Objectives Arterial wall inflammation and remodelling are the characteristic features of Takayasu’s arteritis (TAK). It has been proposed that vascular smooth muscle cells (VSMCs) are the main targeted cells of inflammatory damage and participate in arterial remodelling in TAK. Whether VSMCs are actively involved in arterial wall inflammation has not been elucidated. Studies have shown that cellular senescence in tissue is closely related to local inflammation persistence. We aimed to investigate whether VSMCs senescence contributes to vascular inflammation and the prosenescent factors in TAK. Methods VSMCs senescence and senescence-associated secretory phenotype were detected by histological examination, bulk RNA-Seq and single-cell RNA-seq conducted on vascular surgery samples of TAK patients. The key prosenescent factors and the downstream signalling pathway were investigated in a series of in vitro and ex vivo experiments. Results Histological findings, primary cell culture and transcriptomic analyses demonstrated that VSMCs of TAK patients had the features of premature senescence and contributed substantially to vascular inflammation by upregulating the expression of senescence-associated inflammatory cytokines. IL-6 was found to be the critical cytokine that drove VSMCs senescence and senescence-associated mitochondrial dysfunction in TAK. Mechanistically, IL-6-induced non-canonical mitochondrial localisation of phosphorylated STAT3 (Tyr705) prevented mitofusin 2 (MFN2) from proteasomal degradation, and subsequently promoted senescence-associated mitochondrial dysfunction and VSMCs senescence. Mitochondrial STAT3 or MFN2 inhibition ameliorated VSMCs senescence in ex vivo cultured arteries of TAK patients. Conclusions VSMCs present features of cellular senescence and are actively involved in vascular inflammation in TAK. Vascular IL-6-mitochondrial STAT3-MFN2 signalling is an important driver of VSMCs senescence.

The prevalence of Peripheral Artery Disease (PAD) is rising globally, yet early risk stratification remains challenging due to the limitations of traditional obesity metrics. TyG-ABSI, an index combining Triglyceride-Glucose (TyG) with A Body Shape Index (ABSI), is a novel marker reflecting both functional insulin resistance and structural visceral adiposity. However, its predictive value for PAD remains unexplored in large prospective cohorts. We included 390,274 adults from the UK Biobank. Baseline characteristics were analyzed across TyG-ABSI quartiles and PAD status. Associations between TyG-related indices and incident PAD were assessed using multivariable-adjusted Cox regression, Kaplan-Meier survival curves, and restricted cubic splines. Robustness was evaluated via Fine-Gray competing risk models, propensity score matching, subgroup analyses, and external validation in the NHANES database. Consensus k-means clustering, integrating biochemical and insulin resistance markers, identified metabolic phenotypes and stratified PAD risk. Feature selection (LASSO, Boruta, and Minimum Redundancy Maximum Relevance [mRMR]) guided the development of six machine learning models (logistic regression, GBM, XGBoost, AdaBoost, LightGBM, and neural network) for PAD prediction, with interpretability assessed via SHAP analysis. Higher TyG-ABSI and related indices were strongly associated with increased PAD incidence (cumulative incidence at 15 years: 4.16% in the top quartile vs. 0.98% in the bottom quartile; fully-adjusted Hazard Ratio [HR] per 1-SD increase for TyG-ABSI: 1.22, 95% Confidence Interval [CI] 1.17-1.27), which were robust in the NHANES external validation cohort. Clustering analysis revealed four distinct metabolic subgroups, with the highest PAD risk in the insulin resistance/glucose dysfunction cluster (HR vs. healthy phenotype: 7.48, 95% CI 6.82-8.21). Feature selection identified 19 key predictors. Logistic regression provided the most stable and generalizable prediction (validation Area Under the Curve [AUC] = 0.788, 95% CI 0.778-0.798), demonstrating superior generalizability compared to complex ensemble methods. SHAP analysis demonstrated TyG-ABSI, age, and neutrophil count as leading predictors for incident PAD and confirmed the interpretability of the model. TyG-ABSI is a robust, independent predictor of long-term PAD risk. Data-driven phenotyping and interpretable machine learning facilitate more precise risk stratification. Logistic regression offers optimal performance and interpretability, holding potential clinical utility for individualized PAD risk prediction.

Background: Although hundreds of studies have been conducted, our understanding of the pathogenesis, indications for surgical intervention, and disease markers of Takayasu arteritis (TAK) are still limited. Collection of biological specimens, clinical data and imaging data will facilitate translational research and clinical studies. In this study, we aim to introduce the design and protocol for the Beijing Hospital Takayasu Arteritis (BeTA) Biobank. Methods: Based in the Department of Vascular Surgery of Beijing Hospital and Beijing Hospital Clinical Biological Sample Management Center, the BeTA Biobank is composed of clinical data and sample data from patients with TAK requiring surgical treatment. All clinical data of participants are collected, including demographic characteristics, laboratory tests, imaging results, operation information, perioperative complications, follow-up data, etc. Both blood samples including plasma, serum and cells, and vascular tissues or perivascular adipose tissue are collected and stored. These samples will promote the establishment of a multiomic database for TAK and help to identify disease markers and to explore potential targets for specific future drugs for TAK.

Pulmonary embolism (PE) is one of the leading causes of cardiovascular death, resulting in a significant socioeconomic burden. Although current treatments primarily focus on anticoagulation and thrombolysis, there is an urgent need for a better understanding of its pathophysiology and the development of new treatment strategies. Animal models play a crucial role in understanding PE and developing new therapies for the disease, with rodents commonly used due to ethical and cost considerations. However, existing rodent models for PE are limited by a lack of standardized procedures, which hampers reproducibility and cross-study comparisons. This study aims to establish a minimally invasive rat model of PE using autologous blood clots. The model features a minimally invasive blood sampling technique, a standardized thrombus generation procedure, and minimally invasive vein access. Additionally, protocols for quantifying infarcted areas and visualizing the pulmonary arterial tree are provided. These procedures aim to improve the reliability of rodent models for studying PE progression and facilitate the development of novel treatments.

Peripheral artery disease (PAD) confers elevated risk for major adverse cardiovascular events (MACE), yet accurate risk stratification remains a challenge, particularly among patients with advanced disease necessitating endovascular revascularization. This study aimed to improve the prediction of MACE in a clearly defined high-risk PAD population (hospitalized patients undergoing endovascular intervention) by identifying novel protein biomarkers and developing a robust risk model. We prospectively analyzed blood samples from 164 hospitalized PAD patients scheduled for endovascular revascularization, employing untargeted plasma proteomics and metabolomics. Differential protein and metabolite profiles were compared between patients with and without subsequent MACE. Several proteins, including MMP3, MMP19, and PRB2, were markedly elevated in patients who developed MACE. A proteomics-based risk model incorporating these biomarkers achieved high discriminative accuracy (area under the curve > 0.80) for identifying individuals at increased risk. Metabolomic profiling revealed additional pathway alterations, notably involving tryptophan and glycogen metabolism, which provided mechanistic insights into cardiovascular complications but were not directly incorporated into the prediction model. This study demonstrates that integrating protein biomarkers markedly improves risk stratification in advanced PAD patients undergoing surgical intervention. The findings offer promising tools for early detection and enable more personalized management for this high-risk subgroup, while also deepening understanding of disease pathophysiology. However, further validation in larger and more diverse prospective cohorts is warranted before these findings can be broadly applied in clinical practice.