Subtypes and Trajectories of Depression in Older Adults

描述（由申请人提供）：老年人的抑郁症状与认知和身体功能差、医疗合并症、卫生服务使用增加和生活满意度下降有关，并已被证明可预测认知和功能下降、持续抑郁症状和死亡率。随着未来几十年老年人的数量预计将增加，对晚年抑郁症状亚型及其不同轨迹的更深入了解可以帮助公共卫生和精神病学领域。该研究的目的是确定老年人抑郁症状的亚型和轨迹。拟议的研究将利用在NIH支持的研究中收集的纵向数据的二次分析：1)来自代表性社区样本的老年人；2)诊断为重度抑郁症（MD）的老年患者。重要的是要确定哪些抑郁症亚型会导致不良后果或持续存在，以及是否独特的轨迹可以与特定的亚型联系起来。简而言之，晚期抑郁症的亚型可以通过其症状集和病程来区分。该研究的具体目的是：1)确定老年痴呆症治疗的老年人临床样本和社区老年人样本中观察到的晚年抑郁的类别或亚型，并确定与这些亚型相关的人口统计学、临床、社会、心理和合并症因素；2)确定哪些晚期抑郁亚型与不良结局相关，即持续性抑郁症状、认知能力下降、功能下降和死亡率；3)确定社区居住老年人和MD患者抑郁症状的轨迹，并确定与这些轨迹相关的人口统计学、临床、社会、心理和合并症因素；4)制定策略，进一步研究随时间推移的症状背书模式。为了实现这些目标，我们将使用潜在类别和潜在类别轨迹分析来辨别抑郁症状的类别和症状组的轨迹。提出的分析将利用杜克大学老年流行病学研究既定人群（EPESE）（使用耶鲁大学EPESE的研究结果进行验证）、老年人资产和健康动态研究（AHEAD）以及杜克大学老年抑郁症临床研究中心的两个纵向队列的数据。这项研究可以为精神病学领域提供独特的贡献，并导致老年人抑郁症亚型的识别，然后可以映射到遗传和生物成分，以及抑郁症的预防和治疗干预。这项研究的发现也可能有助于确定晚年抑郁症的过渡性表型，这种表型反映了继发于遗传异常或病理性年龄相关的大脑变化的神经退行性变化。

DESCRIPTION (provided by applicant): Depressive symptoms in older adults have been associated with poor cognitive and physical functioning, medical comorbidity, increased health service use, and decreased life satisfaction, and have been shown to predict cognitive and functional decline, persistent depressive symptoms, and mortality. With the number of older adults expected to increase in the coming decades, a greater understanding of subtypes of late life depressive symptoms and their varying trajectories can help the field of public health as well as psychiatry. The goal of the proposed research is to identify subtypes and trajectories of depressive symptoms in older adults. The proposed research will utilize secondary analysis of longitudinal data collected in NIH supported studies of 1) elders from representative community samples and 2) older patients diagnosed with major depression (MD). It is important to identify which subtypes of depression lead to adverse outcomes or persist, and whether unique trajectories can be linked to specific subtypes. In short, subtypes of late life depression may be distinguished both by their set of symptoms and by their course over time. The specific aims of the proposed research are to: 1) Identify classes or subtypes of late life depression observed in clinical samples of older adults treated for MD and in community samples of older adults, and to identify demographic, clinical, social, psychological, and comorbidity factors associated with these subtypes; 2) Identify which subtypes of late life depression are associated with adverse outcomes, i.e., persistent depressive symptomatology, cognitive decline, functional decline, and mortality; 3) Identify trajectories of depressive symptoms in community dwelling older adults and in patients treated for MD, and identify demographic, clinical, social, psychological, and comorbidity factors associated with these trajectories; and 4) Develop strategies to further examine patterns of symptom endorsement over time. To address these aims, we will use latent class and latent class trajectory analyses in order to discern classes of depressive symptoms and trajectories of symptom groups. The proposed analysis will utilize data from the Duke Established Populations for Epidemiologic Studies of the Elderly (EPESE) (with findings validated using the Yale EPESE), the Study of Assets and Health Dynamics Among the Oldest Old (AHEAD), and two longitudinal cohorts from the Duke Clinical Research Center for the Study of Depression in Late Life. This research can provide a unique contribution to the field of psychiatry, and lead to identification of subtypes of depression in older adults which can then be mapped to genetic and biological components and to prevention of depression and treatment interventions. Findings from this study may also assist in the process of identifying a transitional phenotype of late life depression that reflects neurodegenerative changes secondary to genetic abnormalities or pathologic age-associated brain changes.