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 怀孕期间的饮酒和吸烟通常发生在孕产妇的焦虑和抑郁症的背景下。 但是，关于孕产妇使用，吸烟和不同组合的组合鲜为人知 精神病症状相互作用以改变早期中央和自主神经系统（CNS，ANS）的发展 并随后在后代的神经行为结果。为了更好地理解这些关系，这项赠款 提出全面表征物物酒精，吸烟，抑郁和动画模式的建议 通过机器学习和人工智能方法怀孕；执行深度表型 来自婴儿的电气和物理数据；为了检验生理学衍生的总体假设 衡量介导孕产妇暴露与随后的儿童之间的关系 神经发育结果。为了实现这些目标，本研究建议使用现有数据 NIH资助的SIDS和死产的完成的安全通道研究已完成的安全通道研究 （通过）网络以及最近完成的后续研究，该研究招募了通行证子集 参与者。通过队列参与者来自地理和社会经济上不同的地区 南非和美国北部平原。范围通过数据库包括一个详细的 收集的每日酒精和吸烟暴露和产前抑郁/焦虑评估的记录 对于10,325个母体/婴儿二元组和1,000多个变量，可提供广泛的信息 母亲养育的病史，人口统计学和社会经济地位。这些暴露数据将是 根据新生儿中枢神经系统/ANS深度表型分析的分析，该表型从3,355个婴儿的分析得出 脑电图（EEG）和心电图（ECG）在出生时或一个月记录的数据 年龄。除了四个不同的参与者队列中，收集了多余的神经行为结果数据 参加通行证，在12个月（n = 849）和24-37个月大（n = 667）时进行随访。后续研究 数据集包含项目级数据，子测试原始分数以及早期学习量表的缩放分数， 父母的儿童能力重新定义（PARCA-R），婴儿发育范围III 筛查测试和随访（M-Chat-r/f）修订的幼儿中自闭症的修改清单。这 建议的方法有望阐明次优神经发育和弹性的机制，以及 最终为处于危险群体的放映提供了信息。此外，拟议的全面深层表型是 旨在识别标志物，这些标记是产前暴露与 结果。我们预计我们的发现将利用我们同类的地理文化多样性并提供 与人口不足的人群中的健康分布相关的有意义的信息。