Dataset Information

Neuropsychological Subgroups in Non-Demented Parkinson's Disease: A Latent Class Analysis.

ABSTRACT:

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.

SUBMITTER: Brennan L

PROVIDER: S-EPMC5548408 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Neuropsychological Subgroups in Non-Demented Parkinson's Disease: A Latent Class Analysis.

Brennan Laura L Devlin Kathryn M KM Xie Sharon X SX Mechanic-Hamilton Dawn D Tran Baochan B Hurtig Howard H HH Chen-Plotkin Alice A Chahine Lama M LM Morley James F JF Duda John E JE Roalf David R DR Dahodwala Nabila N Rick Jacqueline J Trojanowski John Q JQ Moberg Paul J PJ Weintraub Daniel D

Journal of Parkinson's disease 20170101 2

<h4>Background</h4>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.<h4>Objective</h4>To identify discrete neurocognitive phenotypes in PD patients without dementia.<h4>Methods</h4>LCA was applied to a battery of 8 neuropsychological measures to identify cognitive subtypes in a cohort of 199 ...[more]

PMID: 28387684

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Dataset Information

Neuropsychological Subgroups in Non-Demented Parkinson's Disease: A Latent Class Analysis.

Background

Objective

Methods

Results

Conclusions

Publications

Neuropsychological Subgroups in Non-Demented Parkinson's Disease: A Latent Class Analysis.

Similar Datasets

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