Diabetologia最新文献

Aims/hypothesis: DNA methylation has been shown to be associated with kidney function and diabetic kidney disease (DKD), but prospective studies are scarce. Therefore, we conducted epigenome-wide association studies (EWASs) on early- and late-stage DKD progression using DNA methylation data obtained by analysing baseline blood samples from participants in the Finnish Diabetic Nephropathy Study type 1 diabetes cohort.

Methods: We included 403 individuals with normal AER (early-stage progression group) and 372 individuals with severe albuminuria (late-stage progression group), and followed up DKD progression, defined as a decrease in eGFR to <60 ml/min per 1.73 m² in the early-stage progression group, and end-stage kidney disease (ESKD) in the late-stage group. Replication was conducted in two type 1 diabetes cohorts in addition to publicly available EWAS summary statistics from diabetes and general population cohorts. Significant loci were further characterised by integration with genetic and proteomic data.

Results: We identified 11 methylation sites associated with DKD progression (p<9.4 × 10^-8). Methylation at cg01730944 near the podocyte-specific gene CDKN1C and three other CpGs associated with early-stage DKD progression were independent of baseline eGFR, whereas late-stage progression CpGs were strongly associated with eGFR. The identified lead ESKD risk locus cg17944885 (chr19p13.2, p=2.6 × 10^-17) and several novel methylation sites associated with late-stage DKD progression were supported by the results of previous studies. Proteomic analysis of cis proteins identified potential target genes for two CpGs: cg14999724 methylation was associated with PRG3 and PRG2, and cg12272104 was associated with BSG, FSTL3 and PALM. Furthermore, UK Biobank data show associations between these proteins and severe kidney endpoints. Finally, survival models that included methylation markers in addition to clinical risk factors significantly improved the identification of individuals at risk of early-stage DKD progression.

Conclusions/interpretation: The current study detected 11 loci associated with DKD progression, identifying methylation changes predictive of early-stage DKD progression in type 1 diabetes for the first time. Future research is needed to establish prognostic DNA methylation markers for DKD progression.

Aims/hypothesis: Type 1 diabetes develops gradually, and previous exposures may influence incidence. We aimed to assess the geographical variation in type 1 diabetes incidence in Sweden by considering all residential locations from birth to diagnosis in individuals aged 0-30 years, diagnosed between 2005 and 2022. Significant high- and low-risk clusters were identified for different life stage exposure windows.

Methods: In 21,774 individuals with type 1 diabetes, all residential geographical locations from birth to diagnosis were geocoded. Geostatistical analysis of the incidence of type 1 diabetes was conducted at the municipality level using the most common residential location during four life stage-specific exposure windows (at diagnosis, the first 5 years after birth, 5 years prior to diagnosis, and from birth to diagnosis). Spatial scan statistics were used to identify statistically significant high- and low-risk clusters for each window. Land use and land cover within these clusters were also characterised.

Results: Significant geographical variation in the incidence of type 1 diabetes was observed. The incidence was consistently higher in rural, low-population-density areas, particularly in central Sweden, and lower in major urban areas. The largest number of spatial clusters of both high risk (RR 1.29-16.0) and low risk (RR 0.32-0.73) was identified when using the most common residential location during the first 5 years after birth. High-risk clusters for this exposure window were characterised by forested and agricultural land, while low-risk clusters were characterised by urban land and open land other than agricultural land.

Conclusions/interpretation: Our findings suggest that the development of type 1 diabetes in Sweden varies geographically and is associated with specific features of the local surroundings in early childhood. This is important knowledge as a basis for identifying possible environmental risk factors and the relationship with risk of type 1 diabetes in future studies.

Three diseases primarily affecting the exocrine pancreas-chronic pancreatitis, cystic fibrosis and pancreatic ductal adenocarcinoma-are all associated with a high incidence of diabetes. Together, they may account for more cases of diabetes than autoimmune type 1 diabetes. All forms of pancreatogenic (type 3c) diabetes are characterised by impaired insulin secretion but maintenance of significant islet beta cell mass, even in the presence of virtually complete destruction of the exocrine component of the gland. Pancreatic ductal injury and associated fibrosis are common features in chronic pancreatitis, cystic fibrosis and ductal adenocarcinoma. Increased peri-ductal fibrosis is also seen in type 2 and type 1 diabetes. Here, we review the literature regarding a potential common aetiological role of pancreatic fibrosis in the pathogenesis of type 3c, type 2 and type 1 diabetes. A vicious profibrotic signalling cycle involving injured ducts, pancreatic stellate cells and macrophages with increased levels of pancreatic tissue TGF-β at the core has increasingly been recognised as an essential driver of pancreatic exocrine fibrosis. We propose a second diabetogenic perpetual cycle comprising paracrine signalling between activated pancreatic stellate cells, macrophages and the islets themselves, leading to potentially reversible beta cell failure. The antifibrotic agents pirfenidone and nintedanib, thought to work primarily through suppression of TGF-β function, are used routinely in clinical practice for non-pancreatic indications, with a first trial in pancreatitis underway. Trials evaluating these licensed therapeutics that include primary diabetes-related endpoints and measures aiming to elucidate the underlying mechanisms of action merit consideration in type 3c diabetes and ultimately in type 2 diabetes and in combinatorial regimens in type 1 diabetes.

