Diabetes Care

Background. Type 2 diabetes (T2D) and coronary artery disease (CAD) frequently co-occur, yet the biological pathways that jointly determine risk remain incompletely understood. Most genetic studies have examined shared risk from a single-disease perspective, limiting insight into the mechanisms that generate discordant risk between conditions. Methods. We applied PLACO to multi-ancestry GWAS data of T2D and CAD to identify shared loci, prioritising shared causal signals using colocalisation. Shared variants were clustered by their associations with 77 cardiometabolic traits, and cluster-specific genetic risk scores (GRS) were tested for association with 17 clinical biomarkers and 1,254 binary outcomes in 378,772 UK Biobank (UKB) participants. Two-sample Mendelian randomisation (MR) was used to test the causal role of liver fat. Results. We identified 149 loci shared between T2D and CAD; most novel loci had discordant effects (35 of 42), in contrast to the predominantly concordant signals reported previously. Clustering 187 independent shared variants revealed seven mechanistic clusters, three of them centred on liver fat and defined by discordant T2D?CAD effects. Enrichment analyses and cluster-GRS associations in UKB highlight associations between higher liver fat and T2D risk with a cardioprotective lipid profile and reduced CAD risk. Genetically higher liver fat increased T2D risk but lowered CAD risk in MR analyses; partitioning liver fat instruments by their effect on ApoB-containing lipoproteins indicates that the CAD effects are determined more by effects of circulating ApoB rather than liver fat itself. Conclusions. Liver fat largely sets the direction of T2D risk, whereas the fate of that lipid, retained in the liver with low circulating ApoB or exported as ApoB-containing lipoproteins, sets the direction of CAD risk. This liver-centric partitioning provides a mechanistic framework for the discordant cardiometabolic effects of hepatic lipid and lipid-lowering pathways, with implications for precision prevention.

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: Type 2 diabetes (T2D) is associated with elevated rates of several cancers and is increasingly recognized as a heterogeneous disease, but whether its clinically distinct subtypes carry different cancer risks is unknown. Methods: In this matched retrospective cohort study using electronic health record data from the Epic Cosmos Research Platform (2012-2025), adults with newly diagnosed T2D were classified into severe insulin-deficient (SIDD, 21.6%), mild obesity-related (MOD, 23.5%), mild age-related (MARD, 40.7%), or mixed (14.1%) subtypes using validated algorithms and matched to adults without diabetes on age, sex, and body mass index. Cause-specific Cox models estimated adjusted hazard ratios (HRs) for seven site-specific cancers, accounting for competing risks. Cancer screening uptake was assessed as a secondary outcome. Results: Among 575,139 adults with T2D and 689,719 without diabetes (median follow-up, 3.8 years), MARD had the highest cancer incidence (17.3 per 1,000 person-years). Relative to adults without diabetes, rates of colorectal, pancreatic, liver, endometrial, and ovarian cancer were elevated across subtypes, with the highest hazards in SIDD (HR=3.87, 95% CI=3.51 to 4.27) and mixed phenotypes. Prostate cancer rates were lower in all subtypes, most markedly in MOD (HR=0.60, 95% CI=0.55 to 0.64). Rates of breast cancer were higher among mixed (HR=1.12, 95% CI=1.05 to 1.19) and lower among MOD (HR=0.85, 95% CI=0.80 to 0.90). Mammography and prostate-specific antigen screening were lower across subtypes. Conclusions: Site-specific cancer incidence and screening uptake differed across clinically defined subtypes of T2D. Subtype classification from routine clinical data may inform targeted cancer surveillance, though further study is needed before clinical use.

Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are increasingly used for type 2 diabetes. Despite established metabolic, cardiovascular, and renal benefits, it remains uncertain whether GLP-1 receptor agonists are associated with longer clinically recorded Alzheimer's disease (AD)-type dementia-free survival than sulfonylureas (SU) or SGLT2 inhibitors. Methods Using All of Us electronic health records, we emulated target trials among adults aged 55 years or older with type 2 diabetes, a 12-month washout, and no prior dementia. We compared GLP-1 receptor agonists with SU and SGLT2 inhibitors. Propensity score weighting and doubly robust estimation addressed confounding. Causal survival forests estimated individualized treatment effects on 48-month RMST free from clinically recorded AD-type dementia. Findings In the GLP-1 receptor agonist versus SU comparison (6,328 individuals; 48-month NNT approximately 202), initiation was associated with a small but statistically significant increase in AD-type dementia-free survival (ATE 0.21 months; 95% CI: 0.07-0.35). The highest-benefit stratum gained 0.45 months (95% CI: 0.28-0.62). In the SGLT2 inhibitor comparison (3,070 individuals; 48-month NNT approximately 245), the average effect was not statistically significant (ATE 0.06 months; 95% CI: -0.18 to 0.31), but treatment effects were heterogeneous. The highest-benefit stratum gained 0.83 months (95% CI: 0.49-1.17). Predicted benefit was associated with older age, insulin use, lower HbA1c, and lower BMI. Interpretation GLP-1 receptor agonists may delay clinically recorded AD-type dementia compared with SU. Comparative effectiveness versus SGLT2 inhibitors may vary, supporting further study. Given the hypothesis-generating nature of these findings, diabetes treatment selection should remain guided by glycemic, cardiovascular, renal, and patient-centered considerations. Funding German Federal Ministry of Research, Technology and Space (03LWH0181B)

Body mass index (BMI) alone may underestimate clinically relevant obesity because it does not capture central fat distribution. We compared obesity prevalence in Mexico using BMI-only criteria, adiposity-confirmed criteria, and the clinical obesity definition proposed by the Lancet Diabetes and Endocrinology Commission. We conducted a population-based, cross-sectional study of 13,160 adults aged 18 years or older who participated in the 2018-2019 Mexican National Health and Nutrition Survey (ENSANUT). Obesity prevalence was estimated through survey-weighted analyses that accounted for the complex sampling design. The weighted prevalence of obesity based on BMI was 34.5% (95% CI, 33.1-35.9), while 30.9% (95% CI, 29.6-32.2) met criteria for clinical obesity. One quarter of individuals with clinical obesity had a BMI under 30 kg/m2, a phenotype more common among older adults. Half of adults with a BMI under 30 kg/m2 showed elevated central adiposity. BMI alone underestimates clinically relevant obesity in Mexican adults. Adding waist-based measurements could improve the identification of individuals with excess fat and metabolic risk, both in clinical settings and population monitoring.

Background Data-driven type 2 diabetes (T2D) subtypes differ in their risks of dementia and stroke. We examined whether their metabolomic profiles also differed and whether subtype-related metabolic patterns were associated with dementia, stroke, and all-cause mortality. Methods We analyzed NMR-based metabolomic profiles across previously defined T2D subtypes in the UK Biobank. Subtype-related metabolites were summarized using principal component analysis (PCA), and their associations with incident dementia, stroke, and all-cause mortality were examined using Cox models. Attenuation analyses and two-sample Mendelian randomization further assessed subtype-outcome relationships and the potential causal relevance of outcome-associated metabolites. Results Among 7,671 individuals (mean age 59.85 years; 37% female), the first five PCs explained 76.7% of variance in subtype-related metabolites and mainly reflected lipid and lipoprotein signatures. After adjustment for T2D subtype and confounders, the HDL-remodeling PC increased risks of all-cause dementia (HR 1.17, 95% CI 1.08-1.27), VaD (HR 1.18, 95% CI 1.05-1.32), and all-cause mortality (HR 1.16, 95% CI 1.13-1.19). Lower scores on the LDL cholesterol-enriched axis increase risks of all-cause dementia (HR 0.75, 95% CI 0.62-0.91) and mortality (HR 0.76, 95% CI 0.69-0.83). The VLDL/LDL-enriched PC was inversely associated with mortality (HR 0.93, 95% CI 0.88-0.98). No significant stroke results were observed. Adjustment for the PCA-derived metabolomic patterns generally attenuated subtype-outcome associations, MR analyses identified 197 metabolite-outcome associations that remained significant after FDR correction. Conclusions Metabolomic profiling showed that the metabolic signatures differed across data-driven T2D subtypes and highlighted lipid and lipoprotein remodeling as a major metabolic feature associated with dementia, stroke, and all-cause mortality.

