Diabetes

PURPOSE: To assess the predictive performance of panel protein biomarkers as well as an established algorithm that considers repeat biomarker testing for risk prediction of PDAC among a prospective cohort of patients with New-onset diabetes. PATIENTS AND METHODS: A panel of protein biomarkers (CA19-9, CA125, CEA, LRG1, REG3A and TIMP1) were assayed in 6,516 serially collected pre-diagnostic plasma samples from 2,121 NOD patients from the Consortium of Chronic Pancreatitis Diabetes and Pancreatic Cancer (CPDPC)-initiated NOD study who completed the 3-year study follow-up period. The specimen set included 25 pre-diagnostic samples from the 12 PDAC cases diagnosed during study follow-up. We applied a single threshold (ST) method, which considers biomarker levels at a single time point, as well as a previously established parametrical empirical Bayes (PEB) algorithm, which considers prior biomarker measurements, with case calls made based on pre-specified cutoffs corresponding to 1% 1-year risk. Resultant biomarker data as well as case calls were provided to the EDRN Data Management and Coordinating Center as part of a Prospective-sample-collection-Retrospective-Blinded-Evaluation (ProBE)-compliant Phase 3 biomarker validation study. Area under the Receiver Operating Characteristic Curves (AUC), sensitivity, specificity, population-level positive predictive value (PPV), and negative predictive value (NPV) are reported. RESULTS: The 3-year incidence of PDAC in the NOD cohort was 0.57%. When considering PDAC vs non-cancer controls, respective AUCs of individual protein biomarkers ranged from 0.52-0.94, with CA19-9 achieving the highest overall performance of 0.94 (95% CI: 0.86-1.00). At the pre-defined 1% 1-year risk threshold, CA19-9 yielded sensitivity of 83.3% at 97.2% specificity. Additional markers CEA, CA125, and TIMP1 demonstrated sensitivity of 33.3%, 41.7%, and 8.3%, respectively. In a subset of patients, CA19-9 first tested positive at a median (interquartile range [IQR]) of 7 months (4 to 14 months) prior to clinical PDAC diagnosis. Of the two PDAC cases missed by CA19-9 using the ST method, one (diagnosed with stage III PDAC) was detected using the PEBCA19-9 algorithm. CONCLUSION: In the setting of adult new onset diabetes, CA19-9 is a readily available and promising biomarker that can be leveraged for earlier detection of an underlying pancreatic cancer. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

Type 1 diabetes (T1D) is an autoimmune disease characterized by CD8 T cell-mediated destruction of pancreatic {beta} cells; however, the cellular interactions that organize immune activation within human islets remain poorly understood. Here, we integrated thirteen CD45 immune cell single-cell RNA sequencing datasets from human islets spanning non-diabetic donors, stage 3 T1D, and type 2 diabetes (T2D) to comprehensively define immune cell heterogeneity and decipher the intercellular communication networks that drive islet autoimmunity. We identified distinct macrophage states, including CD14 inflammatory macrophages, CD14/TREM2 macrophages, and quiescent-like macrophages, together with CD8 T cells and mast cells. Trajectory and communication analyses revealed CD14 macrophages as central immune hubs that coordinate antigen presentation, costimulatory signaling, and inflammatory chemokine production. Compared with non-diabetic and type 2 diabetic islets, T1D macrophages displayed a disease-specific inflammatory program characterized by enhanced TNF, IL18, CCL3, CCL4, CCL5, and ICOSLG expression, supporting CD8 T cell recruitment and activation. Spatial transcriptomic analysis of human T1D pancreas further demonstrated a {beta}-cell-macrophage-CD8 T cell inflammatory niche, where macrophage-derived CCL3/CCL4/CCL5 and CD8 T cell-expressed CCR5 suggest a chemokine-mediated mechanism of immune targeting. Together, these findings identify CD14 macrophages as key orchestrators of a feed-forward inflammatory circuit driving human islet autoimmunity.

