Type 2 diabetes (T2D) exhibits clinical heterogeneity, yet most existing classification models are derived from European populations and face challenges in clinical application. Here, we evaluate the generalizability of a tree-like graph structure from Scottish data to 32,501 newly diagnosed T2D patients from a multi-center Chinese cohort comprising over 8.6 million individuals. We observe similar distribution between the Scottish and Chinese individuals in heart and kidney outcomes, but diabetic retinopathy varies across ancestries even within similar phenotypes. To capture T2D Chinese-specific heterogeneity, we apply a variational autoencoder (VAE) framework to identify key clinical features and construct a tree structure using the Discriminative Dimensionality Reduction Tree (DDRTree) algorithm. This Chinese tree model is validated in two independent external cohorts and revealed longitudinal phenotypic shifts trending toward higher-risk branches. Our findings emphasize the need for population-specific classification frameworks to advance precision diabetology through individualized risk prediction and specialized treatment guidelines.

Background:Gestational diabetes mellitus (GDM) is associated with substantial risks of adverse maternal and neonatal outcomes. Contemporary management approaches for GDM exhibit insufficient implementation, resulting in suboptimal glycemic control and preventable perinatal complications. The rapid evolution of mobile health technologies offers potential to enhance GDM care, yet evidence from large real-world studies remains limited. Objective:This study aimed to evaluate the impact of a telemedicine-enhanced integrated management system on pregnancy outcomes and glycemic control in women with GDM and to explore the dose-response relationship between telemedicine engagement intensity and clinical outcomes. Methods:In this real-world, prospective cohort study conducted at a provincial-level medical center in China, women with GDM were categorized into a standard care group and a telemedicine-enhanced group receiving the TangMama smartphone app in addition to standard care. We compared pregnancy outcomes and glycemic parameters between the 2 groups in an inverse probability of treatment weighting population based on propensity scores. Mediation analyses and dose-response analyses were additionally conducted to explore potential mechanisms and engagement effects. Results:A total of 4621 women with GDM were included, with 1711 in the telemedicine-enhanced group and 2910 in the standard care group. Upon inverse probability of treatment weighting analysis, the telemedicine-enhanced group demonstrated significantly lower gestational weight gain (adjusted mean difference -1.49 kg, 95% CI -1.81 to -1.17), reduced rates of excessive gestational weight gain (adjusted odds ratio [aOR] 0.61, 95% CI 0.54-0.69), cesarean section (aOR 0.80, 95% CI 0.71-0.91), hypertensive disorders in pregnancy (aOR 0.76, 95% CI 0.64-0.90), and pre-eclampsia (aOR 0.64, 95% CI 0.49-0.83). Glycemic control in the third trimester was significantly improved, with lower glycated hemoglobin A1c (HbA1c) levels (adjusted mean difference -0.05%, 95% CI -0.08 to -0.03) and higher HbA1c on-target rates. For neonatal outcomes, telemedicine-enhanced management was associated with lower rates of preterm birth (aOR 0.47, 95% CI 0.38-0.59), large-for-gestational age (aOR 0.81, 95% CI 0.69-0.96), neonatal unit admission (aOR 0.80, 95% CI 0.71-0.91), neonatal hypoglycemia (aOR 0.64, 95% CI 0.45-0.93), and neonatal hyperbilirubinemia (aOR 0.69, 95% CI 0.58-0.82). Mediation analyses identified gestational weight gain and third-trimester fasting plasma glucose as significant mediators. Higher telemedicine engagement was associated with improved glycemic control and reduced adverse outcomes in a dose-response manner. Conclusions:Telemedicine-enhanced integrated management is associated with improved maternal glycemic control and substantial reductions of adverse pregnancy outcomes among women with GDM. The observed dose-response relationship between engagement intensity and outcomes underscores the importance of promoting active patient participation. These findings support the broader integration of telemedicine into routine GDM care pathways to optimize maternal and neonatal health.

