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- Title
Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions.
- Authors
Solomonov, Nili; Lee, Jihui; Banerjee, Samprit; Chen, Serena Z.; Sirey, Jo Anne; Gunning, Faith M.; Liston, Connor; Raue, Patrick J.; Areán, Patricia A.; Alexopoulos, George S.
- Abstract
This prognostic study identifies subtypes of late-life depression and examines their depression trajectory by bipartite network clustering during psychosocial interventions. Key Points: Question: Are there subtypes of late-life depression with a distinct course of depression during psychosocial interventions? Findings: In this prognostic study of a combined sample of 535 older adults with depression, bipartite network clustering identified 3 subtypes of late-life depression: individuals with severe depression and a large social network; older, educated individuals with strong social support and frequent social interactions; and individuals with disability. Subtype 2 had the steepest decline of depression, while subtype 1 had the most persistent depression trajectory during psychosocial interventions. Meaning: Knowledge of an older adult's depression subtype at the outset of treatment may inform the selection of interventions and improve treatment planning. Importance: Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions. Objective: To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression. Design, Setting, and Participants: This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023. Interventions: Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management). Main Outcomes and Measures: The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis. Results: The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P <.001) and remission rate (log-rank χ22 = 18.2; P <.001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory. Conclusions and Relevance: In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.
- Subjects
UNIVERSITY of California, San Francisco; OLDER people; BIPARTITE graphs; HAMILTON Depression Inventory; PROBLEM-solving therapy; CHRONIC obstructive pulmonary disease
- Publication
JAMA Psychiatry, 2023, Vol 80, Issue 6, p621
- ISSN
2168-622X
- Publication type
Article
- DOI
10.1001/jamapsychiatry.2023.0815