基于GWAS数据分析肠道菌群与脑出血的关系

林迪慧; 刘新鹏; 黎祺; 秦家碧; 熊震东; 吴欣锐

doi:10.11817/j.issn.1672-7347.2023.230107

您当前的位置：

首页 >

文章列表页 >

基于GWAS数据分析肠道菌群与脑出血的关系

扫描看全文

文章被引用时，请邮件提醒。

提交

论著 | 更新时间：2023-10-24

基于GWAS数据分析肠道菌群与脑出血的关系
增强出版
Association between gut microbiome and intracerebral hemorrhage based on genome-wide association study data
中南大学学报(医学版) 中南大学学报(医学版) 2023年48卷第8期页码：1176-1184
作者机构：

1.吉首大学医学院公共卫生与医学技术系，湖南吉首 416000
2.湘西土家族苗族自治州疾病预防控制中心，湖南吉首 416000
3.中南大学湘雅公共卫生学院流行病与卫生统计学系，长沙 410006
作者简介：

林迪慧，Email: ldhdoct@163.com, ORCID: 0009-0002-6884-6777
吴欣锐，Email: wuxinrui99@qq.com, ORCID: 0000-0003-0475-7126
基金信息：

国家自然科学基金(82073653);湖南省自然科学基金(2022JJ10087;2022JJ40343);湖南省教育厅科学研究项目(21B0513);湖南省卫生健康委员会科研计划项目(202212053368);吉首大学校级科研项目(Jdx22035)
DOI：10.11817/j.issn.1672-7347.2023.230107
中图分类号：
CSTR：
纸质出版日期： 2023-08-28 ，

收稿日期： 2023-03-15 ，

林迪慧, 刘新鹏, 黎祺, 秦家碧, 熊震东, 吴欣锐. 基于GWAS数据分析肠道菌群与脑出血的关系[J]. 中南大学学报(医学版), 2023, 48(8): 1176-1184.

LIN Dihui, LIU Xinpeng, LI Qi, QIN Jiabi, XIONG Zhendong, WU Xinrui. Association between gut microbiome and intracerebral hemorrhage based on genome-wide association study data[J]. Journal of Central South University. Medical Science, 2023, 48(8): 1176-1184.
林迪慧, 刘新鹏, 黎祺, 秦家碧, 熊震东, 吴欣锐. 基于GWAS数据分析肠道菌群与脑出血的关系[J]. 中南大学学报(医学版), 2023, 48(8): 1176-1184. DOI：10.11817/j.issn.1672-7347.2023. 230107

LIN Dihui, LIU Xinpeng, LI Qi, QIN Jiabi, XIONG Zhendong, WU Xinrui. Association between gut microbiome and intracerebral hemorrhage based on genome-wide association study data[J]. Journal of Central South University. Medical Science, 2023, 48(8): 1176-1184. DOI：10.11817/j.issn. 1672-7347.2023.230107

摘要

目的

自发性脑出血(intracerebral hemorrhage，ICH)在脑卒中各亚型中病死率、致残率最高，既往研究表明肠道菌群(gut microbiome，GM)与ICH的危险因素和病理基础密切相关。本研究旨在探索两者的因果关联及GM对ICH发病的潜在作用机制。

方法

从微生物基因组联盟及国际脑卒中协会获取有关GM和ICH的全基因组关联分析(genome wide association study，GWAS)数据，对GWAS数据使用孟德尔随机化(Mendelian randomization，MR)分析探讨GM与ICH的因果关联，运用条件错误发现率(conditional false discovery rate，cFDR)法识别两者的多效性易感遗传变异。

结果

MR分析结果显示：Pasteurellales目、Pasteurellaceae科、

Haemophilus

属的GM与ICH有负向因果效应；Verrucomicrobiae纲、Verrucomicrobiales目、Verrucomicrobiaceae科、

Akkermansia

属、

Holdemanella

属和

LachnospiraceaeUCG010

属的GM与ICH有正向因果效应。通过cFDR法识别出GM与ICH的3个多效性遗传位点，分别为rs331083、rs4315115和rs12553325。

结论

GM与ICH发病存在因果关联和多效性易感遗传变异。

Abstract

Objective

Intracerebral hemorrhage (ICH) has the highest mortality and disability rates among various subtypes of stroke. Previous studies have shown that the gut microbiome (GM) is closely related to the risk factors and pathological basis of ICH. This study aims to explore the causal effect of GM on ICH and the potential mechanisms.