Background and aimsMetabolic dysfunction-associated steatotic liver disease (MASLD) exhibits substantial heterogeneity in progression and cardiovascular outcomes. We aimed to identify reproducible MASLD subtypes in Chinese cohorts, characterize their distinct lipidomic profiles, and evaluate their associations with long-term cardiovascular outcomes.MethodsWe investigated the heterogeneity of MASLD using k-means clustering based on six simple clinical variables in a cohort of 5,329 individuals. The identified clusters were applied in an independent cohort of 1,432 participants. Cardiovascular outcomes were compared across clusters using Kaplan-Meier curves and log-rank tests. Untargeted lipidomic profiling was performed with partial least squares discriminant analysis and least absolute shrinkage and selection operator regression to identify discriminative lipids for XGBoost model. Feature importance was evaluated using SHapley Additive exPlanations.ResultsThree distinct MASLD subtypes were identified. Cluster A (Metabolic-Obesity subtype), characterized by the highest BMI and triglycerides with the lowest risk of cardiovascular outcomes. Cluster B (Dysglycemic subtype) distinguished by increased glycated hemoglobin. Cluster C (Aging-Hypertensive subtype) was primarily associated with age, blood pressure, and fibrosis markers, leading to higher risk of cardiovascular outcomes. Lipidomics identified 1061 metabolites, and ten lipids distinguishing the Cluster A and C were selected for modeling, yielding an area under the receiver operating characteristic curve of 0.900. Pathway enrichment analysis further revealed the Cluster C involved in sphingolipid metabolism.ConclusionsThree distinct MASLD subtypes with varying metabolic features and cardiovascular risks were identified in Chinese cohorts. The Aging-Hypertensive subtype showed the highest cardiovascular risk and was associated with dysregulated sphingolipid metabolism. These findings underscore the clinical heterogeneity of MASLD and highlight the need for risk classification and personalized intervention strategies.

The mechanisms underlying metabolic remodeling in metabolic dysfunction-associated steatotic liver disease (MASLD) remain unclear. Targeting the process of de novo lipogenesis (DNL) in the liver has the potential to mitigate MASLD. Here we show that interferon-related developmental regulator 1 (IFRD1) expression negatively correlates with MASLD/metabolic-associated steatohepatitis (MASH) progression in human liver tissues. In multiple mouse models, Ifrd1-/- mice exhibit an exacerbated MASLD phenotype, while hepatocyte-specific IFRD1 expression suppresses MASH progression. Mechanistically, IFRD1 promotes GLUD1's mitochondrial localization via direct interaction, stabilizing the enzyme's activity to enhance α-ketoglutarate (α-KG) production. α-KG reduces H3K36me3 level at lipogenic genes, thereby inhibiting DNL and ameliorating MASH. α-KG supplementation reverses MASH exacerbation in Ifrd1-CKO mice. Collectively, our research establishes the IFRD1-GLUD1-α-KG axis as a critical metabolic-epigenetic regulatory hub, providing novel targets for inhibiting hepatic DNL and developing therapeutic agents for MASLD/MASH.

The human decidua establishes immune tolerance at the maternal-fetal interface and is essential for successful embryo implantation and development. Here, we conducted a spatial transcriptomic analysis of human decidua from early pregnancies in both healthy donors and patients with recurrent pregnancy loss (RPL). Our analysis revealed two distinct spatial domains, named implantation zone (IZ) and glandular-secretory zone (GZ), corresponding to the layers of decidua compacta and spongiosa, respectively. The decidual natural killer cell subset (dNK1) and the decidual macrophage subset (dM2), both associated with growth promotion and immune regulation, were predominantly localized in the healthy IZ but were significantly reduced in RPL patients. In contrast, cytotoxic CD8+ T cells, sparsely distributed in the healthy decidual IZ and GZ domains, were elevated in both domains under RPL conditions. Spatial cell-cell interaction analysis indicated a broad exhibition but a marked downregulation of immunoregulatory interactions in the IZ of RPL patients. Through integrated single-cell chromatin accessibility and transcription factor occupancy analyses, we identified FOSL2 as a pivotal regulator orchestrating the spatial transformation of dNK1 cells. Decreased FOSL2 expression correlated with compromised IL-15-induced dNK1 cell transformation and diminished immunoregulatory capabilities. Our findings delineate the intricate spatial and regulatory architecture of immune tolerance within the human decidua, providing new insights into immune tolerance dysregulation in RPL.

