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 total of 990 subjects were enrolled consisting of a control group (<i>n</i> = 455), a type 2 diabetes mellitus group (DM, <i>n</i> = 204), a DKD group (<i>n</i> = 182) and a chronic kidney disease group (CKD, <i>n</i> = 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 <i>vs</i>. Control, (ii) DKD <i>vs</i>. DM, (iii) DKD <i>vs</i>. CKD, and (iv) CKD <i>vs</i>. 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, <i>Gemmiger</i> 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.

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Publication Details

Journal
Gut Microbes
Published
2025-03-06
DOI
https://doi.org/10.1080/19490976.2025.2473506
Citations
17
Primary Topic
Gut microbiota and health
Type
article
Field-Weighted Citation Impact
13.70

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article

Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease

Heng‐Chih Pan, Chi‐Hsiao Yeh, Chih-Wei Tu, Heng-Jung Hsu et al.
17 citations
Gut Microbes
Gut microbiota and health
13.70
article

Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease

Heng‐Chih Pan, Chi‐Hsiao Yeh, Chih-Wei Tu, Heng-Jung Hsu, Wayne Huey‐Herng Sheu, Zhao‐Qing Shen, Yu-Chieh Liao, Yun-Hsuan Chan, Chi‐Chun Lai, Ting‐Fen Tsai, Yi-Ju Chou, I‐Wen Wu, Chi-Jen Lo, Huey‐Kang Sytwu, Chun‐Yu Chen, Tsung-Hsien Tsai, Chieh-Hua Lin, Chin-Chan Lee, Mei‐Ling Cheng
article en
17 citations

Abstract

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 (<i>n</i> = 455), a type 2 diabetes mellitus group (DM, <i>n</i> = 204), a DKD group (<i>n</i> = 182) and a chronic kidney disease group (CKD, <i>n</i> = 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 <i>vs</i>. Control, (ii) DKD <i>vs</i>. DM, (iii) DKD <i>vs</i>. CKD, and (iv) CKD <i>vs</i>. 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, <i>Gemmiger</i> 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.

Gut MicrobesVol. 17(1)
Memorial Hospital of South Bend (US), National Yang Ming Chiao Tung University (TW), National Health Research Institutes (TW), Chang Gung University (TW), Taipei Veterans General Hospital (TW), Chang Gung Memorial Hospital (TW), Acer (United States) (US), Keelung Chang Gung Memorial Hospital (TW), Acer (Taiwan) (TW), National Defense Medical Center (TW)
Chang Gung Memorial Hospital, Ministry of Health and Welfare
Zero hunger
Openalex Percentile: Top 1%
Gut microbiota and health
13.70
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