Distribution-sensitive predictors of gait speed in adults aged 45–85 years in the CLSA

Abstract

Gait speed, known as the ‘sixth vital sign’ is an indicator of functional health and aging. Identifying drivers of midlife gait changes is critical for timely intervention, yet most evidence focuses on older adults and overlooks non-linear, heterogeneous effects. We applied machine learning with quantile regression to the Canadian Longitudinal Study on Aging ( n = 23,419) to identify predictors of gait speed over 3 years. While higher BMI and comorbidity burden were consistently associated with slower gait across quantiles, distinct predictors emerged at distributional extremes. Among slower walkers, higher grip strength, vegetable consumption, and more frequent travel outside the local community predicted faster gait, with physical activity and lung function contributing at lower-to-middle quantiles. Among faster walkers, higher fruit consumption and cognitive performance predicted faster gait. These findings highlight the importance of distribution-sensitive modeling to identify modifiable, stage-specific predictors for tailored interventions to improve health span and quality of life in aging populations.