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Extension and refinement of the predictive value of different classes of markers in ADNI: Four‐year follow‐up data
Author(s) -
Gomar Jesus J.,
ConejeroGoldberg Concepcion,
Davies Peter,
Goldberg Terry E.
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2013.11.009
Subject(s) - positron emission tomography , neuroimaging , logistic regression , alzheimer's disease neuroimaging initiative , magnetic resonance imaging , medicine , cognition , oncology , psychology , standardized uptake value , alzheimer's disease , disease , nuclear medicine , radiology , neuroscience
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‐β, 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.