Disturbed blood flow induces early endothelial inflammation in atherosclerosis, yet the precise mechanisms of endothelial sensing of disturbed flow remain incompletely understood. In this study, we integrated proteomic profiling of human endothelial cells (ECs) subjected to disturbed flow with coronary artery disease risk related genes from large-scale genome-wide association studies (GWAS). We identified mortality factor 4-like protein 1 (MORF4L1, also called MRG15) as a critical modulator in the pathogenesis of atherosclerosis induced by disturbed flow. We show that the expression of MRG15 is markedly reduced in ECs of human aortic atherosclerotic plaques, as well as in ECs exposed to disturbed flow in mouse carotid artery and in cultured human umbilical vein endothelial cells (HUVECs). Endothelial-specific deletion of Mrg15 significantly worsened, while its overexpression attenuated endothelial inflammation and atherosclerotic lesions in turbulent blood flow- or Western diet-induced mouse atherosclerosis model. Single-cell transcriptomics showed that Mrg15 deficiency increased endothelial inflammation and intercellular adhesion molecule 1 (Icam1) expression, and enhanced integrin-mediated adhesion pathways. Mechanistically, MRG15 facilitated the recruitment of enhancer of zeste homolog 2 (EZH2) to maintain repressive histone H3 lysine 27 trimethylation (H3K27me3) marks on the promoters of ICAM1 and integrin subunit alpha 5 (ITGA5). Disturbed blood flow rapidly led to an elevation of protein neddylation, which subsequently induced neddylation-dependent degradation of MRG15 within endothelial cells. This degradation of MRG15 alleviated the transcriptional repression of ICAM1 and ITGA5, thereby enhancing monocyte adhesion to ECs. These findings highlight endothelial MRG15 as a mechanosensitive suppressor of atherosclerosis induced by disturbed flow. Consequently, MRG15 emerges as a promising novel therapeutic target for atherosclerosis. ### Competing Interest Statement The authors have declared no competing interest.

Objective To explore the association between serum uric acid levels and the cumulative incidence risk of atrial fibrillation,and to evaluate the predictive value of different uric acid levels for the onset of atrial fibrillation.Methods A retrospective selection of 451 879 participants from the large-scale prospective epidemiological cohort UK Biobank,aged 40-69 years,all completed a median follow-up of 13.6 years.Participants were divided into groups based on the interquartile range of serum uric acid levels(Q1-Q4)related to gender and whether they were diagnosed with hyperuricemia.Cox proportional hazards model,sensitivity analysis,and other methods were used to compare baseline data and atrial fibrillation incidence during follow-up among each group of participants.Results Individuals with higher baseline uric acid levels tended to be older,more obese,and had lower education levels and a history of cancer,along with significantly higher levels of triglyceride,low-density lipoprotein cholesterol,and C-reactive protein,but lower high-density lipoprotein cholesterol levels(P<0.001);the highest uric acid group showed the highest diabetes prevalence(6.49%).Participants with higher serum uric acid levels(log-rank P<0.05)or diagnosed with hyperuricemia had significantly higher incidence of atrial fibrillation(P<0.001).After adjusting for potential confounders,compared to Q1 uric acid level group,the Q4 level was associated with a 20%increased risk of atrial fibrillation(HR=1.20,95%CI 1.16-1.25).Each 74.7 μmol/L increase in uric acid level was associated with a 9%increased incidence risk of atrial fibrillation(HR=1.09,95%CI 1.08-1.11).Individuals with hyperuricemia had a 20%increased incidence risk of atrial fibrillation(HR=1.20,95%CI 1.17-1.24).A nonlinear association was observed between uric acid levels and the incidence risk of atrial fibrillation(P for nonlinearity<0.01).Subgroup analysis indicated significant heterogeneity of the risk effect across subgroups,with a higher risk associated with hyperuricemia in females.Conclusions Elevated blood uric acid levels may increase the cumulative risk of atrial fibrillation,and this pathogenic effect is significantly correlated with age,race,cancer history,body mass index,and sex.

