Diabetes Care

OBJECTIVE To characterize the prevalence and penetrance of maturity-onset diabetes of the young (MODY) in a multi-ancestry population using a genotype-first design. RESEARCH DESIGN AND METHODS We analyzed whole-genome sequencing and clinical data from 374,973 unrelated All of Us participants (42.0% non-European ancestry). We identified pathogenic or likely pathogenic (P/LP) variants in 10 established MODY genes and assessed carrier prevalence, diabetes penetrance, and glycemic profiles. We evaluated age-dependent diabetes risk by comparing carriers with non-carriers stratified by type 2 diabetes polygenic risk score (T2D PRS). RESULTS We identified 370 carriers of P/LP MODY gene variants (0.099%; 1 in 1,013), with similar carrier prevalence among European- and African-ancestry participants (0.105% in both groups). Diabetes penetrance was incomplete (13.4% by age 40; 43.5% by age 60) and varied by etiology: highest for GCK (56.0% by age 60), intermediate for HNF genes (HNF1A/HNF1B/HNF4A; 45.4%), and lowest for non-GCK/HNF genes (ABCC8/INS/KCNJ11/NEUROD1/PDX1/RFX6; 29.0%). In multivariable Cox models using non-carriers in the middle 80% of the T2D PRS as the reference, non-GCK/HNF gene variant carriers had modestly increased diabetes risk (HR, 1.57), similar to non-carriers in the top 10% of T2D PRS (HR, 1.64). These associations were observed in both European- and non-European-ancestry individuals. HbA1c profiles differed by etiology, with stable mild hyperglycemia in GCK variant carriers and greater variability among HNF and non-GCK/HNF gene variant carriers. CONCLUSIONS MODY gene variants showed incomplete, etiology-dependent penetrance across ancestries. Carriers of P/LP variants in lower-penetrance genes had diabetes risk comparable to that of non-carriers with high polygenic susceptibility.

Background: Poor glycemic control is associated with cardiovascular disease (CVD) risk. However, it is unknown whether glycemic control is related to abdominal aortic calcification (AAC), a marker of subclinical CVD. We investigated the association between glycated hemoglobin (HbA1c) and moderate-to-high automated AAC among middle-aged to older adults from the general population. Methods:We included UK Biobank Imaging Study participants free of atherosclerotic CVD at baseline. HbA1c was measured at baseline (2006-2010) and categorized as normoglycemia (<39.0 mmol/mol), prediabetes (39.0-47.9 mmol/mol), undiagnosed diabetes (HbA1c [&ge;]48 mmol/mol), and diagnosed diabetes. Machine learning-derived AAC24 (ML-AAC24) scores were estimated using a validated automated algorithm applied to dual-energy X-ray absorptiometry lateral spine images (2014-2022). The associations of HbA1c with moderate-to-high ML-AAC24 (defined as a score [&ge;]2) were assessed using logistic regression adjusting for cardiovascular risk factors. Results: Of the included 48,912 participants (mean {+/-} SD age 55 {+/-} 7.6 years, 52% women), 9.7% had prediabetes (HbA1c 39.0-47.9 mmol/mol [5.7-6.4%]), 0.4% had undiagnosed diabetes, and 2.7% had diagnosed diabetes. Each 1-SD increase in log-transformed HbA1c was associated with higher odds of moderate-to-high ML-AAC24 (adjusted odds ratio [aOR] 1.12, 95% CI: 1.09-1.16). Amongst individuals with normal HbA1c, this association was consistent but somewhat weaker for each 1-SD increase in log-transformed HbA1c (aOR 1.07, 95% CI 1.03-1.10). Compared to participants with normal HbA1c, those with prediabetes (aOR 1.19, 95% CI: 1.08-1.30) or diagnosed diabetes (1.64, 95% CI: 1.39-1.94) had higher odds of moderate-to-high ML-AAC24. These associations were consistent in stratified analyses by sex, age groups, body mass index, smoking status and total cholesterol Conclusions: Linear associations between HbA1c levels and ML-AAC24 were observed in UK adults, even in those with normal HbA1c levels. These findings indicate that AAC may develop early in the dysglycemic continuum, supporting earlier cardiometabolic risk assessment even amongst people with ?normal? levels.

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.

