Lipoprotein(a) (Lp(a)) is a low-density lipoprotein (LDL) cholesterol-like particle bound to apolipoprotein(a). Increased Lp(a) levels are an independent, heritable causal risk factor for atherosclerotic cardiovascular disease (ASCVD) as they are largely determined by variations in the Lp(a) gene (LPA) locus encoding apo(a). Lp(a) is the preferential lipoprotein carrier for oxidized phospholipids (OxPL), and its role adversely affects vascular inflammation, atherosclerotic lesions, endothelial function and thrombogenicity, which pathophysiologically leads to cardiovascular (CV) events. Despite this crucial role of Lp(a), its measurement lacks a globally unified method, and, between different laboratories, results need standardization. Standard antilipidemic therapies, such as statins, fibrates and ezetimibe, have a mediocre effect on Lp(a) levels, although it is not yet clear whether such treatments can affect CV events and prognosis. This narrative review aims to summarize knowledge regarding the mechanisms mediating the effect of Lp(a) on inflammation, atherosclerosis and thrombosis and discuss current diagnostic and therapeutic potentials.

The cardiovascular implications of non-alcoholic fatty liver disease (NAFLD) have been associated with heart failure with preserved ejection fraction (HFpEF). The purpose of this review was to conduct a bibliographic search regarding the correlation between NAFLD and the echocardiographic parameters of left ventricular diastolic function. A systematic literature search was conducted in PubMed and Embase for original research data reporting on the association of NAFLD with diastolic function markers [E/e′, left atrial volume index (LAVi), left ventricular mass index (LVMi)]. Meta-analysis was performed using the meta and dmetar packages in R studio v.1.4.1106, with p < 0.05 values being considered significant. Results are expressed as the standardized mean difference (SMD) for continuous variables and as the odds ratio (OR) for categorical variables, with respective 95% confidence intervals (CI). Heterogeneity between studies was expressed with index Ι². From the preliminary search, 2619 articles were found from which 31 studies were included in the final statistical analysis. The meta-analysis of 8 studies which reported on the prevalence of diastolic dysfunction showed that it was increased in patients with NAFLD (OR: 2.07, 95% CI 1.24–3.44 with p = 0.01, I²: 80% with p < 0.01). The meta-analysis of 21 studies showed significantly higher E/e′ in NAFLD patients (SMD 1.02, 95% CI 0.43–1.61 with p < 0.001, I²: 97% with p < 0.001). Individuals with NAFLD had increased LAVi (SMD: 0.87, 95% CI 0.38–1.37 with p < 0.001, I²: 96% with p < 0.001) and LVMi (SMD: 0.89, 95% CI 0.31–1.48 with p = 0.003, I²: 100% with p < 0.001). To conclude, in the meta-analysis of 31 observational studies, NAFLD patients were found to have affected left ventricular diastolic function, supporting the hypothesis of NAFLD being associated with HFpEF.

Artificial intelligence (AI) is transforming cardiovascular medicine by enabling the analysis of high-dimensional biomedical data with unprecedented precision. Initially employed to automate human tasks such as electrocardiogram (ECG) interpretation and imaging segmentation, AI’s true potential lies in uncovering hidden disease data patterns, predicting long-term cardiovascular risk, and personalizing treatments. Unlike human cognition, which excels in certain tasks but is limited by memory and processing constraints, AI integrates multimodal data sources—including ECG, echocardiography, cardiac magnetic resonance (CMR) imaging, genomics, and wearable sensor data—to generate novel clinical insights. AI models have demonstrated remarkable success in early dis-ease detection, such as predicting heart failure from standard ECGs before symptom on-set, distinguishing genetic cardiomyopathies, and forecasting arrhythmic events. However, several challenges persist, including AI’s lack of contextual understanding in most of these tasks, its “black-box” nature, and biases in training datasets that may contribute to disparities in healthcare delivery. Ethical considerations and regulatory frameworks are evolving, with governing bodies establishing guidelines for AI-driven medical applications. To fully harness the potential of AI, interdisciplinary collaboration among clinicians, data scientists, and engineers is essential, alongside open science initiatives to promote data accessibility and reproducibility. Future AI models must go beyond task automation, focusing instead on augmenting human expertise to enable proactive, precision-driven cardiovascular care. By embracing AI’s computational strengths while addressing its limitations, cardiology is poised to enter an era of transformative innovation beyond traditional diagnostic and therapeutic paradigms.

