Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

• Published in: Radiology Vol. 266; no. 2; pp. 583 - 591
• Main Authors: Shaffer, Jennifer L., Petrella, Jeffrey R., Sheldon, Forrest C., Choudhury, Kingshuk Roy, Calhoun, Vince D., Coleman, R. Edward, Doraiswamy, P. Murali
• Format: Journal Article
• Language: English
• Published: United States Radiological Society of North America, Inc 01.02.2013
• Subjects: Aged; Alzheimer Disease - cerebrospinal fluid; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Area Under Curve; Biomarkers - analysis; Chi-Square Distribution; Cognition Disorders - cerebrospinal fluid; Cognition Disorders - diagnostic imaging; Cognition Disorders - pathology; Female; Fluorodeoxyglucose F18; Humans; Image Interpretation, Computer-Assisted; Logistic Models; Magnetic Resonance Imaging - methods; Male; Original Research; Positron-Emission Tomography - methods; Predictive Value of Tests; Radiopharmaceuticals; Regression Analysis; Retrospective Studies; Risk Factors
• ISSN: 0033-8419; 1527-1315; 1527-1315
• DOI: 10.1148/radiol.12120010

To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone. All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data. The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score). Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P < .00001). FDG PET contributed more information to routine tests (P < .00001) than CSF (P = .32) or MR imaging (P = .08). Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.