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.

PURPOSE: To assess the predictive performance of panel protein biomarkers as well as an established algorithm that considers repeat biomarker testing for risk prediction of PDAC among a prospective cohort of patients with New-onset diabetes. PATIENTS AND METHODS: A panel of protein biomarkers (CA19-9, CA125, CEA, LRG1, REG3A and TIMP1) were assayed in 6,516 serially collected pre-diagnostic plasma samples from 2,121 NOD patients from the Consortium of Chronic Pancreatitis Diabetes and Pancreatic Cancer (CPDPC)-initiated NOD study who completed the 3-year study follow-up period. The specimen set included 25 pre-diagnostic samples from the 12 PDAC cases diagnosed during study follow-up. We applied a single threshold (ST) method, which considers biomarker levels at a single time point, as well as a previously established parametrical empirical Bayes (PEB) algorithm, which considers prior biomarker measurements, with case calls made based on pre-specified cutoffs corresponding to 1% 1-year risk. Resultant biomarker data as well as case calls were provided to the EDRN Data Management and Coordinating Center as part of a Prospective-sample-collection-Retrospective-Blinded-Evaluation (ProBE)-compliant Phase 3 biomarker validation study. Area under the Receiver Operating Characteristic Curves (AUC), sensitivity, specificity, population-level positive predictive value (PPV), and negative predictive value (NPV) are reported. RESULTS: The 3-year incidence of PDAC in the NOD cohort was 0.57%. When considering PDAC vs non-cancer controls, respective AUCs of individual protein biomarkers ranged from 0.52-0.94, with CA19-9 achieving the highest overall performance of 0.94 (95% CI: 0.86-1.00). At the pre-defined 1% 1-year risk threshold, CA19-9 yielded sensitivity of 83.3% at 97.2% specificity. Additional markers CEA, CA125, and TIMP1 demonstrated sensitivity of 33.3%, 41.7%, and 8.3%, respectively. In a subset of patients, CA19-9 first tested positive at a median (interquartile range [IQR]) of 7 months (4 to 14 months) prior to clinical PDAC diagnosis. Of the two PDAC cases missed by CA19-9 using the ST method, one (diagnosed with stage III PDAC) was detected using the PEBCA19-9 algorithm. CONCLUSION: In the setting of adult new onset diabetes, CA19-9 is a readily available and promising biomarker that can be leveraged for earlier detection of an underlying pancreatic cancer. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

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.

Diabetes is a highly prevalent complication of cystic fibrosis (CF), affecting 50% of adults with CF and over 80% of those with exocrine pancreatic insufficiency (PI) by age 50 years. Development of cystic fibrosis-related diabetes (CFRD) is associated with increased morbidity and mortality mostly due to advancement of chronic obstructive lung disease. Highly effective modulator therapy (HEMT), using precision medications targeting the cystic fibrosis transmembrane conductance regulator (CFTR), improves CFTR function and CF lung disease, but its impact on diabetes pathogenesis remains uncertain. We sought to determine whether two types of HEMT, ivacaftor and elexacaftor/tezacaftor/ivacaftor (ETI), alter diabetes prevalence in two large cohorts of individuals with CF and exocrine PI. For comparison, a non-highly-effective modulator, lumacaftor/ivacaftor (LUM/IVA), was also assessed. Data were provided by the CFTR2 project, a multinational CF registry (for ivacaftor and LUM-IVA), and by the CF Genome Project (CFGP), a predominantly US-based CF cohort (for ETI). Among 32,753 individuals with CF (2,803 treated), ivacaftor was associated with reduced diabetes prevalence (age-adjusted OR=0.55). In contrast, lumacaftor/ivacaftor (not highly effective) was not associated with diabetes prevalence (n=32,749). Among 2,854 individuals with CF (2,458 treated), ETI was associated with reduced diabetes prevalence (age-adjusted OR=0.47). Overall, HEMT (ivacaftor and ETI) was associated with a 25-39% reduction in diabetes prevalence in CF, while a non-highly-effective modulator (lumacaftor/ivacaftor) showed no difference. Precision targeted amelioration of CFTR dysfunction can delay onset of diabetes in a high-risk CF population.