BACKGROUND:Acute myocardial infarction comprises two distinct entities, myocardial infarction with obstructive coronary artery disease (MI-CAD) and myocardial infarction with nonobstructive coronary arteries (MINOCA), characterized by divergent pathogenesis and potential links to particulate air pollution. This study aimed to investigate the differential impacts of such pollution on the onset of MI-CAD and MINOCA to inform precise prevention and control of myocardial infarction. METHODS:Based on the Chinese Cardiovascular Association Database-Chest Pain Center Registry, we performed a nationwide, time-stratified, case-crossover study from 2015 to 2021. Hourly concentrations of fine particulate matter (PM2.5) and inhalable particulate matter (PM10) were acquired through nearby fixed-site monitoring. We combined a conditional logistic regression model with polynomial distributed lag nonlinear models to differentiate the exposure-response relationships of the hourly concentrations of PM2.5 and PM10 with the onset of MI-CAD and MINOCA over 72 hours. We further calculated the attributable fractions (AFs) due to particulate air pollution accordingly. RESULTS:A total of 918,730 patients with MI-CAD and 83,802 patients with MINOCA were included. The risks of MINOCA and MI-CAD onset were highest at the concurrent exposure hour and diminished within 30 hours. Exposure to PM2.5 and PM10 was associated with a 1-2-fold higher risk of MINOCA compared with MI-CAD. Each interquartile range increase in the concentrations of PM2.5 and PM10 resulted in a 2.17% (95% CI: 0.82-3.53%) and 1.57% (95% CI: 0.23-2.94%) increased risk of MINOCA onset, respectively. The corresponding effect estimates for MI-CAD were 1.14% (95% CI: 0.74-1.55%) and 0.47% (95% CI: 0.13-0.81%), respectively. There were no apparent thresholds for these associations. The AFs of MINOCA attributable to PM2.5 and PM10 were 2.43% and 1.72%, respectively, which were almost one to two times greater than those of MI-CAD (1.27% and 0.54%, respectively). CONCLUSIONS:This nationwide study provides robust evidence that, compared with MI-CAD, MINOCA is more sensitive to particulate air pollution. This effect could occur during concurrent hours and under health-based air quality guidelines.

While many polygenic risk scores (PRSs) of coronary artery disease (CAD) have been developed to stratify disease risks in Europeans, their performances in the Chinese population are suboptimal due to population heterogeneity. Considering the complex genetic architecture of CAD, we train a multi-ancestry multi-trait PRS for CAD, termed PRSCAD+, which is optimized in a prospective Chinese cohort to integrate information from large-scale genome-wide association studies (GWASs) of CAD and 15 related traits in East Asians and Europeans. The hazard ratio (HR) for incident CAD is 1.26 (95% confidence interval: 1.21-1.31) per standard deviation of PRSCAD+, which is stronger than published PRSs, East Asian-specific multi-trait PRS, and the single-trait PRS of CAD. PRSCAD+ also increases the concordance index (C-index) by an average of 1.1% over 14 published PRSs (ΔC ranges from 0.4% to 1.6%; P < 0.05). Addition of PRSCAD+ to traditional clinical risk factors led to a significant improvement in the C-index by 1.3% (P < 0.05). In an external validation set (mean age 58 years), PRSCAD+ achieved an odds ratio of 2.40 (2.18-2.65) and an area under the receiver operating characteristic curve of 0.799 (0.782-0.816) for predicting early-onset CAD. Furthermore, we observed significant gradients across the quintiles of PRSCAD+ in both datasets. These results demonstrate that PRSCAD+ can improve risk prediction and stratification of CAD in the Chinese population by incorporating genetic information of related traits from both European and East Asian studies.

