Interrupting glucagon signaling decreases gluconeogenesis and the fractional extraction of amino acids by liver from blood, resulting in lower glycemia. The resulting hyperaminoacidemia stimulates α cell proliferation and glucagon secretion via a liver/α cell axis. We hypothesized that α cells detect and respond to circulating amino acids’ levels via a unique amino acid transporter repertoire. We found that Slc7a2/SLC7A2 is the most highly expressed cationic amino acid transporter in α cells, with its expression being 3-fold greater in α than β cells in both mouse and human. Employing cell culture, zebrafish, and knockout mouse models, we found that the cationic amino acid arginine and SLC7A2 are required for α cell proliferation in response to interrupted glucagon signaling. Ex vivo and in vivo assessment of islet function in Slc7a2–/– mice showed decreased arginine-stimulated glucagon and insulin secretion. We found that arginine activation of mTOR signaling and induction of the glutamine transporter SLC38A5 was dependent on SLC7A2, showing that the role of both in α cell proliferation is dependent on arginine transport and SLC7A2. Finally, we identified single nucleotide polymorphisms in SLC7A2 associated with HbA1c. Together, these data indicate a central role for SLC7A2 in amino acid–stimulated α cell proliferation and islet hormone secretion.

Phenotyping and genotyping initiatives within the Integrated Islet Distribution Program (IIDP), the largest source of human islets for research in the U.S., provide standardized assessment of islet preparations distributed to researchers and enable the integration of multiple data types. Data from islets of the first 299 organ donors without diabetes analyzed using this pipeline highlights substantial heterogeneity in islet cell composition associated with hormone secretory traits, sex, reported race and ethnicity, genetically predicted ancestry, and genetic risk for type 2 diabetes (T2D). While α and β cell composition influenced insulin and glucagon secretory traits, the abundance of δ cells showed the strongest association with insulin secretion and was also associated with the genetic risk score (GRS) for T2D. These findings have important implications for understanding mechanisms underlying diabetes heterogeneity and islet dysfunction and may provide insight into strategies for personalized medicine and β cell replacement therapy.

Type 1 diabetes is characterised by the autoimmune destruction of pancreatic β-cells, leading to an absolute or near-absolute insulin deficiency. Although traditionally associated with childhood onset, it can manifest at any age, and it is increasingly recognised that there is significant heterogeneity in its clinical presentation. This review examines the intricate interplay between genetic susceptibility, environmental factors, and autoimmune mechanisms that contribute to the pathogenesis of type 1 diabetes. The role of clinical phenotype, along with diagnostic and clinical measurements of autoantibodies and C-peptide, in the classification of type 1 diabetes is discussed, alongside the challenges in diagnosing and classifying the disease. Emerging insights from studies into the heterogeneity of type 1 diabetes phenotypes and mechanistic endotypes underscore the need for refined diagnostic criteria, particularly in identifying autoimmunity in individuals initially diagnosed with type 2 diabetes. The impact of obesity and insulin resistance on disease progression and clinical management is also examined. Overall, this review aims to provide a comprehensive understanding of the evolving landscape of type 1 diabetes, highlighting critical areas for future research and potential therapeutic approaches tailored to individual patient profiles. PLAIN LANGUAGE SUMMARY: Type 1 diabetes is an autoimmune disease where the body does not produce insulin, leading to high blood sugar levels. Traditionally thought to start in children and young adults, type 1 diabetes can occur at any age. However, many factors contribute to when someone develops type 1 diabetes and how rapidly the disease progresses, including a person's combination of genetic factors, including specific genes that can either be protective or high-risk for the development of type 1 diabetes. Although it is widely assumed that a triggering event or events initiate the autoimmune process, the trigger or triggers remain unknown. However, it is this autoimmune process that causes progressive destruction of insulin-producing Β-cells in the pancreas that eventually leads to high blood sugar and the diagnosis of diabetes. Although we have several tools to diagnose and classify diabetes, including measuring autoimmune markers (antibodies) in the blood, there is significant variation in how individuals with type 1 diabetes can present, which can make recognizing and appropriately treating type 1 diabetes more challenging. Finding better ways to characterize the unique characteristics of each subgroup of individuals may provide new insights into how we can best tailor treatment to each of these patient groups.

