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    下一篇

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

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] 杨宇凡,李悦,谢灏,林春华. 胆固醇水平与神经源性膀胱风险的因果关系[J]. 山东大学学报 (医学版), 2026, 64(5): 67-73.
[2] 段盈竹,董波,于睿. 内在情感与类风湿关节炎患者冠状动脉粥样硬化风险关系的孟德尔随机化分析[J]. 山东大学学报 (医学版), 2026, 64(4): 63-71.
[3] 赵万霞,詹群璋,金婷,刘玉新,曲崇正,吴剑纯. 基于孟德尔随机化分析肠道菌群、血液代谢物和肥胖的因果关系[J]. 山东大学学报 (医学版), 2026, 64(4): 72-82.
[4] 吴志晓,赵红洋. 孟德尔随机化分析免疫细胞表型与孤独症谱系障碍的因果关联[J]. 山东大学学报 (医学版), 2026, 64(3): 83-92.
[5] 陈婵,李巨章,何稳,吴巧珍. 基于两样本孟德尔随机化研究抑郁症和抗抑郁药物靶基因与睡眠呼吸暂停的关联[J]. 山东大学学报 (医学版), 2026, 64(1): 28-36.
[6] 王乐,罗清馨,吴思佳,吴雨桐,葛祎蕾,俞一凡,韦云,吉寒冰,刘铁梅,张紫妍,修佳伟,薛付忠,李洪凯. 虚弱和癫痫关联研究:前瞻性队列和孟德尔随机化分析[J]. 山东大学学报 (医学版), 2025, 63(9): 20-30.
[7] 孟晓梅,郝亚平,王亮,于晓,唐与晓. 血清Isthmin1、Gremlin2水平与2型糖尿病患者视网膜病变的相关性[J]. 山东大学学报 (医学版), 2025, 63(9): 102-107.
[8] 申路佳,逯天威,巩伟明,赵岩松,王淑康,袁中尚. 代谢风险评分在2型糖尿病人群心血管结局预测中的应用[J]. 山东大学学报 (医学版), 2025, 63(8): 69-78.
[9] 陈莹莹,王鲁,胡锡峰,朱高培,薛付忠. 基于贝叶斯网络的2型糖尿病患者并发脑卒中风险预测[J]. 山东大学学报 (医学版), 2025, 63(8): 94-102.
[10] 王雪梅,杨豪,宋洋,程世超,张婷婷,王艳春. 抗糖尿病药物与女性恶性肿瘤的因果关联:一项两样本孟德尔随机化分析[J]. 山东大学学报 (医学版), 2025, 63(6): 67-77.
[11] 黄馨,王梦雪,付书璠,张琦悦,徐力. 代谢综合征及其组分与消化系统恶性肿瘤的因果关联:两样本孟德尔随机化研究[J]. 山东大学学报 (医学版), 2025, 63(5): 86-94.
[12] 李建锋,张展,丁新华,高奋堂,何勤利,谢萍. 欧洲人群饮食因素与认知功能障碍关系的孟德尔随机化分析[J]. 山东大学学报 (医学版), 2025, 63(4): 36-43.
[13] 王小磊,方骏,王安,朱武晖,史光军. 两样本孟德尔随机化分析肠道菌群与肝外胆管癌的因果关系[J]. 山东大学学报 (医学版), 2025, 63(4): 44-50.
[14] 杨慧,苏士晶,李芬. 基于双向孟德尔随机化法探讨组织蛋白酶与衰弱的因果关联[J]. 山东大学学报 (医学版), 2025, 63(2): 67-76.
[15] 常宇,胡云峰,王会丰,郭静,张跳,郝雅琴,刘雨. 阑尾切除术与结直肠癌发病风险关联的孟德尔随机化研究[J]. 山东大学学报 (医学版), 2025, 63(2): 77-83.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!