Methods

Genome wide association study (GWAS) data on GM and ICH were obtained from Microbiome Genome and International Stroke Genetics Consortium. Based on the GWAS data

we first performed Mendelian randomization (MR) analysis to evaluate the causal association between GM and ICH. Then

a conditional false discovery rate (cFDR) method was conducted to identify the pleiotropic variants.

Results

MR analysis showed that Pasteurellales

Pasteurellaceae

and

Haemophilus

were negatively correlated with the risk of ICH

while

Verrucomicrobiae

Verrucomicrobiales

Verrucomicrobiaceae

Akkermansia

Holdemanella

and

LachnospiraceaeUCG010

were positively correlated with ICH. By applying the cFDR method

3 pleiotropic loci (rs331083

rs4315115

and rs12553325) were found to be associated with both GM and ICH.

Conclusion

There is a causal association and pleiotropic variants between GM and ICH.

关键词

肠道菌群脑出血孟德尔随机化条件错误发现率全基因组关联分析

Keywords

gut microbiomeintracerebral hemorrhageMendelian randomizationconditional false discovery rategenome wide association study

references

Qureshi AI, Mendelow AD, Hanley DF. Intracerebral haemorrhage[J]. Lancet, 2009, 373(9675): 1632-1644. https:// doi.org/10.1016/S0140-6736(09)60371-8https://doi.org/10.1016/S0140-6736(09)60371-8.

van Asch CJ, Luitse MJ, Rinkel GJ, et al. Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis[J]. Lancet Neurol, 2010, 9(2): 167-176. https://doi.org/10.1016/S1474-4422(09)70340-0https://doi.org/10.1016/S1474-4422(09)70340-0.

Broderick JP, Brott TG, Duldner JE, et al. Volume of intracerebral hemorrhage. A powerful and easy-to-use predictor of 30-day mortality[J]. Stroke, 1993, 24(7): 987-993. https://doi.org/10.1161/01.str.24.7.987https://doi.org/10.1161/01.str.24.7.987.

Winek K, Meisel A, Dirnagl U. Gut microbiota impact on stroke outcome: Fad or fact?[J]. Cerebral Blood Flow Metab, 2016, 36(5): 891-898. https://doi.org/10.1177/0271678X16636890https://doi.org/10.1177/0271678X16636890.

Morais LH, Schreiber HT, Mazmanian SK. The gut microbiota-brain axis in behaviour and brain disorders[J]. Nat Rev Microbiol, 2021, 19(4): 241-255. https://doi.org/10.1038/s41579-020-00460-0https://doi.org/10.1038/s41579-020-00460-0.

张培培. 肠道菌群失调可增加脑卒中风险[D]. 石家庄: 河北医科大学, 2018.

ZHANG Peipei. Dysbiosis of gut microbiota can increase the risk of stroke[D]. Shijiazhuang: Hebei Medical University, 2018.

李裕思, 许华冲, 王俊月, 等. 16S rRNA基因测序技术分析肝阳上亢型脑出血患者的肠道菌群结构[J]. 中国实验方剂学杂志, 2019, 25(8): 83-88. https://doi.org/:10.13422/j.cnki.syfjx. 20190550https://doi.org/:10.13422/j.cnki.syfjx.20190550.

LI Yusi, XU Huachong, WANG Junyue,et al. Analysis of bacterial flora structure of patients with cerebral hemorrhage due to hyperactivity of liver-yang by 16S rRNA gene sequencing technique[J]. Chinese Journal of Experimental Traditional Medical Formulae, 2019, 25: 83-88. https://doi.org/:10.13422/j.cnki.syfjx.20190550https://doi.org/:10.13422/j.cnki.syfjx.20190550.

Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology[J]. Statistics Med, 2008, 27(8): 1133-1163. https://doi.org/10.1002/sim.3034https://doi.org/10.1002/sim.3034.

Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?[J]. Int J Epidemiol,2003, 32(1): 1-22. https://doi.org/10.1093/ije/dyg070https://doi.org/10.1093/ije/dyg070.

Yuan S, Mason AM, Burgess S, et al. Genetic liability to insomnia in relation to cardiovascular diseases: a Mendelian randomisation study[J]. Eur J Epidemiol, 2021, 36(4): 393-400. https://doi.org/10.1007/s10654-021-00737-5https://doi.org/10.1007/s10654-021-00737-5.

Sanna S, van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases[J]. Nat Genet,2019,51(4):600-605. https://doi.org/10.1038/s41588-019-0350-xhttps://doi.org/10.1038/s41588-019-0350-x.

Xu F, Fu Y, Sun TY, et al. The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for complex human diseases[J]. Microbiome, 2020, 8(1):145. https://doi.org/10.1186/s40168-020-00923-9https://doi.org/10.1186/s40168-020-00923-9.

Hughes DA, Bacigalupe R, Wang J, et al. Genome-wide associations of human gut microbiome variation and implications for causal inference analyses[J]. Nat Microbiol, 2020, 5(9): 1079-1087. https://doi.org/10.1038/s41564-020-0743-8https://doi.org/10.1038/s41564-020-0743-8.

Liley J, Wallace C. A pleiotropy-informed Bayesian false discovery rate adapted to a shared control design finds new disease associations from GWAS summary statistics[J/OL]. PLoS Genet, 2015, 11(2): e1004926[2022-12-23]. https://doi.org/10.1371/journal.pgen.1004926https://doi.org/10.1371/journal.pgen.1004926.

Peng C, Shen J, Lin X, et al. Genetic sharing with coronary artery disease identifies potential novel loci for bone mineral density[J]. Bone, 2017, 103: 70-77. https://doi.org/10.1016/j.bone.2017.06.016https://doi.org/10.1016/j.bone.2017.06.016.

Wang X, Lin X, Li D, et al. Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach[J]. J Neurol Sci, 2017, 380: 262-272. https://doi.org/10.1016/j.jns.2017.07.044https://doi.org/10.1016/j.jns.2017.07.044.

Wu X, Lin X, Li Q, et al. Identification of novel SNPs associated with coronary artery disease and birth weight using a pleiotropic cFDR method[J]. Aging (Albany NY), 2020, 13(3): 3618-3644. https://doi.org/10.18632/aging.202322https://doi.org/10.18632/aging.202322.

Goodrich JK, Davenport ER, Beaumont M, et al. Genetic determinants of the gut microbiome in UK twins[J]. Cell Host Microbe, 2016, 19(5): 731-743. https://doi.org/10.1016/j.chom.2016.04.017https://doi.org/10.1016/j.chom.2016.04.017.

Devan WJ, Falcone GJ, Anderson CD, et al. Heritability estimates identify a substantial genetic contribution to risk and outcome of intracerebral hemorrhage[J]. Stroke, 2013, 44(6): 1578-1583. https://doi.org/10.1161/STROKEAHA.111. 000089https://doi.org/10.1161/STROKEAHA.111.000089.

Kurilshikov A, Medina-Gomez C, Bacigalupe R, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition[J]. Nat Genet, 2021, 53(2): 156-165. https://doi.org/10.1038/s41588-020-00763-1https://doi.org/10.1038/s41588-020-00763-1.

Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[J]. Nucleic Acids Res, 2013, 41(Database issue):D590-D596. https://doi.org/10.1093/nar/gks1219https://doi.org/10.1093/nar/gks1219.

Woo D, Falcone GJ, Devan WJ, et al. Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage[J]. The Am J Hum Genet, 2014, 94(4): 511-521. https://doi.org/10.1016/j.ajhg.2014. 02.012https://doi.org/10.1016/j.ajhg.2014.02.012.

