A Bayesian Network Analysis of Gait Speed Change Upon Transition to Uneven Surfaces in Older Adults

BackgroundGait adaptability, defined as the ability to adjust walking performance to environmental challenges, likely reflects complex interactions among the central nervous system (CNS) and other physiological systems, however, the drivers of lower gait adaptability in older adults are poorly understood. MethodsWe applied a Bayesian network framework to quantify multisystem interactions contributing to percent change in gait speed (%GSC) on transition from even to uneven surface in 159 older adults (63% women). Neuroimaging measures include total gray matter and white matter hyperintensities, striatal dopaminergic neurotransmission, and resting state functional connectivity. Other measures were obtained for domains important for locomotor control: health history, lifestyle, psychological well-being, cognition, and musculoskeletal and peripheral nervous systems (neurological exam). The Bayesian network estimated direct and indirect dependencies among variables, and predictive accuracy of %GSC from the Bayesian network was compared with that of multivariable linear regression using 10-fold cross-validation. ResultsParticipants exhibited slower gait on uneven compared to even surfaces (mean %GSC = -6.32%). The Bayesian network outperformed linear regression in predicting %GSC and identified four direct paths to %GSC from: BMI, muscle strength, striato-cortical sensorimotor connectivity, and purpose in life. Indirect paths to %GSC showed interrelations among CNS and non-CNS variables, including striatal dopaminergic neurotransmission, total gray matter volume, medications, proprioception, and sex. ConclusionsGait adaptability in older adults is influenced by interactions among functional connectivity, body composition, muscle strength, and psychological well-being. Strengthening both neural and physical systems through targeted interventions may mitigate declines in gait instability and preserve mobility with aging.