Diabetes Research and Clinical Practice

Objective: Type 1 diabetes is related to an increased risk of structural brain alterations, cerebral microbleeds (CMBs), and cognitive deficits. We explored brain volumes and their direct and combined associations with CMBs on cognitive performance in middle-aged individuals with type 1 diabetes. Research Design and Methods: Adults with type 1 diabetes (n=163; mean age 46+/-8 years; diabetes duration 31+/-10 years; 53% women) and 48 matched controls underwent brain MRI and clinical and neuropsychological evaluations. Volumetric MRI measures adjusted to intracranial volume included total brain volume (TBV), white matter volume (WMV), and total volumes of cortex, thalamus, hippocampus, nucleus accumbens, and choroid plexus. Results: Individuals with type 1 diabetes had smaller TBV, WMV, and volumes of cortex, thalamus, and nucleus accumbens, and larger choroid plexus compared to controls (Cohen d=0.39-0.54). Those with type 1 diabetes and 3 or more CMBs had smaller TBV, WMV, and volumes of cortex, thalamus, and nucleus accumbens, compared to those with 0-2 CMBs (Cohen d=0.54-0.92). We found no direct associations between brain volumes and processing speed or executive functions. However, TBV, WMV, nucleus accumbens, and choroid plexus volumes had significant negative synergistic interactions with CMBs on processing speed and executive functions (standardized betas: -0.61 to -0.51 and 0.54 to 0.75, FDR-corrected p=0.006-0.048). Conclusions: Smaller global and regional brain volumes and larger choroid plexus volumes were found in middle-aged individuals with type 1 diabetes compared to healthy controls. Together with CMB burden, structural brain volumetric alterations were associated with accelerated cognitive deficits.

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 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.

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.

Background: Diabetes mellitus and hypertension are increasingly prevalent non-communicable diseases that often coexist due to their interrelated pathophysiology and commonalities of risk factors. Effective management of these two comorbid conditions often involves polypharmacy, defined as the concurrent use of five or more medications, which for therapeutically relevant outcomes requires a high-medication adherence. Limited data exist on the extent of polypharmacy and its impact on adherence among Tanzanian patients with these comorbidities. This study therefore aimed at evaluating the prevalence of polypharmacy and its impact on medication adherence levels among this population. Methodology: A cross-sectional study involving 396 outpatients was conducted at Muhimbili National Hospital. Consecutive sampling was used to recruit eligible participants. Data was collected using structured-questionnaire which captured information on socio-demographics, clinical characteristics and adherence behaviors. Polypharmacy was defined as using five or more medications. Medication adherence was assessed using the Medication Adherence Report Scale (MARS-5). Multivariable logistic regression was performed to identify factors associated with adherence. Results: 71% of the study participants were on five or more medications, indicating high polypharmacy prevalence, with a median of six medications. Medication adherence was reported at 55.1%. Factors associated with lower adherence included moderate (APR: 0.83, P = 0.001) and high fasting glucose (APR: 0.66, P < 0.001), herbal medicine use (APR: 0.72, P < 0.001), and uncontrolled blood pressure. Conclusion: This study reveals a high prevalence of polypharmacy with moderate medication adherence among patients with comorbid T2DM and hypertension. These findings suggest a targeted intervention utilizing such as patient education and medication reviews are essential to improve adherence and management in Tanzania.

Introduction. Diabetes is a growing global health challenge, necessitating effective management strategies. Glycosylation, a highly regulated post-translational protein modification, has emerged as a pivotal factor in diabetes pathophysiology. However, the modulation of protein glycosylation by antidiabetic treatment is still largely unknown. This study explored the longitudinal effects of four distinct antidiabetic therapies - metformin, insulin, sodium-glucose cotransporter-2 (SGLT2) inhibitors, and glucagon-like peptide-1 receptor agonists (GLP-1RA) - on plasma protein and immunoglobulin G (IgG) glycosylation in patients with type 2 diabetes (T2D). Research Design and Methods. Plasma protein and IgG N-glycans were enzymatically released, purified and chromatographically profiled in a cohort of 124 patients, examined at four time points, to assess therapy-induced glycan alterations. Linear mixed models adjusting for covariates and multiple testing (FDR<0.05) were used to investigate the associations between plasma protein and IgG N-glycosylation and antidiabetic therapy. Results. Our findings reveal that metformin, SGLT2 inhibitors, and GLP-1RA induce significant alterations in IgG glycosylation, including the increased core fucosylation and galactosylation, features associated with a reduced inflammatory IgG potential. Notably, IgG monogalactosylation, previously linked to cardioprotective effects in women, was elevated in response to GLP-1RA and SGLT2 inhibitor treatments. Plasma protein glycosylation changes were more limited, with distinct alterations observed for each therapy. Metformin and GLP-1RA similarly reduced certain fucosylated and sialylated glycans, while SGLT2 inhibitors decreased a high-mannose glycan, previously positively associated with diabetes progression. Insulin therapy had a minimal effect on protein glycosylation, with only one plasma glycan significantly altered. Conclusions. Our findings emphasise the importance of protein glycosylation as a dynamic and responsive marker in T2D treatment. The distinct glycan alterations observed in response to metformin, SGLT2 inhibitors, and GLP-1 receptor agonists provide novel insights into the molecular effects of these therapies, potentially contributing to the development of glycan-based biomarkers for personalized diabetes management.

