ObjectivesIn stable coronary artery disease (CAD), coronary microvascular dysfunction (CMD) is a prevalent cause of myocardial ischemia independent of epicardial stenoses. Although the index of microcirculatory resistance (IMR) is the invasive reference standard for CMD assessment, its routine use is limited by procedural demands and cost. We investigated whether circulating endothelial microparticles (EMPs, CD144+) correlate with IMR and could serve as a non-invasive surrogate for CMD in stable CAD.MethodsFifty patients with stable CAD and 20 controls with normal angiograms underwent invasive physiological assessment, including IMR, fractional flow reserve (FFR), and coronary flow reserve (CFR), using a pressure-temperature sensor-tipped guidewire. Peripheral venous blood was analyzed for CD144+ EMPs by flow cytometry. Associations between EMPs and physiological indices were evaluated, with diagnostic utility assessed via ROC curve analyses.ResultsDemographic and clinical profiles were similar across groups (all P > 0.05). Circulating EMP counts were markedly higher in CAD patients than controls (145.62 ± 101.35 vs. 119.98 ± 71.93 particles/μL, P < 0.001), paralleled by elevated IMR values (22.14 ± 7.42 vs. 16.4 ± 4.41, P = 0.002). Within the CAD cohort, EMP levels correlated positively with IMR (r = 0.684, P < 0.001), but not in controls (r = 0.066, P = 0.783). Neither FFR nor CFR was associated with EMPs. Patients with IMR ≥ 23 had significantly higher EMP concentrations than those with IMR < 23 (P < 0.001). For discriminating CMD (IMR ≥ 23), EMPs yielded an AUC of 0.76 (95% CI: 0.62–0.89); at the optimal threshold of 130 particles/μL, sensitivity was 70.0%, specificity 70.6%, and negative predictive value 85.7%.ConclusionsCirculating CD144+ EMPs strongly correlate with invasive IMR in stable CAD. The moderate ROC performance (AUC 0.76) and high NPV suggest that peripheral EMP measurement holds promise as a non-invasive screening tool for CMD. However, these findings are exploratory and require prospective validation in larger, multi-center cohorts before clinical translation.

IntroductionCurrently, there are many diagnostic strategies for in-stent restenosis (ISR) used clinically, including invasive coronary angiography (ICA), coronary computed tomography angiography (CCTA), and Fractional Flow Reserve (FFR). CCTA is not recommended for post-stent implantation patients owing to suboptimal image quality caused by artifacts. The FFR application is limited by its procedural complexity. Precise evaluation may be achieved by using computed tomography-derived fractional flow reserve (CT-FFR), which combines computational fluid dynamics (CFD) with CCTA. Anatomical and functional assessments of ISR lesions could be integrated effectively as well in this way. However, the computational complexity and prolonged processing time may hinder its utility in clinical use.MethodsThis study is a multicenter, prospective, diagnostic study, aiming to establish a deep learning-based CT-FFR model for the accurate assessment of ISR and to validate its diagnostic performance using invasive FFR as the reference standard. This study will be carried out in Beijing Anzhen Hospital and 6 subcenters in China. We planned to prospectively enroll 331 post-stent implantation patients with available CCTA data since June 2022, and invasive FFR will be performed within 3 months when clinically indicated. Patient recruitment is currently ongoing. Among them, 250 patients from Beijing Anzhen Hospital will be used to adapt and extend the existing DEEPVESSEL model, a deep learning-based CT-FFR computational software designed for the non-invasive functional assessment of coronary artery disease and previously validated in de novo coronary lesions, for application in the assessment of in-stent restenosis (ISR), and 81 patients from the other 6 subcenters will be used in external validation. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value with their corresponding 95% confidence intervals (CIs) were calculated for CT-FFR. The receiver operating characteristic (ROC) curve was analyzed, and the area under the curve (AUC) was calculated. The McNemar test and Bland-Altman plot will be used to examine the diagnostic consistency between CT-FFR and invasive FFR. The correlation was analyzed by Spearman's correlation coefficient.Trial registration numberChiCTR2200058822.

