双歧杆菌
肠道菌群
肥胖
微生物群
生物
超重
乳酸菌
生物信息学
细菌
免疫学
遗传学
内分泌学
作者
Hao Wu,Yuan Li,Yuxuan Jiang,Xinran Li,Shenglan Wang,Changqing Zhao,Ximiao Yang,Baocheng Chang,Juhong Yang,Jie Qiao
标识
DOI:10.3389/fmicb.2024.1488656
摘要
Background 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. Methods 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. Conclusion Bifidobacterium pseudocatenulatum holds potential as a therapeutic agent for managing diet-induced obesity, underscoring its relevance in microbiome-based obesity research and intervention.
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