Introduction and Objective: Type 1 diabetes (T1D) exhibits substantial heterogeneity, yet the key clinical phenotypes remain unclear. We applied DDRTree machine learning to identify the key phenotype characterizing T1D heterogeneity and validate the existence of age-related endotypes. Methods: We performed DDRTree dimensionality reduction analysis using data on 13 phenotypes (age at onset, BMI, blood pressure, ALT, HbA1c, creatinine, triglycerides, HDL-C, total cholesterol, GADA, IA-2A, ZnT8A) from 879 patients with T1D to identify the most important clinical phenotype. We then evaluated the rationality of classifying patients into age-related endotypes (<7, 7-13, ≥13 years) and compared clinical, immunological, and genetic profiles across groups to confirm the presence of age-related endotypes. Finally, we validated the existence of these endotypes in an external UK Biobank cohort and investigated longitudinal cardiovascular outcomes associated with age-at-onset phenotypes. Results: Age at onset emerged as the most important phenotype for T1D heterogeneity, explaining 64.6% of the variance in the DDRTree dimensionality reduction. Classification into age-related endotypes showed significant differences in DDRTree dimensional coordinates (p = 1.61×10-54), supporting the rationality of this grouping. Autoantibody positivity (IA-2A, ZnT8A: p < 0.001), HLA genetic risk (p < 0.001), and metabolic characteristics differed significantly, further confirming age-related endotypes. Moreover, phenotypic heterogeneity by onset age attenuated with disease duration in both the internal and UK Biobank cohorts. Regarding long-term outcomes, older-onset T1D patients exhibited higher cardiovascular risk (sHR = 2.93, 95% CI: 1.49-5.78 for ≥13 vs. <7 years). Conclusion: DDRTree analysis identified age at onset as the key phenotype of T1D heterogeneity. Dimensionality reduction demonstrated excellent discriminative performance, supporting the existence of age-related endotypes. Disclosure S. Chang: None. H. Tan: None. T. Yue: None. Y. Ding: None. Z. Gu: None. Y. Shi: None. L. Pan: None. C. Guo: None. J. Weng: None. X. Zheng: None. Funding Foundation programs supporting included Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0509100; 2023ZD0509102), the National Natural Science Foundation (8247087), and Program for Innovative Research Team of The First Affiliated Hospital of University of Science and Technology of China (CXGG02)

