genotypes were explored for all population (Supplemental Figure 3, https://links.lww.com/MD/A696). No significant alteration in the mRNA expression levels was found for the 3 variants.
Sensitivity Analysis and Publication Bias
Sensitivity analyses were performed to assess the influence of each individual study on the pooled OR in each comparison in the polymorphisms of IL-10 -592C/A, IL-10 -819C/T, and IL-10 -1082G/A. The recalculated ORs were not significantly influenced, which suggested our results were robust and reliable. Begg funnel plot and Egger test were performed to evaluate the potential publication bias of literatures. The shapes of the funnel plots showed no evidence of obvious asymmetry (Figure 5). The Egger test results did not support the existence of publication bias. The Nfs0.05 values for IL-10 -592C/A, IL-10 -1082G/A, and IL-10 -819C/T polymorphisms were 242, 208, 51, respectively, which were consistently greater than the number of studies included in this meta-analysis.
It is now accepted that inflammation play a significant role in the pathophysiology of CVD.4,5 IL-10 is a potent anti-inflammatory cytokine with multiple functions taking part in inflammation reaction as well as the development of CVD.4,11,12 Recently, the associations between 3 IL-10 gene polymorphisms and the risk of CVD have been intensively investigated; however, the results are inconsistent. Thus, we conducted a systematic review with meta-analysis and TSA to obtain a more precise conclusion.
Although data from some individual studies suggested a relationship, the overall result of the present meta-analysis argued against an association of IL-10 -592C/A or IL-10 -1082G/A polymorphism with CVD risk in all genetic models. We also performed genotype-based mRNA expression analysis using the data from 270 individual. The biological results are in accordance with the observed association. Moreover, further sub-analysis of either gene polymorphism based on ethnicity, disease subtype, or quality score did not suggest a significantly different result. There are 3 potential reasons for the results. First, because of the complex nature of CVD, it is unlikely that a SNP in a single gene would be associated with an increased risk of CVD, without a contribution from other polymorphic susceptibility genes. Second, IL-10 -592C/A or IL-10 -1082G/A polymorphism itself might exhibit null contribution to the susceptibility of CVD. Third, other factors, such as age, medical treatment, and nutrient status, can also influence the risk of CVD. However, TSA did not allow us to draw any solid conclusion on the association between IL-10 -592C/A or IL-10 -1082G/A polymorphism and CVD risk. Thus, these issues need to be further studied.
As for IL-10 -819C/T polymorphism, our result showed that the individuals who carry the T allele have 10% decreased risk of CVD compared with the CC homozygote carriers, and a significantly decreased risk of CVD was also found in allele model. This may be because of the fact that IL-10 -819 T allele potently alters the IL-10 gene activity resulting in a marked increase of plasma IL-10 concentration. Moreover, TSA provided firm evidence of -819C/T polymorphism associated with decreased risk of CVD. In the subgroup analysis stratified by disease subtype, we discovered that -819C/T polymorphism had a significant correlation with CAD in all genetic models except heterozygous model. The result of TSA suggested evidence was sufficient enough for this relationship. However, in stroke subgroup, the data were obtained only from two studies. So, the findings in this subgroup should be interpreted with caution.
To make the conclusion more credible, we performed the FPRP analysis, publication bias analysis and sensitivity analysis. All significant associations passed the FPRP analyses, indicating that these associations were robust. Funnel plots suggested that no obvious publication bias was detected. The Nfs0.05 for 3 polymorphisms were greater than the number of studies included in this meta-analysis, also indicating a low probability of publication bias. The sensitivity analysis revealed that the results are robust and no single study could alter the pooled ORs obviously.
For meta-analysis, the existence of heterogeneity among the available studies affects the reliability of the results in a large extent. Thus, we defined a limited number of potential heterogeneity factors before performing our meta-analysis. As for IL-10 -592C/A polymorphism, results from subgroup analysis suggested that the ethnicity and disease subtype might be the sources of heterogeneity. Regarding IL-10 -1082G/A polymorphism, the ethnicity might contribute to the between-study heterogeneity.
As far as we know, this is the first comprehensive meta-analysis exploring the association between 3 IL-10 gene polymorphisms and CVD risk up to now. Previous meta-analysis mainly focused on IL-10 -1082G/A polymorphism and stroke risk,49–52 whereas our meta-analysis included more studies concerning 3 well-characterized polymorphisms in the IL-10 gene and two CVD outcomes (CAD and stroke). Our meta-analysis also has some advantages. First, the search and selection studies were conducted strictly. Second, no evidence of publication bias was found by Begg funnel plot and Egger test. Third, TSA was performed, which could reduce the type I error rate. In addition, we performed false-positive report probability analysis to preclude false association resulting from multiple calculations.
Despite the clear strengths of this meta-analysis, including the large sample size and the implementation of TSA, several limitations should be addressed. First, the included studies were published in English and Chinese, whereas studies published in other languages were ignored. Second, there was significant heterogeneity in some of the pooled analysis, which may have affected the meta-analysis results even though we adopted the random-effects model. Third, several studies deviate from HWE expectations. Though, when the analysis was restricted to the studies in HWE, the pooled results did not alter significantly. Fourth, 2 studies in our meta-analysis included population with Latinos, and we did not find studies performed in other mixed ethnicities, so it is hard to make a definite conclusion about the population-specific genetic differences between the 3 polymorphisms and CVD risk; further studies should pay attention to the ethnic-specific effects on CVD susceptibility.
In conventional meta-analyses, IL-10 -592C/A and IL-10 -1082G/A polymorphisms were not likely to exert any influence on the susceptibility of CVD, whereas the IL-10 -819C/T polymorphism might be a protective factor for CVD, especially for CAD outcome, suggesting potential implications for genotyping the IL-10 -819C/T polymorphism in CAD risk appraisal. After TSA adjustment for sparse data and multiple updating in cumulative meta-analysis, it seems unsure that IL-10 -592C/A and IL-10 -1082G/A polymorphisms were not associated with the risk of CVD. Considering our main limitations, larger well-designed studies are necessary. Moreover, other IL polymorphisms and gene–gene interactions should also be considered in future studies.
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