Diabetes Care

Objective: Despite the complex and non-linear progression of diabetes, its shared pathways with atherosclerotic cardiovascular disease (ASCVD) are conventionally described using models based on single time points. We identified longitudinal diabetes clusters before diagnosis using deep learning and studied their association with ASCVD events and mortality. Methods: We analyzed 157,670 visits from 15,871 adults (25-65 years) without diabetes from four pooled U.S. cohorts (median follow-up: 22 years [IQR: 9-30]). A gated recurrent unit model with decay (GRU-D) was used to predict 1-year risk of diabetes or censoring within 10 years, by learning longitudinal embeddings across 25 clinical characteristics and biomarkers. Parallel Factor Analysis-2 (PARAFAC-2) and Gaussian mixture models (GMM) were used to group longitudinal participant representations as clusters. Landmark time Cox proportional hazards regressions, relative to last observation in the training window, were used to study covariate-adjusted associations of clusters with ASCVD and mortality. Prognostic utility of clusters beyond the PREVENT risk score was assessed using Harrell's C-index. Findings were replicated in a fifth cohort. Results: The analytic sample was aged 49 years [SD: 11], 58% female, and 68% white; 1,202 (8%) developed diabetes within the first 10 years. We identified five clusters (Cluster A to E) that differed in their clinical characteristics over time. Cluster E (46%) had the highest cumulative incidence of diabetes in the study period, followed by Cluster C (40%) and Cluster A (38%). Cluster C, which was defined by older age, high blood pressure, and suboptimal renal function at the first visit, had higher rates of ASCVD (HR: 1.09, 95%CI: 0.98-1.21) and mortality (HR: 1.08, 95%CI: 1.00-1.16), relative to Cluster A despite being similar in age and BMI at the first visit. Relative to Cluster A, all other clusters had similar or lower rates of ASCVD and mortality. We observed substantial cluster effects for three clusters (Clusters C to E), which were based on only two cohorts. The two clusters (Clusters A and B) that included participants from all four cohorts were reproduced in the fifth cohort and showed similar rates of outcomes. Clusters did not improve ASCVD prognosis, relative to a model that included only the PREVENT risk score. Conclusions: Longitudinal clusters reveal substantial heterogeneity in the period before diabetes diagnosis, and their risk for ASCVD and mortality. However, clusters discovered may, in part, be explained by cohort effects from variations in recruitment and visit patterns after recruitment.

Background: Acute pancreatitis (AP) is an established risk factor for diabetes, with approximately 20% of children developing either prediabetes or diabetes within one year of their first episode. Little is known about the diabetes pathophysiology or which individuals are at highest risk. We aimed to evaluate whether genetic risk scores (GRS) for type 1 (T1D) and polygenic risk scores (PRS) type 2 diabetes (T2D) are associated with progression to dysglycemia following AP. Methods: Clinical data were available for 123 children (mean age (IQR), 12 (8-15) years; mean body mass index (BMI), 21.8) with AP who were followed for >1 year. Array genotyping coupled with imputation using the TOPMed reference panel was performed. Genetic ancestry was predicted using a random forest classifier. GRS for T1D and T2D were calculated using either an ancestry-appropriate (T1D-GRS) or a multi-ancestry (T2D-PRS) weighted framework. To evaluate risk compared to the population we used predefined GRS thresholds from UK Biobank. Results: Among the 123 subjects, 24 developed dysglycemia (5 with diabetes and 19 with prediabetes). The majority (75.6%, n=93) of children were of European ancestry. Comparison of the T1D-GRS burden with the UK BioBank showed numerically higher proportions for any given threshold. At the top 5% threshold, 9.7% of our cohort were classified as high-risk compared to 5% in UK Biobank (p<0.05). The elevated T1D-GRS could be primarily attributed to non-HLA variants and was more enriched in those testing positive for [&ge;]1 islet-autoantibody. The T2D-PRS was also elevated in the dysglycemic group but only reached statistical significance in those who were obese. Conclusion: These findings highlight the potential role of both T1D-GRS and T2D-PRS in investigating diabetes susceptibility following AP.

