A Multidimensional Approach to Understanding Prenatal Health and Psychosocial Factors in Relation to the Maternal Inflammatory Milieu and Offspring Neurodevelopment

项目总结 怀孕前和出生前的心理压力、人口统计、创伤、健康和营养都有潜力 来改变后代的神经发育结果。产前环境的这些方面都是假设的 通过母体-胎盘-胎儿生物学(MPF)的应激敏感方面影响发育中的胎儿大脑， 例如免疫和内分泌功能。然而，以前的研究主要集中在单变量上 没有考虑广泛的有潜在影响的先入为主和产前因素的分析 MPF生物学和发育中的胎儿大脑。考虑到这些内容的高度复杂性和互动性 关系，多变量分析非常适合于识别高维分析中的典型模式 这可能揭示了潜在的炎症机制，通过这些机制，妊娠和产前 环境可能会影响后代的大脑发育。炎症的最佳特征可能是考虑到 多种细胞因子同时发挥作用，因为它们似乎是相互依赖的-经常共同作用的细胞因子 通过细胞因子网络进行交流。细胞因子网络的使用已成为一种日益流行的方法 对炎症进行概念化和分析，因为特定的细胞因子网络与某些 精神病态或精神障碍的异质性表现。此外，特定的细胞因子网络 在怀孕期间与后代神经发育结果的改变有关。此外， 这些研究很少或根本没有在独立的数据集中重现。鉴于越来越多的人意识到 在神经科学研究重复性有限的情况下，由美国国立卫生研究院带头的紧急推动， 朝着设计严谨的实验和增加重复性的方向发展。建议的总体目标是 这项研究旨在研究多种孕前和产前因素如何影响母体系统。 妊娠期间的炎症和后代神经发育的相关变化，而 满足对创新研究设计的更高严谨性和重复性的需求，该设计将复制 对三个高维数据集进行复杂的多变量分析。这样做的具体目的是 建议是：1)找出产妇炎症环境的心理社会因素 在多变量水平上怀孕；2)确定有效的母体炎症环境的健康指标 在多变量水平上怀孕；3)确定具有不同母体系统模式的参与者的亚组 炎症，并检查健康和心理社会因素之间可能相关的相互作用，以及 与每个簇相关的神经发育结果。这一建议意在创新性地分析 细胞因子网络活性作为一条潜在的途径，通过它在怀孕前和产前的方面 环境改变后代神经发育的多变量统计技术。此外，给出了 在科学重复性方面的频繁挑战，分析结果将在三个方面进行交叉验证 独立的数据集。

PROJECT SUMMARY Preconceptional and prenatal psychological stress, demographics, trauma, health, and nutrition all have potential to alter offspring neurodevelopmental outcomes. These aspects of the prenatal environment are hypothesized to influence the developing fetal brain via stress-sensitive aspects of maternal-placental-fetal biology (MPF), such as immune and endocrine functioning. However, previous research has centered around univariate analyses that do not consider the wide array of preconceptional and prenatal factors with potential to influence MPF biology and the developing fetal brain. Given the highly complex and interactive nature of these relationships, multivariate analyses are well-suited to identify canonical patterns in high-dimensionality analyses that may shed light on potential inflammatory mechanisms by which the preconceptional and prenatal environment may influence offspring brain development. Inflammation may be best characterized by considering multiple cytokines at once, as they appear to act co-dependently—cytokines that frequently work together to communicate form cytokine networks. The use of cytokine networks has become an increasingly popular method of conceptualizing and analyzing inflammation, given that specific cytokine networks are associated with certain psychopathologies or heterogenous presentations of mental disorders. Further, specific cytokine networks during pregnancy have been associated with altered offspring neurodevelopmental outcomes. Furthermore, little to none of these studies have been reproduced in independent datasets. Given the increasing awareness of limited reproducibility in neuroscience research, there has been an urgent push, spearheaded by the NIH, towards rigorously designed experiments and increased reproducibility. The overall goal of the proposed study is to examine how multiple preconceptional and prenatal factors affect maternal systemic inflammation during pregnancy and associated alterations in offspring neurodevelopment, while meeting the need for greater rigor and reproducibility in an innovative study design which will replicate complex multivariate analyses across three high-dimensionality datasets. The specific aims of this proposal are: 1) Identify maternal psychosocial contributors to the maternal inflammatory milieu during pregnancy at a multivariate level; 2) Identify potent health indicators of the maternal inflammatory milieu during pregnancy at a multivariate level; 3) Identify subgroups of participants with distinct patterns of maternal systemic inflammation and examine potentially relevant interactions between health and psychosocial factors, as well as neurodevelopmental outcomes associated with each cluster. This proposal intends to innovatively analyze cytokine network activity as a potential pathway by which aspects of the preconceptional and prenatal environment alter offspring neurodevelopment using multivariate statistical techniques. Additionally, given frequent challenges with reproducibility in science, results from the analyses will be cross validated across three independent datasets.