Continuous glucose monitoring (CGM) is increasingly used for inpatient glycemic surveillance, but evidence in non-critical care wards remains limited, particularly in real-world public healthcare settings. Intermittent capillary glucose testing may fail to detect transient, nocturnal, or asymptomatic dysglycemia. We sought to evaluate whether CGM improves detection of clinically significant dysglycemia compared with seven-point capillary glucose monitoring in hospitalized patients with type 2 diabetes mellitus or hyperglycemia. This is a prospective, observational, non-randomized, real-world study performed in a tertiary referral center in Mexico. 56 hospitalized patients were included: 28 underwent flash CGM and 28 underwent seven-point capillary glucose monitoring. Patients were followed for up to 6 hospitalization days. The main analytical focus was detection of clinically significant dysglycemia, including hypoglycemia <70 mg/dL, clinically significant hypoglycemia <54 mg/dL, and severe hyperglycemia >250 mg/dL. Secondary outcomes included time in range, mean daily glucose, insulin requirements, infectious complications, length of stay, and mortality. CGM detected more hypoglycemia <70 mg/dL than capillary monitoring (71.4% vs 35.7%, p=0.005), more clinically significant hypoglycemia <54 mg/dL (median 3 [IQR 0-6.5] vs 0, p=0.030), and more severe hyperglycemia >250 mg/dL (median 8.5 [IQR 0.5-17] vs 0 [IQR 0-9.52], p=0.030). Time in range was not significantly different between groups (59.86 +/- 23.46% vs 69.28 +/- 24.99%, p=0.151). After adjustment for age, diabetes duration, and admission hyperglycemia, CGM remained associated with hypoglycemia detection (OR 4.7, 95% CI 1.2-19.0, p=0.027). We concluded that CGM improved detection of clinically significant dysglycemia during up to 6 hospitalization days. Although CGM did not improve time in range or short-term clinical outcomes, it provided superior glycemic surveillance compared with intermittent capillary glucose testing.

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 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.

Introduction: Gestational diabetes mellitus (GDM) is a condition characterized by glucose intolerance that is first identified during pregnancy and typically resolves after childbirth. This condition can lead to various complications, both prenatal and postnatal, including type 2 diabetes (T2D). However, not all women with GDM progress to T2D, and the molecular mechanisms underlying this heterogeneity remain poorly understood. Here, we hypothesized that GDM participants could be clustered into T2D-like subtypes. Methods: To derive T2D-like subtypes, we applied K-means clustering to GDM participants using the predefined T2D subtype cluster centers established in the QPHI cohort. Differential methylation analysis was performed, and the top-ranked sites were used for further analysis. Additionally, we estimated system-specific age acceleration and its association with subtype-specific complications. Results: Our study demonstrated the effectiveness of the novel clustering approach, originally developed for T2D, in GDM. Additionally, the exploratory analysis of the top-ranked CpG sites suggested potential subtype-related methylation patterns. The identified pathways also suggested overlapping molecular mechanisms underlying both GDM and T2D. Conclusion: These findings support the feasibility of classifying GDM into T2D-like clinical subtypes and suggest potential epigenetic differences that warrant validation in larger longitudinal cohorts.

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).

