Social, Cognitive, and Behavioral Influences on Early Life Aging in Autism

ABSTRACT Autism spectrum disorder (ASD) is characterized by disabling social impairments and restrictive, repetitive behaviors that emerge in early childhood and persist throughout the lifespan, affecting 2.2% of adults in the United States. As they age, autistic adults face a range of adverse outcomes, including significantly higher rates of chronic disease, neurodegenerative conditions, and early mortality. My recent electroencephalography (EEG) findings further reveal altered trajectories of functional brain aging in ASD, in line with reports of excessive cognitive aging. However, the mechanisms underlying these age-related declines remain unknown, and by the time that age-related decline manifests behaviorally and cognitively crucial opportunities for risk prevention have already passed. New ‘epigenetic clock’ techniques index the progression of cellular aging processes based on DNA methylation (DNAm) patterns, providing proxy measures of biological age that predict later cognitive, health, functional declines, and mortality. I will explore if these sensitive measures of individual aging trajectories may help to identify autistic individuals at high risk of poor outcomes before patterns of brain activity or behavior begin to change, specifically asking: (1) Is biological risk for poor aging outcomes increased in autistic adults at midlife? (2) Are variations in epigenetic risk linked to brain aging markers? (3) Which clinical and environmental differences during childhood and early adulthood contribute to biological risk variations in this population? The proposed career development award will allow me to address these aims through new integrated methods. Facilitated by a multidisciplinary team of expert mentors (Dr. Lord, Dr. Carroll, Dr. Geschwind, and Dr. Senturk), I will build upon my existing expertise in developmental neuroscience and ASD to acquire new training in epigenetic and longitudinal lifespan methodologies. I will collect epigenetic and neural (EEG) aging measures from a unique and deeply phenotyped cohort of individuals with (N=118) and without ASD (N=39) who have been prospectively followed since age two and are currently 32-36 years old (The ‘Early Diagnosis Study; EDX). Biological age will be quantified from saliva-derived DNAm patterns using three different well-established epigenetic clock algorithms. Brain aging will be measured using EEG markers of peak frequency (7-13Hz), which captures characteristic age-related oscillatory slowing. Together, these studies will inform potential strategies to identify and address age-related risks in ASD from earlier in development. The proposed training goals will be the catalyst for a novel and innovative research program focused on lifespan changes in ASD across multiple levels of measurement and lay the foundation for a longitudinal R01 investigation of epigenetic, neural, and cognitive aging in the EDX cohort. This research program will address a crucial gap in our understanding of long-term lifespan influences in ASD and provide crucial opportunities to mitigate long-term personal and public health burdens in the rapidly growing population of older autistic adults.