With global aging, the prevalence of calcific aortic valve stenosis (CAVS) has significantly increased, and even mild CAVS elevates mortality risk, highlighting an urgent need for preventive strategies. In this study, we analyzed 153,312 participants from the UK Biobank. We found that a higher Life’s Essential 8 (LE8) score was independently associated with a decreased CAVS risk [hazard ratio (HR)/standard deviation (SD): 0.72, 95% confidence interval (CI): 0.68–0.76, P < 2E−16], whereas a higher genetic risk score was independently associated with an increased CAVS risk (HR/SD: 1.63, 95% CI: 1.54–1.71, P < 2E−16). Restricted cubic spline analysis revealed approximately linear inverse associations between LE8 score and CAVS risk across all genetic risk groups. Although no multiplicative interaction was found between cardiovascular health (CVH) and genetic risk for CAVS, a significant additive interaction was identified (relative excess risk due to interaction: 3.77, 95% CI: 1.30–8.50). Among participants with high genetic risk, those with ideal CVH had a lower 10-year cumulative CAVS incidence rate than those with poor CVH (0.33% vs. 1.80%, P < 0.001). Besides, a significant multiplicative interaction was observed between LE8 score and age (P = 0.007). Similar trends were also observed for early-onset and late-onset CAVS. In conclusion, regardless of genetic risk groups, a higher LE8 score was associated with a lower CAVS risk in an approximately linear pattern. In particular, high genetic risk and poor CVH had a synergistic effect on CAVS risk, meaning that participants with high genetic risk and poor CVH would amplify their CAVS risk. Therefore, early and sustained optimization of CVH, particularly among those with high genetic risk, is essential to mitigate the risk of CAVS and its subtypes.

Background/Objectives: This study aimed to establish a CCTA-based radiomics model of perivascular adipose tissue (PCAT) to identify high-risk NCLs in patients with NSTE-ACS, potentially facilitating early risk stratification. Methods: In this prospective cohort of 542 NSTE-ACS patients, pericoronary adipose tissue (PCAT) radiomic features of non-culprit lesions (NCLs) were extracted from baseline coronary CTA. Patients were assigned to training (n = 379) and validation (n = 163) cohorts. Machine learning algorithms were applied to develop a radiomics signature (Rad model) to predict 4-year NCL-related major adverse cardiovascular events (MACEs). A combined clinic-radiomics model was constructed to enhance predictive performance. Additionally, the association between the baseline Rad model and longitudinal non-calcified plaque progression (ΔNCPV%/year) was evaluated in a subcohort (n = 60) with serial CCTA. Results: Over a median 4.0-year follow-up, NCL-related MACE occurred in 84 patients (15.5%). The Rad model (comprising nine features) independently predicted MACE (adjusted hazard ratio, 1.988; 95% CI, 1.753–2.254; p < 0.001). In the validation cohort, the combined model yielded higher discrimination for 4-year MACE than the clinical model alone (AUC, 0.793 vs. 0.703; p < 0.05). In the serial CCTA subgroup, a higher baseline Rad model was significantly associated with annualized non-calcified plaque volume progression (standardized β, 0.477; p < 0.001). Conclusions: A CCTA-based PCAT radiomics model is associated with future NCL-related MACE and accelerated plaque progression in patients with NSTE-ACS. This approach may serve as a non-invasive tool for individualized risk stratification.

Objectives: The objective of this study was to evaluate the baseline blood urea nitrogen-to-serum albumin ratio (BAR) in patients with acute heart failure (AHF) upon admission as a predictor of long-term mortality. Methods: In this retrospective cohort study, a total of 2,556 patients who were hospitalized for AHF at Beijing Anzhen Hospital between 2017 October and 2020 January were enrolled. Multivariate Cox regression analysis was performed to explore the association between BAR and out-of-hospital all-cause mortality. The risk stratification capability of BAR was also calculated using Kaplan–Meier survival analysis. Results: Of the 2,556 patients with AHF, 1,196 had with heart failure with reduced ejection fraction, 1,066 had heart failure with preserved ejection fraction, and 294 had heart failure with mildly reduced ejection fraction. After a median follow-up of 1.84 years (IQR: 1.15, 2.57), 665 (26.0%) patients experienced out-of-hospital all-cause mortality. Univariate Cox regression analysis revealed that BAR was significantly associated with long-term mortality in all patients with AHF ( p < 0.05). After adjusting for age, sex, and traditional clinical risk factors, BAR remained independently associated with mortality in both the overall population and each heart failure subgroup. Conclusion: BAR was associated with long-term mortality in patients with AHF after discharge, regardless of left ventricular ejection fraction at admission. BAR exhibited risk stratification capacity for long-term mortality outcomes in patients with AHF.