Diabetologia

Background. Cardiovascular disease accounts for most mortality in type 2 diabetes (T2D), yet treatment is anchored on glucose-derived metrics and fasting insulin is rarely measured. We tested whether fasting insulin carries cardiovascular-mortality information across the dysglycaemic spectrum and approaches non-diabetic levels with longer oral-therapy T2D duration. Methods and Findings. We analysed six NHANES cycles [2007-2018] linked to National Death Index follow-up through 2019. Baseline characteristics were described in participants with complete kidney-function data: normoglycaemic (n = 2,913), pre-diabetes (n = 3,993), and oral-therapy T2D (n = 1,381). Survey-weighted Cox models used the covariate-complete mortality sample (pre-diabetes n = 4,019; oral-therapy T2D n = 1,387) and adjusted for age, sex, race/ethnicity, BMI, smoking, physical activity, education, poverty:income ratio, and insulin assay generation. Per +1 natural-log-unit fasting insulin, all-cause hazard ratios (HRs) were 1.69 (95% CI 1.14-2.50; P = 0.008) in pre-diabetes and 0.71 (0.51-0.98; P = 0.039) in oral-therapy T2D; cardiovascular HRs were 3.09 (1.69-5.63; P < 0.001) and 0.50 (0.26-0.99; P = 0.046), respectively. Cancer mortality was not associated with fasting insulin. In oral-therapy T2D, geometric-mean fasting insulin remained 1.6- to 2.3-fold the normoglycaemic referent across duration bands. After BMI adjustment, fasting insulin declined during the first five years (-4.0%/year, P = 0.018) and was flat thereafter (-0.1%/year, P = 0.66). Conclusions. Fasting insulin predicted cardiovascular mortality in pre-diabetes. In oral-therapy T2D, the inverse association was most consistent with survivor effects and accumulated renal and vascular damage. Fasting insulin remained above normoglycaemic levels throughout treated T2D.

BCG is a well recognized old generic drug that can mold the human immune system through Immune Training. Broad basic science studies support Immune Training, but formal randomized placebo control trials are frequently lacking for confirmation of human benefits. Here we report on a randomized, double-blind, placebo-controlled phase 2 trial with up to 8 years of follow-up. The primary outcome was mean percent change from baseline in HbA1c. At year 5, HbA1c in the BCG group was reduced from baseline by 7.04% (P=0.0006) and at year 8 by 12.84% (P=0.0001), whereas HbA1c did not change from baseline in the placebo group at either time point. Longitudinal percentage changes in HbA1c from baseline using mixed model repeated measures analysis with a comparison of BCG versus Placebo arms was significant (p=0.0017). This shows durable long term improvements lowering of HbA1c only with BCG immunotherapy. Secondary outcomes were also significant. There was a statistically significant reduction in average insulin dose only in the BCG-treated group (P=0.024). Glycemic improvements occurred without recovery of C-peptide from negligible baseline levels, suggesting no pancreas contribution. Blood sugar values obtained from continuous glucose monitoring (CGM) similarly showed BCG immunotherapy lowers blood sugars compared to the Placebo arm (P=0.0189). CGM data also showed BCG immunotherapy resulted in an up to 183.7% improvement over baseline in time spent in normoglycemia (blood sugar in the normal range of 70-99 mg/dL) (P=0.0391). A sensitivity analysis confirmed BCG specific reductions in HbA1c were robust and durable at least to Year 08 (P<0.0001). BCG was safe. These finding support improved blood sugar control through immune training of metabolism without a contribution of the pancreas even after long standing T1D. Trial registration: NCT02081326.

