Distinct latent symptom profiles in late-onset depressive symptoms in community-dwelling older adults

• Published in: Aging & mental health 2023-11, Vol.27 (11), p.2202-2210
• Main Authors: Taiwo, Zinat, Tighe, Elizabeth L., Tone, Erin B., Light, Sharee N., Dotson, Vonetta M.
• Format: Article
• Language: English
• Subjects: Adults; Age of onset; Aged; Anhedonia; Cognitive ability; Cognitive impairment; Demography; Depression - diagnosis; Depression - epidemiology; Depression - psychology; depressive symptoms; Emotions; Geriatric psychiatry; Hedonic response; Humans; Independent Living; Late-onset depression; Latent Class Analysis; Mental depression; older adult; Older people; Pattern recognition; Positive emotions; Probability; Sadness; symptom profile
• ISSN: 1360-7863; 1364-6915; 1364-6915
• DOI: 10.1080/13607863.2023.2211545

To examine the symptom profiles of late-onset depressive symptoms in a sample of older adults. The sample included 1,192 participants from the National Alzheimer's Coordinating Center Data Set. Participants were ≥65 years old, community-dwelling, and without cognitive impairment or a prior history of depression. Depressive symptoms were assessed using the Geriatric Depression Scale, 15-item (GDS-15). Latent class analysis (LCA) was used to identify and group participants based on profiles of depressive symptoms. LCA revealed three distinct symptom profiles: (1) an Anhedonia/Amotivation profile with a higher probability of endorsing a combination of low positive emotion and amotivation (6%), (2) an Amotivation/Withdrawal profile with a high probability of endorsing only amotivational depressive symptoms (35%), and (3) an asymptomatic profile with no probability of endorsing any depressive symptoms (59%). Amotivational depressive symptoms were observed across both symptomatic profiles, while depressed mood (e.g. sadness) did not predominantly characterize any profile in this sample. There were also significant differences among symptom profiles in terms of demographic and clinical characteristics. Findings highlight the importance of understanding depression at the symptom pattern level. A profile-based diagnostic approach may help improve the recognition of depressive symptoms in older adults.