Diabetes Research and Clinical Practice

Objective: Patients and physicians frequently focus on HbA1c and weight alone. We hypothesized that individuals with similar HbA1c and BMI may present markedly distinct metabolic backgrounds. We investigated whether the adipo-B index- composite of adipose insulin resistance (adipo-IR) and beta-cell function (HOMA-B)-can uncover hidden heterogeneity in this clinically homogeneous population. Methods: A total of 399 newly diagnosed, drug-naive Japanese subjects with T2DM were analyzed. Histograms of HbA1c and BMI demonstrated peak distributions within HbA1c 8-10% and BMI 24-26. Based on these distributions, a clinically homogeneous subgroup was defined to minimize confounding by glycemic severity and adiposity. Metabolic parameters including FBG, insulin, FFA, HOMA-R, HOMA-B, adipo-IR, adipo-B, T-C, TG, HDL-C and non-HDL-C were analyzed. Simple regression, multivariable linear regression, and subgroup stratification analyses were performed. Results: Despite comparable HbA1c and BMI by design, adipo-B stratification revealed significant differences in HOMA-B, FFA, non-HDL-C, and TG, whereas HOMA-R stratification identified only higher insulin and adipo-IR without differences in lipids or HOMA-B. Thus, adipo-B-but not HOMA-R-identified a lipotoxic, beta-cell-stressed phenotype invisible to conventional markers. Simple regression showed significant positive correlations between adipo-B and HbA1c, FBG, FFA, T-C, TG, and non-HDL-C, and negative correlations with insulin and HOMA-B. Multivariable linear regression confirmed that adipo-B was independently associated with non-HDL cholesterol, TG, and FFA after adjustment for HbA1c and BMI. Conclusion: Even among patients with identical HbA1c and BMI, the adipo-B index uncovers clinically relevant metabolic heterogeneity, supporting its role as a functional marker of the adipose-pancreas axis and a potential tool for precision phenotyping in early T2DM.

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.

Background: Metabolic syndrome (MetS) has been associated with cognitive decline. Considering its increasing prevalence worldwide, the goal of this study was to evaluate the feasibility and efficacy of a short-term, self-administered computerized cognitive training programme in individuals with metabolic syndrome and low cognitive performances. Methods: Thirty six participants, aged 40-72 years (mean age: 57.8 years), were randomly assigned to the cognitive training or the passive control group. The cognitive training component of Long Lasting Memories (LLM) Care was used as an interactive software to enhance participants' cognitive functions. Up to 24 sessions, each lasting 45 minutes, were self-administered at home twice per week for 3 months. Thorough cognitive assessments with were performed at baseline (randomization), at the end of intervention, and 12 months after baseline. The primary outcome was performance at nine neuropsychological tests, and the secondary outcome was a self-reported questionnaire assessing everyday functional abilities. Primary analyses were performed employing mixed-effect models using the intention-to-treat principle. Results: Low adherence was observed in the study, as only 9 participants (50%) completed at least 8 sessions of the cognitive training programme (range 9-24 sessions, median 15 sessions). No statistically significant effect of the cognitive training programme on performance in neuropsychological tests or everyday functioning was found. At the end of the 3-month intervention programme, effect for visual memory enhancement in immediate ({beta} = 1.58, 95% CI = -1.84 to 4.99, Cohen's d = 0.39) and delayed recall ({beta} = 2.17, 95% CI = -1.68 to 6.01, Cohen's d = 0.45) was moderate in favour of the intervention group, and at 12-month follow-up, semantic verbal fluency gains for the intervention group were detected ({beta} = 2.78, 95% CI = -0.92 to 6.49, Cohen's d = 0.70), though with wide confidence intervals. Conclusions: Despite some small effects observed in memory and verbal fluency, cognitive training did not yield statistically significant improvements. The observed low adherence and limited benefits on mild cognitive deficits in mostly middle-aged individuals with MetS are likely associated with the self-administered and short-term nature of the computerized intervention. This highlights the need for more intensive and clinician-delivered approaches to enhance engagement. Registry: ClinicalTrials.gov, TRN: NCT05658354, Registration date: 08 December 2022. Keywords: Metabolic syndrome, cognitive deficits, cognitive training, computerized, adults

