Abstract 4362097: Uncovering 24-Hour Hemodynamic Phenotypes: Synchronization Patterns in Cuff-less Ambulatory Blood Pressure Monitoring

Abstract

Introduction: Twenty-four-hour ambulatory blood pressure monitoring (ABPM) offers a more accurate reflection of blood pressure (BP) behavior and cardiovascular risk than office measurements. Cuff-less, FDA-cleared devices like Biobeat allow continuous, real-world tracking of systolic BP (SBP), diastolic BP (DBP), heart rate (HR), cardiac output (CO), and pulse pressure (PP = SBP − DBP). Studying the temporal correlation between these signals, especially SBP and HR, may help uncover dynamic cardiovascular phenotypes driven by autonomic regulation. Methods: We analyzed 24-hour hemodynamic data from 1,122 adults using the Biobeat ABPM at the AHA Comprehensive Hypertension Center, University of Miami (IRB #20210585). Data were sampled ~every 15 minutes. Participants were categorized as normotensive (NT), isolated systolic hypertensive (ISH), or hypertensive (HT), with ISH and HT combined into a single HT group. For each patient, Pearson correlation coefficients were calculated between SBP-HR, SBP-CO, and PP-HR time series after min-max normalization. K-Medoids clustering (k=2–9) was applied to group patients based on correlation values. Optimal cluster number was determined using silhouette scores. Results: Three clusters consistently emerged across all correlation analyses (Figures 1,2 and 3). Cluster 1 (−0.5 to 0.28, 17%) reflected inverse to weak positive correlations; Cluster 2 (0.28 to 0.64, 39%) moderate positive; and Cluster 3 (0.64 to 0.96, 44%) strong positive. Younger age and greater height were associated with stronger correlations (p<0.05), while BMI showed no clear trend. Baseline SBP decreased, and DBP increased across clusters. NT individuals were most frequent in Cluster 3, while HT individuals predominated in Cluster 2. Cluster 1 had the lowest representation from both groups, indicating fewer patients with weak or inverse synchronization. Distribution testing confirmed these differences were significant (p<0.01), revealing distinct BP-HR synchronization phenotypes linked to BP status. Conclusion: In a real-world ABPM cohort, stronger SBP-HR, SBP-CO, and PP-HR synchronization was associated with normotension, younger age, and taller stature. These physiologic phenotypes highlight the value of continuous wearable data in uncovering dynamic patterns beyond static BP thresholds.