BackgroundStructured cognitive screening in diabetes is recommended only from age 65, leaving younger adults with prolonged type 2 diabetes mellitus (T2DM) exposure unmonitored. We quantified the association of HbA1c and diabetes duration with Montreal Cognitive Assessment (MoCA) screen-positive status in adults aged [&le;] 60 years with T2DM of [&ge;] 5 years, and examined against matched controls how the MoCA cutoff (< 26, < 24, < 23) affects the group contrast in a lower-education population. MethodsCross-sectional study at a tertiary-care centre in North India: 110 adults with T2DM and 50 controls frequency-matched for age, sex, and education. Within diabetics, HbA1c and duration were modelled continuously by modified Poisson regression at each pre-specified cutoff and by Spearman correlation with MoCA total score. Prevalence ratios (PR) for diabetics versus controls at each cutoff were adjusted for age, sex, education, BMI, and hypertension. ResultsWithin diabetics, MoCA total score correlated with HbA1c ({rho} = -0.456) and duration ({rho} = -0.318; both p < 0.001). Screen-positive prevalence rose 5% per 1% HbA1c at < 26 (PR 1.05, 95% CI 1.02-1.09), 12% at < 24 (PR 1.12, 1.06-1.18), and 17% at < 23 (PR 1.17, 1.09-1.26), and 11% per year of duration at < 23 (PR 1.11, 1.06-1.17). Median education was 10 and 9 years; 76.0% of controls screened positive at < 26, and the diabetes association was cutoff-dependent: adjusted PR 1.08 (0.89-1.29) at < 26, 1.70 (1.10-2.63) at < 24, and 6.72 (2.23-20.23) at < 23, where 44.5% of diabetics versus 6.0% of controls screened positive (absolute difference 38.5 percentage points, 95% CI 25.0 to 48.7). ConclusionsIn working-age adults with long-standing T2DM, screen-positive prevalence rose continuously with HbA1c at every cutoff, a glycaemic signal independent of threshold choice. The standard < 26 cutoff flagged over three-quarters of both groups and could not triage; < 23 separated diabetics from controls. Screen-positive status is not a diagnosis; MoCA triage at a population-appropriate cutoff in this group warrants prospective validation against a neuropsychological reference standard.

Hyperglycaemia is a hallmark of diabetes and a major risk factor for diabetic kidney disease (DKD). However, the molecular consequences of long-term cumulative hyperglycaemia (CH) remain unclear. As a stable epigenetic modification, DNA methylation may capture past glycaemic exposure. Here, we assessed CH-associated DNA methylation in 1,245 participants with type 1 diabetes (T1D) from Finland and the United Kingdom-Republic of Ireland cohorts. We identified 17 CH-associated CpGs, with the strongest association at cg19693031 (TXNIP). Longitudinal analyses demonstrate that these CH-associated DNA methylation levels remain stable despite short-term glycaemic fluctuations, suggesting lasting epigenetic imprints of earlier metabolic control. Integrative analyses combining genomic, epigenetic, and proteomic data characterized these CpGs and potential target proteins. Mendelian randomization suggested a causal association between cg20853880 (KLF11) and DKD, supported by chromatin accessibility and kidney KLF11 expression. Our findings suggest that epigenetic changes contribute to metabolic memory and may mediate the effects of hyperglycaemia on DKD.

BackgroundContinuous glucose monitoring-derived time in range (TIR; glucose 70-180 mg/dL) has emerged as a complementary glycemic metric, but its association with DKD (Diabetic Kidney Disease) has not been systematically quantified. We aimed to synthesize the evidence on the association between CGM-derived TIR and DKD in adults with type 1 or type 2 diabetes by conducting a systematic review and meta-analysis of the current evidence. MethodsThis systematic review and meta-analysis adhered to PRISMA guidelines. PubMed, Embase, Cochrane CENTRAL, and Scopus were searched through March, 2026. Eligible studies were studies in adults with type 1 and type 2 diabetes that reported an association between CGM-derived TIR and any DKD outcome (albuminuria, eGFR decline, or composite nephropathy) with a quantifiable per-unit effect estimate. Random-effects meta-analysis (REML with Knapp-Hartung adjustment) pooled odds ratios per 10% TIR increment. Risk of bias was assessed using ROBINS-E and certainty of evidence using the GRADE framework. PROSPERO: CRD420261377763. ResultsEleven studies (n = 7,182; seven T2D, four T1D; ten cross-sectional, one retrospective cohort) were included. The pooled OR per 10% TIR was 0.89 (95% CI 0.84-0.95; P = 0.002; I{superscript 2} = 69.6%), and every leave-one-out iteration preserved significance (OR range 0.88-0.91; all P [&le;] 0.005). In the four studies adjusting for HbA1c, the association persisted (OR 0.91, 0.86-0.97; P = 0.015) with no detectable residual heterogeneity (I{superscript 2} = 0%), indicating the signal is not fully attributable to average glycemia. ConclusionsHigher CGM-derived TIR may be associated with lower odds of DKD (OR 0.89 per 10% increment). The HbA1c-adjusted subgroup (k = 4; I{superscript 2} = 0%) provided the most internally consistent signal, though its small size limits inference. Prospective studies with standardized CGM protocols and pre-specified hard kidney endpoints are needed to establish causality.