Aims Metformin remains the primary treatment for type 2 diabetes, yet over 40% of patients fail to maintain glycaemic control. We aimed to identify patients unlikely to respond to metformin prior to treatment initiation and to evaluate whether on-treatment management can improve glycaemic outcomes in suboptimal responders, informing early treatment decisions. Materials and Methods We analyzed 59,881 longitudinal HbA1c measurements from 7,105 patients with type 2 diabetes receiving metformin monotherapy using real-world electronic health records from Kaiser Permanente Northern California with up to six years of follow-up. We integrated demographic, clinical, genetic, and pharmacological factors to characterize metformin responder phenotypes and quantify the impact of adherence and weight control on time to glycaemic failure. Results Three distinct trajectory-based phenotypes were identified: good (63.6%), poor (8.9%), and non-responders (27.5%). Poor responders initially achieved glycaemic targets but lost control within 2.5 years, while non-responders showed minimal HbA1c reduction and failed within 1 year. Five baseline factors-HbA1c, age at diagnosis, body mass index, sex, and estimated glomerular filtration rate-classified phenotypes with good discrimination (area under the receiver operating characteristic curve = 0.84). Incorporating on-treatment HbA1c further enhanced identification of non-responders. Among suboptimal responders, weight control and improved adherence delayed glycaemic failure by approximately 7 months; however, eventual glycaemic failure remained likely. Conclusions We characterized three clinically relevant metformin responder phenotypes and showed that suboptimal responders can be identified early using baseline features. Poor and non-responders are unlikely to achieve durable glycaemic control with metformin alone and may require alternative treatment strategies.

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.

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.

Diabetic peripheral neuropathy (DPN) is a common and disabling condition for which no disease-modifying therapies are available. Glycemic and metabolic drivers do not fully explain why only a subset of individuals with diabetes develop DPN, and genetic contributors remain poorly defined. We aimed to perform a multi-population genome-wide association study (GWAS) of DPN to highlight potential new etiological pathways and therapeutic targets. Methods We performed a multi-population GWAS of neuropathy in people with and without diabetes using the VA Million Veteran Program and UK Biobank, followed by replication in the All of Us Research Program (AoU), and gene-based and gene-set analyses to identify implicated pathways. Causal relationships between circulating serine levels and DPN were further tested using two sample Mendelian randomization. To further evaluate pathogenic potential, we analyzed rare, high impact variants in GWAS implicated genes among individuals with unresolved inherited neuropathies using the GENESIS platform. Findings Among individuals with type 2 diabetes, we identified seven genome wide significant loci (p<5x10-): PHGDH and PSPH (key serine synthesis genes), TEAD1, CYP4F11, LARGE1, FTO, and COBLL1. No loci were significant in individuals without diabetes or with type 1 diabetes. Four loci (PHGDH, TEAD1, FTO and CYP4F11) replicated in AoU (p <0.05). Mendelian randomization demonstrated that higher genetically predicted serine levels were associated with lower DPN risk, consistent with a causal role of serine metabolism in disease pathogenesis. Rare-variant burden analyses revealed associations of predicted deleterious variants with inherited neuropathy case status in PHGDH (odds ratio [OR] 12.7 [95% CI 7.9, 20.4]), PSPH (OR 8.5 [7.2, 10.2]), PHKG1 (OR 4.8 [3.7, 6.3]), and LARGE1 (OR 0.007 [0.0004, 0.1]). Interpretation Convergent genetic evidence across common and rare variation implicates serine synthesis as a key pathway in DPN. These findings link diabetic and inherited neuropathies through a shared metabolic mechanism, identifying serine metabolism as a potential therapeutic target.

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.

