The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study

被引:21
作者
Sun, Kewang [1 ,2 ]
Gao, Yan [3 ]
Wu, Huaqing [4 ]
Huang, Xiangyan [2 ]
机构
[1] Weifang Med Coll, Sch Med Lab, Weifang, Peoples R China
[2] 960th Hosp PLA Jonit Logist Support Force, Dept Blood Transfus, Jinan, Peoples R China
[3] 960th Hosp PLA Jonit Logist Support Force, Dept Gen Med, Jinan, Peoples R China
[4] Beijing Technol & Business Univ, Sch Math & Stat, Beijing, Peoples R China
关键词
Mendelian randomized study; gut microbiota; type; 2; diabetes; causal inference; genetic variation; CHAIN FATTY-ACIDS; INSTRUMENTS; INFERENCE; IMPACT;
D O I
10.3389/fpubh.2023.1255059
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Type 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet. Methods: The summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables. Results: The outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996-1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993-0.998, P = 3.78 x 10(-3)), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993-0.998, P = 8.08 x 10(-3)) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001-1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000-1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001-1.005, P = 4.38 x 10(-3)), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000-1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable. Conclusion: This MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomization study
    Li, Hanjing
    Li, Candong
    FRONTIERS IN MICROBIOLOGY, 2023, 14
  • [2] The causal relationship between gut microbiota and leukemia: a two-sample Mendelian randomization study
    Chen, Guanjun
    Kuang, Zheshu
    Li, Fan
    Li, Jianchang
    FRONTIERS IN MICROBIOLOGY, 2023, 14
  • [3] Causal Relationship Between Gut Microbiota and Autoimmune Diseases: A Two-Sample Mendelian Randomization Study
    Xu, Qian
    Ni, Jing-Jing
    Han, Bai-Xue
    Yan, Shan-Shan
    Wei, Xin-Tong
    Feng, Gui-Juan
    Zhang, Hong
    Zhang, Lei
    Li, Bin
    Pei, Yu-Fang
    FRONTIERS IN IMMUNOLOGY, 2022, 12
  • [4] The causal relationship between gut microbiota and lymphoma: a two-sample Mendelian randomization study
    Li, Biyun
    Han, Yahui
    Fu, Zhiyu
    Chai, Yujie
    Guo, Xifeng
    Du, Shurui
    Li, Chi
    Wang, Dao
    FRONTIERS IN IMMUNOLOGY, 2024, 15
  • [5] Causal relationship between gut microbiota and gynecological tumor: a two-sample Mendelian randomization study
    Xiong, Yajun
    Zhang, Xiaonan
    Niu, Xiaoya
    Zhang, Long
    Sheng, Yanbing
    Xu, Aiguo
    FRONTIERS IN MICROBIOLOGY, 2024, 15
  • [6] Causal relationship between gut microbiota and kidney diseases: a two-sample Mendelian randomization study
    Feng, Zhoushan
    Zhang, Yuliang
    Lai, Yiyu
    Jia, Chunhong
    Wu, Fan
    Chen, Dunjin
    FRONTIERS IN IMMUNOLOGY, 2024, 14
  • [7] The causal relationship between gut microbiota and preterm birth: a two-sample Mendelian randomization study
    Zhu, Tao
    Shen, Dandan
    Cai, Xiao
    Jin, Yuanling
    Tu, Haixia
    Wang, Shouxing
    Pan, Qianglong
    JOURNAL OF MATERNAL-FETAL & NEONATAL MEDICINE, 2025, 38 (01)
  • [8] Causal relationship between gut microbiota and diabetic nephropathy: a two-sample Mendelian randomization study
    Yan, Shuxiang
    Wang, Hua
    Feng, Baiyu
    Ye, Lin
    Chen, Anqun
    FRONTIERS IN IMMUNOLOGY, 2024, 15
  • [9] Causal relationship between gut microbiota and rosacea: a two-sample Mendelian randomization study
    Li, Jiaqi
    Yang, Fengjuan
    Liu, Yuling
    Jiang, Xian
    FRONTIERS IN MEDICINE, 2024, 11
  • [10] Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study
    Yiwen Long
    Lanhua Tang
    Yangying Zhou
    Shushan Zhao
    Hong Zhu
    BMC Medicine, 21