Dysregulated hormone secretion and erosion of endocrine cell identity are features of type 1 and type 2 diabetes, but the transcriptional programs maintaining adult human islet identity and function remain poorly defined. The large MAF transcription factor MAFB is expressed in human - and {beta}-cells, marks their most functionally mature subpopulations, and is downregulated in diabetes, but its role in adult human islets has not been tested directly. Using shRNA-mediated MAFB knockdown (KD) in whole and CD26+ -cell-enriched human pseudoislets, we found that whole pseudoislet MAFB KD impaired glucagon synthesis and secretion while only modestly reducing insulin content and cAMP-potentiated insulin release. Single-cell profiling detected no {beta}-cell transcriptional response beyond MAFB KD itself, consistent with buffering by the related {beta}-cell-enriched MAFA transcription factor. In contrast, -cell-restricted MAFB KD unmasked a cell-autonomous requirement for MAFB in stimulus-secretion coupling. MAFB deficiency also destabilized -cell identity, downregulating canonical -cell and neuroendocrine secretory genes while ectopically inducing mesenchymal and extracellular matrix remodeling programs. In addition, MAFB-dependent downregulation of electron transport chain genes was confined to a large -cell subcluster, manifesting as impaired islet-wide mitochondrial respiration within the broader -cell population. Together, these findings identify MAFB as an essential adult human -cell maintenance factor that links diabetes-associated downregulation to impaired glucagon secretion, -cell identity erosion, and mitochondrial dysfunction. RESEARCH IN CONTEXTO_LIWhat is already known about this subject? O_LIMAFB is expressed in adult human - and {beta}-cells, marks their most functionally mature subpopulations, and is downregulated in type 1 and type 2 diabetes C_LIO_LIIn human stem cell models, MAFB is essential for generating insulin-producing {beta}-like cells, whereas glucagon-producing -like cells are reduced but still formed C_LIO_LINeither model addresses adult human islets: rodent MafB becomes -cell restricted after birth, and stem cell models capture differentiation, not maintenance C_LI C_LIO_LIWhat is the key question? O_LIIs MAFB required to maintain identity and secretory function in adult human islet cells? C_LI C_LIO_LIWhat are the new findings? O_LIMAFB knockdown in primary human pseudoislets impaired glucagon synthesis and secretion but minimally affected {beta}-cells, consistent with buffering by MAFA C_LIO_LIKnockdown in CD26+ -cell-enriched pseudoislets revealed a cell-autonomous requirement for MAFB in stimulus-secretion coupling, and destabilized -cell identity by inducing mesenchymal and extracellular matrix programs C_LIO_LIMAFB loss downregulated electron transport chain genes in the largest -cell subcluster and reduced mitochondrial respiration C_LI C_LIO_LIHow might this impact on clinical practice in the foreseeable future? O_LIPreserving MAFB activity in adult human -cells may represent a strategy to limit -cell dysfunction in diabetes C_LI C_LI

Insulin production is a cardinal feature of pancreatic {beta} cells. Studies in rodents show that {beta} cells can switch between low and high insulin gene activity states and that elevated insulin production makes {beta} cells more vulnerable to stresses associated with diabetes. In people, genetically elevated insulin production increases the risk of type 1 diabetes. Via effects on obesity, hyperinsulinemia contributes to the pathogenesis of type 2 diabetes. Here, we characterize {beta} cells in low and high INS gene activity states sorted from primary human islets transduced with INS-GFP adenovirus and differentiated INS-EGFP knock-in embryonic stem cells (SC{beta} cells). We profile {beta} cell function, protein synthesis, resilience to diabetes associated stress, single {beta} cell transcriptomes and their co-activity networks, and purified {beta} cell proteomes. We show that human {beta} cells transition between distinct states. High INS cells have elevated maturity marker mRNAs and proteins, increased protein translation, are larger, but also more susceptible to cell death when exposed to diabetes-relevant stresses. We also catalogue thousands of differences in proteins in high INS stem cell-derived {beta} cells compared directly with high INS primary {beta} cells. Our study improves our understanding of the delicate balance between insulin production and {beta} cell resilience and guides the engineering of better {beta} cells. Blurbtranscriptional, proteomic, and functional analyses of insulin gene expression states in human {beta} cells from donor islets and stem cells Key findingsO_LIWe identify high and low INS gene activity states in human insulin-producing cells from donor islets and embryonic stem cell differentiations. C_LIO_LIWe characterize the relationship between insulin production and fragility, demonstrating that increased insulin production comes at a cost of reduced resilience to multiple stresses. C_LIO_LIFunctional, transcriptomic, and proteomic analyses identify similarities and differences between how primary and stem cell-derived {beta} cells manage stress and insulin production. C_LIO_LIWe report a comprehensive side-by-side proteomic analysis of purified primary and stem cell- derived {beta} cells in the high INS state and identify differences in protein production and secretion machinery, providing a roadmap for making better {beta} cells. C_LI

