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 [18–20]. 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
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 [29,31–39], six in Caucasian [40–45] 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 [46–53] and four in English [54–57]) were conducted in Asian and 10 in Caucasian [40,58–66]. 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 [61,69–75]) 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.
–, 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 age . | Sample size . | . | ||
---|---|---|---|---|---|---|---|---|
First author . | Year . | Country . | Ethnicity . | Cases . | Controls . | Cases . | Controls . | Genotyping 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 age . | Sample size . | . | ||
---|---|---|---|---|---|---|---|---|
First author . | Year . | Country . | Ethnicity . | Cases . | Controls . | Cases . | Controls . | Genotyping 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 |
First author . | . | . | . | Cases . | . | . | . | Controls . | . | . | HWE . |
---|---|---|---|---|---|---|---|---|---|---|---|
IL-1 | |||||||||||
IL-1α −899C/T | CC | CT | TT | C | T | CC | CT | TT | C | T | |
Um JY | 292 | 68 | 3 | 652 | 74 | 554 | 81 | 5 | 1189 | 91 | 0.57 |
Wei YS | 115 | 37 | 3 | 267 | 43 | 146 | 23 | 1 | 315 | 25 | 0.99 |
Zhang GZ | 84 | 23 | 3 | 191 | 29 | 97 | 13 | 0 | 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 | 2 | 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 | C | T | CC | CT | TT | C | T | |
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 | 9 | 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 | 7 | 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 | 6 | 101 | 29 | 87 | 39 | 4 | 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 | C | T | CC | CT | TT | C | T | |
Um JY | 332 | 30 | 1 | 694 | 32 | 593 | 46 | 1 | 1232 | 48 | 0.99 |
Blading J | 66 | 35 | 4 | 167 | 43 | 240 | 125 | 24 | 605 | 173 | 0.38 |
Zhang GZ | 97 | 13 | 0 | 207 | 13 | 106 | 4 | 0 | 216 | 4 | 0.98 |
Dong RF | 52 | 24 | 6 | 128 | 36 | 57 | 20 | 5 | 134 | 30 | 0.25 |
Ma XL | 34 | 19 | 12 | 87 | 43 | 82 | 42 | 8 | 206 | 58 | 0.71 |
IL-6 | |||||||||||
−174G/C | GG | GC | CC | G | C | GG | GC | CC | G | C | |
Revilla M | 37 | 39 | 6 | 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 | 9 | 142 | 68 | 0.67 |
Song XJ | 54 | 7 | 5 | 115 | 17 | 93 | 4 | 1 | 190 | 6 | 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 | 0 | 189 | 35 | 156 | 52 | 4 | 364 | 60 | 0.99 |
Sun Y | 32 | 20 | 40 | 84 | 100 | 59 | 28 | 23 | 146 | 74 | 0.000 |
Liu DF | 138 | 19 | 0 | 295 | 19 | 153 | 10 | 0 | 316 | 10 | 0.92 |
Tong YQ | 747 | 1 | 0 | 1495 | 1 | 743 | 5 | 0 | 1491 | 5 | 0.99 |
Balcerzyk A | 21 | 43 | 16 | 85 | 75 | 40 | 76 | 22 | 156 | 120 | 0.37 |
Chakraborty B | 57 | 35 | 8 | 149 | 51 | 73 | 39 | 8 | 185 | 55 | 0.68 |
