白细胞介素-1B-511C/T基因多态性与冠状动脉粥样硬化性心脏病关联的meta分析

doi:10.3969/j.issn.1674-8115.2022.02.010

摘要/Abstract

摘要：

目的·评估白细胞介素-1B（interleukin-1B，IL-1B）-511C/T基因多态性与冠状动脉粥样硬化性心脏病（冠心病）发生风险的关联。方法·检索PubMed、Web of Science、Scopus和Embase数据库中已公开发表的关于IL-1B-511C/T基因多态性与冠心病的病例对照研究。检索时间为各数据库建库至2021年6月。由2名研究人员独立进行文献筛选及数据提取。提取的数据包括第一作者姓名、发表年份、研究对象的种族、匹配因素、对照组来源、基因分型方法、样本量、基因型计数、等位基因计数、是否满足哈迪-温伯格平衡定律。采用Stata 16.0软件进行meta分析。结果·共纳入9篇文献，包括2 190例冠心病患者和2 385例对照者。Meta分析结果显示，IL-1B-511C/T基因多态性与冠心病发生风险间无明显统计学相关（T vs C：OR =1.21，95% CI 0.91~1.61；TT+CT vs CC：OR=1.24，95% CI 0.88~1.75；CC+CT vs TT：OR=1.28，95% CI 0.86~1.90）。亚组分析结果显示，在中国人群中与携带C等位基因和CC基因型的个体相比，携带T等位基因和TT+CT基因型的个体发生冠心病的风险分别增加了85%和116%（T vs C：OR=1.85，95% CI 1.02~3.36；TT+CT vs CC：OR=2.16，95% CI 1.10~4.24）。在以医院为基础的病例对照研究中，与携带CC+CT基因型的人群相比，携带TT基因型的人群发生冠心病的风险增加了59%（OR=1.59，95% CI 1.03~2.47）。结论·IL-1B-511C/T基因多态性与冠心病发生风险间无明显关联。在中国人群中，携带T等位基因和TT+TC基因型的个体的冠心病发生风险较高。

关键词: 白细胞介素-1, 基因多态性, -511C/T, 冠状动脉粥样硬化性心脏病, meta分析

Abstract:

Objective·To evaluate the association between interleukin-1B (IL-1B)-511C/T gene polymorphism and the risk of coronary atherosclerotic heart disease (CHD).

Methods·Published case-control studies on IL-1B-511C/T gene polymorphism and CHD in PubMed, Web of Science, Scopus and Embase databases were retrieved from the establishment of each database to June 2021. Two researchers independently conducted literature screening and data extraction. The extracted data included the name of the first author, the year of publication, the race of the research objects, matching factors, the source of the control group, genotyping method, sample size, genotype frequency, allele frequency, and whether to meet Hardy-Weinberg equilibrium. Meta-analysis was performed by using Stata 16.0 software.

Results·A total of 9 literatures were included, including 2 190 patients with CHD and 2 385 controls. Meta-analysis showed that there was no significant correlation between IL-1B -511C/T gene polymorphism and the risk of CHD (T vs C: OR =1.21, 95% CI 0.91?1.61; TT+CT vs CC: OR=1.24, 95% CI 0.88?1.75; CC+CT vs TT: OR=1.28, 95% CI 0.86?1.90). Subgroup analysis showed that compared with individuals with C allele and CC genotype in the Chinese population, the individuals with T allele and TT+CT genotype had a 85% and 116% increased risk of CHD, respectively (T vs C: OR=1.85, 95% CI 1.02?3.36; TT+CT vs CC: OR=2.16, 95% CI 1.10?4.24). In hospital-based case-control studies, compared with individuals with CC+CT genotype, the individuals with TT genotype had a 59% increased risk of CHD (OR=1.59, 95% CI 1.03?2.47).

Conclusion·There is no significant association between IL-1B-511C/T gene polymorphism and the risk of CHD. In the Chinese population, individuals with T allele and TT+TC genotype have a higher risk of CHD.

Key words: interleukin-1, gene polymorphism, -511C/T, coronary atherosclerotic heart disease (CHD), meta-analysis

中图分类号:

R541.4

谭颖超, 杨珺玥, 王莉娜. 白细胞介素-1B-511C/T基因多态性与冠状动脉粥样硬化性心脏病关联的meta分析[J]. 上海交通大学学报(医学版), 2022, 42(2): 197-204.

Yingchao TAN, Junyue YANG, Lina WANG. Association between interleukin-1B-511C/T gene polymorphism and coronary atherosclerotic heart disease: a meta-analysis[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2022, 42(2): 197-204.