Background Besides hyperglycemia, type 2 diabetes (T2D) is characterized by dyslipidemia, which is typically assessed using traditional clinical lipid measurements. However, molecular plasma lipids beyond these traditional markers can provide additional information about an individuals health status. For molecular lipids to be used effectively, certain characteristics, such as their temporal variability, need to be determined. Methods We analyzed the plasma lipidome for three consecutive time points, each three months apart, of 51 individuals with T2D using targeted liquid chromatography coupled to mass spectrometry (LC-MS). 513 lipid species across 25 (sub)classes were quantified by this approach and the temporal variability were calculated. Moreover, to identify sex differences in the plasma lipidome, we analyzed 914 samples of a cross-sectional T2D cohort with the same approach. Results Neutral lipids and phosphatidylserine had the highest temporal variability which was independent of their platform-specific variability. In contrast, glycosphingolipids were found to be relatively stable over time in individuals with T2D. Acyl-chain analysis revealed generally similar variability in the acyl-chain groups but indicated a higher temporal variability in medium-length acyl-chains. Lipid-sex association analysis showed markedly higher sphingomyelins, phosphatidylcholines, and phosphatidylethanolamines in women and higher acylcarnitines in men. Overall, approximately one-third of measured lipids showed significant sex differences independent of age, BMI, diabetes duration, glycemic control, and medication use. Conclusions Our findings provide insights into temporal variability of molecular lipids. This variability should be considered when assessing novel lipid biomarkers. Likewise, sex differences in these lipids need to be considered in precision medicine for diabetes management.

Non-invasive glucose monitoring (NIGM) has been pursued for decades, yet no device has achieved regulatory approval despite numerous studies reporting high accuracy. This systematic review and meta-analysis of 32 studies (38 cohorts: 20 NIGM, 18 iCGM; N = 1,693) investigated methodological factors underlying this accuracy-regulatory gap. The pooled Mean Absolute Relative Difference (MARD) for NIGM (10.21%; 95% CI: 8.73-11.69%) showed no significant difference from iCGM (11.82%; 95% CI: 10.36-13.29%; p = 0.13), with extreme heterogeneity (I{superscript 2} = 95.2%). Meta-regression revealed that study duration was the strongest predictor of NIGM accuracy ({beta} = 3.94, p < 0.001), with MARD degrading from 8.7% in short-term to 15.2% in long-term studies, while iCGM accuracy remained stable. Only 15% of NIGM cohorts validated in the hypoglycemia range, compared to 89% of iCGM studies (p < 0.001). These findings suggest that reported NIGM accuracy is substantially influenced by methodological asymmetries.

Disposition index (DI) is an informative measure of {beta}-cell function adjusted for insulin resistance, but its assessment is procedurally demanding, requiring dynamic testing with timed sampling and insulin or C-peptide-based estimation of insulin sensitivity and secretion. A simple glucose-only metric derived from the oral glucose tolerance test (OGTT) could provide a practical approach to estimating DI. We developed the Recovery-Burden Index (RBI), a glucose-only geometric metric that quantifies post-peak glucose recovery relative to total glucose excursion during OGTT. Using densely sampled venous OGTT profiles with measured DI, RBI was evaluated for prediction of continuous DI by leave-one-out (LOO) cross-validated R2 and for discrimination of DI-defined {beta}-cell dysfunction by AUROC. Performance was compared with conventional glycemic metrics. RBI predicted continuous DI more accurately than conventional glycemic metrics, with LOO R2 of 0.43, Pearson r = 0.70, and Spearman{rho} = 0.75. RBI30-180 performed similarly, with cross-validated R2 of 0.42, Pearson r = 0.72, and Spearman{rho} = 0.75. RBI also discriminated DI-defined {beta}-cell dysfunction, with AUROC values of 0.90 for RBI and 0.91 for RBI30-180. Reduced sampling schedules preserved much of the RBI signal, whereas truncation at 120 min attenuated continuous DI prediction, supporting the contribution of late recovery-phase information. RBI extracts {beta}-cell-relevant information from the OGTT glucose profile using a single transparent glucose-only index. These findings highlight post-peak recovery as a key feature for estimating DI-associated {beta}-cell compensation and support further validation of RBI in extended or CGM-augmented OGTT settings.

ImportanceSemaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1RA), is a highly effective medication to treat type 2 diabetes and obesity. However, concerns about potential suicidality persist, creating clinical uncertainty about its neuropsychiatric safety. ObjectiveTo assess risks of suicidality after initiating semaglutide compared to initiating SGLT2i and by duration of continuous semaglutide treatment. DesignActive-comparator, new-user target trial emulation to estimate inverse probability-weighted marginal cause-specific hazard ratios (HRs). For duration-of-treatment analyses, we used clone-censor-weight methods to estimate exposure-adjusted effects. SettingVeterans Health Administration. ParticipantsU.S. Veterans with type 2 diabetes receiving care from March 1, 2018 to September 1, 2025. ExposureInitiation of semaglutide vs SGLT2i; duration of semaglutide use ([&le;]6, 7-12, >12 months). OutcomesIncident suicidal ideation; suicide attempt or death; and a composite outcome. ResultsA total of 102,361 Veterans met inclusion criteria, including 11,478 new initiators of semaglutide and 90,883 new initiators of an SGLT2i. After overlap weighting, baseline characteristics were well balanced between treatment groups (mean [SD] age, 60.1 [11.7] years; BMI, 37.8 [6.8] kg/m2; hemoglobin A1c, 7.0% [1.4]; 85.5% male; 61.9% non-Hispanic White). During a median follow-up of 2.2 years, 9077 incident suicidal ideation events and 696 suicide attempts or deaths occurred. The incidence rate of suicidal ideation was 56.3 and 37.7 per 1000 person-years among semaglutide initiators and SGLT2i initiators, respectively (hazard ratio [HR], 0.99; 95% CI, 0.93-1.06; P = 0.86). For suicide attempts or deaths, the incidence rates were 4.30 and 2.64 per 1000 person-years, respectively (HR, 1.05; 95% CI, 0.84-1.31; P = .86). In adherence-adjusted analyses, sustained semaglutide treatment for more than 12 months, compared with 6 or fewer months, was associated with a 74% lower risk of suicide attempts or deaths (HR, 0.27; 95% CI, 0.14-0.54; P<.001). ConclusionAmong U.S. Veterans with type 2 diabetes, initiators of semaglutide were not observed to have an increased risk of suicidality compared with initiators of SGLT2i. Those with longer semaglutide treatment (beyond 12 months) had decreased risk of suicide attempt or death, suggesting longer term treatment is safe and may protect against for those outcomes.

Aims/Hypothesis: Behavioral and phenotypic characteristics do not fully explain variability in African Americans with youth-onset type 2 diabetes (Y-T2D) treated with metformin with or without liraglutide. We hypothesized that biological heterogeneity, including genetic variation in the metformin transporter OCT1, influences metformin pharmacokinetics and hepatic glucose flux. Therefore, we sought to characterize metformin pharmacokinetics in Y-T2D and evaluate genetic variants known to modulate metformin efficacy in adults to determine the mechanisms underlying variation in treatment response. Methods: We evaluated genetic variants related to metformin transport and mechanisms of action in 30 Y-T2D using a candidate-gene approach to evaluate the association of pharmacogenetic variants with fasting glucose and gluconeogenesis. In a subset of Y-T2D randomized to 3 months of metformin (n=11) or metformin and liraglutide (n=8), we constructed a metformin population pharmacokinetic model and evaluated gene variant associations. Results: A one-compartment first-order absorption and elimination pharmacokinetic model provided the optimal fit. Metformin pharmacokinetic parameters were similar by group and not related to glycemia. The rs628031_OCT1 A allele was associated with greater metformin clearance. The rs622342_OCT1 C allele was associated with lower post-treatment fractional gluconeogenesis ({beta} [95% CI] = -8.8 [-14.13, -3.47] %, Adjusted R2 = 0.56, P = 0.003). The rs7903146_TCF7L2 T allele was associated with greater reductions in fasting glucose among those treated with metformin + liraglutide ({beta} = -1.32 [-2.42, -0.22] mmol/L, Adjusted R2 = 0.8, P<0.002), but baseline glucose and gluconeogenesis (P<0.0001) were the strongest predictors of post-treatment glycemia. Conclusion/interpretation: In Y-T2D, OCT1 gene variants rs628031 and rs622342 were associated with metformin clearance and gluconeogenesis, respectively. TCF7L2 variant rs7903146 may contribute to differences in glycemic response in youth treated with metformin and liraglutide. These findings suggest genetic variants may be important for understanding variable metformin response in Y-T2D.

