## 4 Discussion
revealed in a longitudinal study of 4121 older Japanese participants that various gait parameters, including gait speed, stride length, cadence, and stride length variability, were predictive of the onset of disability [32]. They established specific cutoff values for each parameter (gait speed: 1.10 m/s; stride length: 1.15 m; stride length variability, 2.86%) and noted a higher risk of disability onset with an increasing number of parameters, indicating low gait function. Particularly in older adults with sarcopenia, characterized by low muscle mass and compromised physical performance, gait speed is slower and stride length is shorter, with higher stride length variability [33]. Consistent with prior studies, our research found that older adults with reduced muscle mass or sarcopenia exhibited impaired gait, including slower gait speed and shorter stride lengths. Stride length emerged as a significant predictor of sarcopenia, aligning with previous findings that shorter stride length reflects underlying deficits in muscle strength and coordination [34]. While we did not categorize stride length into 'normal' or 'low' thresholds, the observed reductions in the poor PP and sarcopenia groups underscore its potential as a marker of mobility impairment. This finding suggests that stride length, as a continuous measure, captures subtle changes in neuromuscular function that may precede or accompany sarcopenia, enhancing its utility in mobility assessments. Moreover, our data unveiled a novel role for double-limb stance in gait, demonstrating its effectiveness both independently and in combination for predicting poor physical performance and sarcopenia. Notably, the combined model integrating gait speed and double-limb stance was developed, yielding an AUC of 0.702 (95% CI: 0.622-0.781, \(p<0.001\)), with enhanced predictive capability demonstrated by a sensitivity of 78.9% and a specificity of 76.3% in predicting sarcopenia. This combined model represents an unexpected yet promising advancement over individual gait parameters, as prior studies have typically focused on single metrics like gait speed or stride length for sarcopenia screening [26; 33]. For instance, [PERSON] et al. [32] reported gait speed (cutoff: 1.10 m/s) and stride length (cutoff: 1.15 m) as predictors of disability onset, with AUCs generally lower than our combined model's 0.702, suggesting that integrating double-limb stance enhances predictive power beyond what single parameters achieve. We interpret this improvement as a reflection of the complementary nature of gait speed, which captures overall mobility efficiency, and double-limb stance, which reflects balance and stability deficits linked to muscle weakness and sarcopenia [34; 35]. This finding was not entirely anticipated, given the limited attention double-limb stance has received in prior sarcopenia research compared to gait speed. Its novelty may stem from its ability to detect subtle neuromuscular changes that single metrics overlook, a hypothesis supported by [PERSON] et al. [36], who noted double-limb stance variability's association with dynamic balance. Alternatively,enhanced accuracy could reflect population-specific gait patterns in our Korean cohort, raising questions about the model's generality across diverse ethnic groups or settings (e.g., institutionalized vs. community-dwelling adults). Future longitudinal studies could test this model's applicability and refine its thresholds, potentially establishing it as a broadly actionable biomarker for early sarcopenia detection. Our findings underscore the significance of assessing gait parameters for identifying the risk of sarcopenia in older adults. Future research should prioritize longitudinal studies to enhance our comprehension of how these parameters forecast the development and progression of sarcopenia. Such studies can guide tailored interventions aimed at preventing falls, disabilities, and mortality among the elderly. This study offers several notable strengths that enhance its contribution to the field.