Aims CGM devices report glucose only within fixed limits (typically 40-400 mg/dL; 2.2-22.2 mmol/L), truncating extreme values to a boundary ("capping"). We characterised prevalence, duration, and consequences of capping in type 1 diabetes trial data. Materials and Methods We analysed 46,990,617 CGM readings from 948 participants across four publicly available clinical trial datasets (Dexcom G4 Platinum or G6 sensors). Capping prevalence, run duration, and associations with age, HbA1c and sex were characterised across all datasets. In the 77 participants of the Replace-BG trial CGM-plus-blood glucose monitor (BGM) arm, CGM-derived metrics were compared with contemporaneous BGM measurements across 1,162 non-overlapping 14-day windows. Results Between 93.5% and 100% of participants had at least one capped reading, and capped values comprised 0.47-0.98% of all readings. In the three datasets for which duration could be calculated, over 70% of upper-cap runs exceeded 15 minutes and over one third exceeded 60 minutes. Upper-limit capping was inversely associated with age (Spearman {rho} -0.20 to -0.47, p[&le;]0.002) in three of the datasets, and positively associated with baseline HbA1c ({rho} 0.39-0.62, p<0.001) in all four datasets. A within-participant analysis showed that capping burden did not predict CGM-BGM divergence in any summary metric (all p>0.2), and a systematic CGM-BGM offset in mean glucose and time in range (TIR) reflected the physiological lag between blood and interstitial fluid rather than capping artefact. Conclusions Sensor limit capping is near-universal in type 1 diabetes, produces sustained periods of right-censored glucose data disproportionately affecting younger patients, and does not substantially distort standard summary metrics at the population level. Clinicians and trialists should be aware that CGM data can confirm extreme glucose events but cannot quantify their severity.

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.

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.

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.

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.

Type 2 Diabetes (T2D) is a growing public health burden in West Africa, yet the effectiveness of lifestyle interventions for glycemic control in this region remains unclear. This systematic review and meta-analysis evaluated the impact of lifestyle interventions on Fasting Blood Glucose (FBG) and Glycated Hemoglobin (HbA1c) levels among adults with T2D in West Africa. A systematic search of PubMed, Scopus, Africa Journals Online, and Cairn.info was conducted following PRISMA guidelines. Randomized controlled trials (RCTs) and quasi-experimental studies evaluating lifestyle interventions (physical activity, dietary modification, and combined/educational interventions) for glycemic control in adults with T2D in West Africa were included. Meta-analysis was performed via a random-effects model with restricted maximum likelihood (REML) estimation, using mean differences (MD) as the effect measure for both FBG and HbA1c outcomes. Heterogeneity was assessed via I2 statistics, and sensitivity, subgroup, and meta-regression analyses were conducted to examine potential moderators of the observed heterogeneity. Ten studies comprising 645 participants were included. Six studies reported FBG outcomes; however, two were excluded from the FBG meta-analysis due to missing control group post-test values and absence of a control group respectively, leaving four studies for pooling. A separate set of four studies contributed to the HbA1c meta-analysis. For FBG, lifestyle interventions were associated with reduction in FBG levels (pooled MD = -1.81 mmol/L, 95% CI: -2.33 to -1.30, p < 0.001), with moderate heterogeneity (I2 = 50.76%). The certainty of evidence assessed using the GRADE approach was rated as low for FBG outcomes and very low for HbA1c outcomes, reflecting concerns about imprecision and inconsistency across studies. Leave-one-out sensitivity analysis confirmed robustness of this finding, with estimates ranging from -1.707 to -2.087 mmol/L. Neither intervention duration nor sample size significantly moderated FBG effect sizes, with the model explaining approximately 15.7% of observed heterogeneity. For HbA1c, lifestyle interventions were also associated with reduction in HbA1c levels (pooled MD = -1.044%, 95% CI: -1.594 to -0.495, p = 0.0002), though heterogeneity was exceptionally high (I2 = 98.08%), limiting interpretability of the pooled estimate. Exploratory meta-regression identified intervention duration and sample size as statistically associated with HbA1c effect size, though the model was saturated given the small number of studies and findings should not be interpreted as confirmatory evidence of moderation. Conclusion: Lifestyle interventions, including supervised physical activity, dietary modification, and community-based diabetes education, were generally associated with improvements in glycemic control among adults with type 2 diabetes in West Africa. Evidence was more consistent for fasting blood glucose, while findings for HbA1c were highly heterogeneous and should be interpreted with caution. These results suggest potential benefit, but variability across studies highlights the need for more standardized and rigorously designed trials in the region.

