您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报 (医学版) ›› 2019, Vol. 57 ›› Issue (11): 103-109.doi: 10.6040/j.issn.1671-7554.0.2019.322

• • 上一篇    

腰围和冠心病因果关系的孟德尔随机化研究

刘新辉1,2,李洪凯1,2,李明卓1,2,于媛媛1,2,司书成1,2,侯蕾1,2,刘璐1,2,李文超1,2,袁同慧1,2,李云霞1,2,周宇畅1,2,薛付忠1,2   

  1. 1.山东大学公共卫生学院生物统计学系, 山东 济南 250012;2.山东大学健康医疗大数据研究院, 山东 济南 250002
  • 发布日期:2022-09-27
  • 通讯作者: 薛付忠. E-mail:xuefzh@sdu.edu.cn
  • 基金资助:
    国家自然科学基金(81773547)

A Mendelian randomization study on the causal relationship between waist circumference and incidence of coronary heart disease

LIU Xinhui1,2, LI Hongkai1,2, LI Mingzhuo1,2, YU Yuanyuan1,2, SI Shucheng1,2, HOU Lei1,2, LIU Lu1,2, LI Wenchao1,2, YUAN Tonghui1,2, LI Yunxia1,2, ZHOU Yuchang1,2, XUE Fuzhong1,2   

  1. 1. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    2. Healthcare Big Data Institute, Shandong University, Jinan 250002, Shandong, China
  • Published:2022-09-27

PDF (PC)

75

摘要： 目的 采用两样本孟德尔随机化方法探索腰围与冠状动脉粥样硬化性心脏病发病风险间的因果关系。 方法 利用大规模全基因组关联研究数据库,选择相互独立且与腰围密切相关的遗传位点作为工具变量,通过Wald比值法、逆方差加权法、MR-Egger回归法以及加权中位数法等两样本孟德尔随机化方法,以比值比(OR值)作为评价指标对腰围与冠心病间的因果关系进行研究。 结果 本研究利用样本量为232 101关于腰围的欧洲人群数据库,与样本量为86 995关于冠心病的欧洲人群数据库,选择其中的39个SNP作为工具变量。运用上述两样本孟德尔随机化方法得出的因果效应估计值相近,其中IVW法的OR值为1.531(95%CI: 1.248~1.877; P<0.001),MR-Egger回归结果表明遗传多效性不会对结果造成偏倚(截距=0.003,P=0.768)。 结论 两样本孟德尔随机化分析结果显示,腰围与冠心病的发病风险间呈正向因果关系,即腰围每增加一个标准差(SD=12.5 cm)会导致冠心病发病风险增加约50%。

关键词: 腰围, 冠心病, 两样本孟德尔随机化, 因果推断

Abstract: Objective To explore the causality between waist circumference(WC)and incidence of coronary heart disease(CHD)using 2-sample mendelian randomization(2MR). Methods Mutually independent genetic variants which were closely related to WC were chosen as instrumental variable(IV)from Genome Wide Association Study(GWAS)databases. By using odds ratio(OR)as outcome indicator, the causal relationship between WC and CHD was analyzed using 2MR methods, including Wald ratio method, inverse-variance weighted(IVW)method, mendelian randomization-Egger(MR-Egger)regression and weight median method. Results WC dataset of 232 101 Europeans and CHD dataset of 86 995 Europeans were used. Totally 39 SNPs were selected as IV. Similar effect estimations were obtained using 2MR methods, of which IVW results showed that the OR between WC and CHD was 1.531(95%CI:1.248- 山 东 大 学 学 报 (医 学 版)57卷11期 -刘新辉,等.腰围和冠心病因果关系的孟德尔随机化研究 \=-1.877;P<0.001). Besides, results of MR-Egger regression indicated that genetic pleiotropy did not bias the effect estimation(slope=0.003, P=0.768). Conclusion This large study using 2MR, shows there is a causal relationship between WC and incidence of CHD, and the risk of CHD will increase by approximately 50% when WC increases one standard deviation(SD=12.5cm).