White matter hyperintensities (WMH) are important imaging biomarkers for cerebral small vessel disease, and their automatic segmentation across data with different distributions is crucial for assessing brain health and supporting diagnosis. However, cross-domain WMH segmentation remains challenging in privacy-sensitive and label-scarce clinical settings. Existing methods either relied on source domain data, violating privacy constraints, or lacked spatial guidance, which resulted in poor generalization, such as low sensitivity to small lesions. To address these challenges, we developed a source-free domain adaptation (SFDA) framework enhanced by federated spatial prior modeling. Our method used a dual-path pseudo-label generator that leveraged spatial priors to improve boundary accuracy and enhance the detection of small lesions. These priors were optimized via federated learning across multiple sites without sharing raw data, boosting model generalization while preserving privacy. The model was then fine-tuned using refined pseudo-labels. Experimental results demonstrated that our method consistently outperforms state-of-the-art UDA and SFDA methods, achieving 3-10% DSC improvement in most sites across 3 public and 7 private datasets. It also showed superior performance in small lesion detection and boundary delineation. Our method offered a robust, privacy-preserving solution for WMH segmentation and provided valuable support for early diagnosis and risk assessment of cerebrovascular diseases.

Objective: Transferring large-scale medical foundation models to specific clinical tasks remains challenging, particularly in multi-center scenarios with heterogeneous data distributions and privacy constraints. Existing adaptation strategies provide limited solutions for collaboratively adapting foundation models across institutions while preserving their transferable representations. Methods: We propose FedSAM-3D, a foundation model adaptation framework for multi-center medical image segmentation. Built upon the SAM-Med3D backbone, FedSAM-3D defines the collaborative optimization space within adapter parameters while keeping the pretrained backbone unchanged. Through federated optimization within this constrained adaptation space, our framework enables efficient cross-center knowledge aggregation without exchanging full model parameters, while allowing each client to adapt the foundation model to local medical data distributions. Results: FedSAM-3D was evaluated on multi-center abdominal organ and brain tumor segmentation datasets under federated adaptation and zero-shot evaluation settings. Across both tasks and multiple clinical datasets, FedSAM-3D generally outperformed ablation variants and existing segmentation methods, demonstrating improved adaptation performance and robustness across heterogeneous medical data distributions. Moreover, FedSAM-3D achieved improved generalization on unseen external datasets, including cross-modality evaluation, highlighting its ability to enhance the transferability of medical foundation models. Conclusion: FedSAM-3D provides an effective paradigm for federated transfer of medical foundation models, achieving improved adaptation performance and generalization while avoiding direct sharing of raw medical data across institutions. Significance: FedSAM-3D provides a parameter-efficient approach for transferring medical foundation models across institutions without directly sharing raw data, facilitating their potential deployment in diverse clinical environments. Our code is available at https://github.com/huavhuahua/FedSAM-3D.

Pan-vascular diseases comprise a spectrum of atherosclerosis-driven vascular disorders that involve multiple vital organs, including the heart, brain, kidneys, and peripheral circulation. Despite being distributed across different clinical specialties due to increasing medical subspecialization, conditions such as coronary artery disease, ischemic stroke, and peripheral artery disease are interconnected manifestations of a unified, systemic vascular pathology. This conceptual shift highlights the need to consider these disorders within an integrated pan-vascular framework rather than as isolated clinical entities. Inflammation is a central driver in pan-vascular pathogenesis, accelerating atherosclerosis and increasing cardiovascular event risk. In the inflammatory cascade of pan-vascular diseases, chemokines play a pivotal role as regulators, facilitating the recruitment and activation of immune cells. C-C motif chemokine ligand 17 (CCL17) is essential for T cell development in the thymus. It binds to the C-C chemokine receptor 4 (CCR4) and exhibits chemotactic activity towards T lymphocytes, mainly T helper 2 (Th2) cells and regulatory T cells. This review summarizes the biological properties of CCL17, its mechanistic roles in pan-vascular pathologies, and its clinical translational potential as a biomarker and therapeutic target.

