Distinct signatures of gut microbiota and metabolites in different types of diabetes: a population-based cross-sectional study

Background Patients with type 1 diabetes (T1D) and type 2 diabetes (T2D) present intestinal disturbances. Recent epidemiological data have showed that, worldwide, over half of newly diagnosed T1D patients were adults. However, the gut microbial alterations in adult-onset T1D are unclear. We aimed to identify the signatures of gut microbiota and metabolites in patients with adult-onset T1D systematically, comparing with T2D patients and healthy controls (HCs).Methods This study enrolled 218 subjects from February 2019 to April 2022 (discovery cohort: 36 HCs, 51 patients with adult-onset T1D and 56 patients with T2D; validation cohort: 28 HCs, 27 patients with adult-onset T1D and 20 patients with T2D). Gut microbial profiles of the study subjects were investigated by metagenomic sequencing, and their faecal and serum metabolites were measured with targeted metabolomics. The study was registered on ClinicalTrials.gov (NCT05252728).Findings Patients with adult-onset T1D had significant differences in the composition of bacteria and their metabolites, characterized by notable depletion of short-chain fatty acid-producing bacteria, especially Eubacterium rectale. This was associated with a severe loss of phenolic acids and their derivatives, including gallic acid (associated with glucose metabolism) and 3,4-dihydroxyhydrocinnamic acid (linked with glucose metabolism and pancreatic beta cell autoimmunity). A predictive model based on six bacteria and six metabolites simultaneously discriminated adult-onset T1D from T2D and HCs with high accuracy. Interestingly, bacterial-viral or bacterial-fungal trans-kingdom relationships, especially positive correlations between bacteriophages and beneficial bacteria, were significantly reduced in adult-onset T1D compared to HCs.Interpretation Adult-onset T1D patients exhibit unique changes in host-microbiota-metabolite interactions. Gut microbiota and metabolite-based algorithms could be used as additional tools for differential diagnosis of different types of diabetes and beyond.