Background Existing insulin resistance (IR) indices are predominantly developed in diabetic cohorts, limiting their generalizability. We developed a novel deep neural network-derived IR index (DNN-IR) using a Mixture-of-Experts (MoE) framework and evaluated its predictive performance for incident cardiovascular disease (CVD) and mortality in general populations. Methods We utilized data from three cohorts: the cross-sectional REACTION study (Fujian subcohort, 2011-2012) for DNN-IR derivation and internal validation; and two prospective cohorts, NHANES (1999-2018, linked to the National Death Index) and CHARLS (2011-2018), for external validation. The DNN-IR was developed using a deep learning model based on a Mixture-of-Experts (MoE) architecture, trained on the REACTION dataset. We evaluated the DNN-IR's utility in predicting incident CVD, cardiovascular mortality, and non-cardiovascular mortality among 13,889 NHANES and 7,047 CHARLS participants. Predictive performance was assessed via the area under the receiver operating characteristic curve (AUC). Multivariable logistic regression, restricted cubic splines, and Kaplan-Meier analyses characterized the associations between DNN-IR and clinical outcomes. Results In the REACTION cohort, DNN-IR demonstrated superior predictive performance for atherosclerotic outcomes, achieving AUROCs of 0.89 (training) and 0.84 (internal validation). In the external CHARLS cohort (median follow-up: 7 years; 1,135 incident CVD cases [16.1%]), DNN-IR yielded AUROCs of 0.72 for incident CVD and 0.77 for all-cause mortality. Fully adjusted models showed that each 1-SD increment in DNN-IR was associated with a 23% higher CVD risk (OR=1.23, 95% CI: 1.14-1.32), exhibiting a predominantly linear dose-response relationship (P-nonlinearity=0.453). In NHANES, DNN-IR robustly predicted cardiovascular (AUROC=0.77) and all-cause mortality (AUROC=0.72), alongside specific mortalities like diabetes (0.91), Alzheimer's disease (0.88), and kidney disease (0.96). Higher DNN-IR levels correlated with stepwise increases in cumulative mortality (log-rank P<0.001). Conclusions The MoE-derived DNN-IR index demonstrated robust and stable performance in predicting atherosclerosis, incident CVD, cardiovascular mortality, and all-cause mortality in the general population. Further validation in larger, more diverse cohorts is warranted to support its broad clinical applicability.

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)

Background and Objectives: Plasma biomarkers, particularly brain-derived phosphorylated-tau (BD-p-tau) species, hold promise as screening tools for Alzheimer's disease (AD). However, their ability to reflect AD pathology remains understudied in community settings. In a community-based sample, we examined associations between plasma biomarkers and cerebral amyloid (A{beta}) deposition, and whether kidney function modified these associations. Methods: This cohort study included cognitively unimpaired, late middle-aged adults with 18F-Florbetaben PET imaging and NULISAseq-derived plasma biomarker measurements. Analyses were restricted to NULISAseq biomarkers related to AD pathology (A{beta}38, A{beta}40, A{beta}42, ACHE, BACE1, BASP1, BD-p-tau181, BD-p-tau217, CD63, IGFBP7, KLK6, MAPT-tau, PSEN1, SFRP1, total p-tau181, p-tau217, and p-tau231). As a measure of kidney function, cystatin C-based estimated glomerular filtration rate was calculated and categorized by chronic kidney disease (CKD) stage as normal/high ([&ge;]90 mL/min/1.73 m2); mildly decreased (60-89 mL/min/1.73 m2); and moderately/severely decreased or failure (<60 mL/min/1.73 m2). Bidirectional stepwise linear regression analysis was performed to identify plasma biomarkers associated with brain A{beta} deposition. Linear regression models including plasma biomarker-by-CKD stage interactions tested effect modification by kidney function. Results: A total of 541 Hispanic, non-Hispanic Black, and non-Hispanic White participants were included. Stepwise linear regression retained plasma BD-p-tau217 (B = 0.22; 95% CI 0.19 to 0.25; p < 0.001), which was positively associated with brain A{beta} deposition, alongside A{beta}42 (B = - 0.08; 95% CI - 0.11 to - 0.06; p < 0.001), IGFBP7 (B = - 0.05; 95% CI - 0.08 to - 0.02; p = 0.004), and BACE1 (B = - 0.04; 95% CI - 0.07 to - 0.01; p = 0.009), which were negatively associated with brain A{beta} deposition. A significant BD-p-tau217-by-CKD stage interaction demonstrated a weaker association between BD-p-tau217 and brain A{beta} deposition among individuals with moderately/severely decreased kidney function or failure than those with normal/high kidney function (B = - 0.17; 95% CI - 0.25 to - 0.08; p < 0.001). Discussion: In a real-world sample, BD-p-tau217 emerged as the plasma biomarker most strongly associated with brain A{beta} deposition, although this association may be attenuated in the presence of moderate/severe kidney dysfunction or kidney failure. IGFBP7 and BACE1 were identified as candidate plasma biomarkers of brain A{beta} deposition, warranting replication in independent cohorts.

