Interleukins (ILs) are the most typical inflammatory and immunoregulatory cytokines. Evidences have shown that polymorphisms in ILs are associated with cerebral infarction risk. However, the results remain inconclusive. The present study was to evaluate the role of ILs polymorphisms in cerebral infarction susceptibility. Relevant case-control studies published between January 2000 and December 2015 were searched and retrieved from the electronic databases of Web of Science, PubMed, Embase and the Chinese Biomedical Database. The odds ratio (OR) with its 95% confidence interval (CI) were employed to calculate the strength of association. A total of 55 articles including 12619 cerebral infarction patients and 14436 controls were screened out. Four ILs (IL-1, IL-6, IL-10 and IL-18) contained nine single nucleotide polymorphisms (SNPs; IL-1α −899C/T, IL-1β −511C/T and IL-1β +3953C/T; IL-6 −174G/C and −572C/G; IL-10 −819C/T and −1082A/G; IL-18 −607C/A and −137G/C). Our result showed that IL-1α −899C/T and IL-18 −607C/A (under all the genetic models), and IL-6 −572C/G (under the allelic model, heterogeneity model and dominant model) were associated with increased the risk of cerebral infarction (P<0.05). Subgroup analysis by ethnicity showed that IL-6 −174G/C polymorphism (under all the five models) and IL-10 −1082A/G polymorphism (under the allelic model and heterologous model) were significantly associated with increased the cerebral infarction risk in Asians. Other genetic polymorphisms were not related with cerebral infarction susceptibility under any genetic models. In conclusion, IL-1α −899C/T, IL-6 −572C/G and IL-18 −607C/A might be risk factors for cerebral infarction development. Further studies with well-designed and large sample size are still required.

INTRODUCTION

Cerebral infarction (or ischaemic stroke), resulting from a blockage in the blood vessels supplying blood to the brain, or leakage outside the vessel walls, is the leading cause of acquired disability in adults and the second leading cause of dementia [1]. It constitutes the majority of cases of cerebrovascular accidents, and can be atherothrombotic or embolic [2]. According to the Oxford Community Stroke Project classification, cerebral infarction is classified as total anterior circulation infarct, partial anterior circulation infarct, lacunar infarct or posterior circulation infarct [3]. The incidence of cerebral infarction ranged from 210 to 600 per 100000 inhabitants per year according to the geographical difference [4,5]. Approximate 20% mortality is occurred at 1 month after the first stoke [5]. The risk factors are age, gender, tobacco smoking, hypertension, dyslipidaemia, diabetes and atrial fibrillation [6,7]. Increasing number of traditional risk factors was shown to be associated with long-term mortality in patients with cerebral infarction [8]. The symptoms of cerebral infarction are determined by the parts of the brain affected, and the pathology and pathophysiology of this disease are still not well understood [9]. Although many improvements such as surgical evacuation and thrombolytic drugs have been made for patients with cerebral infarction during the last decades, there is no specific treatment due to the severity of bleeding [10]. Preventing cerebral infarctions will be important in reducing the high morbidity and mortality rate [11]. Therefore, it is urgent to identify some important biomarkers to predict this disease and guide the treatment at its early onset.

Cerebral infarction is a complex multifactorial polygenic disease. It is well known that inflammation response affects brain tissue after a stroke, and cells and elements of the immune system are involved in all stages of ischaemic cascade [12]. Interleukins (ILs), a multifunctional group of immunomodulators that primarily mediate the leucocyte cross-talk, is critical to mounting any successful inflammation and immune responses [13]. There are 38 ILs so far, and they mainly regulate the immune cell proliferation, growth, differentiation, survival, activation and functions [14]. In addition, ILs are known to be involved in the pathogenesis of human inflammatory and autoimmune diseases [15,16]. Studies have shown that ILs are associated with atherosclerosis [17], and play an important role in cardiovascular disease [1820]. ILs may be major players in the development and progression of cerebral infarction, and the detection of serum ILs might be helpful to assess the severity, therapeutic efficacy and prognosis of patients with cerebral infarction. The increasing of serum IL-6 levels may be related with the occurrence and development of acute cerebral infarction [21]. The lower serum IL-10 concentration was significantly associated with an increased likelihood of cerebral infarction [22,23]. The serum level of IL-18 was significantly elevated in the patients with acute cerebral infarction, and correlated with the volumes of infarction and the clinical neurologic impairment degree scores [24]. IL-33 was shown to be involved in the pathogenesis and/or progression of acute cerebral infarction [25]. Moreover, some specific ILs such as IL-6 might be an independently predictive biomarker for future mortality in the elderly after an ischaemic stroke [26].

Genetic polymorphisms of ILs may affect local serum levels of the proteins and reflect lifelong inflammation status. Recent data suggest that single nucleotide polymorphisms (SNPs) in ILs may contribute to modulating the effects of inflammatory cytokines on cerebral infarction [27]. Although many studies have identified the role of ILs polymorphisms in cerebral infarction risk, the results still remain inconclusive. For example, Rezk et al. [28] inferred that IL-1β −511C/T polymorphism might be associated with more severe functional and neurological impairments in patients with ischaemic stroke, whereas Zhang et al. [29] found no significant association between the IL-1β −511 C/T variant and ischaemic stroke. Therefore, we conducted this meta-analysis to review all the published articles on this issue and reevaluate the relationship between polymorphisms of ILs in cerebral infarction susceptibility to obtain a relatively reliable result.

MATERIALS AND METHODS

Literature search strategy

We performed a comprehensive literature search in the electronic databases of the Web of Science, PubMed, Embase and the Chinese Biomedical Database to retrieve relevant articles published between January 2000 and December 2015. The following MeSH terms: ‘cerebral infarction or brain infarction or cerebral ischaemic stroke’, ‘interleukin or IL or cytokine’, and ‘polymorphism or variant or mutation’ as well as their combinations were used as the searching keywords in conjunction with a highly sensitive search strategy. The references of retrieved articles were manually searched to obtain more related resources. Our study only focused on articles written in English and Chinese. When the same authors or laboratories published more than one articles in the same subjects, only the most recent full-text article was included.

Inclusion and exclusion criteria

Eligible studies had to meet the following criteria: (1) case-control study evaluating the correlation of IL genetic polymorphisms in the pathogenesis of cerebral infarction; (2) the patients should be diagnosed by neuroimaging evidence with both CT and MRI, and meet the diagnostic criteria for cerebral infarction according to the World Health Organization's diagnostic criteria [30]; (3) the controls should be age-, sex-, ethnic-matched participants without other cardiovascular and cerebrovascular diseases and (4) the genotype information was available to be extracted, and the result was presented in odds ratio (OR) with its 95% confidence intervals (CI). The exclusion criteria were: (1) review reports or conference papers; (2) without control group; (3) with duplicated date and (4) studies not conducted in humans.

Data extraction

According to the PRISMA guidelines, two of our authors assessed the quality of relevant articles independently. They should reach a final consensus on each item, and any disagreement was solved by discussed with the third author. The following information was extracted: the first author's name, published year, country, ethnicity, mean age, sample size, genotype frequencies, genotyping method and Hardy–Weinberg equilibrium (HWE) in controls.

