Clinical-pathological correlations of BAV and the attendant thoracic aortopathies. Part 2: Pluridisciplinary perspective on their genetic and molecular origins

Circulating Matrix Metalloproteinases for Prediction of Aortic Dilatation in Children with Bicuspid Aortic Valve: A Single-Center, Observational Study

Circulating biomarkers have been proposed for early identification of aortic dilatation progression associated with bicuspid aortic valve (BAV), but matrix metalloproteinases (MMPs) are distinguished as signatures of increased extracellular matrix degradation, a landmark of aneurysm formation. The current study aims to identify the role of MMP-1, MMP-2, MMP-9, and the MMP inhibitor, TIMP-1, in identifying aortic dilation in children with BAV. We conducted a study on 73 children divided into two study groups, depending on the presence of aortic dilatation (group 1-43 BAV controls and group 2-30 children with BAV and aortic dilatation). Each patient underwent a cardiac ultrasound and, in each case, serum MMP-1, MMP-2, MMP-9, and TIMP-1 were quantified using xMAP technology. Comparison of the MMPs between the two study groups revealed significantly higher values only in the case of TIMP-1, among BAV controls. Moreover, the same TIMP-1 inversely correlated with aortic annulus absolute size and z score, as well as with ascending aorta z score. No particular correlation between the aortic phenotype and the presence of aortic dilatation was found. Future longitudinal research starting at pediatric ages could show the significance of MMPs screening in BAV individuals as predictors of aortic aneurysm formation.

Effect of deletion of the protein kinase PRKD1 on development of the mouse embryonic heart

Congenital heart disease (CHD) is the most common congenital anomaly, with an overall incidence of approximately 1% in the United Kingdom. Exome sequencing in large CHD cohorts has been performed to provide insights into the genetic aetiology of CHD. This includes a study of 1891 probands by our group in collaboration with others, which identified three novel genes-CDK13, PRKD1, and CHD4, in patients with syndromic CHD. PRKD1 encodes a serine/threonine protein kinase, which is important in a variety of fundamental cellular functions. Individuals with a heterozygous mutation in PRKD1 may have facial dysmorphism, ectodermal dysplasia and may have CHDs such as pulmonary stenosis, atrioventricular septal defects, coarctation of the aorta and bicuspid aortic valve. To obtain a greater appreciation for the role that this essential protein kinase plays in cardiogenesis and CHD, we have analysed a Prkd1 transgenic mouse model (Prkd1em1) carrying deletion of exon 2, causing loss of function. High-resolution episcopic microscopy affords detailed morphological 3D analysis of the developing heart and provides evidence for an essential role of Prkd1 in both normal cardiac development and CHD. We show that homozygous deletion of Prkd1 is associated with complex forms of CHD such as atrioventricular septal defects, and bicuspid aortic and pulmonary valves, and is lethal. Even in heterozygotes, cardiac differences occur. However, given that 97% of Prkd1 heterozygous mice display normal heart development, it is likely that one normal allele is sufficient, with the defects seen most likely to represent sporadic events. Moreover, mRNA and protein expression levels were investigated by RT-qPCR and western immunoblotting, respectively. A significant reduction in Prkd1 mRNA levels was seen in homozygotes, but not heterozygotes, compared to WT littermates. While a trend towards lower PRKD1 protein expression was seen in the heterozygotes, the difference was only significant in the homozygotes. There was no compensation by the related Prkd2 and Prkd3 at transcript level, as evidenced by RT-qPCR. Overall, we demonstrate a vital role of Prkd1 in heart development and the aetiology of CHD.

Predictive machine learning models for ascending aortic dilatation in patients with bicuspid and tricuspid aortic valves undergoing cardiothoracic surgery: a prospective, single-centre and observational study

OBJECTIVES

The objective of this study was to develop clinical classifiers aiming to identify prevalent ascending aortic dilatation in patients with bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV).

DESIGN AND SETTING

A prospective, single-centre and observational cohort.

PARTICIPANTS

The study involved 543 BAV and 491 TAV patients with aortic valve disease and/or ascending aortic dilatation, excluding those with coronary artery disease, undergoing cardiothoracic surgery at the Karolinska University Hospital (Sweden).

MAIN OUTCOME MEASURES

Predictors of high risk of ascending aortic dilatation (defined as ascending aorta with a diameter above 40 mm) were identified through the application of machine learning algorithms and classic logistic regression models.

EXPOSURES

Comprehensive multidimensional data, including valve morphology, clinical information, family history of cardiovascular diseases, prevalent diseases, demographic details, lifestyle factors, and medication.

RESULTS

BAV patients, with an average age of 60.4±12.4 years, showed a higher frequency of aortic dilatation (45.3%) compared with TAV patients, who had an average age of 70.4±9.1 years (28.9% dilatation, p <0.001). Aneurysm prediction models for TAV patients exhibited mean area under the receiver-operating-characteristic curve (AUC) values above 0.8, with the absence of aortic stenosis being the primary predictor, followed by diabetes and high-sensitivity C reactive protein. Conversely, prediction models for BAV patients resulted in AUC values between 0.5 and 0.55, indicating low usefulness for predicting aortic dilatation. Classification results remained consistent across all machine learning algorithms and classic logistic regression models.

CONCLUSION AND RECOMMENDATION

Cardiovascular risk profiles appear to be more predictive of aortopathy in TAV patients than in patients with BAV. This adds evidence to the fact that BAV-associated and TAV-associated aortopathy involves different pathways to aneurysm formation and highlights the need for specific aneurysm preventions in these patients. Further, our results highlight that machine learning approaches do not outperform classical prediction methods in addressing complex interactions and non-linear relations between variables.

Genetics in bicuspid aortic valve disease: Where are we?

Bicuspid aortic valve (BAV) is the most common congenital heart defect, found in up to 2% of the population and associated with a 30% lifetime risk of complications. BAV is inherited as an autosomal dominant trait with incomplete penetrance and variable expressivity due to a complex genetic architecture that involves many interacting genes. In this review, we highlight the current state of knowledge about BAV genetics, principles and methods for BAV gene discovery, clinical applications of BAV genetics, and important future directions.