Neurodevelopmental Effects of Prenatal Exposure to Maternal Drinking, Smoking and Adverse Psychosocial Factors: Deep Phenotyping of Infant CNS and ANS Function

产前暴露于母亲饮酒、吸烟和不良心理社会因素对神经发育的影响：婴儿 CNS 和 ANS 功能的深层表型分析

• 批准号: 10367527
• 负责人: William P. Fifer
• 金额: $ 43.12万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367527
• 关键词: 3 year old; Address; Age; Age-Months; Alcohol consumption; Alcohols; Anxiety; Artificial Intelligence; Autonomic nervous system; Birth; Child; Cognitive; Data; Data Set; Databases; Development; Documentation; Early Intervention; Early identification; Electrocardiogram; Electroencephalogram; Electrophysiology (science); Emotional; Enrollment; Ethnic group; Exposure to; Fetal Alcohol Exposure; Fetal Alcohol Spectrum Disorder; Follow-Up Studies; Funding; Geography; Goals; Grant; Infant; Infant Development; Lead; Learning; Life; Machine Learning; Measures; Mediating; Mediator of activation protein; Medical History; Mental Depression; Mental Health; Methodology; Mission; Mothers; Motor Skills; National Institute on Alcohol Abuse and Alcoholism; Neonatal; Newborn Infant; Outcome; Parents; Participant; Pattern; Perinatal; Phenotype; Physiological; Physiology; Pregnancy; Psychopathology; Psychosocial Factor; Public Health; Reporting; Research; Research Design; Research Personnel; Risk; Risk Factors; Shapes; Signal Transduction; Smoking; Socioeconomic Factors; Socioeconomic Status; South Africa; Substance abuse problem; Sudden infant death syndrome; Techniques; Testing; Toddler; Underrepresented Populations; United States; United States National Institutes of Health; alcohol exposure; alcohol risk; antepartum depression; autism spectrum disorder; base; cognitive ability; cohort; demographics; drinking; experience; fetal; follow-up; health disparity; health disparity populations; health economics; improved; infant outcome; innovation; insight; maternal alcohol use; maternal anxiety; maternal depression; maternal risk; neurobehavioral; neurodevelopment; neurodevelopmental effect; neurophysiology; northern plains; offspring; prenatal; prenatal exposure; psychiatric symptom; racial diversity; racial population; recruit; resilience; screening; secondary analysis; signal processing; smoking during pregnancy; smoking exposure; social; socioeconomics; stillbirth; substance use

PROJECT SUMMARY Alcohol use and smoking during pregnancy often occur in the context of maternal anxiety and depression. However, little is known regarding how different combinations of maternal alcohol use, smoking, and psychiatric symptoms interact to alter early central and autonomic nervous system (CNS, ANS) development and subsequent neurobehavioral outcomes in offspring. To better understand these relationships, this grant proposes to comprehensively characterize maternal alcohol, smoking, depression, and anxiety patterns during pregnancy via machine learning and artificial intelligence methodologies; to perform deep phenotyping of electrocortical and physiological data from infants; to test the overall hypothesis that physiology-derived measures mediate the relationship between maternal prenatal exposures and subsequent child neurodevelopmental outcomes. To accomplish these goals, this research proposes to use existing data from the completed Safe Passage Study conducted by the NIH funded Prenatal Alcohol in SIDS and Stillbirth (PASS) Network and from recently completed follow-up studies which enrolled subsets of the PASS participants. PASS cohort participants are from geographically and socio-economically diverse regions in South Africa and the Northern Plains of the United States. The extant PASS database comprises a detailed documentation of daily alcohol and smoking exposure and prenatal depression/anxiety assessments collected for 10,325 maternal/infant dyads and more than 1,000 variables which provide extensive information on maternal-fetal-infant medical history, demographics, and socio-economic status. These exposure data will be analyzed in relation to neonatal CNS/ANS deep phenotyping profiles derived from the analysis of 3,355 infant electroencephalograms (EEG) and electrocardiogram (ECG) data recorded either at birth or at one month of age. The extant neurobehavioral outcomes data were collected in four distinct cohorts of participants originally enrolled in PASS and followed up at 12 months (n=849) and 24-37 months of age (n=667). The follow-up study datasets contain item-level data, sub-test raw scores, and scaled-scores for the Mullen scales of early learning, the Parent Report of Children’s Abilities-Revised (PARCA-R), the Bayley scales of infant development III screening test and the Modified Checklist for Autism in Toddlers Revised with Follow up (M-CHAT-R/F). The proposed approach is expected to elucidate mechanisms of suboptimal neurodevelopment and resilience, and ultimately inform screenings for at-risk groups. Additionally, the proposed comprehensive deep phenotyping is intended to identify markers that are potential mediators of the relationship between prenatal exposures and outcomes. We anticipate that our findings will capitalize on the geocultural diversity of our cohorts and provide meaningful information associated with health disparities in underrepresented populations.

项目摘要 怀孕期间饮酒和吸烟往往发生在母亲焦虑和抑郁的背景下。 然而，关于母亲饮酒、吸烟和 精神症状相互作用，改变早期中枢和自主神经系统（CNS，ANS）的发展 以及后代的神经行为结果。为了更好地理解这些关系， 建议全面描述母亲的酒精，吸烟，抑郁和焦虑模式， 通过机器学习和人工智能方法怀孕;进行深度表型分析， 婴儿的皮层电和生理数据;以检验生理学来源的总体假设 措施调解产妇产前暴露和随后的孩子之间的关系 神经发育结果。为了实现这些目标，这项研究建议使用现有的数据， 由美国国立卫生研究院资助的SIDS和死产产前酒精研究已经完成， （PASS）网络和最近完成的纳入PASS子集的随访研究 参与者PASS队列参与者来自地理和社会经济多样化的地区， 南非和美国北方平原。现存的PASS数据库包括详细的 记录每日酒精和吸烟暴露以及收集的产前抑郁/焦虑评估 对10，325对母婴和1，000多个变量进行了分析， 母亲-胎儿-婴儿病史、人口统计学和社会经济状况。这些暴露数据将 与来自3，355名婴儿分析的新生儿CNS/ANS深层表型特征相关的分析 在出生时或出生后一个月记录脑电图（EEG）和心电图（ECG）数据， 年龄现存的神经行为结果数据最初收集在四个不同的参与者队列中 入组PASS并在12个月（n=849）和24-37个月（n=667）时进行随访。后续研究 数据集包含项目级数据、子测试原始分数和早期学习的马伦量表的缩放分数， 儿童能力父母报告修订版（PARCA-R）、贝利婴儿发展量表III 筛查测试和修订的幼儿自闭症检查表（M-CHAT-R/F）。的 提出的方法有望阐明次优神经发育和恢复力的机制， 最终为高危人群提供信息。此外，建议的全面深度表型分析是 旨在确定作为产前暴露与 结果。我们预计，我们的研究结果将利用我们的同龄人的地理文化多样性，并提供 有意义的信息与代表性不足的人口的健康差距。