Objective: To determine whether continuous glucose monitoring (CGM) identifies clinically relevant glycemic heterogeneity and subclinical end-organ alterations in adults without diabetes. Research Design and Methods: We analyzed 1,017 AI-READI Year 3 participants without diabetes (558 with normoglycemia and 459 with prediabetes by A1C). Fifty-two metrics from 10-day blinded CGM were reduced to nonredundant glycemic axes. Partial Spearman correlations between representative CGM metrics and clinical measures across 13 domains were adjusted for age, sex, and BMI and controlled for false discovery rate. CGM-derived subphenotypes were identified using unsupervised UMAP-HDBSCAN-based clustering. Results: Among 462 glycemic-clinical associations tested, 99 (21.4%) remained significant after false discovery rate correction. Hyperglycemia-related metrics, including mean glucose, time above range, and time in tight range, showed more associations than variability metrics. The strongest signals involved cardiometabolic, cardiovascular, and cognitive measures. Greater hyperglycemia and glucose excursions were associated with lower language performance, slower processing speed, and lower cognitive efficiency ({rho} {approx} -0.10 to -0.14; all P < 0.01). Clustering identified four reproducible glycemic subphenotypes: Healthy, Mild Hyperglycemia, High Variability, and Hyperglycemia. CGM phenotypes reclassified A1C-defined groups: 58.1% of participants with normoglycemia fell into dysglycemic phenotypes, whereas 18.8% of participants with prediabetes fell into more favorable phenotypes. The Hyperglycemia phenotype had the most adverse cardiometabolic profile and lower cognitive performance. Conclusions: In adults without diabetes, CGM revealed glycemic patterns associated with distinct subclinical alterations. CGM-based phenotyping may complement A1C for characterizing early dysglycemia and selecting individuals for longitudinal risk-stratification studies.

Background: Cardio-kidney-metabolic (CKM) diseases share risk factors, but their shared and distinct risk factors for disease progression remain unclear. We examined whether classical risk factors predict baseline CKM indicators and their longitudinal changes in a population-based German National Cohort (NAKO) sample. Methods: In 2,650 NAKO-IMMEDIATE participants selected for deep-phenotyping, baseline values and 5-year changes in glycated hemoglobin (HbA1c), systolic blood pressure (SBP), pulse wave velocity (PWV), and estimated glomerular filtration rate (eGFR) were linearly regressed on age, sex, education, alcohol and tobacco consumption, body mass index (BMI), and residuals of BMI-adjusted waist circumference (WC) with mutual adjustment. Results: Baseline values for HbA1c were 34.6{+/-}5.8 mmol/mol, for PWV 8.3{+/-}1.5 m/s, for SBP 128.0{+/-}15.7 mmHg and for eGFR 96.0{+/-}14.6 ml/min/1.73 m2. Cross-sectionally, all risk factors were associated with one or more of the CKM indicators, with age and BMI being associated with all four indicators. Over 5 years, HbA1c (1.2{+/-}4.2 mmol/mol), PWV (0.5{+/-}1.2 m/s) and SBP (0.6{+/-}14.2 mmHg) increased, whereas eGFR (-2.3{+/-}9.5 ml/min/1.73 m2) decreased. Age (0.03 m/s per 5 year, 95% CI 0.01, 0.06 m/s) was associated with higher and BMI (-0.02 m/s per 1kg/m2, -0.03, -0.01) with lower PWV changes. No other risk factors were associated with CKM indicator changes. Conclusion: Classical risk factors were more frequently related to baseline CKM indicators than to their 5-year changes. Within longitudinal analyses, only age and BMI were related to PWV 5-year changes. Keywords: Cardio-kidney-metabolic function, risk factors, longitudinal studies

Abstract Objective. To determine whether glucagon-like peptide 1 receptor agonist (GLP-1 RA) initiation is associated with excess skeletal muscle loss on repeat whole-body MRI. Research Design and Methods. In the German National Cohort, 58 adults with diabetes who initiated a GLP-1 RA between whole-body MRI examinations 4.6 years apart were matched on sex, age, BMI, and interval to 130 never-exposed controls with diabetes. Deep-learning-segmented tissue volumes were compared within matched sets. Results. Skeletal muscle volume declined 2.75 percentage points more in initiators (95% CI -4.51 to -0.99; P = 0.003), with 2.54 percentage points greater weight loss (P = 0.021). Adipose tissue differences (-4.8% internal torso, -3.2% subcutaneous) were nonsignificant. Conclusions. In this first population-based comparison of whole-body MRI before and after GLP-1 RA initiation with matched unexposed controls, initiators lost roughly 60% more muscle. Skeletal muscle loss is a measurable component of GLP-1 RA weight loss in routine care.

