Zheyu Wang1,2, Zhuojun Tang1, Yuxin Zhu1,2, Corinne Pettigrew3, Anja Soldan3, Alden Gross4,5, Marilyn Albert3. 1. Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 2. Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Maryland, USA. 3. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 4. Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, Maryland, USA. 5. Johns Hopkins University Center on Aging and Health, Baltimore, Maryland, USA.
Abstract
INTRODUCTION: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention. METHODS: An AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort. RESULTS: Higher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum. DISCUSSION: The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.
INTRODUCTION: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention. METHODS: An AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort. RESULTS: Higher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum. DISCUSSION: The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.
Authors: Marilyn S Albert; Steven T DeKosky; Dennis Dickson; Bruno Dubois; Howard H Feldman; Nick C Fox; Anthony Gamst; David M Holtzman; William J Jagust; Ronald C Petersen; Peter J Snyder; Maria C Carrillo; Bill Thies; Creighton H Phelps Journal: Alzheimers Dement Date: 2011-04-21 Impact factor: 21.566
Authors: Rahul S Desikan; Florent Ségonne; Bruce Fischl; Brian T Quinn; Bradford C Dickerson; Deborah Blacker; Randy L Buckner; Anders M Dale; R Paul Maguire; Bradley T Hyman; Marilyn S Albert; Ronald J Killiany Journal: Neuroimage Date: 2006-03-10 Impact factor: 6.556
Authors: Clifford R Jack; David S Knopman; Stephen D Weigand; Heather J Wiste; Prashanthi Vemuri; Val Lowe; Kejal Kantarci; Jeffrey L Gunter; Matthew L Senjem; Robert J Ivnik; Rosebud O Roberts; Walter A Rocca; Bradley F Boeve; Ronald C Petersen Journal: Ann Neurol Date: 2012-04-09 Impact factor: 10.422
Authors: Alden L Gross; Keenan A Walker; Abhay R Moghekar; Corinne Pettigrew; Anja Soldan; Marilyn S Albert; Jeremy D Walston Journal: Front Aging Neurosci Date: 2019-09-06 Impact factor: 5.750