Statistical analysis

The relationship between IL genetic polymorphisms and cerebral infarction susceptibility was measured by the pooled OR and 95% CI. The Z test was used to estimate the statistical significance of pooled ORs (P-value less than 0.05 were considered statistically significant). For each genetic polymorphism, the allelic model, homologous model, heterogeneous model, dominant model and recessive model were calculated. Between-study heterogeneity was evaluated by the Q-statistic test and the I2 test. If the effect was homologous (the Q-test showed a P > 0.05 and I2 test exhibited <50%), the fixed-effect model was employed; otherwise, the random-effect model was used. All the statistical analysis was performed using the RevMan statistical software (version 5.3, the Cochrane Collaboration, Oxford, England).

RESULTS

Study characteristics

After applying the inclusion and exclusion criteria, we totally screened out 55 related articles, containing four genes (IL-1, IL-6, IL-10 and IL-18). Figure 1 presented the flow diagram of the selection of studies.

Flow chart of selection process in this meta-analysis

Figure 1
Flow chart of selection process in this meta-analysis
Figure 1
Flow chart of selection process in this meta-analysis

For IL-1, 17 articles contained three SNPs (IL-1α −899C/T, IL-1β −511C/T and IL-1β +3953C/T). Ten of them were conducted in Asian [29,3139], six in Caucasian [4045] and one in African [28]. All the genotype frequencies in controls followed the HWE.

For IL-6, 22 articles were included, containing two SNPs (−174G/C and −572C/G). Twelve (eight were written in Chinese [4653] and four in English [5457]) were conducted in Asian and 10 in Caucasian [40,5866]. All the genotype frequencies in controls except the studies of Song et al., Li et al., Sun et al. and Tuttolomondo et al. were conformed to the HWE.

For IL-10, two polymorphisms (−819C/T and −1082A/G) from 10 articles (two were written in Chinese [67,68] and eight in English [61,6975]) were included. Seven studies were conducted in Asians and three in Caucasians. The genotype distributions in all controls were consistent with HWE except the studies conducted by Zhang et al. and Marousi et al.

For IL-18, 8 articles (three in English [7678] and five in Chinese [7983]) contained 2 polymorphisms (−607C/A and −137G/C). All of them were conducted in Chinese population. The genotype distributions in all controls were consistent with HWE.

Table 1 listed the detailed characteristics of included studies. Table 2 exhibited the distribution information of genotypes in cerebral infarction cases and matched-controls.

Table 1
Main characteristics of included studies in this meta-analysis

–, Not available; ARMS-PCR, amplification refractory mutation system PCR methods; PCR-RFLP, PCR-restriction fragment length polymorphism; PCR-SSP, PCR-sequence specific primer; RT-PCR, reverse transcription-PCR.

