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.
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| Hybels, Celia F; Landerman, Lawrence R; Blazer, Dan G (2013) Latent subtypes of depression in a community sample of older adults: can depression clusters predict future depression trajectories? J Psychiatr Res 47:1288-97 |
| Kuchibhatla, M N; Fillenbaum, G G; Hybels, C F et al. (2012) Trajectory classes of depressive symptoms in a community sample of older adults. Acta Psychiatr Scand 125:492-501 |
| Hybels, Celia F; Landerman, Lawrence R; Blazer, Dan G (2012) Age differences in symptom expression in patients with major depression. Int J Geriatr Psychiatry 27:601-11 |
| Hybels, Celia F; Blazer, Dan G; Landerman, Lawrence R et al. (2011) Heterogeneity in symptom profiles among older adults diagnosed with major depression. Int Psychogeriatr 23:906-22 |
| Hybels, Celia F; Pieper, Carl F; Blazer, Dan G et al. (2010) Trajectories of mobility and IADL function in older patients diagnosed with major depression. Int J Geriatr Psychiatry 25:74-81 |
| Hybels, Celia F; Blazer, Dan G; Pieper, Carl F et al. (2009) Profiles of depressive symptoms in older adults diagnosed with major depression: latent cluster analysis. Am J Geriatr Psychiatry 17:387-96 |