Ning J, Huang S, Chen S, et al. Investigating casual associations among gut microbiota, metabolites, and neurodegenerative diseases: A mendelian randomization study[J]. J Alzheimers Dis, 2022, 87(1): 211-222. https://doi.org/10.3233/JAD-215411https://doi.org/10.3233/JAD-215411.

Johnson AD, Handsaker RE, Pulit SL, et al. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap[J]. Bioinformatics, 2008, 24(24): 2938-2939. https://doi.org/10.1093/bioinformatics/btn564https://doi.org/10.1093/bioinformatics/btn564.

Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies[J]. Intern J Epidemiol, 2011, 40(3): 755-764. https://doi.org/10.1093/ije/dyr036https://doi.org/10.1093/ije/dyr036.

Burgess S, Thompson SG. Use of allele scores as instrumental variables for Mendelian randomization[J]. Int J Epidemiol, 2013, 42(4): 1134-1144. https://doi.org/10.1093/ije/dyt093https://doi.org/10.1093/ije/dyt093.

高雪, 王慧, 王彤. 孟德尔随机化中多效性偏倚校正方法简介[J]. 中华流行病学杂志, 2019, 40(3): 360-365. https://doi.org/10.3760/cma.j.issn.0254-6450.2019.03.020https://doi.org/10.3760/cma.j.issn.0254-6450.2019.03.020.

GAO Xue, WANG Hui, WANG Tong. Review on correction methods related to the pleiotropic effect in Mendelian randomization [J]. Chinese Journal of Epidemiology, 2019, 40: 360-365. https://doi.org/10.3760/cma.j.issn.0254-6450.2019. 03.020https://doi.org/10.3760/cma.j.issn.0254-6450.2019.03.020.

Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-525. https://doi.org/10.1093/ije/dyv080https://doi.org/10.1093/ije/dyv080.

Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol, 2013, 37(7): 658-665. https://doi.org/10.1002/gepi.21758https://doi.org/10.1002/gepi.21758.

林丽娟, 魏永越, 张汝阳, 等. 孟德尔随机化方法在观察性研究因果推断中的应用[J]. 中华预防医学杂志, 2019, 53(6): 619-624. https://doi.org/10.3760/cma.j.issn.0253-9624.2019. 06.015https://doi.org/10.3760/cma.j.issn.0253-9624.2019.06.015.

LIN Lijuan, WEI Yongyue, ZHANG Ruyang, et al. Application of mendelian randomization. methods in causal inference of observational study [J]. Chinese Journal of Preventive Medicine, 2019: 619-624. https://doi.org/10.3760/cma.j.issn. 0253-9624.2019.06.015https://doi.org/10.3760/cma.j.issn.0253-9624.2019.06.015.

Bowden J, Davey SG, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314. https://doi.org/10.1002/gepi. 21965https://doi.org/10.1002/gepi.21965.

Thompson JR, Minelli C, Abrams KR, et al. Meta-analysis of genetic studies using Mendelian randomization—a multivariate approach[J]. Stat Med, 2005, 24(14): 2241-2254. https://doi.org/10.1002/sim.2100https://doi.org/10.1002/sim.2100.

Yavorska OO, Burgess S. Mendelian randomization: an R package for performing Mendelian randomization analyses using summarized data[J]. Int J Epidemiol, 2017, 46(6): 1734-1739. https://doi.org/10.1093/ije/dyx034https://doi.org/10.1093/ije/dyx034.

Lawlor DA. Commentary: Two-sample Mendelian randomization: opportunities and challenges[J]. Int J Epidemiol, 2016, 45(3): 908-915. https://doi.org/10.1093/ije/dyw127https://doi.org/10.1093/ije/dyw127.

Purcell S, Neale B, Todd-Brown K, et al. PLINK: A tool set for whole-genome association and population-based linkage analyses[J]. Am J Hum Genet, 2007, 81(3): 559-575. https://doi.org/10.1086/519795https://doi.org/10.1086/519795.

Andreassen OA, Thompson WK, Schork AJ, et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate[J/OL]. PLoS Genet, 2013, 9(4): e1003455[2022-11-26]. https://doi.org/10.1371/journal.pgen.1003455https://doi.org/10.1371/journal.pgen.1003455.

