Subtypes and Trajectories of Depression in Older Adults

老年人抑郁症的亚型和轨迹

• 批准号: 7248953
• 负责人: CELIA F HYBELS
• 金额: $ 7.8万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7248953
• 关键词: Address; Adult; Age; Aged, 80 and over; Aging; Area; Arts; Biological; Biometry; Brain; Chronic; Class; Clinical; Clinical Research; Cognitive; Communities; Comorbidity; Data; Data Set; Diagnosis; Disease remission; Elderly; Epidemiologic Studies; Epidemiology; Genetic; Geriatric Psychiatry; Goals; Health; Health Services; Impaired cognition; Intervention; Label; Lead; Learning; Life; Link; Major Depressive Disorder; Maps; Medical; Mental Depression; Minor; Molecular Abnormality; Nerve Degeneration; Normalcy; Numbers; Outcome; Participant; Pathologic; Patients; Pattern; Phenotype; Physical Function; Population; Prevalence; Prevention; Process; Psychiatry; Public Health; Recovery; Research; Research Personnel; Sampling; Score; Secondary to; Severities; Surveys; Symptoms; Techniques; Time; United States National Institutes of Health; Work; base; clinically significant; cohort; depressive symptoms; functional decline; geriatric depression; health care service utilization; middle age; minor depressive disorder; mortality; older patient; programs; psychologic; satisfaction; social; young adult

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.

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、医疗服务使用增加和生活满意度下降有关，并且已被证明可以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数量预计将增加，更好地了解晚年抑郁症状的亚型及其不同的轨迹可以有助于公共卫生和精神病学领域。拟议研究的目标是确定老年人抑郁症状的亚型和轨迹。拟议的研究将利用对 NIH 支持的研究中收集的纵向数据进行二次分析，这些研究涉及 1) 来自代表性社区样本的老年人和 2) 被诊断患有重度抑郁症 (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，以及独特的轨迹是否可以与特定亚型联系起来。简而言之，晚年抑郁症的亚型可以通过其症状和随时间的推移来区分。拟议研究的具体目标是： 1) 确定在接受 MD 治疗的老年人临床样本和老年人社区样本中观察到的晚年抑郁症的类别或亚型，并确定与这些亚型相关的人口、临床、社会、心理和合并症因素； 2) 确定晚年抑郁症的哪些亚型与不良后果相关，即持续抑郁症状、认知能力下降、功能下降和死亡； 3) 确定社区居住的老年人和接受MD治疗的患者的抑郁症状轨迹，并确定与这些轨迹相关的人口、临床、社会、心理和合并症因素； 4) 制定策略以进一步检查随着时间的推移症状认可的模式。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。拟议的分析将利用杜克老年人流行病学研究人群 (EPESE) 的数据（使用耶鲁大学 EPESE 验证的结果）、老年人资产和健康动态研究 (AHEAD) 以及杜克晚年抑郁症研究临床研究中心的两个纵向队列数据。这项研究可以为精神病学领域做出独特的贡献，并有助于识别老年人抑郁症的亚型，然后将其映射到遗传和生物成分以及抑郁症的预防和治疗干预措施。这项研究的结果还可能有助于识别晚年抑郁症的过渡表型，该表型反映了继发于遗传异常或病理性年龄相关大脑变化的神经退行性变化。