Individuals with type 1 diabetes (T1D) or permanent neonatal diabetes (PND) due to an INS gene mutation (INS-PND) have a marked reduction in pancreas volume by MRI compared with control individuals with no diabetes (ND). One possible explanation for this is loss of islet-acinar insulin signaling in these forms of severe insulin deficiency. To test the hypothesis that insulin deficiency drives the loss of pancreas volume in diabetes, we used a standardized and validated MRI protocol to measure pancreas volumes in individuals with various forms of monogenic diabetes, including maturity onset diabetes of the young (MODY) and PND (HNF4A-MODY, GCK-MODY, HNF1A-MODY, HNF1B-MODY, INS-MODY, or INS-PND; n = 37), and compared their pancreas volumes with those of previously reported individuals with T1D (n = 93) or healthy control participants with ND (n = 90). Across all monogenic diabetes groups, individuals receiving insulin therapy had significantly smaller pancreas volume compared with those not requiring insulin. These results support the hypothesis that insulin signaling to the exocrine pancreas determines pancreas volume in multiple types of diabetes. ARTICLE HIGHLIGHTS Individuals with type 1 diabetes (T1D) have a markedly smaller pancreas, but the mechanism responsible for the reduction in size is unknown. How pancreas volume differs in individuals with specific forms of monogenic diabetes and how pancreas volume relates to the severity of insulin deficiency are unknown. Measured by MRI, individuals with permanent neonatal diabetes due to an INS gene mutation (INS-PND) or the HNF1B gene associated with maturity onset diabetes of the young had smaller pancreas than individuals without diabetes. Across all types of monogenic diabetes, individuals receiving insulin replacement therapy had smaller pancreas than individuals not using insulin. These results support the conclusion that insulin deficiency is a major factor contributing to changes in pancreas volume in T1D, INS-PND, and other forms of monogenic diabetes.

This Viewpoint offers an account of the events surrounding the discovery of the biological action of glucagon-like peptide-1 and the roles of the individuals who contributed to the discovery.

Introduction and Objective: To investigate the role of cell composition within pancreatic islet and exocrine compartment in the pathophysiology of type 1 diabetes (T1D), we employed immunohistochemistry (IHC) and co-detection by indexing (CODEX), to simultaneously map cell coordinates and quantify protein expression. Building on our previous work demonstrating that the overall human islet endocrine cell mass and composition are unchanged in early-stage type 2 diabetes (T2D), we expanded our analysis to samples from the Human Pancreas Analysis Program (HPAP), which included control (ND), auto-antibody positive (AAB), and T1D donors. Methods: We developed a computational pipeline to analyze the imaging data with millions of cells and tens of protein markers. The key steps include (1) image pre-processing, (2) deep learning-based cell segmentation, (3) cell type annotation, and (4) cell neighborhood analysis to identify specific cellular spatial arrangements. A ground-truth dataset of ~500,000 expert-annotated cells was built to benchmark the pipeline. Results: Our approach effectively segmented cells in regions across diverse cell densities. The pipeline accurately annotated cell types in benchmarking experiments and was robust for rare cell types (e.g., gamma islet cells and B lymphocytes). We further profiled pancreatic cell composition and spatial distribution for 15 HPAP samples (5.5±2.7×105 cells/sample; 6 ND, 4 AAB, and 5 T1D). Compared to ND samples, we observed beta cell loss (p=0.004) and increased colocalization between stellate and ductal cells (p=0.01) in T1D samples and decreased colocalization between delta and COL4A1+ cells (p=0.01) in both AAB and T1D samples. Conclusion: By exploring cell organization and integrating with other modalities including physiological and scRNA-seq data, this approach will provide insight into the connections between spatial cellular organization, cell-cell interactions, and islet function in different forms of diabetes. F. Feng: None. A.L. Hopkirk: None. K. Patel: None. K. Lee: None. A. Eskaros: None. X. Luo: None. A.C. Powers: None. J. Liu: None. D.C. Saunders: None. M. Brissova: None.

