| Literature DB >> 35441611 |
Alice Giotta Lucifero1, Matias Baldoncini2, Ilaria Brambilla3, Monica Rutigliano4, Gabriele Savioli5, Renato Galzio6, Alvaro Campero7, Michael T Lawton8, Sabino Luzzi9.
Abstract
Introduction The interleukin-6 (IL-6), a proinflammatory cytokine, supports the adaptive immune response and regulates inflammatory processes. The -174 G>C and -572 G>C promoter polymorphisms of the IL-6 gene take part in the pathogenesis of intracranial aneurysms (IAs) and influence the clinical presentation of subarachnoid hemorrhage. This meta-analysis purposes to evaluate whether and which IL-6 allelic variations are related to a risk of IAs formation. Methods A PRISMA-based literature search was performed on the PubMed/Medline and Web of Science databases. The keywords used were "interleukin-6," "IL-6," "polymorphism," "interleukin-6 genotype," combined with "intracranial aneurysms" and "subarachnoid hemorrhage." Only human case-control studies, with a study (IAs) and a control group, written in English, and published in the last 15 years were selected. A meta-analysis was performed, estimating odds ratios and 95% confidence intervals in fixed- or random-effects models, as applicable. Statistical analysis was conducted with RevMan 5.0 software. Results 9 studies were eligible. No associations were found between -174 G>C polymorphisms and IAs susceptibility. Notable results were reported by the analysis of -572G>C polymorphisms. -572GG/GC/CC genotypes were strongly related to IAs occurrence with a statistical significance of p=0.03, p=0.0009, and p=0.00001, respectively. Conclusion A higher incidence of -572G>C promoter polymorphisms were demonstrated in the IAs group, highlighting the pivotal role of inflammatory genes in the natural history of brain aneurysms. Additional studies are required considering the racial heterogenicity and the need to widen the population sample.Entities:
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Year: 2022 PMID: 35441611 PMCID: PMC9179066 DOI: 10.23750/abm.v92iS4.12669
Source DB: PubMed Journal: Acta Biomed ISSN: 0392-4203
Figure 1.PRISMA flow diagram on the meta-analysis selection process
Overview of Data reported in the Literature about IL-6 Gene Polymorphisms and IAs
| Author, Year | Study type | Country | Timeframe | № of patients | Age | Gender | Genotype | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| IAs Group | Control Group | IAs Group (average y-o) | Control Group (average y-o) | IAs Group [№of male (%)] | Control Group [№ of male (%)] | Polymorphism | Allele | IAs Group (№ of patients) | Control Group (№ofpatients) | NOS | |||||
| Morgan et al., 2006 ( | POS | UK | 2002-2003 | 91 | 2720 | 55 | 56 | 36 ( | 2720 ( | -174G>C | GG | 40 | 867 | 6 | |
| GC | 40 | 1358 | |||||||||||||
| CC | 6 | 495 | |||||||||||||
| -572G>C | GG | 79 | 2359 | ||||||||||||
| GC | 8 | 244 | |||||||||||||
| CC | 4 | 9 | |||||||||||||
| Fontanellaet al, 2008 ( | POS | Italy | 2003-2006 | 179 | 156 | 53.7 | 53.7 | 58 ( | 50 ( | -174G>C | GG | 78 | 66 | 8 | |
| GC | 86 | 71 | |||||||||||||
| CC | 15 | 19 | |||||||||||||
| -572G>C | GG | 149 | 131 | ||||||||||||
| GC | 26 | 23 | |||||||||||||
| CC | 4 | 2 | |||||||||||||
| Sun et al, 2008 ( | ROS | China | 200S-2007 | 240 | 240 | 45.2 | 41.8 | 104 ( | 116 ( | -572G>C | GG | 59 | 9 | 6 | |
| GC | 130 | 82 | |||||||||||||
| CC | 51 | 149 | |||||||||||||
| Zhang et al, 2011 ( | POS | China | 2006-2008 | 182 | 182 | 36 | 33 | 103 ( | 95 ( | -572G>C | GG | 145 | 165 | 6 | |
| GC | 32 | 16 | |||||||||||||
| CC | 5 | 1 | |||||||||||||
| Pera et al, 2012 ( | POS | Poland | 2002-2009 | 276 | 581 | 50.5 | 56 | 120 ( | 274 ( | -174G>C | GG | 82 | 186 | 6 | |
| GC | 138 | 275 | |||||||||||||
| CC | 56 | 120 | |||||||||||||
| Liu et al, 2012 ( | ROS | China | 2012 | 220 | 220 | 47.4 | 45.6 | 95 ( | 103 ( | -572G>C | GG | 33 | 11 | 7 | |
| GC | 66 | 77 | |||||||||||||
| CC | 121 | 132 | |||||||||||||
| Sathyan et al, 201S ( | ROS | India | 2014 | 220 | 2S0 | 51.2 | NA | 123 ( | NA ■ | -174G>C | GG | 144 | 153 | 6 | |
| GC | 63 | 80 | |||||||||||||
| CC | 8 | 11 | |||||||||||||
| -572G>C | GG | 57 | 81 | ||||||||||||
| GC | 126 | 111 | |||||||||||||
| CC | 37 | 52 | |||||||||||||
| Bayri et al, 201S ( | POS | Turkey | 201S | 120 | 120 | NA | NA | NA | NA ■ | -174G>C | GG | 72 | 66 | 6 | |
| GC | 36 | 42 | |||||||||||||
| CC | 12 | 12 | |||||||||||||
| -572G>C | GG | 94 | 83 | ||||||||||||
| GC | 24 | 33 | |||||||||||||
| CC | 2 | 4 | |||||||||||||
| Xu et al, 2021 ( | POS | China | 2016-2020 | 384 | 384 | 57.1 | 66.5 | 117 ( | 117( | -174G>C | GG | 0 | 0 | 6 | |
| GC | 0 | 0 | |||||||||||||
| CC | 384 | 384 | |||||||||||||
| -572G>C | GG | 17 | 18 | ||||||||||||
| GC | 137 | 141 | |||||||||||||
| CC | 230 | 225 | |||||||||||||
Figure 2.Forest plot for -174GG polymorphism
Figure 4.Forest plot for -174CC polymorphism
Figure 5.Forest plot for -572GG polymorphism. (A) Fixed and (B) random model.
Figure 7.Forest plot for -572CC polymorphism. (A) Fixed and (B) random model
Figure 8.Funnel plot for -174G>C polymorphisms
Figure 9.Funnel plot for -572G>C polymorphisms