Key words: Waist circumference, Coronary heart disease, Two-sample mendelian randomization, Causal inference

中图分类号: 

  • R541.4
[1] Members WG, Mozaffarian D, Benjamin EJ, et al. Executive summary: heart disease and stroke statistics-2016 update: a report from the American heart association[J]. Circulation, 2016, 127(1): 143-152.
[2] Kones R. Primary prevention of coronary heart disease: integration of new data, evolving views, revised goals, and role of rosuvastatin in management. a comprehensive survey[J]. Drug Des Devel Ther, 2011, 5(1): 325-380.
[3] Clark AL, Fonarow GC, Horwich TB. Waist circumference, body mass index, and survival in systolic heart failure: the obesity paradox revisited[J]. J Card Fail, 2011, 17(5): 374-380.
[4] Buchholz AC, Bugaresti JM. A review of body mass index and waist circumference as markers of obesity and coronary heart disease risk in persons with chronic spinal cord injury[J]. Spinal cord, 2005, 43(9): 513-518.
[5] Dale CE, Fatemifar G, Palmer TM, et al. Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a Mendelian randomization analysis[J]. Circulation, 2017, 135(24): 2373-2388.
[6] Rexrode KM, Carey VJ, Hennekens CH, et al. Abdominal adiposity and coronary heart disease in women[J]. JAMA, 1998, 280(21): 1843-1848.
[7] Flint AJ, Rexrode KM, Hu FB, et al. Body mass index, waist circumference, and risk of coronary heart disease: a prospective study among men and women[J]. Obes Res Clin Pract, 2010, 4(3): e171-e181.
[8] Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations[J]. Int J Epidemiol, 2004, 33(1): 30-42.
[9] Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology[J]. Stat Med, 2008, 27(8): 1133-1163.
[10] Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators[J]. Am J Epidemiol, 2013, 178(7): 1177-1184.
[11] Shungin D, Winkler TW, Croteau-Chonka DC, et al. New genetic loci link adipose and insulin biology to body fat distribution[J]. Nature, 2015, 518(7538): 187-196.
[12] Pritchard JK, Przeworski M. Linkage disequilibrium in humans: models and data[J]. Am J Hum Genet, 2001, 69(1): 1-14.
[13] Noyce AJ, Kia DA, Hemani G, et al. Estimating the causal influence of body mass index on risk of Parkinson disease: a Mendelian randomisation study[J]. PLoS Med, 2017, 14(6): e1002314. doi: 10.1371/journal.pmed.1002314.
[14] Saade S, Cazier JB, Ghassibe-Sabbagh M, et al. Large scale association analysis identifies three susceptibility loci for coronary artery disease[J]. PLoS One, 2011, 6(12): e29427. doi: 10.1371/journal.pone.0029427.
[15] Staiger DO, Stock JH. Instrumental variables regression with weak instruments[J]. Econometrica, 1997, 65(3): 557-586.
[16] Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome[J]. Elife, 2018, 7: e34408. doi: 10.7554/eLife.34408.
[17] 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.
[18] Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets[J]. Nat Genet, 2016, 48(5): 481-487.
[19] Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genetic Epidemiol, 2013, 37(7): 658-665.
