Psychiatric and cognitive disorders are common and frequently co-occur in PD. In order to empirically delineate distinct classes of PD patients presenting with similar symptom profiles across both psychiatric and cognitive assessments, we utilized latent variable modeling to characterize profiles of non-motor symptoms among PD patients without significant global cognitive impairment. Overall, the findings lend support to the notion of multiple, distinct classes of PD patients, with each class characterized by a distinct psychiatric and cognitive profile. Furthermore, patient-level characteristics appeared to serve as significant predictors of PD patients’ likelihood of membership in the various identified classes.
Results from the LCA models indicated that a 4-class model best captured group-level variability in PD patients’ psychiatric and cognitive symptom profiles. Only one-third of the study sample could be considered intact from this standpoint (Class 4; IntG). Close to one-fifth of the sample (Class 1; PsyG) demonstrated a profile of numerous, significant psychiatric symptoms with intact cognition, and approximately one-quarter (Class 3; CogG) had worse cognitive abilities across a range of domains, without significant co-morbid psychiatric symptoms. The final quarter of the sample (Class 2; Psy-CogG) had both significant psychiatric symptoms and worse cognitive abilities across a range of domains. Our results are consistent with two recent cluster analyses of Neuropsychiatric Inventory(10
) (NPI) scores in PD patients(3
) that identified several clusters of patients, including one with minimal neuropsychiatric symptoms, one or more clusters with primarily affective symptoms, and another with primarily psychotic symptoms.
A number of notable associations emerged when examining the relationship between patient-level characteristics and latent class membership. Patients in Class 2 (Psy-CogG), the most impaired group overall, were older, had more advanced disease, and higher UPDRS motor scores on average than all the other groups, with many of these differences meeting statistical significance. This is consistent with previous research demonstrating that such factors are associated with both cognitive impairment and some psychiatric symptoms in PD(22
). Patients in Class 1, who had significant psychiatric symptoms but were intact cognitively, were the youngest group with the shortest disease duration and fewest motor symptoms on average, consistent with some research findings that younger PD patients may have an elevated risk of psychiatric disorders early in the disease course(35
Class membership was also predicted by medication use. Specifically, higher levodopa dosages were associated with a greater likelihood of belonging to both classes with impaired cognitive functioning, and in the case of Class 2 (Psy-CogG), also a higher frequency of psychosis, as opposed to classes with relatively intact cognitive functioning. This is consistent with previous research demonstrating that higher levodopa dosages can be associated with cognitive impairment and psychosis, particularly in older patients and in those with more advanced disease(19
). Conversely, patients taking a dopamine agonist were significantly more likely to belong to the groups characterized by low psychiatric symptom severity, consistent with preliminary evidence that this medication class may improve certain psychiatric symptoms (e.g., depression)(6
), although the association between infrequent dopamine agonist use and high psychosis prevalence in Class 2 (Psy-CogG) suggests that some medication treatment decisions in PD are made in response
to the presence of certain psychiatric symptoms.
There are several limitations to this study. First, our convenience sample, which was drawn from tertiary care clinics, was predominately male, white, college educated, and had mild to moderate severity of PD. Second, while there were significant site differences for age, levodopa dose, attention, and executive function, we chose not to include site as a predictor in the multinomial logistic regression portion of the LCA model due to the fact that the majority of patients were recruited from one of the two sites (i.e., the University of Pennsylvania). Had we included site as a predictor, we would have run the risk of small, or empty, cells when estimating the joint distribution of the four categorical latent variables and independent variables. Thus, additional studies in larger numbers of PD patients with greater demographic and clinical diversity are needed to verify and generalize our findings. Larger samples would also allow for more flexibility when modeling predictors and covariates. Third, although our study looked at a range of neuropsychiatric and cognitive variables, the ones included were those available from our assessment battery. Future studies should consider other non-motor symptoms or impairments for inclusion in symptom profiling. Finally, we were not able to assess the clinical impact of the psychiatric symptoms or worse cognitive performance in the affected groups, but the scores on the depression, anxiety, apathy and daytime sleepiness scales in the two psychiatric groups were above the cut-offs recommended to indicate clinically-significant symptoms(18
), and in general neuropsychiatric symptoms in non-demented PD patients are associated with poorer quality of life(27
Identifying discrete classes of patients with similar psychiatric and cognitive profiles and also certain demographic and clinical characteristics may shed light on the underlying etiology of different patterns of non-motor impairment. For instance, the group with psychiatric symptoms only (Class 1), younger on average with mild PD of short duration, may have psychosocially-mediated psychiatric symptoms, whereas the group with both psychiatric symptoms and worse cognitive functioning (Class 2), older and with longer disease duration, may have psychiatric and cognitive symptoms secondary to progressive disease-related neuropathological changes. As a result, more appropriate assessment and treatment plans may be developed that target discrete patient subgroups, addressing the shortcomings of traditional approaches that target individual symptoms (e.g., “target symptom” approach) or empirically derived clusters of highly correlated symptoms (e.g., factor analysis). Further research is needed to determine if identified classes are stable across time, and if the classes help predict function, quality of life, and long-term patient outcomes. Additionally, studies should also assess whether symptom profiles are amenable to treatment at a class level.