Human Microbiome and Perinatal Depression

人类微生物组和围产期抑郁症

基本信息

批准号：
9767273
负责人：
Jack Anthony Gilbert
金额：
$ 8.32万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-20 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9767273
关键词：
Address Adherence Adverse effects Affect African American Age Antidepressive Agents Appointment Bacteria Behavioral Bioinformatics Child Clinical Clinical Research Clinical Trials Cognitive Therapy Collection Complex Complication Consent Data Depressed mood Development Diagnosis Diet Disease Eligibility Determination Endocrine system Epidemiology Exclusion Family Feasibility Studies Feces Germ-Free Gestational Age Goals Guidelines Habits Health Healthcare Hispanics Home environment Hormonal Human Human Microbiome Immune system Immunology Infant Insurance Coverage Intestines Knowledge Link Longitudinal Studies Low income Machine Learning Major Depressive Disorder Mental Depression Mental Health Microbe Microbiology Minority Monitor Mothers Mus Nested Case-Control Study Neurologic Neurotransmitters Nutritional Observational Study Participant Patient Recruitments Perinatal Pharmaceutical Preparations Pharmacological Treatment Phenotype Postpartum Period Pregnancy Pregnancy Complications Pregnant Women Premature Birth Prenatal care Preparation Prevalence Probiotics Process Prospective cohort study Questionnaires Race Reporting Research Research Design Research Infrastructure Risk Role Safety Sample Size Sampling Schedule Socioeconomic Status Structure System Systems Biology Techniques Testing Therapeutic Time Transportation Visit Woman Work adverse outcome alternative treatment antepartum depression antidepressant effect bacterial community demographics depressive symptoms drug development dysbiosis effective therapy fecal microbiome fecal transplantation follow-up gut microbiome gut microbiota gut-brain axis health data improved infant outcome insight maternal stress microbial microbial community microbiome microbiome analysis microorganism mood symptom network models neuropsychiatry novel novel marker nutrition perinatal mental health peripartum depression probiotic therapy process optimization programs prospective relating to nervous system sample collection social stigma stool sample targeted treatment treatment as usual uptake

项目摘要

Project Summary/Abstract Depression is the most common complication of pregnancy and affects 10-20% of women. Up to 75% of women with perinatal depression do not receive mental health care. In lower income African American and Hispanic women, the prevalence of perinatal depression (PND) is particularly high and the treatment rates are lower. Cognitive behavioral therapy (CBT) is an effective treatment but practical barriers such as time commitment, insurance coverage, and transportation, as well as stigma, limit uptake of CBT particularly for minority women. Similarly, concern about the adverse effects of antidepressant treatment on maternal and infant outcomes limits uptake of pharmacological treatments. A novel avenue for the development of safe therapeutics for depression focuses on the brain-gut axis and is supported by initial demonstrations that probiotics can improve mood symptoms. Our long-term goal is to assist in the discovery of potential microbes that are correlated with PND, and could be novel biomarkers for the diagnosis and treatment of this disorder, particularly in minority women. As a first step, we propose a multi-PI feasibility study involving the implementation and optimization of processes for participant recruitment and stool sample collection, as well as the initial assessments of longitudinal differences in gut microbiota between pregnant women with and without major depressive disorder (MDD). To meet these goals, we will leverage an established research infrastructure to conduct a prospective, nested case-control study. We will prospectively collect depression diagnoses and gut microbiome samples on 300 women at four points during pregnancy and after delivery to yield a total of 58 women, 29 cases with MDD and 29 controls without MDD. The two groups will be matched on key covariates (e.g., age, race, socio- economic status, gestational age, health). At their scheduled usual care, prenatal appointment, consented participants will complete mental health, demographics, microbiome-related, and dietary and physical habits questionnaires and provide a fecal sample either at that time of their appointment or at home. To achieve our first aim - to determine the feasibility of a larger similarly designed study - we will estimate the total number of potential participants, the number assessed and not assessed for eligibility (and reasons), the number excluded and reasons for exclusion, the number lost to follow-up, and the data available for analysis at each of the 4 visits. To achieve our second aim - to determine whether the gut microbiome dynamically differs in pregnant women with and without MDD - we will employ a suite of systems level approaches, including multivariate statistical techniques, network modeling and machine learning techniques. This R03 will allow us to optimize our research processes for the prospective collection of stool samples and mental health data in preparation for larger nested case-control studies, and ultimately clinical trials of probiotic therapies for PND.

项目概要/摘要抑郁症是最常见的妊娠并发症，影响 10-20% 的女性。高达 75% 的女性患有围产期抑郁症的人无法接受心理保健。低收入非裔美国人和西班牙裔女性围产期抑郁症（PND）患病率特别高，且治疗率较低。认知行为疗法（CBT）是一种有效的治疗方法，但存在实际障碍，例如时间投入、保险范围、交通以及耻辱限制了 CBT 的接受，特别是对少数族裔妇女而言。同样，对抗抑郁治疗对母婴结局的不利影响的担忧也限制了采取药物治疗。开发抑郁症安全疗法的新途径重点关注脑肠轴，并得到益生菌可以改善情绪的初步证明的支持症状。我们的长期目标是协助发现与 PND 相关的潜在微生物，并可能成为诊断和治疗这种疾病的新型生物标志物，尤其是少数族裔女性。作为第一步，我们提出了一项多 PI 可行性研究，涉及实施和优化参与者招募和粪便样本收集的流程，以及初步评估患有和不患有重度抑郁症的孕妇肠道微生物群的纵向差异（MDD）。为了实现这些目标，我们将利用现有的研究基础设施进行前瞻性、巢式病例对照研究。我们将前瞻性地收集抑郁症诊断和肠道微生物样本 300 名妇女在怀孕期间和产后的四个时间点进行了调查，得出总共 58 名妇女，其中 29 例患有 MDD 和 29 个没有 MDD 的控件。两组将在关键协变量（例如年龄、种族、社会地位）上进行匹配经济状况、胎龄、健康状况）。在他们安排的常规护理、产前预约、同意参与者将完成心理健康、人口统计、微生物组相关以及饮食和身体习惯进行问卷调查并在预约时或在家中提供粪便样本。为了实现我们的第一个目标 - 确定更大的类似设计研究的可行性 - 我们将估计总数量潜在参与者、已评估和未评估资格（及原因）的人数、排除的人数排除的原因、失访人数以及 4 项中每项可用于分析的数据访问。为了实现我们的第二个目标 - 确定肠道微生物组在怀孕期间是否存在动态差异患有或不患有 MDD 的女性 - 我们将采用一套系统级方法，包括多变量统计技术、网络建模和机器学习技术。这个 R03 将使我们能够优化我们前瞻性收集粪便样本和心理健康数据的研究流程正在准备中用于更大规模的巢式病例对照研究，以及最终针对 PND 的益生菌疗法的临床试验。