Tuttolomondo A | 40 | 46 | 10 | 126 | 66 | 14 | 33 | 1 | 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 | 4 | 22 | 16 | 30 | 54 | 14 | 21 | 13 | 49 | 47 | 0.69 |
−572C/G | CC | CG | GG | C | G | CC | CG | GG | C | G | |
Wei YS | 84 | 71 | 5 | 239 | 81 | 116 | 57 | 2 | 289 | 61 | 0.22 |
Yamada Y | 412 | 199 | 25 | 1023 | 249 | 1138 | 760 | 112 | 3036 | 984 | 0.60 |
Liang J | 103 | 89 | 7 | 295 | 103 | 127 | 66 | 3 | 320 | 72 | 0.23 |
Liu DF | 34 | 33 | 3 | 101 | 36 | 51 | 24 | 5 | 126 | 34 | 0.65 |
Tong YQ | 373 | 326 | 49 | 1072 | 424 | 424 | 267 | 57 | 1115 | 381 | 0.26 |
Pan Y | 55 | 44 | 7 | 154 | 58 | 59 | 32 | 1 | 150 | 34 | 0.33 |
Xiao H | 103 | 89 | 7 | 295 | 103 | 127 | 66 | 3 | 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 | C | T | CC | CT | TT | C | T | |
Zhang GZ | 28 | 90 | 86 | 146 | 262 | 27 | 48 | 56 | 102 | 160 | 0.03 |
Jin L | 12 | 82 | 95 | 106 | 272 | 7 | 37 | 48 | 51 | 133 | 0.99 |
Tuttolomondo A | 63 | 14 | 19 | 140 | 52 | 26 | 17 | 5 | 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 | A | G | AA | AG | GG | A | G | |
Zhang GZ | 202 | 2 | 0 | 406 | 2 | 120 | 11 | 0 | 251 | 11 | 0.88 |
Munshi A | 92 | 241 | 147 | 425 | 535 | 63 | 218 | 189 | 344 | 596 | 0.99 |
Jin L | 161 | 27 | 1 | 349 | 29 | 78 | 12 | 2 | 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 | 0 | 334 | 28 | 83 | 32 | 0 | 198 | 32 | 0.22 |
Kumar P | 11 | 77 | 162 | 99 | 401 | 4 | 37 | 209 | 45 | 455 | 0.31 |
Ozkan A | 11 | 26 | 5 | 48 | 36 | 19 | 18 | 11 | 56 | 40 | 0.28 |
IL-18 | |||||||||||
−607C/A | CC | CA | AA | C | A | CC | CA | AA | C | A | |
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 | G | C | GG | GC | CC | G | C | |
Li XQ | 76 | 19 | 3 | 171 | 25 | 62 | 33 | 5 | 157 | 43 | 0.98 |
Wang YJ | 174 | 42 | 2 | 390 | 46 | 146 | 66 | 6 | 358 | 78 | 0.90 |
Ren DL | 161 | 29 | 3 | 351 | 35 | 96 | 23 | 1 | 215 | 25 | 0.96 |
Wei GY | 91 | 54 | 8 | 236 | 70 | 85 | 25 | 4 | 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 author . | . | . | . | Cases . | . | . | . | Controls . | . | . | HWE . |
---|---|---|---|---|---|---|---|---|---|---|---|
IL-1 | |||||||||||
IL-1α −899C/T | CC | CT | TT | C | T | CC | CT | TT | C | T | |
Um JY | 292 | 68 | 3 | 652 | 74 | 554 | 81 | 5 | 1189 | 91 | 0.57 |
Wei YS | 115 | 37 | 3 | 267 | 43 | 146 | 23 | 1 | 315 | 25 | 0.99 |
Zhang GZ | 84 | 23 | 3 | 191 | 29 | 97 | 13 | 0 | 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 | 2 | 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 | C | T | CC | CT | TT | C | T | |
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 | 9 | 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 | 7 | 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 | 6 | 101 | 29 | 87 | 39 | 4 | 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 | C | T | CC | CT | TT | C | T | |
Um JY | 332 | 30 | 1 | 694 | 32 | 593 | 46 | 1 | 1232 | 48 | 0.99 |
Blading J | 66 | 35 | 4 | 167 | 43 | 240 | 125 | 24 | 605 | 173 | 0.38 |