Abstract BACKGROUND Atherosclerotic cardiovascular disease remains the leading cause of death worldwide. Most current pharmacotherapies target conventional risk factors that promote atherosclerosis (e.g., hyperlipidemia) rather than intrinsic resilience factors that protect against atherosclerosis in the face of risk factors. Here, we investigated the role of desert hedgehog ( DHH ), a canonical ligand of the hedgehog signaling pathway, as a novel resilience factor that restrains endothelial mesenchymal transition (EndoMT) and protects against atherosclerosis. METHODS Single-cell RNA sequencing (scRNA-seq) was performed on atheroprone and atheroprotective regions of the ApoE knockout mouse aorta to identify mechanoresponsive genes associated with atherosclerosis. Endothelial cell-specific Dhh knockout mice were subjected to partial carotid ligation and hypercholesterolemic conditions to investigate the role of endothelial Dhh in atherosclerosis progression. scRNA-seq, bulk RNA sequencing, endothelial lineage tracing, immunoprecipitation-coupled mass spectrometry, and surface plasmon resonance were used to in-vestigate the role and mechanism of DHH in EndoMT. Pharmacological interventions and recombinant DHH administration were performed in vivo to evaluate therapeutic potential of DHH targeting. DHH expression was also examined in human atherosclerotic arteries and serum samples from patients with coronary artery disease. RESULTS DHH protein expression was enriched in arterial endothelium from mice, porcine, and humans. However, DHH expression was significantly reduced in atherosclerotic arteries and serum from patients with coronary artery disease. scRNA-seq of atheroprone and atheroresistant region of mouse aorta identified Dhh as a novel mechanoresponsive gene enriched in aortic regions exposed to unidirectional laminar flow. Endothelial cell-specific Dhh knockout ( Dhh ecKO ) mice exhibited increased atherosclerotic lesion area, large necrotic cores, and reduced collagen content following partial carotid ligation. Similarly, under hypercholesterolemic conditions, both male and female Dhh ecKO mice showed aggravated atherosclerosis progression. scRNA-seq of Dhh ecKO mouse aortas revealed an increased proportion of endothelial cells undergoing mesenchymal transition, indicating enhanced EndoMT. These findings were corroborated by bulk RNA-sequencing of DHH depleted HUVECs and endothelial lineage tracing in inducible Dhh ecKO mice. Mechanistically, DHH directly interacted with plasminogen activator inhibitor type 1 (PAI-1) and suppressed PAI-1-induced EndoMT. PAI-1 promoted EndoMT in ECs through activation of canonical TGF-β signaling (SMAD2/3) and noncanonical AKT/ERK1/2 signaling via interaction with low-density lipoprotein receptor-related protein (LRP1). Neutralization of PAI-1 or inhibition of LRP1, AKT/ERK1/2, or SMAD3 signaling abolished DHH deficiency-induced EndoMT. DHH competitively inhibited PAI-1 binding to LRP1, thereby attenuating downstream pro-EndoMT signaling. Intriguingly, treatment with PAI-1 inhibitor TM5275 mitigated endothelial Dhh deficiency induced EndoMT in vivo . Of translational relevance, recombinant mouse DHH protein administration reduced atherosclerosis progression, stabilized plaque, and decreased the expression of EndoMT markers in ApoE knockout mice. CONCLUSIONS Desert hedgehog (DHH) is an intrinsic endothelial cell-enriched resilience factor that protects against EndoMT and atherosclerosis by preventing PAI-1 signaling. The present study implicates endothelial DHH as a potential therapeutic target for atherosclerotic cardiovascular disease. Graphical abstract Desert hedgehog (DHH) is an intrinsic endothelial cell-enriched resilience factor that protects against EndoMT and atherosclerosis by preventing PAI-1 binding to LRP1 and downstream AKT/ERK, as well as SMAD2/3 signaling. Clinical Perspective What Is New? DHH was identified as a flow-responsive resilience gene that is downregulated by disturbed flow in endothelial cells. Endothelial-specific deletion of Dhh promoted endothelial-to-mesenchymal transition (En-doMT) and atherosclerosis. DHH directly interacts with PAI-1 and preclude PAI-1 mediated pro-EndoMT signaling. What Are the Clinical Implications? DHH maintains endothelial homeostasis during atherosclerosis. Targeting endothelial DHH-PAI-1 axis may represent a potential therapeutic strategy to reduce EndoMT and limit plaque progression. Lower circulating DHH level may serve as a potential biomarker of endothelial dysfunction and plaque vulnerability in atherosclerotic disease.

Large-for-gestational-age (LGA) births occur in many pregnancies with an apparently metabolically healthy phenotype, limiting risk identification based on conventional clinical characteristics alone. We explored the association between second-trimester maternal serum lipidomic profiles and LGA risk in this apparently healthy population using a nested case-control design within an ongoing prospective pregnancy cohort. The study included a derivation cohort of 135 participants and an independent temporal validation cohort of 66 participants. Lipidomic profiles were analyzed by using liquid chromatography and high-resolution mass spectrometry. Multivariate modeling identified 11 circulating lipid biomarkers (seven glycerophospholipids, two glycerolipids, and two sphingolipids) associated with LGA risk. Integrating these lipid biomarkers with routine clinical factors substantially improved predictive performance compared with clinical variables alone. These findings suggest that metabolic alterations are detectable before clinical manifestations become apparent and support serum lipidomic profiling as a complementary approach for early risk stratification in pregnancies traditionally considered low risk.

