Diabetologia

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.

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.

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: Renal effects of statins in type 2 diabetes mellitus (T2DM) remain uncertain. We evaluated whether statin exposure is associated with time to dialysis initiation. Methods: We conducted a retrospective cohort study of adults with T2DM, indexing follow-up at diagnosis during first hospital admission (day 0) between january 2017 and march 2025. Statin use was modeled as time-varying from statin days; (classified in 3 categories: baseline users, new users, and never users). The primary outcome was dialysis. Analysis estimated cause-specific hazards, censoring deaths; proportional hazards were checked with prespecified windows of statin exposure (0?1, 1?3, > 3 years). Competing-risk analyses (Fine?Gray) assessed the sub-distribution hazard of dialysis with death as a competing event in two models: (i) prevalent users at baseline and (ii) new-users with post-initiation intervals of 30 and 90 days. An Observational Medical Outcomes Partnership Common Data Model standardized dataset of a Brazilian quaternary hospital, and the Real-World Data tool MD Clone were used in the study. Results: Of 36,246 adults identified, 32,125 entered the time-varying cohort (39,943 risk intervals; 656 dialysis events); median follow-up among censored patients was 753 days. At baseline, 70.3% never used statins, 5.5% were users (? 0 days), and 24.2% initiated after diagnosis. Crude dialysis incidence was 4.51 vs. 12.31 per 1,000 patient-years during unexposed vs. exposed time. In the adjusted time-varying Cox model, current statin exposure was associated with a modestly higher hazard of dialysis (HR = 1.043, 95% CI 1.011?1.077). In the new-users analysis, HRs were 0.83 (95% CI 0.66?1.05), and 0.73 (95% CI 0.57?0.92) with a 30-day and 90-day intervals, respectively. Conclusions: In this retrospective cohort of hospitalized diabetic patients at baseline, statin initiation at least 90-days in advance is associated with reduced indication of renal replacement therapy.

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.

Secondary hyperparathyroidism persists in the majority of kidney transplant recipients and is associated with adverse graft and cardiovascular outcomes. The immunosuppressive drug class used post-transplant may modulate parathyroid hormone (PTH) levels through distinct mechanisms: calcineurin inhibitors (CNI) stabilize PTH mRNA, while mTOR inhibitors (mTORi) suppress parathyroid cell proliferation in experimental models. We report supporting evidence from two independent analyses. In a multinational real-world database analysis (TriNetX Global Collaborative Network), kidney transplant recipients with documented mTORi use and eGFR in the target range had lower PTH than those on CNI across eGFR strata examined (15-30, 30-45, 45-60, 60-75, >75 mL/min/1.73 m2), with risk ratios for PTH >130 pg/mL ranging from 0.47 to 0.67 in propensity-matched analyses (all p < 0.05). The known confounders - calcium (higher in CNI) and phosphate (higher in mTORi) - both act to oppose this pattern, strengthening the possibility of a drug effect. In a longitudinal single-center cohort (n = 118; 796 PTH measurements), a linear mixed-effects model with time-varying mTORi exposure confirmed a 42% lower PTH during on-mTORi periods after adjustment for eGFR, transplant vintage, diabetes, age, and sex (fold-change 0.58 [95% CI 0.50-0.68]; p < 0.0001). These findings suggest a direct PTH-lowering effect of mTORi. Immunosuppression choice may be considered in the management of post-transplant hyperparathyroidism in selected patients.

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 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: 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: APOL1 high-risk variants markedly increase susceptibility to kidney disease among individuals of African ancestry; however, only a subset of carriers develops clinically significant CKD or ESKD. This discrepancy highlights a gap between genetic risk and clinical trajectory. Current prognostic tools rely primarily on eGFR and albuminuria, which incompletely reflect the underlying biological processes driving APOL1-associated kidney injury. We hypothesized that plasma biomarkers reflecting inflammatory and tubular injury pathways could identify biologically active disease states within this genetically high-risk population and improve prognostic stratification. Methods: Participants from the Mount Sinai BioMe Biobank carrying two APOL1 high-risk alleles (G1, G1; G1, G2; or G2 G2) were followed for a median of 6 years. Baseline plasma biomarkers of inflammation and tubular injury (TNFR1, TNFR2, KIM-1, MCP-1, YKL-40, IL-18, suPAR) were measured. The composite outcome was sustained 40% decline in eGFR or ESKD. Multivariable Cox models assessed associations between biomarkers and outcomes. A weighted biomarker risk score was derived from tertile-based hazard ratios and categorized into low-, moderate-, and high-risk groups. Results: Among 498 participants (median eGFR 83 ml/min/1.73 m2), 80 (16.1%) reached the composite outcome. Higher concentrations of TNFR1, TNFR2, suPAR, KIM-1, and IL-18 were independently associated with kidney events after multivariable adjustment. Event rates were 7% in the low-risk group, 16% in the moderate-risk group, and 36% in the high-risk group. Conclusions: Plasma biomarkers reflecting inflammatory and tubular injury pathways reveal marked heterogeneity in kidney outcomes among individuals with high-risk APOL1 genotypes. Integration of these signals into a biology-weighted score identifies distinct prognostic phenotypes beyond genotype and traditional clinical measures, supporting multidomain biomarker frameworks for risk stratification and potential trial enrichment in APOL1-associated kidney disease.