Mean ageSample size
First authorYearCountryEthnicityCasesControlsCasesControlsGenotyping methods
IL-1 
Seripa D 2003 Italy Caucasian 65.8±10.4 63.7±14.0 101 110 PCR-RFLP 
Um JY 2003 Korea Asian 61.0±14.5 62.2±9.8 363 640 PCR-RFLP 
Blading J 2004 Ireland Caucasian 69 (35–99) 37.1 (18–65) 105 389 PCR-RFLP 
Dziedzic T 2004 Poland Caucasian 65.2±14.7 64.8±14.8 183 180 PCR-RFLP 
lacoviello L 2005 Italy Caucasian 35±7 35±8 134 134 PCR-RFLP 
Rubattu S 2005 Italy Caucasian 35.95±8.12 34.7±6.9 115 180 PCR-RFLP 
Wei YS 2005 China Asian 66.9±9.5 65.7±10.2 155 170 PCR-RFLP 
Lai JT 2006 China Asian 56.85±13.10 27.16±5.25 112 95 PCR-RFLP 
Zhang GZ 2006 China Asian 56±8 55±6 110 110 PCR-RFLP 
Banerjee I 2008 India Asian 58.6±14.2 57.4±8.8 112 212 PCR-RFLP 
Zee RYL 2008 USA Caucasian 62.1±0.5 61.7±0.5 258 258 PCR-RFLP 
Dong RF 2009 China Asian 60.31±10.51 58.77±10.83 82 82 PCR-RFLP 
Li N 2010 China Asian 63.88±7.36 62.87±7.57 371 371 PCR-RFLP 
Ma XL 2012 China Asian 46–75 44–70 65 130 PCR-RFLP 
Zhao N 2012 China Asian 59.2±10.71 62.32±10.68 1124 1163 PCR-RFLP 
Zhang Z 2013 China Asian 66.6±8.4 66.1±5.2 440 486 PCR-RFLP 
Rezk NA 2015 Egypt African 61.2±11.6 62.8±10.8 176 320 PCR-RFLP 
IL-6 
Revilla M 2002 Spain Caucasian 64.9±9.5 64.8±9.1 82 82 PCR-RFLP 
Pola R 2003 Italy Caucasian 76.8±8.4 76.2±7.1 119 133 PCR-RFLP 
Blading J 2004 Ireland Caucasian 69 (35–99) 37.1 (18–65) 105 389 PCR-RFLP 
Flex A 2004 Italy Caucasian 76.2±9.4 76.1±6.8 237 223 PCR-RFLP 
Wei YS 2004 China Asian 62.7±10.3 60.9±9.1 160 175 PCR-RFLP 
Chamorro A 2005 Spain Caucasian 67.0±10 64.0±10 273 105 PCR-RFLP 
Song XJ 2005 China Asian 68.23±9.58 66.08±8.62 66 98 PCR-RFLP 
Lalouschek W 2006 Austria Caucasian 53 (49–57) 49 (43–56) 404 415 PCR-RFLP 
Li HJ 2006 China Asian 64.92±11.16 63.91±11.96 112 105 PCR-RFLP 
Yamada Y 2006 Japan Asian 67.2±11.1 60.6±11.3 636 2010 PCR-SSP 
Banerjee I 2008 India Asian 58.6±14.2 57.4±8.8 112 212 PCR-RFLP 
Liang J 2009 China Asian 59.9±9.8 61.5±11.1 199 196 PCR-RFLP 
Sun Y 2009 China Asian 59.12±12.13 58.71±11.83 92 110 PCR-RFLP 
Liu DF 2010 China Asian 61.5±13.5 58.5±9.5 157 163 PCR-RFLP 
Tong YQ 2010 China Asian 61.52±9.68 60.61±9.11 748 748 Sequencing 
Pan Y 2011 China Asian 62.6±10.2 61.4 ±10.5 106 92 PCR-RFLP 
Xiao H 2011 China Asian 59.9±9.8 61.5 ±11.1 200 196 PCR-RFLP 
Balcerzyk A 2012 Poland Caucasian 8.75 (0.5–18) 7.5 (0.2–18) 80 138 PCR-RFLP 
Chakraborty B 2012 India Asian 54.0±10.9 52.5 ±9.8 100 120 PCR-RFLP 
Tuttolomondo A 2012 Italy Caucasian 71.9±9.75 71.4 ±7.45 96 48 PCR-RFLP 
Xuan Y 2014 China Asian 45.4±9.5 44.8±10.1 430 461 PCR-RFLP 
Bazina A 2015 Croatia Caucasian 54 (51–57) 55 (50–61) 114 187 RT-PCR 
Ozkan A 2015 Turkey Caucasian 63.57±15.3 62.29±12.6 42 48 RT-PCR 
IL-10 
Zhang GZ 2007 China Asian 55±9 35±5 204 131 PCR-RFLP 
Munshi A 2010 India Asian 49.3±17.34 47.01±16.78 480 470 ARMSPCR 
Jin L 2011 China Asian – – 189 92 PCR-RFLP 
Marousi S 2011 Greece Caucasian 68 (58–76) 69 (58–77) 145 145 RT-PCR 
Sultana S 2011 India Asian 53.72±11.11 54.06±10.98 238 226 ARMS PCR 
Tuttolomondo A 2012 Italy Caucasian 71.9±9.75 71.4±7.45 96 48 PCR-RFLP 
He W 2015 China Asian – – 260 260 PCR-RFLP 
Jiang XH 2015 China Asian 66.11±10.54 65.43±11.62 181 115 PCR-RFLP 
Kumar P 2015 India Asian 50.97±12.70 52.83±12.59 250 250 PCR-RFLP 
Ozkan A 2015 Turkey Caucasian 63.57±15.3 62.29±12.6 42 48 RT-PCR 
IL-18 
Zhang N 2010 China Asian 68.3±11.4 67.5±6.6 423 384 PCR-SSP 
Li XQ 2011 China Asian 62 (47–76) 59 (46–75) 98 100 PCR-SSP 
Wang YJ 2011 China Asian 64.2±13.1 63.9±12.9 218 218 PCR-SSP 
Ren DL 2012 China Asian 66.06±7.96 64.52±6.57 193 120 PCR-SSP 
Lu JX 2013 China Asian 65.7±8.8 64.6±9.9 386 364 PCR-RFLP 
Wei GY 2013 China Asian 58.5±12.1 59.6±12.8 153 114 PCR-RFLP 
Dai XL 2014 China Asian 63.88±7.36 62.87±7.57 371 371 PCR-RFLP 
Shi JH 2015 China Asian 62.4±9.3 61.8±10.6 322 322 PCR-RFLP 
Mean ageSample size
First authorYearCountryEthnicityCasesControlsCasesControlsGenotyping methods
IL-1 
Seripa D 2003 Italy Caucasian 65.8±10.4 63.7±14.0 101 110 PCR-RFLP 
Um JY 2003 Korea Asian 61.0±14.5 62.2±9.8 363 640 PCR-RFLP 
Blading J 2004 Ireland Caucasian 69 (35–99) 37.1 (18–65) 105 389 PCR-RFLP 
Dziedzic T 2004 Poland Caucasian 65.2±14.7 64.8±14.8 183 180 PCR-RFLP 
lacoviello L 2005 Italy Caucasian 35±7 35±8 134 134 PCR-RFLP 
Rubattu S 2005 Italy Caucasian 35.95±8.12 34.7±6.9 115 180 PCR-RFLP 
Wei YS 2005 China Asian 66.9±9.5 65.7±10.2 155 170 PCR-RFLP 
Lai JT 2006 China Asian 56.85±13.10 27.16±5.25 112 95 PCR-RFLP 
Zhang GZ 2006 China Asian 56±8 55±6 110 110 PCR-RFLP 
Banerjee I 2008 India Asian 58.6±14.2 57.4±8.8 112 212 PCR-RFLP 
Zee RYL 2008 USA Caucasian 62.1±0.5 61.7±0.5 258 258 PCR-RFLP 
Dong RF 2009 China Asian 60.31±10.51 58.77±10.83 82 82 PCR-RFLP 
Li N 2010 China Asian 63.88±7.36 62.87±7.57 371 371 PCR-RFLP 
Ma XL 2012 China Asian 46–75 44–70 65 130 PCR-RFLP 
Zhao N 2012 China Asian 59.2±10.71 62.32±10.68 1124 1163 PCR-RFLP 
Zhang Z 2013 China Asian 66.6±8.4 66.1±5.2 440 486 PCR-RFLP 
Rezk NA 2015 Egypt African 61.2±11.6 62.8±10.8 176 320 PCR-RFLP 
IL-6 
Revilla M 2002 Spain Caucasian 64.9±9.5 64.8±9.1 82 82 PCR-RFLP 
Pola R 2003 Italy Caucasian 76.8±8.4 76.2±7.1 119 133 PCR-RFLP 
Blading J 2004 Ireland Caucasian 69 (35–99) 37.1 (18–65) 105 389 PCR-RFLP 
Flex A 2004 Italy Caucasian 76.2±9.4 76.1±6.8 237 223 PCR-RFLP 
