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：
10187656
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/10187656
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

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.

项目摘要/摘要重度抑郁症（MDD）在全球范围内影响> 3亿人。这是残疾的主要贡献者和自杀，因此是许多不利生活和健康结果的横切风险因素。这是可遗传的，并且全基因组协会最近提供了信息。但是，几乎所有当前的MDD样品都没有丰富在临床严重程度最高（即表型分布的极端尾巴）的个体中，临床预测的弱点。我们建议专注于“表型极端MDD”。我们将定义这些在多年的随访中，个人在人口规模上进行经验，以占领个人 MDD临床特征较差（例如，耐药性，致密的治疗方式，精神病）和差结果（例如，社会功能差，残疾，自杀）。表型极端MDD的情况不成比例有助于MDD的全球燃烧。我们证明我们可以识别这些人并初步数据暗示这些人对MDD风险等位基因的遗传燃烧更大。我们将解决一个额外的通过多个高度动力的复制层在独立队列中的弱点。我们需要知道很快，有前途的模型是否可以复制和概括，我们已经为此建立了基础架构。在AIM 1中，我们将在瑞典人口的训练集中凭经验识别“表型极端MDD” 在独立样本中复制（另一个来自瑞典的⅔，来自丹麦的数据集，挪威），然后对来自英国（苏格兰一代，英国生物银行）的独立样本进行概括，美国（Psychemerge）。在AIM 2中，我们将使用使用经验表型的极限MDD定义来验证 AIM 1种群中的基因组数据（即谱系和SNP可her性，与其他MDD定义形成对比，评估表型极限MDD的个体是否具有MDD的遗传风险评分较高）。在AIM 3中，我们将开发极限MDD的临床上有用的预测算法：我们可以在首先预测谁随后会发展表型极限MDD？我们将对所有目标都具有出色的统计能力。这些目标的成功完成将使我们的变革性，三级预防意图有效和在一个人的早期，对表型的随后发展具有临床上有用的预测治疗史。这是实现部署这些模型的总体翻译目标的基础在国家尺度上，以改善最严重患病的MDD患者的健康状况。