Unveiling Dynamic Autonomic Regulation in health and disease through Network Physiology

Obiettivo

Cardiovascular disease (CVD) remains the leading cause of death in Europe. Many fatal events are preventable through better management of indirect risk factors. Obesity promotes metabolic disturbances, including hypertension and diabetes, while also disrupting autonomic nervous system regulation and increasing cardiovascular risk. Baroreflex impairment, arrhythmias and nocturnal blood pressure dysregulation are overt signs of Parkinson’s disease and create conditions for sudden cardiac death. Sleep disorders, such as periodic limb movements, trigger repeated nocturnal sympathetic surges, increasing long-term cardiovascular risk and potentially signaling prodromal neurodegeneration. Despite their differences, obesity, Parkinson’s disease, and periodic limb movements converge on autonomic dysfunction as a key mechanism linking them to CVD. Nonetheless, understanding dynamic autonomic regulation requires advanced analytical approaches. In the emerging research field of Network Physiology, data-driven tools model organ systems as interconnected nodes, capturing high-order interactions beyond traditional pairwise analyses and thus allowing quantification of collective dynamics among multiple systems. Building on a Network Physiology approach, this project will advance methodology by integrating time series modelling, information theory, and dynamic system analysis, and leverage a network-based perspective to (1) assess high-order interactions in physiological systems and characterize autonomic regulation across health and disease, (2) explore central-autonomic contributions to CVD-related complications, and (3) derive clinical markers for risk prediction and therapeutic targeting. By integrating network science with clinical research, the project aims to uncover mechanisms of autonomic dysfunction and enable early interventions to reduce preventable CVD deaths.