Wei YS 2004 China Asian 62.7±10.3 60.9±9.1 160 175 PCR-RFLP 
Chamorro A 2005 Spain Caucasian 67.0±10 64.0±10 273 105 PCR-RFLP 
Song XJ 2005 China Asian 68.23±9.58 66.08±8.62 66 98 PCR-RFLP 
Lalouschek W 2006 Austria Caucasian 53 (49–57) 49 (43–56) 404 415 PCR-RFLP 
Li HJ 2006 China Asian 64.92±11.16 63.91±11.96 112 105 PCR-RFLP 
Yamada Y 2006 Japan Asian 67.2±11.1 60.6±11.3 636 2010 PCR-SSP 
Banerjee I 2008 India Asian 58.6±14.2 57.4±8.8 112 212 PCR-RFLP 
Liang J 2009 China Asian 59.9±9.8 61.5±11.1 199 196 PCR-RFLP 
Sun Y 2009 China Asian 59.12±12.13 58.71±11.83 92 110 PCR-RFLP 
Liu DF 2010 China Asian 61.5±13.5 58.5±9.5 157 163 PCR-RFLP 
Tong YQ 2010 China Asian 61.52±9.68 60.61±9.11 748 748 Sequencing 
Pan Y 2011 China Asian 62.6±10.2 61.4 ±10.5 106 92 PCR-RFLP 
Xiao H 2011 China Asian 59.9±9.8 61.5 ±11.1 200 196 PCR-RFLP 
Balcerzyk A 2012 Poland Caucasian 8.75 (0.5–18) 7.5 (0.2–18) 80 138 PCR-RFLP 
Chakraborty B 2012 India Asian 54.0±10.9 52.5 ±9.8 100 120 PCR-RFLP 
Tuttolomondo A 2012 Italy Caucasian 71.9±9.75 71.4 ±7.45 96 48 PCR-RFLP 
Xuan Y 2014 China Asian 45.4±9.5 44.8±10.1 430 461 PCR-RFLP 
Bazina A 2015 Croatia Caucasian 54 (51–57) 55 (50–61) 114 187 RT-PCR 
Ozkan A 2015 Turkey Caucasian 63.57±15.3 62.29±12.6 42 48 RT-PCR 
IL-10 
Zhang GZ 2007 China Asian 55±9 35±5 204 131 PCR-RFLP 
Munshi A 2010 India Asian 49.3±17.34 47.01±16.78 480 470 ARMSPCR 
Jin L 2011 China Asian – – 189 92 PCR-RFLP 
Marousi S 2011 Greece Caucasian 68 (58–76) 69 (58–77) 145 145 RT-PCR 
Sultana S 2011 India Asian 53.72±11.11 54.06±10.98 238 226 ARMS PCR 
Tuttolomondo A 2012 Italy Caucasian 71.9±9.75 71.4±7.45 96 48 PCR-RFLP 
He W 2015 China Asian – – 260 260 PCR-RFLP 
Jiang XH 2015 China Asian 66.11±10.54 65.43±11.62 181 115 PCR-RFLP 
Kumar P 2015 India Asian 50.97±12.70 52.83±12.59 250 250 PCR-RFLP 
Ozkan A 2015 Turkey Caucasian 63.57±15.3 62.29±12.6 42 48 RT-PCR 
IL-18 
Zhang N 2010 China Asian 68.3±11.4 67.5±6.6 423 384 PCR-SSP 
Li XQ 2011 China Asian 62 (47–76) 59 (46–75) 98 100 PCR-SSP 
Wang YJ 2011 China Asian 64.2±13.1 63.9±12.9 218 218 PCR-SSP 
Ren DL 2012 China Asian 66.06±7.96 64.52±6.57 193 120 PCR-SSP 
Lu JX 2013 China Asian 65.7±8.8 64.6±9.9 386 364 PCR-RFLP 
Wei GY 2013 China Asian 58.5±12.1 59.6±12.8 153 114 PCR-RFLP 
Dai XL 2014 China Asian 63.88±7.36 62.87±7.57 371 371 PCR-RFLP 
Shi JH 2015 China Asian 62.4±9.3 61.8±10.6 322 322 PCR-RFLP 
Table 2
Information of genotype distribution in cerebral infarction cases and controls among included studies in this meta-analysis
First authorCasesControlsHWE
IL-1 
IL-1α −899C/T CC CT TT CC CT TT  
Um JY 292 68 652 74 554 81 1189 91 0.57 
Wei YS 115 37 267 43 146 23 315 25 0.99 
Zhang GZ 84 23 191 29 97 13 207 13 0.80 
Banerjee I 38 62 12 138 86 104 89 19 297 127 0.99 
Dong RF 46 26 10 118 46 68 12 148 16 0.31 
Li N 121 207 43 449 293 154 183 34 491 251 0.14 
Zhao N 11 189 924 211 2037 10 220 933 240 2086 0.75 
Zhang Z 145 232 63 522 335 200 237 49 637 358 0.22 
Rezk NA 48 84 44 180 172 180 118 22 478 162 0.91 
IL-1β −511C/T CC CT TT CC CT TT  
Seripa D 41 47 13 129 73 39 58 13 136 84 0.47 
Dziedzic T 94 69 20 257 109 87 79 14 253 107 0.79 
lacoviello L 66 59 191 77 52 61 21 165 103 0.91 
Rubattu S 47 51 17 145 85 79 83 18 241 119 0.85 
Lai JT 25 55 32 105 119 30 46 19 106 84 0.98 
Zhang GZ 28 51 31 107 113 30 52 28 112 108 0.85 
Zee RYL 113 123 22 349 167 111 120 27 342 174 0.81 
Dong RF 52 23 127 37 46 26 10 118 46 0.15 
Li N 93 170 108 356 386 101 178 92 380 362 0.74 
Ma XL 42 17 101 29 87 39 213 47 0.99 
Zhao N 298 561 265 1157 1091 323 583 257 1229 1097 0.98 
Zhang Z 119 226 95 464 416 108 261 117 477 495 0.26 
Rezk NA 53 87 36 193 159 206 101 13 513 127 0.99 
IL-1β+3953C/T CC CT TT CC CT TT  
Um JY 332 30 694 32 593 46 1232 48 0.99 
Blading J 66 35 167 43 240 125 24 605 173 0.38 
Zhang GZ 97 13 207 13 106 216 0.98 
Dong RF 52 24 128 36 57 20 134 30 0.25 
Ma XL 34 19 12 87 43 82 42 206 58 0.71 
IL-6 
−174G/C GG GC CC GG GC CC  
Revilla M 37 39 113 51 27 40 15 94 70 0.99 
Pola R 56 48 15 160 78 28 58 47 114 152 0.45 
Blading J 33 60 12 126 84 123 198 68 444 334 0.75 
Flex A 100 115 22 315 159 66 99 68 231 235 0.07 
Chamorro A 104 134 35 342 204 46 50 142 68 0.67 
Song XJ 54 115 17 93 190 0.008 
Lalouschek W 143 187 74 473 335 156 192 67 504 326 0.83 
Li HJ 39 24 49 102 122 55 29 21 139 71 0.000 
Banerjee I 77 35 189 35 156 52 364 60 0.99 
Sun Y 32 20 40 84 100 59 28 23 146 74 0.000 
Liu DF 138 19 295 19 153 10 316 10 0.92 
Tong YQ 747 1495 743 1491 0.99 
Balcerzyk A 21 43 16 85 75 40 76 22 156 120 0.37 
Chakraborty B 57 35 149 51 73 39 185 55 0.68 
Tuttolomondo A 40 46 10 126 66 14 33 61 35 0.003 
Xuan Y 205 170 55 580 280 246 171 44 663 259 0.21 
Bazina A 39 53 22 131 97 63 98 26 224 150 0.46 
Ozkan A 22 16 30 54 14 21 13 49 47 0.69 
−572C/G CC CG GG CC CG GG  
Wei YS 84 71 239 81 116 57 289 61 0.22 
Yamada Y 412 199 25 1023 249 1138 760 112 3036 984 0.60 
Liang J 103 89 295 103 127 66 320 72 0.23 
Liu DF 34 33 101 36 51 24 126 34 0.65 
Tong YQ 373 326 49 1072 424 424 267 57 1115 381 0.26 
Pan Y 55 44 154 58 59 32 150 34 0.33 
Xiao H 103 89 295 103 127 66 320 72 0.22 
Xuan Y 267 127 35 661 197 318 122 21 758 164 0.12 
IL-10 
−819C/T CC CT TT CC CT TT  
Zhang GZ 28 90 86 146 262 27 48 56 102 160 0.03 
Jin L 12 82 95 106 272 37 48 51 133 0.99 
