Publications

Receptor-targeted polypharmacology is emerging as a promising strategy for the treatment of obesity, type 2 diabetes, and metabolic dysfunction-associated steatohepatitis. GLP-1-GIP-lanifibranor, a unimolecular conjugate combining GLP-1R/GIPR co-agonism with pan-PPAR activation, enables receptor-guided intracellular delivery of lanifibranor to incretin receptor-expressing cells while limiting systemic off-target exposure. In obese mouse models, the conjugate produced greater reductions in body weight, adiposity, food intake, and hyperglycemia than semaglutide, GLP-1-GIP co-agonism, or lanifibranor alone, while significantly improving insulin sensitivity. Mechanistic analyses demonstrated receptor-dependent delivery and identified PPARδ signaling as a principal mediator of glycemic improvement independent of weight loss. Unlike unconjugated lanifibranor, the conjugate did not induce anemia, fluid retention, renal dysfunction, or adipocyte differentiation, supporting the concept that tissue-restricted PPAR activation may mitigate classical adverse effects of systemic PPAR agonism. These findings establish peptide-directed nuclear receptor targeting as a potentially important platform for next-generation metabolic therapeutics, although substantial translational uncertainties remain regarding clinical efficacy, safety, and long-term applicability.

BACKGROUND: Exercise-induced hypertension (EIH) is recognized as an independent cardiovascular risk factor. Epicardial fat thickness (EFT) has been implicated in various cardiovascular pathologies. However, the relationship between EFT, EIH, and ventricular arrhythmias remains poorly characterized. This study aimed to investigate the predictive value of EFT for both EIH and premature ventricular beats (PVB).

METHODS: A total of 2658 participants were initially screened for eligibility, and normotensive participants of age > 18 were considered for enrollment. All participants underwent a comprehensive clinical evaluation. Data were analyzed considering EIH and PVB. Multivariable logistic regression, ROC and decision curve analysis (DCA) were performed to evaluate the clinical utility of predictive models with and without EFT parameters.

RESULTS: ROC curve analysis demonstrated that EFT had moderate ability for predicting EIH (AUC 0.73, 95% CI 0.69-0.78). Multivariable logistic regression revealed that EFT was independently associated with EIH (OR 2.27, 95% CI 1.72-2.98). For the prediction of PVB among individuals with EIH, EFT demonstrated good accuracy (AUC 0.78, 95% CI 0.69-0.86). Among patients with EIH, epicardial fat was strongly associated with increased odds of arrhythmias (OR 3.58, 95% CI 2.35-5.46, p < 0.001). DCA revealed that incorporating EFT scores into predictive models provided superior net benefit improving the clinical utility of the predictive model.

CONCLUSIONS: EFT is an independent predictor of both exercise-induced hypertension and ventricular arrhythmias, with strong predictive value for premature ventricular beats in patients with EIH. The incorporation of EFT into risk prediction models provides superior clinical net benefit compared to traditional risk factors alone.

Fabry disease is an X-linked lysosomal storage disorder caused by α-galactosidase A deficiency, leading to progressive accumulation of Gb3 and lyso-Gb3 and a complex multisystem phenotype extending beyond substrate storage. Cardiovascular involvement remains the leading cause of morbidity and mortality, yet early detection and risk stratification remain challenging. In this context, a new proteomic study leveraging high-throughput proximity extension assays and machine learning has defined a cardiovascular risk signature in Fabry disease. Differential expression analysis identified widespread proteomic remodeling involving inflammatory signaling, extracellular matrix organization, angiogenesis, and metabolic pathways, supporting a systems-level view of disease pathogenesis. A 10-protein biosignature integrating markers of mitochondrial stress, lysosomal function, vascular remodeling, and immune activation demonstrated the ability to discriminate patients with cardiovascular involvement. Notably, proteins such as GDF15, NT-proBNP, NOS1, CTSF, and TNFRSF11B highlight the interplay between mitochondrial dysfunction, lysosomal impairment, and vascular inflammation. These findings suggest that Fabry cardiomyopathy reflects coordinated dysregulation across metabolic and inflammatory networks and that multi-protein signatures may improve precision phenotyping and cardiovascular risk prediction beyond conventional biomarkers.

Cardiovascular-kidney-metabolic (CKM) syndrome is a multisystem condition integrating metabolic dysfunction, chronic kidney disease (CKD), and cardiovascular disease into a unified framework. CKM syndrome encompasses progressive metabolic derangements, renal injury, and cardiovascular remodeling, which interact to amplify morbidity and mortality. Lifestyle interventions (including structured weight loss, dietary modification, physical activity, and sleep optimization) form a cornerstone of prevention and management. Pharmacologic strategies targeting the renin-angiotensin-aldosterone system, sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, and lipid-lowering therapies provide additional multisystem benefits. In this comprehensive review, we systematically examine the most updated evidence on CKM syndrome, in terms of pathophysiology, epidemiology, clinical manifestations, diagnostic evaluation, and therapeutic strategies. We also highlight future research directions and precision medicine approaches.

Artificial intelligence (AI) is rapidly reshaping cardiovascular (CV) medicine, driving a paradigm shift toward truly personalized and data-driven care. This comprehensive review examines the conceptual foundations, clinical applications, and future implications of AI across the CV continuum, spanning prevention, diagnosis, risk stratification, and therapy. Core AI methodologies (including machine learning, deep learning, natural language processing, and computer vision) are discussed in the context of cardiology's uniquely data-rich environment, encompassing imaging, electrocardiography, electronic health records, wearable devices, and multi-omics data. This systematic review highlights major clinical domains where AI has demonstrated a substantial impact, including CV imaging, ECG interpretation, hypertension and heart failure management, coronary artery disease, acute coronary syndromes, interventional cardiology, and cardiac surgery. AI-driven predictive analytics enable early detection of subclinical disease, improved prognostication, and individualized prevention strategies, while wearable technologies and remote monitoring platforms facilitate continuous, real-world patient surveillance. Emerging applications in pharmacotherapy, drug repurposing, and genomics further reinforce AI's role in advancing precision cardiology. Equally emphasized are the ethical, legal, and social challenges accompanying AI adoption, such as algorithmic bias, data privacy, cybersecurity, interpretability, and regulatory oversight. Our review underscores the necessity of rigorous clinical validation, transparent model design, and seamless integration into clinical workflows to ensure safety, equity, and physician trust. Ultimately, AI is best positioned as an augmentative tool that complements (but does not replace!) clinical expertise. By fostering hybrid intelligence that integrates human judgment with computational power, AI has the potential to redefine CV care delivery, improve outcomes, and support a more proactive, patient-centered healthcare model.