Diabetologia最新文献

Aims/hypothesis: The aim of this work was to explore molecular amino acids (AAs) and related structures of HLA-DQA1-DQB1 that underlie its contribution to the progression from stages 1 or 2 to stage 3 type 1 diabetes.

Methods: Using high-resolution DQA1 and DQB1 genotypes from 1216 participants in the Diabetes Prevention Trial-Type 1 and the Diabetes Prevention Trial, we applied hierarchically organised haplotype association analysis (HOH) to decipher which AAs contributed to the associations of DQ with disease and their structural properties. HOH relied on the Cox regression to quantify the association of DQ with time-to-onset of type 1 diabetes.

Results: By numerating all possible DQ heterodimers of α- and β-chains, we showed that the heterodimerisation increases genetic diversity at the cellular level from 43 empirically observed haplotypes to 186 possible heterodimers. Heterodimerisation turned several neutral haplotypes (DQ2.2, DQ2.3 and DQ4.4) to risk haplotypes (DQ2.2/2.3-DQ4.4 and DQ4.4-DQ2.2). HOH uncovered eight AAs on the α-chain (-16α, -13α, -6α, α22, α23, α44, α72, α157) and six AAs on the β-chain (-18β, β9, β13, β26, β57, β135) that contributed to the association of DQ with progression of type 1 diabetes. The specific AAs concerned the signal peptide (minus sign, possible linkage to expression levels), pockets 1, 4 and 9 in the antigen-binding groove of the α1β1 domain, and the putative homodimerisation of the αβ heterodimers.

Conclusions/interpretation: These results unveil the contribution made by DQ to type 1 diabetes progression at individual residues and related protein structures, shedding light on its immunological mechanisms and providing new leads for developing treatment strategies.

Data availability: Clinical trial data and biospecimen samples are available through the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository portal ( https://repository.niddk.nih.gov/studies ).

Aims/hypothesis: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.

Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.

Results: The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.

Conclusions/interpretation: Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Aims/hypothesis: The temporal suppression of insulin clearance after glucose ingestion is a key determinant of glucose tolerance for people without type 2 diabetes. Whether similar adaptations are observed after the ingestion of a mixed-macronutrient meal is unclear.

Methods: In a secondary analysis of data derived from two randomised, controlled trials, we studied the temporal responses of insulin clearance after the ingestion of a standardised breakfast meal consisting of cereal and milk in lean normoglycaemic individuals (n=12; Lean-NGT), normoglycaemic individuals with central obesity (n=11; Obese-NGT) and in people with type 2 diabetes (n=19). Pre-hepatic insulin secretion rates were determined by the deconvolution of C-peptide, and insulin clearance was calculated using a single-pool model. Insulin sensitivity was measured by an oral minimal model.

Results: There were divergent time course changes in insulin clearance between groups. In the Lean-NGT group, there was an immediate post-meal increase in insulin clearance compared with pre-meal values (p<0.05), whereas insulin clearance remained stable at baseline values in Obese-NGT or declined slightly in the type 2 diabetes group (p<0.05). The mean AUC for insulin clearance during the test was ~40% lower in the Obese-NGT (1.3 ± 0.4 l min^-1 m^-2) and type 2 diabetes (1.4 ± 0.7 l min^-1 m^-2) groups compared with Lean-NGT (1.9 ± 0.5 l min^-1 m^-2; p<0.01), with no difference between the Obese-NGT and type 2 diabetes groups. HOMA-IR and glucagon AUC emerged as predictors of insulin clearance AUC, independent of BMI, age or insulin sensitivity (adjusted R²=0.670). Individuals with increased glucagon AUC had a 40% reduction in insulin clearance AUC (~ -0.75 l min^-1 m^-2; p<0.001).

Conclusions/interpretation: The ingestion of a mixed-macronutrient meal augments differing temporal profiles in insulin clearance among individuals without type 2 diabetes, which is associated with HOMA-IR and the secretion of glucagon. Further research investigating the role of hepatic glucagon signalling in postprandial insulin kinetics is warranted.

Trial registration: ISRCTN17563146 and ISRCTN95281775.

Aims/hypothesis: We compared the effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) on renal outcomes in individuals with type 2 diabetes, focusing on the changes in eGFR and albuminuria.

Methods: This was a multicentre retrospective observational study on new users of diabetes medications. Participant characteristics were assessed before and after propensity score matching. The primary endpoint, change in eGFR, was analysed using mixed-effects models. Secondary endpoints included categorical eGFR-based outcomes and changes in albuminuria. Subgroup and sensitivity analyses were performed to assess robustness of the findings.

Results: After matching, 5701 participants/group were included. Participants were predominantly male, aged 61 years, with a 10 year duration of diabetes, a baseline HbA_1c of 64 mmol/mol (8.0%) and BMI of 33 kg/m². Chronic kidney disease (CKD) was present in 23% of participants. During a median of 2.1 years, from a baseline of 87 ml/min per 1.73 m², eGFR remained higher in the SGLT2i group compared with the GLP-1RA group throughout the observation period by 1.2 ml/min per 1.73 m². No differences were detected in albuminuria change. The SGLT2i group exhibited lower rates of worsening CKD class and favourable changes in BP compared with the GLP-1RA group, despite lesser HbA_1c decline. SGLT2i also reduced eGFR decline better than GLP-1RA in participants without baseline CKD.

Conclusions/interpretation: In individuals with type 2 diabetes, treatment with SGLT2i was associated with better preservation of renal function compared with GLP-1RA, as evidenced by slower decline in eGFR. These findings reinforce SGLT2i as preferred agents for renal protection in this patient population.

