Brain Metabolic Resilience in Alzheimer's Disease: A Predictor of Cognitive Decline and Conversion to Dementia

Objective: Brain atrophy measured by structural imaging has been used to quantify resilience against neurodegeneration in Alzheimer's disease. Considering glucose hypometabolism is another marker of neurodegeneration, we quantified metabolic resilience (MR) based on Fluorodeoxyglucose positron emission tomography (FDG PET) and investigated its clinical implications. Methods: We quantified the MR and other resilience metrics, including brain resilience (BR) and cognitive resilience (CR), using partial least squares path modeling from the ADNI database. A linear mixed-effects model and a Cox proportional hazards model were used to identify the impact of each resilience on longitudinal cognitive function and conversion to dementia, respectively. Results: A total of 848 participants were included in this study. All resilience metrics (CR, BR, and MR) were associated with slower cognitive decline. Results from the ANOVA test, AIC and BIC values showed that the additional inclusion of MR improved the performances of the linear mixed effect models. In survival analysis, all resilience variables were negatively associated with the risk of conversion to dementia. In line with the results of the linear mixed effects models, the additional inclusion of MR into the models with different resilience variables increased the C-index. Conclusion: Relative preservation of brain glucose metabolism is a valuable predictor of future cognitive decline and conversion to dementia, adding value to existing resilience metrics. While the utility of FDG PET in clinical settings is limited by cost and accessibility, it might have potential usefulness as a prognostic marker, especially in a context of resilience.