Tuttolomondo A 63 14 19 140 52 26 17 69 27 0.69 
He W 43 113 104 199 321 33 111 116 177 343 0.73 
Jiang XH 32 73 76 137 225 18 44 53 80 150 0.24 
−1082A/G AA AG GG AA AG GG  
Zhang GZ 202 406 120 11 251 11 0.88 
Munshi A 92 241 147 425 535 63 218 189 344 596 0.99 
Jin L 161 27 349 29 78 12 168 16 0.23 
Marousi S 47 71 27 165 125 53 71 21 177 113 0.94 
Sultana S 154 44 40 352 124 163 47 16 373 79 0.000 
Tuttolomondo A 58 14 24 130 62 20 17 11 57 39 0.18 
He W 41 124 95 206 314 29 108 123 166 354 0.77 
Jiang XH 153 28 334 28 83 32 198 32 0.22 
Kumar P 11 77 162 99 401 37 209 45 455 0.31 
Ozkan A 11 26 48 36 19 18 11 56 40 0.28 
IL-18 
−607C/A CC CA AA CC CA AA  
Zhang N 122 227 74 471 375 81 207 96 369 399 0.29 
Li XQ 25 55 18 105 91 23 56 21 102 98 0.48 
Ren DL 58 99 36 215 171 17 71 32 105 135 0.08 
Lu JX 116 188 82 420 352 77 195 92 349 379 0.38 
Dai XL 43 207 121 293 449 34 183 154 251 491 0.14 
Shi JH 88 180 54 356 288 68 183 71 319 325 0.05 
−137G/C GG GC CC GG GC CC  
Li XQ 76 19 171 25 62 33 157 43 0.98 
Wang YJ 174 42 390 46 146 66 358 78 0.90 
Ren DL 161 29 351 35 96 23 215 25 0.96 
Wei GY 91 54 236 70 85 25 195 33 0.48 
Dai XL 108 170 93 386 356 92 178 101 362 380 0.74 
Shi JH 230 81 11 541 103 220 84 18 524 120 0.05 
First authorCasesControlsHWE
IL-1 
IL-1α −899C/T CC CT TT CC CT TT  
Um JY 292 68 652 74 554 81 1189 91 0.57 
Wei YS 115 37 267 43 146 23 315 25 0.99 
Zhang GZ 84 23 191 29 97 13 207 13 0.80 
Banerjee I 38 62 12 138 86 104 89 19 297 127 0.99 
Dong RF 46 26 10 118 46 68 12 148 16 0.31 
Li N 121 207 43 449 293 154 183 34 491 251 0.14 
Zhao N 11 189 924 211 2037 10 220 933 240 2086 0.75 
Zhang Z 145 232 63 522 335 200 237 49 637 358 0.22 
Rezk NA 48 84 44 180 172 180 118 22 478 162 0.91 
IL-1β −511C/T CC CT TT CC CT TT  
Seripa D 41 47 13 129 73 39 58 13 136 84 0.47 
Dziedzic T 94 69 20 257 109 87 79 14 253 107 0.79 
lacoviello L 66 59 191 77 52 61 21 165 103 0.91 
Rubattu S 47 51 17 145 85 79 83 18 241 119 0.85 
Lai JT 25 55 32 105 119 30 46 19 106 84 0.98 
Zhang GZ 28 51 31 107 113 30 52 28 112 108 0.85 
Zee RYL 113 123 22 349 167 111 120 27 342 174 0.81 
Dong RF 52 23 127 37 46 26 10 118 46 0.15 
Li N 93 170 108 356 386 101 178 92 380 362 0.74 
Ma XL 42 17 101 29 87 39 213 47 0.99 
Zhao N 298 561 265 1157 1091 323 583 257 1229 1097 0.98 
Zhang Z 119 226 95 464 416 108 261 117 477 495 0.26 
Rezk NA 53 87 36 193 159 206 101 13 513 127 0.99 
IL-1β+3953C/T CC CT TT CC CT TT  
Um JY 332 30 694 32 593 46 1232 48 0.99 
Blading J 66 35 167 43 240 125 24 605 173 0.38 
Zhang GZ 97 13 207 13 106 216 0.98 
Dong RF 52 24 128 36 57 20 134 30 0.25 
Ma XL 34 19 12 87 43 82 42 206 58 0.71 
IL-6 
−174G/C GG GC CC GG GC CC  
Revilla M 37 39 113 51 27 40 15 94 70 0.99 
Pola R 56 48 15 160 78 28 58 47 114 152 0.45 
Blading J 33 60 12 126 84 123 198 68 444 334 0.75 
Flex A 100 115 22 315 159 66 99 68 231 235 0.07 
Chamorro A 104 134 35 342 204 46 50 142 68 0.67 
Song XJ 54 115 17 93 190 0.008 
Lalouschek W 143 187 74 473 335 156 192 67 504 326 0.83 
Li HJ 39 24 49 102 122 55 29 21 139 71 0.000 
Banerjee I 77 35 189 35 156 52 364 60 0.99 
Sun Y 32 20 40 84 100 59 28 23 146 74 0.000 
Liu DF 138 19 295 19 153 10 316 10 0.92 
Tong YQ 747 1495 743 1491 0.99 
Balcerzyk A 21 43 16 85 75 40 76 22 156 120 0.37 
Chakraborty B 57 35 149 51 73 39 185 55 0.68 
Tuttolomondo A 40 46 10 126 66 14 33 61 35 0.003 
Xuan Y 205 170 55 580 280 246 171 44 663 259 0.21 
Bazina A 39 53 22 131 97 63 98 26 224 150 0.46 
Ozkan A 22 16 30 54 14 21 13 49 47 0.69 
−572C/G CC CG GG CC CG GG  
Wei YS 84 71 239 81 116 57 289 61 0.22 
Yamada Y 412 199 25 1023 249 1138 760 112 3036 984 0.60 
Liang J 103 89 295 103 127 66 320 72 0.23 
Liu DF 34 33 101 36 51 24 126 34 0.65 
Tong YQ 373 326 49 1072 424 424 267 57 1115 381 0.26 
Pan Y 55 44 154 58 59 32 150 34 0.33 
Xiao H 103 89 295 103 127 66 320 72 0.22 
Xuan Y 267 127 35 661 197 318 122 21 758 164 0.12 
IL-10 
−819C/T CC CT TT CC CT TT  
Zhang GZ 28 90 86 146 262 27 48 56 102 160 0.03 
Jin L 12 82 95 106 272 37 48 51 133 0.99 
Tuttolomondo A 63 14 19 140 52 26 17 69 27 0.69 
He W 43 113 104 199 321 33 111 116 177 343 0.73 
Jiang XH 32 73 76 137 225 18 44 53 80 150 0.24 
−1082A/G AA AG GG AA AG GG  
Zhang GZ 202 406 120 11 251 11 0.88 
Munshi A 92 241 147 425 535 63 218 189 344 596 0.99 
Jin L 161 27 349 29 78 12 168 16 0.23 
Marousi S 47 71 27 165 125 53 71 21 177 113 0.94 
Sultana S 154 44 40 352 124 163 47 16 373 79 0.000 
Tuttolomondo A 58 14 24 130 62 20 17 11 57 39 0.18 
He W 41 124 95 206 314 29 108 123 166 354 0.77 
Jiang XH 153 28 334 28 83 32 198 32 0.22 
Kumar P 11 77 162 99 401 37 209 45 455 0.31 
Ozkan A 11 26 48 36 19 18 11 56 40 0.28 
IL-18 
−607C/A CC CA AA CC CA AA  
Zhang N 122 227 74 471 375 81 207 96 369 399 0.29 
Li XQ 25 55 18 105 91 23 56 21 102 98 0.48 
Ren DL 58 99 36 215 171 17 71 32 105 135 0.08 
Lu JX 116 188 82 420 352 77 195 92 349 379 0.38 
Dai XL 43 207 121 293 449 34 183 154 251 491 0.14 
Shi JH 88 180 54 356 288 68 183 71 319 325 0.05 
−137G/C GG GC CC GG GC CC  
Li XQ 76 19 171 25 62 33 157 43 0.98 
Wang YJ 174 42 390 46 146 66 358 78 0.90 
Ren DL 161 29 351 35 96 23 215 25 0.96 
Wei GY 91 54 236 70 85 25 195 33 0.48 
Dai XL 108 170 93 386 356 92 178 101 362 380 0.74 
Shi JH 230 81 11 541 103 220 84 18 524 120 0.05 

