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

社会、认知和行为对自闭症早期衰老的影响

• 批准号: 10676874
• 负责人: Abigail H Dickinson
• 金额: $ 18.21万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-05 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676874
• 关键词: Acceleration; Address; Adult; Adverse event; Affect; Age; Age Years; Aging; Algorithms; American; Anxiety; Behavior; Behavioral; Biological; Biological Aging; Body mass index; Brain; Cardiovascular Diseases; Cell Aging; Characteristics; Child; Childhood; Chronic; Chronic Disease; Chronology; Clinical; Clinical Data; Clinical Management; Cognitive; Cognitive aging; Communication; DNA Methylation; Data; Deterioration; Development; Diabetes Mellitus; Dimensions; Disease; Disparity; Early Diagnosis; Electroencephalography; Environmental Risk Factor; Epigenetic Process; Face; Foundational Skills; Foundations; Frequencies; Future; General Population; Goals; Health; Health Care Costs; Impaired cognition; Impairment; Individual; Individual Differences; Investigation; K-Series Research Career Programs; Life; Life Expectancy; Link; Longevity; Longitudinal cohort; Machine Learning; Malignant Neoplasms; Measurement; Measures; Mental Depression; Mentors; Methodology; Methods; Modeling; Molecular; Morbidity - disease rate; National Institute of Mental Health; Nerve Degeneration; Neurodegenerative Disorders; Neurosciences; Older Population; Outcome; Participant; Pathway interactions; Pattern; Persons; Pharmaceutical Preparations; Phase; Phenotype; Physiological; Population; Predisposition; Prevention; Prevention strategy; Procedures; Process; Proxy; Public Health; Reporting; Research; Rest; Risk; Risk Factors; Saliva; Sampling; Schools; Sensory; Social isolation; Stress; Surrogate Markers; Symptoms; Techniques; Time; Training; United States; Variant; Vulnerable Populations; adult with autism spectrum disorder; adverse outcome; age related; aging brain; autism spectrum disorder; autistic; behavior influence; catalyst; clinically relevant; cognitive function; cohort; comorbidity; early childhood; emerging adult; environmental intervention; epigenetic marker; experience; functional decline; high risk; improved; improved outcome; indexing; individuals with autism spectrum disorder; innovation; insight; methylation pattern; middle age; mortality; multidisciplinary; neural; neurophysiology; novel; patient population; preventive intervention; programs; promote resilience; prospective; protective factors; psychosocial stressors; repetitive behavior; resilience factor; risk minimization; risk mitigation; sex; skills; social; young adult

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.

摘要 自闭症谱系障碍(ASD)的特征是社会功能障碍和限制性、重复性 在儿童早期出现并持续终生的行为，影响了2.2%的成年人 美国。随着年龄的增长，自闭症成人面临一系列不良后果，包括显著更高的发病率 慢性病、神经退行性疾病和早期死亡。我最近的脑电(EEG) 研究结果进一步揭示了ASD患者功能性脑老化的轨迹改变，这与过度 认知老化。然而，这些与年龄相关的下降背后的机制仍然未知，而且 时间是这样的 与年龄相关的下降表现为行为和认知上的关键风险预防机会 已经过去了。新的‘表观遗传时钟’技术索引细胞衰老过程的进程，基于 DNA甲基化(DNaM)模式，提供生物年龄的代理测量，预测以后的认知，健康， 功能衰退和死亡率。我将探索这些衡量个体衰老轨迹的敏感指标是否可能 在大脑活动或行为模式开始之前，帮助识别有不良结果的高危自闭症患者 要改变，特别要问：(1)自闭症成年人在中年时衰老不良的生物学风险是否会增加？ (2)表观遗传风险的变化是否与脑老化标记物有关？(3)哪些临床和环境因素 儿童期和成年期早期的差异会导致这一人群的生物风险差异吗？ 拟议的职业发展奖将使我能够通过新的综合方法来实现这些目标。 在多学科专家导师团队(洛德博士、卡罗尔博士、格施温德博士和森图尔克博士)的推动下， 我将利用我在发育神经科学和自闭症方面的现有专业知识，获得新的培训 表观遗传学和纵向寿命方法。我将收集表观遗传学和神经(EEG)老化测量 来自患有ASD(N=118)和没有ASD(N=39)的独特且表型深刻的个体队列 从2岁开始前瞻性跟踪，目前32-36岁(‘早期诊断研究；edX)。 生物年龄将根据唾液衍生的dNaM模式进行量化，使用三种不同的成熟模式 表观遗传时钟算法。大脑老化将使用峰值频率(7-13赫兹)的脑电标记物进行测量， 捕捉到了典型的与年龄相关的振荡减速。总之，这些研究将为潜在的战略提供信息 从发育的早期识别和解决自闭症的年龄相关风险。 拟议的培训目标将成为一项以寿命为重点的创新研究计划的催化剂 ASD在多个测量级别上的变化，并为纵向R01调查奠定基础 在edX队列中的表观遗传、神经和认知老化。这项研究计划将解决一个关键的差距 我们对ASD的长期寿命影响的了解，并提供了缓解长期ASD的关键机会 快速增长的自闭症老年人口的个人和公共卫生负担。