Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods: A Systematic Review and Meta-analysis

Abstract

Background and objective: 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.

Figure 1. Conceptual Model of Resistance and Resilience in Normative and Pathologic Brain and Cognitive Aging

An individual's level of (successful) cognitive aging is determined by 2 distinct modes of cognitive preservation, resistance, and (brain and cognitive) resilience. Recent research in the cognitive decline and dementia field posits neurodegenerative disorders as the product of multiple proteinopathies and other pathologic events occurring in conjunction. Similarly, heterogeneity in cognitive aging trajectories is determined by varying degrees of resistance- and resilience-related mechanisms that interact with these processes and synergistically contribute to successful aging. Icons in this figure are modified from Servier Medical Art, licensed under a Creative Commons Attribution 3.0 Unported License (smart.servier.com/).

Figure 2. Generic Diagram of the Residual Approach, Study Selection Flowchart, and Histogram of Selected Studies Publication Year

(A) Person-specific residuals (ε) are computed as the difference between an observed outcome variable and that predicted by a number of variables of interest. Individuals who present a better outcome than predicted (generally) have a higher level of resilience or resistance. Note however that the directionality of the residuals ultimately depends on the outcome and predictor variables in the model, and hence, in some studies a negative residual indicates higher resilience/resistance. (B) Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart of study selection. (C) Distribution of publication year for the 54 studies eligible for inclusion in the systematic review. This figure illustrates an increasing number of studies over the past decade that use a residual method–based measure of resilience or resistance.

Figure 3. Residuals Measures of Resistance and Resilience-Related Constructs

Schematic diagram depicting a gross categorization of the different residual methods we identified in the reviewed studies. Within the framework of resistance and (brain and cognitive) resilience, according to what outcome variable has been modeled and subsequently residualized, we identified age-based, brain-based, and cognition-based residual methods. Arrows point from the predictor variables to the outcome variable residualized (e.g., we found 2 different ways of calculating age-based residuals: predicting chronological age with respect to molecular change measures or with respect to brain integrity measures). Note that while resistance and brain resilience concepts partially overlap, as both include preservation of brain structure (brain integrity), we distinguish them depending on whether risk factor (age) or pathology (molecular changes) is included in the equation. Similarly, brain and cognitive resilience partially overlap, as both may include pathology (molecular changes) as predictors, and we distinguish them based on whether the residualized outcome represents brain integrity or cognition. These 2 models may reflect different phenotypes of resilience, as cognitive resilience relative to the molecular markers could (partly) be explained by brain resilience. Icons in this figure are modified from Servier Medical Art, licensed under a Creative Commons Attribution 3.0 Unported License (smart.servier.com/).

Figure 4. Applications of Residual-Method Based Measures of Resistance and Resilience

This figure illustrates a summary of the diverse range of applications and analyses for which residual measures were employed. The studies included in the systematic review demonstrated the use of residual measures to facilitate research aimed to better understand the role of resistance and resilience in brain and cognitive aging. AD = Alzheimer disease; GWAS = genome-wide association study; MCI = mild cognitive impairment; SES = socioeconomic status.

Figure 5. Forest Plots Illustrating Associations of Residual Method–Based Measures of Resistance and Resilience With Longitudinal Cognitive Outcomes

(A) Random-effects meta-analysis of association between resistance and risk of progression to dementia/Alzheimer disease (AD). Note that in the age-based residual measures of resistance, a negative value indicates higher resistance level. Therefore, a hazard ratio >1 indicates a favorable outcome, such that a higher resistance level is associated with a reduced risk of converting to AD/dementia. (B) Random-effects meta-analysis of association between cognitive resilience and risk of progression to dementia/AD. In this case, the cognition-based or brain-based residual measures are proportional to the level of cognitive resilience, with a higher positive value indicating a higher cognitive resilience level. A hazard ratio <1 indicates a favorable outcome, where a higher cognitive resilience level is associated with a reduced risk of converting to AD/dementia. (C) Random-effects meta-analysis of association between cognitive resilience and rate of cognitive decline. A positive standardized regression coefficient indicates a favorable outcome, where a higher cognitive resilience level is associated with a slower rate of decline. The outcome indicates the specific cognitive domain/neuropsychological test used to assess cognitive decline in each study. Size of rectangles are proportional to the weight of the study in the random-effects model. CI = confidence interval; CN = cognitively normal; MCI = mild cognitive impairment; MMSE = Mini-Mental State Examination; nCN = number of cognitively normal patients; nDem = number of patients with dementia/AD; nMCI = number of patients with mild cognitive impairment; RE = random effects. ^aRE model: random-effects model using the DerSimonian-Laird estimator. ^bThese 2 studies contain a partially overlapping sample of patients as they used the same cohort.