AimsThe oral glucose tolerance test (OGTT) is effective for detecting post-load dysglycemia, but it is burdensome and therefore not routinely used. Continuous glucose monitoring (CGM) offers a convenient way to capture real-world glucose patterns, yet it remains unclear whether CGM-derived metrics reflect OGTT-defined dysglycemia. We therefore aimed to evaluate CGM-derived and clinical metrics for predicting OGTT 2-hour glucose, classifying OGTT-defined dysglycemia, and assessing day-to-day repeatability. MethodsWe analyzed a cohort with paired free-living CGM and OGTT. Multiple CGM-derived metrics and clinical measures were compared for prediction of OGTT 2-hour glucose, classification of OGTT-defined dysglycemia, and day-to-day stability. Predictive performance was assessed primarily by leave-one-out (LOO) R2, and day-to-day repeatability by intraclass correlation coefficients (ICC). ResultsThe glycemic persistence index (GPI), a metric integrating the magnitude and duration of glycemic elevation, was the strongest single predictor of OGTT 2-hour glucose (LOO R2 0.439). GPI also showed strong day-to-day repeatability (ICC 0.665) and ranked first on a combined prediction-stability score. For classification of OGTT-defined dysglycemia, HbA1c had a slightly higher AUC than GPI, but GPI plus HbA1c performed best overall, indicating complementary information. ConclusionsGPI was a strong predictor of OGTT 2-hour glucose and showed a favorable balance between predictive performance and day-to-day stability, supporting its potential utility as a CGM-derived marker of dysglycemia.

Aims: Sarcopenia and sarcopenic obesity are associated with increased risks of cardiovascular (CV) disease and mortality. This study examined the associations of body composition and daily physical activity with mortality, CV events and cancer in patients with diabetes. Methods: This prospective cohort study included patients with diabetes treated at a specialised clinic in Japan between January 2018 and March 2023. Body composition, including visceral adipose tissue (VAT), was assessed by bioelectrical impedance analysis. Daily physical activity was evaluated using the non-exercise activity thermogenesis (NEAT) questionnaire, and handgrip strength (HGS) was measured by dynamometry. Cox proportional hazards models were used to assess associations with mortality, CV events, and cancer. Results: Among 2,024 patients (mean age 63.0 years, BMI 24.6 kg/m^2, HbA1c 7.8%), NEAT, HGS, and VAT were not independently associated with all-cause mortality. Higher VAT was associated with increased cancer risk (HR 1.485; 95% CI 1.101-2.003; p = 0.009). Higher HGS was inversely associated with CV event risk (HR 0.951; 95% CI 0.919-0.984; p = 0.004). NEAT was not associated with any outcome. Conclusions: Higher VAT was associated with increased cancer risk, whereas higher HGS was protective against CV events. Incorporating body composition and HGS assessments into clinical practice may improve risk stratification and management in patients with diabetes.

