Dissecting the neurobiology of anxiety across diagnostic categories – the extension of the Marburg/Münster affective disorders cohort study (MACS) regarding anxiety disorders

跨诊断类别剖析焦虑的神经生物学——关于焦虑症的马尔堡/明斯特情感障碍队列研究 (MACS) 的延伸

• 批准号: 437618337
• 负责人: Professor Dr. Udo Dannlowski
• 金额: --
• 依托单位: Klinik für Psychische Gesundheit
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/437618337?language=en
• 关键词: Dissecting; neurobiology; anxiety; across; diagnostic

Advances in etiology research in anxiety disorders (AXD) are often hampered by small sample sizes, high heterogeneity between studies, and lack of replication in independent samples. Furthermore, there are only few longitudinal MRI studies and transdiagnostic approaches are completely missing. However, transdiagnostic approaches are particularly important for AXD, considering the high comorbidity of about 40-70 % between AXD and affect disorders (AD, ie, major depressive disorder (MDD) and bipolar disorder (BD)), which can be explained at least in part by a high intersection of genetic and environmental risk factors. Thus, there is an emerging need for a novel generation of studies employing a) large-scale samples, b) longitudinal designs, c) structural and functional imaging approaches in d) well-defined, deeply phenotyped patients and control samples carrying genetic, environmental, and both risk factors, which are e) analysed with latest genetic and epigenetic methods and f) innovative multi-omics molecular approaches. To truly advance the field, g) a transdiagnostic approach with several patient groups based on h) multivariate analyses of multimodal data from these patient groups and i) replication in other samples will be a necessary next step.The aim of the proposed project is the investigation of a total of 300 patients with AXD (panic disorder, agoraphobia, social phobia, multiple phobias, generalized anxiety disorder) at two time points, two years apart. Data collection, quality control and analysis procedures correspond to those of the DFG FOR2107 "Neurobiology Affective Disorders" (www.FOR2107.de). Phenotyping includes multimodal MRI imaging, GWAS, cytokines, microbiome, environmental and lifetime risks, neuropsychology, personality traits and clinical parameters. As of May 2019, 2577 subjects (healthy, MDD, BD) were examined in the FOR2107 for the first and 1262 for the second measurement time. Of the patients with AD, 42% have a comorbid AXD. The present application comprises the recruitment and analysis of 300 patients with AXD at the first and 225 patients at the second measurement point after two years. This approach extends the existing, unique data of the FOR2107 in manifold ways at manageable costs. The data analyzes will include both univariate, hypothesis-driven approaches, but especially multi-dimensional analyzes with machine learning techniques for the multi-omics approaches.The proposed project will open up a large, unique cohort of patients with AXD, making it possible for the first time to analyze gene-environment interactions based on multiple (epi-) genetic and (endo) phenotypic data in a transdiagnostic and longitudinal design. This allows new pathophysiological entities to be defined on a neurobiological basis. These will fundamentally broaden the understanding of the etiology of AXD and contribute to the prevention, prediction of individual disease progression, and the development of new therapies.

焦虑症(AXD)病因学研究的进展往往受到样本量小、研究之间高度异质性以及缺乏独立样本重复的阻碍。此外，只有很少的纵向MRI研究，完全缺乏跨诊断方法。然而，考虑到AXD和情感障碍(AD，即严重抑郁障碍(MDD)和双相情感障碍(BD))之间高达40%-70%的共患率，跨诊断方法对AXD特别重要，这至少可以部分地由遗传和环境风险因素的高度交叉来解释。因此，出现了对新一代研究的新需求，该研究采用a)大规模样本，b)纵向设计，c)结构和功能成像方法，d)明确的、深入的表型患者和携带遗传、环境和两者危险因素的对照样本，e)使用最新的遗传和表观遗传学方法进行分析，以及f)创新的多组学分子方法。为了真正推动这一领域的发展，g)基于h)来自这些患者组的多模式数据的多变量分析和i)在其他样本中复制将是必要的下一步。拟议的项目的目标是在两个时间点，相隔两年的两个时间点对总共300名AXD(惊恐障碍、广场恐怖症、社交恐惧症、多重恐惧症、广泛性焦虑症)患者进行调查。数据收集、质量控制和分析程序符合DFG FOR2107“神经生物学情感障碍”(www.FOR2107.de)。表型分型包括多模式磁共振成像、GWAS、细胞因子、微生物组、环境和终生风险、神经心理学、人格特征和临床参数。截至2019年5月，FOR2107首次检查2577名受试者(健康、MDD、BD)，第二次测量1262名受试者。在AD患者中，42%的患者合并AXD。本申请包括300名AXD患者在两年后的第一个测量点和225个第二个测量点的招募和分析。这种方法以可管理的成本以多种方式扩展了FOR2107现有的独特数据。数据分析将包括单变量、假设驱动的方法，特别是针对多组学方法的机器学习技术的多维分析。拟议的项目将开辟一个庞大的、独特的AXD患者队列，使其首次有可能在跨诊断和纵向设计中基于多(表观)遗传和(Endo)表型数据分析基因-环境相互作用。这允许在神经生物学的基础上定义新的病理生理实体。这将从根本上拓宽对AXD病因的理解，并有助于预防、预测个别疾病的进展，以及开发新的治疗方法。