A Trans-Nordic Study of Extreme Major Depression

跨北欧的极度抑郁症研究

• 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
• 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

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 严重抑郁障碍(MDD)影响着全球3亿人。它是导致残疾的主要因素 和自杀，因此是许多不利生活和健康后果的交叉风险因素。它是可遗传的，而且 全基因组关联最近很有启发意义。然而，目前几乎所有的MDD样本都没有被浓缩 在具有最高临床严重性(即，表型分布的极端尾部)的个体中，关键的 临床预测的弱点。我们建议关注“表型极端MDD”。我们将对这些进行定义 在多年的跟踪调查中，根据经验对个体进行种群规模，以捕捉患有显著疾病的个体 MDD临床特征较差(例如，耐药、密集的治疗模式、精神病)和较差 结果(例如，社会功能差、残疾、自杀)。表型极度MDD不成比例的病例 为全球MDD的负担作出贡献。我们表明，我们可以识别这些人和初步数据 提示这些个体具有更大的MDD风险等位基因的遗传负担。我们将解决另一个问题 通过独立队列中的多个高度强大的复制层在该领域中表现疲软。我们需要知道 快速确定一个有前途的模式是否能够复制和推广，我们已经为此建立了基础设施。 在目标1中，我们将在瑞典人群的⅓训练集中经验性地识别“表型极端MDD” 在独立样本中复制(来自瑞典的另一个⅔和来自丹麦的协调数据集 挪威)，然后推广到来自英国的独立样本(苏格兰一代，英国生物库)，以及 美国(心理医学)。在目标2中，我们将使用以下方法验证经验表型极端MDD定义 目标1群体中的基因组数据(即，系谱和SNP遗传力，与其他MDD定义对比， 评估表型极端MDD的个体是否携带较高的MDD遗传风险分数)。在《目标3》中， 我们将为极端MDD开发临床上有用的预测算法：我们能在第一次呈现时预测谁 随后会发展成表型极端MDD吗？我们将对所有目标拥有非凡的统计能力。 这些目标的成功实现将使我们的变革性、三级预防意图能够有效和 临床上有用的预测一个人早期表型极端MDD的后续发展 治疗病史。这是实现部署这些模型的总体转换目标的基础 在全国范围内，以改善病情最严重的MDD患者的健康。