Importance: Higher coffee intake has been associated with lower risk of type 2 diabetes (T2D), but the underlying biological pathways remain incompletely understood. Objective: To examine associations of coffee intake with insulin sensitivity, adiposity, and T2D risk, and assess whether coffee intake modifies associations between pathway-specific genetic susceptibility and incident T2D. Design, Setting, and Participants: Cross-sectional analyses among 806 participants without T2D in the VITamin D and OmegA-3 TriaL (VITAL) clinical sub-cohort, who underwent repeated dietary assessment, clinical phenotyping, and dual-energy X-ray absorptiometry imaging at baseline and year-2. Prospective analyses among 333,053 UK Biobank participants without T2D at baseline who had dietary and genetic data and were followed for a median of 13.3 years. Exposures: Coffee intake assessed by food frequency questionnaires. In UK Biobank, 12 pathway-specific polygenic scores (pPS) representing distinct T2D pathophysiological mechanisms were evaluated. Main Outcomes and Measures: The primary outcomes, in VITAL, were HbA1c, oral glucose tolerance test-derived measures of glucose response and insulin sensitivity, beta-cell function, and overall, truncal, and visceral adiposity; in UK Biobank, was incident T2D. Results: In VITAL, higher coffee intake was associated with higher insulin sensitivity (standardized beta; per cup/day, 0.046; P = .004) and lower visceral adipose tissue mass (beta -0.047; P = .006), after adjusting for demographic, lifestyle, and clinical factors, including body mass index. In UK Biobank, higher coffee intake was associated with lower T2D incidence (hazard ratio per cup/day, 0.96; 95% CI, 0.95-0.97), lower triglyceride-to-HDL cholesterol ratio (beta: -0.01; P = 2.51 x 10-19), and lower visceral adipose tissue mass (beta: -0.01; P = 4.28 x 10-9). Associations of 3 pPS related to insulin resistance and fat distribution with incident T2D were attenuated among participants consuming higher amount of coffee than among non-consumers (P for interaction < .0043). Conclusions and Relevance: Higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower risk of T2D. Together with the attenuation of associations between pathway-specific genetic susceptibility and T2D risk among higher coffee consumers, these findings suggest that insulin resistance and visceral adiposity-related pathways may contribute to the association between coffee intake and T2D risk.

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.

Introduction Type 2 diabetes (T2D) prevalence, severity, duration, and control are associated with dementia incidence, but prior literature is focused on specific pharmacologic, dietary, and exercise interventions in isolation while controlling for other co-occurring factors. Accounting for comprehensive life course experiences of the timing of diabetes onset, severity, treatment, and progression over a period of decades would provide a more comprehensive description of how life course diabetes progression and control is associated with dementia. Trajectories of diabetes diagnosis, pharmacological management, and disease progression are heterogeneous, and classifying these trajectories presents a significant methodological challenge. Methods Using deidentified survey and electronic health record data from Kaiser Permanente Northern California (KPNC) from the Research Program on Genes, Environment, and Health (RPGEH), we defined annual "states" for each eligible participant with T2D diagnosed between ages 50 and 70 based on KPNC, diabetes diagnosis, glycated hemoglobin, antidiabetes prescription count, and kidney dysfunction. We then employed sequence and cluster analyses to group participants into clusters with similar trajectories of these states. Finally, we estimated hazard ratios for incidence of Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD) for each of these clusters as well as individuals with type 1 or other diabetes types, relative to participants without diabetes at age 70, using covariate-adjusted Cox proportional hazards models. Results Using the 18,688 participants with T2D included in the diabetes trajectory assessment, sequence and cluster analysis identified 9 clusters of T2D treatment and control histories between ages 50 and 70. Clusters differed markedly in timing of onset of T2D, glucose control, antidiabetes drug use and kidney function. Associations of these clusters with incident AD/ADRD after age 70 was heterogeneous and patterned by diabetes control and treatment history, particularly by diabetes duration and treatment regime. Conclusions In conclusion, in this real-world data context, we find increased diabetes severity, increased medication use, and faster progression to kidney disease is associated with increased risk of dementia. We find some patterns of diabetes severity and control are associated with greater dementia risk. This information may be useful in the context of targeted screening and allocation of preventative services for ADRD.

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.

