Autonomic Regulation, Vascular Adaptation, and the Conditions of Daily Life: Insights into Hypertensive Disorders of Pregnancy - PROJECT SUMMARY Hypertensive disorders of pregnancy (HDP) now affect nearly one in six pregnancies in the United States and remain a leading contributor to maternal morbidity and mortality. Yet, the early physiologic signatures that precede clinical onset remain poorly defined. Emerging research reveals that HDP often begins with subtle changes in autonomic and vascular function that precede any clear clinical symptoms. However, few studies have investigated continuous physiologic patterns from early pregnancy, and even fewer have integrated multiple signals (e.g., blood pressure [BP], heart rate [HR], heart rate variability [HRV]) to capture early disruptions in maternal cardiovascular (CV) adaptation that may signal elevated HDP risk. The purpose of this study is to use wearable technology, ecological momentary assessments (EMAs), and machine learning (ML) to characterize maternal autonomic and vascular adaptation and identify early physiologic and contextual predictors of HDP. This project fills a critical gap by generating longitudinal HRV and BP data and characterizing how autonomic signals relate to vascular change across gestation. We will quantify autonomic and vascular dynamics using continuous measures of heart rate (HR), time- and frequency-domain HRV metrics (e.g., root mean square of successive differences [RMSSD], low- and high-frequency power [LF, HF]), respiratory rate, and temperature. These physiologic signals will be evaluated (using mixed-effects modeling) as predictors of downstream vascular adaptation, as measured by repeated assessments of mean arterial pressure (MAP), pulse pressure (PP), and shock index (SI), to identify early physiologic patterns that precede HDP risk (Aim 1). To determine how the conditions of daily life (CoDL) influence these adaptations, we will pair weekly EMA-derived stress indices with physiologic trajectories to evaluate effects on autonomic balance and vascular function (Aim 2). Finally, we will develop and internally validate ML models that combine wearable-derived physiologic features with contextual data to improve early HDP risk prediction beyond standard demographic and clinical models (Aim 3). Guided by the Maternal Adaptation Framework, conceptually informed by the Roy Adaptation Model, this project tests the hypothesis that contextual stressors disrupt early autonomic and vascular regulation, thereby increasing vulnerability to HDP. The long-term goal is to advance person-centered, precision-based maternal care by defining early, modifiable physiologic pathways that can inform timely intervention. This study leverages two ongoing wearable-based cohorts, Weight Of It All (R01NR019254; PI Carlson) and BioBAYB2 (Flinn Foundation/ABRC; PI Erickson), which provide continuous physiologic data from the late first trimester through birth. The aims of this project directly support NINR’s priorities to address CoDL and to develop precision approaches to optimize maternal health outcomes for all. The project also includes a comprehensive training plan to develop a registered nurse for an independent research career as a nurse scientist with expertise in longitudinal physiologic data, contextual modeling, and ML approaches to maternal health.