Abstract Background Atherosclerosis is a chronic inflammatory vascular disorder with persistent residual inflammation even after standard lipid-lowering therapy. Mounting evidence from bench to bedside suggests that diabetes and obesity accelerate atherosclerosis development. Tirzepatide (TZP), a dual Glucagon-Like Peptide-1 Receptor/Glucose-Dependent Insulinotropic Polypeptide Receptor (GLP-1R/GIPR) agonist approved for treating diabetes and obesity, has demonstrated proven cardiometabolic efficacy in large cardiovascular outcome trials. However, it remains largely uncertain whether TZP attenuates atherosclerosis independent of its anti-diabetic and anti-obese effects through direct actions on the vasculature. Methods We established atherosclerotic mouse models under diabetic, obese, and non-diabetic/non-obese conditions. Analysis of covariance (ANCOVA) and pair-feeding experiments were applied to experimentally decouple weight-dependent metabolic improvement from intrinsic vasculoprotection. Molecular and cell biological assays in human umbilical vein endothelial cells (HUVECs) and human aortic endothelial cells (HAECs) were performed to dissect the underlying signaling mechanisms. Results TZP markedly reduced aortic plaque burden and inflammation, restrained necrotic core enlargement, and improved plaque stability across all experimental mouse models. Both ANCOVA and pair-feeding experiments confirmed that these atheroprotective effects were independent of food intake and body weight loss. Furthermore, TZP attenuated systemic and vascular inflammation in Tumor Necrosis Factor-α (TNF)-treated C57BL/6J mice, and this protection occurred without changes in body weight or blood glucose levels. Mechanistically, TZP directly targeted endothelial cells and activated the cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/endothelial nitric oxide synthase (eNOS) pathway, increased eNOS phosphorylation and nitric oxide bioavailability, consequently downregulating the expression of the pro-inflammatory adhesion molecules Vascular Cell Adhesion Molecule-1 (VCAM–1) and Intercellular Adhesion Molecule-1 (ICAM–1). Conclusions TZP arrests atherosclerosis progression through weight loss-independent anti-inflammatory mechanisms. These findings implicate TZP as a promising therapeutic drug for mitigating residual vascular inflammation in patients with atherosclerotic cardiovascular disease (ASCVD), irrespective of glycemic status or obesity. Clinical Perspective What Is New? Tirzepatide exerts direct anti-atherosclerotic effects in preclinical mouse models of atherosclerosis under diabetic, obese, and non-obese conditions. Tirzepatide directly targets endothelial GLP-1R/GIPR and downstream cAMP/PKA/eNOS signaling pathway to suppress NF-κB-driven vascular inflammation, thereby uncovering a previously unrecognized vasculoprotective mechanism underlying its cardiovascular benefits What Are the Clinical Implications? Tirzepatide exerts direct vascular protective effects independent of body weight reduction, suggesting that its cardiovascular benefits may extend beyond glycemic control and obesity management. Tirzepatide may represent a promising therapeutic drug for addressing residual vascular inflammation in ASCVD patients, including those without overt diabetes or obesity

