Beyond gait speed: a multidimensional motor signature of motoric cognitive risk syndrome identified through domain-specific anomaly detection.

Motoric cognitive risk (MCR) syndrome, defined by subjective cognitive complaints and slow gait speed, identifies older adults at increased risk of major neurocognitive disorders (NCDs). Yet, gait speed reflects a composite output shaped by heterogeneous neuromusculoskeletal and cognitive processes, limiting its clinical specificity. This study aimed to refine the motor signature of MCR by quantifying domain-specific gait deviations relative to a normative reference cohort using an anomaly-detection approach. Ninety-seven adults (≥ 55 years) completed two 3-min 30-s treadmill walking bouts at their preferred speed. Participants were categorized into three groups: older adults with MCR (n = 20), healthy older adults with slow gait (sHOA; n = 20) matched to MCR on demographic characteristics with comparably slow gait speed, and healthy older adults (HOA; n = 57). Linear spatiotemporal and nonlinear trunk acceleration-derived variables were organized into ten functional gait domains, conceptually grouped into gait pattern (pace, rhythm, phases, postural control, symmetry), fluctuation amplitude (variability), and temporal structure of fluctuations (regulation, signal complexity, divergence of movement trajectories, and attractor complexity). For each domain, a Gaussian mixture model trained on HOA data defined a normative reference space, from which individual anomaly scores quantified deviations across groups. Both sHOA and MCR showed higher deviations in gait pattern domains (pace and phases) than HOA, consistent with their slower gait speed. Only MCR exhibited additional deviations in domains related to fluctuation amplitude and temporal structure, reflected by increased step-to-step variability and trunk acceleration fluctuations that were more divergent, more predictable, and less complex. These findings reveal a multidimensional motor signature of MCR. Domain-specific anomaly scores may provide individualized, clinically interpretable biomarkers to support early detection and monitoring of older adults at increased risk of major NCDs.