Extension and refinement of the predictive value of different classes of markers in ADNI: Four-year follow-up data
Background: This study examined the predictive value of different classes of markers in the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) over an extended 4-year follow-up in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Methods: MCI patients were assessed for clinical, cognitive, magnetic resonance imaging (MRI), positron emission tomography-fluorodeoxyglucose (PET-FDG), and cerebrospinal fluid (CSF). markers at baseline and were followed on a yearly basis for 4 years to ascertain progression to AD. Logistic regression models were fitted in clusters, including demographics, APOE genotype, cognitive markers, and biomarkers (morphometric, PET-FDG, CSF, amyloid-beta, and tau). Results: The predictive model at 4 years revealed that two cognitive measures, an episodic memory measure and a Clock Drawing screening test, were the best predictors of conversion (area under the curve = 0.78). Conclusions: This model of prediction is consistent with the previous model at 2 years, thus highlighting the importance of cognitive measures in progression from MCI to AD. Cognitive markers were more robust predictors than biomarkers. (C) 2014 The Alzheimer's Association. All rights reserved.
Faculty, Northwell Health, Northwell Resident
School of Medicine; Northwell Health