High-altitude populations exhibit lower cardiovascular disease incidence and mortality with significant differences between indigenous highlanders and migrants. However, related genetic adaptation and population-specific mechanisms remain underexplored. We conducted a comprehensive multi-omics investigation of three cardiovascular disease patient cohorts from distinct altitudes: indigenous high-altitude residents (IHA, n = 31), high-altitude migrants (HAM, n = 18), and low-altitude residents (LAR, n = 50). IHA cardiovascular patients exhibited distinctive genetic signatures with significant enrichment of UGT1A family gene variants. Lipidomic profiling identified 118 differential lipid species between IHA and HAM patients, with significant enrichment in sphingolipid metabolism pathways. Integration of genomic and lipidomic data identified 102 significant gene-metabolite associations in IHA patients, particularly between UGT1A variants and sphingomyelin species. Comparative analysis with LAR patients revealed both shared and population-specific metabolic signatures. Our findings offer unique perspectives on cardiovascular disease in high-altitude environments, revealing complex interactions between genetic adaptation, environmental exposure, and disease pathophysiology.

The C-reactive protein–triglyceride–glucose index (CTI), which integrates inflammation, lipid metabolism, and glucose metabolism, has emerged as a composite biomarker for metabolic and cardiovascular disease. This study aimed to evaluate the association between CTI and major adverse cardiovascular and cerebral events (MACCEs) in patients with acute coronary syndrome (ACS) and type 2 diabetes mellitus (T2DM), and to determine whether CTI adds prognostic information beyond conventional clinical variables and its individual components. This single-center observational cohort study included 5,472 patients with ACS and T2DM treated at the Cardiovascular Centre of Beijing Friendship Hospital from January 2013 to January 2021. CTI was calculated as 0.412 × ln(C-reactive protein [mg/L]) + ln(triglyceride [mg/dL] × fasting plasma glucose [mg/dL]/2). The primary endpoint was MACCEs, defined as a composite of all-cause death, non-fatal myocardial infarction, non-fatal stroke, and ischemia-driven revascularization. Kaplan–Meier curves and multivariable Cox regression were supplemented by comparisons of CTI, the triglyceride-glucose (TyG) index, and ln(CRP), Harrell C-index, time-dependent area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration, decision curve analysis, ACS-subtype analyses, multiple imputation, proportional-hazards diagnostics, landmark analyses, and competing-risk analyses. During a median follow-up of 3.04 (IQR: 1.07–5.02) years, 1,075 MACCEs occurred. In univariable Cox analysis, each 1-unit increase in CTI was associated with a higher risk of MACCEs (HR 1.24, 95

Background:Three-vessel coronary artery disease (3V-CAD) often requires revascularization with either percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). The SYNTAX score is widely used for anatomical risk stratification, but whether magnetocardiography (MCG) provides incremental information beyond anatomical assessment remains uncertain. Objectives:To evaluate, in an exploratory pilot analysis, whether MCG-derived parameters add information beyond the SYNTAX score for modeling clinician-selected revascularization category in 3V-CAD and to examine the noncausal association between model-treatment concordance and major adverse cardiovascular and cerebrovascular events (MACCEs). Methods:Prospective cohort data were analyzed retrospectively. Candidate MCG parameters were screened using Pearson correlation, random forest analysis, and stepwise logistic regression, and selected variables were combined with the SYNTAX score. Model discrimination, calibration, bootstrap internal validation, and LASSO penalized logistic regression sensitivity analysis were assessed. Model-treatment concordance was explored using Kaplan-Meier analysis and multivariable Cox regression. Results:Among 544 patients, 543 complete cases were available for model evaluation, including 42 CABG events. In the overall cohort, the combined MCG-SYNTAX model did not materially improve discrimination compared with the SYNTAX-only model (AUC, 0.853 vs. 0.847; p = 0.628). Discrimination was also similar in the low-risk subgroup (SYNTAX < 22; AUC, 0.824 vs. 0.848; p = 0.540). In the intermediate-high-risk subgroup (SYNTAX ≥ 22), the combined model had a numerically higher apparent AUC, but this finding was considered hypothesis-generating. Bootstrap internal validation did not support a maintained incremental value of the combined model (optimism-corrected AUC, 0.834 vs. 0.848 for the SYNTAX-only model). In LASSO sensitivity analysis using all appended MCG-derived candidate variables, only the SYNTAX score was retained. The exploratory follow-up analysis showed an unadjusted difference in MACCE-free survival between concordance groups, but this association was not retained after multivariable adjustment and was not interpreted causally. Conclusions:In this single-center pilot modeling study, selected MCG parameters showed limited and unstable incremental value beyond the SYNTAX score for modeling clinician-selected revascularization category in 3V-CAD. These findings do not support clinical implementation at this stage and require external validation. Trial Registration: Chinese Clinical Trial Registry: ChiCTR2200066942.

