**Background:** Type 2 diabetes (T2DM) incidence is rising rapidly, and the gut microbiota is increasingly recognized as a key factor in metabolic disease. Goto-Kakizaki (GK) rats are a spontaneous, non-obese model of T2DM that shares common glucose metabolism features with human T2DM patients. This study aimed to systematically characterize faecal gut microbes and metabolites in GK rats using metagenomic and untargeted metabolomic approaches and to analyze their relationship with glucose and insulin resistance.
**Methods:** Ten male GK rats (model group) and ten male Wistar rats (control group), aged 5-6 weeks, were observed for 10 weeks. One rat from each group was excluded (one GK rat had FBG <11.1 mmol/L; one control rat escaped), leaving n=9 per group. Rats were fed standard breeding feed (not high-sugar/fat diet). Body weight and fasting blood glucose (FBG) were measured every two weeks. At week 11, after overnight fasting, blood was collected for FBG, insulin, HOMA-IR, and HOMA-β assessment. Faecal samples were collected for metagenomic sequencing (Illumina NovaSeq) and untargeted LC-MS/MS metabolomics. Statistical analyses included Student's t-tests, OPLS-DA, and correlation analyses.
**Key Results:** GK rats showed significantly lower body weight (P<0.05), higher FBG (mean >11.1 mmol/L at week 5, approaching 16.7 mmol/L at week 11), higher insulin levels, higher HOMA-IR, and lower HOMA-β compared to controls (P<0.05, P<0.01). Metagenomic sequencing yielded 4.15×10^10 raw bases and identified 12,302 species. At the phylum level, Firmicutes (54.85% control vs. 57.75% model) and Bacteroidetes (27.16% vs. 23.82%) dominated, with no significant differences. At the species level, GK rats had significantly decreased Prevotella sp. CAG:604 and Lactobacillus murinus (P<0.05) and significantly increased Allobaculum stercoricanis (P<0.01). FBG and HOMA-IR were positively correlated with Allobaculum stercoricanis and negatively correlated with Lactobacillus murinus (P<0.05, P<0.01). Metabolomics identified 815 positive-ion and 678 negative-ion metabolites, with 596 found in KEGG. Fourteen potential metabolic biomarkers were identified, including glycochenodeoxycholic acid (down), uric acid (up), glutathione (up), glycocholic acid (down), β-sitostenone (up), allantoic acid (up), N-acetylaspartate (up), N-acetyl-L-glutamic acid (up), sphinganine (up), 4-pyridoxic acid (up), 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate (up), 13(S)-HODE (up), linoleic acid (up), and 3-α,7-α,26-trihydroxy-5-β-cholestane (down). FBG and HOMA-IR were positively correlated with 13(S)-HODE, glutathione, uric acid, 4-pyridoxic acid, and allantoic acid, and negatively correlated with 3-α,7-α,26-trihydroxy-5-β-cholestane and chenodeoxycholic acid glycine conjugate (P<0.05, P<0.01). Sphinganine and linoleic acid were positively correlated with Allobaculum stercoricanis (P<0.01). Six metabolic pathways showed significant differences: arginine biosynthesis (P=0.0056, impact 0.198), primary bile acid biosynthesis (P=0.0057, impact 0.057), purine metabolism (P=0.0149, impact 0.0), alanine/aspartate/glutamate metabolism (P=0.0217, impact 0.098), linoleic acid metabolism (P=0.0413, impact 1.0), and nitrogen metabolism (P=0.0494, impact 1.0).
**Clinical Implications:** This study demonstrates that GK rats exhibit distinct gut microbial dysbiosis and metabolite alterations that correlate with hyperglycemia and insulin resistance. The identified microbial species (Allobaculum stercoricanis, Prevotella sp. CAG:604, Lactobacillus murinus) and metabolites (bile acids, uric acid, sphinganine, 13(S)-HODE, linoleic acid, 4-pyridoxic acid) may serve as potential biomarkers or therapeutic targets for T2DM. The findings suggest that T2DM involves not only carbohydrate dysregulation but also disordered lipid and protein metabolism. Limitations include the lack of drug intervention verification and long-term dynamic observations. Future research should explore faecal microbiota transplantation, metabolite supplementation, and drug screening based on these findings.