Mechanistic Interpretation of Beat‐to‐Beat Cardiovascular Variability in Hypertension Using a Closed‐Loop Cardiovascular Model

Beat-to-beat heart rate variability (HRV) and blood pressure variability (BPV) are gaining attention as noninvasive markers of autonomic dysfunction, particularly, for improving hypertension management. However, interpretation is complicated by coexisting autonomic dysregulation and vascular mechanical alterations in hypertension. This study used a numerical model to disentangle these contributions, which cannot be isolated in vivo. A closed-loop mathematical model was developed to simulate cardiovascular variability during head-up tilt (HUT), incorporating four heart chambers, systemic and pulmonary circulations, and short-term autonomic regulation mechanisms via arterial and cardiopulmonary reflexes. Beat-to-beat heart rate and blood pressure fluctuations were simulated by introducing central autonomic noise into sympathetic efferent pathways and incorporating respiratory-induced intrathoracic pressure variations. Three hypertension-related alterations-elevated peripheral resistance, reduced arterial compliance, and enhanced left ventricular contractility-were simulated individually. Elevated vascular resistance suppressed both HRV and BPV via baroreflex saturation. Reduced arterial compliance increased BPV due to impaired pressure buffering but reduced HRV through diminished reflex feedback. Enhanced ventricular contractility amplified both HRV and BPV via stronger baroreflex-driven oscillations and greater pulsatile pressure transmission. Generally, HRV was influenced by autonomic efferent fluctuations, respiratory vagal modulation, and baroreflex-mediated feedback to pressure changes from respiration and orthostatic stress. BPV was more directly governed by mechanical fluctuations in stroke volume. HRV and BPV patterns emerge from interactions between mechanical and reflex mechanisms that modulate the transmission of underlying variability into measurable signals. This modeling framework offers mechanistic insights into beat-to-beat cardiovascular variability in hypertension and may support more accurate clinical interpretation.