| Literature DB >> 32390362 |
Liming Hu1, Bingyang Li1, Xin Liao1, Junxia Yan2.
Abstract
PURPOSE: Inflammatory cytokines are thought to be involved in the pathogenesis of intracranial aneurysm (IA), although results among studies in the literature are inconsistent. This article sought to review studies on the associations among polymorphisms in inflammatory cytokine genes and IA risk and to provide recommendations for future research.Entities:
Keywords: Intracranial aneurysm; inflammatory cytokines; meta-analysis; polymorphism
Mesh:
Substances:
Year: 2020 PMID: 32390362 PMCID: PMC7214114 DOI: 10.3349/ymj.2020.61.5.391
Source DB: PubMed Journal: Yonsei Med J ISSN: 0513-5796 Impact factor: 2.759
Literature Search Strategy
| Web of Science | ||
|---|---|---|
| 1 | TS=(“intracranial aneurysm*”) OR TS=(“cerebral aneurysm*”) OR TS=(“subarachnoid hemorrhage*”) | 40547 |
| Databases=WOS, BCI, KJD, RSCI, SCIELO Timespan=All years | ||
| Search language=Auto | ||
| 2 | TS=(“inflammatory cytokine*”) OR TS=(“interleukin*”) OR TS=(“IL”) OR TS=(“tumor necrosis factor*”) OR TS=(“TNF*”) | 761024 |
| Databases=WOS, BCI, KJD, RSCI, SCIELO Timespan=All years | ||
| Search language=Auto | ||
| 3 | #2 AND #1 | 840 |
| Databases=WOS, BCI, KJD, RSCI, SCIELO Timespan=All years | ||
| Search language=Auto | ||
| 4 | #3 NOT TS=(animal) | 170 |
| Databases=WOS, BCI, KJD, RSCI, SCIELO Timespan=All years | ||
| Search language=Auto | ||
| 5 | #4 NOT (TS=”case report”) | 167 |
| Databases=WOS, BCI, KJD, RSCI, SCIELO Timespan=All years | ||
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| 6 | #5 NOT (TS=review) | 132 |
| Databases=WOS, BCI, KJD, RSCI, SCIELO Timespan=All years | ||
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Characteristics of Studies Included in the Meta-Analysis of Inflammatory Cytokine Gene Polymorphisms
| Gene | SNPs | Author and reference | Year | Country | Sample size | Mean age (yr) | Male (n, %) | Genotype* | Allele (M/m)† | OR (95% CI) | NOS | HWE | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Case | Control | Case | Control | Case | Control | Case | Control | Case | Control | Dominant model | Allelic model | |||||||
| rs1800629 | Borges, et al. | 2018 | Brazil | 33 | 81 | 54.0±9.0 | 52.0±6.0 | - | 38 (46.91) | 21/3/9 | 47/15/19 | 45/21 | 109/53 | 0.79 (0.34–1.82) | 0.96 (0.52–1.77) | 6 | 0.00 | |
| G>A | Sathyan, et al. | 2015 | India | 220 | 250 | 51.2±11.4 | - | 123 (55.91) | - | 185/41/0 | 192/51/1 | 411/41 | 435/53 | 0.82 (0.52–1.29) | 0.82 (0.53–1.26) | 6 | 0.21 | |
| Fontanella, et al. | 2007 | Italy | 171 | 144 | 54.1±14.0 | 53.4±14.2 | 56 (32.75) | 70 (48.61) | 136/30/5 | 92/50/2 | 302/40 | 234/54 | 0.46 (0.28–0.75) | 0.57 (0.37–0.89) | 7 | 0.09 | ||
| rs1800587 | Sathyan, et al. | 2015 | India | 220 | 250 | 51.2±11.4 | - | 123 (55.91) | - | 109/83/27 | 118/108/17 | 301/137 | 344/142 | 0.95 (0.66–1.37) | 0.91 (0.68–1.20) | 6 | 0.25 | |
| C>T | Fontanella, et al. | 2010 | Italy | 215 | 155 | 55.0±14.5 | 53.7±14.0 | 74 (34.42) | 50 (32.26) | 82/110/23 | 63/80/12 | 274/156 | 206/104 | 1.11 (0.73–1.69) | 1.13 (0.83–1.53) | 7 | 0.05 | |
| rs16944 | Sathyan, et al. | 2015 | India | 220 | 250 | 51.2±11.4 | - | 123 (55.91) | - | 84/101/38 | 90/115/39 | 269/177 | 295/193 | 0.97 (0.66–1.41) | 0.99 (0.76–1.29) | 6 | 0.82 | |
