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

• Published in: Scientific reports Vol. 9; no. 1; p. 2235
• Main Authors: Varatharajah, Yogatheesan, Ramanan, Vijay K, Iyer, Ravishankar, Vemuri, Prashanthi
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 19.02.2019; Nature Publishing Group UK
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - genetics; Alzheimer Disease - physiopathology; Alzheimer's disease; Amyloid; Biomarkers; Cerebrospinal fluid; Cognition; Cognition & reasoning; Cognitive ability; Cognitive Dysfunction - genetics; Cognitive Dysfunction - physiopathology; Complement receptors; Dementia; Dementia disorders; Demography; Female; Genetic factors; Genetic Predisposition to Disease; Health risk assessment; Humans; Learning algorithms; Machine learning; Magnetic resonance imaging; Male; Medical imaging; Middle Aged; Models, Genetic; Models, Neurological; Neuroimaging; Pathology; Pathophysiology; Resilience, Psychological; Tau protein
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-38793-3

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.