Use of latent variable modeling to delineate psychiatric and cognitive profiles in Parkinson disease

A range of psychiatric symptoms and cognitive deficits occur in Parkinson disease (PD), and symptom overlap and comorbidity complicate the classification of nonmotor symptoms. The objective of this study was to use analytic-based approaches to classify psychiatric and cognitive symptoms in PD.

Cross-sectional evaluation of a convenience sample of patients in specialty care.

Two outpatient movement disorders centers at the University of Pennsylvania and Philadelphia Veterans Affairs Medical Center.

One hundred seventy-seven patients with mild-moderate idiopathic PD and without significant global cognitive impairment.

Subjects were assessed with an extensive psychiatric, neuropsychological, and neurological battery. Latent class analysis (LCA) was used to statistically delineate group-level symptom profiles across measures of psychiatric and cognitive functioning. Predictors of class membership were also examined.

Results from the LCA indicated that a four-class solution best fit the data. The 32.3% of the sample had good psychiatric and normal cognitive functioning, 17.5% had significant psychiatric comorbidity but normal cognition, 26.0% had few psychiatric symptoms but had poorer cognitive functioning across a range of cognitive domains, and 24.3% had both significant psychiatric comorbidity and poorer cognitive functioning. Age, disease severity, and medication use predicted class membership.

LCA delineates four classes of patients in mild-moderate PD, three of which experience significant nonmotor impairments and comprise over two thirds of patients. Neuropsychiatric symptoms and cognitive deficits follow distinct patterns in PD, and further study is needed to determine whether these classes are generalizable, stable, predict function, quality of life, and long-term outcomes and are amenable to treatment at a class level.