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Updated: Jun 30, 2025
Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
A two-sample bidirectional Mendelian randomization analysis investigates associations between gut microbiota and type
Siyuan Song1, Qiling Zhang1, Li Zhang1
1Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.
Objective:
This study sought to elucidate the causal association between gut microbiota (GM) composition and type 2 diabetes mellitus (T2DM) through a comprehensive two-sample bidirectional Mendelian randomization analysis.
Method:
T2DM data were sourced from the IEU OpenGWAS Project database, complemented by 211 gut microbiota (GM) datasets from the MiBioGen Federation. The primary analytical approach employed was inverse variance weighted (IVW), supplemented by MR-Egger regression and weighted median (WME) methods to investigate their potential interplay. Results were assessed using odds ratios (OR) and 95% confidence intervals (CI). The robustness and reliability of the findings were confirmed through leave-one-out analysis, heterogeneity testing, and assessment of horizontal pleiotropy. Furthermore, we explored the potential mediating role of metabolites in the pathway linking GM to T2DM.
Result:
A set of 11 Single Nucleotide Polymorphisms (SNPs) linked to GM were identified as instrumental variables (IVs). The IVW analysis revealed that increased abundance of the genus Actinomyces, genus Bilophila, genus Lachnoclostridium, genus Ruminococcus gnavus group, and genus Streptococcus corresponded to a heightened risk of T2DM. Conversely, higher levels of genus Eubacterium oxidoreducens group, genus Oscillospira, genus Ruminococcaceae UCG003, genus Ruminococcaceae UCG010, and genus Sellimonas were associated with a reduced risk of T2DM. However, following false discovery rate (FDR) correction, only the abundance of genus Lachnoclostridium retained a significant positive correlation with T2DM risk (OR = 1.22, q value = 0.09), while the other ten GM showed suggestive associations with T2DM. Reverse MR analysis did not reveal any causal relationship between T2DM and the increased risk associated with the identified GM. Additionally, metabolites did not exhibit mediating effects in this context.
Conclusion:
This study effectively pinpointed specific GM associated with T2DM, potentially paving the way for novel biomarkers in the prevention and treatment of this condition. The findings suggested that probiotics could emerge as a promising avenue for managing T2DM in the future. Furthermore, the analysis indicated that metabolites do not appear to act as mediators in the pathway from GM to T2DM.