Non-HLA donor-recipient (D-R) genetic mismatches contribute to kidney allograft injury and long-term graft loss, but their clinical use is limited by the unavailability of donor DNA after transplantation. We tested whether non-invasively obtained, recipient-derived samples could be used to infer donor genotype and D-R mismatches. Genomic DNA (g-DNA) of 11 unselected kidney transplant recipients and donors underwent whole-exome sequencing (100x). Additional customized probes were added for intronic coverage (300x) of 55 targeted non-HLA genes of reported clinical relevance. Variants identified from sequencing results were compared with plasma cell-free DNA (cfDNA), urine cell-pellet DNA (U-DNA) obtained from the same recipients. Genome-wide-, exonic-, or non-synonymous exonic- mismatches in transmembrane or secreted proteins, and mismatches within target genes were benchmarked using donor g-DNA to generate mismatch scores for each D-R pair. Within each of these genomic scales of mismatch, U-DNA identified D-R mismatches significantly better than the corresponding cfDNA (P<0.001 for each comparison). U-DNA also identified gene-level mismatches in the LIMS1 gene, and correctly inferred established donor-origin risk alleles, including SHROOM3 and APOL1. Our findings demonstrate proof-of-concept that U-DNA in tandem with recipient genome, can non-invasively infer relevant non-HLA loci/mismatches circumventing the need for the donor genomic DNA.

Background and hypothesis: Sodium-glucose cotransporter-2 inhibitors (SGLT2-inhibitors) slow chronic kidney disease progression, but evidence in non-diabetic kidney transplant recipients is limited. We evaluated associations between SGLT2-inhibitor use and major adverse kidney events (MAKE), major adverse cardiovascular events (MACE), and all-cause mortality. Methods: In this retrospective cohort study using the TriNetX federated research network, adult non-diabetic kidney transplant recipients transplanted between January 2015 and January 2022 were identified. SGLT2-inhibitor users initiating therapy [&ge;]1000 days post-transplant were compared with non-users after 1:1 propensity score matching. The primary outcome was MAKE, defined as dialysis initiation or death. Secondary outcomes included all-cause mortality and MACE. Results: Propensity score matching yielded 867 pairs of SGLT2-inhibitor users and non-users. SGLT2-inhibitor use was associated with lower risks of MAKE (adjusted hazard ratio [aHR] 0.64, 95% CI 0.45-0.91) and all-cause mortality (aHR 0.55, 95% CI 0.36-0.85). No significant association was observed for MACE (aHR 0.86, 95% CI 0.64-1.17). No increased risk of urinary tract infections was observed among SGLT2-inhibitor users. Conclusion: SGLT2-inhibitor use was associated with lower risks of MAKE and all-cause mortality in non-diabetic kidney transplant recipients.

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.

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.

Abstract Background Alport Syndrome (AS), caused by pathogenic variants in type IV collagen genes COL4A3/4/5, is a leading monogenic cause of Kidney Failure (KF). Clinical course varies widely, and disease specific predictors of progression relevant to clinical care and trial design remain incompletely defined. Methods In this retrospective cohort study of individuals with AS in the UK National Registry of Rare Kidney Diseases, patients were classified as having AS or heterozygous genotypes and followed to assess proteinuria progression, eGFR slope and kidney survival. Proteinuria and eGFR trajectories were analysed using mixed effects regression models; kidney survival using Kaplan Meier analysis. Results Among 1032 participants (median follow up 11.6 years; 47% female), 475 (46%) had AS genotypes (Male XLAS or autosomal recessive AS). eGFR decline accelerated with advancing CKD stage across all genotypes (p<0.001). Proteinuria increased as eGFR declined and occurred earlier in AS genotypes. After reaching proteinuria thresholds of more than 1.0 and 3.0g/g, kidney survival over the subsequent 5 years did not differ significantly between genotypes (logrank p=0.14, p=0.17, respectively), although modest differences emerged over longer follow-up. Across eGFR thresholds (90, 60, and 45mL/min/1.73m2), higher proteinuria was associated with shorter time to KF; for example, at eGFR 45mL/min/1.73m2, median time to KF was 3.0 years (IQR, 1.6-5.4) for above-median vs 6.5 years (5.1-not estimable) for below-median proteinuria (p<0.0001). Almost all patients who reached KF had developed proteinuria of more than 0.3g/g. Conclusion In this national cohort, eGFR decline accelerated with CKD stage and proteinuria was strongly associated with progression to KF across genotypes. The non linearity of eGFR decline may inform its interpretation in clinical practice and use as a trial endpoint. Once comparable proteinuria levels were reached, differences in outcomes by genotype were attenuated, supporting proteinuria as a key prognostic marker and strengthening rationale for its use as a surrogate endpoint in AS clinical trials