The optimal revascularization strategy for coronary small vessel disease remains controversial due to limited lumen gain and high restenosis risk associated with stent implantation. Drug-coated balloon may offer an alternative by delivering antiproliferative drugs without leaving a permanent implant. This study aimed to evaluate the efficacy and safety of a paclitaxel-coated balloon developed by Hangzhou Revita Medical Technology Co., Ltd. for the treatment of coronary small vessel disease and very small vessel disease. This prospective, multicenter, randomized controlled trial enrolled 216 patients with reference vessel diameters between 1.5 and 2.5 mm at eight centers in China. Participants were randomly assigned (1:1) to treatment with a paclitaxel-coated balloon (Drug coated balloon group, n = 109) or a conventional balloon (Uncoated balloon group, n = 107). Patients with RVD < 2.0 mm were defined as the very small vessel disease (VSVD). The primary endpoint was in-lesion late lumen loss at 9 months. Secondary endpoints included minimal lumen diameter, percent diameter stenosis, binary restenosis, and device-oriented major adverse cardiovascular events. At 9 months, angiographic follow-up was completed in 176 patients. The drug coated balloon group showed significantly lower in-lesion LLL compared with the uncoated balloon group (0.15 ± 0.34 mm vs. 0.36 ± 0.43 mm, p < 0.001), larger MLD (1.39 ± 0.41 mm vs. 1.13 ± 0.48 mm, p < 0.001), and reduced DS

In-stent restenosis (ISR) continues to be a significant problem after percutaneous coronary intervention (PCI), negatively affecting patient care. This review offers a thorough examination of current ISR diagnostic methods - which combine anatomical and functional assessments with cutting-edge technologies - with holistic recommendations for ISR management, from optimized prevention during PCI to effective treatment. Anatomically, coronary angiography (CAG) persists as the gold standard, while intravascular ultrasound (IVUS) and optical coherence tomography (OCT) enhance stent optimization and ISR detection through high-resolution imaging. Functionally, fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) quantify ischemic risk, whereas non-invasive techniques like Computed Tomography-derived Fractional Flow Reserve (CT-FFR) and quantitative flow ratio (QFR) are transforming clinical paradigms. Multimodal imaging fusion and artificial intelligence markedly improve diagnostic accuracy and efficiency. Biomarkers and genomics are valuable tools for assessing ISR risk. Future directions emphasize integrated anatomical-functional-molecular assessments and AI-driven personalized management to refine ISR care and patient prognosis.

Cardiovascular diseases (CVDs), which have high morbidity and mortality, have become one of the world's largest public health concerns. Although primary hospitalization can partially relieve symptoms, many patients continue to have poor prognoses and lowered quality of life after discharge. Cardiac rehabilitation (CR) is an important recommendation for patients undergoing cardiac surgery, as well as those who suffer from severe cardiovascular events or chronic cardiac disease. However, standard, center-based CR options tend to be ineffective and hard to access, leading to low compliance. The development of digital health technologies (DHTs), especially wearables-which can be combined with mobile applications-has enabled home-based CR, that is, remote CR. This model has substantially enhanced CR efficiency and patient adherence. With advancements in artificial intelligence (AI), including machine learning (ML) algorithms and deep learning, large-scale data from wearables and other DHTs can be effectively retrieved and interpreted. Further incorporation of AI into DHTs may provide real-time fitness telemonitoring, accurate risk recognition and prediction, individualized exercise recommendations, and improved patient adherence. In this review, we succinctly highlight several applications of both AI and wearables in remote CR, and evaluate their collective roles in patient CR execution from several perspectives. We hope our review will help advance further integration of AI and DHT into home-based CR, and plan to direct future research toward refining the use of AI in new-era digital CR.