A ccurate and individualized prediction of functional outcome after acute ischemic stroke (AIS) remains challenging. Conventional prognostic markers such as baseline National Institutes of Health Stroke Scale (NIHSS) score and age show substantial inter-patient variability and do not capture the spatial heterogeneity of tissue injury and hemodynamic disturbance. Although multimodal CT imaging is routinely acquired in acute stroke care, its integration into interpretable and generalizable outcome prediction frameworks remains limited. In this multicenter retrospective study of 720 AIS patients from five institutions, we developed an interpretable multimodal prognostic model integrating non-contrast CT, CT angiography, CT perfusion, and clinical variables to predict 90-day functional outcome (mRS). The model was trained in a derivation cohort and externally validated in four independent cohorts. Feature attribution methods, including SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM), were used to identify key predictors and derive a spatially resolved risk representation by integrating NIHSS, Tmax, and age into a composite biomarker (C-SHAP), enabling region-specific prognostic interpretation. Among 720 patients with AIS, our model achieved superior and stable performance across the derivation and four external validation cohorts, with AUCs ranging from 0.72 to 0.82, consistently outperforming clinical-only, imaging-only, and conventional multimodal models. SHAP analysis identified baseline NIHSS, Tmax, and age as the most influential predictors, with patient-specific contributions varying by clinical context. Spatial attribution using Grad-CAM localized outcome-relevant information to functionally meaningful brain regions. The derived Combined SHAP (C-SHAP) biomarker captured regionally coherent prognostic risk patterns and showed stronger associations with 90-day mRS than lesion burden alone. Integrating C-SHAP with ischemic lesion distribution enabled more individualized and informative outcome assessment. Integration of multimodal CT imaging with clinical variables enables accurate, generalizable, and interpretable prediction of 90-day functional outcome after acute ischemic stroke. The proposed spatially resolved risk representation extends beyond lesion-based assessment and supports individualized prognostic evaluation in clinical practice.

PCSK5 (proprotein convertase subtilisin/kexin 5) is essential for heart development. However, its role in myocardial infarction (MI) remains unexplored. In this study, we found that the plasma levels of PCSK5 were elevated in MI patients and exhibited potential in predicting cardiac function improvement. PCSK5 expression was upregulated in cardiac endothelial cells (ECs) of MI patients. Pcsk5 deficiency in ECs impaired angiogenesis and cardiac recovery post-MI, and delayed tissue repair following hindlimb ischemic injury in male mice. In contrast, the endothelial-specific Pcsk5 delivery enhanced angiogenesis and cardiac function post-MI. Mechanistically, PCSK5 directly cleaved VEGFA, activating its signaling and promoting angiogenic activity. The residues Arg158 and Asn164 of PCSK5 were crucial for its function. Semaglutide increased vascular densities and cardiac function post-MI, partially through EC-derived Pcsk5 in male mice. This study identified PCSK5 as a pro-angiogenic factor secreted by ECs, with the potential to become a therapeutic target for ischemic diseases.