BackgroundVertical sleeve gastrectomy (VSG) improves glycaemic control in type 2 diabetes (T2D) through mechanisms that extend beyond weight loss. The interaction between glucocorticoid metabolism and inflammation in this context remains unclear. MethodsWe investigated the role of 11{beta}-hydroxysteroid dehydrogenase type 1 (11{beta}HSD1) in mediating the metabolic effects of VSG in humans and mice. Subcutaneous adipose tissue biopsies were collected before and 6 months after VSG. Parallel studies were conducted in lean and high-fat diet-fed mice undergoing VSG or sham surgery, alongside 11{beta}HSD1 knockout models. Glucose tolerance and expression of 11{beta}HSD1 and interleukin-6 (IL6) were assessed. Mechanistic interactions were examined in IL6-treated human hepatocytes. ResultsVSG reduced 11{beta}HSD1 and IL6 expression in human adipose tissue and improved insulin resistance. In lean mice, VSG improved glucose tolerance and downregulated both markers independently of weight loss. 11{beta}HSD1 knockout mice exhibited improved glucose tolerance despite increased adiposity, partially recapitulating the VSG phenotype. Both interventions reduced circulating and tissue IL6 levels. IL6 stimulation increased HSD11B1 expression in hepatocytes. Conclusions11{beta}HSD1 links glucocorticoid metabolism, inflammation, and glucose homeostasis following VSG. Targeting this pathway may offer a strategy to replicate key metabolic benefits of metabolic bariatric surgery.

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: Recently, proteinuria has been accepted as a surrogate end point for clinical trials in focal segmental glomerulosclerosis (FSGS) ang IgA nephropathy. However, proteinuria has not been evaluated in Apolipoprotein L1 (APOL1)-mediated kidney disease (AMKD). Methods: Real world data (RWD) analysis of 128 patients of African ancestry with APOL1 high risk genotypes, without diabetes, enrolled in the Million Veteran Program (MVP; n=109) or the biorepository at Vanderbilt University (BioVU; n=19), who had urine albumin-creatinine ratio (UACR) >= 420 mg/g (PCR~0.9 g/g) with a concurrent GFR value. The main predictor was change in the log-UACR at 12 months. The primary outcome was annual GFR slope over 24 months. Secondary outcomes included a kidney composite of a sustained 30% GFR decline, end stage kidney disease (ESKD) or death and ESKD as a single outcome. Linear regression and Cox proportional hazards models were used to assess the effect of changes in UACR and the outcomes. Results: In the pooled analysis the mean age was 56.8 (SD 15.5) y, 116 were male (90.6%) and three patients had diagnosis of FSGS at baseline. Mean baseline eGFR was 46.8 (SD 16.1) mL/min/1.73m2, mean baseline UACR was 1240.8 (1107.7) mg/g, mean eGFR slope was -4.67[-6.00, -3.33] mL/min/1.73m2/year and the geometric mean percentage changes in the UACR at 12 months were -57.5% [-65.0%, -48.4%]. For every 1 unit of log (UACR) increment at 12 months, the annual eGFR slope decreased by -1.80 [-2.56, -1.03] mL/min/1.73m2 in the pooled analysis. For every 1 unit of log (UACR) increment at 12 months, the Cox regression showed a 61% increase in the risk of a kidney composite (p=0.002) and a 98% increase in the risk of ESKD (p<0.001). It was estimated that a 50% reduction of UACR at 12 months was associated with a 28% reduction in the kidney composite endpoint (adjusted hazard ratio [aHR]=0.72; 95% confidence interval [CI]:0.59-0.88; p=0.002), and a 38% reduction in the risk of ESKD (aHR=0.62; 95% CI:0.49-0.80; p<0.001). Conclusions and relevance: Changes in UACR at 12 months significantly modify the rate of decline of GFR over 24 months and clinically meaningful endpoints, supporting the use of UACR changes as surrogate endpoint in AMKD.

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.