Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers

Abstract

Purpose: 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.

Materials and methods: 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).

Results: 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).

Conclusion: 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.

Figure 1:

Example components from separate ICAs. Z threshold was 1.5. Both components significantly differentiated converters from nonconverters. Left: The MR imaging component (Comp) highlights in red the bilateral medial temporal lobes, inferior and lateral temporal lobes, and anterior and inferior frontal lobes, consistent with atrophy in these regions in converters. Negative signal, noted in blue, is seen in the periventricular white matter, consistent with higher levels of white matter disease in converters. Right: The FDG PET component highlights in red the temporoparietal lobes, right greater than left, and the posterior cingulate region, consistent with hypometabolism in these regions in converters.

Figure 2:

Receiver operating characteristic curves for all of the logistic regression models for predicting conversion from MCI to AD. Of the three biomarkers alone, FDG PET added the most prognostic information with an area under the curve (AUC) of 0.874, compared with MR imaging (area under the curve = 0.741) and CSF proteins (area under the curve = 0.695).