How You Walk, Not Just How Fast, May Reveal Early Dementia Risk

For years, clinicians have used a deceptively simple measurement to flag older adults at risk of dementia: how fast they walk. Slow gait combined with subjective memory complaints defines a condition known as motoric cognitive risk syndrome, or MCR, which has been shown in large international cohorts to predict major neurocognitive disorders years before diagnosis. But walking speed has always been a blunt instrument. It is a composite output, the end product of muscles, joints, sensory feedback, and brain circuits, and it can slow down for many reasons that have nothing to do with cognitive decline. A new study published in GeroScience argues that the real diagnostic information is hiding in the fine structure of movement, not the headline number.

The research, led by Baptiste Perthuy and Leslie M. Decker of the COMETE laboratory at Université de Caen Normandie, together with colleagues including Nick Stergiou of the University of Nebraska at Omaha and Fabien Cignetti of the Grenoble Institute of Neurosciences, took an unusual approach. Rather than comparing group averages, the team built a normative model of healthy walking and then measured how far each individual deviated from it, domain by domain. Ninety-seven adults aged 55 and over completed two treadmill walking bouts of three minutes and thirty seconds at their preferred speed. The participants fell into three groups: twenty older adults with MCR, twenty healthy older adults with slow gait who were matched to the MCR group on demographics and walked at comparably slow speeds, and fifty-seven healthy older adults who served as the reference population.

The methodological core of the study lies in how walking was decomposed. The researchers organized a battery of linear spatiotemporal measures and nonlinear variables derived from trunk acceleration into ten functional gait domains. These were conceptually grouped into three tiers: gait pattern, covering pace, rhythm, gait phases, postural control, and symmetry; fluctuation amplitude, essentially step-to-step variability; and the temporal structure of those fluctuations, which includes regulation, signal complexity, divergence of movement trajectories, and attractor complexity. This domain-based framework reflects a growing consensus in the biomechanics literature that gait is not a single quantity but a bundle of partly independent control processes, each with its own neural substrates.

To quantify deviation, the team trained a Gaussian mixture model on data from the healthy older adults. A Gaussian mixture model is a probabilistic machine learning technique that fits a set of overlapping bell-shaped distributions to data, effectively learning the shape of normal variation in each gait domain. Once trained, the model defines a normative reference space, and any new individual can be assigned an anomaly score that expresses how improbable their gait profile is relative to that healthy distribution. This approach, borrowed from normative modeling in computational psychiatry, sidesteps a chronic weakness of case-control studies: it does not assume that a clinical group is merely shifted in the average, but instead maps each person’s position within or outside the healthy manifold.

The results split cleanly into two layers. Both the MCR group and the healthy slow walkers showed elevated deviations in gait pattern domains, specifically pace and phases, which is exactly what one would expect given their slower walking speed. In other words, the coarse, speed-related signature of gait does not distinguish MCR from ordinary age-related slowing. That finding alone is a pointed critique of gait speed as a stand-alone clinical marker: two people can walk at the same slow tempo for entirely different reasons, and a stopwatch cannot tell them apart.

The second layer is where the study becomes striking. Only the MCR group showed additional deviations in the domains related to fluctuation amplitude and temporal structure. Their walking exhibited increased step-to-step variability, and their trunk acceleration fluctuations were more divergent, more predictable, and less complex than those of healthy walkers. Each of those adjectives carries technical weight. Greater divergence of movement trajectories, typically quantified through Lyapunov exponents, indicates that small perturbations during walking grow faster over time, a hallmark of reduced local dynamic stability. Greater predictability and reduced complexity, often measured with entropy-based statistics, suggest that the gait pattern has become more rigid and stereotyped, losing the adaptive variability that characterizes healthy neuromotor control.

These nonlinear findings resonate with a theoretical framework that Stergiou and Decker helped develop more than a decade ago, which proposes that healthy movement occupies an optimal zone of variability, and that pathology pushes movement either toward excessive randomness or excessive regularity. The MCR signature observed here, with both elevated variability and reduced complexity, fits the picture of a neuromotor system that has lost fine-grained regulation. It also aligns with neuroimaging work linking MCR to cortical atrophy and white matter hyperintensities, since the brain networks that support executive function and attention are deeply involved in orchestrating the moment-to-moment regulation of gait fluctuations.

The clinical implications are considerable. Because the anomaly scores are computed per domain and per individual, they could function as personalized, interpretable digital biomarkers. A clinician could, in principle, see not just that a patient’s gait is abnormal, but which specific control processes are deviating from the healthy reference and by how much. That granularity matters for early detection, since the MCR signature identified here was visible even in a modest sample of twenty affected individuals, and it matters for monitoring, because domain-specific scores could in future studies track whether an intervention is restoring healthy gait dynamics rather than merely speeding someone up. The treadmill protocol itself, two short bouts at preferred speed with trunk-worn accelerometry, is simple enough to imagine translating into routine geriatric assessment.

Important caveats remain. The study is cross-sectional, so it demonstrates association rather than prediction; it cannot yet say whether the multidimensional signature precedes cognitive decline or emerges alongside it. The MCR group is small, and treadmill walking, while well controlled, differs in known ways from overground walking. The authors note that data are available from the corresponding author upon reasonable request, and the study, conducted within the PRESAGE project in Normandy with support from European and regional funders, was designed with the kind of immersive and instrumented facilities that allow unusually rich movement recording. Whether the anomaly-detection framework generalizes to community-based cohorts and to overground or free-living walking will be the decisive test.

Even so, the study marks a conceptual shift in how the field might think about the motor face of cognitive risk. Gait speed tells you that something may be wrong; the temporal architecture of movement fluctuations begins to tell you what. If subsequent longitudinal work confirms that the complexity and stability domains identified here predict conversion to major neurocognitive disorders ahead of conventional markers, the humble act of walking a few minutes on a treadmill, read through the right mathematical lens, could become one of the most accessible windows into the aging brain that medicine currently possesses.

Subject of Research: Multidimensional gait analysis and anomaly detection for identifying motoric cognitive risk syndrome in older adults

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

Article References: Perthuy, B., Vinzant, H., Brifault, C., Cabibel, V., Laillier, R., Sultan, A., Denise, P., Lefèvre, N., Dalibot, A., Stergiou, N., Cignetti, F., & Decker, L. M. (2026). Beyond gait speed: a multidimensional motor signature of motoric cognitive risk syndrome identified through domain-specific anomaly detection. GeroScience. https://doi.org/10.1007/s11357-026-02493-4

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02493-4

Keywords: motoric cognitive risk syndrome, gait analysis, gait speed, gait variability, nonlinear dynamics, anomaly detection, Gaussian mixture model, digital biomarkers, dementia risk, older adults, GeroScience, treadmill walking