[20] Bowden J, Del Greco MF, Minelli C, et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization[J]. Stat Med, 2017, 36(11): 1783-1802.
[21] Rosoff DB, Lohoff F. Educational attainment causally impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study in~ 780,000 study participants[J]. bioRxiv, 2019: e557595. doi: https://doi.org/10.1101/557595.
[22] Bowden J, Davey Smith G, 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.
[23] 魏丽娟, 董惠娟. Meta分析中异质性的识别与处理[J]. 第二军医大学学报, 2006, 27(4): 449-450. WEI Lijuan, DONG Huijuan. The identification and solution of heterogeneity in meta-analysis[J]. Academic Journal of Second Military Medical University, 2006, 27(4): 449-450.
[24] 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.
[25] Renehan A, Tyson M, Egger M, et al. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies[J]. Lancet, 2008, 371(9612): 569-578.
[26] Thorp ML. Body mass index and risk for end-stage renal disease[J]. Ann Intern Med, 2006,144(9):700-701.
[27] Whitlock G, Lewington S, Sherliker P, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies[J]. Lancet, 2009, 373(9669): 1083-1096.
[28] 乐生龙. 不同身体形态评价指标与脂代谢水平关系的研究[D]. 上海:上海交通大学, 2013.
[29] Rexrode KM, Buring JE, Manson JE. Abdominal and total adiposity and risk of coronary heart disease in men[J]. Int J Obes Relat Metab Disord, 2001, 25(7): 1047-1056.
[30] Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease[J]. Nature, 2006, 444(7121): 875-880.
[31] Lawlor DA. Commentary: Two-sample Mendelian randomization: opportunities and challenges[J]. Int J Epidemiol, 2016, 45(3): 908-915.
[1] 张凯,司书成,李吉庆,刘晓雯,赵英琪,薛付忠. 睡眠性状与肠易激综合征的孟德尔随机化研究[J]. 山东大学学报 (医学版), 2022, 60(8): 109-114.
[2] 谢佳莹, 祁佳, 宋铭, 李育林, 王迪, 贾旭, 张薇, 钟明, 尚嫄嫄. 血清蛋白质β-折叠水平与冠心病的相关性[J]. 山东大学学报 (医学版), 2022, 60(1): 21-26.
[3] 杨璇,李岩志,马伟,贾崇奇. 基于两样本孟德尔随机化的肺功能与新型冠状病毒肺炎病死风险的因果关系[J]. 山东大学学报 (医学版), 2021, 59(7): 104-111.
[4] 刘聪聪,陈晓璐,司书成,王博洁,李云霞,李文超,袁同慧,薛付忠. 山东省人群血清尿酸变异性与冠心病发病风险的关系[J]. 山东大学学报 (医学版), 2020, 58(12): 109-116.
[5] 李明卓,孙秀彬,王春霞,杨洋,刘新辉,刘言训,薛付忠,袁中尚. 血脂正常人群HDL-C纵向变化与冠心病的关联性分析:一项回顾性队列研究[J]. 山东大学学报 (医学版), 2019, 57(8): 110-116.
[6] 林伟强,王春霞,李明卓,孙秀彬,刘言训,薛付忠,袁中尚. 中老年人血压变化轨迹与冠心病发病的关系[J]. 山东大学学报 (医学版), 2019, 57(4): 106-112.
[7] 王春霞,许艺博,杨宁,夏冰,王萍,薛付忠. 基于健康管理队列的冠心病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 66-71.
[8] 屈庆喜, 赵鑫, 孙文宇, 宋光民, 孟祥斌, 毕研文. 同期冠状动脉旁路移植术加颈动脉内膜剥脱术治疗冠心病合并颈动脉狭窄的疗效[J]. 山东大学学报（医学版）, 2015, 53(5): 71-74.
[9] 刘天骄, 郭媛, 李婷婷, 张建宁, 李俊, 张鹏. 血清CTRP9、APN水平与急性冠脉综合征的相关性[J]. 山东大学学报（医学版）, 2014, 52(9): 58-62.
[10] 姜红梅1，2，陈文强1. 老年冠心病心衰患者运动康复治疗中心理干预的临床意义[J]. 山东大学学报（医学版）, 2014, 52(4): 85-88.
[11] 黄芬，蒋钰 . IGT患者高甘油三酯血症-腰围表型与早期认知障碍的关系[J]. 山东大学学报（医学版）, 2014, 52(3): 72-74.
[12] 张旭平，张冬，李梓，刘鲁祁. 糖尿病与原位大隐静脉病变的相关性[J]. 山东大学学报(医学版), 2013, 51(11): 66-70.
[13] 孙雅文1，高优美1，唐芳2，薛付忠1，张成琪2，于红霞1. 济南市健康体检女性膳食模式与测量指标的相关性[J]. 山东大学学报(医学版), 2013, 51(11): 98-101.
[14] 梁凯1，侯新国1，王新华2，宋允胜2，井庆平3，曲勇4，孙宇1，宋君1，董晓琳1，许聿新1，李振作1，付春莉1，李文娟1，王云霞1，矫磊1，曹立君1，尹小芳1，范琳琳1，陈丽1. 人体肥胖测量参数对糖代谢异常风险的预测价值研究[J]. 山东大学学报(医学版), 2012, 50(9): 59-62.
[15] 陈伟，谈红，李晓燕，杨燕，韦修莹，陈英剑，张红明. 冠心病患者外周血内皮祖细胞数量差异及其对内皮功能的影响[J]. 山东大学学报(医学版), 2012, 50(8): 73-76.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发