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.

BackgroundHypertension is a highly heritable cardiovascular disorder and a major determinant of cardiometabolic disease, including diabetes. However, the regulatory genes and tissue-specific mechanisms underlying blood pressure variations remain incompletely understood. MethodsLeveraging a well-characterized prospective population-based cohort comprised of 27,308 participants from the Singapore Chinese Health Study (SCHS), we evaluated genome-wide genetic associations for five blood pressure traits: hypertension status, systolic blood pressure, diastolic blood pressure, mean arterial pressure (MAP) and pulse pressure (PP). Additionally, we conducted a transcriptome-wide association study (TWAS), integrating gene expression data from 49 tissues, followed by colocalization and fine-mapping to prioritize regulatory genes. Association of identified variants with incident diabetes was additionally evaluated in the longitudinal data. ResultsWe validated 10 blood pressure loci (P between 1.64 x 10-20 - 4.10 x 10-8) and identified an East-Asian specific splice donor variant at the COL21A1 gene associated with PP (rs149344559, P = 6.78 x 10-10). Integrative analyses prioritized FGF5 in kidney cortex and ENPEP in pituitary tissue as candidate regulatory genes. The blood pressure-lowering allele at ENPEP (T allele, rs1879056) was associated with reduced risk of incident diabetes. Mediation analysis demonstrated that approximately 21% of the genetic association with diabetes was mediated through MAP (Pindirect-effect = 2 x 10-16). ConclusionThis study refines genetic predispositions for blood pressure among East-Asians. We further delineate tissue-specific regulatory pathways underlying blood pressure variations and identify ENPEP-mediated dysfunctions linking blood pressure genetics to diabetes risk, underscoring integrated disease mechanisms.

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.

Background Lifestyle interventions can increase the probability of remission of prediabetes to normal glucose tolerance, but their economic value remains unclear. We assessed the within-trial and lifetime-horizon modeled cost-effectiveness of intensive and conventional lifestyle interventions in risk-stratified participants with prediabetes. Methods A health economic evaluation was conducted alongside the 12-month multicenter PLIS trial (n=1,105). High-risk participants were randomized to intensive (HR-INT) or conventional (HR-CONV); low-risk participants to conventional lifestyle intervention (LR-CONV) or control (only short single consultation; LR-CTRL) with risk stratification based on insulin secretion, insulin sensitivity, and liver fat content. Within-trial analyses estimated incremental costs per additional remission to normoglycemia and per quality-adjusted life year (QALY). Lifetime cost-effectiveness was modelled using a four-state Markov Model. Findings At 12 months, HR-INT and LR-CONV increased remission compared with their respective comparators. The incremental cost per additional remission was {euro}7,081 (95% CI: dominated-47,277) for HR-INT and {euro}4,278 (1,312-11,793) for LR-CONV from a health insurance perspective. A willingness-to-pay of {euro}22,000 (HR-INT) and {euro}7,500 (LR-CONV) per additional remission corresponded to 90% probability of cost-effectiveness. Neither intervention was cost-effective in terms of QALYs gained within the 12-months period. Lifetime modelling suggested that both HR-INT and LR-CONV are not only cost-effective, but also cost-saving, relative to HR-CONV and LR-CTRL, respectively. Also in the probabilistic sensitivity analysis, most simulations indicated dominance (71.7% for HR and 88% for LR). Interpretation Based on short-term economic evaluation, the interventions assessed were cost-effective regarding additional participants with remission, not for incremental QALYs gained. Lifetime modelling suggests cost savings for both risk groups. Targeting populations with lifestyle interventions to achieve prediabetes remission seems to generate good value for money in the long term.

