Background: Methods to detect early cognitive decline and account for heterogeneity of deficits in Parkinson's disease (PD) are needed. Quantitative methods such as latent class analysis (LCA) offer an objective approach to delineate discrete phenotypes of impairment.
Objective: To identify discrete neurocognitive phenotypes in PD patients without dementia.
Methods: LCA was applied to a battery of 8 neuropsychological measures to identify cognitive subtypes in a cohort of 199 non-demented PD patients. Two measures were analyzed from each of four domains: executive functioning, memory, visuospatial abilities, and language. Additional analyses compared groups on clinical characteristics and cognitive diagnosis.
Results: LCA identified 3 distinct groups of PD patients: an intact cognition group (54.8%), an amnestic group (32.2%), and a mixed impairment group with dysexecutive, visuospatial and lexical retrieval deficits (13.1%). The two impairment groups had significantly lower instrumental activities of daily living ratings and greater motor symptoms than the intact group. Of those diagnosed as cognitively normal according to MDS criteria, LCA classified 23.2% patients as amnestic and 9.9% as mixed cognitive impairment.
Conclusions: Non-demented PD patients exhibit distinct neuropsychological profiles. One-third of patients with LCA-determined impairment were diagnosed as cognitively intact by expert consensus, indicating that classification using a statistical algorithm may improve detection of initial and subtle cognitive decline. This study also demonstrates that memory impairment is common in non-demented PD even when cognitive impairment is not clinically apparent. This study has implications for predicting eventual emergence of significant cognitive decline, and treatment trials for cognitive dysfunction in PD.
Keywords: Parkinson’s disease; cognition; latent class analysis; neuropsychology.