Aims/Hypothesis: Atypical forms of diabetes often provide diagnostic challenges and are suboptimally treated. We sought to determine whether type 1 and 2 diabetes polygenic scores (PS) would help improve classification of islet autoantibody-negative (IAb-) atypical diabetes cases. Methods: We implemented type 1 diabetes PS (T1D PS) and type 2 diabetes PS (T2D PS) in 309 IAb- individuals' genome sequencing from the Rare and Atypical Diabetes Network (RADIANT), the largest and most comprehensively phenotyped collection of unsolved and atypical diabetes cases. We performed regression analyses to assess associations between PS, clinical characteristics, and glycemic measures. Results: Both T1D and T2D PS were significantly higher in RADIANT than in ancestry-matched controls from UK Biobank. Both PS were associated with C-peptide measures throughout participant oral glucose tolerance tests (OGTT), although in opposite directions; T1D PS was associated with lower C-peptide measures, while T2D PS was associated with higher measures. Participants with T1D PS above a previously established optimal PS value for differentiating cases and controls (T1D PS GRS2>12.88) had 13-fold higher odds of both insulin deficiency on OGTT and use of both basal and bolus insulin therapy. Conclusions/Interpretation: Individuals in RADIANT with ambiguous diabetes subtypes displayed genetic enrichment for both type 1 and type 2 diabetes, despite ascertainment to remove standard type 1 and type 2 diabetes. T1D PS identified individuals with an unrecognized "type 1 diabetes-like" phenotype with insulin deficiency and need for intensive insulin in the absence of islet autoantibodies. PS may help identify individuals with atypical diabetes who will ultimately benefit from insulin therapy.

Glycemic control is key to prevention of complications of diabetes. Although eosinophils have been implicated in metabolic regulation, most human studies have been cross-sectional and have yielded inconsistent findings. We conducted a retrospective cohort study of adults with uncontrolled diabetes receiving primary care at Mass General Brigham. We evaluated the relationship between eosinophil count and HbA1c over one year, adjusting for demographic, socioeconomic, and clinical covariates. Among 21,447 patients included in the study, eosinophil count increased by a mean of 2.6 cells/L (SD 157.2 cells/L) and HbA1c decreased by a mean of 0.82% (SD 2.09%) during follow-up. Increases in eosinophil count were associated with reductions in HbA1c (parameter estimate -0.366 per 1,000 cells/L; 95% CI -0.526 to -0.206; P < 0.001), whereas baseline eosinophil count was not significantly associated with HbA1c changes. The association remained significant in a sensitivity analysis excluding systemic glucocorticoid users. Increases in eosinophil count were associated with improved glycemic control in adults with diabetes, whereas baseline eosinophil count was not. These findings extend prior cross-sectional observations and suggest that changes in eosinophils may represent a biomarker of glycemic improvement. Further prospective studies are needed to determine whether this relationship reflects underlying immune-metabolic mechanisms or treatment-related changes.

Abstract Objective: To identify clinical and genetic factors associated with variation in glycemic response to glucagon-like peptide-1 receptor agonist (GLP-1RA) therapy among adults with type 2 diabetes, with a focus on common GLP1R variations, polygenic risk load, and pancreas-specific regulation annotation. Research Design and Methods: We conducted a retrospective cohort study using electronic health record (EHR)-linked biobank data from the All of Us research workbench platform that included 5784 adults with type 2 diabetes who initiated GLP-1RA therapy. Baseline HbA1c was measured within 3 months before medication initiation, and follow-up HbA1c was measured after 3 months. The patients with type 2 diabetes were classified as good responders (HbA1c reduction [&ge;] 2.5 percentage point) or poor responders (HbA1c reduction <0.5 percentage point). Models adjusted for demographic characteristics, anthropometric and metabolic measures, blood pressure, body mass index (BMI), lipid profile, liver function tests, polygenic risk score, and GLP1R variant carrier status were compared between the two groups. Common GLP1R variations were further investigated for carrier frequency and associated HbA1c levels before and after medication use. Results: The cohort included 3194 good responders and 2590 poor responders. Good responders were younger than poor responders (55.2 vs. 58.6 years) and had significantly higher glycemic improvement. HbA1c levels fell from 9.2% to 6.3% in good responders and 8.4% to 8.1% in poor responders, resulting in an absolute HbA1c reduction of 2.9% and 0.3%, respectively. Good responders also showed larger decreases in fasting glucose, BMI, systolic and diastolic blood pressure, triglycerides, total cholesterol, LDL cholesterol, and liver enzymes, as well as minor improvements in HDL-C. After multivariable adjustment, Poor responders had a greater T2D polygenic risk score (0.38 vs. 0.21), more GLP1R coding variant carrier status (10.1% vs. 8.0%), and a higher overall GLP1R variant burden (22.8% vs. 19.2%). Variant-level studies revealed rs2268650 and rs2003132 enrichment among poor responders, with negative post-treatment HbA1c patterns in carriers, whereas good-response carriers showed significant HbA1c improvement. Conclusions: Response to GLP-1RA in T2D is associated with baseline clinical and metabolic status, as well as inherited genetic susceptibility, which includes common GLP1R variation and a larger polygenic risk burden. Integrating clinical and pharmacogenomic profiling may improve patient classification and provide insight into treatment failure in poor responders.