Zhang GZ | 97 | 13 | 0 | 207 | 13 | 106 | 4 | 0 | 216 | 4 | 0.98 |
Dong RF | 52 | 24 | 6 | 128 | 36 | 57 | 20 | 5 | 134 | 30 | 0.25 |
Ma XL | 34 | 19 | 12 | 87 | 43 | 82 | 42 | 8 | 206 | 58 | 0.71 |
IL-6 | |||||||||||
−174G/C | GG | GC | CC | G | C | GG | GC | CC | G | C | |
Revilla M | 37 | 39 | 6 | 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 | 9 | 142 | 68 | 0.67 |
Song XJ | 54 | 7 | 5 | 115 | 17 | 93 | 4 | 1 | 190 | 6 | 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 | 0 | 189 | 35 | 156 | 52 | 4 | 364 | 60 | 0.99 |
Sun Y | 32 | 20 | 40 | 84 | 100 | 59 | 28 | 23 | 146 | 74 | 0.000 |
Liu DF | 138 | 19 | 0 | 295 | 19 | 153 | 10 | 0 | 316 | 10 | 0.92 |
Tong YQ | 747 | 1 | 0 | 1495 | 1 | 743 | 5 | 0 | 1491 | 5 | 0.99 |
Balcerzyk A | 21 | 43 | 16 | 85 | 75 | 40 | 76 | 22 | 156 | 120 | 0.37 |
Chakraborty B | 57 | 35 | 8 | 149 | 51 | 73 | 39 | 8 | 185 | 55 | 0.68 |
Tuttolomondo A | 40 | 46 | 10 | 126 | 66 | 14 | 33 | 1 | 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 | 4 | 22 | 16 | 30 | 54 | 14 | 21 | 13 | 49 | 47 | 0.69 |
−572C/G | CC | CG | GG | C | G | CC | CG | GG | C | G | |
Wei YS | 84 | 71 | 5 | 239 | 81 | 116 | 57 | 2 | 289 | 61 | 0.22 |
Yamada Y | 412 | 199 | 25 | 1023 | 249 | 1138 | 760 | 112 | 3036 | 984 | 0.60 |
Liang J | 103 | 89 | 7 | 295 | 103 | 127 | 66 | 3 | 320 | 72 | 0.23 |
Liu DF | 34 | 33 | 3 | 101 | 36 | 51 | 24 | 5 | 126 | 34 | 0.65 |
Tong YQ | 373 | 326 | 49 | 1072 | 424 | 424 | 267 | 57 | 1115 | 381 | 0.26 |
Pan Y | 55 | 44 | 7 | 154 | 58 | 59 | 32 | 1 | 150 | 34 | 0.33 |
Xiao H | 103 | 89 | 7 | 295 | 103 | 127 | 66 | 3 | 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 | C | T | CC | CT | TT | C | T | |
Zhang GZ | 28 | 90 | 86 | 146 | 262 | 27 | 48 | 56 | 102 | 160 | 0.03 |
Jin L | 12 | 82 | 95 | 106 | 272 | 7 | 37 | 48 | 51 | 133 | 0.99 |
Tuttolomondo A | 63 | 14 | 19 | 140 | 52 | 26 | 17 | 5 | 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 | A | G | AA | AG | GG | A | G | |
Zhang GZ | 202 | 2 | 0 | 406 | 2 | 120 | 11 | 0 | 251 | 11 | 0.88 |
Munshi A | 92 | 241 | 147 | 425 | 535 | 63 | 218 | 189 | 344 | 596 | 0.99 |
Jin L | 161 | 27 | 1 | 349 | 29 | 78 | 12 | 2 | 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 | 0 | 334 | 28 | 83 | 32 | 0 | 198 | 32 | 0.22 |
Kumar P | 11 | 77 | 162 | 99 | 401 | 4 | 37 | 209 | 45 | 455 | 0.31 |
Ozkan A | 11 | 26 | 5 | 48 | 36 | 19 | 18 | 11 | 56 | 40 | 0.28 |
IL-18 | |||||||||||
−607C/A | CC | CA | AA | C | A | CC | CA | AA | C | A | |
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 | G | C | GG | GC | CC | G | C | |
Li XQ | 76 | 19 | 3 | 171 | 25 | 62 | 33 | 5 | 157 | 43 | 0.98 |
Wang YJ | 174 | 42 | 2 | 390 | 46 | 146 | 66 | 6 | 358 | 78 | 0.90 |
Ren DL | 161 | 29 | 3 | 351 | 35 | 96 | 23 | 1 | 215 | 25 | 0.96 |
Wei GY | 91 | 54 | 8 | 236 | 70 | 85 | 25 | 4 | 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.