Immune checkpoint inhibitor (ICI)-related myocarditis is a rare but often fatal adverse event that has gained increasing attention due to the widespread clinical use of ICIs. This review explores the development of immune cells and their critical roles in cardiac autoimmunity, with particular focus on defects in central tolerance, the role of immune checkpoints, T cell trafficking to the heart, and the involvement of resident cardiac macrophages. It also introduces the key pathogenic mechanisms of ICI-related myocarditis, including the activation of autoreactive T cells that recognize self or shared antigens, the crosstalk between T cells and macrophages, and macrophage polarization. In addition, we also discuss current and emerging targetable therapeutic pathways, including cytokine modulation, the JAK/STAT pathway, and the use of CTLA-4 immunoglobulin. We further summarize practical diagnostic and prognostic approaches, including the role and limitations of troponin-based monitoring, echocardiography, magnetic resonance imaging, PET-CT applications, and endomyocardial biopsy. This review aims to establish a mechanistic framework that integrates pathogenic mechanisms, diagnostic and prognostic approaches, and therapeutically targetable pathways in ICI-related myocarditis.

Background: The relationship between left atrial (LA) size, LA strain, and long-term prognosis in patients with coronary chronic total occlusion (CTO) remains unclear. This study aimed to evaluate the association of LA size and LA strain with clinical outcomes in CTO patients using cardiac magnetic resonance (CMR). Methods: This retrospective study included 168 patients with left ventricular ejection fraction (LVEF) ≥ 40%. The primary endpoint was the composite of major adverse cardiovascular and cerebrovascular events (MACCE). Model 1 was established by adjusting for clinically relevant parameters and standard CMR metrics. Models 2–4 were developed using Cox regression based on Model 1, with additional adjustment for each LA strain parameter separately. Results: A total of 168 patients with an LVEF ≥ 40% were analyzed, of whom 39 (23.2%) experienced MACCE during a mean follow-up of 45.9 months (median, 42 months). A preliminary model suggested that LA maximum volume index (LAVImax) was independently associated with MACCE (HR 1.05, 95% CI 1.02–1.08, p = 0.004). Specifically, compared to the first quartile of LAVImax, the second, third, and fourth quartiles were associated with an increased risk of MACCE (Q2: HR 4.50, 95% CI 1.42–14.27, p = 0.011; Q3: HR 4.40, 95% CI 1.29–14.96, p = 0.018; Q4: HR 5.55, 95% CI 1.71–18.06, p = 0.004). In Models 2–4, higher LAVImax remained independently associated with MACCE (all p < 0.05), after adjusting for LA reservoir strain, conduit strain and booster strain, separately. In contrast, none of the LA strain parameters were associated with MACCE. Conclusions: Among CTO patients with LVEF ≥ 40%, LAVImax was independently associated with MACCE.