The discovery of insulin transformed type 1 diabetes (T1D) from a lethal disease to a chronic health condition where individuals can lead long and productive lives. However, T1D is still associated with considerable morbidity and mortality, underscoring the need for disease-modifying therapies to delay clinical onset and preserve residual pancreatic β-cell function in those newly diagnosed with T1D. Notably, the first disease-modifying therapy (teplizumab, a monoclonal antibody targeting CD3+ on T lymphocytes) was approved by the US Food and Drug Administration in November 2022 to delay the clinical onset of T1D, thus opening new avenues to treat T1D as an immunologic disease rather than simply as a metabolic disease with lifelong insulin administration. In this Scientific Statement, we will integrate and summarize information about the pathogenesis of T1D, highlight gaps in current knowledge, and propose future activities that may lead to additional approaches to treat the underlying autoimmunity and β-cell defects in diabetes. Hopefully, these efforts, when combined with other rapidly improving T1D therapeutics including automated insulin delivery and cell replacement therapy, will lead to better long-term outcomes for those living with T1D.

Genome-wide association studies (GWAS) have identified hundreds of genetic loci linked to pancreatic islet dysfunction and type 2 diabetes (T2D). Many of these loci map to islet genes and their enhancers; however, a complete understanding of the gene regulatory contributions to T2D is lacking. We have previously shown that cell-type specific DNA hypomethylation patterns identify gene enhancers that contribute to the heritability of cell-type relevant traits. Here, we determine a reference dataset of islet hypomethylated regions (HMRs) from DNA methylomes of donors without T2D. Comparing islet HMRs to profiles from functionally diverse cell types, we identify ~4,800 islet-specific HMRs and demonstrate their enrichment for key genes and transcription factor motifs essential for beta cell function. We further show that islet-specific HMRs are highly enriched for GWAS variants that contribute specifically to T2D heritability. Leveraging genotype-phenotype data from the Vanderbilt BioVU biobank, which links patient DNA to electronic health records (EHR), we perform phenome- and lab-wide association scans that replicate islet HMR associations with T2D and uncover novel links to related clinical traits. To overcome challenges posed by stringent GWAS significance thresholds, we develop an HMR-WAS, which allows detection of T2D signals in our smaller BioVU cohort. Our comprehensive framework reveals enhancers of genes involved in insulin secretion and glucose metabolism and demonstrates the impact of T2D-associated variants on their transcriptional activity. By integrating HMRs with patient EHR, our work underscores the potential to reveal new enhancer loci associated with disease risk, especially those missed in a conventional GWAS. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by NIH awards (R01 GM147078 to E.H., R01 DK129469, U01DK135017 to M.B.), Department of Defense Idea award (W81XWH-20-1-0522 to E.H.), an American Cancer Society (ACS) Institutional Research Grant (#IRG-15-169-56 to E.H.), the Vanderbilt University Stanley Cohen Innovation Fund (to E.H), the VU School of Medicine Dean Faculty Fellow Award (to E.H) and funds from the Vanderbilt Ingram Cancer Center. Human pancreatic islets and/or other resources were provided by the NIDDK-funded IIDP (RRID:SCR_014387) at City of Hope, NIH Grant # U24DK098085. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Vanderbilt University Medical Center (VUMC) Institutional Review Board (IRB) oversees use of the Vanderbilt BioVU patient data repository and gave ethical approval for this project (IRB# 190418). Human islets used in this study were obtained from organ donors through the Integrated Islet Distribution Program (IIDP). De-identified donor information, medical histories and clinical characteristics were correlated with data generated in this study. The VUMC IRB determined that studies on de-identified human pancreatic specimens do not qualify as human subject research and are thus exempt from human studies approval. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study have been deposited in the Gene Expression Omnibus (GEO), but the GEO repository (GSE302385) is currently private. As such, all data produced in this study are available upon reasonable request to the authors.