Introduction: Ethnic minority populations are disproportionately affected by type 2 diabetes (T2D). We investigated ethnic differences in the risks of diabetes-related complications and mortality in the Netherlands, and identified clinical, sociodemographic and environmental determinants associated with these differences. Methods: We included 175,112 adults with T2D from the dynamic prospective primary care cohort DIAMANT. DIAMANT data were linked to national registries from Statistics Netherlands and GECCO, a database integrating geographic, environmental and contextual exposures. Ethnic differences in complications risks were estimated using Cox proportional hazards models. Potential mediating factors were explored using machine-learning-based variable selection and association decomposition approaches. Results: At baseline, mean age was 65.4 (SD 12.3) years, 46.6% were women and median T2D duration was 11.3 [IQR 7.2; 15.8] years. Substantial heterogeneity in complication risk was observed across ethnic groups compared with Dutch-origin individuals. Retinopathy risk was consistently higher across nearly all non-Dutch groups (HRs 1.37-2.37). For macrovascular complications, elevated risks were mainly observed among Surinamese and Turkish individuals, including heart failure (HR 1.30 and 1.46, respectively). In contrast, individuals of Indonesian and Moroccan origin showed similar or lower risk for most complications. Environmental exposures (e.g. air pollution, temperature) and sociodemographic factors (e.g. main benefit, household composition) accounted for a substantial attenuation of several observed associations. Discussion: Substantial ethnic differences exist in risks of T2D complications and mortality, which showed to be heterogeneous across outcomes and populations. Our findings suggest that a considerable proportion of these disparities is attributable to differences in environmental and sociodemographic context, highlighting the importance of interventions that take into account differences in environmental and socio-demographic context.

Background. In people with type 2 diabetes (T2D), postprandial glucose weighs on glycaemic control, and carbohydrate counting is difficult. Patients can describe their meal but rarely quantify it. Objective. To assess (1) whether a small open large language model (LLM), fine-tuned on public data, estimates the carbohydrate content of a meal described in words, and (2) what this carbohydrate adds to the prediction of the postprandial glucose rise in two public cohorts. Methods. Step 1: the Qwen3-4B LLM was fine-tuned with QLoRA on Nutrition5k dishes and compared with an automatically matched USDA food composition table and with Qwen3-4B and Qwen3-8B used without training, on test dishes (internal control) and on 100 real meals of ShanghaiT2DM patients, whose reference carbohydrate was computed from the recorded quantities. Step 2: the maximum glucose rise between 0 and 120 minutes ({Delta}Gmax) was predicted by personalised gradient boosting (M2), compared with a spline linear regression and with the Chronos-2 foundation model, using only information available at meal start and person-level cross-validation, in ShanghaiT2DM (people with T2D recruited in two Shanghai hospitals; carbohydrate estimated by the LLM) and CGMacros (free-living adults in Santa Barbara, California; carbohydrate entered in the dataset, no text). Step 3: the model was transferred between datasets. 95% confidence intervals (CI) were obtained by cluster BCa bootstrap (2,000 resamples). Results. On the 100 real meals, the carbohydrate mean absolute error (MAE) of the fine-tuned model was 12.2 [9.9, 15.2] g, almost half that of the table (22.6 g; paired difference -10.5 [-16.8, -6.0] g) and nearly a third of that of the same model without fine-tuning (34.3 g), with a bias of -10.3 g; on test dishes it was 2.2 [2.0, 2.5] g. Personalised gradient boosting (M2) reached a correlation between predicted and observed rises of r = 0.63 [0.55, 0.69] in CGMacros (1,286 meals, 45 adults) and 0.49 [0.42, 0.54] in ShanghaiT2DM (2,339 meals, 99 people with T2D), about twice the linear reference (r = 0.32 and 0.23); for a rise above 50 mg/dL, its area under the ROC curve (AUROC) was 0.80 and 0.74, versus 0.64 and 0.68 with pre-meal glucose alone. Adding carbohydrate to pre-meal glucose changed r by 0.095 [0.068, 0.128] in CGMacros and by 0.004 [-0.010, 0.017] in ShanghaiT2DM; difference between datasets: 0.091 [0.060, 0.126]. Applied from one dataset to the other, the model lost correlation ({Delta}r = -0.11 [-0.17, -0.05] towards ShanghaiT2DM and -0.12 [-0.19, -0.04] towards CGMacros). The full "text [-&gt;] LLM [-&gt;] glucose" chain was evaluated in ShanghaiT2DM only. Conclusion. An open 4-billion-parameter LLM, fine-tuned with QLoRA on public data and usable offline, almost halves the error of carbohydrate counting for real meals described in words compared with an automatically matched table, with an underestimation of large meals. Personalised gradient boosting, based on pre-meal glucose and the person's usual response, doubles the correlation of a linear reference for predicting the glucose rise in both cohorts. Carbohydrate improved this prediction in CGMacros but not in ShanghaiT2DM; the two datasets differ in many respects, and the cause of this gap is not established.