Objective To develop clinically operational, population-representative risk-score models for detecting metabolic syndrome (MetS) in U.S. adults by incorporating the NHANES survey design. Research Design and Methods We analyzed 36,812 U.S. adults from NHANES 1988--2018. Seven models of increasing clinical complexity were trained and evaluated, ranging from basic demographics to full biochemical panels. We used a new deep-learning methodology for survey data with a predictive uncertainty quantification model. Results A model combining anthropometrics, vital signs and a basic lipid panel achieved an AUC of 0.923 at an estimated cost of 0.40 eur per individual. Adding diabetes-specific biomarkers, including fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), yielded only marginal improvements. Conclusions This low-cost population-representative screening tool for MetS may help identify at-risk individuals and support data-driven public health interventions.

Purpose: Genetic risk scores (GRSs) are summaries of genetic data that can improve prediction of disease risk and progression. GRSs are increasing available but rely on high quality input data to produce good output results; with noisy or missing inputs the GRS may be inaccurate. We aimed to develop a method to produce a robust estimate of the GRS when input data is missing, noisy or both. Approach: We developed a neural network approach, named masked-MLP, for robust GRS calculation trained on a set of GRS scores calculated on clean data. The masked-MLP includes additional input data and has noise inserted during training, both which make the model more robust. Results: A GRS for type 1 diabetes (T1D) calculated on input data with 10\% of the data corrupted had a Spearman rank correlation to the clean GRS of 0.669 (0.665-0.674) while the equivalent for the masked-MLP was 0.951 (0.950-0.952). For the same data the area under the receiver operating characteristic curve for separation of T1D from population samples fell from 0.919 (0.904-0.932) to 0.808 (0.787-0.827) for the GRS while the masked-MLP fell to 0.910 (0.895-0.924). Conclusions: The masked-MLP was more robust to noise when calculating a GRS than using standard approaches. Our approach has the potential to ensure both improved research and clinical outcomes due to more reliable GRS calculation.

Background: People with ischemic heart disease (IHD) remain at high risk of recurrent major cardiovascular events despite contemporary therapy. Over two decades, a translational research program has evaluated pressure pain sensitivity (PPS) as a non-invasive marker of central autonomic dysfunction and a mutual risk phenotype in IHD and type 2 diabetes. A PPS-guided non-pharmacological intervention has been shown to substantially reduce five-year all-cause mortality in IHD. Methods: In a randomized controlled trial, 213 adults with stable IHD and elevated PPS, suggesting ANSD, were allocated to PPS-guided intervention (n=106) or control (n=107). The active group received three months of structured education (daily PPS self-measurement, cutaneous sensory nerve stimulation, supportive mental and physical exercises, telemedical feedback) followed by self-directed continuation. Controls received a booklet on general stress-management. The primary endpoint for this prespecified secondary analysis was a composite of eight major cardiovascular events. Results: Over 5 years, at least one major adverse cardiovascular event occurred in 19.8% of the PPS-guided group versus 43.8% of controls (odds ratio 0.32, 95% CI 0.17-0.62, P=0.0003). Incidence rates were directionally in favor of active intervention across all event categories (P=0.004). Conclusions: A brief PPS-guided non-pharmacological intervention, followed by self-directed continuation, was associated with a marked long-term reduction in major adverse cardiovascular events, complementing previously reported large reductions in all-cause mortality in the same cohort. Within the context of a multi-decade PPS research program, these findings support PPS-guided care as a low-resource autonomic intervention ready for pragmatic scale-up testing as an adjunct to cardiometabolic care.