| C>T | Fontanella, et al. | 2010 | Italy | 215 | 155 | 55.0±14.5 | 53.7±14.0 | 74 (34.42) | 50 (32.26) | 94/88/33 | 64/68/23 | 276/154 | 196/114 | 0.91 (0.60–1.38) | 0.96 (0.71–1.30) | 7 | 0.48 | |
| Slowik, et al. | 2006 | Poland | 231 | 231 | 49.9±12.7 | 50.4±12.4 | 99 (42.86) | 99 (42.86) | 100/99/32 | 111/106/14 | 299/163 | 328/134 | 1.21 (0.84–1.75) | 1.33 (1.01–1.76) | 7 | 0.08 | ||
| rs1800795 | Sathyan, et al. | 2015 | India | 220 | 250 | 51.2±11.4 | - | 123 (55.91) | - | 144/63/8 | 153/80/11 | 351/79 | 386/102 | 0.83 (0.56–1.22) | 0.85 (0.61–1.18) | 6 | 0.90 | |
| G>C | Bayri, et al. | 2015 | Turkey | 120 | 120 | - | - | - | - | 72/36/12 | 66/42/12 | 180/60 | 174/66 | 0.81 (0.49–1.36) | 0.88 (0.59–1.32) | 6 | 0.18 | |
| Pera, et al. | 2012 | Poland | 276 | 581 | 50.5±12.7 | 56.0±17.7 | 120 (43.48) | 274 (47.16) | 82/138/56 | 186/275/120 | 302/250 | 647/515 | 1.11 (0.82–1.52) | 1.04 (0.85–1.28) | 6 | 0.32 | ||
| Fontanella, et al. | 2008 | Italy | 179 | 156 | 53.7±14.1 | 53.7±14.0 | 58 (32.40) | 50 (32.05) | 78/86/15 | 66/71/19 | 242/116 | 203/109 | 0.95 (0.62–1.47) | 0.89 (0.65–1.23) | 8 | 0.99 | ||
| Morgan, et al. | 2006 | UK | 91 | 2720 | 55 (24–80) | 56 (49–64) | 36 (40.0) | 2720 (100) | 40/40/6 | 867/1358/495 | 120/52 | 3092/2348 | 0.54 (0.35–0.83) | 0.57 (0.41–0.79) | 6 | 0.36 | ||
| rs1800796 | Sathyan, et al. | 2015 | India | 220 | 250 | 51.2±11.4 | - | 123 (55.91) | - | 57/126/37 | 81/111/52 | 240/200 | 273/215 | 1.42 (0.95–2.13) | 0.95 (0.73–1.23) | 6 | 0.23 | |
| G>C | Bayri, et al. | 2015 | Turkey | 120 | 120 | - | - | - | - | 94/24/2 | 83/33/4 | 212/28 | 199/41 | 0.62 (0.35–1.11) | 0.64 (0.38–1.08) | 6 | 0.75 | |
| Liu, et al. | 2012 | China | 220 | 220 | 47.4±11.3 | 45.6±10.7 | 95 (43.18) | 103 (46.82) | 33/66/121 | 11/77/132 | 132/308 | 99/341 | 0.30 (0.15–0.61) | 0.68 (0.50–0.92) | 7 | 0.96 | ||
| Zhang, et al. | 2011 | China | 182 | 182 | 36.0±4.2 | 33.0±4.5 | 103 (56.59) | 95 (52.20) | 145/32/5 | 165/16/1 | 322/42 | 346/18 | 2.48 (1.34–4.59) | 2.51 (1.41–4.45) | 6 | 0.38 | ||
| Fontanella, et al. | 2008 | Italy | 179 | 156 | 53.7±14.1 | 53.7±14.0 | 58 (32.40) | 50 (32.05) | 149/26/4 | 131/23/2 | 324/34 | 285/27 | 1.06 (0.59–1.89) | 1.11 (0.65–1.88) | 8 | 0.40 | ||
| Sun, et al. | 2008 | China | 240 | 240 | 45.2±11.7 | 41.8±9.0 | 104 (43.33) | 116 (48.33) | 59/130/51 | 9/82/149 | 248/232 | 100/380 | 0.12 (0.06–0.25) | 0.25 (0.19–0.33) | 6 | 0.58 | ||
| Morgan, et al. | 2006 | UK | 91 | 2720 | 55 (24–80) | 56 (49–64) | 36 (40.0) | 2720 (100) | 79/8/4 | 2359/244/9 | 166/16 | 4962/262 | 1.42 (0.76–2.64) | 1.83 (1.08–3.10) | 6 | 0.32 | ||
| rs3212227 | Sathyan, et al. | 2015 | India | 220 | 250 | 51.2±11.4 | - | 123 (55.91) | - | 88/96/36 | 83/115/31 | 272/168 | 281/177 | 0.85 (0.58–1.25) | 0.98 (0.75–1.28) | 6 | 0.37 | |
| A>C | Li, et al. | 2012 | China | 164 | 258 | 53.1±13.1 | 50.0±8.9 | 60 (36.59) | 108 (41.86) | 29/100/35 | 80/136/42 | 158/170 | 296/220 | 2.09 (1.29–3.38) | 1.45 (1.10–1.91) | 6 | 0.21 | |
SNPs, single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; -, not available; NOS, Newcastle-Ottawa quality assessment scale; HWE, Hardy-Weinberg equilibrium.