Background: Acute complete occlusive myocardial infarction (ACOMI) represents the most severe high-risk subtype of acute myocardial infarction (AMI) that requires urgent revascularization. However, conventional STEMI-based electrocardiogram (ECG) diagnosis exhibits low sensitivity for the identification of ACOMI, leading to frequent missed diagnosis. Current AI-ECG models are designed to detect AMI or STEMI rather than ACOMI, limiting their clinical applicability. Against this background, we aimed to develop an interpretable dual-modal AI-ECG model to accurately identify ACOMI and validate its performance in real-world populations. Methods: We conducted a dual-center retrospective cohort study in China between Dec 28 2018 and Dec 31 2025. All enrolled participants had a definitive diagnosis of AMI and paired pre-procedural 12-lead ECG and coronary angiography datasets. The development cohort from Zhongshan Hospital, Fudan University including 6762 cases was randomly split into training, internal validation and internal test subsets. We developed a dual-modal deep learning framework integrating ECG signal backbone and image backbone. Model performance was comprehensively evaluated from discrimination, calibration and net clinical benefit, and further compared with STEMI criteria and physicians. After confirming the optimal fused dual-modal model, we further validated its discrimination performance using an independent external retrospective cohort of 889 patients from Shanghai Tenth People’s Hospital, Tongji University. Findings: The development cohort (Zhongshan Hospital) comprised 6762 paired ECG–angiography records from 5332 patients, among which 2075 were confirmed ACOMI. The external test cohort (Tenth People’s Hospital) included 889 ECG records from 877 patients, and 251 records were confirmed ACOMI. In the internal test, our AI-ECG model demonstrated superior diagnostic performance compared with conventional STEMI criteria and ECG experts for ACOMI, achieving an AUROC of 0.878 (95% CI 0.858–0.897) and an AUPRC of 0.825 (95% CI 0.793–0.855). At the optimal threshold, the model yielded a sensitivity of 0.779 and specificity of 0.833, far exceeding the sensitivity of standard STEMI criteria (0.312, P<0.001) while maintaining comparable specificity (0.845, P=0.529).In the external test cohort, the model retained robust generalisability with an AUROC of 0.841 (95% CI 0.811–0.870) and AUPRC of 0.701 (95% CI 0.642–0.759), with a markedly higher sensitivity (0.741 vs 0.203 for STEMI criteria, P<0.001). Calibration curves and low stable Brier scores (0.132 internal; 0.137 external) confirmed strong alignment between predicted probabilities and observed ACOMI events. Decision curve analysis (DCA) demonstrated sustained positive net clinical benefit of the AI-ECG model across all clinically relevant risk thresholds in both cohorts. Kaplan–Meier cumulative incidence curves showed nearly in-hospital major adverse cardiovascular events (MACEs) rates between CAG-confirmed ACOMI patients and AI-predicted ACOMI patients (5.5% vs 5.4%). Interpretation: The dual-modal AI-ECG model demonstrated superior diagnostic performance. It addresses the low-sensitivity limitation of conventional STEMI criteria and supports timely revascularisation decision-making for patients with suspected acute coronary syndrome.

Predicting stroke outcome remains challenging due to inherent heterogeneity, misalignment of multimodal clinical data, and the availability of well-annotated longitudinal datasets. Current methodologies often lack robustness and generalizability across these tasks. We propose a few-shot contrastive learning framework that integrates brain MRI images and structured clinical records for cross-task prognosis prediction, addressing both morphological and functional outcomes. Our method combines Model-Agnostic Meta-Learning (MAML) with a two-step contrastive learning strategy including self-awareness learning that captures task-specific features and domain learning that facilitates cross-dataset generalization. To handle inconsistencies in tabular data, a Misalignment Separation technique was adopted. The framework jointly trains a domain encoder on multimodal inputs, capturing shared and task-specific prior knowledge to enhance predictive robustness. Evaluations on 309 patients for morphological outcome and 341 patients for functional outcome, as well as on external validation datasets, demonstrated that our approach outperformed SimCLR and conventional supervised methods, and could effectively integrate cross-task datasets. This framework highlights the potential of multimodal few-shot learning for robust stroke prognosis prediction for small-sample datasets.

BACKGROUND:Extracellular vesicles (EVs) are involved in exercise-induced cardiac protection. However, the effects and underlying mechanisms of tissue-specific molecular cargo packaged within these EVs, including PIWI (P-element induced wimpy testis)-interacting RNA (piRNA), remain poorly understood. In particular, the mechanistic contribution of exercised intracardiac EV-associated piRNAs to doxorubicin-induced cardiotoxicity (DCT) has not been defined. METHODS:Transgenic reporter mice and a cardiomyocyte-specific Rab27a (RAB27A, member RAS oncogene family) knockout strategy were used to investigate the contribution of cardiomyocyte-derived EVs to exercise-induced cardioprotection against DCT. To screen functionally relevant cardioprotective piRNA cargo, heart tissues from patients with dilated cardiomyopathy and experimental DCT models were analyzed, including cardiomyocyte-specific knockout mice, human embryonic stem cell-derived cardiomyocytes, and primary murine cardiomyocytes. RESULTS:We found that cardiomyocyte-derived EVs post-exercise were enriched for a cardiac-specific protective piRNA (piR-mmu-57256903), designated as an exercise-induced protective piRNA (EPPIR). EPPIR levels were significantly reduced in heart tissue from patients with dilated cardiomyopathy and DCT models. Functionally, EPPIR protects the heart against DCT by regulating KDM6B (lysine [K]-specific demethylase 6B)-H3K27me3 (trimethylated histone H3 at lysine 27)-Dtna epigenetic axis. In addition, EPPIR acts as a cardiomyocyte-specific suppressor of Tp53 (tumor protein p53). CONCLUSIONS:We identify a previously unrecognized role for cardiomyocyte-derived EV-associated piRNA EPPIR in mediating exercise-induced cardioprotection. EPPIR exerts its protective effects through coordinated regulation of the KDM6B-Dtna axis and cardiomyocyte-specific suppressor of Tp53, providing mechanistic insight and highlighting a potential therapeutic strategy for DCT.

