Extension and refinement of the predictive value of different classes of markers in ADNI: Four-year follow-up data |
| |
| Institution: | 1. The Litwin-Zucker Research Center, The Feinstein Institute for Medical Research, Manhasset, NY, USA;2. Fundacion para la Investigacion y Docencia Maria Angustias Gimenez, Hermanas Hospitalarias, Sant Boi de Llobregat, Spain;3. Hofstra North Shore Long Island Jewish School of Medicine, Hempstead, NY, USA;1. Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany;2. Department of Psychiatry, University of Patras, Rion Patras, Greece;3. Department of Nuclear Medicine, Technische Universität München, Munich, Germany;4. Institute of Medical Statistics and Epidemiology, Technische Universität München, Munich, Germany;5. Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK;6. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;7. Neuroepidemiology and Ageing Research Unit, School of Public Health, The Imperial College of Science, Technology and Medicine, London, UK;8. West London Cognitive Disorders Treatment and Research Unit, West London Mental Health Trust, London, UK;9. Department of Nuclear Medicine, University of Cologne, Cologne, Germany;10. Department of Psychiatry and Psychotherapy, University Medical Center of Mainz, Mainz, Germany;1. Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Public Health, Department of Cardiology, Qilu Hospital of Shandong University, Jinan, China;2. Cardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, China;3. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China;1. Department of Psychiatry, University of California, San Francisco, CA, USA;2. Department of Neurology, University of California, San Francisco, CA, USA;3. Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA;4. Veterans Affairs Medical Center, San Francisco, CA, USA;5. Northern California Institute for Research and Education, San Francisco, CA, USA;6. Department of Neurology, Boston University, Boston, MA, USA;7. Department of Biostatistics, Boston University, Boston, MA, USA;8. Institute for Social Research, University of Michigan, Ann Arbor, MI, USA;9. Department of Medicine, University of Michigan, Ann Arbor, MI, USA;10. Veterans Affairs Center for Practice Management and Outcomes Research, Ann Arbor, MI, USA;1. Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA;2. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA;3. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA;4. Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA;1. Banner Alzheimer''s Institute, Phoenix, AZ, USA;2. Arizona Alzheimer''s Consortium, Phoenix, AZ, USA;3. Pentara Corporation, Salt Lake City, UT, USA;4. Department of Mathematics and Statistics, Arizona State University, Tempe, AZ, USA;5. Rush Alzheimer''s Disease Center, Rush University Medical Center, Chicago, IL, USA;6. Department of Family Medicine, Rush University Medical Center, Chicago, IL, USA;7. Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA;8. Department of Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA;9. Department of Psychiatry, University of Arizona, Tucson, AZ, USA;10. Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, USA |
| |
| Abstract: | BackgroundThis 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.MethodsMCI 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).ResultsThe 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).ConclusionsThis 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. |
| |
| Keywords: | Mild cognitive impairment Alzheimer's disease Cognition MRI PET-FDG CSF |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
