Diabetologia最新文献

Aims/hypothesis: The high heritability of type 1 diabetes has enabled the development of polygenic risk scores (PRSs) as disease risk screening tools. PRSs can identify individuals at the highest genetic risk in a population, who can benefit from autoantibody and metabolic surveillance, to avoid ketoacidosis at diagnosis and to access preventive therapies. However, PRSs for type 1 diabetes developed from European data perform less well in non-European ancestries. We aimed to develop a PRS with comparable performance among different ancestries.

Methods: Using the PRS-CSx method, and data from large European, East Asian, African American and Hispanic type 1 diabetes genome-wide association studies (N_{total_cases}=29,469), we developed a trans-ancestry PRS (TA-PS), combining a non-HLA component incorporating over a million variants with the HLA component of a published European PRS (GRS2x). We tested the performance of the PRS using area under the receiver operating curve (AUROC), sensitivity and specificity in a multi-ancestry type 1 diabetes case-control cohort (N_total=4657; N_non-European=556) from Montreal, Canada. We validated our results in two independent type 1 diabetes case-control cohorts (Children's Hospital of Philadelphia, Center for Applied Genomics [CHOP-CAG] and Genetic Risk Assessment for Chinese Eaglet-T1D [GRACE]) and two population-based cohorts (All of Us and UK Biobank).

Results: In the multi-ancestry Montreal-based cohort, TA-PS showed an AUROC of 0.89 which was significantly higher than the respective measure obtained with GRS2x in the same population (AUROC of 0.85). We obtained better overall sensitivity at the 90th percentile cut-off using TA-PS (0.71 in Europeans, 0.77 in South Asians), compared with sensitivity of 0.32 in African Americans and 0.56 in Europeans using GRS2x. The specificity obtained using TA-PS was slightly lower than that of GRS2x, albeit still acceptable (≥0.83 across all ancestries). These results were validated in the four independent cohorts.

Conclusions/interpretation: We developed a trans-ancestry PRS that outperformed the European-based GRS2x. Importantly, TA-PS provides a comparable prediction in various ancestries, which supports its use in population-wide screening programmes.

Aims/hypothesis: Growing evidence implicates gut microbiota-derived metabolites in metabolic homeostasis. Indole, a microbial tryptophan metabolite, has been reported to enhance glucagon-like peptide-1 (GLP-1) secretion in vitro, and its derivatives have been inversely associated with risk of type 2 diabetes. We hypothesised that indole acts via the gastrointestinal tract to modulate glucose homeostasis, and tested this hypothesis using in vitro and in vivo models.

Methods: We measured GLP-1 secretion from cultured murine enteroendocrine cells, and evaluated intraperitoneal glucose tolerance and hormone secretion in mice following indole treatment. Subsequently, the impact of indole on intestinal epithelial cell fate and L cell number was examined using murine ileal organoid cultures and in vivo. Finally, we explored the effect of chronic indole administration on metabolic outcomes in a murine model of type 2 diabetes.

Results: Indole stimulated in vitro GLP-1 secretion in a concentration-dependent manner, and improved acute glucose management in vivo. Additionally, we demonstrate that indole drives enteroendocrine L cell differentiation in murine ileal organoids, resulting in increased L cell density and longer-term glucoregulatory benefits in vivo. Finally, sub-chronic indole administration improved glucose tolerance and insulin sensitivity in a diabetic mouse model.

Conclusions/interpretation: Our findings identify indole as a glucose-lowering molecule that acts on the gut, and raise the possibility of incorporating indole into nutraceutical supplements to aid in the treatment or prevention of type 2 diabetes. This study highlights the importance of gut microbiota-derived metabolites in metabolic health and opens new avenues for developing novel strategies to combat type 2 diabetes.

Data availability: RNA sequencing data are available from the Gene Expression Omnibus under accession number GSE306720.