Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease
Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their host remains elusive for DKD. This study aims to elucidate the functional capability of gut microbiota to affect kidney function of DKD patients. A to...
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Taylor & Francis Group
2025-12-01
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| Series: | Gut Microbes |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19490976.2025.2473506 |
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| author | I-Wen Wu Yu-Chieh Liao Tsung-Hsien Tsai Chieh-Hua Lin Zhao-Qing Shen Yun-Hsuan Chan Chih-Wei Tu Yi-Ju Chou Chi-Jen Lo Chi-Hsiao Yeh Chun-Yu Chen Heng-Chih Pan Heng-Jung Hsu Chin-Chan Lee Mei-Ling Cheng Wayne Huey-Herng Sheu Chi-Chun Lai Huey-Kang Sytwu Ting-Fen Tsai |
| author_facet | I-Wen Wu Yu-Chieh Liao Tsung-Hsien Tsai Chieh-Hua Lin Zhao-Qing Shen Yun-Hsuan Chan Chih-Wei Tu Yi-Ju Chou Chi-Jen Lo Chi-Hsiao Yeh Chun-Yu Chen Heng-Chih Pan Heng-Jung Hsu Chin-Chan Lee Mei-Ling Cheng Wayne Huey-Herng Sheu Chi-Chun Lai Huey-Kang Sytwu Ting-Fen Tsai |
| author_sort | I-Wen Wu |
| collection | DOAJ |
| description | Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their host remains elusive for DKD. This study aims to elucidate the functional capability of gut microbiota to affect kidney function of DKD patients. A total of 990 subjects were enrolled consisting of a control group (n = 455), a type 2 diabetes mellitus group (DM, n = 204), a DKD group (n = 182) and a chronic kidney disease group (CKD, n = 149). Full-length sequencing of 16S rRNA genes from stool DNA was conducted. Three findings are pinpointed. Firstly, new types of microbiota biomarkers have been created using a machine-learning (ML) method, namely relative abundance of a microbe, presence or absence of a microbe, and the hierarchy ratio between two different taxonomies. Four different panels of features were selected to be analyzed: (i) DM vs. Control, (ii) DKD vs. DM, (iii) DKD vs. CKD, and (iv) CKD vs. Control. These had accuracy rates between 0.72 and 0.78 and areas under curve between 0.79 and 0.86. Secondly, 13 gut microbiota biomarkers, which are strongly correlated with anthropometric, metabolic and/or renal indexes, concomitantly identified by the ML algorithm and the differential abundance method were highly discriminatory. Finally, the predicted functional capability of a DKD-specific biomarker, Gemmiger spp. is enriched in carbohydrate metabolism and branched-chain amino acid (BCAA) biosynthesis. Coincidentally, the circulating levels of various BCAAs (L-valine, L-leucine and L-isoleucine) and their precursor, L-glutamate, are significantly increased in DM and DKD patients, which suggests that, when hyperglycemia is present, there has been alterations in various interconnected pathways associated with glycolysis, pyruvate fermentation and BCAA biosynthesis. Our findings demonstrate that there is a link involving the gut-kidney axis in DKD patients. Furthermore, our findings highlight specific gut bacteria that can acts as useful biomarkers; these could have mechanistic and diagnostic implications. |
| format | Article |
| id | doaj-art-4ccdec56d2ad471d8edd6a2b9e5cc42e |
| institution | DOAJ |
| issn | 1949-0976 1949-0984 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
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| series | Gut Microbes |
| spelling | doaj-art-4ccdec56d2ad471d8edd6a2b9e5cc42e2025-08-20T03:22:22ZengTaylor & Francis GroupGut Microbes1949-09761949-09842025-12-0117110.1080/19490976.2025.2473506Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney diseaseI-Wen Wu0Yu-Chieh Liao1Tsung-Hsien Tsai2Chieh-Hua Lin3Zhao-Qing Shen4Yun-Hsuan Chan5Chih-Wei Tu6Yi-Ju Chou7Chi-Jen Lo8Chi-Hsiao Yeh9Chun-Yu Chen10Heng-Chih Pan11Heng-Jung Hsu12Chin-Chan Lee13Mei-Ling Cheng14Wayne Huey-Herng Sheu15Chi-Chun Lai16Huey-Kang Sytwu17Ting-Fen Tsai18Department of Nephrology, Chang Gung Memorial Hospital, Keelung, TaiwanInstitute of Population Health Sciences, National Health Research Institutes, Miaoli, TaiwanAdvanced Tech BU, Acer Inc, New Taipei City, TaiwanInstitute of Population Health Sciences, National Health Research Institutes, Miaoli, TaiwanDepartment