Fecal Microbiome and Urine Metabolome Profiling of Type 2 Diabetes

• Published in: Journal of microbiology and biotechnology Vol. 35; pp. e2411071 - 9
• Main Authors: Yi, Hye-Min, Won, Seok, Pak, Juhan, Park, Seong-Eun, Kim, Mi-Ri, Kim, Ji-Hyun, Park, Eun-Young, Hwang, Sun-Young, Lee, Mee-Hyun, Son, Hong-Seok, Kwak, Suryang
• Format: Journal Article
• Language: English
• Published: Korea (South) 한국미생물·생명공학회 11.03.2025
• Subjects: Adult; Aged; Bacteria - classification; Bacteria - genetics; Bacteria - isolation & purification; Biomarkers - urine; Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - metabolism; Diabetes Mellitus, Type 2 - microbiology; Diabetes Mellitus, Type 2 - urine; Feces - microbiology; Female; Gas Chromatography-Mass Spectrometry; Gastrointestinal Microbiome; Humans; Male; Metabolome; Metabolomics - methods; Microbiota; Middle Aged; RNA, Ribosomal, 16S - genetics; Urine - chemistry; 생물학; Type 2 diabetes; biomarker; fecal microbiome; urine metabolome
• ISSN: 1017-7825; 1738-8872
• DOI: 10.4014/jmb.2411.11071

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.