Building predictive algorithms to identify resilience and resistance to Alzheimer's disease

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PROJECT SUMMARY There are two observed phenomena that defy the traditional Alzheimer’s disease (AD) trajectory; those who resist the accumulation of AD pathology (amyloid and/or tau) despite evidence of risk factors, and those who present with AD pathology but remain resilient to cognitive decline. Classifying these individuals who will likely manifest resistance or resilience to AD over their lifetime is critical for informing clinical practice and transforming clinical trial recruitment. It remains unclear how combinations of risk factors, whether demographic, vascular or neuroimaging, may help to increase accuracy for predicting an individuals’ likelihood of manifesting resistance or resilience to AD. Further, very little is understood about how sex, race and their interaction influence these phenomena. Relatively limited sample sizes and low racial diversity have so far hampered studies. The overall goal of this proposal is to develop and validate robust predictive algorithms of resistance and resilience to AD by harmonizing data from 13 well characterized and racially diverse cohorts of clinically normal older adults (n=~15,000). This innovative proposal could transform approaches for both clinical decision making and clinical trials. Based on a simple set of easily accessible medical information, such as demographics, vascular risk, APOEe4 status, and brain volumetric data when available, our validated models will provide interpretable patient- level predictions of resistance and resilience with 10-year risk estimates of AD pathological burden and cognitive decline given a patient’s profile. Similarly, our predictive algorithms will provide a predictive framework of who should be invited for initial screening and serve to predict those most likely to accumulate Ab/tau or exhibit short term decline within the course of a clinical trial. We propose to harmonize data from 13 cohorts of ~15,000 clinically normal individuals, to accomplish the following aims: (1) build predictive algorithms to classify those who are resistant to either amyloid or tau and validate these models to demonstrate their utility in clinical practice and AD prevention trials, (2) build and validate predictive algorithms to classify those who are cognitively resilient in the face of abnormal levels of amyloid or tau, and (3) examine how intersections between sex and race can produce more refined individualized risk profiles that are reflective of these two critical population strata that are known risk factors for AD. Our strong interdisciplinary team spans the breadth of cognitive neuroscience, PET and MR neuroimaging, biostatistics, behavioral neurology, and epidemiology. Our multi-PI team reflect four critical areas of expertise that are essential to this proposal: (1) data harmonization, (2) neuroimaging, (3) machine learning, and (4) cognitive resilience. We have published a range of data harmonization approaches for both cognitive and PET neuroimaging data, which can be flexibly applied to different data types. Using these approaches, we will identify higher-order interactions between multiple risk factors (demographics, vascular risk, neuroimaging) to build individualized risk profiles of both resistance and resilience to AD. This innovative proposal has the potential to transform the way we approach clinical practice and clinical trial design.