Purpose: Machine learning-based coronary computed tomography fractional flow reserve (CT-FFR) holds great potential for assessing coronary ischemic status. The current literature lacks a comprehensive description of the routine implementation of CT-FFR in real world. To investigate the clinical characteristics and acceptance of CT-FFR in clinical decision-making among Chinese patients and subsequently assess the diagnostic accuracy of invasive coronary angiography as the reference. Materials and Methods: In this retrospective single-center study, 4564 patients were included. In the first part, we conducted a baseline analysis of patients and their epicardial coronary arteries. Then, we analyzed hospitalization and revascularization in the context of application of CT-FFR, using logistic regression and Sankey diagrams. Finally, we performed a diagnostic analysis of 2718 vessels in 906 patients. Results: The baseline analysis included a total of 4564 patients. A statistically significant distinction was observed in the traditional risk factors for coronary heart disease between 2 groups with CT-FFR 0.8 cutoff values. Logistic regression analysis and Sankey plots revealed a association between CT-FFR ≤0.8 and subsequent hospitalization. Finally, a diagnostic analysis was performed on 2718 vessels, and the optimal diagnostic model efficacy was achieved by using a CT-FFR cutoff value of 0.8 in conjunction with stenosis ≥70% for CCTA. Conclusions: Our study provides evidence that machine learning-based CT-FFR values exhibit a probably positive correlation with individuals presenting high-risk factors for coronary artery disease. Furthermore, we observed a influence of CT-FFR on the clinical decisions made by physicians. The integration of CT-FFR and CCTA has the potential to enhance diagnostic efficacy.

IntroductionCardiovascular disease (CVD), a leading global health burden, is incompletely explained by traditional risk factors. Obesity and depression, both rising globally, may synergistically elevate CVD risk through shared pathways like chronic inflammation and autonomic dysfunction.MethodsUsing data from 9,925 Chinese adults (2015–2020 China Health and Retirement Longitudinal Study), we analyzed BMI categories (normal: <24.0 kg/m², overweight: 24.0–27.9 kg/m², obesity: ≥28.0 kg/m²) and depression severity (CESD-10 scores: none <10, mild/moderate 10–19, severe ≥20). Cox models estimated CVD hazard ratios (HRs), adjusted for sociodemographic and clinical confounders.ResultsAmong 1,644 incident CVD cases, overweight (HR=1.17) and obesity (HR=1.22) independently increased risk, as did mild/moderate (HR=1.43) and severe depression (HR=1.82). A marked joint effect emerged for obesity with severe depression (HR=2.72), while overweight and severe depression showed antagonistic interaction (HR=0.65). Subgroup analyses revealed heightened risks in non-diabetic individuals (HR=2.84 for obese/severe depression) and those aged 45–55 (HR=4.77).ConclusionsThese findings highlight obesity and depression as independent and interactive CVD risk factors, with synergistic effects in specific subgroups. Integrated interventions addressing both conditions are critical, particularly for non-diabetic and middle-aged populations, to mitigate CVD burden through combined mental and physical health strategies.

Background Mental stress-induced myocardial ischemia (MSIMI) is a recognized phenomenon in patients with coronary artery disease (CAD), particularly among those with comorbid anxiety or depression. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been recommended as a sensitive modality for MSIMI detection; however, data on its longitudinal consistency in high-risk populations remain limited. Objective To evaluate the detection of MSIMI using SPECT in patients with CAD and comorbid anxiety or depression, and to assess the consistency of MSIMI findings between baseline and one-year follow-up. Methods Patients with angiographically confirmed CAD who underwent coronary revascularization between December 2018 and December 2019 were prospectively enrolled if they had comorbid anxiety or depression (defined by PHQ-9 and GAD-7 scores). Mental stress test was performed using the Stroop color and word test (SCWT) at ≥ 4 weeks post revascularization. All patients underwent two-day 99 mTc-MIBI SPECT MPI at rest and during mental stress. MSIMI was defined as the presence of any of the following: reversible myocardial perfusion defect (RMPD), transient ischemic dilation (TID), reverse redistribution (RR), or a reduction in ejection fraction of ≥ 5% (ΔEF≥5%). Follow-up imaging was performed at 12 months. Phi correlation was used to assess consistency between baseline and follow-up MSIMI, and Spearman correlation was used to assess the area of RMPD. Results Among 205 enrolled patients, 105 (51.2%) demonstrated MSIMI at baseline (mean 42.8 days post revascularization). After a mean follow-up of 14.25 ± 4.42 months, 93 patients underwent repeat imaging, with MSIMI detected in 42 (45.1%). RMPD was the most frequent abnormality at both time points (baseline: 56.2%; follow-up: 78.6%). The overall consistency of MSIMI between baseline and follow-up was not significant (phi = 0.172, P = 0.097). However, RMPD showed significant consistency (phi = 0.293，P = 0.005), and the area of RMPDwas significantly correlated between the two time points (r = 0.413, p = 0.005). Conclusion In patients with CAD and comorbid anxiety or depression, RMPD is the most common SPECT-defined MSIMI phenotype and demonstrates moderate temporal consistency over a one-year follow-up period. These findings support the potential utility of SPECT MPI for serial assessment of MSIMI in this high-risk population.

