Diabetes

Pancreatic islets regulate blood glucose homeostasis. Although islet architecture is stable under homeostatic conditions, increased metabolic demand drives compensatory islet expansion. In mice, islets are organized as a {beta} cell core surrounded by a mantle of and {delta} cells. The formation of islet architecture during development requires expression of Roundabout receptors 1 and 2 (Robo1/2) in endocrine cells and of Slits 2 and 3 (Slit2/3) from islet-extrinsic sources. Furthermore, expression of Robo2 in endocrine cells is required to maintain islet architecture in the adult mouse. However, the cellular sources of Slit2/3 in the adult pancreas and their expression dynamics during islet expansion remain unknown. Here, we identify distinct stromal populations, including fibroblasts and pericytes, as well as neurons within intrapancreatic ganglia, as the sources of Slit2/3. We further show that Slit3 expression is increased in Ob/Ob mice, and that SLIT2 expression is elevated in stromal cell populations of humans with type 2 diabetes. The expression of neither Slit2 nor Slit3 was affected by deletion of Robo2 in {beta} cells. Together, these findings define the cellular origins of Slit2/3 and their expression dynamics in the adult pancreas, supporting a potential role for Slit signaling in the diabetic islet microenvironment.

PRV-101 is a multivalent formalin-inactivated Coxsackievirus B (CVB) vaccine developed to prevent CVB infections, which are associated with increased risk of islet autoimmunity. While PRV-101 induces robust neutralizing antibody responses, its T-cell immunogenicity is unknown. We analyzed peripheral blood mononuclear cells from 25 healthy adults receiving three high or low PRV-101 doses or placebo in a Phase I randomized, placebo-controlled trial. CVB-reactive CD8 T-cell responses were assessed using HLA Class I multimers, and CD4 and T follicular helper (Tfh) responses were measured by activation-induced marker assays following stimulation with a CVB peptide library. PRV-101 elicited minimal CVB-reactive CD8 T-cell responses but robust CD4 and Tfh responses, peaking at week 12 and persisting through week 32. Responses were observed in both seronegative and seropositive individuals, consistent with effective immune priming and boosting. Tfh frequencies correlated with neutralizing antibody titers. Female participants exhibited higher peak Tfh responses than males. We conclude that PRV-101 elicits a CVB-protective immune profile, dominated by Tfh responses supporting durable humoral immunity and devoid of potentially diabetogenic cytotoxic T-cell responses. This profile invites further investigations in vaccine trials for type 1 diabetes prevention.

Type 1 diabetes (T1D) is characterized by the autoimmune destruction of pancreatic beta cells. While most beta cells are lost, a subset of beta cells persists years and even decades after disease onset. Studying these surviving cells is challenging, and thus how they escape immune killing remains poorly understood. Here, we applied a gene regulatory network inference-based clustering approach on existing islet scRNAseq data from cadaveric donors with T1D, autoantibody positive donors at risk for T1D, and non-diabetic donors to analyze beta cells from patients with established T1D. This approach identified a novel beta cell subtype enriched in T1D donors defined by the activity of several transcription factors which have well-characterized roles in beta cell survival, most notably IRF1. We found increased expression of immunomodulatory genes (e.g. SOCS1/3, HLA-E) as well as decreased expression of autoantigens and secretory genes, suggesting dedifferentiation. We identified inflammatory cytokines as a driver of this phenotype by reanalyzing public data from primary human beta cells stimulated with inflammatory cytokines in vitro. We additionally find a similar transcriptional program active in a subset of alpha cells, consistent with cell-extrinsic inflammatory cytokine signaling in vivo. Overall, we propose that this population represents a resilient beta cell phenotype, and that the transcriptional program active in these cells may identify targets for T1D prevention and reversal.