Aims/hypothesis: The role of red meat in type 1 diabetes risk remains unclear. We examined whether maternal and early-life red meat intake is associated with the development of type 1 diabetes and whether such associations are modified by genetic susceptibility.

Methods: We analysed data from 15,717 children participating in the All Babies In Southeast Sweden (ABIS) cohort, followed for type 1 diabetes diagnosis via national registers until the age of 24-26 years. Dietary intake was assessed through food frequency questionnaires during pregnancy and at ages 1, 2.5 and 5 years. Cox models estimated adjusted HRs and 95% CIs for type 1 diabetes in relation to red meat, including beef, pork and sausage, analysed as high vs low intake frequency and per serving/week. Analyses were stratified by HLA risk genotype and family history of type 1 diabetes.

Results: Frequency of red meat intake during pregnancy or at age 1 was not associated with type 1 diabetes risk. The corresponding HRs per serving/week were 0.98 (95% CI 0.90, 1.07) and 0.98 (95% CI 0.88, 1.08), respectively. In type-specific analyses, higher frequency of beef intake at age 5 was associated with an increased risk of type 1 diabetes (HR 1.29 [95% CI 1.05, 1.58]), with a similar tendency for exposure at age 2.5 (HR 1.12 [95% CI 0.93, 1.36]). The association at age 5 was evident among children with high-risk HLA genotypes (HR 1.40 [95% CI 1.11, 1.78]) or a family history of type 1 diabetes (HR 1.56 [95% CI 1.08, 2.26]). In contrast, no statistically significant association was observed among children with low-risk HLA genotypes (HR 0.34 [95% CI 0.10, 1.19]) or without a family history of type 1 diabetes (HR 1.20 [95% CI 0.92, 1.56]). No associations were found for higher frequency of beef consumption during pregnancy or at age 1, nor for pork and sausage at any age.

Conclusions/interpretation: Childhood beef consumption may contribute to type 1 diabetes development in genetically at-risk individuals. Further research is needed to confirm this finding and clarify underlying mechanisms.

Aims/hypothesis: In people with type 2 diabetes and obesity, Roux-en-Y gastric bypass (RYGB) can induce remission of diabetes. While RYGB has been reported to improve beta cell function in individuals with type 2 diabetes, it is unclear whether this is accompanied by changes in beta cell mass. In this explorative proof-of-concept study, we compared beta cell mass, measured by [⁶⁸Ga]Ga-NODAGA-exendin-4 positron emission tomography/computed tomography (PET/CT) imaging, between individuals achieving remission of type 2 diabetes following RYGB and those not achieving remission.

Methods: Individuals with (n=8) and without (n=9) remission of type 2 diabetes up to 4 years after RYGB were injected with 100 ± 5.6 MBq of [⁶⁸Ga]Ga-NODAGA-exendin-4 to quantify beta cell mass using PET/CT imaging. Beta cell function was determined by the AUC for C-peptide and the ratio between the AUC for C-peptide and AUC for glucose obtained from a combined arginine stimulation test (ARGT) and OGTT. Acquired variables are expressed as mean ± SD or median (IQR) based on normality.

Results: Individuals with remission of type 2 diabetes had a shorter diabetes duration than those without remission. After RYGB, beta cell function was higher in individuals with remission of type 2 diabetes than individuals without remission, based on both ARGTs (AUC_C-peptide/fasting glucose 1.1 ± 0.41 vs 0.32 ± 0.16 nmol × min/mmol, p=0.001) and OGTTs (AUC_C-peptide:AUC_glucose 0.15 [0.11-0.24] vs 0.032 [0.023-0.054], p=0.005). In contrast, beta cell mass did not differ between individuals with or without remission of type 2 diabetes (3.6 [3.4-5.4] vs 3.8 [1.9-4.5] kBq/MBq, p=0.87) and did not correlate with beta cell function or body weight parameters. HOMA2-%B, also representing beta cell function, was better in the remission group and significantly improved in these individuals after RYGB, whereas it remained unchanged in non-remitters.

Conclusions/interpretation: Individuals with remission of type 2 diabetes after RYGB have better beta cell function than those not achieving remission, but the groups did not differ with respect to beta cell mass. Our preliminary data argue against a stimulating effect of RYGB on beta cell mass, although revival of non-functional (so-called dormant) beta cells is a possible explanation for remission.

Trial registration: ClinicalTrials.gov NCT02542059.