N, number of included studies; Ph, I2, test of heterogeneity; F, fixed-effect model; R, random-effect model.
. | . | . | Test of association . | Test of heterogeneity . | |||
---|---|---|---|---|---|---|---|
SNPs . | Comparisons . | N . | OR (95% CI) . | P . | Ph . | I2 . | Model . |
IL-1 IL-1α −899C/T | T versus C | 9 | 1.69 (1.33, 2.14) | <0.0001 | <0.0001 | 82% | R |
TT versus CC | 2.32 (1.34, 3.99) | 0.002 | 0.0007 | 70% | R | ||
CT versus CC | 1.66 (1.44, 1.91) | <0.00001 | 0.07 | 45% | F | ||
TT + CT versus CC | 1.89 (1.46, 2.44) | <0.00001 | 0.003 | 65% | R | ||
TT versus CT + CC | 1.76 (1.18, 2.64) | 0.006 | 0.0009 | 70% | R | ||
IL-1β −511C/T | T versus C | 13 | 1.11 (0.91, 1.35) | 0.32 | <0.0001 | 85% | R |
TT versus CC | 1.27 (0.88, 1.84) | 0.21 | <0.0001 | 80% | R | ||
CT versus CC | 1.04 (0.84, 1.29) | 0.72 | 0.0001 | 69% | R | ||
TT + CT versus CC | 1.09 (0.85, 1.40) | 0.51 | <0.0001 | 80% | R | ||
TT versus CT + CC | 1.23 (0.93, 1.62) | 0.14 | <0.0001 | 71% | R | ||
IL-1β +3953C/T | T versus C | 5 | 1.24 (1.00, 1.54) | 0.05 | 0.09 | 50% | F |
TT versus CC | 1.47 (0.83, 2.60) | 0.19 | 0.12 | 48% | F | ||
CT versus CC | 1.21 (0.93, 1.57) | 0.16 | 0.40 | 1% | F | ||
TT + CT versus CC | 1.24 (0.97, 1.60) | 0.09 | 0.29 | 20% | F | ||
TT versus CT + CC | 1.43 (0.82, 2.51) | 0.21 | 0.11 | 50% | F | ||
IL-6 | |||||||
−174G/C | C versus G | 18 | 1.12 (0.88, 1.43) | 0.37 | <0.0001 | 86% | R |
CC versus GG | 1.13 (0.68, 1.88) | 0.64 | <0.0001 | 85% | R | ||
GC versus GG | 1.04 (0.92, 1.17) | 0.56 | 0.02 | 47% | F | ||
CC + GC versus GG | 1.09 (0.85, 1.41) | 0.48 | <0.0001 | 75% | R | ||
CC versus GC + GG | 1.11 (0.71, 1.72) | 0.65 | <0.0001 | 83% | R | ||
−572C/G | G versus C | 8 | 1.31 (1.03, 1.66) | 0.03 | <0.0001 | 84% | R |
GG versus CC | 1.48 (0.88, 2.48) | 0.14 | 0.006 | 64% | R | ||
CG versus CC | 1.38 (1.04, 1.83) | 0.03 | <0.0001 | 82% | R | ||
GG + CG versus CC | 1.40 (1.05, 1.88) | 0.02 | <0.0001 | 84% | R | ||
GG versus CG + CC | 1.28 (0.81, 2.02) | 0.29 | 0.03 | 55% | R | ||
IL-10 | |||||||
−819C/T | T versus C | 5 | 0.93 (0.80, 1.09) | 0.38 | 0.64 | 0% | F |
TT versus CC | 0.97 (0.71, 1.33) | 0.86 | 0.34 | 12% | F | ||
CT versus CC | 0.91 (0.54, 1.52) | 0.71 | 0.03 | 62% | R | ||
TT + CT versus CC | 0.93 (0.70, 1.22) | 0.59 | 0.19 | 35% | F | ||
TT versus CT + CC | 0.92 (0.75, 1.13) | 0.42 | 0.56 | 0% | F | ||