Type 1 diabetes (T1D) exhibits age-related heterogeneity in clinical progression and immune pathology, yet the underlying molecular mechanisms remain poorly understood. Here, we integrate microbiome, metabolome, lipidome, and transcriptome profiling from 108 newly diagnosed pediatric patients with T1D, along with 56 healthy controls, to investigate age-related endotypes. Patients were stratified into early-onset (E-T1D, <7 years), intermediate-onset (I-T1D, 7-12 years), and late-onset (L-T1D, ≥13 years) groups. Multi-omics analyses revealed distinct molecular signatures among T1D subgroups. The most enriched microbial signatures were the genus Acetatifactor in E-T1D, the phylum Firmicutes A in I-T1D, and the family Bacteroidaceae in L-T1D (Linear Discriminant Analysis scores = 3.49, 5.56, and 5.78, respectively). For metabolites, pipecolic acid increased most in E-T1D, testosterone in I-T1D, while N-acetylhomocitrulline was most enriched in L-T1D. Lipidomic profiling revealed subgroup-specific alterations, with increased levels of LPA(16:1) in E-T1D, TG(16:0/18:2/18:3) in I-T1D, and TG(18:0/18:1/18:1) in L-T1D. The proportion of peripheral B cells to total lymphocytes was the highest in E-T1D (median = 11.64%) and associated with upregulated immune-related pathways, lowest in L-T1D (median = 5.99%) and linked to metabolic processes, while I-T1D (median = 8.47%) exhibited intermediate features of both groups. Integration of multi-omics interaction networks and experimental validation revealed that the microbial species Dialister invisus may promote peripheral B cell proliferation via docosapentaenoic acid, potentially contributing to early-onset T1D. Together, these findings provide a molecular framework for understanding age-related T1D endotypes and suggest potential targets for precision intervention. Workflow and key findings of the study.A multi-omics integration strategy was applied to newly diagnosed pediatric type 1 diabetes (T1D) patients stratified by age at diagnosis: early-onset (E-T1D), intermediate-onset (I-T1D), and late-onset (L-T1D), to delineate age-related T1D endotypes. Comprehensive profiling included gut microbiome, serum metabolome, lipidome, and peripheral immune transcriptome analyses. An integrated multi-omics interaction network revealed 665 direct microbiota-gene connections and 2,608 microbiota-metabolite/lipid-gene triadic interactions, highlighting a D. invisus-docosapentaenoic acid (DPA)-STMN1 axis mediating B-cell activation in early-onset T1D.

Introduction and Objective: Environmental factors such as the gut microbiota may play distinct roles in the pathogenesis of type 1 diabetes (T1D) presenting with or without diabetic ketoacidosis (DKA) at onset. This study aimed to employ a deep learning model to identify multi-omics signatures in patients with DKA-onset T1D and to investigate the underlying mechanisms. Methods: Newly diagnosed T1D patients (n=69) were randomly enrolled and stratified into two groups: DKA and Non-DKA, based on the presence of ketoacidosis at onset. A deep learning model, Multi-Omics Variational Autoencoders (MOVE), was utilized to assess the importance of features from gut microbiome, serum metabolome, lipidome, and proteome profiles between the groups. Key microbiota and their potential associations with metabolic or protein features were screened through model perturbation analysis. Results: The MOVE model demonstrated strong capability in processing T1D multi-omics data (loss value = 0.9403). Analysis revealed distinct multi-omics signatures between the groups. The DKA group was characterized by elevated levels of s__Bacteroides clarus, 2-Arachidonyl Glycerol ether, and Phosphatidylcholine (PC) (36:2). In contrast, the Non-DKA group featured a higher abundance of f__Burkholderiaceae, along with increased levels of D-(+)-Proline, LPC (16:0), and the protein CCL28. Integrated importance evaluation of clinical, gut microbiome, metabolome, lipidome, and proteome signatures highlighted the significant role of gut microbiota in DKA onset. Perturbation-based analysis using MOVE suggested that s__Lachnospira sp000437735 might ameliorate ketoacidosis occurrence at T1D onset by facilitating the metabolism of benzyl alcohol to benzaldehyde. Conclusion: The application of a deep learning model provides novel molecular insights and potential therapeutic targets for elucidating the heterogeneity of T1D onset and for exploring individualized prevention and treatment strategies. Disclosure H. Tan: None. T. Yue: None. Y. Ding: None. Z. Gu: None. Y. Shi: None. L. Pan: None. S. Chang: None. C. Guo: None. J. Weng: None. X. Zheng: None. Funding Foundation programs supporting included Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0509100; 2023ZD0509102), the National Natural Science Foundation (8247087), and Program for Innovative Research Team of The First Affiliated Hospital of University of Science and Technology of China (CXGG02)