Purpose:Combining different types of medical imaging data, through multimodal fusion, promises better segmentation of anatomical structures, such as the pancreas. Strategic implementation of multimodal fusion could improve our ability to study diseases such as diabetes. However, where to perform fusion in deep learning models is still an open question. It is unclear if there is a single best location to fuse information when analyzing pairs of imperfectly aligned images or if the optimal fusion location depends on the specific model being used. Two main challenges when using multiple imaging modalities to study the pancreas are (1) the pancreas and surrounding abdominal anatomy have a deformable structure, making it difficult to consistently align the images and (2) breathing by the individual during image collection further complicates the alignment between multimodal images. Even after using state-of-the-art deformable image registration techniques, specifically designed to align abdominal images, multimodal images of the abdomen are often not perfectly aligned. We examine how the choice of different fusion points, ranging from early in the image processing pipeline to later stages, impacts the segmentation of the pancreas on imperfectly registered multimodal magnetic resonance (MR) images. Approach:Our dataset consists of 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas segmentation labels drawn mainly based on the T2w images. Because the T2w images were acquired in an interleaved manner across two breath holds and the T1w images on one breath hold, there were three different breath holds impacting the alignment of each pair of images. We used deeds, a state-of-the-art deformable abdominal image registration method to align the image pairs. Then, we trained a collection of basic UNets with different fusion points, spanning from early to late layers in the model, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images. To investigate whether performance differences on key fusion points are generalized to other architectures, we expanded our experiments to nnUNet. Results:The single-modality T2w baseline using a basic UNet model had a median Dice score of 0.766, whereas the same baseline on the nnUNet model achieved 0.824. For each fusion approach, we analyzed the differences in performance with Dice residuals, by subtracting the baseline score from the fusion score for each datapoint. For the basic UNet, the best fusion approach was from early/mid fusion and occurred in the middle of the encoder with a median Dice residual of + 0.012 compared with the baseline. For the nnUNet, the best fusion approach was early fusion through naïve image concatenation before the model, with a median Dice residual of + 0.004 compared with the baseline. After Bonferroni correction, the distributions of the Dice scores for these best fusion approaches were found to be statistically significant ( p < 0.05 ) via the paired Wilcoxon signed-rank test against the baseline. Conclusions:Fusion in specific blocks can improve performance, but the best blocks for fusion are model-specific, and the gains are small. In imperfectly registered datasets, fusion is a nuanced problem, with the art of design remaining vital for uncovering potential insights. Future innovation is needed to better address fusion in cases of imperfect alignment of abdominal image pairs. The code associated with this project is available here https://github.com/MASILab/influence_of_fusion_on_pancreas_segmentation.

OBJECTIVE:To estimate the impact of state-level insulin out-of-pocket caps on changes in out-of-pocket and total costs of insulin and healthcare for insulin users with employer-sponsored insurance. STUDY SETTING AND DESIGN:We evaluated changes in costs using a quasi-experimental (triple difference-in-differences; "DDD") design to analyze multi-carrier claims from insulin users enrolled in fully insured (state-regulated) and self-funded (generally exempt) employer-sponsored plans in 10 states with caps by January 2021 compared to no-cap states pre-/post-cap implementation. Primary outcomes were changes in insulin out-of-pocket spending, total (plan + member) paid for insulin, and total healthcare costs. Secondary outcomes were intermediary (e.g., pharmaceutical) changes in out-of-pocket and total costs. DATA SOURCES AND ANALYTIC SAMPLE:In the policy year (no-cap states: 2021), we identified 218,441 insulin-users in the Health Care Cost Institute 2.0 Dataset (cap states: 27,834 in fully insured and 22,131 in self-funded plans; no-cap states: 97,239 in fully insured and 71,237 in self-funded plans) and 215,635 in the year prior. PRINCIPAL FINDINGS:We found evidence of modest decreases in 30-day standardized (DDD: -$5 [95% CI: -$6 to -$4]; p < 0.001) and annual (DDD: -$67 [95% CI: -$82 to -$51]; p < 0.001) insulin out-of-pocket spending. Savings increased by spending quantile (e.g., 95th-percentile change:-$347 [95% CI: -$460 to $233]). Difference-in-differences (DiD) comparing fully insured to self-funded plans within cap-states showed larger changes (e.g., 95th-percentile annual insulin out-of-pocket:-$484 [95% CI: -$651 to -$318]), likely due to policy spillover effects (i.e., fully insured plans decreased out-of-pocket in no-cap states). Change in annual total paid for healthcare was not statistically significant (DDD:-$1082 [95% CI: -$2918 to $755]; p < 0.25). We saw no evidence of caps increasing out-of-pocket or total spending on insulin, prescriptions, or healthcare. CONCLUSIONS:Our findings suggest early caps had modest effects on out-of-pocket spending among fully insured insulin users, with larger savings for those at the top of the spending distribution and no total cost increases. Policy effects may be greater than observed; they likely lag implementation and develop over time.

