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Genetically Predicted Gut Microbiota and Lymphoma Risk: A Mendelian Randomization Study
Danhua Li1, Tongfei Zhou1, Meng Liu2
1Foshan Key Laboratory of Precision Therapy in Oncology and Neurology, Department of Pulmonary Oncology, The First People's Hospital of Foshan (Foshan Hospital affiliated with Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Foshan, Guangdong, China.
Introduction:
Growing evidence links gut microbiota (GM) to hematological malignancies; however, its role in lymphoma remains unclear. This study aimed to investigate the potential causal relationships between genetically predicted gut microbial taxa and lymphoma subtypes using a Mendelian randomization (MR) framework.
Methods:
Using genome-wide association study (GWAS) summary data for 211 gut microbial taxa and 10 lymphoma subtypes, we performed bidirectional Mendelian randomization (MR) and sensitivity analyses to assess causality. Reverse MR was also used to evaluate reverse causation. Steiger directionality tests were applied to verify causal direction. False discovery rate (FDR) correction was applied to account for multiple testing.
Results:
We identified 22 genera exhibiting nominal associations based on IVW estimates (P < 0.05): Hodgkin lymphoma (4 genera), non-Hodgkin lymphoma (3), Diffuse Large B-cell lymphoma (DLBCL, 3), Follicular lymphoma (1), non-Follicular lymphoma (nFL, 2), T/NK lymphoma (1), Mantle cell lymphoma (4), Marginal zone lymphoma (1), Macroglobulinemia (2), and non-Hodgkin NAS (1). Additionally, choline showed nominal inverse associations with DLBCL (OR=0.77, 95% CI=0.59-1.00, P <0.05) and nFL risk (OR=0.82, 95% CI=0.71-0.94, P <0.01). None of these associations remained statistically significant after false discovery rate (FDR) correction.
Discussion:
The observed associations differed substantially across lymphoma subtypes, indicating that gut microbiota-related effects are unlikely to operate through a single shared mechanism. Such heterogeneity is consistent with the distinct immunological and metabolic features of individual lymphoma entities. Although several biologically plausible mechanisms may underlie these associations, the findings should be interpreted with caution, given the use of genus-level microbial traits and summary-level GWAS data. In addition, population specificity and residual pleiotropy cannot be fully excluded despite extensive sensitivity analyses.
Conclusion:
This MR study provides preliminary genetic evidence supporting potential associations between genetically predicted gut microbial taxa and lymphoma risk. The heterogeneity observed across entities underscores the complexity of microbiota-lymphoma relationships. Further studies integrating functional experiments and high-resolution microbial data are warranted to clarify the biological relevance of these findings.
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