Correlation between ILs polymorphisms and susceptibility to cerebral infarction

Table 3 showed the summary risk estimates for association between ILs polymorphisms and cerebral infarction.

Table 3
Meta-analysis on the association between ILs polymorphisms and cerebral infarction risk in total population

N, number of included studies; Ph, I2, test of heterogeneity; F, fixed-effect model; R, random-effect model.

Test of associationTest of heterogeneity
SNPsComparisonsNOR (95% CI)PPhI2Model
IL-1 IL-1α −899C/T T versus C 1.69 (1.33, 2.14) <0.0001 <0.0001 82% 
 TT versus CC  2.32 (1.34, 3.99) 0.002 0.0007 70% 
 CT versus CC  1.66 (1.44, 1.91) <0.00001 0.07 45% 
 TT + CT versus CC  1.89 (1.46, 2.44) <0.00001 0.003 65% 
 TT versus CT + CC  1.76 (1.18, 2.64) 0.006 0.0009 70% 
IL-1β −511C/T T versus C 13 1.11 (0.91, 1.35) 0.32 <0.0001 85% 
 TT versus CC  1.27 (0.88, 1.84) 0.21 <0.0001 80% 
 CT versus CC  1.04 (0.84, 1.29) 0.72 0.0001 69% 
 TT + CT versus CC  1.09 (0.85, 1.40) 0.51 <0.0001 80% 
 TT versus CT + CC  1.23 (0.93, 1.62) 0.14 <0.0001 71% 
IL-1β +3953C/T T versus C 1.24 (1.00, 1.54) 0.05 0.09 50% 
 TT versus CC  1.47 (0.83, 2.60) 0.19 0.12 48% 
 CT versus CC  1.21 (0.93, 1.57) 0.16 0.40 1% 
 TT + CT versus CC  1.24 (0.97, 1.60) 0.09 0.29 20% 
 TT versus CT + CC  1.43 (0.82, 2.51) 0.21 0.11 50% 
IL-6        
−174G/C C versus G 18 1.12 (0.88, 1.43) 0.37 <0.0001 86% 
 CC versus GG  1.13 (0.68, 1.88) 0.64 <0.0001 85% 
 GC versus GG  1.04 (0.92, 1.17) 0.56 0.02 47% 
 CC + GC versus GG  1.09 (0.85, 1.41) 0.48 <0.0001 75% 
 CC versus GC + GG  1.11 (0.71, 1.72) 0.65 <0.0001 83% 
−572C/G G versus C 1.31 (1.03, 1.66) 0.03 <0.0001 84% 
 GG versus CC  1.48 (0.88, 2.48) 0.14 0.006 64% 
 CG versus CC  1.38 (1.04, 1.83) 0.03 <0.0001 82% 
 GG + CG versus CC  1.40 (1.05, 1.88) 0.02 <0.0001 84% 
 GG versus CG + CC  1.28 (0.81, 2.02) 0.29 0.03 55% 
IL-10        
−819C/T T versus C 0.93 (0.80, 1.09) 0.38 0.64 0% 
 TT versus CC  0.97 (0.71, 1.33) 0.86 0.34 12% 
 CT versus CC  0.91 (0.54, 1.52) 0.71 0.03 62% 
 TT + CT versus CC  0.93 (0.70, 1.22) 0.59 0.19 35% 
 TT versus CT + CC  0.92 (0.75, 1.13) 0.42 0.56 0% 
−1082A/G G versus A 10 0.76 (0.57, 1.02) 0.07 <0.0001 82% 
 GG versus AA  0.78 (0.46, 1.34) 0.37 0.0003 74% 
 AG versus AA  0.76 (0.54, 1.07) 0.12 0.004 63% 
 GG + AG versus AA  0.74 (0.52, 1.05) 0.09 0.0004 70% 
 GG versus AG + AA  0.80 (0.51, 1.24) 0.31 <0.0001 80% 
IL-18        
−607C/A A versus C 0.76 (0.69, 0.84) <0.00001 0.76 0% 
 AA versus CC  0.56 (0.45, 0.68) <0.00001 0.68 0% 
 CA versus CC  0.71 (0.59, 0.84) <0.0001 0.43 0% 
 AA + CA versus CC  0.66 (0.55, 0.77) <0.00001 0.48 1% 
 AA versus CA + CC  0.70 (0.60, 0.82) <0.0001 0.93 0% 
−137G/C C versus G 0.83 (0.62, 1.10) 0.20 0.003 72% 
 CC versus GG  0.75 (0.55, 1.03) 0.08 0.43 0% 
 GC versus GG  0.82 (0.57, 1.16) 0.26 0.005 70% 
 CC + GC versus GG  0.81 (0.57, 1.14) 0.23 0.003 73% 
 CC versus GC + GG  0.84 (0.64, 1.11) 0.21 0.58 0% 
Test of associationTest of heterogeneity
SNPsComparisonsNOR (95% CI)PPhI2Model
IL-1 IL-1α −899C/T T versus C 1.69 (1.33, 2.14) <0.0001 <0.0001 82% 
 TT versus CC  2.32 (1.34, 3.99) 0.002 0.0007 70% 
 CT versus CC  1.66 (1.44, 1.91) <0.00001 0.07 45% 
 TT + CT versus CC  1.89 (1.46, 2.44) <0.00001 0.003 65% 
 TT versus CT + CC  1.76 (1.18, 2.64) 0.006 0.0009 70% 
IL-1β −511C/T T versus C 13 1.11 (0.91, 1.35) 0.32 <0.0001 85% 
 TT versus CC  1.27 (0.88, 1.84) 0.21 <0.0001 80% 
 CT versus CC  1.04 (0.84, 1.29) 0.72 0.0001 69% 
 TT + CT versus CC  1.09 (0.85, 1.40) 0.51 <0.0001 80% 
 TT versus CT + CC  1.23 (0.93, 1.62) 0.14 <0.0001 71% 
IL-1β +3953C/T T versus C 1.24 (1.00, 1.54) 0.05 0.09 50% 
 TT versus CC  1.47 (0.83, 2.60) 0.19 0.12 48% 
 CT versus CC  1.21 (0.93, 1.57) 0.16 0.40 1% 
 TT + CT versus CC  1.24 (0.97, 1.60) 0.09 0.29 20% 
 TT versus CT + CC  1.43 (0.82, 2.51) 0.21 0.11 50% 
IL-6        
−174G/C C versus G 18 1.12 (0.88, 1.43) 0.37 <0.0001 86% 
 CC versus GG  1.13 (0.68, 1.88) 0.64 <0.0001 85% 
 GC versus GG  1.04 (0.92, 1.17) 0.56 0.02 47% 
 CC + GC versus GG  1.09 (0.85, 1.41) 0.48 <0.0001 75% 
 CC versus GC + GG  1.11 (0.71, 1.72) 0.65 <0.0001 83% 
−572C/G G versus C 1.31 (1.03, 1.66) 0.03 <0.0001 84% 
 GG versus CC  1.48 (0.88, 2.48) 0.14 0.006 64% 
 CG versus CC  1.38 (1.04, 1.83) 0.03 <0.0001 82% 
 GG + CG versus CC  1.40 (1.05, 1.88) 0.02 <0.0001 84% 
 GG versus CG + CC  1.28 (0.81, 2.02) 0.29 0.03 55% 
IL-10        
−819C/T T versus C 0.93 (0.80, 1.09) 0.38 0.64 0% 
 TT versus CC  0.97 (0.71, 1.33) 0.86 0.34 12% 
 CT versus CC  0.91 (0.54, 1.52) 0.71 0.03 62% 
 TT + CT versus CC  0.93 (0.70, 1.22) 0.59 0.19 35% 
 TT versus CT + CC  0.92 (0.75, 1.13) 0.42 0.56 0% 
−1082A/G G versus A 10 0.76 (0.57, 1.02) 0.07 <0.0001 82% 
 GG versus AA  0.78 (0.46, 1.34) 0.37 0.0003 74% 
 AG versus AA  0.76 (0.54, 1.07) 0.12 0.004 63% 
 GG + AG versus AA  0.74 (0.52, 1.05) 0.09 0.0004 70% 
 GG versus AG + AA  0.80 (0.51, 1.24) 0.31 <0.0001 80% 
IL-18        
−607C/A A versus C 0.76 (0.69, 0.84) <0.00001 0.76 0% 
 AA versus CC  0.56 (0.45, 0.68) <0.00001 0.68 0% 
 CA versus CC  0.71 (0.59, 0.84) <0.0001 0.43 0% 
 AA + CA versus CC  0.66 (0.55, 0.77) <0.00001 0.48 1% 
 AA versus CA + CC  0.70 (0.60, 0.82) <0.0001 0.93 0% 
−137G/C C versus G 0.83 (0.62, 1.10) 0.20 0.003 72% 
 CC versus GG  0.75 (0.55, 1.03) 0.08 0.43 0% 
 GC versus GG  0.82 (0.57, 1.16) 0.26 0.005 70% 
 CC + GC versus GG  0.81 (0.57, 1.14) 0.23 0.003 73% 
 CC versus GC + GG  0.84 (0.64, 1.11) 0.21 0.58 0% 