Diabetic kidney disease (DKD) substantially contributes to premature mortality in individuals with Type 2 diabetes (T2D). Although the 2024 American Diabetes Association (ADA) guideline incorporates the risk stratification of DKD progression into routine diabetes care, its predictive validity for mortality remains underexplored in real-world populations. Using data from 6936 adults with T2D enrolled in the US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, this study examined whether baseline DKD progression risk categories-based on estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR)-can predict all-cause and cardiovascular mortality. Risk of DKD progression was classified as low, moderately increased, high, or very high. Mortality outcomes were identified via linkage to the National Death Index until December 31, 2019. Cox proportional hazards models were used to estimate hazard ratios (HRs), adjusting for demographics, comorbidities, lifestyle behaviors, laboratory values, and medication use. Over a median follow-up of 98 months, 1781 all-cause and 623 cardiovascular deaths occurred. Compared with the low-risk group, the very high-risk group had significantly higher risks of all-cause mortality (adjusted HR: 2.53; 95% CI: 2.15-2.98) and cardiovascular mortality (adjusted HR: 2.47; 95% CI: 1.87-3.27), with a clear dose-response gradient across risk categories (p for trend < 0.001). Notably, this graded relationship remained stable across most examined subgroups, and significant interactions were observed specifically among individuals aged < 65 years and those using renin-angiotensin system (RAS) inhibitors. These findings confirm the robust prognostic utility of the ADA-endorsed risk stratification matrix of DKD progression for mortality in T2D and highlight its potential to inform risk-based individualized management strategies in clinical practice. This study also provides pragmatic evidence supporting the implementation of guideline recommendations.

BACKGROUND:Type 2 diabetes (T2D) causes multisystem complications, but an integrated multi-omics framework for cross-system, multi-outcome analysis is lacking. We aimed to comprehensively construct the proteomic and metabolomic atlas of major T2D outcomes and to identify predictive panels that balance performance and clinical feasibility. METHODS:Among UK Biobank participants with T2D, we established proteomic (n = 3104), metabolomic (n = 28,834), and multi-omics (n = 3059) subcohorts. Using cross-sectional and longitudinal analyses, we systematically evaluated the associations of plasma proteins and metabolites with 19 T2D-related outcomes. Predictive models were developed using machine learning-based molecular feature selection and were compared with the clinical risk model. RESULTS:The study identified molecular signals that consistently exhibited positive or negative associations across multiple T2D outcomes, revealing shared biological pathways. We also uncovered outcome-specific and heterogeneous molecular signatures. Furthermore, protein-based models substantially outperformed clinical models (median delta C-index = 0.108; range: 0.063-0.143), while combined models achieved the best performance (median delta C-index = 0.109; range: 0.080-0.150) with consistent improvements in reclassification metrics, whereas metabolites provided only modest incremental gains (median delta C-index = 0.027; range: 0.006-0.070). Evaluation across varying selection thresholds identified a simplified panel of 174 proteins that maintained robust predictive performance. CONCLUSION:This large-scale multi-omics study systematically constructs the molecular atlas of T2D complications, providing new insights into disease biology and potential therapeutic targets. It further defines the predictive value of proteomic and metabolomic profiles and proposes a clinically feasible and practical framework for risk prediction and precision intervention.

Dear Editor, Metabolic dysfunction-associated steatohepatitis(MASH)has emerged as a major contributor to chronic liver disease world-wide.Being categorized under the spectrum of metabolic dysfunction-associated steatotic liver disease(MASLD),MASH arises from simple steatosis in the context of metabolic dysfunc-tion and can progress to advanced fibrosis,cirrhosis,and hepatocellular carcinoma[1,2].