Obesity is a chronic inflammatory disease associated with immune dysregulation. However, alterations in adaptive immune function remain unclear, particularly in the setting of childhood obesity and weight loss. We defined peripheral T cell dysregulation in a cross-sectional cohort of pediatric participants across weight categories and in a longitudinal cohort of adolescents with severe obesity undergoing bariatric surgery. We found increased expression of activation markers (including PD-1 and CD69) in non-naive CD8+ T cells whereas non-naive CD4+ T cells were skewed towards Tfh, Th17, and mixed Th2/Th17 populations. Consistent with a hyperactive state, T cells had enhanced capacity for inflammatory cytokine production (including IFN-{gamma} and TNF-), along with enrichment of gene sets associated with cytokine signaling, cell proliferation, and cell death. Notably, these phenotypic, functional, and transcriptional alterations were not fully resolved after bariatric surgery, despite clinically meaningful weight loss. Together, these findings demonstrate that pediatric obesity leads to dysregulation of adaptive immune function with incomplete normalization after weight loss. SUMMARYThe impact of pediatric obesity on immune cell function is not well understood. This study demonstrates that both CD4+ and CD8+ T cells are dysregulated in children living with obesity and further identifies that this dysregulated state persists following clinically significant weight loss.

BackgroundObesity arises from a complex interplay of genetic and environmental factors, with alterations of transcriptional networks that integrate metabolic, immune, and regulatory pathways. Conventional measures such as body mass index (BMI) quantify body size but fail to capture the molecular heterogeneity underlying divergent metabolic outcomes. We therefore sought to construct a gene expression-based transcriptomic representation of obesity, using BMI as a practical training anchor, and to use this framework to delineate transcriptional programs associated with metabolically healthy and pathogenic obesity, with subsequent projection to mouse transcriptomic data for cross-species validation. MethodsTranscriptome data of human visceral adipose tissue (N= 1,298) were used to derive the transcriptomic BMI model, and genes contributing to the model were functionally annotated by gene set enrichment analysis. The human-trained model was subsequently applied to mouse selection lines (N = 18) with divergent obesity phenotypes. In the human cohort, post hoc stratification into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) groups was performed using a downstream classification framework incorporating observed BMI together with predicted BMI, to assess whether model-derived predicted BMI reflected obesity-related pathophysiologic status. ResultsModel-selected genes were involved in coordinated regulation of lipid metabolism, immune activation, and growth signaling, extending to mitochondrial and translational pathways. Cross-species analyses uncovered conserved metabolic polarization: DU6 mice exhibited lipid-anabolic and inflammatory remodeling, whereas DU6P mice displayed oxidative, mitochondrial, and GH-axis-enriched transcriptional states. In human cohorts, MHO individuals showed upregulation of mitochondrial energetics and protein synthesis, while MUO individuals were characterized by increased autophagy, lipid catabolism, and stress-adaptive signaling on the transcriptional level. Together, these findings define a conserved molecular continuum linking oxidative efficiency to metabolic health and inflammation to metabolic vulnerability. ConclusionsThis integrative transcriptomic framework bridges human and mouse adipose biology to uncover conserved mechanisms underlying obesity phenotypes. By contrasting mitochondrial and translational programs with inflammatory and catabolic pathways, it provides mechanistic insight into metabolic resilience and a foundation for precision approaches to obesity management.

Variants in the human PAX4 gene are associated with both monogenic and complex forms of diabetes, yet their pathogenic effects remain difficult to define in models that accurately mimic human islet architecture and neonatal metabolic transitions. Here, we created a porcine PAX4 loss-of-function model using CRISPR/Cas9 cytidine deaminase base editing to introduce a premature stop codon in the PAX4 coding sequence. PAX4 knockout piglets developed severe hyperglycemia within 24 hours of birth, followed by rapid postnatal clinical deterioration and uniform death by day 3. Biochemical analysis showed significant diabetic decompensation, including electrolyte imbalances, hyperosmolality, azotemia, dyslipidemia, and metabolic acidosis. Gross and histological examinations revealed notable pancreatic hypoplasia with preservation of exocrine tissue. Single-nucleus RNA sequencing and immunohistochemistry demonstrated an almost complete loss of insulin-and somatostatin-producing {beta}-and {delta}-cells, respectively, with relative preservation of glucagon-expressing -cells. Overall, these results establish PAX4 as a crucial factor in pancreatic endocrine development and postnatal glucose regulation in a large-animal model. This platform offers a human-relevant system for studying diabetes-associated PAX4 variants and for testing regenerative and gene-based therapies for insulin-deficient diabetes.

