The Impact of Adverse Childhood Experiences on Autonomic Functioning Across the Lifecourse

Using data from the 1946 National Survey of Health and Development (NSHD) and the Avon Longitudinal Study of Parents and Children (ALSPAC), I plan to investigate how adverse childhood experiences (ACEs) impact resting blood pressure, heart rate, and heart rate variability across the life course and determine what modifiable factors buffer this relationship. Historically, ACEs have been measured using either cumulative risk (total score) models or specificity (single adversity) models. However, recent research conducted by Ellis et al. (2022) and Usacheva et al (2022) have proposed the efficacy of a dimensional model that is informed by the Threat-Deprivation Framework and Harshness-Unpredictability Model.

My dissertation project will include four unified parts. My first study will use longitudinal resting heart rate and blood pressure data from the NSHD and ALSPAC cohorts to investigate how exposure to ACEs impacts resting heart rate and blood pressure across the life course. I will use latent growth curve analysis techniques to look at differences in the starting points and heart rate and blood pressure trajectories over time. Additionally, I will look at how these trajectories differ based on the 3 methods (cumulative risk, specificity, and dimensional models) to classify adversity.

In my second study, I will use cross-sectional HRV data from the NSHD to investigate the impact of childhood adversity on midlife HRV and autonomic functioning. Additionally, I will investigate this relationship using the 3 adversity methods (cumulative risk, specificity, and dimensional models) to define adversity. I will use linear regression, t-test, and ANOVA techniques to test the cross-sectional associations.

My third study will employ longitudinal ALSPAC heart rate and blood pressure data to use a structured life course modelling approach to illustrate how the type, timing, and duration of ACEs influence heart rate and blood pressure across the life course. I plan to identify critical periods of the life course where ACEs might be the most detrimental to the individual informing intervention strategies or whether an accumulation model best fits the data. The ACEs classification will be informed by the results of studies 1 and 2. SLCMA is advantageous because it takes into consideration the dynamic impact events that occur in an individual's life can influence health across the life course into later life.

Using g-estimation, my fourth study will employ longitudinal ALSPAC and NSHD heart rate and blood pressure data and the ACE classification results from studies 1 and 2, to identify which individual characteristics or interventions (primary and/or secondary prevention programs) can shape the relationship between ACEs and autonomic regulation. G-estimation is beneficial because it allows for mediation analyses using time-varying confounders.