Zhang N, Wang H, Wang X, et al. Combination effect between gut microbiota and traditional potentially modifiable risk factors for first-ever ischemic stroke in Tujia, Miao and Han populations in China[J]. Front Mol Neurosci, 2022, 15: 922399. https://doi.org/10.3389/fnmol.2022.922399https://doi.org/10.3389/fnmol.2022.922399.

Ariesen M, Claus S, Rinkel G, et al. Risk factors for intracerebral hemorrhage in the general population[J]. Stroke,2003, 34(8): 2060-2065. https://doi.org/10.1161/01.STR. 0000080678.09344.8Dhttps://doi.org/10.1161/01.STR.0000080678.09344.8D.

O’Donnell MJ, Chin SL, Rangarajan S, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study[J]. Lancet, 2016, 388(10046): 761-775. https://doi.org/10.1016/S0140-6736(16)30506-2https://doi.org/10.1016/S0140-6736(16)30506-2.

Kostic AD, Gevers D, Siljander H, et al. The dynamics of the human infant gut microbiome in development and in progression toward Type 1 diabetes[J]. Cell Host Microbe, 2015, 17(2): 260-273. https://doi.org/10.1016/j.chom.2015.01.001https://doi.org/10.1016/j.chom.2015.01.001.

Sarwar N, Gao P, Seshasai SR, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies[J]. Lancet, 2010, 375(9733): 2215-2222. https://doi.org/10.1016/S0140-6736(10)60484-9https://doi.org/10.1016/S0140-6736(10)60484-9.

Caesar R, Fåk F, Bäckhed F. Effects of gut microbiota on obesity and atherosclerosis via modulation of inflammation and lipid metabolism[J]. J Intern Med, 2010, 268(4): 320-328. https:// doi.org/10.1111/j.1365-2796.2010.02270.xhttps://doi.org/10.1111/j.1365-2796.2010.02270.x.

Viswanathan A, Greenberg SM. Cerebral amyloid angiopathy in the elderly[J]. Ann Neurol, 2011, 70(6): 871-880. https://doi.org/10.1002/ana.22516https://doi.org/10.1002/ana.22516.

Knudsen KA, Rosand J, Karluk D, et al. Clinical diagnosis of cerebral amyloid angiopathy: Validation of the Boston Criteria[J]. Neurology, 2001, 56(4): 537-539. https://doi.org/10.1212/wnl.56.4.537https://doi.org/10.1212/wnl.56.4.537.

Xiong Z, Peng K, Song S, et al. Cerebral intraparenchymal hemorrhage changes patients’ gut bacteria composition and function[J]. Front Cell Infect Microbiol, 2022, 12: 829491. https://doi.org/10.3389/fcimb.2022.829491https://doi.org/10.3389/fcimb.2022.829491.

Yoon HS, Cho CH, Yun MS, et al. Akkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in mice[J]. Nat Microbiol, 2021, 6(5): 563-573. https://doi.org/10.1038/s41564-021-00880-5https://doi.org/10.1038/s41564-021-00880-5.

Yuan J, Chen Q, Xiong X, et al. Quantitative profiling of oxylipins in acute experimental intracerebral hemorrhage[J]. Front Neurosci, 2020, 14. https://doi.org/10.3389/fnins.2020. 00777https://doi.org/10.3389/fnins.2020.00777.

Zheng J, Yu Z, Ma L, et al. Association between blood glucose and functional outcome in intracerebral hemorrhage: a systematic review and meta-analysis[J/OL]. World Neurosurg, 2018, 114: e756-e765[2022-12-31]. https://doi.org/10.1016/j.wneu.2018.03.077https://doi.org/10.1016/j.wneu.2018.03.077.

Jang LG, Choi G, Kim SW, et al. The combination of sport and sport-specific diet is associated with characteristics of gut microbiota: an observational study[J]. J Int Soc Sports Nutr, 2019, 16(1). https://doi.org/10.1186/s12970-019-0290-yhttps://doi.org/10.1186/s12970-019-0290-y.