Background: Insulin resistance is a core pathophysiologic feature of metabolic disease, but its reference-standard assessment by steady-state plasma glucose (SSPG) testing is procedurally demanding and labor-intensive, limiting use in routine clinical care and large-scale research. Because OGTT glucose profiles are widely available, we aimed to develop a glucose-only metric to characterize dynamic glucose responses and estimate SSPG-measured insulin resistance. Methods: We developed the Width-Delay Index (WDI), a glucose-only OGTT metric integrating relative exposure width, delayed exposure timing, and glycemic floor. In a dataset of 32 subjects with 16-point venous OGTT profiles and paired SSPG measurements, WDI performance was assessed using leave-one-out cross-validation (LOOCV) for SSPG prediction, together with insulin-resistance discrimination and sparse-sampling robustness analyses. Results: The 15-120 min OGTT window yielded the strongest WDI performance. WDI15-120 predicted SSPG with LOOCV R2 = 0.57 (95% CI, 0.27-0.77), Pearson r = 0.77, and Spearman rho = 0.74. WDI15-120 showed higher predictive performance than standard OGTT glucose measures and insulin-derived indices, including HOMA-IR, Matsuda index, and disposition index. WDI15-120 also discriminated insulin-resistant from insulin-sensitive subjects with AUROC = 0.969. When recalculated from conventional 5-point OGTT sampling, WDI15-120 retained substantial performance, with LOOCV R2 = 0.41 and AUROC = 0.945. Conclusions: WDI provides a simple, glucose-only, physiologically interpretable approach for estimating SSPG-measured insulin resistance from OGTT glucose dynamics.

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.

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.

ABSTRACT Background: To examine trends in Indonesia's diabetes care cascade from 2013 to 2023, identify key determinants, and assess progress toward global targets of 80% diagnosis and 80% glycemic control among those diagnosed. Methods: We analyzed nationally representative data from Indonesia's Health Surveys in 2013, 2018, and 2023. Diabetes was defined using fasting plasma glucose and oral glucose tolerance tests. We estimated diagnosis, treatment, and control rates and examined sociodemographic predictors of cascade progression using survey-weighted logistic regression models. Results: Between 2013 and 2023, the prevalence of diabetes among adults aged [&ge;]15 years remained stable, ranging from 10.7% to 11.8%. Diagnosis increased from 15.1% (95% CI: 13.4-16.7) to 20.7% (18.5-22.9), treatment nearly doubled from 10.5% (9.1-11.9) to 19.0% (16.9-21.2), and control rose modestly from 4.6% (3.6-5.6) to 6.5% (5.2-7.8). Older age, urban residence, higher socioeconomic status, and insurance coverage were associated with greater progression through the cascade. Wealth-related inequalities persisted in 2023: one-third of cases were diagnosed among the richest (35.3% [29.0-41.7]) versus only 11.0% (7.9-14.2%) among the poorest. Compared with the lowest quintile, wealthier individuals had higher odds of diagnosis (AOR 3.55 [2.11-5.98] for diagnosis, 3.59 [2.01-6.41] for treatment, and 2.24 [1.12-4.51] for control). Conclusions: Indonesia achieved meaningful improvements in the diabetes care cascade over the past decade, yet remains far below global 80/80 targets, with nearly 80% of cases undiagnosed and control below 10%. Persistent wealth-based inequities highlight that near-universal insurance coverage has not been translated into equitable care access, underscoring the need for equity-focused screening and primary care strengthening. Keywords: Diabetes, Care Cascade, Health Services, Indonesia

Introduction: Lipids are considered both drivers and biomarkers of cardiometabolic diseases. As glucagon-like peptide-1 receptor agonists (GLP-1RAs) are widely used for diabetes and obesity management, it is crucial to understand how they affect the related comorbidities through the circulating lipidome. This study investigated the lipidomic changes induced by liraglutide treatment when compared to placebo in people with type 2 diabetes (T2D) to characterise lipid remodelling and its association with clinical outcomes. Research design and methods: This post-hoc study analysed plasma samples using liquid chromatography-mass spectrometry (LC-MS/MS) from LIRAFLAME, a randomised, double-blind, placebo-controlled, parallel-group trial. A hundred people with T2D received up to 1.8 mg of liraglutide or placebo once daily for 26 weeks. Plasma samples were collected at baseline, week 13 and week 26. Results: Liraglutide treatment resulted in a statistically significant increase in multiple lysophospholipid subclasses, including LPCs, LPC(O)s, LPC(P)s, LPEs, and LPE(P)s, observed at 13 weeks and sustained at 26 weeks compared to placebo. These increases were not mediated by the change in BMI. Triglyceride concentrations decreased at 13 weeks, while fatty acid levels declined at 26 weeks, consistent with enhanced lipid remodelling. The increase in LPC(O)s was associated with favourable decreases in ALAT, MCP-1, and UACR, suggesting anti-inflammatory effects with hepatic, renal, and cardiovascular benefits. Conclusions: Compared to placebo, 26 weeks of liraglutide treatment resulted in a favourable lipidomic shift from a triglyceride-rich profile towards one enriched in lysophospholipids. This lipid remodelling was associated with improvements in hepatic, renal, and inflammatory markers.

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.