Brain-Predicted Age Difference Moderates the Association Between Muscle Strength and Mobility

Abstract

Background: Approximately 35% of individuals over age 70 report difficulty with mobility. Muscle weakness has been demonstrated to be one contributor to mobility limitations in older adults. The purpose of this study was to examine the moderating effect of brain-predicted age difference (an index of biological brain age/health derived from structural neuroimaging) on the relationship between leg strength and mobility.

Methods: In community dwelling older adults (N = 57, 74.7 ± 6.93 years; 68% women), we assessed the relationship between isokinetic leg extensor strength and a composite measure of mobility [mobility battery assessment (MBA)] using partial Pearson correlations and multifactorial regression modeling. Brain predicted age (BPA) was calculated from T1 MR-images using a validated machine learning Gaussian Process regression model to explore the moderating effect of BPA difference (BPAD; BPA minus chronological age).

Results: Leg strength was significantly correlated with BPAD (r = -0.317, p < 0.05) and MBA score (r = 0.541, p < 0.001). Chronological age, sex, leg strength, and BPAD explained 63% of the variance in MBA performance (p < 0.001). BPAD was a significant moderator of the relationship between strength and MBA, accounting for 7.0% of MBA score variance [△R ² = 0.044, F(1,51) = 6.83, p = 0.01]. Conditional moderation effects of BPAD indicate strength was a stronger predictor of mobility in those with a great BPAD.

Conclusion: The relationship between strength and mobility appears to be influenced by brain aging, with strength serving as a possible compensation for decline in neural integrity.

Keywords: brain aging; dynapenia; physical function; sarcopenia; weakness.

FIGURE 1

Summary of brain age prediction using a supervised machine learning process. (A) Structural T-1 MRI scans labeled with chronological age from a training set of healthy individuals are loaded into a machine learning regression model. (B) Validation of model accuracy is conducted using cross-validation methods from a portion of the original dataset excluded from the model. Model generated predicted age values are compared with actual age values to determine model accuracy. (C) Model coefficients from the trained model are applied to a new test dataset to determine individual brain age prediction (61.7 years in this example). (D) A standardized metric for statistical comparison is created (brain-predicted age difference) by subtracting chronological age from predicted age to reflect rate of brain aging, with positive and negative values indicating older and younger brains, respectively. *Reprint permission from Elsevier from Trends in Neuroscience, 40 (12), Cole J. H. and Franke K., Predicting age using neuroimaging: innovative brain ageing biomarkers, 681–90, 2017.

FIGURE 2

Heterogeneity of brain-predicted age. Brain-predicted age from brainageR regression model. Scatterplot depicting chronological age (x-axis) by brain-predicted age (y-axis). Dashed line is the line of identity and solid black line is the regression line of chronological age on brain-predicted age.

FIGURE 3

Correlation matrix of chronological age, brain-predicted age difference, normalized leg strength and functional performance. Values represent Pearson’s r for each bivariate correlation. Weak = 0.00–0.49, Moderate = 0.50–0.69, Strong = 0.70–1.0 (Jurs et al., 1998). BPAD, brain-predicted age difference; LE, lower extremity; MBA, mobility battery assessment; 6MWT, six-minute walk test; FSST, four square step test; SCP, stair climb power; 5CR, five times chair rise; CFT, complex functional task.

FIGURE 4

Conditional effects of brain-predicted age difference (BPAD) on the strength-function relationship. (A) Normalized leg extensor strength and composite mobility battery assessment (MBA) score demonstrate a weaker relationship for low (younger) brain age (16th percentile). In contrast, normalized leg extensor strength is a stronger predictor of MBA score for average and high (older) brain age (84th percentile). (B) Johnson–Neyman plot indicating conditional effects of brain-predicted age difference (BPAD) on the relationship between leg extensor strength and mobility battery assessment (MBA) score performance with a 95% confidence interval (dashed line). Note the vertical boundary lines indicate the range of BPAD where normalized leg extensor strength is a significant predictor of MBA score.

FIGURE 5

Conceptual framework of our proposed Motoric Aging and Compensation Hypothesis (MACH). (Left panel A) In the context of relatively “younger” brains and adequate neural mechanisms of mobility, strength is not needed as a functional compensation. (Left panel B) With accelerated brain age and decline in neural integrity, strength becomes a stronger predictor of functional performance and maintains functional capacity. (Right panel A) Brain age difference does not moderate the predictive relationship between strength and habitual motor tasks (e.g., gait), indicating neural processing may not be as integral during simpler, more automatic mobility tasks. (Right panel B) Increased task complexity of goal-directed motor tasks necessitates greater neural contribution to functional performance, with brain age moderating the relationship between strength and mobility.