Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
Tiago Azevedo, Richard A. I. Bethlehem, David J. Whiteside, Nol Swaddiwudhipong, James B. Rowe, Píetro Lió, Timothy Rittman, the Alzheimer’s Disease Neuroimaging Initiative, Lisa C. Silbert, Betty Lind, Rachel Crissey, Jeffrey Kaye, Raina Carter, Sara Dolen, Joseph F. Quinn, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Karen Dagerman, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Jaimie Ziolkowski, Judith L. Heidebrink, Zbizek Nulph, Joanne Lord, Lisa Zbizek-Nulph, Ronald C. Petersen, Sara S. Mason, Colleen S. Albers, David S. Knopman, Kris Johnson, Javier Villanueva-Meyer, Valory Pavlik, Nathaniel Pacini, Ashley Lamb, Joseph S. Kass, Rachelle S. Doody, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Akiva Mintz, Beau M. Ances, John C. Morris, David Winkfield, Maria Carroll, Georgia Stobbs-Cucchi, Angela Oliver, Mary L. Creech, Mark A. Mintun, Stacy Schneider, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Daniel Marson, Hillel Grossman, Martin Goldstein, Jonathan Greenberg, Effie Mitsis, Raj C. Shah, Melissa Lamar, Ajay Sood, Kimberly S. Blanchard, Debra Fleischman, Konstantinos Arfanakis, Patricia Samuels, Ranjan Duara, Maria T. Greig‐Custo, Rosemarie Rodriguez, Marilyn Albert, Daniel Varón, Chiadi U. Onyike, Leonie Farrington, Scott Rudow, Rottislav Brichko, Maria T. Greig, Stephanie Kielb, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Martin Sadowski, Thomas Wısnıewskı, Melanie Shulman, Arline Faustin, Julia Rao, Karen M. Castro, Anaztasia Ulysse, Shannon Chen, Mohammed O. Sheikh, Jamika Singleton-Garvin, P. Murali Doraiswamy, Jeffrey R. Petrella, Olga James
Abstract
BACKGROUND: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. METHODS: We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. RESULTS: We show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. CONCLUSIONS: This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.