Climate change has amplified the variability and intensity of cold weather, contributing to a growing health burden. Cold exposure serves as a significant, yet preventable, environmental trigger for acute chest pain–related life-threatening cardiovascular diseases (CVDs), such as acute coronary syndrome, acute aortic dissection, and pulmonary embolism. This scientific statement synthesizes multidisciplinary evidence from meteorology, environmental epidemiology, basic science, and clinical research to offer an updated evaluation of the impact of cold exposure on these acute chest pain-related life-threatening CVDs. The evidence consistently demonstrates that cold weather significantly increases the incidence of such events, often with delayed effects lasting several days to weeks. Vulnerable groups, including the elderly, individuals with chronic conditions, and those of lower socioeconomic status, are particularly at risk. Data also suggest that interventions, including central heating, integrated health warning systems, and appropriate personal protective measures, can effectively mitigate the associated risks. Based on this evidence, the statement provides expert consensus recommendations across clinical, policy, and behavioral domains. Strengthening prevention and response to cold-related cardiovascular risks is essential for building climate-resilient health systems and mitigating the health impacts of climate change.

The burden of metabolic dysfunction and coronary artery disease (CAD) in young adults has rapidly increased. We aimed to investigate whether the association between metabolically unhealthy status and prevalent CAD varies by age in both obese and non-obese individuals, and whether specific metabolic clustering patterns are associated with an elevated likelihood of obstructive CAD among young adults. This hospital-based study involved patients with and without angiographically confirmed obstructive CAD (coronary stenosis ≥ 50

BACKGROUND The Liver Imaging Reporting and Data System (LI-RADS) is widely used for the diagnosis of hepatocellular carcinoma, but feature scoring by radiologists is subjective and time-consuming. An urgent need exists for an objective and efficient radiologist-supervised automated LI-RADS categorization system. AIM To develop an evidence-based radiologist-supervised automated LI-RADS grade 3 (LR-3), 4 (LR-4) and 5 (LR-5) categorization system (Evi-LIRADS) through quantitative feature characterization, following LI-RADS v2018. METHODS This retrospective multicenter study (April 2012-November 2022) included untreated patients with suspected hepatocellular carcinoma undergoing gadoxetic acid-enhanced magnetic resonance imaging. Lesions from center 1 were partitioned into a development set (275 lesions used for five-fold cross-validation) and an internal testing set (62 lesions). Lesions from centers 2 (85 lesions) and 3 (104 lesions) constituted two external testing sets. Evi-LIRADS was designed by emulating the decision-making process of radiologists through a series of image processing algorithms, to recognize nonrim arterial phase hyper-enhancement, nonperipheral washout, and enhancing capsule, which provided detailed assessments of feature locations and patterns, improving the transparency of feature classification. Based on the three major image features and the automatically segmented lesion size, LI-RADS categories were assigned using LI-RADS v2018 algorithm. Feature classification was evaluated using area under the receiver operating characteristic curve. LI-RADS categorization was assessed by accuracy. RESULTS The internal dataset included 337 patients from center 1, while external datasets comprised 76 patients from center 2 and 97 patients from center 3. For feature classification, areas under the receiver operating characteristic curves were 0.975, 0.898, and 0.940 for arterial phase hyper-enhancement; 0.803, 0.824, and 0.850 for washout; 0.759, 0.800, and 0.784 for capsule across three datasets. Three-class LI-RADS categorization among LR-3, LR-4 and LR-5 achieved accuracies of 80.6%, 74.1%, and 77.9%, respectively, surpassing comparison methods (58.6%-69.6%). LI-RADS categorization between LR-3 and combined LR-4/LR-5 achieved 95.2%, 88.2%, and 90.4% accuracies for the three datasets, respectively. The visualization provided detailed feature locations and patterns. Evi-LIRADS saved an average of 21.1 seconds per patient (58.8% of the time) compared with radiologists, excluding radiologists' quality control time. CONCLUSION Following LI-RADS guidelines and radiologists' decision-making process, Evi-LIRADS was developed through quantitative feature characterization, demonstrating good accuracy, robust generalization, improved efficiency, enhanced clinical relevance, and improved transparency.

