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) ›› 2023, Vol. 61 ›› Issue (5): 102-107.doi: 10.6040/j.issn.1671-7554.0.2023.0085

• 公共卫生与管理学 • 上一篇    

氨基酸与2型糖尿病因果关系的孟德尔随机化分析

张天鑫1,2,张婷3,黄鑫1,2,韩佳沂1,2,王淑康1,2   

  1. 1.山东大学齐鲁医学院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;3.山东省公共卫生临床中心药学部, 山东 济南 250102
  • 发布日期:2023-05-15
  • 通讯作者: 王淑康. E-mail:wsk2001@sdu.edu.cn
  • 基金资助:
    国家自然科学基金(82173624,81872712);山东省自然科学基金(ZR2019ZD02)

A mendelian randomization analysis on the causal associations between amino acids and type 2 diabetes

ZHANG Tianxin1,2, ZHANG Ting3, HUANG Xin1,2, HAN Jiayi1,2, WANG Shukang1,2   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Institute of Medical Dataology, Shandong University, Jinan 250002, Shandong, China;
    3. Department of Pharmacy, Shandong Public Health Clinical Center, Jinan 250102, Shandong, China
  • Published:2023-05-15

PDF (PC)

163

摘要： 目的 探讨氨基酸与2型糖尿病(T2D)的双向因果关联。 方法 利用氨基酸和T2D全基因组关联研究汇总数据,通过逆方差加权法、加权中位数法、MR-Egger回归法、MR稳健调整轮廓得分法、MR多效性残差及异质性检测法(MR-PRESSO)进行两样本双向孟德尔随机化(MR)分析,并采用Cochrans Q检验、MR-Egger回归截距项检验、MR-PRESSO全局检验评估结果的异质性和水平多效性。 结果 逆方差加权法结果显示,亮氨酸(OR=1.239,95%CI: 1.078~1.424,P=0.002 5)、缬氨酸(OR=1.222,95%CI: 1.071~1.394,P=0.002 9)会增加T2D发病风险,甘氨酸(OR=0.885,95%CI: 0.831~0.942,P=0.000 1)则降低T2D发病风险。反向MR分析结果显示,T2D分别与亮氨酸、异亮氨酸、缬氨酸、丙氨酸、苯丙氨酸、酪氨酸的增加和甘氨酸的降低有因果关联。多种MR方法的结果基本一致,且敏感性分析未发现潜在的多效性。 结论 较高水平的亮氨酸、缬氨酸及较低水平的甘氨酸与T2D发病风险具有双向因果关联,T2D对异亮氨酸、丙氨酸、苯丙氨酸、酪氨酸有正向因果效应。

关键词: 氨基酸, 2型糖尿病, 孟德尔随机化, 因果推断, 支链氨基酸

Abstract: Objective To investigate the bidirectional causal association between amino acids and type 2 diabetes(T2D). Methods Data of genome-wide association study on amino acids and T2D were collected. Inverse variance weighted method, weighted median method, MR-Egger regression method, MR robust adjusted profile score method and MR pleiotropy residual sum and outlier method(MR-PRESSO)were adopted for two-sample bidirectional mendelian randomization(MR)analysis. Cochrans Q test, MR-Egger intercept test, and MR-PRESSO global test were used to evaluate the heterogeneity and pleiotropy of the results. Results The results of inverse variance weighted method showed that leucine(OR=1.239, 95%CI: 1.078-1.424, P=0.002 5)and valine(OR=1.222, 95%CI: 1.071-1.394, P=0.002 9)increased the risk of T2D, while glycine(OR=0.885, 95%CI: 0.831-0.942, P=0.000 1)decreased the risk. The results of reverse MR analysis showed that the risk of T2D was causally associated with increased leucine, isoleucine, valine, alanine, phenylalanine and tyrosine, and decreased glycine. The results of multiple MR methods suggested robustness of causal associations and no potential pleiotropy was found in the sensitivity analysis. Conclusion Higher levels of leucine and valine and lower level of glycine have bidirectional causal associations with T2D risk. T2D has positive causal effects on isoleucine, alanine, phenylalanine and tyrosine.