PURPOSE:Machine learning-based coronary computed tomography fractional flow reserve (CT-FFR) holds great potential for assessing coronary ischemic status. The current literature lacks a comprehensive description of the routine implementation of CT-FFR in real world. To investigate the clinical characteristics and acceptance of CT-FFR in clinical decision-making among Chinese patients and subsequently assess the diagnostic accuracy of invasive coronary angiography as the reference. MATERIALS AND METHODS:In this retrospective single-center study, 4564 patients were included. In the first part, we conducted a baseline analysis of patients and their epicardial coronary arteries. Then, we analyzed hospitalization and revascularization in the context of application of CT-FFR, using logistic regression and Sankey diagrams. Finally, we performed a diagnostic analysis of 2718 vessels in 906 patients. RESULTS:The baseline analysis included a total of 4564 patients. A statistically significant distinction was observed in the traditional risk factors for coronary heart disease between 2 groups with CT-FFR 0.8 cutoff values. Logistic regression analysis and Sankey plots revealed a association between CT-FFR ≤0.8 and subsequent hospitalization. Finally, a diagnostic analysis was performed on 2718 vessels, and the optimal diagnostic model efficacy was achieved by using a CT-FFR cutoff value of 0.8 in conjunction with stenosis ≥70% for CCTA. CONCLUSIONS:Our study provides evidence that machine learning-based CT-FFR values exhibit a probably positive correlation with individuals presenting high-risk factors for coronary artery disease. Furthermore, we observed a influence of CT-FFR on the clinical decisions made by physicians. The integration of CT-FFR and CCTA has the potential to enhance diagnostic efficacy.

BACKGROUND:Artificial intelligence (AI)-assisted assessment of left ventricular ejection fraction (LVEF) has been increasingly adopted in clinical practice. In this study we aimed to assess the reliability and reproducibility of AI-assisted LVEF assessment in a diverse, real-world, multicentre setting. METHODS:We conducted a retrospective multicentre study involving 354 cardiac magnetic resonance examinations. A standardized LVEF reassessment (M-LVEF) was performed using QMass 8.1 (Medis Medical Imaging, Leiden, Netherlands) and systematically compared with original report-derived LVEF values (R-LVEF) generated using vendor-specific AI tools. For interobserver reproducibility assessment, 3 operators independently analyzed 30 randomly selected cases using fully manual and AI-assisted modes, the latter also performed with QMass 8.1 software. Operator A performed 2 measurements in both modes for intraobserver analysis. RESULTS:In overall and single-centre analyses, the consistency between R-LVEF and M-LVEF was good or excellent (intraclass correlation coefficient ≥ 0.86), but the 95% limits of agreement all exceeded ± 5%. In 44.3% of cases > 5% differences between R-LVEF and M-LVEF were observed, and 17.5% showed > 10% differences. The AI-assisted method not only significantly reduced LVEF assessment time compared with manual analysis, but also showed superior reproducibility. This was evidenced by greater interobserver agreement among 3 operators (intraclass correlation coefficients, 0.968, 0.974, 0.984 for AI vs 0.913, 0.924, 0.971 for manual, respectively) and greater intraobserver reproducibility, all with significantly lower coefficients of variation (5.6%, 8.8%, 9.3% vs 2.5%, 3.8%, 4.4%, respectively; 3.5% vs 1.0% [P < 0.05). CONCLUSIONS:There are individual differences in AI-assisted LVEF assessment, but it has significant advantages in efficiency and reproducibility, making AI a worthwhile tool to facilitate cardiac magnetic resonance quantification.