Background: Mucopolysaccharidosis type IIIB (MPS IIIB) is a devastating neurodegenerative lysosomal storage disorder caused by alpha-N-acetylglucosaminidase (NAGLU) deficiency. There is currently no approved therapy. We report the 3-month outcomes of a novel intracerebroventricular (ICV) gene therapy in a child with MPS IIIB. Methods: In an open-label, single-center, investigator-initiated trial (ChiCTR2600121466), a single dose of RDGT-101 (2.0E14; vg of an AAV9 vector encoding human NAGLU) was administered via ICV infusion. Primary outcomes were safety and tolerability. Secondary outcomes included serum NAGLU activity, urinary heparan sulfate (HS) excretion, and neurocognitive function. Exploratory analyses included hematological parameters. Results: The patient achieved serum NAGLU activity (17.06 nmol/mL/hour) approaching that of healthy controls (17.75 {+/-} 1.37 nmol/mL/hour) by Month 3, accompanied by a 58.4% reduction in urinary HS. Clinically, previously severe hand and toe contractures resolved, allowing for full extension. Neurocognitive improvements were observed, including clear articulation, logical conversation, and sustained eye contact. Hematological analyses revealed normalized red blood cell indices and improved iron utilization. No dose-limiting toxicities, serious adverse events, or clinically significant laboratory abnormalities were observed. Conclusions: A single ICV infusion of RDGT-101 was safe and well-tolerated in this patient with MPS IIIB. Early biochemical correction was accompanied by marked improvements in somatic, neurocognitive, and hematological parameters. These findings support further investigation of ICV AAV9 gene therapy for MPS IIIB.

Background: Chronic low-grade inflammation drives cardiovascular-kidney-metabolic (CKM) syndrome. Clonal hematopoiesis of indeterminate potential (CHIP), an age-related driver of systemic inflammation, is linked to several cardiometabolic disorders. However, whether CHIP modifies CKM progression and contributes to heterogeneity in cardiovascular disease (CVD) risk within the CKM framework remains uninvestigated. Methods: This cohort study included 307,025 UK Biobank participants at CKM stages 0-3 free of baseline CVD. CHIP status was identified via whole-exome sequencing (WES). The association between CHIP and baseline CKM severity was examined, along with the independent and joint effects of CHIP and CKM stages on incident CVD risk. The joint effects of CHIP and polygenic risk scores (PRS) were further assessed, and the incremental predictive value of incorporating CHIP into the AHA PREVENT equations was evaluated. Results: CHIP carriers were more likely to present with advanced CKM stages [OR 1.14 (1.09-1.20), P < 0.001] and exhibited higher incident CVD risk during follow-up [HR 1.13 (1.08-1.18), P < 0.001]. Significant joint effects between CHIP and CKM stages were observed, with the highest risk among CHIP carriers at CKM stage 3 [HR 1.63 (1.50-1.78), P < 0.001]. Large or multiple CHIP mutations conferred greater hazards, with distinct gene-specific effects observed. Moreover, CHIP and high genetic risk also jointly amplified CVD susceptibility. Most importantly, incorporating CHIP into AHA PREVENT significantly improved risk discrimination. Conclusions: CHIP is a significant risk factor associated with more advanced CKM stages and amplifies incident CVD risk. Integrating CHIP into existing prevention strategies may refine CVD risk stratification.