Purpose:Although elevated body mass index (BMI) is a well-known risk factor for type 2 diabetes, the disease's presence in some lean adults and absence in others with obesity suggests that more detailed measurements of body composition may uncover abdominal phenotypes of type 2 diabetes. With artificial intelligence (AI) and computed tomography (CT), we can now leverage robust image segmentation to extract detailed measurements of size, shape, and tissue composition from abdominal organs, abdominal muscle, and abdominal fat depots in 3D clinical imaging at scale. This creates an opportunity to empirically define body composition signatures linked to type 2 diabetes risk and protection using large-scale clinical data. Approach:We studied imaging records of 1728 de-identified patients from Vanderbilt University Medical Center with BMI collected from the electronic health record. To uncover BMI-specific diabetic abdominal patterns from clinical CT, we applied our design four times: once on the full cohort ( n = 1728 ) and once on lean ( n = 497 ), overweight ( n = 611 ), and obese ( n = 620 ) subgroups separately. Briefly, our experimental design transforms abdominal scans into collections of explainable measurements, identifies which measurements most strongly predict type 2 diabetes and how they contribute to risk or protection, groups scans by shared model decision patterns, and links those decision patterns back to interpretable abdominal phenotypes in the original explainable measurement space of the abdomen using the following steps. (1) To capture abdominal composition: we represented each scan as a collection of 88 automatically extracted measurements of the size, shape, and fat content of abdominal structures using TotalSegmentator. (2) To learn key predictors: we trained a 10-fold cross-validated random forest classifier with SHapley Additive exPlanations (SHAP) analysis to rank features and estimate their risk-versus-protective effects for type 2 diabetes. (3) To validate individual effects: for the 20 highest-ranked features, we ran univariate logistic regressions to quantify their independent associations with type 2 diabetes. (4) To identify decision-making patterns: we embedded the top-20 SHAP profiles with uniform manifold approximation and projection and applied silhouette-guided K-means to cluster the random forest's decision space. (5) To link decisions to abdominal phenotypes: we fit one-versus-rest classifiers on the original anatomical measurements from each decision cluster and applied a second SHAP analysis to explore whether the random forest's logic had identified abdominal phenotypes. Results:Across the full, lean, overweight, and obese cohorts, the random forest classifier achieved a mean area under the receiver operating characteristic curve (AUC) of 0.72 to 0.74. SHAP highlighted shared type 2 diabetes signatures in each group-fatty skeletal muscle, older age, greater visceral and subcutaneous fat, and a smaller or fat-laden pancreas. Univariate logistic regression confirmed the direction of 14 to 18 of the top 20 predictors within each subgroup ( p < 0.05 ). Clustering the model's decision space further revealed type 2 diabetes-enriched abdominal phenotypes within the lean, overweight, and obese subgroups. Conclusions:We found similar abdominal signatures of type 2 diabetes across the separate lean, overweight, and obese groups, which suggests that the abdominal drivers of type 2 diabetes may be consistent across weight classes. Although our model had a modest AUC, the explainable components allowed for a clear interpretation of feature importance. In addition, in both lean and obese subgroups, the most important feature for identifying type 2 diabetes was fatty skeletal muscle.

Purpose: Understanding how the pancreas changes is critical for detecting deviations in type 2 diabetes and other pancreatic disease. We measure pancreas size and shape using morphological measurements from ages 0 to 90. Our goals are to 1) identify reliable clinical imaging modalities for AI-based pancreas measurement, 2) establish normative morphological aging trends, and 3) detect potential deviations in type 2 diabetes. Approach: We analyzed a clinically acquired dataset of 2533 patients imaged with abdominal CT or MRI. We resampled the scans to 3mm isotropic resolution, segmented the pancreas using automated methods, and extracted 13 morphological pancreas features across the lifespan. First, we assessed CT and MRI measurements to determine which modalities provide consistent lifespan trends. Second, we characterized distributions of normative morphological patterns stratified by age group and sex. Third, we used GAMLSS regression to model pancreas morphology trends in 1350 patients matched for age, sex, and type 2 diabetes status to identify any deviations from normative aging associated with type 2 diabetes. Results: When adjusting for confounders, the aging trends for 10 of 13 morphological features were significantly different between patients with type 2 diabetes and non-diabetic controls (p < 0.05 after multiple comparisons corrections). Additionally, MRI appeared to yield different pancreas measurements than CT using our AI-based method. Conclusions: We provide lifespan trends demonstrating that the size and shape of the pancreas is altered in type 2 diabetes using 675 control patients and 675 diabetes patients. Moreover, our findings reinforce that the pancreas is smaller in type 2 diabetes. Additionally, we contribute a reference of lifespan pancreas morphology from a large cohort of non-diabetic control patients in a clinical setting.