Type 1 diabetes (T1D) results from autoimmune destruction of pancreatic {beta}-cells. While anti-CD3 therapy can delay disease progression and preserve residual {beta}-cell function, disease reversal will likely require both immune modulation and {beta}-cell regeneration. We found that the combination of harmine and exendin-4 (H+E) reduced inflammation-induced human {beta}-cell apoptosis, suppressed cytokine signaling and immunogenicity pathways, and improved {beta}-cell function. Although H+E alone did not reverse diabetes in NOD mice, low-dose anti-CD3 followed by H+E normalized blood glucose, increased insulin levels, improved glucose tolerance, expanded {beta}-cell mass, and enhanced diabetes remission. These effects were associated with reduced pro-inflammatory T-cell responses, increased regulatory T cells, and greater expression of exhaustion-related T-cell markers, without broad lymphocyte depletion. Similar immunomodulatory effects were observed in activated human PBMCs. Transcriptomic analyses identified the lncRNA SNHG6 as a key mediator of H+E action; SNHG6 protected {beta}-cells from cytokine-induced stress, apoptosis, and immunogenicity. Together, these findings demonstrate that H+E promotes {beta}-cell recovery and resilience while reducing {beta}-cell immunogenicity, enabling remission of recent-onset T1D when combined with anti-CD3 therapy. SNHG6 emerges as a novel regulator of {beta}-cell protection during inflammation.

The decline in first-phase insulin response (FPIR) during the presymptomatic period of type 1 diabetes (T1D) is well established. In-situ functional studies with pancreas tissue slices showed that {beta}-cell loss of glucose-responsiveness was independent of T-cell infiltration into islets in recent-onset T1D cases. However, the mechanisms driving {beta}-cell dysfunction before the onset of T1D remain unclear. In pancreas tissue from donors across the natural history of T1D, we utilized an in-situ, whole-islet phenotypical and transcriptomic approach to unravel novel targets in the glucose-stimulus coupled secretion pathway that are similarly impaired in T-cell infiltrated and non-infiltrated islets. Specifically, we observed that islets from autoantibody positive (single(s) or multiple(m) AAb+) donors exhibited activation of post-transcriptional gene regulation along with reduced protein translation, processing in the endoplasmic reticulum (ER), and ER stress. Disrupted mitochondrial metabolism and bioenergetics were prominent in islets from multiple AAb+ and T1D donors with disease durations [&le;]7 years. In addition, T1D islets presented reduced mitochondrial protein import, quality control, and dynamics, together with downregulated genes in insulin secretory pathways. During infiltration, these pathways remain dysregulated while immune/inflammatory transcripts were increased. These studies identified novel mechanisms of {beta}-cell dysregulation before symptomatic onset and independent of T-cell infiltration in T1D pathogenesis.

Acute glucose-stimulated beta-cell secretion is a key dimension of beta-cell function, but the hyperglycemic clamp used to assess it is technically demanding and resource-intensive, limiting its use at scale. We aimed to develop and validate the oral glucose tolerance test (OGTT) Early C-peptide (OEC) Index for estimating clamp-derived acute C-peptide response to glucose (ACPRg). We defined OEC as the 0-60-min incremental C-peptide area under the curve (AUC) normalized to the geometric mean of incremental and total glucose AUCs, with a fasting-glucose correction of 100/G0. OEC was evaluated in paired OGTT-clamp data from RISE Adult Medication (baseline n=257; internal validation) and BetaFat (baseline n=70; external validation). Predictive R^2 and Pearson r for log-ACPRg were assessed using participant-level 5-fold cross-validation in RISE and external validation without refitting in BetaFat, with the C-peptidogenic, insulinogenic, and CIR30 indices as comparators. In RISE Adult, cross-validated R^2 for OEC was 0.47, 0.61, and 0.54 at baseline, 12, and 15 months, respectively, with corresponding Pearson r of 0.69, 0.78, and 0.74; nested cross-validation showed only modest attenuation. Applied without refitting in BetaFat, OEC yielded R^2 of 0.55, 0.41, and 0.39 at baseline, 12, and 24 months, with corresponding r of 0.76, 0.70, and 0.64. OEC had higher predictive R^2 point estimates than all three comparators at every visit in both cohorts. OEC provides a simple, transparent OGTT-based estimate of acute beta-cell secretory capacity that retained predictive performance in BetaFat and may broaden access to physiological assessment of early beta-cell function.

