title: Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study.
abstract: 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.
  
  
  RESULTS
  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 - (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 Aβ42to 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).
  
  
  CONCLUSIONS
  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.
  
  
  BACKGROUND
  None.
authors.affiliation: Department of Clinical Neurosciences, Royal Free Campus, UCL Institute of Neurology, University College London, London, UK. Electronic address: a.schrag@ucl.ac.uk.
authors.forename: Anette
authors.initials: A
authors.lastname: Schrag
authors.affiliation: Department of Clinical Neurosciences, Royal Free Campus, UCL Institute of Neurology, University College London, London, UK.
authors.forename: Uzma Faisal
authors.initials: UF
authors.lastname: Siddiqui
authors.affiliation: Department of Clinical Neurosciences, Royal Free Campus, UCL Institute of Neurology, University College London, London, UK.
authors.forename: Zacharias
authors.initials: Z
authors.lastname: Anastasiou
authors.affiliation: Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Philadelphia Veterans Affairs Medical Center, Philadelphia, PA, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Weintraub
authors.affiliation: Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK.
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Schott
mesh_terms: D000368:Aged; D016229:Amyloid beta-Peptides / Q000134:cerebrospinal fluid; D001057:Apolipoproteins E / Q000235:genetics; D015415:Biomarkers / Q000378:metabolism*; D003072:Cognition Disorders / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000209:etiology*; D015331:Cohort Studies; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests; D017063:Outcome Assessment, Health Care; D010300:Parkinson Disease / Q000150:complications*; D010446:Peptide Fragments / Q000134:cerebrospinal fluid; D011237:Predictive Value of Tests; D012372:ROC Curve; D012720:Severity of Illness Index; D016875:tau Proteins / Q000134:cerebrospinal fluid
chemical_list: D016229:Amyloid beta-Peptides; D001057:Apolipoproteins E; D015415:Biomarkers; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42); D016875:tau Proteins