Background: Consensus continuous glucose monitoring (CGM) metrics, including time in range (TIR), time above range (TAR), time below range (TBR), mean glucose, glucose management indicator, and glycemic variability, are essential for modern glucose assessment. However, these whole-day summaries do not explicitly partition nocturnal basal from daytime ambulatory glycemic burden. Objective: To develop and evaluate a complementary domain-based CGM framework that quantifies basal and daytime ambulatory glycemic exposure across oral glucose tolerance test (OGTT)-derived dysglycemia phenotypes. Methods: In this observational, clinic-based study, 253 individuals underwent OGTT with insulin measurement and CGM. Participants were classified using a prespecified OGTT-derived phenotyping algorithm, implemented through a deterministic rules-based web calculator, and collapsed into five groups: NoDM, Increased insulin resistance, Midzone Glycemia, Prediabetes, and Diabetes. CGM files were uniformly reprocessed by selecting the latest contiguous episode and retaining the most recent 15 calendar days with data. The 24-hour profile was partitioned into nocturnal basal (00:00 to <06:00) and daytime ambulatory (06:00 to <24:00) domains. Derived indices included Area of Basal Glycemia (ABG), Area of Prandial/Daytime Ambulatory Glycemia (APG), incremental ABG (iABG), incremental APG (iAPG), and exploratory deficit indices dABG and dAPG. Results: The final dataset contributed 3,647 analyzable CGM days. APG remained higher than ABG across all groups. Mean ABG/APG increased from 80.45/86.38 mg/dL in NoDM to 111.96/124.70 mg/dL in Diabetes. Mean iABG/iAPG increased from 5.65/6.60 to 34.12/38.91 mg/dL, whereas dABG/dAPG declined as dysglycemia worsened. Conclusions: The ABG/APG framework provides interpretable, domain-resolved CGM burden metrics that separate basal from daytime ambulatory exposure and distinguish total burden from above-threshold excess. These indices are proposed as adjunctive metrics to support dysglycemia phenotyping, early risk recognition, and treatment monitoring, but are not intended to replace established consensus CGM metrics or diagnostic criteria. External, prospective validation is required.

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: Despite the benefits of statin therapy in individuals with diabetes, fewer than 70% of adults with diabetes meet contemporary guidelines for statin therapy and reducing low-density lipoprotein cholesterol (LDL) to <100 mg/dL. Evidence describing delays in statin initiation after diabetes diagnosis and associated clinical outcomes may motivate process of care interventions to improve guideline recommended care in individuals newly diagnosed with type 2 diabetes mellitus (T2D). Objective: To examine the timing of statin initiation and achievement of LDL <100 mg/dL after diabetes diagnosis, and to determine the association of early LDL reduction among statin initiators with incident atherosclerotic cardiovascular disease (ASCVD). Design: Retrospective observational cohort study using data from 2005-2021 Setting: Veterans Affairs Health Care System (VA) Participants: Individuals with newly diagnosed T2D Exposure: Primary exposure was ASCVD risk based on ACC/AHA Pooled Cohort Equations; secondary exposure was LDL <100 mg/dL in the first year after T2D diagnosis among statin initiators Main Outcomes and Measures: Co-primary outcomes were initiation of statin therapy and achievement of LDL <100 mg/dL within 5 years of diabetes diagnosis; incident 5-year ASCVD was a secondary outcome. Results: Among 100,406 individuals with newly diagnosed T2D, 59,615 were prescribed statin therapy within five years (59.4%), and 44,783 (57.5%) of those with LDL above goal achieved LDL <100 mg/dL within 5 years. Relative to those at low (<7.5%) 10-year ASCVD risk, individuals at intermediate (7.5-20%) and high (>20%) risk were more likely to be initiated on a statin (intermediate: Hazard Ratio [HR] 1.14 [95% CI 1.11, 1.17]; high: HR 1.16 [95% CI 1.13, 1.19]) and to achieve LDL <100 mg/dL (intermediate: HR 1.23 [95% CI 1.19, 1.26]; high: HR 1.34 [95% CI 1.30, 1.38]). Among those prescribed a statin within one year of diabetes diagnosis, achieving LDL <100 mg/dL in the first year after diabetes diagnosis was associated with lower risk of 5-year incident ASCVD (HR 0.84 [95% CI 0.77, 0.92]). Conclusions and Relevance: Gaps in guideline-directed primary prevention of ASCVD arise early following initial diabetes diagnosis. Guideline recommended early LDL lowering among statin initiators was associated with improved clinical outcomes.

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.