Background: Large-scale biobanks have advanced genomic and epidemiologic research, but many rely on infrequent biological sampling and limited digital phenotyping. The Yamanashi Multi-omics Cohort (YMoC) was established to support longitudinal assessment of molecular, clinical, and behavioural changes in a screening-defined cohort of adults at elevated metabolic risk without diagnosed diabetes. Methods: YMoC is a longitudinal cohort of 215 adults aged 30-70 years in Yamanashi Prefecture, Japan, who met prespecified glycaemic eligibility criteria at health check-up, including fasting plasma glucose 100-125 mg/dL (5.6-6.9 mmol/L) and HbA1c <6.5%. Participants underwent three in-person visits over six months. Measurements include 75-g oral glucose tolerance testing with serial sampling, clinical biochemistry, anthropometry, liver elastography, and collection of blood, urine, stool, and saliva for multi-omics profiling. Between visits, participants wore a Fitbit Inspire 3 and completed daily app-based questionnaires using the Taohealth app. Current molecular data include genome-wide single nucleotide polymorphism array genotyping and longitudinal plasma proteomics in a subset. Conclusions: YMoC is designed to evaluate within-person molecular and phenotypic trajectories in a screening-defined metabolic-risk cohort. The cohort provides a dense longitudinal resource linking clinical assessments, biospecimens, omics assays, and digital phenotyping, including analyses of insulin-resistance-related markers such as homeostasis model assessment of insulin resistance (HOMA-IR).

Type 2 diabetes (T2D) is a complex metabolic disorder characterized by hyperglycemia and insulin resistance. Although genome-wide association studies (GWAS) have identified >600 T2D risk loci, the causal genes and the relevant tissues mediating these associations remain largely unresolved. To address this challenge, we performed tissue-specific, ancestry-aware transcriptome-wide association studies (TWAS) across six T2D-relevant tissues: subcutaneous adipose, visceral adipose, brain hypothalamus, liver, skeletal muscle, and pancreas. We conducted ancestry-specific multi-tissue TWAS in European ancestry (EUR) data using summary statistics from the largest EUR GWAS (242,283 cases and 1,569,734 controls) and pre-trained gene expression prediction models derived from 689 EUR individuals from the Genotype-Tissue Expression (GTEx) Project. Conditional analyses were performed to identify independent TWAS signals. We identified 684-750 significant gene-T2D associations per tissue (P < 1.919 x 10-6), implicating both established and novel candidate genes. Among these, JAZF1 and IDE showed consistent association signals across all six tissues, whereas TCF7L2 and WSF1 exhibited heterogeneous effects restricted to a subset of T2D-relevant tissues. Conditional analyses further refined these signals to 289-322 independent TWAS signals per tissue. Together, these finding highlight substantial regulatory heterogeneity in the genetic architecture of T2D and underscore the importance of tissue context in interpreting disease-associated loci. Cross-ancestry replication of EUR-derived TWAS signals was evaluated in African American (AFA) individuals. We conducted an AFA-TWAS using summary statistics from the largest AFA GWAS (50,251 cases and 103,909 controls) in combination with gene expression prediction models trained in 111 AFA individuals from GTEx. We observed significant enrichment of EUR-derived T2D TWAS signals in the AFA TWAS across subcutaneous adipose, visceral adipose, skeletal muscle, and pancreas, whilst enrichment was weaker in liver, likely reflecting limited sample size. Overall, our findings demonstrate that integrating tissue-specific and ancestry-aware TWAS refines the identification of causal genes for T2D, with cross-ancestry replication supporting the robustness of these signals and cross-tissue analyses revealing context-specific effects. However, they also highlight the limited availability of non-EUR datasets and the need for larger, more diverse ancestry-specific transcriptomic resources.