Body mass index (BMI) alone may underestimate clinically relevant obesity because it does not capture central fat distribution. We compared obesity prevalence in Mexico using BMI-only criteria, adiposity-confirmed criteria, and the clinical obesity definition proposed by the Lancet Diabetes and Endocrinology Commission. We conducted a population-based, cross-sectional study of 13,160 adults aged 18 years or older who participated in the 2018-2019 Mexican National Health and Nutrition Survey (ENSANUT). Obesity prevalence was estimated through survey-weighted analyses that accounted for the complex sampling design. The weighted prevalence of obesity based on BMI was 34.5% (95% CI, 33.1-35.9), while 30.9% (95% CI, 29.6-32.2) met criteria for clinical obesity. One quarter of individuals with clinical obesity had a BMI under 30 kg/m2, a phenotype more common among older adults. Half of adults with a BMI under 30 kg/m2 showed elevated central adiposity. BMI alone underestimates clinically relevant obesity in Mexican adults. Adding waist-based measurements could improve the identification of individuals with excess fat and metabolic risk, both in clinical settings and population monitoring.

Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L, ATF2, SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.

Objective: To determine whether continuous glucose monitoring (CGM) identifies clinically relevant glycemic heterogeneity and subclinical end-organ alterations in adults without diabetes. Research Design and Methods: We analyzed 1,017 AI-READI Year 3 participants without diabetes (558 with normoglycemia and 459 with prediabetes by A1C). Fifty-two metrics from 10-day blinded CGM were reduced to nonredundant glycemic axes. Partial Spearman correlations between representative CGM metrics and clinical measures across 13 domains were adjusted for age, sex, and BMI and controlled for false discovery rate. CGM-derived subphenotypes were identified using unsupervised UMAP-HDBSCAN-based clustering. Results: Among 462 glycemic-clinical associations tested, 99 (21.4%) remained significant after false discovery rate correction. Hyperglycemia-related metrics, including mean glucose, time above range, and time in tight range, showed more associations than variability metrics. The strongest signals involved cardiometabolic, cardiovascular, and cognitive measures. Greater hyperglycemia and glucose excursions were associated with lower language performance, slower processing speed, and lower cognitive efficiency ({rho} {approx} -0.10 to -0.14; all P < 0.01). Clustering identified four reproducible glycemic subphenotypes: Healthy, Mild Hyperglycemia, High Variability, and Hyperglycemia. CGM phenotypes reclassified A1C-defined groups: 58.1% of participants with normoglycemia fell into dysglycemic phenotypes, whereas 18.8% of participants with prediabetes fell into more favorable phenotypes. The Hyperglycemia phenotype had the most adverse cardiometabolic profile and lower cognitive performance. Conclusions: In adults without diabetes, CGM revealed glycemic patterns associated with distinct subclinical alterations. CGM-based phenotyping may complement A1C for characterizing early dysglycemia and selecting individuals for longitudinal risk-stratification studies.