IntroductionThe bidirectional relationship between depression and type 2 diabetes (T2D) is well-established. Women are disproportionately affected by their co-occurrence, particularly during midlife, yet sex- and age-specific studies on phenotypic and mechanistic factors underlying risk for their co-occurrence are limited. The purpose of this study was to identify combined risk profiles (i.e., depression, T2D) in women during midlife and to determine if microRNAs (miRs) that are associated with high-risk profiles provide mechanistic insights into multimorbidity. Materials and MethodsThis study included baseline data from women during midlife (ages 40-64 years) who participated in the Diabetes Prevention Program (DPP) (n = 603). Unsupervised k-means clustering was used to identify multimorbid risk profiles. Clinical characteristics included for risk profiling included Beck Depression Inventory (BDI-I), age, BMI, waist circumference, triglycerides, high HDL, FBG, and HbA1c. Associations between risk profiles and individual miRs and principal components of co-expressed miRs were determined via logistic regression models adjusted for participant race and ethnicity. False discovery rate (q< 0.05) was used to control for multiple comparisons. ResultsTwo distinct profiles were identified, with the high-risk profile characterized by younger age yet higher adiposity, glycemic biomarkers, and depression symptom burden compared to the low-risk profile. MiR-320a and miR-320c were associated with increased odds of high-risk profile assignment, and a co-expression cluster enriched for miRs belonging to the miR-320 family (PC3) was significantly associated with increased odds of high-risk profile assignment. Across all models, Black race demonstrated at least threefold higher odds of high-risk profile assignment. DiscussionThese findings highlight distinct multimorbid risk profiles in women during midlife, emphasizing the potential utility of integrated, multidimensional approaches for risk stratification. Findings also revealed mechanisms that may underly risk for co-occurrence of T2D and depression in women during midlife and potential therapeutic targets for prevention and treatment.

High-fat diet (HFD) combined with streptozotocin (STZ) is widely used to model type 2 diabetes (T2D) in rodents, but is often associated with high mortality, non-responders, and inconsistent outcomes. STZ is conventionally administered using body weight-adjusted dosing (mg/kg), despite evidence that heavier animals, including HFD-fed mice, exhibit more severe glycaemic responses. Here, we performed metabolic phenotyping in chow- and HFD-fed C57BL/6J mice treated with low or high fixed doses (mg instead of mg/kg) of anomer-equilibrated STZ. HFD combined with low-dose STZ induced a stable T2D-like phenotype characterized by sustained obesity, moderate hyperglycaemia, insulin resistance, and partial {beta}-cell loss, with low inter-individual variability. In contrast, high-dose STZ induced a T1D-like phenotype with extensive {beta}-cell loss. A semi-mechanistic mathematical model was developed and validated against independent experimental data, reproducing the observed dynamics of fasting glucose in response to fixed-dose STZ. The model further predicted that weight-adjusted (mg/kg) dosing could introduce variability in glycaemic responses, particularly in HFD-fed mice. Together, these results demonstrate that fixed-dose, anomer-equilibrated STZ induces a stable T2D-like phenotype, providing an alternative to conventional weight-adjusted dosing in HFD-fed mice.

Background: Epidemiological evidence links type 2 diabetes (T2D) to an increased risk of dementia, including Alzheimers disease (AD). However, previous syntheses often combined heterogeneous diabetes and dementia definitions and have not comprehensively quantified AD incidence among individuals with T2D. We aimed to estimate both the incidence of AD among individuals with T2D and the association between T2D and AD using studies with well-defined T2D and AD outcomes. Methods: We systematically searched MEDLINE, CINAHL (via EBSCO), Embase (via Ovid), and Scopus from inception to April 2026 for studies investigating the incidence of AD among individuals with T2D or the association between T2D and AD. Data were pooled using random-effects models and presented as incidence rates and adjusted relative risks (RRs) with 95% confidence intervals (CIs). Results Of the 9,430 articles identified, 40 studies involving 27,102,559 participants were included. Twenty-three studies contributed incidence data, and 26 reported adjusted relative risks (aRR). The pooled incidence of AD among individuals with T2D was 4.71 per 1,000 person-years (95% CI 3.31, 6.71). T2D was associated with an increased risk of AD (aRR 1.53, 95% CI 1.38, 1.70). Subgroup findings were generally consistent, results were robust in sensitivity analyses, and no publication bias was detected. Conclusions: This study provides a comprehensive quantification of the AD burden associated with T2D by focusing on well-defined AD and T2D outcomes and advancing the field beyond prior broad dementia syntheses. Integrating incidence and relative risk estimates clarifies both the absolute and relative burden of AD in T2D and extends previous syntheses that primarily emphasised relative risk. Individuals with T2D experienced approximately five AD cases per 1,000 person-years and a 53% higher risk of AD, supporting the rationale for integrating cognitive risk prevention into diabetes care.

