Neuropsychological phenotypes among men with and without HIV disease in the multicenter AIDS cohort study.

AIDS(2018)

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摘要
Objective: Mild forms of HIV-associated neurocognitive disorder (HAND) remain prevalent in the combination antiretroviral therapy (cART) era. This study's objective was to identify neuropsychological subgroups within the Multicenter AIDS Cohort Study (MACS) based on the participant-based latent structure of cognitive function and to identify factors associated with subgroups. Design: The MACS is a four-site longitudinal study of the natural and treated history of HIV disease among gay and bisexual men. Methods: Using neuropsychological domain scores, we used a cluster variable selection algorithm to identify the optimal subset of domains with cluster information. Latent profile analysis was applied using scores from identified domains. Exploratory and posthoc analyses were conducted to identify factors associated with cluster membership and the drivers of the observed associations. Results: Cluster variable selection identified all domains as containing cluster information except for Working Memory. A three-profile solution produced the best fit for the data. Profile 1 performed below average on all domains, Profile 2 performed average on executive functioning, motor, and speed and below average on learning and memory, Profile 3 performed at or above average across all domains. Several demographic, cognitive, and social factors were associated with profile membership; these associations were driven by differences between Profile 1 and the other profile. Conclusion: There is an identifiable pattern of neuropsychological performance among MACS members determined by all domains except Working Memory. Neither HIV nor HIV-related biomarkers were related with cluster membership, consistent with other findings that cognitive performance patterns do not map directly onto HIV serostatus. Copyright (C) 2018 Wolters Kluwer Health, Inc. All rights reserved.
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关键词
cognition,epidemiology,HIV,latent profile analysis,model-based clustering
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