Our objective was to better understand the genetic bases of dilated cardiomyopathy (DCM), a leading cause of systolic heart failure. We conducted the largest genome-wide association study performed so far in DCM, with 2719 cases and 4440 controls in the discovery population. We identified and replicated two new DCM-associated loci on chromosome 3p25.1 [lead single-nucleotide polymorphism (SNP) rs62232870, P = 8.7 × 10⁻¹¹ and 7.7 × 10⁻⁴ in the discovery and replication steps, respectively] and chromosome 22q11.23 (lead SNP rs7284877, P = 3.3 × 10⁻⁸ and 1.4 × 10⁻³ in the discovery and replication steps, respectively), while confirming two previously identified DCM loci on chromosomes 10 and 1, BAG3 and HSPB7. A genetic risk score constructed from the number of risk alleles at these four DCM loci revealed a 3-fold increased risk of DCM for individuals with 8 risk alleles compared to individuals with 5 risk alleles (median of the referral population). In silico annotation and functional 4C-sequencing analyses on iPSC-derived cardiomyocytes identify SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine transporter whose involvement in myocardial dysfunction and DCM is supported by numerous observations in humans and animals. At the 22q11.23 locus, in silico and data mining annotations, and to a lesser extent functional analysis, strongly suggest SMARCB1 as the candidate culprit gene. This study provides a better understanding of the genetic architecture of DCM and sheds light on novel biological pathways underlying heart failure. Step 1: Through the largest genome-wide association study performed so far in dilated cardiomyopathy, we identified and replicated two new loci on chromosome 3p25.1 and 22q11.23. Step 2: Combined in silico and functional analyses at the associated loci revealed the best culprit gene at each locus: SLC6A6 (chromosome 3) and SMARCB1 (chromosome 22). The discovery of these two new players shed light on novel biological pathways and putative new therapeutic targets.

In the ESC 2023 guidelines, cardiomyopathies are conservatively defined as ‘myocardial disorders in which the heart muscle is structurally and functionally abnormal, in the absence of coronary artery disease, hypertension, valvular disease, and congenital heart disease sufficient to cause the observed myocardial abnormality’. They are morpho-functionally classified as hypertrophic, dilated, restrictive, and arrhythmogenic right ventricular cardiomyopathy with the addition of the left ventricular non-dilated cardiomyopathy that describes intermediate phenotypes not fulfilling standard disease definitions despite the presence of myocardial disease on cardiac imaging or tissue analysis. The new ESC guidelines provide ‘a guide to the diagnostic approach to cardiomyopathies, highlight general evaluation and management issues, and signpost the reader to the relevant evidence base for the recommendations’. The recommendations and suggestions included in the document provide the tools to build up pathways tailored to specific cardiomyopathy (phenotype and cause) and define therapeutic indications, including target therapies where possible. The impact is on clinical cardiology, where disease-specific care paths can be assisted by the guidelines, and on genetics, both clinics and testing, where deep phenotyping and participated multi-disciplinary evaluation provide a unique tool for validating the pathogenicity of variants. The role of endomyocardial biopsy remains underexploited and confined to particular forms of restrictive cardiomyopathy, myocarditis, and amyloidosis. New research and development will be needed to cover the gaps between science and clinics. Finally, the opening up to disciplines such as bioinformatics, bioengineering, mathematics, and physics will support clinical cardiologists in the best governance of the novel artificial intelligence–assisted resources.

Risk-reducing surgery (RRS) is recommended in BRCA-mutated carriers because of their increased risk of developing ovarian cancer, while its role is still discussed for women harboring mutations in non-BRCA homologous repair genes. The aim of this study was to retrospectively evaluate the occurrence of pathological findings in a high-risk population undergoing RRS in San Matteo Hospital, Pavia between 2012 and 2022, and correlate their genetic and clinical outcomes, comparing them with a control group. The final cohort of 190 patients included 85 BRCA1, 63 BRCA2, 11 CHEK2, 7 PALB2, 4 ATM, 1 ERCC5, 1 RAD51C, 1 CDH1, 1 MEN1, 1 MLH1 gene mutation carriers and 15 patients with no known mutation but with strong familial risk. Occult invasive serous carcinoma (HGSC) and serous tubal intraepithelial carcinoma (STIC) were diagnosed in 12 (6.3%) women, all of them BRCA carriers. No neoplastic lesion was diagnosed in the non-BRCA group, in women with familial risk, or in the control group. Oral contraceptive use and age ≤45 at surgery were both found to be favorable factors. While p53 signature and serous tubal intraepithelial lesion (STIL) were also seen in the control group and in non-BRCA carriers, STIC and HGSC were only found in BRCA1/2 mutation carriers.

X-ray Coronary Angiography (XCA) enables visualization of coronary arteries for disease and morphology assessment. Accurate segmentation of major coronary vessels is crucial for automated analysis of their geometric features but presents challenges due to anatomical complexity. This study introduces CoroSAM, an adaptation of the LiteMedSAM Foundation Model, employing parameter-efficient fine-tuning for interactive coronary artery segmentation in XCA images. The proposed approach incorporates Convolutional Adapter layers within the image encoder's TinyViT blocks to enhance domain-specific feature extraction while maintaining computational efficiency. A point-based prompting strategy directly encodes vessel endpoints and branch points as additional input channels. Model evaluation employed 5-fold cross-validation on the ARCADE dataset and zero-shot testing on external datasets (XCAD, DCA1). Performance was compared with state-of-the-art models for user-guided segmentation. CoroSAM demonstrated superior performance on the ARCADE test set (Dice=0.87, Precision=0.86, Recall=0.89) while requiring fewer trainable parameters compared to competitive models. Statistical analysis confirmed significant improvements over alternative Adapter configurations. Zero-shot generalization yielded competitive performance on external datasets (XCAD: Dice=0.82; DCA1: Dice=0.73), demonstrating robust transferability across different image qualities. Integrating specialized Convolutional Adapters and channel-encoded point prompts enables accurate delineation of major coronary vessels with minimal user intervention. CoroSAM's architecture facilitates efficient inference on standard computing hardware without dedicated GPUs, providing a practical tool for clinical applications. This approach establishes an adaptation framework that effectively balances segmentation accuracy with computational efficiency, making it suitable for routine analysis workflows.

Pregnancy in women with cardiovascular disease (CVD) represents a distinct clinical condition, as the physiological adaptations of pregnancy may interact with underlying cardiac pathology in variable and sometimes unpredictable ways. Consequently, maternal and foetal risk cannot be assumed a priori and requires structured, patient- and disease-specific assessment. The new ESC guidelines on cardiovascular disease and pregnancy incorporate updated evidence following the 2018 version. The contents span from models of multidisciplinary care (the Pregnancy Heart Team, PHT), pre-pregnancy counselling, and pregnancy-related risk assessment, to novel recommendations that are both shared by all pregnancies in women with CVD and specific to each distinct disease. Due to limited prospective or randomized studies largely prevented by ethical reasons, most recommendations are based on evidence level C. This document provides a concise synthesis of innovations in shared management strategies and disease-specific pathways in pregnancy of women with known CVD. It also acknowledges that not all clinical scenarios can be fully captured by guideline-based recommendations. Beyond guidelines, and primarily because approximately 50% of recommendations are Level C evidence, there is considerable decision-making margin for cardiovascular specialists to adhere recommendations. For rare unpredictable complications/events the limited available evidence does not allow recommendations to be made. Looking to the future, the expectation is that AI, which is already generating data and models of care in pregnancy, can help support widespread access to PHT, both in hospitals equipped with the necessary requirements and remotely to all healthcare facilities serving pregnant women with CVDs.