Polygenic risk scores (PRS) assume additive SNP effects, yet genetic risk also arises from interactions between loci and environmental factors that contribute to broad-sense heritability. We developed an extended PRS (ePRS) framework for type 2 diabetes (T2D) that incorporates locus-by-locus non-additive effects beyond those captured by additive single-locus PRS or linkage disequilibrium (LD) tagging. These were modelled as cumulative burden (G+G; summed allele counts), statistical epistasis (GxG; allele count products), and gene-environment effects derived from cardiometabolic variables in electronic health records. Across 235,000 UK Biobank participants, five complementary ePRS models captured largely non-overlapping high-risk individuals, suggesting that a key to individual risk predictions comprise the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score improved case detection beyond clinical predictors, including individuals within clinically normal ranges. These findings were generalized to celiac disease, with similar complementarity across models, with potential for clinical use pending prospective validation.

Background: Observational research reports positive associations between type 2 diabetes mellitus (T2DM) and obesity-related cancers (ORCs), but causality remains unclear due to confounding (namely the shared risk factor of obesity, commonly approximated as body mass index, BMI), immortal time bias, and detection-time bias. Here, we aimed to use causal inference methods to minimise the above problems and estimate causal associations between new-onset T2DM and incident cancer. Methods: We performed a cohort study within UK Biobank, comparing new-onset T2DM with unexposed individuals matched 1 to 3 on BMI, age, and sex using a sequential longitudinal approach. The primary outcomes were total incident cancer, divided into ORCs and non-obesity-related cancers (NORCs). The secondary outcomes were site-specific cancers. We developed Cox models to estimate time-split hazard ratios (tsHRs) and 95% confidence intervals (CIs) stratified by sex. Findings: 23,771 participants with new-onset T2DM were matched with 71,170 unexposed participants. During a median follow-up of 5 years, there were 7694 (T2DM: 2432; unexposed: 5262) incident cancers. In men, there was evidence for an effect of T2DM on obesity-related cancer (tsHR 1.39, 95% CI 1.21-1.59), particularly on hepatocellular carcinoma (tsHR 3.97, 95% CI 2.38-6.65), pancreatic (tsHR 1.77, 95% CI 1.15-2.72) and kidney (tsHR 1.62, 95% CI 1.13-2.32) cancers. In women, there was evidence for an effect on obesity-related cancers (tsHR 1.33, 95% CI 1.16-1.52). Importantly, there were no associations with post-menopausal breast and endometrial cancers, two cancer types consistently associated with elevated BMI. There was no effect of new-onset T2DM on incidence of NORCs. There was evidence of detection-time bias, particularly in men. Interpretation: This is the first large-scale study to demonstrate evidence of a BMI-independent associations between new-onset T2DM and incident cancer. In men, this was primarily driven by hepatocellular carcinoma, pancreatic cancer, and kidney cancer. In women, the underlying cancers driving this relationship were less clearly defined. Funding: This study was funded by Cancer Research UK and administered through the Manchester Cancer Research Centre MB-PhD scheme (SEBCATP-2023/100010).