Trisomy 21 or Down syndrome (DS) affects multi-organ systems across the lifespan. The presence of an extra chromosome, along with genome dosage imbalance due to triplicated genes, contributes to the DS phenotypes. Of the DS mouse models, few are aneuploid with a freely segregating extra chromosome. We previously showed that the aneuploid Ts65Dn mice exhibit metabolic deficits consistent with the metabolic profile of DS. However, the genotype-phenotype relationships in Ts65Dn mice are complicated by the presence of triplicated genes unrelated to human chromosome 21 (Hsa21). To address this issue, we leveraged a refined model, Ts66Yah, where the extra triplicated genes in Ts65Dn have been removed. Deep phenotyping and multi-omics analyses showed that Ts66Yah mice develop pronounced and widespread metabolic disturbances. Despite sexual dimorphism in weight gain, body temperature, lipid and lipoprotein profiles, hepatic injury and adipose fibrosis, both male and female Ts66Yah mice share a common phenotype of pronounced glucose intolerance and insulin resistance, reduced mitochondrial respiratory capacity in visceral fat, altered serum inflammatory cytokine profile, and dysregulated serum and liver metabolomes. Pan-tissue transcriptomes also reveal signatures of immune activation, disrupted metabolic processes and cellular respiration, altered cytokine signaling, enhanced oxidative stress, and extracellular matrix remodeling. These combined changes across tissues disrupt metabolic homeostasis more severely in Ts66Yah than in Ts65Dn mice. Several phenotypes, including glucose intolerance, insulin resistance, tissue fibrosis, and oxidative stress were further exacerbated by an obesogenic diet. This foundational data establishes Ts66Yah as a valuable reference model for the mechanistic and comparative study of metabolic dysfunction in DS.

Background. Type 2 diabetes (T2D) and coronary artery disease (CAD) frequently co-occur, yet the biological pathways that jointly determine risk remain incompletely understood. Most genetic studies have examined shared risk from a single-disease perspective, limiting insight into the mechanisms that generate discordant risk between conditions. Methods. We applied PLACO to multi-ancestry GWAS data of T2D and CAD to identify shared loci, prioritising shared causal signals using colocalisation. Shared variants were clustered by their associations with 77 cardiometabolic traits, and cluster-specific genetic risk scores (GRS) were tested for association with 17 clinical biomarkers and 1,254 binary outcomes in 378,772 UK Biobank (UKB) participants. Two-sample Mendelian randomisation (MR) was used to test the causal role of liver fat. Results. We identified 149 loci shared between T2D and CAD; most novel loci had discordant effects (35 of 42), in contrast to the predominantly concordant signals reported previously. Clustering 187 independent shared variants revealed seven mechanistic clusters, three of them centred on liver fat and defined by discordant T2D?CAD effects. Enrichment analyses and cluster-GRS associations in UKB highlight associations between higher liver fat and T2D risk with a cardioprotective lipid profile and reduced CAD risk. Genetically higher liver fat increased T2D risk but lowered CAD risk in MR analyses; partitioning liver fat instruments by their effect on ApoB-containing lipoproteins indicates that the CAD effects are determined more by effects of circulating ApoB rather than liver fat itself. Conclusions. Liver fat largely sets the direction of T2D risk, whereas the fate of that lipid, retained in the liver with low circulating ApoB or exported as ApoB-containing lipoproteins, sets the direction of CAD risk. This liver-centric partitioning provides a mechanistic framework for the discordant cardiometabolic effects of hepatic lipid and lipid-lowering pathways, with implications for precision prevention.

The release of glucagon from pancreatic alpha cells is a core component of hypoglycaemic counter regulation. Several mechanisms regulate glucagon release including paracrine control by neighbouring cell types, and changes in extracellular glucose. While the inhibitory effect of glucose on glucagon secretion is well established, the exact way in which glucose metabolism contributes to alpha cell function remains unclear. Here, we use live-cell imaging of the redox potential in alpha cells within intact islets to investigate whether non-mitochondrial glucose metabolism contributes to the potentiation of glucagon secretion at low glucose. Our findings show that increased glucose metabolism through the pentose phosphate pathway elevates the cytosolic redox potential in alpha cells. Using a combination of antioxidant treatment and pre-incubation in 5 mM glucose, we find that the cytosolic redox potential affects PKA activity in alpha cells and that changes in whole body redox state affects the counterregulatory response in mice. These findings indicate that prior glucose-driven redox potential charging is essential for maintaining glucagon secretion at low glucose.

Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are increasingly used for type 2 diabetes. Despite established metabolic, cardiovascular, and renal benefits, it remains uncertain whether GLP-1 receptor agonists are associated with longer clinically recorded Alzheimer's disease (AD)-type dementia-free survival than sulfonylureas (SU) or SGLT2 inhibitors. Methods Using All of Us electronic health records, we emulated target trials among adults aged 55 years or older with type 2 diabetes, a 12-month washout, and no prior dementia. We compared GLP-1 receptor agonists with SU and SGLT2 inhibitors. Propensity score weighting and doubly robust estimation addressed confounding. Causal survival forests estimated individualized treatment effects on 48-month RMST free from clinically recorded AD-type dementia. Findings In the GLP-1 receptor agonist versus SU comparison (6,328 individuals; 48-month NNT approximately 202), initiation was associated with a small but statistically significant increase in AD-type dementia-free survival (ATE 0.21 months; 95% CI: 0.07-0.35). The highest-benefit stratum gained 0.45 months (95% CI: 0.28-0.62). In the SGLT2 inhibitor comparison (3,070 individuals; 48-month NNT approximately 245), the average effect was not statistically significant (ATE 0.06 months; 95% CI: -0.18 to 0.31), but treatment effects were heterogeneous. The highest-benefit stratum gained 0.83 months (95% CI: 0.49-1.17). Predicted benefit was associated with older age, insulin use, lower HbA1c, and lower BMI. Interpretation GLP-1 receptor agonists may delay clinically recorded AD-type dementia compared with SU. Comparative effectiveness versus SGLT2 inhibitors may vary, supporting further study. Given the hypothesis-generating nature of these findings, diabetes treatment selection should remain guided by glycemic, cardiovascular, renal, and patient-centered considerations. Funding German Federal Ministry of Research, Technology and Space (03LWH0181B)

Type 2 diabetes (T2D) is a complex metabolic disorder characterized by hyperglycemia and insulin resistance. Although genome-wide association studies (GWAS) have identified >600 T2D risk loci, the causal genes and the relevant tissues mediating these associations remain largely unresolved. To address this challenge, we performed tissue-specific, ancestry-aware transcriptome-wide association studies (TWAS) across six T2D-relevant tissues: subcutaneous adipose, visceral adipose, brain hypothalamus, liver, skeletal muscle, and pancreas. We conducted ancestry-specific multi-tissue TWAS in European ancestry (EUR) data using summary statistics from the largest EUR GWAS (242,283 cases and 1,569,734 controls) and pre-trained gene expression prediction models derived from 689 EUR individuals from the Genotype-Tissue Expression (GTEx) Project. Conditional analyses were performed to identify independent TWAS signals. We identified 684-750 significant gene-T2D associations per tissue (P < 1.919 x 10-6), implicating both established and novel candidate genes. Among these, JAZF1 and IDE showed consistent association signals across all six tissues, whereas TCF7L2 and WSF1 exhibited heterogeneous effects restricted to a subset of T2D-relevant tissues. Conditional analyses further refined these signals to 289-322 independent TWAS signals per tissue. Together, these finding highlight substantial regulatory heterogeneity in the genetic architecture of T2D and underscore the importance of tissue context in interpreting disease-associated loci. Cross-ancestry replication of EUR-derived TWAS signals was evaluated in African American (AFA) individuals. We conducted an AFA-TWAS using summary statistics from the largest AFA GWAS (50,251 cases and 103,909 controls) in combination with gene expression prediction models trained in 111 AFA individuals from GTEx. We observed significant enrichment of EUR-derived T2D TWAS signals in the AFA TWAS across subcutaneous adipose, visceral adipose, skeletal muscle, and pancreas, whilst enrichment was weaker in liver, likely reflecting limited sample size. Overall, our findings demonstrate that integrating tissue-specific and ancestry-aware TWAS refines the identification of causal genes for T2D, with cross-ancestry replication supporting the robustness of these signals and cross-tissue analyses revealing context-specific effects. However, they also highlight the limited availability of non-EUR datasets and the need for larger, more diverse ancestry-specific transcriptomic resources.