Capillary leak syndrome is an under-diagnosed condition leading to serious hypoalbuminemia with diffuse edema, pulmonary edema, severe hypotension, and possibly death. Sepsis leading to hemophagocytic lymphohistiocytosis (HLH) is a major risk factor; however, capillary hyper-permeability is the core underlying pathophysiological mechanism. Endothelial dysfunction plays a major role in cardiometabolic disease through insulin resistance, lipotoxicity, and, eventually, oxidative stress and chronic inflammation. We review the literature concerning the aforementioned mechanisms as well-established risk factors for adverse COVID-19 outcomes. We especially focus on data regarding the underlying endothelial effects of SARS-CoV-2 infection, including direct damage and increased vascular leakage through a hyper-inflammatory cascade and diminished nitric oxide bioavailability. Interestingly, an increased incidence of hypoalbuminemia has been observed in patients with severe COVID-19, especially those with underlying cardiometabolic disease. Importantly, low albumin levels present a strong, positive association with poor disease outcomes. Therefore, in this review article, we highlight the important role of cardiovascular risk factors on endothelium integrity and the possible link of endothelial damage in the hypoalbuminemia-associated adverse prognosis of COVID-19 patients.

Little is known about the role of serum and tissue mediators in the progression of ascending aortic aneurysms and dissections. We examined how the tissue expression of microRNAs and matrix metalloproteinases (MMPs), as well as the serum levels of osteoprotegerin, adiponectin, and high sensitivity C-reactive protein (hsCRP) are associated with these entities. We enrolled 21 patients with ascending aortic aneurysm, 11 with acute Stanford type A aortic dissection and 18 controls. The serum levels of osteoprotegerin, adiponectin, and hsCRP, as well as the tissue expression of MMPs 2 and 9 and tissue microRNAs 29 and 195 were compared among groups. There was no difference regarding serum osteoprotegerin, adiponectin, and tissue MMP2 and MMP9 levels. hsCRP was higher in the dissection group ( P = .03). Tissue expression of microRNA 29 was 2.11-fold higher in the dissection ( P = .001) and 2.99-fold higher in the aneurysm group ( P < .001), compared with the control group. Tissue expression of microRNA 195 was 2.72-fold higher in the dissection ( P < .001) and 2.00-fold lower in the aneurysm group ( P = .08), compared with to the control group. These findings support the contribution of microRNAs in the progression of aneurysm formation and dissection, suggesting a role as potential biomarkers and future therapeutic targets.

Background: HER2-positive breast cancer patients receiving chemotherapy and targeted therapy (including anthracyclines and trastuzumab) face an elevated risk of cardiotoxicity, which can lead to long-term cardiovascular complications. Identifying predictive biomarkers is essential for early intervention. Circulating microRNAs (miRNAs), known regulators of gene expression and cardiovascular function, have emerged as potential indicators of cardiotoxicity. This study aims to evaluate the differential expression of circulating miRNAs in HER2-positive breast cancer patients undergoing chemotherapy and to assess their prognostic ability for therapy-induced cardiotoxicity using machine learning models. Methods: Forty-seven patients were assessed for cardiac toxicity at baseline and every 3 months, up to 15 months. Blood samples were collected at baseline. MiRNA expression profiling for 84 microRNAs was performed using the miRCURY LNA miRNA PCR Panel. Differential expression was calculated via the 2^−∆∆Ct method. The five most upregulated and five most downregulated miRNAs were further assessed using univariate logistic regression and receiver operating characteristic (ROC) analysis. Five machine learning models (Decision Tree, Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), k-Nearest Neighbors (KNN)) were developed to classify cardiotoxicity based on miRNA expression. Results: Forty-five miRNAs showed significant differential expression between cardiac toxic and non-toxic groups. ROC analysis identified hsa-miR-155-5p (AUC 0.76, p = 0.006) and hsa-miR-124-3p (AUC 0.75, p = 0.007) as the strongest predictors. kNN, SVM, and RF models demonstrated high prognostic accuracy. The decision tree model identified hsa-miR-17-5p and hsa-miR-185-5p as key classifiers. SVM and RF highlighted additional miRNAs associated with cardiotoxicity (SVM: hsa-miR-143-3p, hsa-miR-133b, hsa-miR-145-5p, hsa-miR-185-5p, hsa-miR-199a-5p, RF: hsa-miR-185-5p, hsa-miR-145-5p, hsa-miR-17-5p, hsa-miR-144-3p, and hsa-miR-133a-3p). Performance metrics revealed that SVM, kNN, and RF models outperformed the decision tree in overall prognostic accuracy. Pathway enrichment analysis of top-ranked miRNAs demonstrated significant involvement in apoptosis, p53, MAPK, and focal adhesion pathways, all known to be implicated in chemotherapy-induced cardiac stress and remodeling. Conclusions: Circulating miRNAs show promise as biomarkers for predicting cardiotoxicity in breast cancer patients. Machine learning approaches may enhance miRNA-based risk stratification, enabling personalized monitoring and early cardioprotective interventions.