In:
Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 97, No. 10 ( 2021-09-7), p. 474-488
Abstract:
There is 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 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 methodologic variability in how the residual measures were derived and validated. Despite methodologic differences across studies, meta-analytic assessments showed significant associations of levels of resistance (hazard ratio [HR] [95% confidence interval (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). Discussion 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 methodologic standardization is needed to increase comparability across studies and, ultimately, application in clinical practice.
Type of Medium:
Online Resource
ISSN:
0028-3878
,
1526-632X
DOI:
10.1212/WNL.0000000000012499
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2021
