Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics

Authors

Yogatheesan Varatharajah, University of Illinois Urbana-Champaign
Vijay K. Ramanan, Mayo Clinic
Ravishankar Iyer, University of Illinois Urbana-Champaign
Prashanthi Vemuri, Mayo Clinic
Michael W. Weiner, University of California, San Francisco
Paul Aisen, University of Southern California
Ronald Petersen, Mayo Clinic
Clifford R. Jack, Mayo Clinic
Andrew J. Saykin, Indiana University Bloomington
William Jagust, University of California, Berkeley
John Q. Trojanowki, University of Pennsylvania
Arthur W. Toga, University of Southern California
Laurel Beckett, University of California, Davis
Robert C. Green, Harvard Medical School
John Morris, Washington University in St. Louis
Leslie M. Shaw, University of Pennsylvania
Zaven Khachaturian, Prevent Alzheimer’s Disease
Greg Sorensen, Siemens AG
Maria Carrillo, Alzheimer’s Association
Lew Kuller, University of Pittsburgh
Marc Raichle, Washington University in St. Louis
Steven Paul, Cornell University
Peter Davies, Albert Einstein College of Medicine of Yeshiva University
Howard Fillit, AD Drug Discovery Foundation
Franz Hefti, Acumen Pharmaceuticals
David Holtzman, Washington University in St. Louis
M. Marcel Mesulam, Northwestern University
William Potter, National Institute of Mental Health (NIMH)
Peter Snyder, Brown University
Adam Schwartz, Eli Lilly and Company
Tom Montine, University of Washington, Seattle
Ronald G. Thomas, University of California, San Diego

Document Type

Article

Publication Date

12-1-2019

Journal

Scientific Reports

Volume

9

Issue

1

URL with Digital Object Identifier

10.1038/s41598-019-38793-3

Abstract

© 2019, The Author(s). In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression of CR1 (complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.