Hyperglycaemia is a hallmark of diabetes and a major risk factor for diabetic kidney disease (DKD). However, the molecular consequences of long-term cumulative hyperglycaemia (CH) remain unclear. As a stable epigenetic modification, DNA methylation may capture past glycaemic exposure. Here, we assessed CH-associated DNA methylation in 1,245 participants with type 1 diabetes (T1D) from Finland and the United Kingdom-Republic of Ireland cohorts. We identified 17 CH-associated CpGs, with the strongest association at cg19693031 (TXNIP). Longitudinal analyses demonstrate that these CH-associated DNA methylation levels remain stable despite short-term glycaemic fluctuations, suggesting lasting epigenetic imprints of earlier metabolic control. Integrative analyses combining genomic, epigenetic, and proteomic data characterized these CpGs and potential target proteins. Mendelian randomization suggested a causal association between cg20853880 (KLF11) and DKD, supported by chromatin accessibility and kidney KLF11 expression. Our findings suggest that epigenetic changes contribute to metabolic memory and may mediate the effects of hyperglycaemia on DKD.

Type 2 diabetes (T2D) is characterized by substantial clinical heterogeneity, yet its neurobiological substrates remain unclear. Here, we leverage normative modelling of cortical morphometric networks to quantify individual-level brain deviations using structural MRI data from 3,723 participants (1,941 T2D patients and 1,782 healthy controls) across three independent cohorts. Data-driven clustering reveals two robust T2D biotypes characterized by widespread positive (biotype 1) and negative (biotype 2) brain deviations. Despite comparable overall metabolic burdens, these biotypes feature distinct brain-metabolic coupling patterns: biotype 1 is primarily associated with lipid metabolism, whereas biotype 2 reflects a multifactorial metabolic burden spanning glycaemic control, adiposity, lipid, vascular risks, and disease duration. These divergent brain vulnerability profiles have distinct cognitive consequences; notably, biotype 2 shows poorer performance in complex cognitive tasks, aligning with negatively deviated connectivity gradients along the sensorimotor-to-association axis. Spatial transcriptomic analyses further link biotype 2 deviations to gene expression patterns enriched in insulin signalling, mitochondrial functions, tight junctions, and neurodegenerative disease pathways, alongside specific involvement of inhibitory neurons and oligodendrocyte lineage cells. Our findings provide a neurobiological understanding of T2D heterogeneity and establish a data-driven framework for characterizing personalized brain vulnerability, with implications for advancing precision diabetes medicine.

Polygenic risk scores (PRS) assume additive SNP effects, yet genetic risk also arises from interactions between loci and environmental factors that contribute to broad-sense heritability. We developed an extended PRS (ePRS) framework for type 2 diabetes (T2D) that incorporates locus-by-locus non-additive effects beyond those captured by additive single-locus PRS or linkage disequilibrium (LD) tagging. These were modelled as cumulative burden (G+G; summed allele counts), statistical epistasis (GxG; allele count products), and gene-environment effects derived from cardiometabolic variables in electronic health records. Across 235,000 UK Biobank participants, five complementary ePRS models captured largely non-overlapping high-risk individuals, suggesting that a key to individual risk predictions comprise the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score improved case detection beyond clinical predictors, including individuals within clinically normal ranges. These findings were generalized to celiac disease, with similar complementarity across models, with potential for clinical use pending prospective validation.