−1082A/G | G versus A | 10 | 0.76 (0.57, 1.02) | 0.07 | <0.0001 | 82% | R |
GG versus AA | 0.78 (0.46, 1.34) | 0.37 | 0.0003 | 74% | R | ||
AG versus AA | 0.76 (0.54, 1.07) | 0.12 | 0.004 | 63% | R | ||
GG + AG versus AA | 0.74 (0.52, 1.05) | 0.09 | 0.0004 | 70% | R | ||
GG versus AG + AA | 0.80 (0.51, 1.24) | 0.31 | <0.0001 | 80% | R | ||
IL-18 | |||||||
−607C/A | A versus C | 6 | 0.76 (0.69, 0.84) | <0.00001 | 0.76 | 0% | F |
AA versus CC | 0.56 (0.45, 0.68) | <0.00001 | 0.68 | 0% | F | ||
CA versus CC | 0.71 (0.59, 0.84) | <0.0001 | 0.43 | 0% | F | ||
AA + CA versus CC | 0.66 (0.55, 0.77) | <0.00001 | 0.48 | 1% | F | ||
AA versus CA + CC | 0.70 (0.60, 0.82) | <0.0001 | 0.93 | 0% | F | ||
−137G/C | C versus G | 6 | 0.83 (0.62, 1.10) | 0.20 | 0.003 | 72% | R |
CC versus GG | 0.75 (0.55, 1.03) | 0.08 | 0.43 | 0% | F | ||
GC versus GG | 0.82 (0.57, 1.16) | 0.26 | 0.005 | 70% | R | ||
CC + GC versus GG | 0.81 (0.57, 1.14) | 0.23 | 0.003 | 73% | R | ||
CC versus GC + GG | 0.84 (0.64, 1.11) | 0.21 | 0.58 | 0% | F |
. | . | . | Test of association . | Test of heterogeneity . | |||
---|---|---|---|---|---|---|---|
SNPs . | Comparisons . | N . | OR (95% CI) . | P . | Ph . | I2 . | Model . |
IL-1 IL-1α −899C/T | T versus C | 9 | 1.69 (1.33, 2.14) | <0.0001 | <0.0001 | 82% | R |
TT versus CC | 2.32 (1.34, 3.99) | 0.002 | 0.0007 | 70% | R | ||
CT versus CC | 1.66 (1.44, 1.91) | <0.00001 | 0.07 | 45% | F | ||
TT + CT versus CC | 1.89 (1.46, 2.44) | <0.00001 | 0.003 | 65% | R | ||
TT versus CT + CC | 1.76 (1.18, 2.64) | 0.006 | 0.0009 | 70% | R | ||
IL-1β −511C/T | T versus C | 13 | 1.11 (0.91, 1.35) | 0.32 | <0.0001 | 85% | R |
TT versus CC | 1.27 (0.88, 1.84) | 0.21 | <0.0001 | 80% | R | ||
CT versus CC | 1.04 (0.84, 1.29) | 0.72 | 0.0001 | 69% | R | ||
TT + CT versus CC | 1.09 (0.85, 1.40) | 0.51 | <0.0001 | 80% | R | ||
TT versus CT + CC | 1.23 (0.93, 1.62) | 0.14 | <0.0001 | 71% | R | ||
IL-1β +3953C/T | T versus C | 5 | 1.24 (1.00, 1.54) | 0.05 | 0.09 | 50% | F |
TT versus CC | 1.47 (0.83, 2.60) | 0.19 | 0.12 | 48% | F | ||
CT versus CC | 1.21 (0.93, 1.57) | 0.16 | 0.40 | 1% | F | ||
TT + CT versus CC | 1.24 (0.97, 1.60) | 0.09 | 0.29 | 20% | F | ||
TT versus CT + CC | 1.43 (0.82, 2.51) | 0.21 | 0.11 | 50% | F | ||
IL-6 | |||||||
−174G/C | C versus G | 18 | 1.12 (0.88, 1.43) | 0.37 | <0.0001 | 86% | R |
CC versus GG | 1.13 (0.68, 1.88) | 0.64 | <0.0001 | 85% | R | ||