White adipose tissue and pancreatic islets play central roles in the regulation of metabolic homeostasis. Although ectopic lipid accumulation is established as a driver of impaired insulin secretion, the acute contribution of adipocyte lipolysis to islet function remains poorly documented. Here, we investigated a mouse model with inducible adipocyte-specific deletion of both adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL), which leads to defective adipocyte lipolysis. Despite preserved ex vivo islet function, these mice displayed a marked reduction in insulin secretion in response to stimulation of adipocyte {beta}3-adrenoceptors, as well as following glucose and arginine challenges. Mechanistically, we identified non-esterified fatty acids as critical mediators of lipolysis-driven insulin secretion, engaging pancreatic signaling of the free fatty acid receptors FFAR4 (a.k.a. GPR120) and FFAR1 (a.k.a. GPR40). The regulation of insulin secretion by adipocyte lipolysis was preserved in high-fat diet-induced obesity. These findings identify an underappreciated adipose-islet crosstalk that couples adipocyte lipolysis to insulin secretion and links lipid and glucose metabolism.

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.

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.

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.

Insulinomas are rare and benign human pancreatic adenomas that overproduce insulin and display increased beta cell mass. We and others have shown that transcriptomic and genomic profiling on insulinomas provides a data mine for identifying targets that can be manipulated to induce human beta cell regeneration. Majority of causative genetic variants in insulinomas involve epigenetic regulatory genes. Yet, specifically how these variants lead to human beta cell expansion and increased function is largely unknown. Here, we performed bulk and single-nucleus epigenomic and transcriptomic profiling to define regulatory alterations in human insulinomas. Bulk ATAC-seq and H3K27Ac ChIP-seq revealed significant enrichment of AP-1 transcription factor binding motifs within beta cell-associated open chromatin/enhancer regions in normal islets, accompanied by robust expression of AP-1 family members; in contrast, insulinomas exhibited marked reductions in both AP-1 motif enrichment and AP-1 expression. Our snRNA-seq and snATAC-seq profiling across four independent insulinomas identified a consistent and previously unrecognized signature defined by suppression of AP-1 transcription factors and widespread loss of chromatin accessibility at AP-1 binding sites, particularly at enhancers governing beta cell identity. Collectively, these results establish AP-1-mediated regulatory programs as critical determinants of beta cell maturation and define their disruption as a signature among human insulinomas.

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.

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.

Glycosuria, whether genetically induced or triggered by SGLT2 inhibitors, activates compensatory glucose-producing pathways that limit glucose lowering in type 2 diabetes. To define these pathways, we studied renal Glut2 knockout mice, which progressively lose Slc5a2 (encoding SGLT2) expression yet maintain normoglycemia despite marked urinary glucose loss. Metabolic profiling and isotope tracing revealed coordinated adaptations in mannose and glutamine metabolism during glycosuria. Skeletal muscle reduced glucose utilization and instead oxidized mannose, while whole-body glycolysis declined, establishing a systemic glucose-sparing state. Disruption of glutamine transport or mannose utilization caused hypoglycemia in mice treated with an SGLT2 inhibitor, demonstrating dependence on these substrates to maintain glucose homeostasis during glycosuria. Multiomic profiling revealed increased expression and chromatin accessibility of mannose and glutamine transport pathways. These findings identify a kidney-driven metabolic program that preserves systemic glucose homeostasis during glycosuria and may inform strategies to optimize the glucose-lowering efficacy of SGLT2 inhibitors.