Background: Type 2 diabetes (T2D) is associated with elevated rates of several cancers and is increasingly recognized as a heterogeneous disease, but whether its clinically distinct subtypes carry different cancer risks is unknown. Methods: In this matched retrospective cohort study using electronic health record data from the Epic Cosmos Research Platform (2012-2025), adults with newly diagnosed T2D were classified into severe insulin-deficient (SIDD, 21.6%), mild obesity-related (MOD, 23.5%), mild age-related (MARD, 40.7%), or mixed (14.1%) subtypes using validated algorithms and matched to adults without diabetes on age, sex, and body mass index. Cause-specific Cox models estimated adjusted hazard ratios (HRs) for seven site-specific cancers, accounting for competing risks. Cancer screening uptake was assessed as a secondary outcome. Results: Among 575,139 adults with T2D and 689,719 without diabetes (median follow-up, 3.8 years), MARD had the highest cancer incidence (17.3 per 1,000 person-years). Relative to adults without diabetes, rates of colorectal, pancreatic, liver, endometrial, and ovarian cancer were elevated across subtypes, with the highest hazards in SIDD (HR=3.87, 95% CI=3.51 to 4.27) and mixed phenotypes. Prostate cancer rates were lower in all subtypes, most markedly in MOD (HR=0.60, 95% CI=0.55 to 0.64). Rates of breast cancer were higher among mixed (HR=1.12, 95% CI=1.05 to 1.19) and lower among MOD (HR=0.85, 95% CI=0.80 to 0.90). Mammography and prostate-specific antigen screening were lower across subtypes. Conclusions: Site-specific cancer incidence and screening uptake differed across clinically defined subtypes of T2D. Subtype classification from routine clinical data may inform targeted cancer surveillance, though further study is needed before clinical use.

Background: Large-scale biobanks have advanced genomic and epidemiologic research, but many rely on infrequent biological sampling and limited digital phenotyping. The Yamanashi Multi-omics Cohort (YMoC) was established to support longitudinal assessment of molecular, clinical, and behavioural changes in a screening-defined cohort of adults at elevated metabolic risk without diagnosed diabetes. Methods: YMoC is a longitudinal cohort of 215 adults aged 30-70 years in Yamanashi Prefecture, Japan, who met prespecified glycaemic eligibility criteria at health check-up, including fasting plasma glucose 100-125 mg/dL (5.6-6.9 mmol/L) and HbA1c <6.5%. Participants underwent three in-person visits over six months. Measurements include 75-g oral glucose tolerance testing with serial sampling, clinical biochemistry, anthropometry, liver elastography, and collection of blood, urine, stool, and saliva for multi-omics profiling. Between visits, participants wore a Fitbit Inspire 3 and completed daily app-based questionnaires using the Taohealth app. Current molecular data include genome-wide single nucleotide polymorphism array genotyping and longitudinal plasma proteomics in a subset. Conclusions: YMoC is designed to evaluate within-person molecular and phenotypic trajectories in a screening-defined metabolic-risk cohort. The cohort provides a dense longitudinal resource linking clinical assessments, biospecimens, omics assays, and digital phenotyping, including analyses of insulin-resistance-related markers such as homeostasis model assessment of insulin resistance (HOMA-IR).

Obesity is a global epidemic characterized by metabolic dysfunction, with white adipose tissue playing a pivotal role in these processes. Noncoding RNAs, such as long non-coding RNAs (lncRNAs) and short non-coding RNAs (e.g., microRNAs), have been identified as an emerging class of regulatory molecules that can influence metabolic function. Here, the Dleu2/miR-15a/16-1 cluster (known as 13q14-Minimal Deleted Region, i.e., MDR), which encodes the lncRNA Dleu2 and miR-15a/16-1, a previously unrecognized player in metabolic function, is shown to contribute to obesity and insulin resistance. Using a combination of phenotypic and molecular approaches, this study establishes that MDR governs metabolic regulation for the first time. In a nutshell, this study identifies a new role of a lncRNA-miRNA cluster, previously implicated exclusively in cancer, in the regulation of obesity, thereby extending its biological significance beyond oncology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/745519v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@424a1borg.highwire.dtl.DTLVardef@f6e3eorg.highwire.dtl.DTLVardef@10ebf0borg.highwire.dtl.DTLVardef@120803c_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDeletion of MDR contributes to obesity, insulin resistance, and impaired energy metabolism C_LIO_LILoss of MDR reduces circulating adiponectin levels, indicating metabolic dysfunction C_LIO_LIMDR regulates satiety signaling in visceral adipose tissue and increases serum leptin levels C_LIO_LIMDR modulates several unrecognized new transcriptional regulators in obesity C_LIO_LIFirst evidence to establish the metabolic role of MDR beyond cancer biology C_LI

