A Trans-Nordic Study of Extreme Major Depression

跨北欧的极度抑郁症研究

基本信息

批准号：
10376800
负责人：
PATRICK F SULLIVAN
金额：
$ 79.52万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-10 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10376800
关键词：
Address Affect Age Algorithms Body mass index Clinical Collaborations Computer software Data Data Set Denmark Development Diagnosis Diagnostic Family Foundations Future Generations Genetic Genetic Risk Genomics Goals Health Healthcare Healthcare Systems Hereditary Disease Heritability Individual Infrastructure Inherited Intention Intervention Life Major Depressive Disorder Medical Medical History Mental disorders Methods Modeling Norway Outcome Paper Patients Pattern Persons Phenotype Population Preventive Psychiatry Psychoses Quality of life Recording of previous events Reproducibility Research Personnel Retrospective cohort Risk Risk Factors Sample Size Sampling Scheme Scotland Secure Severities Smoking Social Functioning Suicide Sweden Tail Testing Training Twin Multiple Birth Validation Work base biobank clinically relevant cohort completed suicide data analysis pipeline data harmonization density design disability follow-up genetic information genetic pedigree genome wide association study genome-wide genomic data improved meetings phenotypic data prediction algorithm predictive modeling psychiatric genomics recruit risk variant sex tertiary prevention therapy resistant translational goal

项目摘要

Project Summary/Abstract Major depressive disorder (MDD) affects >300 million people worldwide. It is a leading contributor to disability and suicide, and thus a cross-cutting risk factor for many adverse life and health outcomes. It is heritable, and genome-wide association recently been informative. However, nearly all current MDD samples are not enriched in individuals with the highest clinical severity (i.e., the extreme tail of the phenotype distribution), a critical weakness for clinical prediction. We propose to focus on “phenotype extreme MDD”. We will define these individuals empirically on a population scale over years of follow-up in order to capture individuals with markedly worse MDD clinical features (e.g., treatment-resistance, dense patterns of treatment, psychosis) and poor outcomes (e.g., poor social function, disability, suicide). Cases with phenotype extreme MDD disproportionally contribute to the global burden of MDD. We show that we can identify these individuals and preliminary data suggest these individuals have a greater inherited burden of MDD risk alleles. We will address an additional weakness in the field via multiple, highly powered layers of replication in independent cohorts. We need to know quickly whether a promising model can replicate and generalize, and we have built the infrastructure for this. In Aim 1, we will empirically identify “phenotype extreme MDD” in a training set of ⅓ of the Swedish population with replication in independent samples (the other ⅔ from Sweden and harmonized datasets from Denmark and Norway) and then generalization to independent samples from the UK (Generation Scotland, UK Biobank), and the US (PsycheMERGE). In Aim 2, we will validate the empirical phenotype extreme MDD definition using genomic data in the Aim 1 populations (i.e., pedigree- and SNP-heritability, contrast with other MDD definitions, evaluate whether individuals with phenotype extreme MDD carry higher genetic risk scores for MDD). In Aim 3, we will develop clinically useful prediction algorithms for extreme MDD: can we predict at first presentation who will subsequently develop phenotype extreme MDD? We will have exceptional statistical power for all Aims. Successful completion of these aims will enable our transformative, tertiary-preventive intention of valid and clinically useful prediction of the subsequent development of phenotype extreme MDD early in a person’s treatment history. This is foundational to achieve the overarching translational goal of deploying these models on national scales in order to improve the health of MDD patients who are most severely ill.

项目概要/摘要重度抑郁症 (MDD) 影响着全球超过 3 亿人，它是导致残疾的主要原因。和自杀，因此是许多不良生活和健康结果的交叉风险因素。全基因组关联最近提供了丰富的信息，然而，几乎所有当前的 MDD 样本都没有得到富集。在临床严重程度最高的个体（即表型分布的极端尾部）中，一个关键的我们建议重点关注“表型极端 MDD”。经过多年的跟踪，在人口规模上对个体进行了经验性的跟踪，以捕获具有显着特征的个体。更严重的 MDD 临床特征（例如，治疗抵抗、密集治疗模式、精神病）和较差结果（例如，社会功能差、残疾、自杀）表现型极端 MDD 的病例不成比例。我们证明我们可以识别这些个人和初步数据。表明这些人具有更大的 MDD 风险等位基因负担。我们将解决另一个问题。我们需要知道通过独立群体中的多个、强大的高度复制层来发现该领域的弱点。快速确定一个有前途的模型是否可以复制和推广，我们已经为此构建了基础设施。在目标 1 中，我们将在 ⅓ 瑞典人口的训练集中凭经验识别“表型极端 MDD” 在独立样本中进行复制（另外⅔来自瑞典，以及来自丹麦和的统一数据集）挪威），然后推广到英国的独立样本（苏格兰一代，英国生物银行），以及在目标 2 中，我们将使用美国 (PsycheMERGE) 验证经验表型极端 MDD 定义。 Aim 1 人群中的基因组数据（即谱系遗传力和 SNP 遗传力，与其他 MDD 定义相比，评估具有极端表型 MDD 的个体是否具有较高的 MDD 遗传风险评分）。我们将为极端 MDD 开发临床上有用的预测算法：我们能否在第一次演示时预测谁随后会发展出极端MDD表型吗？我们将为所有目标提供卓越的统计能力。成功完成这些目标将使我们能够实现有效且有效的变革性三级预防意图。对一个人早期极端表型 MDD 的后续发展进行临床有用的预测这是实现部署这些模型的总体转化目标的基础。在全国范围内，以改善病情最严重的重度抑郁症患者的健康。