Bellino, Michele

BACKGROUND: Bicuspid aortic valve (BAV) is a frequent congenital heart defect with a high heritability. Despite this, only a limited number of genes have been associated with the disease, and the molecular mechanisms remain unexplained in most cases. This study aimed to further understand the genetic architecture of BAV. METHODS: A genome-wide association study meta-analysis including 9631 cases among 65 677 participants was performed. Genes were prioritized using transcriptomic analyses based on RNA sequencing in relevant tissues, including human fetal and adult aortic valves. The impact of the knockdown or knockout of 4 candidate genes on cardiac development was verified in zebrafish. A polygenic risk score was developed, its association with BAV was evaluated in an independent cohort, and its association with a wide range of phenotypes (n=976) was evaluated in UK Biobank (n=355 618 individuals). RESULTS: Thirty-six genomic loci were identified, including 32 that were not described previously. Among the prioritized genes, KANK2 and ERBB4 were identified as potentially causal through transcriptomic analyses, colocalization, and Mendelian randomization based on gene expression in human aortic valves (n=484), whereas PRDM6 and STRN were prioritized using similar analyses from aortic (n=326) and left ventricular tissues (n=326), respectively. Targeting 4 candidate genes ( WNT4 , LEF1 , STRN , and KANK2 ) in zebrafish led to disruption in cardiac development. A polygenic risk score was associated with an odds ratio of 2.07 (95% CI, 1.90–2.25; P =5.43×10 -62 ) per SD for BAV and significantly associated with thoracic aortic aneurysm and atrial fibrillation in UK Biobank. CONCLUSIONS: This study supports a significant polygenic contribution to BAV, where the combination of multiple common variants in genes involved in heart morphogenesis disrupts aortic valve development.

ABSTRACT Aims Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

Abstract Background Little is known about clinical outcomes in patients with bicuspid aortic valve (BAV) and ascending aortic diameters ≥50 mm where the elective surgical-indication zone begins. Purpose To assess incident aortic dissection (AoD), post-surgical survival and guideline implementation. Methods Multi-center retrospective study of BAV patients (≥18 years) with maximal root or tubular-ascending aortic diameters ≥50mm detected by transthoracic echocardiography at baseline. Ascertainment of aortic surgery, AoD and death was carried-out at their respective institutions. The primary outcome was AoD: “confirmed” AoD by surgery or death certificate, or “possible” AoD defined as sudden cardiac death of unknown cause by death certificate. Secondary outcomes were aortic surgery, post-surgical survival and guideline implementation. Results We included 506 consecutive BAV patients, mean age 61±14 years, 83% men, mean maximal aortic diameter 52±2 mm at baseline, ascending aorta was the most common segment ≥50mm (85%). During a median follow-up of 7.7 years, 356 (71%) underwent elective surgery (89% of class 1 patients). Early-surgery (≤6 months from baseline) occurred in 195 patients and 311 patients remained “under-surveillance”. Surgery under-surveillance (>6 months from baseline) occurred in 161/311 (52%) patients at 2.6 [IQR: 1.5–4.7] years of follow-up. Surgical mortality was 1.4%. Ten-year post-surgical survival was >90% and similar between early-surgery and surgery under-surveillance (p=0.8). Of 8 AoD events, 3 confirmed and 5 possible (all occurring in unoperated patients under-surveillance), 5 events occurred with maximal aortic baseline diameter ≥55 mm and/or >1 year without clinical follow-up; therefore, under guideline non-compliance. Incidence of confirmed plus possible aortic dissection was 0.37% per year with size 50–54mm, 1.13% per year between 55–59mm, and 10.41% per year with size ≥60mm. Aortic stenosis was associated with AoD (p=0.04) and all-cause death in unoperated patients (p=0.001). Conclusions Our results suggest that BAV patients can be safely followed as per guidelines after aortic diameters enter the elective surgical zone (≥50mm), with excellent post-surgical survival regardless of whether surgery is early- or under-surveillance. Appropriate guideline implementation for surgical thresholds and interval clinical follow-up could have prevented most AoD events. Valvular disease guideline should also be followed to prevent AoD and improve survival in these patients. Funding Acknowledgement Type of funding sources: None.