Causal relationships of gut microbiota, plasma metabolites, and metabolite ratios with diffuse large B-cell lymphoma: a Mendelian randomization study.

BACKGROUND

Recent studies have revealed changes in microbiota constitution and metabolites associated with tumor progression, however, no causal relation between microbiota or metabolites and diffuse large B-cell lymphoma (DLBCL) has yet been reported.

METHODS

We download a microbiota dataset from the MiBioGen study, a metabolites dataset from the Canadian Longitudinal Study on Aging (CLSA) study, and a DLBCL dataset from Integrative Epidemiology Unit Open genome-wide association study (GWAS) project. Mendelian randomization (MR) analysis was conducted using the R packages, TwoSampleMR and MR-PRESSO. Five MR methods were used: MR-Egger, inverse variance weighting (IVW), weighted median, simple mode, and weighted mode. Reverse MR analyses were also conducted to explore the causal effects of DLBCL on the microbiome, metabolites, and metabolite ratios. Pleiotropy was evaluated by MR Egger regression and MR-PRESSO global analyses, heterogeneity was assessed by Cochran's Q-test, and stability analyzed using the leave-one-out method.

RESULTS

119 microorganisms, 1,091 plasma metabolite, and 309 metabolite ratios were analyzed. According to IVW analysis, five microorganisms were associated with risk of DLBCL. The genera (OR: 3.431,  = 0.049) andgenera (OR: 2.406,  = 0.029) were associated with higher risk of DLBCL. Further, 27 plasma metabolites were identified as having a significant causal relationships with DLBCL, among which citrate levels had the most significant protective causal effect against DLBCL ( = 0.006), while glycosyl-N-tricosanoyl-sphingadienine levels was related to higher risk of DLBCL ( = 0.003). In addition, we identified 19 metabolite ratios with significant causal relationships to DLBCL, of which taurine/glutamate ratio had the most significant protective causal effect ( = 0.005), while the phosphoethanolamine/choline ratio was related to higher risk of DLBCL ( = 0.009). Reverse MR analysis did not reveal any significant causal influence of DLBCL on the above microbiota, metabolites, and metabolite ratios ( > 0.05). Sensitivity analyses revealed no significant heterogeneity or pleiotropy ( > 0.05).

CONCLUSION

We present the first elucidation of the causal influence of microbiota and metabolites on DLBCL using MR methods, providing novel insights for potential targeting of specific microbiota or metabolites to prevent, assist in diagnosis, and treat DLBCL.