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Enriching Amnestic Mild Cognitive Impairment Populations for Clinical Trials: Optimal Combination of Biomarkers to Predict Conversion to Dementia

Article type: Research Article

Authors: Yu, Penga; * | Dean, Robert A.a | Hall, Stephen D.a | Qi, Yuanb | Sethuraman, Gopalana | Willis, Brian A.a | Siemers, Eric R.a | Martenyi, Ferenca | Tauscher, Johannes T.a | Schwarz, Adam J.a | for the Alzheimer's Disease Neuroimaging Initiative1

Affiliations: [a] Eli Lilly and Company, Indianapolis, IN, USA | [b] Department of Computer Science, Purdue University, West Lafayette, IN, USA

Correspondence: [*] Correspondence to: Peng Yu, Eli Lilly and Company, Indianapolis, IN 46285, USA. Tel.: +1 317 277 5528; Fax: +1 317 276 6545; E-mail: yu_peng_py@lilly.com.

Note: [1] Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). 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 list of ADNI investigators is available at http://adni.loni.ucla.edu/research/active-investigators/.

Keywords: Alzheimer's disease, apolipoprotein E, biomarker, cerebrospinal fluid, conversion, FDG-PET, magnetic resonance imaging, mild cognitive impairment, prodromal, progression

DOI: 10.3233/JAD-2012-120832

Journal: Journal of Alzheimer's Disease, vol. 32, no. 2, pp. 373-385, 2012

Accepted 14 June 2012
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Published: 9 October 2012
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Supplementary Materials:

Supplementary Materials.

Abstract

The goal of this study was to identify the optimal combination of magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) biomarkers to predict conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) dementia within two years, for enriching clinical trial populations. Data from 63 subjects in the Alzheimer's Disease Neuroimaging Initiative aMCI cohort who had MRI and FDG-PET imaging along with CSF data at baseline and at least two years clinical follow-up were used. A Bayesian classification method was used to determine which combination of 31 variables (MRI, FDG-PET, CSF measurements, apolipoprotein E (ApoE) genotype, and cognitive scores) provided the most accurate prediction of aMCI to AD conversion. The cost and time trade-offs for the use of these biomarkers as inclusion criteria in clinical trials were evaluated. Using the combination of all biomarkers, ApoE genotype, and cognitive scores, we achieved an accuracy of 81% in predicting aMCI to AD conversion. With only ApoE genotype and cognitive scores, the prediction accuracy decreased to 62%. By comparing individual modalities, we found that MRI measures had the best predictive power (accuracy = 78%), followed by ApoE, FDG-PET, CSF, and the Alzheimer's disease assessment scale-cognitive subscale. The combination of biomarkers from different modalities, measuring complementary aspects of AD pathology, provided the most accurate prediction of aMCI to AD conversion within two years. This was predominantly driven by MRI measures, which emerged as the single most powerful modality. Overall, the combination of MRI, ApoE, and cognitive scores provided the best trade-off between cost and time compared with other biomarker combinations for patient recruitment in clinical trial.

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