Background Chronic gastritis and duodenitis (CGD) are highly prevalent among patients with type 2 diabetes (T2D). However, the prognostic impact of their comorbidity and the potential role of MRI-derived phenotype-tailored dietary strategies remain unclear. Methods This prospective cohort study included 453,768 UK Biobank participants. Primary endpoints were myocardial infarction, stroke, end-stage renal disease (ESRD), dementia, Parkinson's disease, and all-cause mortality. Time-dependent multivariable Cox regression assessed outcome associations, while additive interaction analyses evaluated synergistic effects between T2D and CGD. Eight healthy dietary pattern scores were analyzed. Latent profile analysis classified MRI-derived body composition phenotypes based on fat distribution and organ volume. Results T2D and CGD were positively associated, and their comorbidity increased risks of cardiovascular events, ESRD, dementia, and all-cause mortality. Additive interaction analyses demonstrated synergistic effects on myocardial infarction and all-cause mortality. The comorbidity was further associated with aggravated lipid metabolic abnormalities and multiorgan atrophy. Higher adherence to the Healthful Plant-Based Diet Index (HPDI) and Dietary Approaches to Stop Hypertension (DASH) diets attenuated the excess mortality risk related to this synergy. Dietary associations varied across T2D, CGD, and comorbid populations, while MRI-based latent profiles modified diet-outcome relationships. A nomogram integrating demographic, dietary, and body composition data demonstrated reliable long-term predictive performance for myocardial infarction, stroke, and all-cause mortality. Conclusions Comorbid T2D and CGD substantially increase adverse clinical risks and exhibit synergistic effects on myocardial infarction and all-cause mortality. These findings support routine CGD screening in T2D care and provide population-based evidence for MRI-derived phenotype-tailored dietary strategies.

Objectives: Diabetes affects over 500 million people globally and glycemia is inadequately managed. Metformin is the most frequently prescribed initial treatment for type 2 diabetes globally, yet glycemic response trajectories to metformin in routine real-world care and predictors of treatment response have not been well described. We aimed to identify glycemic response trajectories in adults prescribed metformin monotherapy as initial type 2 diabetes treatment and predictors of poor glycemic response to metformin. Design: Observational cohort study using latent class mixed models to identify hemoglobin A1c (HbA1c) trajectory classes, followed by random forests machine learning to predict trajectory class membership. Setting: US Veterans Affairs Healthcare System Participants: Adults treated with metformin alone for >30 days after diabetes diagnosis with a minimum of two HbA1c measurements from 90 days prior to two years after the first metformin prescription (N=140,413). Exposures: Demographic, laboratory, vital sign, and comorbidity data were included as predictors of metformin response trajectory Main Outcomes and Measures: We included all HbA1c measurements (487,604 total) for two years after metformin initiation to define metformin glycemic response trajectories. Results: We identified three HbA1c trajectories: stably low (89.7% of sample, mean HbA1c decrease from 7.2% to 6.6%), brisk response (7.1% of sample, mean HbA1c decrease from 11.4% to 7.0%), and non-response (3.1% of sample, mean HbA1c increase from 8.9% to 10.8%). Of those in the stably low and brisk response classes at 2 years, 91% maintained HbA1c at approximately 7% on metformin alone for 5 years after drug initiation. Prediction models could accurately predict brisk response (91% accuracy) but not metformin non-response (59% accuracy). Conclusions: Most individuals treated initially with metformin monotherapy have a beneficial and durable glycemic response. Predicting individuals who will not respond to metformin may be challenging but is evident within six months with recommended glycemic surveillance. The findings support current guidelines for HbA1c surveillance when initiating diabetes treatment.

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.