Enhancing Pediatric Health Equity for Cardiovascular and Brain Health Outcomes Using Machine Learning and Social Determinants of Health in Multiethnic Pediatric Populations

Background

Childhood and adolescence are critical periods for brain development and overall health. Hypertension and metabolic disorders such as obesity and diabetes are becoming more prevalent in the pediatric population, raising questions about their potential to impair brain development and function. The social determinants of health (SDOH) can exacerbate or mitigate the effects of these conditions. This project leverages extensive data from the Adolescent Brain Cognitive Development (ABCD) study to explore how these factors interact over time to affect neurodevelopment and cognitive outcomes in a diverse cohort of children transitioning into adolescence.

Project Description and Objectives

This research initiative is dedicated to understanding the multifaceted impact of pediatric cardiovascular health and metabolic disorders, such as elevated blood pressure and glycated hemoglobin (HbA1c) levels, on adolescent brain development and executive function. By meticulously analyzing detailed neuroimaging data and extensive health metrics over the critical transition from childhood into adolescence, our project seeks to delineate how these health conditions influence neurodevelopmental trajectories. The primary objective is to develop comprehensive predictive models integrating clinical, behavioral, and environmental factors to identify at-risk children accurately. This effort aims not only to enhance early detection but also to facilitate the implementation of effective, tailored interventions that can preempt or ameliorate adverse developmental outcomes.

A secondary but equally vital objective of our project is to examine the potential modifying effects of social determinants of health on the relationship between metabolic and cardiovascular conditions and brain health. By understanding these interactions, we can tailor interventions more precisely to the needs of diverse populations, potentially reducing health disparities and improving overall outcomes.

Research Methods

Utilizing state-of-the-art machine learning techniques, we will analyze a rich dataset that includes neuroimaging, cognitive assessments, and physical health measures from the ABCD study. This analysis will employ advanced statistical methods to identify patterns and establish causal links between cardiovascular and metabolic health and their impact on neurodevelopment. In parallel, we will integrate data on social determinants of health to assess how socioeconomic status, access to healthcare, and neighborhood environment interact with physiological health to affect cognitive development and brain structure.

This multidisciplinary approach will enable us to develop robust predictive models to forecast the onset of neurological and cognitive impairments associated with children’s cardiovascular and metabolic health. These models will be critical in guiding the development of targeted preventive and therapeutic strategies, ultimately contributing to better health outcomes for children as they grow into adulthood. By combining cutting-edge research methods with one of the largest and most diverse pediatric datasets available, this project aims to significantly enhance our understanding of pediatric health and development. The findings will contribute to the scientific community and support public health officials and policymakers in crafting more effective health interventions tailored to the needs of children and adolescents.