OBJECTIVE:A longstanding question in type 1 diabetes (T1D) research pertains to the selective loss of β-cells whilst neighboring islet α-cells remain unharmed. We examined molecular mechanisms that may underly this differential vulnerability, by investigating the role of RNA editing, a cellular process that prevents double-stranded RNA (dsRNA)-mediated interferon response, in mouse α- and β-cells. METHODS:The enzyme responsible for RNA editing, Adar, was selectively deleted in vivo in mouse β-cells, α-cells, or in both cell types. Subsequent analyses were performed to investigate the impact of deficient RNA editing in α- or β-cells on the interferon response, islet inflammation, cell viability and metabolic outcomes. RESULTS:Mosaic disruption of the Adar gene in mouse β-cells triggers a massive interferon response, islet inflammation and mutant β-cell destruction. Surprisingly, wild type β-cells are also eliminated, whereas neighboring α-cells are unaffected. α-cell Adar deletion leads to only a slight elevation in interferon signature and does not elicit inflammation nor a metabolic phenotype. Concomitant deletion of Adar in α- and β-cells leads to elimination of both cell populations, suggesting that in contrast to β-cells, α-cell death requires both cell autonomous deficiency in RNA editing and exogenous cytokines. CONCLUSIONS:We demonstrate differential sensitivity of mouse α- and β-cells to deficient RNA editing. The resistance of α-cells to RNA editing deficiency and to cytokines mirrors their persistence in T1D, and constitutes a molecularly defined model of differential islet cell vulnerability.

Introduction and Objective: Hyperglycemia, insulin resistance, and hyperglucagonemia are key features of type 2 diabetes (T2D). While insulin resistance increases insulin secretion and beta-cell proliferation in mice, it does not have the same impact on transplanted human beta cells. Since mouse and human glucagon are identical, distinguishing between secreted glucagon from transplanted human islets versus the host mouse pancreas has not been possible. We used a glucagon-knockout immunodeficient mouse model (NSG-GKO) to investigate the impact of high-fat diet on human alpha cell function in vivo. Methods: NSG-GKO mice transplanted with normal human islets (1000 IEQ, n=4 donors) were fed either a HFD (HFD+Islets [I]) or regular diet (RD+I) for 12 weeks. Body weight and random glucose were measured weekly. GTT, serum lipids, fasted (6h) and stimulated (15 min after glucose/arginine [G/A] injection) human insulin and glucagon were assessed every 4 weeks. After 12 weeks, the human grafts were analyzed. Results: By week 3 of the diet, HFD+I mice were glucose intolerant. After 12 weeks, they exhibited a 2.8-fold increase in body weight, higher serum cholesterol, triglycerides, and glucose. Fasted insulin levels at 8 weeks were increased 2.2-fold in HFD-I (p=0.0009) and glucagon slightly increased (43.5±5.3 vs 61.0±8.3pg/mL). G/A stimulated human insulin secretion in the RD+I group (basal vs. stimulated: 0.53±0.08, 0.95±0.18ng/mL, p=0.024) but not in the HFD+I mice (1.21 ± 0.16 vs. 0.76 ± 0.18 ng/mL, p=0.075). In contrast, G/A-stimulated human glucagon secretion was 1.4-fold greater in the HFD+I group compared to the RD+I group. We observed increased amyloid deposits (4.6±1 vs. 8.8±0.7% of insulin area, p=0.0016) in grafts of HFD+I mice and a reduced number of MAFB+ and NKX6.1+ beta cells after 12 weeks of HFD. ARX+, MAFB+ alpha cells were similar in both cohorts. Conclusion: Transplanted human alpha and beta cells have a different response to HFD, with impaired insulin but increased glucagon secretion, suggesting these contribute to hyperglucagonemia and hyperglycemia. C. Dai: None. K.C. Coate: None. R. Brantley: None. E. Acker: None. A. Eskaros: None. A.K. Singh: None. N. Dey: None. R. Jenkins: None. K. Tellez: None. Y. Hang: None. M. Brissova: None. J.J. Wright: None. S. Kim: None. A.C. Powers: None.