Key words: Amino acids, Type 2 diabetes, Mendelian randomization, Causal inference, Branched-chain amino acids

中图分类号: 

  • R587.1
[1] Sun H, Saeedi P, Karuranga S, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 [J]. Diabetes Res Clin Pract, 2022, 183: 109119. doi: 10.1016/j.diabres.2021.109119.
[2] Gar C, Rottenkolber M, Prehn C, et al. Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes [J]. Crit Rev Clin Lab Sci, 2018, 55(1): 21-32.
[3] 文江平, 郝洁, 陶丽新, 等. 血浆氨基酸水平与2型糖尿病发生的巢式病例对照研究[J].中华糖尿病杂志, 2017, 9(12): 764-769. WEN Jiangping, HAO Jie, TAO Lixin, et al. Association of plasma amino acids and the risk of developing diabetes in a rural Chinese population: a nested case-control study [J]. Chinese Journal of Diabetes Mellitus, 2017, 9(12): 764-769.
[4] Ahola-Olli AV, Mustelin L, Kalimeri M, et al. Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts [J]. Diabetologia, 2019, 62(12): 2298-2309.
[5] Lawlor DA, Harbord RM, Sterne JAC, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology [J]. Stat Med, 2008, 27(8): 1133-1163.
[6] Julkunen H, Cichońska A, Slagboom PE, et al. Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population [J]. Elife, 2021, 10: e63033. doi: 10.7554/eLife.63033.
[7] Zheng J, Baird D, Borges MC, et al. Recent developments in mendelian randomization studies [J]. Curr Epidemiol Rep, 2017, 4(4): 330-345.
[8] Brion MJA, Shakhbazov K, Visscher PM. Calculating statistical power in mendelian randomization studies [J]. Int J Epidemiol, 2013, 42(5): 1497-1501.
[9] Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data [J]. PLoS Genet, 2017, 13(11): e1007081. doi: 10.1371/journal.pgen.1007081.
[10] Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data [J]. Genet Epidemiol, 2013, 37(7): 658-665.
[11] 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.
[12] 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.
[13] Zhao Q, Wang J, Hemani G, et al. Statistical inference in two-sample summary-data mendelian randomization using robust adjusted profile score [J]. Ann Stat, 2020, 48(3): 1742-1769.
[14] Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases [J]. Nat Genet, 2018, 50(5): 693-698.
[15] Bowden J, Spiller W, Del Greco MF, et al. Improving the visualization, interpretation and analysis of two-sample summary data mendelian randomization via the radial plot and radial regression [J]. Int J Epidemiol, 2018, 47(4): 1264-1278.
[16] Newgard CB, An J, Bain JR, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance [J]. Cell Metab, 2009, 9(4): 311-326.
[17] Wang-Sattler R, Yu Z, Herder C, et al. Novel biomarkers for pre-diabetes identified by metabolomics [J]. Mol Syst Biol, 2012, 8: 615. doi: 10.1038/msb.2012.43.
[18] Badoud F, Lam KP, DiBattista A, et al. Serum and adipose tissue amino acid homeostasis in the metabolically healthy obese [J]. J Proteome Res, 2014, 13(7): 3455-3466.
[19] Palmer ND, Stevens RD, Antinozzi PA, et al. Metabolomic profile associated with insulin resistance and conversion to diabetes in the insulin resistance atherosclerosis study [J]. J Clin Endocrinol Metab, 2015, 100(3): E463-E468.
[20] Rebholz CM, Yu B, Zheng Z, et al. Serum metabolomic profile of incident diabetes [J]. Diabetologia, 2018, 61(5): 1046-1054.
[21] Merino J, Leong A, Liu CT, et al. Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose [J]. Diabetologia, 2018, 61(6): 1315-1324.
[22] 王凤华, 刘静, 邓秋菊, 等. 血浆20种氨基酸水平与糖尿病风险的关联研究[J]. 中华内科杂志, 2019, 58(4): 270-277. WANG Fenghua, LIU Jing, DENG Qiuju, et al. The association between plasma levels of 20 amino acids and risk of diabetes [J]. Chinese Journal of Internal Medicine, 2019, 58(4): 270-277.