Latent transforming growth factor β-binding protein 4 (LTBP4) has been reported to be associated with heart failure (HF), but its role in HF remains unclear. We observe increased LTBP4 expression in plasma and cardiomyocytes of HF patients, and in a male mouse HF model induced by transverse aortic constriction (TAC). Cardiomyocyte-specific Ltbp4 deficiency attenuates NLRP3 inflammasome activation, cardiac dysfunction, and fibrosis post-TAC. Mechanistically, pressure overload upregulates LTBP4 partially via the transcription factor SP1. Angiotensin II promotes the recruitment of intracellular LTBP4 to the microtubule-organizing center (MTOC) via dynein. Subsequently, LTBP4 facilitates the dynein-mediated NLRP3 translocation to the MTOC and promotes NLRP3-NEK7 interaction, thereby driving NLRP3 inflammasome activation. Additionally, LTBP4 upregulates NLRP3 transcription and correlates positively with NLRP3 and interleukin-1β in HF patients. Here we show that LTBP4 is an important regulator of the NLRP3-NEK7 interaction and NLRP3 inflammasome activation in cardiomyocytes, highlighting its potential as a therapeutic target for HF.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy characterized by progressive fibrofatty replacement of the right ventricular myocardium, ventricular arrhythmias, and an increased risk of sudden cardiac death. Pathogenic variants of desmosomal genes have been implicated in ARVC pathogenesis and may disrupt desmosomal protein function. However, whether and how desmosomal protein dysfunction directly activates cardiac fibroblasts to mediate fibrosis remains poorly understood. To address this, we combined genetic analysis with in vivo and cellular models to investigate the role of desmosomal dysfunction in cardiac fibrosis. The systematic genetic analysis revealed that desmosomal gene variants are predominant in ARVC, accounting for 67.4% of cases in cohort studies and 96.1% of pathogenic variants in ClinVar. We used desmocollin-2 (DSC2) knockout mice to recapitulate key features of ARVC, including right ventricular fibrosis, enlargement, and dysfunction. In vitro, DSC2 deficiency directly activates cardiac fibroblasts, resulting in increased cell proliferation, migration, and fibrosis marker expression. Further analysis identified transforming growth factor beta-2 (TGF-β2) as a critical signaling mediator in cardiac fibrosis of DSC2 deficiency-mediated ARVC. Mechanistically, DSC2 deficiency upregulated transcription factor 7 (TCF7) expression, promoting its binding to TGF-β2 promoter regions to enhance TGF-β2 transcription in cardiac fibroblasts. Pharmacological inhibition of TGF-β2 with pirfenidone (PFD) effectively attenuated cardiac fibrosis and improved right ventricular function in DSC2-deficient hearts. The results of the present study identified DSC2 deficiency-mediated TCF7-TGF-β2 signaling in cardiac fibroblasts, which contributed to ARVC development. Thus, targeting TGF-β2 signaling may be a promising therapeutic strategy for desmosome gene mutation-related ARVC.