BACKGROUND AND AIMS:Patients with three-vessel coronary artery disease (3V-CAD) remain at heterogeneous risk for major adverse cardiovascular and cerebrovascular events (MACCE) after revascularization. Magnetocardiography (MCG) is a noncontact and radiation-free technique that may capture electrophysiological abnormalities not reflected by anatomical risk scores. This study evaluated the exploratory prognostic value of MCG-derived parameters in patients with 3V-CAD. METHODS:This single-center prospective cohort study included 544 patients with 3V-CAD who underwent coronary revascularization. MCG recordings were obtained before revascularization using a 36-channel optically pumped magnetometer-based system. The primary endpoint was MACCE, defined as cardiac death, stroke, myocardial infarction, or unplanned revascularization. Cox regression models were constructed using clinical variables, SYNTAX score, and an MCG composite variable. Model performance was assessed using discrimination, calibration, decision curve analysis, and bootstrap internal validation. RESULTS:During a median follow-up of 725 days, 37 patients (6.8%) experienced MACCE. The SYNTAX score was independently associated with MACCE after adjustment for clinical variables and MCG score (hazard ratio [HR], 1.08; 95% confidence interval [CI], 1.04-1.12; p < 0.001). The MCG composite variable was also associated with MACCE after adjustment for clinical variables and SYNTAX score (HR, 2.74; 95% CI, 1.57-4.79; p < 0.001). Model discrimination increased from 0.596 for clinical variables alone to 0.682 after adding SYNTAX score and to 0.727 after adding MCG. However, the improvement from the clinical + SYNTAX model to the MCG-added model was not statistically significant (p = 0.17). Bootstrap validation showed an optimism-corrected C-index of 0.672. CONCLUSION:MCG-derived parameters were associated with MACCE and may provide complementary electrophysiological information for exploratory risk stratification in patients with 3V-CAD after revascularization. Larger multicenter studies with external validation are needed to confirm its complementary prognostic value. TRIAL REGISTRATION:Chinese Clinical Trial Registry identifier: ChiCTR2200066942.

This study aimed to investigate the mechanism by which Caveolin-1 (Cav-1) deficiency leads to cardiac dysfunction, utilizing both in vivo and in vitro experimental models. Experiments used 43-52-week-old wild-type (WT) and Cav-1 knockout (Cav-1-/-) mice (n=5 per group), and the H9C2 rat cardiomyocyte cell line. In vivo, Cav-1-/-mice received rapamycin (0.25 mg/kg). In vitro, H9C2 cells underwent Cav-1 knockdown/overexpression and were treated with rapamycin (100 nM), chloroquine (20 µM), AMPK activator A-769662, adiponectin (APN, 5 µg/ml), or AdipoR1 overexpression. Cardiac function was assessed by echocardiography (LVEF, LVFS). Protein expression was analyzed via western blotting and immunofluorescence. Autophagic flux was measured using mRFP-GFP-LC3B lentivirus. Apoptosis was evaluated by TUNEL staining and flow cytometry. Data are mean ± SD; statistical analysis used t-tests/ANOVA. Cav-1-/- mice exhibited impaired cardiac function (LVEF: reduced vs. WT, p<0.05), suppressed autophagy, increased apoptosis, and elevated inflammation/fibrosis. In H9C2 cells, Cav-1 knockdown inhibited AMPK phosphorylation, activated mTOR, and repressed autophagy, effects reversed by Cav-1 overexpression or rapamycin/AMPK activation. Bioinformatic and immunofluorescence analyses identified AdipoR1 downregulation in Cav-1-/- hearts; APN/AdipoR1 overexpression rescued autophagy and reduced apoptosis. Cav-1 deficiency induces cardiac dysfunction by suppressing autophagy via the AdipoR1-AMPK-mTOR pathway, highlighting Cav-1 as a potential therapeutic target for cardiac dysfunction.