AD risk score for the early phases of disease based on unsupervised machine learning

• Published in: Alzheimer's & dementia Vol. 16; no. 11; pp. 1524 - 1533
• Main Authors: Wang, Zheyu, Tang, Zhuojun, Zhu, Yuxin, Pettigrew, Corinne, Soldan, Anja, Gross, Alden, Albert, Marilyn
• Format: Journal Article
• Language: English
• Published: United States 01.11.2020
• Subjects: Aged; Alzheimer Disease - diagnosis; Alzheimer's disease; cognitive testing; Disease Progression; Early Diagnosis; Female; Humans; latent variable; machine learning; Male; Middle Aged; multidomain biomarkers; progression; Risk Factors; risk score; unsupervised learning; Unsupervised Machine Learning; risk score; progression; machine learning; Alzheimer's disease; multidomain biomarkers; latent variable; cognitive testing; unsupervised learning
• ISSN: 1552-5260; 1552-5279; 1552-5279
• DOI: 10.1002/alz.12140

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.