Introduction The aim of this study was to compare patient-reported outcomes (PROs) related to hypoglycemia recovery with FLO23011, a glucose/beta-hydroxybutyrate Multi-Energy Substrate for Hypoglycemia (MESH) treatment, versus standard glucose gel in adults with type 1 diabetes, and to explore associations between by continuous glucose monitoring-derived metrics and perceived and objective treatment response. Research Design and Methods In a randomized, open-label, crossover study, 12 adults with type 1 diabetes used either FLO23011 or glucose gel to treat hypoglycemia during two 6-week periods, with continuous glucose monitoring throughout. PROs were assessed using 14-domain questionnaires and exit interviews. CGM analyses from a broader discovery analysis examined patient-relevant recovery signals: glucose-band exposure versus psychological PRO scores, and baseline glycemic variability versus time-in-range response. Results FLO23011 was rated more favorably than glucose gel in 13/14 domains, with statistically significant in 10. Differences included speed of action (8.0 vs. 7.3; P = 0.025), after-effects reduction (8.0 vs. 6.2; P = 0.014), ease-of-use (8.9 vs. 4.9; P = 0.002), and overall management ability (8.5 vs. 7.4; P = 0.019). Interviews described faster cognitive recovery, reduced disruption, and greater confidence. Reduced Level 1 hypoglycemia exposure was associated with higher reduced-worry and management-ability ratings (n=5; {rho} = 0.90; P = 0.037). Higher baseline coefficient of variation was associated with greater time-in-range improvement with FLO23011 (n=9; {rho} = 0.917; P = 0.0005). Conclusions FLO23011 showed more favorable patient-reported recovery than glucose gel. Initial CGM-PRO analyses findings suggest perceived benefit may align with reduced Level 1 hypoglycemia, while baseline variability may identify greater objective response. Results support integrating patient-reported and glycemic outcomes to evaluate hypoglycemia treatments, warranting confirmation in larger blinded studies.

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.

Background: Metformin is one of the most commonly prescribed medications for individuals with diabetes and may provide protection against long-term sequelae of COVID-19. Methods: We evaluated a retrospective cohort of individuals in the National Clinical Cohort Collaborative with type 2 diabetes mellitus and COVID-19 who were prescribed metformin or a dipeptidyl peptidase-4 inhibitor (DPP4i) at least 30 days before the onset of acute COVID-19 between October 1, 2021, and November 15, 2023. We compared the 12-month cumulative incidence of Long COVID diagnosis (ICD-10 U09.9: Post COVID-19 condition, unspecified), probable Long COVID (based on a model-derived phenotype), and mortality between individuals prescribed metformin vs. DPP4i. We applied Super Learner and targeted maximum likelihood estimation to obtain risk ratios while adjusting for covariates of interest. Results: In our sample of 53,332 individuals with type 2 diabetes and COVID-19, we found that metformin prescription was associated with a lower risk of all-cause mortality after COVID-19 (adjusted risk ratio [aRR] 0.61, 95% CI 0.51, 0.73). We also observed that metformin users, compared to DPP4i users, had a slightly lower risk of probable Long COVID (aRR 0.87, 95% CI 0.81, 0.94) but did not detect a significant relationship with Long COVID diagnosis (aRR 0.90, 95% CI 0.68, 1.20), although we observed similar point estimates across Long COVID outcomes. Conclusions: These findings support the hypothesis that metformin prescription during acute COVID-19 may be associated with lower mortality among adults with diabetes. These analyses also provide modest evidence of a protective association against Long COVID in adults with diabetes, although estimates were imprecise.

Metabolic diseases such as type 2 diabetes (T2D) arise through complex interactions between physiological, molecular, and environmental processes. Clinical traits including age, sex, adiposity, and glycaemic status are strongly associated with disease risk and progression, yet most molecular studies examine these factors independently and assume relatively static molecular regulation. Consequently, how physiological state dynamically reshapes molecular organisation across omics layers remains poorly understood. Here, we integrated transcriptomic, proteomic, metabolomic, and genetic data from 3,027 individuals in the IMI DIRECT cohort to characterise the joint molecular effects of age, sex, body mass index (BMI), and glycated haemoglobin (HbA1c). We identified widespread associations between these traits and molecular phenotypes. However, interaction analyses revealed a more complex context-dependent regulation, showing that the molecular effect of one trait frequently depends on the state of another, with sex-specific effects of age being more prominent. We also investigated relationships between different types of molecular phenotypes and how these relationships are modulated by metabolic disease relevant traits, demonstrating that cross-omic molecular coordination is itself dynamically remodelled by physiological and metabolic state. Probabilistic causal inference identified a directionally structured network of age-associated molecules, revealing pathways through which age effects propagate across omics layers, showcased in the example of the mTOR signalling pathway. Integration of this directed network with genetic colocalisation analyses also identified a sub-network relevant for T2D. Collectively, our findings demonstrate that metabolic disease relevant traits not only independently influence molecular phenotype abundance but also jointly reshape the directional organisation of cross-omic molecular networks. These results support a model in which metabolic disease susceptibility emerges through dynamic rewiring of interconnected molecular systems and provide a framework for context-dependent biomarker discovery, disease stratification, and precision metabolic medicine.