How cell type, sex and disease interact and affect gene expression and splicing is an important, but complicated question. Visualizing and testing specific hypotheses around these complex interactions is an important first step to identifying molecular components underpinning complex disease. Using a meta-analytical framework, we develop an analytical path for identifying testable molecular hypotheses of complex interactions between splicing, sex, disease and cell type. We focus on type 1 diabetes (T1D) but the approach is generalizable to any complex disease with defined candidate loci. Previous studies report T1D-associated splicing in candidate genes, differences in disease effects across immune cell types, sex effects on splicing and cell-type-specific splicing. However, identifying and interpreting complex interactions between sex, splicing and disease are challenging. Here we demonstrate how a gene expression study of T1D, designed to evaluate these interactions can be analyzed in a straightforward manner. We find that sex-dependent T1D-associated splicing is markedly more prevalent in CD4 T cells than in CD8 T cells, affecting 72% of T1D candidate genes in CD4 cells compared to 30% in CD8 cells. We pinpoint exons whose rate of inclusion is affected by the interaction of sex and disease. We use long-read RNAseq to identify novel intron retention events and splice sites which are quantified with short-reads leading to a richer description of the regulatory impact of T1D on alternative splicing. We identify a set of candidate isoforms for follow-up molecular studies in BACH2, a transcription factor known to be relevant in disease prevalence.

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.

Pregnancy is a period of extensive metabolic rewiring. Insulin secreting {beta}-cells respond to the metabolic challenges of pregnancy by increasing their mass and size and by altering secretory patterns to maintain glucose homeostasis. If glucose metabolism is not tightly controlled, gestational diabetes may develop. Most studies on {beta}-cell adaptation during pregnancy are derived from rodent models, making translation to the vastly different human gestational setting challenging. In this work, we performed an extensive characterization of pancreatic adaptations throughout porcine pregnancy. Pigs have a long gestational period (114 days) and share a similar size and metabolism to humans, making them an ideal model to bridge the knowledge gap between rodents and humans. By analyzing pancreatic samples from early and late gestational ages, we captured the full trajectory of endocrine remodeling. We observed pregnancy-driven remodeling of endocrine cell types, marked by preferential expansion of pancreatic polypeptide-secreting cells. Proteomic characterization of the pancreas from early and late gestation showed a downregulation of SLC20A2 and ZCCHC7, identifying new protein targets involved in physiological endocrine cell adaptation. Overall, our comprehensive characterization of pancreatic adaptations in the pig model helps bridge the translational gap between rodents and humans and highlights previously unrecognized proteins with therapeutic potential for gestational diabetes.

Weight cycling (WC), defined as weight gain, loss, and regain, is common in obesity, but its metabolic consequences remain unclear. We tested whether WC-aggravated glucose intolerance in obesity is age-dependent and linked to circadian disruption. Young (7w) and mid-aged (12m) mice underwent a 15-week dietary intervention: Lean and Obese mice fed normal chow (NC) and high-fat diet (HFD) throughout, respectively. WC mice undergone HFD-induced weight gain, NC-induced weight loss, and a second HFD-induced weight regain. Late-onset obese (LO) mice ate HFD only paralleling weight regain of WC. In young, but not mid-aged mice, prior obesity accelerated weight regain upon HFD re-exposure, and aggravated glucose intolerance beyond that observed in Obese mice. This occurred without a worse adipose inflammatory profile. Rather, WC young mice exhibited blunting of light/dark-phase oscillation of feeding and energy metabolism, adipose and hepatic core clock gene oscillation, and increased hepatic expression of clock and gluconeogenic genes during the inactive phase. Restricting food availability to the active phase did not alter final weight regain, but improved glucose tolerance selectively in WC mice, normalized hepatic gluconeogenic and clock-genes expression in both liver and adipose tissue. These findings identify circadian disruption as a modifiable mediator of the adverse metabolic impact of WC in young-adulthood obesity. HighlightsO_LIWeight cycling is common in obesity, but whether it worsens metabolic dysfunction beyond persistent obesity remains unclear. C_LIO_LIWe asked whether weight cycling aggravates glucose intolerance in an age-dependent manner and whether circadian disruption contributes to this effect. C_LIO_LIIn young, but not mid-aged mice, weight cycling accelerated weight regain and worsened glucose intolerance, accompanied by blunted diurnal oscillation of behavioral parameters and core clock gene expression, without exaggerated adipose inflammation. C_LIO_LIActive-phase time-restricted feeding improved WC-induced aggravated glucose tolerance and circadian oscillation, identifying circadian disruption as a modifiable mechanism linking weight cycling adverse metabolic outcomes in young-adulthood obesity. C_LI