Background: Type 2 diabetes (T2D) has been associated with reduced abdominal aortic aneurysm (AAA) risk and slower growth in observational studies, but the mechanism for this paradoxical association remains unclear. We hypothesized that T2D's mechanistic heterogeneity obscures opposing, pathway-specific effects on AAA. Methods: We performed two-sample Mendelian Randomization (MR) to investigate the effect of T2D (T2DGGI, 242,283 individuals with T2D and 1,569,730 without) on AAA (AAAgen, 37,214 individuals with AAA and 997,456 without) using 449 genetic instruments. We also performed MR within previously defined clusters representing different mechanisms of T2D. We extended the analysis to individual hyperglycemic, insulin-resistance, and insulin-sensitivity traits. Our hypothesis was tested in a porcine pancreatic elastase murine model of abdominal aortic aneurysm formation. Results: Overall T2D liability showed no net association with AAA liability (OR 1.00, 95% CI 0.96-1.04, P = 0.90), albeit with marked instrument heterogeneity (Cochran's Q = 1356, P = 5 x 10-92) and inconsistency across sensitivity analyses, with a notably protective MR-Egger result (OR 0.87, 95% CI 0.80-0.94, P = 0.0011) and non-zero intercept (P = 2 x 10-4). MR of T2D on AAA within mechanistic clusters showed opposing effects: lipodystrophy (OR 1.47, 95% CI 1.26-1.72) and obesity (OR 1.27, 95% CI 1.18-1.36) cluster membership associated with increased AAA risk, while beta-cell negative proinsulin (OR 0.83, 95% CI 0.76-0.90) and beta-cell positive proinsulin (OR 0.86, 95% CI 0.80-0.93) clusters were protective. This pattern was robust to clustering method. Traits indexing hyperglycemia (fasting glucose, 2-hour glucose, hemoglobin A1c) were associated with reduced AAA risk, while traits indexing insulin resistance (fasting insulin, BMI, waist-hip ratio, triglyceride-to-HDL ratio) were associated with increased AAA risk. In mice, those treated with a high fructose diet as a model for insulin resistance showed increased growth of the abdominal aorta compared with controls, whereas those treated with pancreatoxin streptozotocin as a model for isolated hyperglycemia showed blunted growth. Conclusion: Distinct components of T2D physiology may have differential effects on AAA development. Whereas insulin resistance may promote AAA, hyperglycemia appears to protect against it. Our findings support insulin sensitization as a rational direction for AAA pharmacotherapy and suggest that the pathways mediating the protective effect of hyperglycemia may harbor additional therapeutic targets.

Adult pancreatic {beta}-cells are mostly locked in quiescence, limiting large-scale analysis of their cell cycle. To overcome this limitation, we used synthetic in vitro transcribed mRNAs encoding Cyclin D1 and CDK4 to induce proliferation in up to 70% of primary rat {beta}-cells. Flow cytometry-based analysis of cell-cycle markers revealed substantial heterogeneity in {beta}-cell cell-cycle progression, identifying five distinct groups of proliferating {beta}-cells based on G1 entry timing. Total cell-cycle length ranged from approximately 26 to 34 hours and was primarily determined by variability in G1 duration (13-19 hours). In the fastest-dividing {beta}-cells, G1, S, and G2M lasted approximately 13, 6, and 7 hours, respectively, whereas later-entering {beta}-cells exhibited progressively longer G1 phase. Additionally, we identified a small {beta}-cell subpopulation that failed to complete division and may have exited the cell cycle. These findings reveal cell-cycle entry and G1 progression as a major source of heterogeneity in {beta}-cell proliferation and provide quantitative benchmark for developing strategies to enhance controlled {beta}-cell regeneration in diabetes.