图/表 7

参考文献 29

1	MATHERS C D, LONCAR D. Projections of global mortality and burden of disease from 2002 to 2030[J]. PLoS Med, 2006, 3(11): e442.
2	MALAKAR A K, CHOUDHURY D, HALDER B, et al. A review on coronary artery disease, its risk factors, and therapeutics[J]. J Cell Physiol, 2019, 234(10): 16812-16823.
3	CAMPBELL T C, PARPIA B, CHEN J. Diet, lifestyle, and the etiology of coronary artery disease: the Cornell China study[J]. Am J Cardiol, 1998, 82(10B): 18T-21T.
4	ERBEL R, GÖRGE G. New insights in pathogenesis and etiology of coronary artery disease[J]. Dtsch Med Wochenschr, 2014, 139(Suppl 1): S4-S8.
5	BEDNARSKA J, BEDNARSKA-CHABOWSKA D, ADAMIEC-MROCZEK J. Coronary artery disease: new insights into revascularization treatment of diabetic patients[J]. Adv Clin Exp Med, 2017, 26(7): 1163-1167.
6	STEIN R, FERRARI F, SCOLARI F.Genetics, dyslipidemia, and disease cardiovascular: new insights[J]. Curr Cardiol Rep, 2019, 21(8): 68.
7	MARTINEZ P F, OKOSHI M P. Genetic risk in coronary artery disease[J]. Arq Bras Cardiol, 2018, 111(1): 62-63.
8	MYERS R H, KIELY D K, CUPPLES L A, et al. Parental history is an independent risk factor for coronary artery disease: the Framingham Study[J]. Am Heart J, 1990, 120(4): 963-969.
9	BORGHINI A, ANDREASSI M G. Genetic polymorphisms offer insight into the causal role of microRNA in coronary artery disease[J]. Atherosclerosis, 2018, 269: 63-70.
10	MOREIRA D M, DA SILVA R L, VIEIRA J L, et al. Role of vascular inflammation in coronary artery disease: potential of anti-inflammatory drugs in the prevention of atherothrombosis. Inflammation and anti-inflammatory drugs in coronary artery disease[J]. Am J Cardiovasc Drugs, 2015, 15(1): 1-11.
11	DINARELLO C A. Biologic basis for interleukin-1 in disease[J]. Blood, 1996, 87(6): 2095-2147.
12	DHOREPATIL A, BALL S, GHOSH R K, et al. Canakinumab: promises and future in cardiometabolic diseases and malignancy[J]. Am J Med, 2019, 132(3): 312-324.
13	TABREZ S, JABIR N R, FIROZ C K, et al. Estimation of interleukin-1β promoter (-31 C/T and -511 T/C) polymorphisms and its level in coronary artery disease patients[J]. J Cell Biochem, 2017, 118(9): 2977-2982.
14	CHIBANA H, KAJIMOTO H, UENO T, et al. Interleukin-1β is associated with coronary endothelial dysfunction in patients with mTOR-inhibitor-eluting stent implantation[J]. Heart Vessels, 2017, 32(7): 823-832.
15	RIOS D L S, CERQUEIRA C C S, BONFIM-SILVA R, et al. Interleukin-1β and interleukin-6 gene polymorphism associations with angiographically assessed coronary artery disease in Brazilians[J]. Cytokine, 2010, 50(3): 292-296.
16	MANTEL N, HAENSZEL W. Statistical aspects of the analysis of data from retrospective studies of disease[J]. J Natl Cancer Inst, 1959, 22(4): 719-748.
17	LICASTRO F, CHIAPPELLI M, CALDARERA C M, et al. The concomitant presence of polymorphic alleles of interleukin-1β, interleukin-6 and apolipoprotein E is associated with an increased risk of myocardial infarction in elderly men. Results from a pilot study[J]. Mech Ageing Dev, 2004, 125(8): 575-579.
18	IACOVIELLO L, DI CASTELNUOVO A, GATTONE M, et al. Polymorphisms of the interleukin-1β gene affect the risk of myocardial infarction and ischemic stroke at young age and the response of mononuclear cells to stimulation in vitro[J]. Arterioscler Thromb Vasc Biol, 2005, 25(1): 222-227.
19	ZEE R Y L, HENNESSEY H, MICHAUD S E, et al. Genetic variants within the interleukin-1 gene cluster, and risk of incident myocardial infarction, and ischemic stroke: a nested case-control approach[J]. Atherosclerosis, 2008, 201(1): 124-129.
20	COKER A, ARMAN A, SOYLU O, et al. Lack of association between IL-1 and IL-6 gene polymorphisms and myocardial infarction in Turkish population[J]. Int J Immunogenet, 2011, 38(3): 201-208.
21	REN B J, SHE Q. Study on the association between IL-1β, IL-8 and IL-10 gene polymorphisms and risk of coronary artery disease[J]. Int J Clin Exp Med, 2015, 8(5): 7937-7943.
22	CHEN L, LU F, WANG Z, et al. Influence of interleukin-1β gene polymorphism on the risk of myocardial infarction complicated with ischemic stroke[J]. Exp Ther Med, 2018, 16(6): 5166-5170.
23	MA L J, SU H, WANG Y, et al. Interleukin-1β (IL-1β) C-511T polymorphism is associated with susceptibility to coronary artery disease in type 2 diabetic patients[J]. Eur J Inflamm, 2020, 18(5): 1-12.
24	MUSUNURU K, KATHIRESAN S. Genetics of common, complex coronary artery disease[J]. Cell, 2019, 177(1): 132-145.
25	TABAEI S, MOTALLEBNEZHAD M, TABAEE S S. Systematic review and meta-analysis of association of polymorphisms in inflammatory cytokine genes with coronary artery disease[J]. Inflamm Res, 2020, 69(10): 1001-1013.
26	VOHNOUT B, DI CASTELNUOVO A, TROTTA R, et al. Interleukin-1 gene cluster polymorphisms and risk of coronary artery disease[J]. Haematologica, 2003, 88(1): 54-60.
27	赵娜, 王兴宁, 张瑞. 中国多省份人群IL-1B-511C/T和IL1RN+8006T/C单核苷酸多态性与冠心病相关性的meta分析[J]. 细胞与分子免疫学杂志, 2017, 33(10): 1409-1414.
28	罗涛, 王璐, 田恬, 等. 匹配在观察性研究中的作用: 有向无环图视角[J]. 中华流行病学杂志, 2021, 42(4): 740-744.
29	刘宝花. 病例对照研究在健康管理科研中的理论、设计和应用[J]. 中华健康管理学杂志, 2019, 13(4): 360-366.

