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ORIGINAL RESEARCH article

Front. Microbiol.
Sec. Microorganisms in Vertebrate Digestive Systems
Volume 15 - 2024 | doi: 10.3389/fmicb.2024.1488656
This article is part of the Research Topic Bifidobacteria: Exploring the Roles of These Microbiome Guardians and Their Effects on Human Health View all 6 articles

Machine Learning Prediction of Obesity-Associated Gut Microbiota: Identifying Bifidobacterium pseudocatenulatum as a Potential Therapeutic Target

Provisionally accepted
Hao Wu Hao Wu 1Yuan Li Yuan Li 2Xinran Li Xinran Li 2Shenglan * Wang Shenglan * Wang 2Changle Xinran Zhao Changle Xinran Zhao 3Ximiao Yang Ximiao Yang 1Baocheng Chang Baocheng Chang 2Juhong Yang Juhong Yang 2*Jianjun Qiao Jianjun Qiao 3*
  • 1 Zhejiang institute of tianjin university, Zhejiang, China
  • 2 Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, China
  • 3 Tianjin University, Tianjin, Tianjin, China

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

    The rising prevalence of obesity and related metabolic disorders highlights the urgent need for innovative research approaches. Utilizing machine learning (ML) algorithms to predict obesity-associated gut microbiota and validating their efficacy with specific bacterial strains could significantly enhance obesity management strategies.We leveraged gut microbiome data from 1,563 healthy individuals and 2,043 overweight patients sourced from the GMrepo database. We assessed the anti-obesity effects of Bifidobacterium pseudocatenulatum through experimentation with Caenorhabditis elegans and C3H10T1/2 cells.Results: Our analysis revealed a significant correlation between gut bacterial composition and body weight. The top 40 bacterial species were utilized to develop ML models, with XGBoost demonstrating the highest predictive accuracy. SHAP analysis indicated a negative association between the relative abundance of six bacterial species, including B. pseudocatenulatum, and body mass index (BMI). Furthermore, B. pseudocatenulatum was shown to reduce lipid accumulation in C. elegans and inhibit lipid differentiation in C3H10T1/2 cells.Conclusions: Bifidobacterium pseudocatenulatum holds potential as a therapeutic agent for managing diet-induced obesity, underscoring its relevance in microbiome-based obesity research and intervention.

    Keywords: Overweight, machine learning, XGBoost-SHAP, intestinal microbiota, Bifidobacterium pseudocatenularis

    Received: 30 Aug 2024; Accepted: 05 Dec 2024.

    Copyright: © 2024 Wu, Li, Li, Wang, Zhao, Yang, Chang, Yang and Qiao. 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:
    Juhong Yang, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, China
    Jianjun Qiao, Tianjin University, Tianjin, 300072, Tianjin, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.