[23] Shi L, Brunius C, Lehtonen M, et al. Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort [J]. Diabetologia, 2018, 61(4): 849-861.
[24] Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes [J]. Nat Med, 2011, 17(4): 448-453.
[25] White PJ, McGarrah RW, Herman MA, et al. Insulin action, type 2 diabetes, and branched-chain amino acids: a two-way street [J]. Mol Metab, 2021, 52: 101261. doi: 10.1016/j.molmet.2021.101261.
[26] Patti ME, Brambilla E, Luzi L, et al. Bidirectional modulation of insulin action by amino acids [J]. J Clin Invest, 1998, 101(7): 1519-1529.
[27] Krebs M, Krssak M, Bernroider E, et al. Mechanism of amino acid-induced skeletal muscle insulin resistance in humans [J]. Diabetes, 2002, 51(3): 599-605.
[28] Chen L, Zhang J, Li C, et al. Glycine transporter-1 and glycine receptor mediate the antioxidant effect of glycine in diabetic rat islets and INS-1 cells [J]. Free Radic Biol Med, 2018, 123: 53-61. doi: 10.1016/j.freeradbiomed.2018.05.007.
[29] Wang Q, Holmes MV, Davey Smith G, et al. Genetic support for a causal role of insulin resistance on circulating branched-chain amino acids and inflammation [J]. Diabetes Care, 2017, 40(12): 1779-1786.
[30] Wittemans LBL, Lotta LA, Oliver-Williams C, et al. Assessing the causal association of glycine with risk of cardio-metabolic diseases [J]. Nat Commun, 2019, 10(1): 1060. doi: 10.1038/s41467-019-08936-1.
[31] Vangipurapu J, Stancáková A, Smith U, et al. Nine amino acids are associated with decreased insulin secretion and elevated glucose levels in a 7.4-year follow-up study of 5,181 Finnish men [J]. Diabetes, 2019, 68(6): 1353-1358.
[32] Luzi L, Castellino P, DeFronzo RA. Insulin and hyperaminoacidemia regulate by a different mechanism leucine turnover and oxidation in obesity [J]. Am J Physiol, 1996, 270(2 Pt 1): E273-E281.
[33] Okun JG, Rusu PM, Chan AY, et al. Liver alanine catabolism promotes skeletal muscle atrophy and hyperglycaemia in type 2 diabetes [J]. Nat Metab, 2021, 3(3): 394-409.
[34] Alves A, Bassot A, Bulteau AL, et al. Glycine metabolism and its alterations in obesity and metabolic diseases [J]. Nutrients, 2019, 11(6): 1356. doi: 10.3390/nu11061356.
[1] 李金泉,高美芳,闫飞,董明. 136例2型糖尿病患者肌肉痉挛的发生频率及危险因素[J]. 山东大学学报 (医学版), 2023, 61(5): 20-24.
[2] 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.
[3] 赵美茹,朱迪,刘淋,管庆波,张栩. 简易胰岛素抵抗指标与698例2型糖尿病患者发生高尿酸血症风险的关联[J]. 山东大学学报 (医学版), 2022, 60(12): 44-51.
[4] 于书卷,王美娟,陈丽,曹英娟,吕晓燕,刘雪燕,林鹏,颜景政. 老年2型糖尿病患者轻度认知功能障碍的影响因素[J]. 山东大学学报 (医学版), 2022, 60(11): 108-112.
[5] 吕丽,姜璐,陈诗鸿,庄向华,宋玉文,王殿辉,安文娟,李倩,潘喆. 210例绝经后2型糖尿病发生骨质疏松的相关因素[J]. 山东大学学报 (医学版), 2021, 59(7): 19-25.
[6] 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.
[7] 郑凤杰,宋玉文,孙爱丽,潘喆,王殿辉,娄能俊,吕丽, 庄向华,陈诗鸿. 糖尿病周围神经病变与肌少症的关联性[J]. 山东大学学报 (医学版), 2021, 59(6): 38-44.
[8] 刘萍,宋玉文,王萍,田光伟,郑凤杰,吕丽,杜娇娇,张静,庄向华,陈诗鸿. 维生素D缺乏与2型糖尿病合并抑郁状态的相关性[J]. 山东大学学报 (医学版), 2021, 59(6): 51-56.
[9] 耿玲,张欢欢,张甜甜. 459例试管婴儿备孕期高龄女性膳食蛋白质摄入现状分析[J]. 山东大学学报 (医学版), 2021, 59(11): 29-34.
[10] 张宝文,雷香丽,李瑾娜,罗湘俊,邹容. miR-21-5p靶向调控TIMP3抑制2型糖尿病肾病小鼠肾脏系膜细胞增殖及细胞外基质堆积[J]. 山东大学学报 (医学版), 2020, 1(7): 7-14.
[11] 王余余,高丽,陈少华. 94例2型糖尿病患者急性脑梗死后认知障碍与甲状腺功能的关联性[J]. 山东大学学报 (医学版), 2020, 58(5): 56-61.
[12] 李云霞,李洪凯,马韫韬,于媛媛,孙晓茹,刘新辉, 司书成,侯蕾,袁同慧,刘璐,李文超,薛付忠,刘言训. 基于两样本孟德尔随机化的身高和冠心病风险之间因果关系[J]. 山东大学学报 (医学版), 2020, 58(5): 107-114.
[13] 唐博,邵静,崔静,孙健平. 2型糖尿病发病与高密度脂蛋白关系的机制研究[J]. 山东大学学报 (医学版), 2020, 58(3): 99-106.
[14] 金海燕,张炎,马小莉,韩羽,赵蕙琛,刘元涛,张玉超. MiR-122与miR-33a在2型糖尿病合并冠心病患者中的表达[J]. 山东大学学报 (医学版), 2020, 58(3): 94-98.
[15] 曾雁冰,王秋鹏,方亚. 厦门市糖尿病“三师共管”模式的卫生经济学评价[J]. 山东大学学报 (医学版), 2019, 57(8): 89-94.
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