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ORIGINAL RESEARCH article

Front. Microbiol.
Sec. Evolutionary and Genomic Microbiology
Volume 15 - 2024 | doi: 10.3389/fmicb.2024.1356437
This article is part of the Research Topic Host Genetics and its Interaction with Microbiome in Health, Disease, and Production Efficiency of Livestock Species View all articles

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

Provisionally accepted
Jingrong Qian Jingrong Qian Wen Zheng Wen Zheng *Jun Fang Jun Fang *Shiliang Cheng Shiliang Cheng *Yanli Zhang Yanli Zhang *Xuewei Zhuang Xuewei Zhuang *Chao Song Chao Song *
  • Department of Clinical Laboratory, Shandong Province Third Hospital, Shandong University, Jinan, Shandong Province, China

The final, formatted version of the article will be published soon.

    Background: Recent studies have revealed changes in microbiota constitution and metabolites associated with tumour 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 Terrisporobacter (OR: 3.431, p = 0.049) and genera Oscillibacter (OR: 2.406, p = 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 (p = 0.006), while glycosyl-N-tricosanoyl-sphingadienine levels was related to higher risk of DLBCL (p = 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 (p = 0.005), while the phosphoethanolamine/choline ratio was related to higher risk of DLBCL (p = 0.009). Reverse MR analysis did not reveal any significant causal influence of DLBCL on the above microbiota, metabolites, and metabolite ratios (p > 0.05). Sensitivity analyses revealed no significant heterogeneity or pleiotropy (p > 0.05).We present the first elucdation 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.

    Keywords: Gut Microbiota, Plasma metabolites, Metabolite ratios, DLBCL, Mendelian randomization

    Received: 15 Dec 2023; Accepted: 08 May 2024.

    Copyright: © 2024 Qian, Zheng, Fang, Cheng, Zhang, Zhuang and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Wen Zheng, Department of Clinical Laboratory, Shandong Province Third Hospital, Shandong University, Jinan, Shandong Province, China
    Jun Fang, Department of Clinical Laboratory, Shandong Province Third Hospital, Shandong University, Jinan, Shandong Province, China
    Shiliang Cheng, Department of Clinical Laboratory, Shandong Province Third Hospital, Shandong University, Jinan, Shandong Province, China
    Yanli Zhang, Department of Clinical Laboratory, Shandong Province Third Hospital, Shandong University, Jinan, Shandong Province, China
    Xuewei Zhuang, Department of Clinical Laboratory, Shandong Province Third Hospital, Shandong University, Jinan, Shandong Province, China
    Chao Song, Department of Clinical Laboratory, Shandong Province Third Hospital, Shandong University, Jinan, Shandong Province, China

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