Journal Article Epublish
PubMed: 40147938 DOI: 10.4014/jmb.2411.11071
Fecal Microbiome and Urine Metabolome Profiling of Type 2 Diabetes.
J Microbiol Biotechnol 35:e2411071
Authors: Hye-Min Yi, Seok Won, Juhan Pak, Seong-Eun Park, Mi-Ri Kim, Ji-Hyun Kim, Eun-Young Park, Sun-Young Hwang, Mee-Hyun Lee, Hong-Seok Son, Suryang Kwak
Affiliations
- College of Korean Medicine, Dongshin University, Naju 58245, Republic of Korea. Dangbom Korean Medicine Clinic, Seoul 03192, Republic of Korea.
- Department of Bio and Fermentation Convergence Technology, College of Science and Technology, Kookmin University, Seoul 02707, Republic of Korea.
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Republic of Korea.
- Dangbom Korean Medicine Clinic, Seoul 03192, Republic of Korea.
- College of Korean Medicine, Dongshin University, Naju 58245, Republic of Korea.
Abstract
Type 2 diabetes is a prevalent metabolic disorder with serious health consequences, necessitating both enhanced diagnostic methodologies and comprehensive elucidation of its pathophysiological mechanisms. We compared fecal microbiome and urine metabolome profiles in type 2 diabetes patients versus healthy controls to evaluate their respective diagnostic potential. Using a cohort of 94 subjects (48 diabetics, 46 controls), this study employed 16S rRNA amplicon sequencing for fecal microbiome analysis and GC-MS for urinary metabolomics. While fecal microbiome alpha diversity showed no significant differences between groups, urinary metabolomics demonstrated distinct structural patterns and higher evenness in type 2 diabetes patients. The study identified several diabetes-associated urinary metabolites, including elevated levels of glucose and inositol, along with decreased levels of 6 urine metabolites including glycolic acid, hippurate, and 2-aminoethanol. In the fecal microbiome, genera such as - showed positive correlation with type 2 diabetes, while demonstrated negative correlation. Receiver operating characteristic curve analyses revealed that urinary metabolites exhibited superior diagnostic potential compared to fecal microbiome features, with an area under the curve of 0.90 for the combined metabolite model versus 0.82 for the integrated bacterial taxa model. These findings suggest that urinary metabolomics may offer a more reliable approach for type 2 diabetes diagnosis compared to fecal 16S metabarcoding, while highlighting the potential of multi-marker panels for enhanced diagnostic accuracy.
MeSH
Diabetes Mellitus, Microbiology, Biomarkers, Biotechnology, Metabolomics, Diabetes Mellitus, Type 2