Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study

Abstract

Summary Background Parkinson’s disease is associated with an increased incidence of cognitive impairment and dementia. Predicting who is at risk of cognitive decline early in the disease course has implications for clinical prognosis and for stratification of participants in clinical trials. We assessed the use of clinical information and biomarkers as predictive factors for cognitive decline in patients with newly diagnosed Parkinson’s disease. Methods The Parkinson’s Progression Markers Initiative (PPMI) study is a cohort study in patients with newly diagnosed Parkinson’s disease. We evaluated cognitive performance (Montreal Cognitive Assessment [MoCA] scores), demographic and clinical data, APOE status, and biomarkers (CSF and dopamine transporter [DAT] imaging results). Using change in MoCA scores over 2 years, MoCA scores at 2 years’ follow-up, and a diagnosis of cognitive impairment (combined mild cognitive impairment or dementia) at 2 years as outcome measures, we assessed the predictive values of baseline clinical variables and separate or combined additions of APOE status, DAT imaging, and CSF biomarkers. We did univariate and multivariate linear analyses with MoCA change scores between baseline and 2 years, and with MoCA scores at 2 years as dependent variables, using backwards linear regression analysis. Additionally, we constructed a prediction model for diagnosis of cognitive impairment using logistic regression analysis. Findings 390 patients with Parkinson’s disease recruited between July 1, 2010, and May 31, 2013, and for whom data on MoCA scores at baseline and 2 years were available. In multivariate analyses, baseline age, University of Pennsylvania Smell Inventory Test (UPSIT) scores, CSF amyloid β (Aβ42) to t-tau ratio, and APOE status were associated with change in MoCA scores over time. Baseline age, MoCA and UPSIT scores, and CSF Aβ42 to t-tau ratio were associated with MoCA score at 2 years (using a backwards p-removal threshold of 0·1). Accuracy of prediction of cognitive impairment using age alone (area under the curve 0·68, 95% CI 0·60–0·76) significantly improved by addition of clinical scores (UPSIT, Rapid Eye Movement Sleep Behaviour Disorder Screening Questionnaire [RBDSQ], Geriatric Depression Scale, and Movement Disorder Society Unified Parkinson’s Disease Rating Scale motor scores; 0·76, 0·68–0·83), CSF variables (0·74, 0·68–0·81), or DAT imaging results (0·76, 0·68–0·83). In combination, the five variables showing the most significant associations with cognitive impairment (age, UPSIT, RBDSQ, CSF Aβ42, and caudate uptake on DAT imaging) allowed prediction of cognitive impairment at 2 years (0·80, 0·74–0·87; p=0·0003 compared to age alone). Interpretation In newly diagnosed Parkinson’s disease, the occurrence of cognitive impairment at 2 year follow-up can be predicted with good accuracy using a model combining information on age, non-motor assessments, DAT imaging, and CSF biomarkers.

Can we predict development of impulsive–compulsive behaviours in Parkinson’s disease?

“…Regardless of the site of autonomic dysfunction, this association might suggest a more severe clinical phenotype of the disease in PD with ICB. Dysautonomia may predict worse disease progression in early PD,32 and lower MoCA score and RBD at the time of PD diagnosis have been found as predictive factors of mild cognitive impairment 33…”

Initial cognitive changes in Parkinson's disease

“…Most of these longitudinal evaluations followed early, initially untreated patients. Among those with normal cognition at baseline, one-year PD-MCI incidence in this population has been estimated to be 10% 34 , rising to 20-30% over subsequent years 32, 34, 35, 46 . These rates are not reflective, however, of the proportion developing cognitive impairment, as they exclude those participants transitioning to dementia.…”

“…In particular, poorer performance on language, short-term recall and working memory tasks predicted future development of PD-MCI 47 . Other clinical features that have been associated with a higher risk of developing cognitive impairment include poorer olfactory performance, RBD, depression and greater motor impairment 42, 46 . Biomarkers have been found to have added predictive value, in particular APOE genotype, low CSF Abeta:total tau ratio, and lower putaminal DAT asymmetry 46 .…”

Combining clinical and biofluid markers for early Parkinson's disease detection

“…A previous study has shown the integration of several key clinical factors as well as genetic risks for PD diagnosis yielded good sensitivity and specificity for PD diagnosis 2. Genetic risk or clinical features based prediction models have been studied in the prediction of PD falls or cognitive impairment 3, 4, 5. Body fluid biomarkers, particularly in the cerebrospinal fluid (CSF), have also been widely tested, including α ‐syn, total and phosphorylated tau (t‐tau and p‐tau), and amyloid beta 1‐42 (A β 1‐42 ), but no single biomarker to date has been capable of accurate diagnosis.…”

“…Body fluid biomarkers, particularly in the cerebrospinal fluid (CSF), have also been widely tested, including α ‐syn, total and phosphorylated tau (t‐tau and p‐tau), and amyloid beta 1‐42 (A β 1‐42 ), but no single biomarker to date has been capable of accurate diagnosis. In one study, clinical and body fluid markers were combined to successfully predict cognitive impairment in PD 5. However, to our knowledge, no study has used a similar combined model to predict PD diagnosis.…”