Aims/hypothesis: Diabetic kidney disease (DKD) is a severe diabetic complication that affects one third of individuals with type 1 diabetes. Although several genes and common variants have been shown to be associated with DKD, much of the predicted inheritance remains unexplained. Here, we performed next-generation sequencing to assess whether low-frequency variants, extending to a minor allele frequency (MAF) ≤10% (single or aggregated) contribute to the missing heritability in DKD.

Methods: We performed whole-exome sequencing (WES) of 498 individuals and whole-genome sequencing (WGS) of 599 individuals with type 1 diabetes. After quality control, next-generation sequencing data were available for a total of 1064 individuals, of whom 541 had developed either severe albuminuria or end-stage kidney disease, and 523 had retained normal albumin excretion despite a long duration of type 1 diabetes. Single-variant and gene-aggregate tests for protein-altering variants (PAV) and protein-truncating variants (PTV) were performed separately for WES and WGS data and combined in a meta-analysis. We also performed genome-wide aggregate analyses on genomic windows (sliding window), promoters and enhancers using the WGS dataset.

Results: In the single-variant meta-analysis, no variant reached genome-wide significance, but a suggestively associated common THAP7 rs369250 variant (p=1.50 × 10^-5, MAF=49%) was replicated in the FinnGen general population genome-wide association study (GWAS) data for chronic kidney disease and DKD phenotypes. The gene-aggregate meta-analysis provided suggestive evidence (p<4.0 × 10^-4) at four genes for DKD, of which NAT16 (MAF_PAV≤10%) and LTA (also known as TNFβ, MAF_PAV≤5%) are replicated in the FinnGen general population GWAS data. The LTA rs2229092 C allele was associated with significantly lower TNFR1, TNFR2 and TNFR3 serum levels in a subset of FinnDiane participants. Of the intergenic regions suggestively associated with DKD, the enhancer on chromosome 18q12.3 (p=3.94 × 10^-5, MAF_variants≤5%) showed interaction with the METTL4 gene; the lead variant was replicated, and predicted to alter binding of the MafB transcription factor.

Conclusions/interpretation: Our sequencing-based meta-analysis revealed multiple genes, variants and regulatory regions that were suggestively associated with DKD. However, as no variant or gene reached genome-wide significance, further studies are needed to validate the findings.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed non-alcoholic fatty liver disease (NAFLD), is defined as steatotic liver disease (SLD) in the presence of one or more cardiometabolic risk factor(s) and the absence of harmful alcohol intake. The spectrum of MASLD includes steatosis, metabolic dysfunction-associated steatohepatitis (MASH, previously NASH), fibrosis, cirrhosis and MASH-related hepatocellular carcinoma (HCC). This joint EASL-EASD-EASO guideline provides an update on definitions, prevention, screening, diagnosis and treatment for MASLD. Case-finding strategies for MASLD with liver fibrosis, using non-invasive tests, should be applied in individuals with cardiometabolic risk factors, abnormal liver enzymes and/or radiological signs of hepatic steatosis, particularly in the presence of type 2 diabetes or obesity with additional metabolic risk factor(s). A stepwise approach using blood-based scores (such as the fibrosis-4 index [FIB-4]) and, sequentially, imaging techniques (such as transient elastography) is suitable to rule-out/in advanced fibrosis, which is predictive of liver-related outcomes. In adults with MASLD, lifestyle modification-including weight loss, dietary changes, physical exercise and discouraging alcohol consumption-as well as optimal management of comorbidities-including use of incretin-based therapies (e.g. semaglutide, tirzepatide) for type 2 diabetes or obesity, if indicated-is advised. Bariatric surgery is also an option in individuals with MASLD and obesity. If locally approved and dependent on the label, adults with non-cirrhotic MASH and significant liver fibrosis (stage ≥2) should be considered for a MASH-targeted treatment with resmetirom, which demonstrated histological effectiveness on steatohepatitis and fibrosis with an acceptable safety and tolerability profile. No MASH-targeted pharmacotherapy can currently be recommended for the cirrhotic stage. Management of MASH-related cirrhosis includes adaptations of metabolic drugs, nutritional counselling, surveillance for portal hypertension and HCC, as well as liver transplantation in decompensated cirrhosis.

Transgender identity is often associated with gender dysphoria and minority stress. Gender-affirming hormone treatment (GAHT) includes masculinising or feminising treatment and is expected to be lifelong in most cases. Sex and sex hormones have a differential effect on metabolism and CVD in cisgender people, and sex hormone replacement in hypogonadism is associated with higher vascular risk, especially in ageing individuals. Using narrative review methods, we present evidence regarding metabolic and cardiovascular outcomes during GAHT and propose recommendations for follow-up and monitoring of metabolic and cardiovascular risk markers during GAHT. Available data show no increased risk for type 2 diabetes in transgender cohorts, but masculinising GAHT increases lean body mass and feminising GAHT is associated with higher fat mass and insulin resistance. The risk of CVD is increased in transgender cohorts, especially during feminising GAHT. Masculinising GAHT is associated with a more adverse lipid profile, higher haematocrit and increased BP, while feminising GAHT is associated with pro-coagulant changes and lower HDL-cholesterol. Assigned male sex at birth, higher age at initiation of GAHT and use of cyproterone acetate are separate risk factors for adverse CVD markers. Metabolic and CVD outcomes may improve during gender-affirming care due to a reduction in minority stress, improved lifestyle and closer surveillance leading to optimised preventive medication (e.g. statins). GAHT should be individualised according to individual risk factors (i.e. drug, dose and form of administration); furthermore, doctors need to discuss lifestyle and preventive medications in order to modify metabolic and CVD risk during GAHT. Follow-up programmes must address the usual cardiovascular risk markers but should consider that biological age and sex may influence individual risk profiling including mental health, lifestyle and novel cardiovascular risk markers during GAHT.