Measuring resilience and resistance in aging and Alzheimer disease using residual methods: A systematic review and meta-analysis

Objective: There is currently a lack of consensus on how to optimally define and measure resistance and resilience in brain and cognitive aging. Residual methods use residuals from regression analysis to quantify the capacity to avoid (resistance) or cope (resilience) "better or worse than expected" given a certain level of risk or cerebral damage. We reviewed the rapidly-growing literature on residual methods in the context of aging and Alzheimer's disease (AD), and performed meta-analyses to investigate associations of residual-method based resilience and resistance measures with longitudinal cognitive and clinical outcomes. Methods: A systematic literature search of PubMed and Web-of-Science databases (consulted until March 2020) and subsequent screening led to 54 studies fulfilling eligibility criteria, including 10 studies suitable for the meta-analyses. Results: We identified articles using residual methods aimed at quantifying resistance (n=33), cognitive resilience (n=23) and brain resilience (n=2). Critical examination of the literature revealed that there is considerable methodological variability in how the residual measures were derived and validated. Despite methodological differences across studies, meta-analytic assessments showed significant associations of levels of resistance (HR[95%CI]=1.12[1.07-1.17], p<0.0001) and levels of resilience (HR[95%CI]=0.46[0.32-0.68], p<0.001) with risk of progression to dementia/AD. Resilience was also associated with rate of cognitive decline (β[95%CI]=0.05[0.01-0.08], p