Background Cardiovascular-Kidney-Metabolic (CKM) syndrome encompasses interconnected conditions such as type 2 diabetes (T2D), hypertension, hypertriglyceridemia, metabolic syndrome (MetS), and chronic kidney disease (CKD). As CKM progresses, cardiorenal risks increase. Although Glucagon-like peptide-1 receptor agonists (GLP-1 RA) have demonstrated cardiorenal and cardiometabolic benefits, offering an opportunity to slow CKM progression, their use may vary across social determinants of health (SDoH) and stage 2 CKM subgroups. Objective To evaluate the influence of SDoH on access to GLP-1 RA among patients with T2D and other stage 2 CKM conditions. Methods This cross-sectional study used data from the U.S. National Health and Nutrition Examination Survey (NHANES), 2005?2020. Adults aged [&ge;]30 years with T2D and/or other stage 2 CKM conditions were included. Weighted descriptive analysis, multivariable logistic regression and LASSO were applied to assess associations between SDoH and GLP-1 RA use. Results Among 4,520 participants (representing approximately 84.0 million U.S. adults), weighted mean age was 61.4 years, 48.9% were female, and 61.5% were non-Hispanic White. Among participants with T2D, GLP-1 RA use was higher among individuals with higher education (3.39% vs 1.43%), private insurance (3.00% vs 0.58%), and higher income (4.70% vs 1.87%), while no use was observed among those without routine places for care. In adjusted analyses, individuals with lower income, less than high school education, lack of insurance, and being unmarried had 64%, 51%, 81%, and 40% lower likelihood of GLP-1 RA use, respectively. LASSO identified income, education, insurance, and access to care as predictors. Lower income, lower educational attainment, and lack of insurance were associated with 48%, 34%, and 79% lower likelihood of GLP-1 RA use, respectively, adjusting for age, sex, and race/ethnicity. Conclusion SDoH-driven disparities limit GLP-1 RA access. Expanding GLP-1 RA access by addressing socioeconomic barriers is critical to slowing CKM progression, reducing cardiovascular risk, and mitigating health disparities.

Importance. Metformin may influence risk of dementia, with prior conflicting observations of protection or harm. Objective. To determine the association of randomization to metformin vs. placebo or intensive lifestyle intervention (ILS) in the Diabetes Prevention Program (DPP) with cognitive outcomes (cognitive impairment syndromes and trajectories of cognitive test performance) during the DPP Outcomes Study (DPPOS). Design, Setting & Participants. Prospective long-term follow-up of DPP/DPPOS participants at 27 U.S. centers among adults who were at high risk for type 2 diabetes (T2D) at baseline. Exposures. Randomization to metformin, placebo, or ILS (1996-1999) for 3.2 years followed by open-label metformin in the original randomized metformin group until 2021. Main Outcomes & Measures. Cognitive impairment syndromes were adjudicated in 2022-2024 in 1,483 participants (median age 74 [IQR 68, 80]) using the National Alzheimer's Coordinating Center Uniform Dataset version 3. Cognitive performance in executive and memory domains was ascertained with repeated cognitive tests between 2009 and 2024. Multinomial logistic regression and mixed-effects models were fit to examine associations of randomization to metformin with cognitive outcomes. Results. Total metformin exposure (mean {+/-} SD) was 15.5 {+/-}7.7 years/person in the metformin group. Persons in the placebo and ILS groups received out-of-study metformin usually after developing diabetes with mean metformin total exposure of 4.5 {+/-}5.1 and 3.8 {+/-}4.8 years/person in the placebo and ILS groups, respectively. Overall, the frequency distributions of the cognitive syndromes did not differ significantly by treatment group; however, randomization to metformin was associated with a 60% (OR 0.40 [95%CI 0.17, 0.97]) and 62% (OR 0.38 [95%CI 0.16, 0.89]) lower odds of dementia compared with placebo and ILS, respectively, after adjustment for demographics, education, income, and APOE-{varepsilon}4 genotype. Randomization to metformin was also associated with significantly better memory performance over time ( {beta} =0.58; 95%CI: 0.09, 1.1; p=0.02; Cohen's d=0.1). Conclusions and Relevance. Long-term metformin treatment is associated with a reduced risk of dementia and better memory performance among persons with pre-diabetes or T2D. Estimates were imprecise due to a limited number of dementia cases. Longer follow-up with more dementia cases is needed to confirm our findings.

