Neuropsychological Testing Predicts Cerebrospinal Fluid Amyloid-β in Mild Cognitive Impairment

Article type: Research Article

Authors: Kandel, Benjamin M.^{a; *} | Avants, Brian B.^b | Gee, James C.^b | Arnold, Steven E.^c | Wolk, David A.^d | for the Alzheimer’s Disease Neuroimaging Initiative

Affiliations: [a] Penn Image Computing and Science Laboratory and Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA | [b] Penn Image Computing and Science Laboratory and Department of Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA | [c] Department of Psychiatry, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA | [d] Department of Neurology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA

Correspondence: [*] Correspondence to: Benjamin M. Kandel, B.A., Penn Image Computing and Science Laboratory and Department of Bioengineering, University of Pennsylvania, 3600 Market St, Ste. 370, Philadelphia, PA 19104, USA. Tel.: +1 314 610 7256; bkandel@seas.upenn.edu

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Abstract: Background: Psychometric tests predict conversion of mild cognitive impairment (MCI) to probable Alzheimer’s disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. Objective: To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by cerebrospinal fluid (CSF) amyloid-β (Aβ)1 - 42, in patients with MCI, as compared to neuroimaging biomarkers. Methods: We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status. Results: The 30-min delayed recall score of the Rey Auditory Verbal Learning Test was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67 ± 0.02 and odds ratio of 2.5 ± 0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67 ± 0.03, OR 3.2 ± 1.2), followed by hippocampal volume (AUC 0.64 ± 0.02, OR 2.4 ± 0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68 ± 0.03 (OR 3.38 ± 1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC. Conclusion: Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD.

Keywords: Alzheimer’s disease, magnetic resonance imaging, mild cognitive impairment, positron emission tomography

DOI: 10.3233/JAD-142943

Journal: Journal of Alzheimer's Disease, vol. 46, no. 4, pp. 901-912, 2015