Background. Type 2 diabetes mellitus (T2D) is defined by progressive pancreatic {beta}-cell dysfunction whose molecular underpinnings remain incompletely understood. Single-cohort transcriptomic analyses of donor islets have yielded heterogeneous gene lists of limited cross-study reproducibility, constraining both mechanistic interpretation and biomarker development. Methods. We combined two complementary analytical strategies applied to four public human islet transcriptomic cohorts (GSE25724, GSE20966, GSE38642, and GSE164416; n = 7-57 donors per contrast). For the integrative arm, three microarray datasets and one bulk RNA-seq dataset were processed independently and unified through gene-level random-effects meta-analysis, hallmark pathway scoring (GSVA/MSigDB), and iterative module refinement, yielding a two-axis disease framework. For the diagnostic arm, a consensus multi-method machine learning pipeline, combining LASSO penalized logistic regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest importance scoring, was applied to 184 differentially expressed genes from the RNA-seq cohort, with all normalization steps performed within leave-one-out cross-validation (LOOCV) folds to prevent data leakage. Machine learning classification of the RNA-seq cohort was additionally subjected to external transportability testing in the independent bulk human islet RNA-seq cohort GSE50244 using an overlap-restricted reduced score and a threshold fixed in the discovery cohort. Results. Meta-analysis across all four cohorts identified 337 high-confidence T2D-associated genes (96.1% directional concordance in beta-cell-enriched tissue). These were distilled into two refined 14-gene modules: ImmuneStress (MICB, HLA-DRA, HLA-DPA1, IL1R2, and others) and BetaCellIdentitySecretion (RASGRP1, PPP1R1A, SLC2A2, and others), whose composite IsletDysfunctionScore provided the most stable cross-platform separation of non-diabetic from T2D islets (Hedges' g = 1.80, p = 9.83 x $10^-17$, $\text{I}^2$= 0%). Consistent with progressive disease, IsletDysfunctionScore increased monotonically from non-diabetic to impaired glucose tolerance to T2D. Separately, the machine learning pipeline derived a 10-gene diagnostic panel: GABRA2, SLC2A2, ARG2, DKK3, PRIMA1, TAFA4, HHATL, PARVG, RNU1-70P, and the novel lncRNA ENSG00000284653, that achieved perfect discrimination in LOOCV (AUC = 1.000, sensitivity = 1.000, specificity = 1.000, zero misclassifications across all 57 donors). A leakage-verification experiment confirmed that this performance reflected genuine biological signal: global quantile normalization prior to cross-validation collapsed AUC to 0.380. External testing showed that 8 of the 10 panel genes were measurable in GSE50244. The frozen 8-gene reduced score retained strong discrimination (external AUC = 0.907), with 6 of 8 genes preserving directional concordance, but the discovery-derived threshold did not transfer because the external score distribution was shifted upward and compressed, yielding complete sensitivity but zero specificity at the frozen cutoff Conclusions. Integrating pathway-level meta-analysis with machine learning classification, we present a coherent two-axis model: immune/stress activation and loss of beta-cell identity/secretory competence, together with a compact, biologically interpretable 10-gene diagnostic signature. Panel genes converge on GABA signaling, glucose transport, arginine metabolism, WNT pathway inhibition, and a novel lncRNA, providing both mechanistic hypotheses and high-priority targets for external validation. These findings offer a reproducible transcriptomic scaffold for future mechanistic, biomarker, and clinical translation studies of human islet dysfunction. They also support external transportability of the core biological signal, while indicating that absolute operating thresholds are cohort-dependent and would require recalibration before deployment in independent datasets.

