<p>This study investigated whether different OpenSim musculoskeletal models preserve the intrinsic multivariate structure of sagittal-plane gait kinematics in clinical populations. Four publicly available clinical gait datasets were analyzed, including post-stroke, hip osteoarthritis, fall-risk, and vestibulopathy cohorts. Marker trajectories were standardized and processed using four musculoskeletal models (Gait2354, Gait2392, Rajagopal, and Lai-Uhlrich). Following quasi-static scaling and inverse kinematics, hip flexion, knee flexion, and ankle dorsiflexion trajectories were time-normalized to one gait cycle. Representational consistency was evaluated using Dynamic Time Warping (DTW), principal component analysis (PCA), and nonlinear manifold embedding. Median DTW distances indicated negligible waveform distortion between the closely related Gait2354 and Gait2392 models, moderate differences between the Gait models and Rajagopal or Lai-Uhlrich models which varies from 0.19 to 0.42, and generally lower distortion between Rajagopal and Lai-Uhlrich which varies from 0.05 to 0.29. Distal joints exhibited larger divergence, with ankle median DTW distances reaching 0.66 in the fall-risk cohort. PCA showed that the first principal component frequently explained more than 80% of waveform variance, with combined PC1-PC2 variance often exceeding 90% at distal joints. Centroid displacement and nonlinear separation ratios varied across joints and cohorts, with minimal separation in hip osteoarthritis knee kinematics and larger separations in post-stroke and fall-risk cohorts. Overall, while gait kinematics remain predominantly low-dimensional across musculoskeletal formulations, model selection influences both geometric representation and waveform similarity, particularly at distal joints and in neurologically impaired populations.</p>