Participant-specific interrogation of population-based data to predict cognitive decline from neuropsychiatric symptoms and neuroimaging biomarkers: A machine learning approach (P12-6.001)

Abstract

<h3>Objective:</h3> Develop machine learning (ML) models to predict decline in global cognition and attention from neuroimaging biomarkers of Alzheimer’s disease (AD) and neuropsychiatric symptoms (NPS). <h3>Background:</h3> ML is increasingly being used in brain aging research. <h3>Design/Methods:</h3> We developed four ML models (support vector machine, Gaussian process, random forest, Naïve Bayes) to predict z-scored global cognition and attention domain changes between first and last study visits of each participant. We used a stepwise approach to develop the prediction models by: 1) including AD biomarkers (PiB- &amp; FDG-PET), education, and APOE ɛ4 status; followed by 2) adding measures of depression (Beck Depression Inventory-II; BDI-II), and anxiety (Beck Anxiety Inventory; BAI); then 3) adding 12 NPS including NPS severity as measured by Neuropsychiatric Inventory Questionnaire (NPI-Q). <h3>Results:</h3> Data from 777 cognitively unimpaired (CU) participants for the outcome of global cognition z-score, and 795 CU participants for the outcome of attention z-score was available. 80% of the sample was randomly used to train the model, which was blind tested on the remaining 20%. For global cognition, the best model was able to predict 78% of participants with decreasing z-score using AD biomarkers, education, and APOE ɛ4 status. Adding BDI-II and BAI improved the prediction to 87%; and adding 12 NPS further improved the prediction to 96% of participants with decreasing z-scores. Including NPS severity did not improve the model’s performance. For the attention domain, the best model predicted 89% of participants with decreasing z-score using AD biomarkers, education, and APOE ɛ4 status. After adding BDI-II and BAI measurements, the model was able to predict 97% of participants with decreasing z-scores. Adding 12 NPS, and NPI severity only marginally improved the model’s performance to 98%. <h3>Conclusions:</h3> ML has the potential to successfully predict global and domain-specific cognitive decline in community-dwelling persons, with NPS improving prediction compared to AD neuroimaging biomarkers alone. <b>Disclosure:</b> Mr. Shah has nothing to disclose. Jeremy Syrjanen has nothing to disclose. Dr. Krell-Roesch has nothing to disclose. The institution of Walter Kremers has received research support from Biogen. Dr. Vemuri has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Miller Medical Communications Inc.. The institution of Dr. Vemuri has received research support from NIH. The institution of Dr. Vassilaki has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for F. Hoffmann-La Roche Ltd . Dr. Vassilaki has stock in Abbott Laboratories. Dr. Vassilaki has stock in Johnson and Johnson. Dr. Vassilaki has stock in Medtronic. Dr. Vassilaki has stock in Amgen. Dr. Vassilaki has stock in AbbVie. Dr. Vassilaki has stock in Merck. The institution of Dr. Vassilaki has received research support from NIH. The institution of Dr. Vassilaki has received research support from European Union/ St. Anne’s University Hospital Brno, Czech Republic. The institution of an immediate family member of Dr. Vassilaki has received research support from Avobis Bio, LLC. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Roche. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Merck. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Genentech. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Nestle. Dr. Petersen has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Genentech, Inc.. Dr. Petersen has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Roche, Inc.. Dr. Petersen has received publishing royalties from a publication relating to health care. Dr. Petersen has received publishing royalties from a publication relating to health care. The institution of Dr. Forzani has received research support from NIH. The institution of Dr. Forzani has received research support from NSF. Dr. Forzani has received intellectual property interests from a discovery or technology relating to health care. Dr. Forzani has received personal compensation in the range of $100,000-$499,999 for serving as a Professor with Arizona State University. Dr. Wu has nothing to disclose. The institution of Dr. Geda has received research support from NIH.