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• W4225121832 abstract "In the absence of disease-modifying treatments, identifying potential psychosocial risk factors for dementia is paramount. Depression and anxiety have been identified as potential risk factors. Studies however have yielded mixed findings, lending possibility to the fact that potential constellations of co-occurring depression and anxiety symptoms may better explain the link between affective symptoms and cognitive decline.Data from participants (aged 50 and above) of the PROTECT study was used. Latent Class Analysis (LCA) was conducted on 21,684 participants with baseline anxiety and depression measures. Multiple linear regressions models, using a subset of these participants (N = 6136) who had complete cognition data at baseline and at 2-year follow-up, were conducted to assess for associations between class membership and longitudinal changes in cognition.The LCA identified a 5-class solution: No Symptoms, Sleep, Sleep and Worry, Sleep and Anhedonia, and Co-morbid Depression and Anxiety. Class membership was significantly associated with longitudinal change in cognition. Furthermore, this association differed across different cognitive measures.Limitations included significant attrition and a generally healthy sample which may impact generalisability.Substantial heterogeneity in affective symptoms could explain previous inconsistent findings concerning the association between affective symptoms and cognition. Clinicians should not focus solely on total symptom scores on a single affective domain, but instead on the presence and patterns of symptoms (even if sub-clinical) on measures across multiple affective domains. Identifying particular subgroups that are at greater risk of poor cognitive outcomes may support targeted prevention work." @default.
• W4225121832 created "2022-05-01" @default.
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• W4225121832 date "2022-07-01" @default.
• W4225121832 modified "2023-10-18" @default.
• W4225121832 title "Are subtypes of affective symptoms differentially associated with change in cognition over time: A latent class analysis" @default.
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• W4225121832 doi "https://doi.org/10.1016/j.jad.2022.04.139" @default.
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