*Genotype presented as wild type/heterozygous/homozygous, †M/m, major/minor allele.
Fig. 1PRISMA flow diagram of study selection process. SNPs, single nucleotide polymorphisms, SAH, subarachnoid hemorrhage; IA, intracranial aneurysm.
Main Results of the Pooled ORs in Meta-Analysis of the Associations between Inflammatory Cytokine Gene Polymorphisms and Intracranial Aneurysm
| Gene SNPs | N | Sample size (case/control) | Dominant model | Allelic model | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| OR (95% CI) | I2 (%) | OR (95% CI) | I2 (%) | |||||||
| 3 | 424/475 | 0.65 (0.47–0.89) | 36.5 | 0.212 | 0.007 | 0.74 (0.56–0.97) | 7.7 | 0.338 | 0.030 | |
| 2 | 435/405 | 1.02 (0.77–1.34) | 0.0 | 0.590 | 0.903 | 1.11 (0.91–1.37) | 0.0 | 0.916 | 0.307 | |
| 3 | 666/636 | 1.03 (0.83–1.29) | 0.0 | 0.541 | 0.786 | 1.09 (0.93–1.28) | 35.2 | 0.214 | 0.282 | |
| 5 | 886/3827 | 0.87 (0.73–1.04) | 46.4 | 0.113 | 0.126 | 0.84 (0.69–1.04)* | 56.8 | 0.055 | 0.114 | |
| 7 | 1252/3888 | 0.75 (0.38–1.51)* | 89.8 | <0.001 | 0.422 | 0.91 (0.51–1.62)* | 93.8 | <0.001 | 0.735 | |
| Chinese | 3 | 642/642 | 0.45 (0.07–2.79)* | 95.3 | <0.001 | 0.390 | 0.73 (0.24–2.23)* | 96.6 | <0.001 | 0.579 |
| Caucasian | 4 | 610/3246 | 1.13 (0.87–1.46) | 49.0 | 0.117 | 0.376 | 1.08 (0.75–1.54)* | 61.3 | 0.052 | 0.684 |
| 2 | 384/508 | 1.32 (0.55–3.18)* | 87.9 | 0.004 | 0.537 | 1.19 (0.81–1.74)* | 74.3 | 0.048 | 0.373 | |
SNPs, single nucleotide polymorphisms; N, number of studies; I, Higgins I statistic; P, value for Q test; P value for Z test; OR, odds ratio; CI, confidence interval.
*ORs were calculated in random-effect model.
Fig. 2Forest plots for the associations of inflammatory cytokine gene polymorphisms with IA risk in a dominant model. (A) Forest plot of TNF-α rs1800629 and IA risk in the dominant model. (B) Forest plot of IL-1α rs1800587 and IA risk in the dominant model. (C) Forest plot of IL-1β rs16944 and IA risk in the dominant model. (D) Forest plot of IL6 rs1800795 and IA risk in the dominant model. (E) Forest plot of IL6 rs1800796 and IA risk in the dominant model. (F) Forest plot of IL-12B rs3212227 and IA in the dominant model. IA, intracranial aneurysm; OR, odds ratio; CI, confidence interval.
Fig. 3Begg's funnel plots and Egger's plots for the association of rs1800796 with IA risk in a dominant model. IA, intracranial aneurysm; OR, odds ratio.