IL-1

For IL-1α −899C/T polymorphism, 9 articles included 2933 cerebral infarction patients and 3554 controls. The frequency of T allele was shown to be higher in cases than that in controls (53.5% versus 43.7%), and our result identified that IL-1α −899C/T polymorphism was associated with cerebral infarction risk under each genetic models (T versus C: OR=1.69, 95% CI=1.33–2.14, P<0.0001; TT versus CC: OR=2.32, 95% CI=1.34–3.99, P=0.002; CT versus CC: OR=1.66, 95% CI=1.44–1.91, P<0.00001; TT + CT versus CC: OR=1.89, 95% CI=1.46–2.44, P<0.00001; TT versus CT + CC: OR=1.76, 95% CI=1.18–2.64, P=0.006) as shown in Figure 2. 

Meta-analysis of the relationship between the IL-1α −899C/T polymorphism and cerebral infarction risk under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).
Figure 2
Meta-analysis of the relationship between the IL-1α −899C/T polymorphism and cerebral infarction risk under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).
Figure 2
Meta-analysis of the relationship between the IL-1α −899C/T polymorphism and cerebral infarction risk under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).

For IL-1β −511C/T polymorphism, there were 3271 cerebral infarction cases and 3619 controls from 13 articles. We did not detect a significant association between IL-1β −511C/T polymorphism and cerebral infarction susceptibility under any genetic models in the random-effect model (Table 3).

For IL-1β +3953C/T polymorphism, 5 articles contained 725 patients and 1353 controls. Our result found that there was no positive relationship between IL-1β +3953C/T polymorphism and cerebral infarction risk in the fixed-effect model as well (Table 3).

IL-6

For IL-6 −174G/C polymorphism, 18 articles contained 3369 patients and 3795 controls. Our result did not find a significant relationship between IL-6 −174G/C polymorphism and cerebral infarction occurrence under any genetic models (Table 3). Subgroup analysis by ethnicity showed that this genetic variant was associated with increased the risk to cerebral infarction only in Asians (C versus G: OR=1.65, 95% CI=1.19–2.29, P=0.003; CC versus GG: OR=2.18, 95% CI=1.29–3.65, P=0.003; GC versus GG: OR=1.26, 95% CI=1.04–1.53, P=0.02; CC + GC versus GG: OR=1.45, 95% CI=1.21–1.73, P<0.0001; CC versus GC + GG: OR=2.04, 95% CI=1.22–3.40, P=0.007) as shown in Figure 3. 

Forest plot of the relative strength of the association between IL-6 −174G/C polymorphism and cerebral infarction risk in Asians under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).
Figure 3
Forest plot of the relative strength of the association between IL-6 −174G/C polymorphism and cerebral infarction risk in Asians under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).
Figure 3
Forest plot of the relative strength of the association between IL-6 −174G/C polymorphism and cerebral infarction risk in Asians under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).

For IL-6 −572C/G polymorphism, 8 articles contained 2547 patients and 3958 controls. Our result found that IL-6 −572C/G polymorphism was positively correlated with cerebral infarction risk under the allelic model (G versus C: OR=1.31, 95% CI=1.03–1.66, P=0.03), heterogeneity model (CG versus CC: OR =1.38, 95% CI=1.04–1.83, P=0.03) and dominant model (GG + CG versus CC: OR=1.40, 95% CI=1.05–1.88, P=0.02) in the random-effect model as shown in Figure 4. 

Meta-analysis of correlation of IL-6 −572C/G polymorphism in cerebral infarction risk under the allelic model (A: G versus C), heterogeneity model (B: CG versus CC) and dominant model (C: GG + CG versus CC) in the random-effect model.
Figure 4
Meta-analysis of correlation of IL-6 −572C/G polymorphism in cerebral infarction risk under the allelic model (A: G versus C), heterogeneity model (B: CG versus CC) and dominant model (C: GG + CG versus CC) in the random-effect model.
Figure 4
Meta-analysis of correlation of IL-6 −572C/G polymorphism in cerebral infarction risk under the allelic model (A: G versus C), heterogeneity model (B: CG versus CC) and dominant model (C: GG + CG versus CC) in the random-effect model.

IL-10

For IL-10 −819C/T mutation, 5 articles included 930 patients and 646 controls. Our result found no significant association between this genetic variant and cerebral infarction risk under any comparison models as shown in Table 3. 

For IL-10 −1082A/G polymorphism, 2085 cases and 1785 controls from 10 relevant articles were screened out. This SNP was not associated with increased the susceptibility of cerebral infarction under each genetic models as well (Table 3). Subgroup analysis by ethnicity showed that IL-10 −1082A/G polymorphism was significantly associated with increased the cerebral infarction risk under the allelic model (OR=0.68, 95% CI=0.46–0.99, P=0.04) and heterologous model (OR=0.74, 95% CI=0.60–0.92, P=0.006) as shown in Figure 5. 

Forest plot of the association between IL-10 −1082A/G polymorphism and cerebral infarction risk under the allelic model (A) and heterologous model (B).
Figure 5
Forest plot of the association between IL-10 −1082A/G polymorphism and cerebral infarction risk under the allelic model (A) and heterologous model (B).
Figure 5
Forest plot of the association between IL-10 −1082A/G polymorphism and cerebral infarction risk under the allelic model (A) and heterologous model (B).

