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Residual approaches to capture resilience and resistance in aging and Alzheimer’s disease: A meta‐analysis
Author(s) -
Bocancea Diana I.,
van Loenhoud Anna C.,
Groot Colin,
Barkhof Frederik,
van der Flier Wiesje M.,
Ossenkoppele Rik
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.055128
Subject(s) - context (archaeology) , psychological resilience , psychology , dementia , meta analysis , neuroimaging , residual , cognitive decline , resilience (materials science) , cognition , gerontology , disease , clinical psychology , medicine , psychiatry , computer science , biology , social psychology , paleontology , physics , algorithm , thermodynamics
Background There is currently no consensus on how to optimally define and measure resistance and resilience in aging and Alzheimer´s disease (AD). Residuals from regression analyses can be used to quantify whether an individual´s capacity to avoid (resistance) or cope with (resilience) cerebral damage is higher or lower than expected. We aggregated the rapidly‐growing literature on neuroimaging‐based residual methods in the context of aging and AD into a meta‐analysis in order to investigate associations of residual measures of resilience and resistance with longitudinal cognitive and clinical outcomes. Method A systematic literature search of PubMed and Web‐of‐Science databases (consulted until March 2020) and subsequent screening led to 10 studies eligible for the meta‐analysis. The included studies employed a residual measure to investigate associations between either resistance or resilience and rate of cognitive decline or clinical progression (i.e., conversion to MCI or dementia). We extracted standardized regression coefficients for the former and hazard ratios (HRs) for the latter, and assessed overall effects using random‐effects models in the R(v3.6.1) metafor package(v2.4‐0). Result Resistance was most commonly measured with age‐based residuals (also known as “brain age” methods) that capture the extent to which an individual preserves brain integrity despite chronological aging. Cognitive resilience residuals quantified deviations in cognition from expected levels based on the amount of neurodegeneration [Figure 1]. There is considerable methodological variability in how the residual measures were derived and validated. Despite these methodological differences across studies (reflected in the moderate to high heterogeneity estimates), our meta‐analysis showed significant associations between resistance and risk of dementia/AD (HR[95%CI]=1.12[1.07‐ 1.17], p<0.0001, I 2 =70.2%), with a lower level of resistance indicating a higher risk of progression. Cognitive resilience was significantly associated with risk of progression (HR[95%CI]=0.46[0.32‐0.68], p<0.001, I 2 =94.2%) and also with rate of cognitive decline (β[95%CI]=0.05[0.01‐0.08], p<0.01, I 2 =80.3%), suggesting an overall protective role of resilience across studies (i.e. a lower level of resilience is associated with an increased risk of dementia and a faster decline) [Figure 2]. Conclusion This meta‐analysis supports the validity of residual measures to quantify resilience and resistance, as they capture clinically meaningful information in aging and AD.