Muralidharan J, Moreno-Indias I, Bulló M, et al. Effect on gut microbiota of a 1-y lifestyle intervention with Mediterranean diet compared with energy-reduced Mediterranean diet and physical activity promotion: PREDIMED-Plus Study[J]. Am J Clin Nutr, 2021, 114(3): 1148-1158. https://doi.org/10.1093/ajcn/nqab150https://doi.org/10.1093/ajcn/nqab150.

Carpenter AM, Singh IP, Gandhi CD, et al. Genetic risk factors for spontaneous intracerebral haemorrhage[J]. Nat Rev Neurol, 2016, 12(1): 40-49. https://doi.org/10.1038/nrneurol.2015.226https://doi.org/10.1038/nrneurol.2015.226.

Harshfield E, Georgakis M, Malik R, et al. Modifiable lifestyle factors and risk of stroke[J]. Stroke, 2021, 52(3): 931-936. https://doi.org/10.1161/STROKEAHA.120.031710https://doi.org/10.1161/STROKEAHA.120.031710.

Zhang Y, Tuomilehto J, Jousilahti P, et al. Lifestyle factors on the risks of ischemic and hemorrhagic stroke[J]. Arch Intern Med, 2011, 171(20): 1811-1818. https://doi.org/10.1001/archinternmed.2011.443https://doi.org/10.1001/archinternmed.2011.443.

He K, Rimm EB, Merchant A. Fish consumption and risk of stroke in men[J]. ACC Curr J Rev, 2003, 12(2): 43-44. https://doi.org/10.1016/S1062-1458(03)00047-3https://doi.org/10.1016/S1062-1458(03)00047-3.

Wahab KW, Tiwari HK, Ovbiagele B, et al. Genetic risk of Spontaneous intracerebral hemorrhage: Systematic review and future directions[J]. J Neurol Sci, 2019, 407: 116526. https://doi.org/10.1016/j.jns.2019.116526https://doi.org/10.1016/j.jns.2019.116526.

Dong X, Lu Z, Kang C, et al. The long noncoding RNA RP11-728F11.4 promotes atherosclerosis[J]. Arterios Thromb Vasc Biol, 2021, 41(3): 1191-1204. https://doi.org/10.1161/ATVBAHA.120.315114https://doi.org/10.1161/ATVBAHA.120.315114.

Le Roy T, Lécuyer E, Chassaing B, et al. The intestinal microbiota regulates host cholesterol homeostasis[J]. BMC Biol, 2019, 17(1): 94. https://doi.org/10.1186/s12915-019-0715-8https://doi.org/10.1186/s12915-019-0715-8.

Begley M, Gahan CGM, Hill C. The interaction between bacteria and bile[J]. FEMS Microbiol Rev, 2005, 29(4): 625-651. https://doi.org/10.1016/j.femsre.2004.09.003https://doi.org/10.1016/j.femsre.2004.09.003.

Vojinovic D, Radjabzadeh D, Kurilshikov A, et al. Relationship between gut microbiota and circulating metabolites in population-based cohorts[J]. Nat Commun, 2019, 10(1): 5813. https://doi.org/10.1038/s41467-019-13721-1https://doi.org/10.1038/s41467-019-13721-1.

Qie R, Liu L, Zhang D, et al. Dose-response association between high-density lipoprotein cholesterol and stroke: A systematic review and meta-analysis of prospective cohort studies[J/OL]. Prev Chron Dise, 2021, 18: E45[2022-09-13]. https://doi.org/10.5888/pcd18.200278https://doi.org/10.5888/pcd18.200278.

Holmes MV, Millwood IY, Kartsonaki C, et al. Lipids, lipoproteins, and metabolites and risk of myocardial infarction and stroke[J]. J Am College Cardiol, 2018, 71(6): 620-632. https://doi.org/10.1016/j.jacc.2017.12.006https://doi.org/10.1016/j.jacc.2017.12.006.

浏览量

318

下载量

CSCD

工具集

关联资源

丁苯酞对脑出血后小胶质细胞极化的影响及其机制

青年脑出血病因和危险因素分析

大鼠脑出血后行为学改变和室管膜下区细胞增殖规律