GC versus GG | 1.04 (0.92, 1.17) | 0.56 | 0.02 | 47% | F | ||
CC + GC versus GG | 1.09 (0.85, 1.41) | 0.48 | <0.0001 | 75% | R | ||
CC versus GC + GG | 1.11 (0.71, 1.72) | 0.65 | <0.0001 | 83% | R | ||
−572C/G | G versus C | 8 | 1.31 (1.03, 1.66) | 0.03 | <0.0001 | 84% | R |
GG versus CC | 1.48 (0.88, 2.48) | 0.14 | 0.006 | 64% | R | ||
CG versus CC | 1.38 (1.04, 1.83) | 0.03 | <0.0001 | 82% | R | ||
GG + CG versus CC | 1.40 (1.05, 1.88) | 0.02 | <0.0001 | 84% | R | ||
GG versus CG + CC | 1.28 (0.81, 2.02) | 0.29 | 0.03 | 55% | R | ||
IL-10 | |||||||
−819C/T | T versus C | 5 | 0.93 (0.80, 1.09) | 0.38 | 0.64 | 0% | F |
TT versus CC | 0.97 (0.71, 1.33) | 0.86 | 0.34 | 12% | F | ||
CT versus CC | 0.91 (0.54, 1.52) | 0.71 | 0.03 | 62% | R | ||
TT + CT versus CC | 0.93 (0.70, 1.22) | 0.59 | 0.19 | 35% | F | ||
TT versus CT + CC | 0.92 (0.75, 1.13) | 0.42 | 0.56 | 0% | F | ||
−1082A/G | G versus A | 10 | 0.76 (0.57, 1.02) | 0.07 | <0.0001 | 82% | R |
GG versus AA | 0.78 (0.46, 1.34) | 0.37 | 0.0003 | 74% | R | ||
AG versus AA | 0.76 (0.54, 1.07) | 0.12 | 0.004 | 63% | R | ||
GG + AG versus AA | 0.74 (0.52, 1.05) | 0.09 | 0.0004 | 70% | R | ||
GG versus AG + AA | 0.80 (0.51, 1.24) | 0.31 | <0.0001 | 80% | R | ||
IL-18 | |||||||
−607C/A | A versus C | 6 | 0.76 (0.69, 0.84) | <0.00001 | 0.76 | 0% | F |
AA versus CC | 0.56 (0.45, 0.68) | <0.00001 | 0.68 | 0% | F | ||
CA versus CC | 0.71 (0.59, 0.84) | <0.0001 | 0.43 | 0% | F | ||
AA + CA versus CC | 0.66 (0.55, 0.77) | <0.00001 | 0.48 | 1% | F | ||
AA versus CA + CC | 0.70 (0.60, 0.82) | <0.0001 | 0.93 | 0% | F | ||
−137G/C | C versus G | 6 | 0.83 (0.62, 1.10) | 0.20 | 0.003 | 72% | R |
CC versus GG | 0.75 (0.55, 1.03) | 0.08 | 0.43 | 0% | F | ||
GC versus GG | 0.82 (0.57, 1.16) | 0.26 | 0.005 | 70% | R | ||
CC + GC versus GG | 0.81 (0.57, 1.14) | 0.23 | 0.003 | 73% | R | ||
CC versus GC + GG | 0.84 (0.64, 1.11) | 0.21 | 0.58 | 0% | F |
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).
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).
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.
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).
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).
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.
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
References
Author notes
Equal contributors, they are co-first authors.