of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, Taipei, TaiwanAdvanced Tech BU, Acer Inc, New Taipei City, TaiwanAdvanced Tech BU, Acer Inc, New Taipei City, TaiwanInstitute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli, TaiwanMetabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan, TaiwanCommunity Medicine Research Center, Chang Gung Memorial Hospital, Keelung, TaiwanDepartment of Nephrology, Chang Gung Memorial Hospital, Keelung, TaiwanDepartment of Nephrology, Chang Gung Memorial Hospital, Keelung, TaiwanDepartment of Nephrology, Chang Gung Memorial Hospital, Keelung, TaiwanDepartment of Nephrology, Chang Gung Memorial Hospital, Keelung, TaiwanMetabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan, TaiwanInstitute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli, TaiwanCommunity Medicine Research Center, Chang Gung Memorial Hospital, Keelung, TaiwanNational Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli, TaiwanDepartment of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, Taipei, TaiwanDiabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their host remains elusive for DKD. This study aims to elucidate the functional capability of gut microbiota to affect kidney function of DKD patients. A total of 990 subjects were enrolled consisting of a control group (n = 455), a type 2 diabetes mellitus group (DM, n = 204), a DKD group (n = 182) and a chronic kidney disease group (CKD, n = 149). Full-length sequencing of 16S rRNA genes from stool DNA was conducted. Three findings are pinpointed. Firstly, new types of microbiota biomarkers have been created using a machine-learning (ML) method, namely relative abundance of a microbe, presence or absence of a microbe, and the hierarchy ratio between two different taxonomies. Four different panels of features were selected to be analyzed: (i) DM vs. Control, (ii) DKD vs. DM, (iii) DKD vs. CKD, and (iv) CKD vs. Control. These had accuracy rates between 0.72 and 0.78 and areas under curve between 0.79 and 0.86. Secondly, 13 gut microbiota biomarkers, which are strongly correlated with anthropometric, metabolic and/or renal indexes, concomitantly identified by the ML algorithm and the differential abundance method were highly discriminatory. Finally, the predicted functional capability of a DKD-specific biomarker, Gemmiger spp. is enriched in carbohydrate metabolism and branched-chain amino acid (BCAA) biosynthesis. Coincidentally, the circulating levels of various BCAAs (L-valine, L-leucine and L-isoleucine) and their precursor, L-glutamate, are significantly increased in DM and DKD patients, which suggests that, when hyperglycemia is present, there has been alterations in various interconnected pathways associated with glycolysis, pyruvate fermentation and BCAA biosynthesis. Our findings demonstrate that there is a link involving the gut-kidney axis in DKD patients. Furthermore, our findings highlight specific gut bacteria that can acts as useful biomarkers; these could have mechanistic and diagnostic implications.https://www.tandfonline.com/doi/10.1080/19490976.2025.2473506Diabetic kidney diseasemicrobiotamachine learningbranched-chain amino acids |
| spellingShingle | I-Wen Wu Yu-Chieh Liao Tsung-Hsien Tsai Chieh-Hua Lin Zhao-Qing Shen Yun-Hsuan Chan Chih-Wei Tu Yi-Ju Chou Chi-Jen Lo Chi-Hsiao Yeh Chun-Yu Chen Heng-Chih Pan Heng-Jung Hsu Chin-Chan Lee Mei-Ling Cheng Wayne Huey-Herng Sheu Chi-Chun Lai Huey-Kang Sytwu Ting-Fen Tsai Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease Gut Microbes Diabetic kidney disease microbiota machine learning branched-chain amino acids |
| title | Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease |
| title_full | Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease |
| title_fullStr | Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease |
| title_full_unstemmed | Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease |
| title_short | Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease |
| title_sort | machine learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease |
| topic | Diabetic kidney disease microbiota machine learning branched-chain amino acids |
| url | https://www.tandfonline.com/doi/10.1080/19490976.2025.2473506 |
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