Bicuspid aortic valve (BAV) is a common (0.5–2.0% of general population) congenital heart defect with increased prevalence of aortic dilatation and dissection. BAV has an autosomal dominant inheritance with reduced penetrance and variable expressivity. BAV has been described as an isolated trait or associated with syndromic conditions [e.g., Marfan Marfan syndrome or Loeys-Dietz syndrome (MFS, LDS)]. Identification of a syndromic condition in a BAV patient is clinically relevant to personalize aortic surgery indication. A 4-fold increase in BAV prevalence in a large cohort of unrelated MFS patients with respect to general population was reported, as well as in LDS patients (8-fold). It is also known that BAV is more frequent in patients with thoracic aortic aneurysm (TAA) related to mutations in ACTA2, FBN1, and TGFBR2 genes. Moreover, in 8 patients with BAV and thoracic aortic dilation, not fulfilling the clinical criteria for MFS, FBN1 mutations in 2/8 patients were identified suggesting that FBN1 or other genes involved in syndromic conditions correlated to aortopathy could be involved in BAV. Beyond loci associated to syndromic disorders, studies in humans and animal models evidenced/suggested the role of further genes in non-syndromic BAV. The transcriptional regulator NOTCH1 has been associated with the development and acceleration of calcium deposition. Genome wide marker-based linkage analysis demonstrated a linkage of BAV to loci on chromosomes 18, 5, and 13q. Recently, a role for GATA4/5 in aortic valve morphogenesis and endocardial cell differentiation has been reported. BAV has also been associated with a reduced UFD1L gene expression or involvement of a locus containing AXIN1/PDIA2. Much remains to be understood about the genetics of BAV. In the last years, high-throughput sequencing technologies, allowing the analysis of large number of genes or entire exomes or genomes, progressively became available. The latter issue together with the development of “BigData” analysis methods improving their interpretation and integration with clinical data represents a promising opportunity to increase the disease knowledge and diagnosis in monogenic and multifactorial complex traits. This review summarized the main knowledge on the BAV genetic bases, the role of genetic diagnosis in BAV patient managements and the crucial challenges for the comprehension of genetics of BAV in research and diagnosis.

Inflammatory mediators and metalloproteinases are altered in acute ischemic stroke (AIS) and play a detrimental effect on clinical severity and hemorrhagic transformation of the ischemic brain lesion. Using data from the Italian multicenter observational MAGIC (MArker bioloGici nell'Ictus Cerebrale) Study, we evaluated the effect of inflammatory and metalloproteinases profiles on three-month functional outcome, hemorrhagic transformation and mortality in 327 patients with AIS treated with intravenous thrombolys in according to SITS-MOST (Safe Implementation of Thrombolysis in Stroke-MOnitoring STudy) criteria. Circulating biomarkers were assessed at baseline and 24 h after thrombolysis. Adjusting for age, sex, baseline glycemia and National Institute of Health Stroke Scale, history of atrial fibrillation or congestive heart failure, and of inflammatory diseases or infections, baseline alpha-2macroglobulin (A2M), baseline serum amyloid protein (SAP) and pre-post tissue-plasminogen activator (tPA) variations (Δ) of metalloproteinase 9, remained significantly and independently associated with three-month death [OR (95% CI):A2M:2.99 (1.19-7.53); SAP:5.46 (1.64-18.74); Δmetalloproteinase 9:1.60 (1.12-2.27)]. The addition of baseline A2M and Δmetalloproteinase 9 or baseline SAP and Δmetalloproteinase 9 (model-2 or model-3) to clinical variables (model-1) significantly improved the area under curve for prediction of death [model-2 with A2M: p = 0.0205; model-3 with SAP: p = 0.001]. In conclusion, among AIS patients treated with thrombolysis, circulating A2M, SAP and Δmetalloproteinase 9 are independent markers of poor outcome. These results may prompt controlled clinical research about agents antagonizing their effect.

Experimental studies have shown a significant increase in angiotensin-converting enzyme (ACE) expression in atrial tissue of AF patients. ACE regulates the synthesis of endothelial nitric oxide (NO), which modulates autonomic nervous activity involved in the development of AF. The aim of the study was to evaluate the prevalence of ACE insertion/deletion and endothelial NO synthase (eNOS) T-786C, G894T, and 4a/4b polymorphisms in 148 patients with persistent AF, compared with 210 control subjects. ACE insertion/deletion polymorphism genotype distribution and allele frequency were significantly different between patients and controls (P < 0.0001 and P < 0.0001, respectively). ACE DD genotype was significantly associated with the risk of AF (OR DD/ID + II = 3.24, P < 0.0001). Analysis of eNOS polymorphisms showed no significant difference in genotype distribution and allele frequency between patients and controls. The results suggest a possible role of ACE DD genotype as a predisposing factor to AF and a pathophysiological mechanism of ACE inhibition in reducing the incidence of AF in patients with left ventricular dysfunction.

Background and aims. Familial hypercholesterolemia (FH) is an inherited disorder characterized by high levels of blood cholesterol from birth and premature coronary heart disease. Thus, the identification of FH patients is crucial to prevent or delay the onset of cardiovascular events, and the availability of a tool helping with the diagnosis in the setting of general medicine is essential to improve FH patient identification. Methods. This study evaluated the performance of the Dutch Lipid Clinic Network (DLCN) score in FH patients enrolled in the LIPIGEN study, an Italian integrated network aimed at improving the identification of patients with genetic dyslipidaemias, including FH. Results. The DLCN score was applied on a sample of 1377 adults (mean age 42.9±14.2 years) with genetic diagnosis of FH, resulting in 28.5% of the sample classified as probable FH and 37.9% as classified definite FH. Among these subjects, 43.4% had at least one missing data out of 8, and about 10.0% had 4 missing data or more. When analyzed based on the type of missing data, a higher percentage of subjects with at least 1 missing data in the clinical history or physical examination was classified as possible FH (DLCN score 3-5). We also found that using real or estimated pre-treatment LDL-C levels may significantly modify the DLCN score. Conclusions. Although the DLCN score is a useful tool for physicians in the diagnosis of FH, it may be limited by the complexity to retrieve all the essential information, suggesting a crucial role of the clinical judgement in the identification of FH subjects.