Introduction Diabetes distress describes the psychological and emotional burden of living with diabetes and is associated with reduced self-management and adverse diabetes outcomes. Clinical guidelines recommend routine assessment and management of diabetes distress, but this is not always implemented. Therefore, there is a need to develop approaches to deliver emotional health support in routine clinical care more effectively. We describe here the protocol for a study to I) assess the feasibility of implementation of the D-stress Pathway, comprising Enhanced Usual Care (EUC) and an online, group-based, psychological diabetes distress reduction intervention called REDUCE, ii) evaluate the feasibility of the study protocol iii) detect an effect signal of diabetes distress score and Interstitial Glucose Time in Range and iv) refine initial programme theories of how both interventions (EUC and REDUCE) work, for whom, and under what circumstances. Methods This feasibility study includes a multicentre trial within a cohort design (TWICs) where sites have a staggered exposure to the interventions alongside a realist process evaluation. Four UK NHS diabetes services will recruit 80 adults with type 1 diabetes ([&ge;]1 year) using continuous glucose monitoring (CGM) ([&ge;]3 months). All participants will receive EUC and provide monthly data over 7 months on diabetes distress (measured by the Type 1 Diabetes Distress Assessment System (T1DDAS) and interstitial glucose measured by using continuous glucose monitoring. Participants with elevated diabetes distress, will be offered the six-week, group-based, online REDUCE intervention plus EUC, compared to EUC alone. Up to twenty participants with type 1 diabetes, ten family members/friends, sixteen healthcare professionals delivering EUC and five REDUCE facilitators will be interviewed to explore their experience of receiving training and delivering the D-stress Pathway. Up to 20 EUC consultations and REDUCE sessions will be observed. Analysis Feasibility will be assessed against pre-specified progression criteria and analysed descriptively using summary statistics. Primary outcomes include baseline level of diabetes distress, recruitment rate, intervention uptake, and data completeness, which will be analysed descriptively. Qualitative data will be analysed using framework analysis guided by realist programme theories developed for this study. Ethics Ethics approval has been granted by NHS Research Ethics Committee (REC) (Bromley REC: 25/LO/0469) and Health Research Authority obtained. All participants will provide informed consent. Trial registration no: Registered at ClinicalTrials.gov number NCT07193446 on 26/11/2025. Protocol and statistical analysis plan: The trial protocol and statistical analysis plan can be accessed at ClinicalTrials.gov.

Obesity is a significant public health concern. Early-onset obesity in the context of rare disease can reflect genetically-mediated pathology or elevated susceptibility through indirect mechanisms. Mapping the diverse characteristics and needs of young people with obesity in the rare disease population is a first step toward mechanistic and translational research. We carried out a retrospective comparative analysis of demographic, genotypic, phenotypic and health service utilisation data for young people with obesity (cases: n=500) and without obesity (controls: n=11,444) from the UK 100,000 Genomes Project rare disease cohort. Cases and controls were recruited prior to genomic diagnosis, across clinical disorder categories. We observed significant association between socioeconomic deprivation and obesity risk. Young people with obesity had significantly higher utilisations of acute care and mental health services, indicating an overall higher health burden. A curated panel of 519 candidate obesity-associated genes demonstrated aggregate association with obesity, although no single gene reached significance. Phenotypic comparison between cases and controls highlighted increased multi-organ and neurological system involvement, highlighting the overlap between neurodevelopmental and obesity risks. Within the case group, we conducted cluster analysis to identify early-onset obesity groups with different phenotypic profiles, potentially arising from different causal pathways - this identified six obesity subgroups of interest, with differing involvement of neurodevelopmental and other systems. Our study confirms that obesity co-occurs with a wide range of factors within the rare disease population, and is associated with significant physical and mental health needs, requiring holistic lifelong care.

Emerging evidence indicates that oral and systemic health are interconnected, yet the basis of this relationship remains incompletely understood. In a genome-wide association study of objectively measured dental caries in permanent dentition among Danish children and adolescents (DCCA) (N = 151,521), we identified 14 independent loci. Genes at DCCA-associated loci were enriched for expression in immune, secretory and epithelial cell populations. We found genetic correlations and evidence for shared causal variants with several cardiometabolic traits. Leveraging data from UK Biobank (Nmax = 501,936) and independent pediatric cohorts (Nmax = 3,412), we showed that genetic liability to DCCA associated with dentures, risk of coronary artery disease and type 2 diabetes in adults, and with HbA1C, lipid, liver enzyme levels, and plasma proteins implicated in oral, metabolic and hepatic biology in both populations. Our results provide new insights into the genetic architecture underlying the relationship between DCCA and cardiometabolic disease.

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