IL-18

For IL-18 −607C/A polymorphism, 6 articles contained 1793 cerebral infarction patients and 1661 healthy controls. No significant heterogeneity was detected, and the fixed-effect model was used. Our result found that the frequency of A allele was a little higher in controls than that in patients (55.0% versus 48.1%), but the A allele of IL-18 −607C/A polymorphism was associated with increased the risk of cerebral infarction (A versus C: OR=0.76, 95% CI=0.69–0.84, P<0.00001). This statistically significant was also observed in other genetic models (AA versus CC: OR=0.56, 95% CI=0.45–0.68, P<0.00001; CA versus CC: OR=0.71, 95% CI=0.59–0.84, P<0.0001; AA + CA versus CC: OR=0.66, 95% CI=0.55–0.77, P<0.00001; AA versus CA + CC: OR=0.70, 95% CI=0.60–0.82, P<0.0001). Figure 6 showed the result of IL-18 −607C/A polymorphism in cerebral infarction risk.

Forest plots for association between IL-18 −607C/A polymorphism and cerebral infarction risk under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).
Figure 6
Forest plots for association between IL-18 −607C/A polymorphism and cerebral infarction risk under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).
Figure 6
Forest plots for association between IL-18 −607C/A polymorphism and cerebral infarction risk under the allelic model (A), homologous model (B), heterogeneous model (C), dominant model (D) and recessive model (E).

For IL-18 −137G/C polymorphism, five articles included 1355 cases and 1245 controls. Our result found that IL-18 −137G/C polymorphism was not associated with cerebral infarction risk under any genetic comparison models (Table 3).

Sensitivity analysis and publication bias

We successively omitted each single study respectively to confirm whether each included study affect the overall results. Our result found that the pooled ORs were not significantly changed. The funnel plots were used to evaluate the publication bias. All the plots were found to be roughly symmetrical, indicating no publication bias presented as shown in Figure 7. However, visual inspection of funnel plots did not guarantee that publication bias was absolutely absent.

Funnel plot of IL-1α −899C/T (CT versus CC) and IL-6 −174G/C (GC versus GG) polymorphisms in cerebral infarction.

Figure 7
Funnel plot of IL-1α −899C/T (CT versus CC) and IL-6 −174G/C (GC versus GG) polymorphisms in cerebral infarction.
Figure 7
Funnel plot of IL-1α −899C/T (CT versus CC) and IL-6 −174G/C (GC versus GG) polymorphisms in cerebral infarction.

DISCUSSION

In this meta-analysis, we totally identified 55 relevant articles. Our results found that polymorphisms of IL-1α −899C/T and IL-18 −607C/A (under all the genetic models), and IL-6 −572C/G (under the allelic model, heterogeneity model and dominant model) were associated with increased the risk of cerebral infarction. Other genetic polymorphisms were not related with cerebral infarction susceptibility under any genetic models. Subgroup analysis by ethnicity showed that IL-6 −174G/C polymorphism (under all the five models) and IL-10 −1082A/G polymorphism (under the allelic model and heterologous model) were significantly associated with increased the cerebral infarction risk in Asians. This may be due to the higher frequency of C allele of IL-6 −174G/C and G allele of IL-10 −1082A/G in Asian populations. Our results were consistent with previous meta-analysis conducted by Jin et al. [84] and Yin et al. [85] which showed that IL-10 −1082 A/G polymorphism was associated with ischaemic stroke susceptibility in Asians, not consistent with the results from the studies of Kumar et al. [86] and Jin et al. [87] which showed that IL-6 −174G/C and −572C/G polymorphisms were not be associated with an increased susceptibility to ischaemic stroke, and Ye et al. [88] which inferred that IL-1β −511C/T polymorphism might be moderately associated with increased risk of ischaemic stroke.

Cerebral infarction is a complex vascular and metabolic process leading to neuronal death, and the loss of blood supply results in the death of that area of tissue [89]. The mechanisms for clinical deterioration in patients with ischaemic stroke are not completely understood. Interleukins are a kind of immunomodulating agents. They not only provide communication between immune cells, but also play a role in signalling the brain to produce neurochemical, neuroendocrine, neuroimmune and behavioural changes [90]. Several cytokines are released early after the onset of brain ischaemia, and studies have shown that IL-6 participated in the acute-phase response that follows focal cerebral ischaemia, and its levels on admission are associated with early clinical deterioration [91]. Furthermore, exploring these pathophysiological mechanisms underlying ischaemic tissue damage may direct rational drug design in the therapeutic treatment of stroke [92].

A growing body of evidence has indicated an important role of inflammatory cytokines in the pathogenesis of cerebral lesion following stroke [93]. They are critical to the pathogenesis of tissue damage in cerebral infarction [92]. IL-1 was shown to play a systemic inflammation role in acute brain injury [94]. Elevated IL-4 level in the human serum may be an important factor in cerebral infarction during the acute stage [95]. Increasing the serum IL-6 and IL-8 levels may be related with the occurrence and development of acute cerebral infarction [96]. Elevated IL-8 may contribute to stroke pathophysiology by activating polymorphonuclear leucocyte activation early after ischaemia [97]. IL-18 is involved in stroke-induced inflammation and that initial serum IL-18 levels may be predictive of stroke outcome [98].

Genetic polymorphisms may influence the expression level of ILs, which in turn may be associated with cerebral infarction. Analysis of genetic variation within genes coding for inflammatory mediators can offer some advantage compared with analyses of the plasma protein levels. Olsson et al. [99] showed a relationship between IL-1 receptor antagonist polymorphism and overall ischaemic stroke. Tong et al. [100] found that IL-4 variable number of tandem repeats polymorphism might influence the ischaemic stroke susceptibility in the Chinese Uyghur population. Luo et al. [101] demonstrated that the IL-8+781C/T polymorphism was associated with neurological recovery at the acute stage of atherosclerotic cerebral infarction in the Han Chinese population, and the patients with the CT genotype recovered better than those with other genotypes. Guo et al. [102] identified that genetic variation of rs4742 170 in IL33 is significantly associated with the developing of ischaemic stroke.

Several limitations were presented in this meta-analysis. Firstly, there was significant heterogeneity among included studies, which may affect the precision of outcome. Secondly, most of the included studies were conducted in Asian population, whereas other population should be included in the future analysis. Thirdly, due to lacking the detailed information, we could not perform a precise analysis by adjusting potentially suspected factors such as age, gender, smoking status and environmental factors. Lastly, the interaction of gene–gene and gene–environment should be considered.

In conclusions, our results suggested that polymorphisms of IL-1α −899C/T, IL-6 −572C/G and IL-18 −607C/A were positive correlated with increased the risk of cerebral infarction. Subgroup analysis by ethnicity showed that polymorphisms of IL-6 −174G/C and IL-10 −1082A/G were significantly associated with cerebral infarction risk in Asians. Future analysis with well-designed studies and large sample size are still needed to further investigate the association of polymorphisms in ILs and cerebral infarction.

AUTHOR CONTRIBUTION

Heng Yang conceived and designed the entire study; Niannian Fan and Jiantao Wang performed the literature research and analysed the data; Yili Deng and Jie Zhu were responsible for data acquisition; Jing Mei and Yao Chen performed statistical analysis; Niannian Fan, Jiantao Wang, Yili Deng and Jie Zhu drafted the paper. Heng Yang revised the whole paper. All authors read and agreed with the final version of this manuscript.

Abbreviations

     
  • CI

    confidence interval

  •  
  • IL

    interleukin

  •  
  • OR

    odds ratio

  •  
  • SNP

    single nucleotide polymorphism

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Author notes

1

Equal contributors, they are co-first authors.

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