ObjectiveDiabetic complications collectively represent one of the most urgent unresolved problems in medicine, yet the field continues to study them in near-complete isolation from one another. No unified framework has systematically characterised the shared and divergent molecular signatures of ten clinically critical metabolic transporters across all five major complications, cardiomyopathy (DCM), nephropathy (DN), retinopathy (DR), peripheral neuropathy (DPN), and atherosclerosis and vasculopathy (DAD), through an integrated, multi-method computational pipeline. This study was designed to address that gap directly. MethodsEleven GEO microarray datasets comprising 118 diabetic and 76 control samples were analysed through twelve sequential phases: differential expression analysis, pan-complication overlap, weighted gene co-expression network analysis (WGCNA), GO/KEGG functional enrichment with gene set enrichment analysis (GSEA), STRING protein-protein interaction (PPI) network construction, competing endogenous RNA (ceRNA) network mapping, transcription factor activity inference using a VIPER-style algorithm, immune cell infiltration estimation by single-sample GSEA, diagnostic biomarker modelling using LASSO logistic regression and Random Forest classification, CMap-style drug repurposing by connectivity scoring, and two-sample Mendelian randomisation (MR) employing four independent estimators (inverse-variance weighted [IVW], MR-Egger, weighted median, and weighted mode). ResultsCD36 was the only transporter to achieve significant dysregulation across three independently sourced tissue types (DN, DR, DPN; logFC range 0.88 to 2.18), whilst TLR4 exhibited the highest fold-change in the study (logFC = 3.88, DPN) and the greatest WGCNA module membership (kME = 0.976, DPN). SERCA2 was significantly downregulated in three complications (DCM, DN, and DR) at formal significance thresholds and trended negatively in the remaining two (DPN and DAD), constituting the most consistently suppressed transporter in the study. Its universal downregulation was explicable through four convergent mechanisms spanning transcriptional, oxidative, ceRNA-mediated, and transcription factor-level regulation, and was confirmed as causally relevant to diabetic cardiomyopathy by eQTL Mendelian randomisation (beta = -0.085, p = 0.005). miR-21-5p was identified as the dominant ceRNA regulatory bridge (betweenness centrality = 0.428; 6.7-fold above the second-ranked miRNA), with MALAT1 as the sole lncRNA hub active in all five complications. PPARgamma and TP53 repression emerged as the leading transcription factor-level explanations for the simultaneous metabolic and inflammatory dysregulation characteristic of the diabetic transcriptome. Immune deconvolution revealed DCM as immunologically quiescent, DN as comprehensively infiltrated (ten enriched cell types), and DPN as mast-cell-dominated, identifying a cellular mechanism for TLR4-driven neuroinflammation that has not previously been systematically characterised. GLUT4 achieved perfect diagnostic discrimination for DPN (AUC = 1.000, p < 0.001; LASSO coefficient = -2.143), whilst SGLT2 was the leading DAD diagnostic marker (AUC = 1.000, p = 0.002). Epalrestat was the sole pan-complication drug repurposing candidate (significant connectivity reversal in four of five complications). Mendelian randomisation confirmed causal effects of T2DM genetic liability on all five complications (all p < 0.0001, all four estimators concordant), and eQTL-MR identified TLR4 (beta = +0.073, p = 0.006) and CD36 (beta = +0.070, p = 0.008) as causal risk factors for DN, SERCA2 reduced expression as a causal driver of DCM (beta = -0.085, p = 0.005), and SGLT2 expression as a causal protector against DN (beta = -0.070, p = 0.013). ConclusionsThis twelve-phase investigation identifies a pan-complication CD36/TLR4 inflammatory dyad and a SERCA2 calcium-mitochondrial effector axis, both confirmed at seven independent analytical levels, including causal genomic inference. GLUT4 downregulation defines DPN at the diagnostic level with perfect accuracy and is explicable through a five-layer mechanistic chain from MODY transcription factor inactivation to ceRNA competitive pressure. Epalrestat warrants prospective evaluation beyond its established DPN indication. These findings collectively constitute the most comprehensive computational characterisation of metabolic transporter biology in diabetic complications to date. RESEARCH IN CONTEXTO_ST_ABSWhat is already known about this subject?C_ST_ABSThe five major diabetic complications (cardiomyopathy, nephropathy, retinopathy, peripheral neuropathy, and atherosclerosisare) individually well-characterised, and several key metabolic transporters, including SGLT2, CD36, TLR4, SERCA2, and GLUT4, have established roles in one or more of these conditions. Mendelian randomisation has confirmed that T2DM genetic liability causally increases the risk of each complication independently. However, no study has examined all ten major metabolic transporters across all five complications simultaneously, and the shared versus complication-specific regulatory architectures of these transporters remain entirely uncharacterised. What is the key question?Which metabolic transporters are consistently dysregulated across all five diabetic complications, which are complication-specific, and can their shared regulatory mechanisms, from RNA regulation through to causal genetic evidence be used to identify diagnostic biomarkers and actionable therapeutic targets that transcend individual complication boundaries? What are the key findings and their implications for the field?CD36 and TLR4 constitute a pan-complication inflammatory dyad confirmed at seven independent analytical levels, including Mendelian randomisation causal evidence (both p < 0.01 for diabetic nephropathy). SERCA2 is universally suppressed across all five complications and is a causal driver of diabetic cardiomyopathy by eQTL-MR (p = 0.005). GLUT4 is a perfect single-gene diagnostic for diabetic peripheral neuropathy (AUC = 1.000) and a causal renal protector. Mast cells are identified as the innate cellular effectors of TLR4-driven diabetic neuropathy. Epalrestat demonstrates pan-complication therapeutic potential beyond its licensed DPN indication. These findings provide a unified mechanistic framework and a translational roadmap grounded in causal genomic evidence, with implications for both complication-targeted and pan-complication therapeutic strategies.