Introduction and Objective: Type 1 diabetes (T1D) is a complex autoimmune disorder characterized by the loss of pancreatic islet beta cells. Analyses of pancreatic tissues from organ donors and MRI scans of patients reveal significant changes in both endocrine and exocrine compartments as T1D progresses. To explore the molecular mechanisms behind these changes, we combined single-cell RNA sequencing (scRNA-seq) with co-detection by indexing (CODEX) multiplex tissue imaging, using data from the Human Pancreas Analysis Program (HPAP). Methods: We analyzed transcriptome data from 37 donors: 18 controls without diabetes (ND), 10 autoantibody-positive donors (AAB), and 9 donors with T1D. For a subset of 15 donors, CODEX imaging data and transcriptome data were both available: 6 ND, 4 AAB, and 5 T1D donors. We employed new pipelines for scRNA-seq and CODEX cell annotations, and for pseudo-bulk differential expression analysis. Results: Our pseudo-bulk approach identified differentially expressed genes in the major cell types of the endocrine (alpha, N=16,577 and beta, N=11,446 cells) and exocrine compartments (ductal, N=10,430 and acinar, N=20,058 cells) in both AAB and T1D groups compared to the ND group. To spatially contextualize these changes, we aligned scRNA-seq data with CODEX imaging data (alpha, N=81,243; beta, N=80,292; ductal, N=910,371; acinar, N=393,893 cells) using the cell-cell similarity alignment algorithm CelLink. This algorithm enables the imputation of gene expression within annotated cells in CODEX images by utilizing common marker genes across the two data modalities and developing a pipeline to provide new insights into spatial cell-cell interactions in T1D progression. Conclusion: Our integrative high-dimensional data analysis shows that the pancreatic endocrine and exocrine compartments change before the clinical T1D onset. Further validation of these findings could help identify therapeutic targets within the pancreas to treat or prevent T1D. T.S. Bate: None. X. Luo: None. K.G. Moo: None. F. Feng: None. A.L. Hopkirk: None. K. Patel: None. K. Lee: None. A. Eskaros: None. P. Orchard: None. C. Robertson: None. J. Cartailler: None. D.C. Saunders: None. A.C. Powers: None. J. Liu: None. S.C. Parker: Research Support; Pfizer Inc. M. Brissova: None.

Introduction and Objective: Prediabetes, often classified by static glucose measures, limits identification of impaired beta-cell function beyond response to prevailing insulin sensitivity. We implemented hyperglycemic clamps (HG) to classify prediabetes phenotypes based on stimulated insulin response. Methods: We screened healthy individuals (n=2500, 18-45 yrs, BMI 18.5-25 kg/m2) and recruited (37 normal glycemia, NG; 63 prediabetes, preD) participants based on ADA HbA1c cutoff (5.7). Whole blood was collected at 17 time points during HG clamp. Glucose, insulin, C-peptide, HOMAIR and HOMA B% were measured. Statistical analysis included Wilcoxon test to compare groups and K-means clustering to identify subtypes of prediabetes. Differences between preD clusters at baseline, 1st (10 min) and 2nd phase (120 min) insulin secretion were assessed. Results: Compared to NG, preD had higher glucose (p=0.028) at 120 min, lower C-peptide [baseline (p=0.048), 1st phase (p=0.024), 2nd phase (p=0.006)] and lower HOMA-B at baseline and 2nd phase. K-means clustering using C-peptide and HOMA-B identified two clusters: high insulin cluster (preD1), low insulin cluster (preD2) (Table). Conclusion: HG clamps in prediabetes individuals suggest heterogeneity and majority have lower 2nd phase glucose-stimulated insulin response compared to NG individuals, indicating beta-cell dysfunction as a major contributor to prediabetes in Indians. V. Natarajan: None. B. Attunuru: None. D. Shankar: None. P.R. Somvanshi: None. V. Nayanatara: None. S. Pinninti: None. S. Kulkarni: None. K.R. Choudari: None. L. Staimez: None. A.C. Powers: None. K. Narayan: None. A. Kurpad: None. M. Sasikala: None. DBT Wellcome Trust India Alliance (IA/CRC/23/1/600505)

