Jesus J Gomar1, Concepcion Conejero-Goldberg2, Peter Davies3, Terry E Goldberg4. 1. The Litwin-Zucker Research Center, The Feinstein Institute for Medical Research, Manhasset, NY, USA; Fundacion para la Investigacion y Docencia Maria Angustias Gimenez, Hermanas Hospitalarias, Sant Boi de Llobregat, Spain. 2. The Litwin-Zucker Research Center, The Feinstein Institute for Medical Research, Manhasset, NY, USA. 3. The Litwin-Zucker Research Center, The Feinstein Institute for Medical Research, Manhasset, NY, USA; Hofstra North Shore Long Island Jewish School of Medicine, Hempstead, NY, USA. 4. The Litwin-Zucker Research Center, The Feinstein Institute for Medical Research, Manhasset, NY, USA; Hofstra North Shore Long Island Jewish School of Medicine, Hempstead, NY, USA. Electronic address: tgoldber@nshs.edu.
Abstract
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.
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.
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