Gait Variability Is Associated With Gray Matter Volumes Implicated in Cognitive Function: A Cross-Sectional Analysis...

Aging is associated with both gait impairments and cognitive decline; however, the relationship between specific gait variability parameters, gray matter volume (GMV), and cognitive function remains poorly understood. This study aims to examine the associations between gait variability parameters (derived from stride length, step length, step time, and gait velocity) and GMV and its associations with cognitive function in cognitively normal older adults.

Eighty-seven older adults (48 female) aged 65-80 from the AGUEDA trial participated in this cross-sectional analysis. The Optogait system was used to record gait parameters. T1-weighted brain images were acquired magnetic resonance imaging scanner, and GMV was calculated by whole-brain voxel-based morphometric analysis using SPM12. Cognitive function was calculated from different cognitive tests.

Greater stride length variability was associated with lower GMV (p < .001) in clusters located in the supramarginal gyrus (t = 4.014, k = 179, β = -0.494) and hippocampus (t = 3.670, k = 334, β = -0.394), whereas greater step length variability was linked to lower GMV in the parahippocampal gyrus (t = 3.624, k = 76, β = -0.410). However, greater step time variability was associated with greater GMV in the supplementary motor area (t = 4.117, k = 274, β = 0.449). Gait velocity variability did not show any association with GMV. Furthermore, greater GMV in the supramarginal gyrus was associated with better working memory (β = 0.252, p = .008); greater GMV in the hippocampus was associated with better attentional/inhibitory control (β = 0.275, p = .010); and greater GMV in the parahippocampal gyrus was associated with better EF (β = 0.212, p = .035), attentional/inhibitory control (β = 0.241, p = .019), and working memory (β = 0.233, p = .027).

These results suggest that gait variability could be an indicator of neurocognitive decline in older adults. Understanding these associations is essential for early dementia detection and sheds light on the complex interplay between physical function, brain health, and cognitive function during aging.