BACKGROUND:The prevalence of type 2 diabetes (T2D) is higher in Black than in White Americans, and individuals with T2D have an increased cancer risk. We investigated the association of T2D with the risk of all cancers combined and 21 site-specific cancers among predominantly low-income participants who experienced a disproportionately high risk of both T2D and cancer. METHODS:The study included 76,121 participants (mean age, 52.0 years; 67.2% Black) from the Southern Community Cohort Study. T2D was ascertained at the baseline survey. Incident cancer was ascertained via linkage to state cancer registries. Cox proportional hazards models were used to estimate the associations between T2D and cancer after adjusting for confounders. RESULTS:Among participants, 21.2% (N = 16,137) had baseline T2D, and 9.7% (N = 7,376) were diagnosed with incident cancer. Compared with individuals without T2D, individuals with T2D had a significantly elevated risk of all cancers combined (HR, 1.07; 95% confidence interval, 1.01-1.13) and several site-specific cancers, including cancers of the stomach, colorectum, pancreas, liver/intrahepatic bile duct, kidney, and renal pelvis as well as leukemia. The significant association for most cancers was largely observed within 15 years after T2D diagnosis, except for cancer of the pancreas and liver/intrahepatic bile duct, for which elevated risks remain statistically significant 15 to 30 years after T2D diagnosis. CONCLUSIONS:T2D was associated with the risk of overall and certain site-specific cancers in this predominant low-income population. IMPACT:Preventive measures to reduce the burden from T2D could help reduce the risk of overall cancer and several site-specific cancers.

Spatial 'omics technologies are a powerful tool for mapping the relationship between cellular organization and molecular distributions in healthy and diseased tissue microenvironments. Here, we describe a novel multimodal pipeline that represents experimental and computational advances for spatiomolecular analysis of tissue samples across molecular classes. This adaptable method integrates matrix-assisted laser desorption/ionization imaging mass spectrometry spatial lipidomics, spatial transcriptomics, protein imaging via multiplexed immunofluorescence microscopy, and histopathological staining to uncover spatiomolecular profiles associated with unique cellular niches and pathological features. We demonstrate the power of this approach using two different complex human disease systems: Alzheimer's disease in human brain tissue and type 2 diabetes mellitus in the human pancreas. This work establishes and demonstrates a generalizable framework for multimodal spatial integration, enabling precise mapping of molecular mechanisms that underlie complex tissue pathologies.

METHODS:Investigated the association of multiple cardiometabolic comorbidities with total/major cause-specific mortality and evaluate if this association might be modified by race among predominantly low-income Black and White participants. METHODS:The Southern Community Cohort Study, prospective cohort study. Participants (40-79 years) recruited predominantly from community health centers across 12 states in southeastern United States. Enrollment began in 2002 and concluded in 2009, follow-up until 2020. Cardiometabolic comorbidities (diabetes, hypertension, myocardial infarction, stroke) ascertained at the baseline survey. Cox proportional hazard models used. RESULTS:Study included 76,721 participants; 16,197, 41,944, 5,247, and 4,919 participants with prior diagnosis of diabetes, hypertension, myocardial infarction, and stroke, respectively at baseline. Compared to individuals with no comorbidity, individuals with any single comorbidity experienced a significantly 30 to 90% increased rate of death due to any causes. The increase in mortality was elevated with an increasing number of comorbidities, with HR of 3.81 (95% CI: 3.26-4.46) and a cumulative risk of 62.5% at age 75 years for total mortality for those with four comorbidities. The risk was high for death due to cardiovascular diseases (HR: 6.18, 95% CI: 5.12-7.47). These associations were stronger among Blacks than Whites. Individuals with four comorbidities at age 40 years were estimated to have a 16-year loss in life expectancy compared with those without any comorbidity. CONCLUSION:Cardiometabolic comorbidities were associated with increases in all-cause and major cause-specific mortality, particularly Black Americans. This study calls for effective measures to prevent cardiometabolic comorbidities to reduce premature deaths in underserved Americans.