Non-typhoidal Salmonella is a major cause of bacterial foodborne illness leading to acute gastroenteritis. In individuals with type 2 diabetes (T2D), Salmonella infection is more likely to cause life-threatening extraintestinal infections. The mechanism underlying this susceptibility remains unclear. In this study, 8-week-old TALLYHO mice were fed either a chow or high-fat diet (HFD, 45% fat) for 8 weeks to induce the T2D. As expected, HFD-fed mice gained more weight and developed diabetic-range blood glucose levels by 16 weeks of age. Next, mice from each diet group were orally infected with a fully virulent bioluminescent Salmonella Typhimurium to monitor infection spread by in-vivo imaging. Although both groups developed clinical signs of salmonellosis, Salmonella spread was accelerated and followed an unusual pattern in T2D mice compared with healthy animals. Additionally, hyperglycemia increased gut-derived lipopolysaccharide leakage into the bloodstream. Based on the link between T2D and altered levels of butyrate-producing bacteria in the gut, we analyzed the intestinal short-chain fatty acid (SCFA) profiles in the TALLYHO mice. As expected, intestinal SCFA concentrations, including butyrate, were lower in HFD mice than in chow-fed animals. Given butyrate?s role in gut health and its ability to downregulate Salmonella invasion genes, mice received oral butyrate supplementation. We found that butyrate supplementation reduced the extraintestinal spread of Salmonella in normoglycemic chow-fed animals. Unexpectedly, although butyrate improved intestinal health in hyperglycemic mice, it failed to decrease Salmonella spread in diabetic animals. Taken together, these findings provide novel insights into the pathogenesis of enteric salmonellosis in the context of T2D.

Background Existing insulin resistance (IR) indices are predominantly developed in diabetic cohorts, limiting their generalizability. We developed a novel deep neural network-derived IR index (DNN-IR) using a Mixture-of-Experts (MoE) framework and evaluated its predictive performance for incident cardiovascular disease (CVD) and mortality in general populations. Methods We utilized data from three cohorts: the cross-sectional REACTION study (Fujian subcohort, 2011-2012) for DNN-IR derivation and internal validation; and two prospective cohorts, NHANES (1999-2018, linked to the National Death Index) and CHARLS (2011-2018), for external validation. The DNN-IR was developed using a deep learning model based on a Mixture-of-Experts (MoE) architecture, trained on the REACTION dataset. We evaluated the DNN-IR's utility in predicting incident CVD, cardiovascular mortality, and non-cardiovascular mortality among 13,889 NHANES and 7,047 CHARLS participants. Predictive performance was assessed via the area under the receiver operating characteristic curve (AUC). Multivariable logistic regression, restricted cubic splines, and Kaplan-Meier analyses characterized the associations between DNN-IR and clinical outcomes. Results In the REACTION cohort, DNN-IR demonstrated superior predictive performance for atherosclerotic outcomes, achieving AUROCs of 0.89 (training) and 0.84 (internal validation). In the external CHARLS cohort (median follow-up: 7 years; 1,135 incident CVD cases [16.1%]), DNN-IR yielded AUROCs of 0.72 for incident CVD and 0.77 for all-cause mortality. Fully adjusted models showed that each 1-SD increment in DNN-IR was associated with a 23% higher CVD risk (OR=1.23, 95% CI: 1.14-1.32), exhibiting a predominantly linear dose-response relationship (P-nonlinearity=0.453). In NHANES, DNN-IR robustly predicted cardiovascular (AUROC=0.77) and all-cause mortality (AUROC=0.72), alongside specific mortalities like diabetes (0.91), Alzheimer's disease (0.88), and kidney disease (0.96). Higher DNN-IR levels correlated with stepwise increases in cumulative mortality (log-rank P<0.001). Conclusions The MoE-derived DNN-IR index demonstrated robust and stable performance in predicting atherosclerosis, incident CVD, cardiovascular mortality, and all-cause mortality in the general population. Further validation in larger, more diverse cohorts is warranted to support its broad clinical applicability.