Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target
BackgroundThe 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 signif...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1488656/full |
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| author | Hao Wu Yuan Li Yuxuan Jiang Xinran Li Shenglan Wang Changle Zhao Changle Zhao Ximiao Yang Baocheng Chang Juhong Yang Juhong Yang Jianjun Qiao Jianjun Qiao |
| author_facet | Hao Wu Yuan Li Yuxuan Jiang Xinran Li Shenglan Wang Changle Zhao Changle Zhao Ximiao Yang Baocheng Chang Juhong Yang Juhong Yang Jianjun Qiao Jianjun Qiao |
| author_sort | Hao Wu |
| collection | DOAJ |
| description | BackgroundThe 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.MethodsWe 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.ResultsOur 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.ConclusionBifidobacterium pseudocatenulatum holds potential as a therapeutic agent for managing diet-induced obesity, underscoring its relevance in microbiome-based obesity research and intervention. |
| format | Article |
| id | doaj-art-783452708ba642e98919946f58f3fcdd |
| institution | Kabale University |
| issn | 1664-302X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj-art-783452708ba642e98919946f58f3fcdd2025-02-05T23:04:36ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2025-02-011510.3389/fmicb.2024.14886561488656Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic targetHao Wu0Yuan Li1Yuxuan Jiang2Xinran Li3Shenglan Wang4Changle Zhao5Changle Zhao6Ximiao Yang7Baocheng Chang8Juhong Yang9Juhong Yang10Jianjun Qiao11Jianjun Qiao12Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, ChinaNHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, ChinaYidu Cloud (Beijing) Technology Co., Ltd., Beijing, ChinaNHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, ChinaNHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, ChinaZhejiang Institute of Tianjin University (Shaoxing), Shaoxing, ChinaDepartment of Pharmaceutical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, ChinaZhejiang Institute of Tianjin University (Shaoxing), Shaoxing, ChinaNHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, ChinaNHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, ChinaGuangdong Medical University, Zhanjiang, ChinaZhejiang Institute of Tianjin University (Shaoxing), Shaoxing, ChinaDepartment of Pharmaceutical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, ChinaBackgroundThe 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.MethodsWe 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.ResultsOur 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.ConclusionBifidobacterium pseudocatenulatum holds potential as a therapeutic agent for managing diet-induced obesity, underscoring its relevance in microbiome-based obesity research and intervention.https://www.frontiersin.org/articles/10.3389/fmicb.2024.1488656/fulloverweightmachine learningXGBoost-SHAPintestinal microbiotaBifidobacterium pseudocatenularis |
| spellingShingle | Hao Wu Yuan Li Yuxuan Jiang Xinran Li Shenglan Wang Changle Zhao Changle Zhao Ximiao Yang Baocheng Chang Juhong Yang Juhong Yang Jianjun Qiao Jianjun Qiao Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target Frontiers in Microbiology overweight machine learning XGBoost-SHAP intestinal microbiota Bifidobacterium pseudocatenularis |
| title | Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target |
| title_full | Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target |
| title_fullStr | Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target |
| title_full_unstemmed | Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target |
| title_short | Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target |
| title_sort | machine learning prediction of obesity associated gut microbiota identifying bifidobacterium pseudocatenulatum as a potential therapeutic target |
| topic | overweight machine learning XGBoost-SHAP intestinal microbiota Bifidobacterium pseudocatenularis |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1488656/full |
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