Study included	Year	Area	Race	Source of control group	Genotype method	Matching（Y/N）	HWE（Y/N）	NOS score
LICASTRO， et al^［17］	2004	Italy	Caucasian	PB	PCR-RFLP	Y	Y	≥7
IACOVIELLO， et al^［18］	2005	Italy	Caucasian	PB	Not PCR-RFLP	Y	Y	≥7
ZEE， et al^［19］	2008	US	Caucasian	PB	Not PCR-RFLP	Y	Y	≥7
RIOS， et al^［15］	2010	Brazil	Caucasian	HB	PCR-RFLP	N	Y	≥7
COKER， et al^［20］	2011	Turkish	Caucasian	HB	PCR-RFLP	N	Y	≥7
REN， et al^［21］	2015	China	Chinese	HB	Not PCR-RFLP	N	Y	≥7
TABREZ， et al^［13］	2017	Saudi	Caucasian	HB	PCR-RFLP	N	Y	<7
CHEN， et al^［22］	2018	China	Chinese	HB	Not PCR-RFLP	N	N	≥7
MA， et al^［23］	2020	China	Chinese	HB	PCR-RFLP	Y	Y	≥7

Study included	Year	Area	Race	Source of control group	Genotype method	Matching（Y/N）	HWE（Y/N）	NOS score
LICASTRO， et al^［17］	2004	Italy	Caucasian	PB	PCR-RFLP	Y	Y	≥7
IACOVIELLO， et al^［18］	2005	Italy	Caucasian	PB	Not PCR-RFLP	Y	Y	≥7
ZEE， et al^［19］	2008	US	Caucasian	PB	Not PCR-RFLP	Y	Y	≥7
RIOS， et al^［15］	2010	Brazil	Caucasian	HB	PCR-RFLP	N	Y	≥7
COKER， et al^［20］	2011	Turkish	Caucasian	HB	PCR-RFLP	N	Y	≥7
REN， et al^［21］	2015	China	Chinese	HB	Not PCR-RFLP	N	Y	≥7
TABREZ， et al^［13］	2017	Saudi	Caucasian	HB	PCR-RFLP	N	Y	<7
CHEN， et al^［22］	2018	China	Chinese	HB	Not PCR-RFLP	N	N	≥7
MA， et al^［23］	2020	China	Chinese	HB	PCR-RFLP	Y	Y	≥7

Study included	Sample size/n		Genotype and allele frequency in case group/n					Genotype and allele frequency in control group/n
Study included	Case group	Control group	CC	CT	TT	C	T	CC	CT	TT	C	T
LICASTRO， et al^［17］	139	122	65	60	14	190	88	46	65	11	157	87
IACOVIELLO， et al^［18］	406	419	195	180	31	570	242	174	187	58	535	303
ZEE， et al^［19］	340	341	148	153	39	449	231	164	137	40	465	217
RIOS， et al^［15］	276	138	80	130	66	290	262	47	69	22	163	113
COKER， et al^［20］	167	235	59	72	36	190	144	77	113	45	267	203
REN， et al^［21］	325	342	95	152	78	342	308	114	155	73	383	301
TABREZ， et al^［13］	152	75	58	63	31	179	125	27	33	15	87	63
CHEN， et al^［22］	251	200	134	80	37	348	154	143	37	20	323	77
MA， et al^［23］	134	513	35	65	34	135	133	298	179	36	775	251