Dysregulated hormone secretion and erosion of endocrine cell identity are features of type 1 and type 2 diabetes, but the transcriptional programs maintaining adult human islet identity and function remain poorly defined. The large MAF transcription factor MAFB is expressed in human - and {beta}-cells, marks their most functionally mature subpopulations, and is downregulated in diabetes, but its role in adult human islets has not been tested directly. Using shRNA-mediated MAFB knockdown (KD) in whole and CD26+ -cell-enriched human pseudoislets, we found that whole pseudoislet MAFB KD impaired glucagon synthesis and secretion while only modestly reducing insulin content and cAMP-potentiated insulin release. Single-cell profiling detected no {beta}-cell transcriptional response beyond MAFB KD itself, consistent with buffering by the related {beta}-cell-enriched MAFA transcription factor. In contrast, -cell-restricted MAFB KD unmasked a cell-autonomous requirement for MAFB in stimulus-secretion coupling. MAFB deficiency also destabilized -cell identity, downregulating canonical -cell and neuroendocrine secretory genes while ectopically inducing mesenchymal and extracellular matrix remodeling programs. In addition, MAFB-dependent downregulation of electron transport chain genes was confined to a large -cell subcluster, manifesting as impaired islet-wide mitochondrial respiration within the broader -cell population. Together, these findings identify MAFB as an essential adult human -cell maintenance factor that links diabetes-associated downregulation to impaired glucagon secretion, -cell identity erosion, and mitochondrial dysfunction. RESEARCH IN CONTEXTO_LIWhat is already known about this subject? O_LIMAFB is expressed in adult human - and {beta}-cells, marks their most functionally mature subpopulations, and is downregulated in type 1 and type 2 diabetes C_LIO_LIIn human stem cell models, MAFB is essential for generating insulin-producing {beta}-like cells, whereas glucagon-producing -like cells are reduced but still formed C_LIO_LINeither model addresses adult human islets: rodent MafB becomes -cell restricted after birth, and stem cell models capture differentiation, not maintenance C_LI C_LIO_LIWhat is the key question? O_LIIs MAFB required to maintain identity and secretory function in adult human islet cells? C_LI C_LIO_LIWhat are the new findings? O_LIMAFB knockdown in primary human pseudoislets impaired glucagon synthesis and secretion but minimally affected {beta}-cells, consistent with buffering by MAFA C_LIO_LIKnockdown in CD26+ -cell-enriched pseudoislets revealed a cell-autonomous requirement for MAFB in stimulus-secretion coupling, and destabilized -cell identity by inducing mesenchymal and extracellular matrix programs C_LIO_LIMAFB loss downregulated electron transport chain genes in the largest -cell subcluster and reduced mitochondrial respiration C_LI C_LIO_LIHow might this impact on clinical practice in the foreseeable future? O_LIPreserving MAFB activity in adult human -cells may represent a strategy to limit -cell dysfunction in diabetes C_LI C_LI

Objectives. To evaluate the impact of reimbursement restrictions for glucagon-like peptide-1 receptor agonists (GLP-1RAs) for type 2 diabetes, introduced in Denmark in November 2024, on injectable semaglutide (Ozempic) utilization patterns and subsequent HbA1c changes among discontinuers. Design. Nationwide registry-based cohort study. Setting. Denmark. Participants. Adults treated with injectable semaglutide for diabetes (January 2018 to December 2025). Main outcome measures. We described temporal patterns in injectable semaglutide use before and after the policy change. Among semaglutide continuers after the policy change, we assessed whether continuation met a key criterion in the new policy: non-feasibility of sodium-glucose cotransporter 2 inhibitor use and high cardiovascular risk. We characterized semaglutide discontinuers around the policy implementation (March 2024 to November 2024) and subsequent treatment switches. Among discontinuers, we evaluated HbA1c changes and performed interrupted time series (ITS) analyses. Results. Following the policy change, use of injectable semaglutide declined modestly before subsequently increasing. Among 61,120 individuals classified as semaglutide continuers, 27,143 (44%) were already using an SGLT2i. Of the remaining 33,977 (56%), only 1,862 (5.5%) met a key reimbursement criterion (i.e., non-feasibility of SGLT2i use and high cardiovascular risk). Among 79,305 individuals with a semaglutide fill between March 2024 and November 2024, 22,096 (28%) were classified as discontinuers. Within six months, ~40% of discontinuers switched to other glucose-lowering therapies, most commonly SGLT2is (24%). 37% of discontinuers later reinitiated semaglutide. Among individuals with an on-treatment HbA1c measurement (within 12 months before their last semaglutide fill) and an off-treatment HbA1c measurement (6-12 months after their last semaglutide fill) (n=13,017), mean HbA1c increased from 49 to 57 mmol/mol (mean change +7.4 mmol/mol, 95% CI 7.2-7.6). ITS analysis showed that HbA1c increased by 5.9 mmol/mol (95% CI 5.1-6.7) at six months post-discontinuation, which attenuated to 3.1 mmol/mol (95% CI 2.2-4.1) at 12 months; raw monthly means (not included in the ITS model) suggested this persisted through 18 months. Conclusions. Following the policy change, population-level use of injectable semaglutide declined modestly before increasing again. Most discontinuers did not initiate another glucose-lowering therapy and experienced an increase in HbA1c that did not fully return to pre-discontinuation levels during follow-up.