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<0.01). Conclusion:
This review and meta-analysis supports the usefulness of residual methods
as appropriate measures of resilience and resistance, as they capture
clinically meaningful information in aging and AD. More rigorous
methodological standardization is needed, however, to increase
comparability across studies and, ultimately, application in clinical
practice.