Study included	Sample size/n		Genotype and allele frequency in case group/n					Genotype and allele frequency in control group/n
Study included	Case group	Control group	CC	CT	TT	C	T	CC	CT	TT	C	T
LICASTRO， et al^［17］	139	122	65	60	14	190	88	46	65	11	157	87
IACOVIELLO， et al^［18］	406	419	195	180	31	570	242	174	187	58	535	303
ZEE， et al^［19］	340	341	148	153	39	449	231	164	137	40	465	217
RIOS， et al^［15］	276	138	80	130	66	290	262	47	69	22	163	113
COKER， et al^［20］	167	235	59	72	36	190	144	77	113	45	267	203
REN， et al^［21］	325	342	95	152	78	342	308	114	155	73	383	301
TABREZ， et al^［13］	152	75	58	63	31	179	125	27	33	15	87	63
CHEN， et al^［22］	251	200	134	80	37	348	154	143	37	20	323	77
MA， et al^［23］	134	513	35	65	34	135	133	298	179	36	775	251

Subgroup	Genetic model	Number of study	OR （95% CI）	I² value	P_Hvalue
Race
Chinese	T vs C	3	1.85 （1.02-3.36）	93.3%	0.000
	TT+CT vs CC	3	2.16 （1.10-4.24）	89.4%	0.000
	CC+CT vs TT	3	1.99 （0.87-4.58）	88.9%	0.000
Caucasian	T vs C	6	0.97 （0.82-1.16）	57.6%	0.038
	TT+CT vs CC	6	0.94 （0.80-1.09）	37.2%	0.158
	CC+CT vs TT	6	1.00 （0.70-1.42）	57.8%	0.037
Source of control
PB	T vs C	3	0.89 （0.68-1.15）	67.8%	0.045
	TT+CT vs CC	3	0.88 （0.63-1.24）	66.2%	0.052
	CC+CT vs TT	3	0.78 （0.48-1.29）	57.7%	0.094
HB	T vs C	6	1.42 （1.00-2.03）	89.0%	0.000
	TT+CT vs CC	6	1.50 （0.96-2.34）	85.5%	0.000
	CC+CT vs TT	6	1.59 （1.03-2.47）	76.9%	0.000
Genotype method
PCR-RFLP	T vs C	5	1.27 （0.78-2.06）	91.4%	0.000
	TT+CT vs CC	5	1.24 （0.66-2.33）	89.2%	0.000
	CC+CT vs TT	5	1.63 （0.91-2.90）	79.0%	0.000
Not PCR-RFLP	T vs C	4	1.14 （0.82-1.58）	87.4%	0.000
	TT+CT vs CC	4	1.23 （0.82-1.84）	83.9%	0.000
	CC+CT vs TT	4	0.97 （0.62-1.49）	72.3%	0.000
Matching （Y/N）
Y	T vs C	4	1.20 （0.66-2.21）	95.5%	0.000
	TT+CT vs CC	4	1.25 （0.63-2.50）	93.3%	0.000
	CC+CT vs TT	4	1.26 （0.47-3.36）	92.4%	0.000
N	T vs C	5	1.22 （0.99-1.51）	62.8%	0.030
	TT+CT vs CC	5	1.24 （0.91-1.71）	65.0%	0.022
	CC+CT vs TT	5	1.28 （1.03-1.59）	0	0.707
NOS score
≥7	T vs C	8	1.24 （0.91-1.69）	90.9%	0.000
	TT+CT vs CC	8	1.28 （0.89-1.86）	87.5%	0.000
	CC+CT vs TT	8	1.31 （0.85-2.02）	83.0%	0.000
<7	T vs C	1	0.96 （0.65-1.43）	‒	‒
	TT+CT vs CC	1	0.91 （0.51-1.62）	‒	‒
	CC+CT vs TT	1	1.02 （0.51-2.04）	‒	‒
HWE （Y/N）
Y	T vs C	8	1.15 （0.85-1.55）	89.8%	0.000
	TT+CT vs CC	8	1.16 （0.81-1.64）	85.1%	0.000
	CC+CT vs TT	8	1.25 （0.80-1.93）	82.8%	0.000
N	T vs C	1	1.86 （1.36-2.54）	‒	‒
	TT+CT vs CC	1	2.19 （1.48-3.25）	‒	‒
	CC+CT vs TT	1	1.56 （0.87-2.78）	‒	‒