Associations of neurocognitive and neuropsychiatric patterns with brain structural biomarkers and dementia risk: A latent class analysis

J Alzheimers Dis. 2024 Nov 25:13872877241300181. doi: 10.1177/13872877241300181. Online ahead of print.

Abstract

Background: Neurocognitive and neuropsychiatric symptoms are essential clinical manifestations of age-related cognitive impairment, yet their patterns of co-existence remain unclear through the cognitive continuum.

Objective: To examine the associations of person-centered cluster-derived patterns, based on a comprehensive collection of domain-specific cognitive and neuropsychiatric assessments, with neuroimaging markers and dementia risk.

Methods: 641 participants were included in the analysis from memory clinics in Singapore. Latent class analysis was applied to define clusters of individuals with different clinical patterns. The associations between identified clinical groups with neuroimaging markers of cerebrovascular diseases and neurodegeneration were analyzed using logistic regression models. Cox proportional hazard models were applied for incident dementia.

Results: Three latent classes differing in neurocognitive and neuropsychiatric impairment were identified (Class 1 "memory impairment only"; Class 2 "global cognitive impairment"; Class 3 "global cognitive and neuropsychiatric impairment"). Compared with Class 1, Class 2 and 3 were associated with smaller brain volumes, moderate-to-severe cortical atrophy and medial temporal lobe atrophy, and the presence of all cerebrovascular lesions. Moreover, compared with Class 2, Class 3 had smaller brain volumes, moderate-to-severe cortical atrophy and presence of intracranial stenosis. Additionally, compared to Class 1, Class 2 (hazard ratio [HR] = 3.84, 95%CI 2.11-7.00), and Class 3 (HR = 6.92, 95%CI 2.84-16.83) showed an increased risk of incident dementia.

Conclusions: Participants characterized by multi-domain cognitive impairment and co-occurrence of cognitive and neuropsychiatric impairment showed the highest risk of incident dementia, which may be attributed to both neurodegenerative and cerebrovascular pathologies.

Keywords: Alzheimer's disease; cognition impairment; data driven; elderly; latent class analysis; neuropsychiatric symptoms.