Intestinal microbiota and tuberculosis: Insights from Mendelian randomization.
Peijun LiuYaomei LuoMinghua ZhangPublished in: Medicine (2024)
Respiratory tuberculosis (RTB), a global health concern affecting millions of people, has been observationally linked to the gut microbiota, but the depth and nature of this association remain elusive. Despite these findings, the underlying causal relationship is still uncertain. Consequently, we used the Mendelian randomization (MR) method to further investigate this potential causal connection. We sourced data on the gut microbiota from a comprehensive genome-wide association study (GWAS) conducted by the MiBioGen Consortium (7686 cases, and 115,893 controls). For RTB, we procured 2 distinct datasets, labeled the Fingen R9 TBC RESP and Fingen R9 AB1 RESP, from the Finnish Genetic Consortium. To decipher the potential relationship between the gut microbiota and RTB, we employed MR on both datasets. Our primary mode of analysis was the inverse variance weighting (IVW) method. To ensure robustness and mitigate potential confounders, we meticulously evaluated the heterogeneity and potential pleiotropy of the outcomes. In the TBC RESP (RTB1) dataset related to the gut microbiota, the IVW methodology revealed 7 microbial taxa that were significantly associated with RTB. In a parallel vein, the AB1 RESP (RTB2) dataset highlighted 4 microbial taxa with notable links. Notably, Lachnospiraceae UCG010 was consistently identified across both datasets. This correlation was especially evident in the data segments designated Fingen R9 TBC RESP (OR = 1.799, 95% CI = 1.243-2.604) and Finngen R9 AB1 RESP (OR = 2.131, 95% CI = 1.088-4.172). Our study identified a causal relationship between particular gut microbiota and RTB at the level of prediction based on genetics. This discovery sheds new light on the mechanisms of RTB development, which are mediated by the gut microbiota.
Keyphrases
- global health
- mycobacterium tuberculosis
- genome wide association study
- microbial community
- rna seq
- human health
- single cell
- small molecule
- electronic health record
- magnetic resonance
- big data
- pulmonary tuberculosis
- gene expression
- metabolic syndrome
- machine learning
- risk assessment
- genome wide
- adipose tissue
- skeletal muscle
- weight loss
- artificial intelligence
- optical coherence tomography
- contrast enhanced
- glycemic control
- climate change
- pet ct