Background Obesity is a systemic disorder with heterogeneous fat distribution and complex metabolic complications. Conventional genome-wide association studies (GWAS) typically analyze individual obesity-related traits separately, limiting the identification of shared genetic architecture and key regulatory mechanisms, particularly those involving non-coding variants. Methods We integrated GWAS data for five obesity traits (body mass index, waist circumference, visceral fat, liver fat, and body fat percentage) using genomic structural equation modeling (GSEM) to construct a multivariate phenotype (mvObesity). Functional genomic integration combined adipose chromatin accessibility, enhancer promoter interactions, and expression quantitative trait loci (eQTL) data with transcriptome-wide and proteome-wide (TWAS and PWAS) analyses, fine-mapping, and colocalization. Trait-relevant cell types were identified using single-cell and single-cell polygenic association of GWAS (scPagwas) analyses. Results Multi-omics integration in adipose tissue identified 799 independent SNPs across 548 loci, including 45 previously unreported signals. Fine-mapping and TWAS defined 150 high-confidence candidate genes enriched for neuronal signaling, synaptic organization, and lipid metabolism pathways. MAGMA-based enrichment further revealed significant overrepresentation in brain regions such as the cerebellum, hippocampus, and hypothalamus, indicating central regulatory involvement. Single-cell analyses highlighted adipocytes, preadipocytes, and smooth muscle cells as major genetically influenced types, while cross-tissue TWAS and scRNA-seq supported coordinated neuro-metabolic transcriptional regulation. Multi-omic prioritization identified key genes such as MED13L, GBE1, CADM2, PIK3R3, ERBB4, and PTK2B and demonstrated significant genome-wide and local genetic overlap between mvObesity and cardiometabolic traits. Conclusions This multivariate, multi-omics framework delineates a cross-tissue neuro-adipose regulatory axis underlying obesity, providing mechanistic insight and a genetically informed candidate framework for future precision metabolic intervention research.

The progression of cardiovascular disease shows significant sexual dimorphism: although females generally develop the disease later in life, they exhibit a higher age-related incidence than males. While current studies have separately reported sex differences in atherosclerotic development in Apoe−/− and Ldlr−/−, a comparative assessment of these sex-specific characteristics across both models is lacking. This study therefore aimed to assess the influence of sex on atherosclerosis using both Apoe−/− and Ldlr−/− mice. Eight-week-old mice were fed an atherogenic ALMN diet for 20 weeks to promote plaque development. We performed comprehensive analyses of: (1) systemic metabolic parameters (lipid profile, glucose metabolism); (2) atherosclerotic burden (whole aorta and aortic sinus plaque area); and (3) plaque composition (necrotic core size, collagen content, macrophage infiltration) in mice of both sexes. As a result, male mice showed higher lipid levels, worse glucose tolerance, and reduced insulin sensitivity compared to females in both models. Apoe−/− mice showed minimal sex differences in atherosclerosis with a trend toward increased plaque size in females. Plaque composition did not differ significantly between sexes in Apoe−/− mice. In contrast, Ldlr−/− males exhibited greater whole aortic plaque burden than females, yet plaque stability also remained similar across sexes. This comparative analysis of two widely used murine atherosclerosis models reveals genotype-dependent sexual dimorphism. This study underscores the importance of considering the distinct sex-specific characteristics of Apoe−/− and Ldlr−/− mice when selecting animal models for exploring atherosclerosis pathomechanisms as well as effective pharmacotherapies, and further supports the necessity of developing sex-specific therapies.

ABSTRACT Type 1 diabetes (T1D) in children exhibits substantial heterogeneity in glycemic control, yet the biological mechanisms underlying this variation remain unclear. We aimed to explore endotype heterogeneity in youth with recent‐onset T1D using unsupervised clustering based on multi‐omics data, and to identify associated molecular signatures and underlying mechanisms. In a discovery cohort of 69 children and adolescents with recent‐onset T1D, unsupervised clustering of fecal metagenomic profiles revealed two robust subgroups distinguished by hemoglobin A1c (HbA1c) levels. The High‐HbA1c group was enriched in Bacteroidota, while the Low‐HbA1c group was enriched in Firmicutes and certain Bacteroides species (Bacteroides ovatus, Bacteroides xylanisolvens, Bacteroides nordii, and Bacteroides cellulosilyticus). Metabolomics revealed significant enrichment of tryptophan‐derived metabolites in the Low‐HbA1c group. Bacteroides species signatures are positively correlated with tryptophan metabolite skatole. In an independent validation cohort, Bacteroides signatures discriminated individuals with good versus poor glycemic control (AUC = 0.854). Similar microbial patterns were observed in healthy children stratified by glycemic risk, indicating broader relevance of these signatures. Together, microbiome‐based clustering identified glycemic control‐related subtypes in T1D youth and suggested a potential role of Bacteroides and skatole in glycemic control. Mechanistic studies are warranted to confirm its role as a glycemic control‐related endotype with distinct pathophysiology.