Alzheimer’s disease neurodegenerative biomarkers, particularly neurofilament light chain and tau-related proteins, show consistent associations with early motor decline in older adults according to a systematic review published in 2026. These physical shifts in gait, balance, and mobility often manifest before measurable cognitive symptoms appear, offering clinicians new indicators for early detection.
Motor Performance Links Across 17 Studies
Researchers conducted a systematic review examining studies published between 2018 and 2026 to investigate the connection between neurodegenerative biomarkers and motor outcomes. The review gathered data from 17 eligible studies involving participants across the Alzheimer’s disease continuum, including individuals with mild cognitive impairment, motoric cognitive risk syndrome, cognitive complaints, and cognitively unimpaired older adults at risk of dementia.
The analysis identified several biological indicators showing consistent relationships with physical decline. Lower cerebrospinal fluid amyloid beta 42 concentrations were tied directly to slower gait speed, poorer balance, impaired mobility, and worse performance on mobility assessments. Meanwhile, tau-related biomarkers—including phosphorylated tau, phosphorylated tau 181, phosphorylated tau 217, and total tau—linked to mobility impairment, reduced physical function, and poorer dual-task performance.
Neurofilament Light and Tau Biomarkers Signal Decline
Among all examined indicators, neurofilament light chain demonstrated particularly consistent associations with physical decline. Higher concentrations linked to poorer performance on the Short Physical Performance Battery, slower gait speed, and lower grip strength across multiple studies. Longitudinal evidence further suggested that elevated phosphorylated tau 181, phosphorylated tau 217, and neurofilament light chain levels associated with accelerated motor decline and combined cognitive-motor deterioration over time. These observations appeared not only in people with established cognitive impairment but also in cognitively unimpaired older adults.
Inflammatory biomarkers, by contrast, showed far less consistent relationships with motor outcomes. Only one study identified a significant association between serum interleukin 8 and mobility performance, while other inflammatory markers demonstrated limited evidence of a connection with motor function.
Machine Learning Predicts Parkinson’s Cognitive Decline
Investigators incorporated repeated measurements of total tau and neurofilament light chain taken across three time points. Using descriptive statistics to capture minimum, maximum, mean, and standard deviation, the researchers summarized longitudinal biomarker dynamics to feed machine learning models.
Integrating these longitudinal summaries significantly enhanced cognitive decline prediction compared to baseline-only evaluations. An extreme gradient boosting model with features selected via recursive feature elimination achieved the best performance with an area under the curve of 0.806, marking a substantial improvement over the baseline-only model score of 0.560. Across algorithms, dynamic changes in total tau and diastolic blood pressure emerged as the most consistent predictors, providing actionable insights for patient care and therapeutic trials.
Character-Level Speech Analysis for Dementia Screening
In parallel with biomarker evaluations, artificial intelligence researchers have turned to fine-grained linguistic analysis to catch early cognitive changes. Traditional clinical screening tools like the Mini-Mental State Examination and the Montreal Cognitive Assessment remain standard practice, but researchers note they are often limited by subjectivity, educational bias, and a tendency to identify dementia only at later stages.
By applying recurrence quantification analysis to character-encoded speech, investigators generated recurrence plots that revealed temporal dynamics in speech patterns such as pauses, repetitions, and hesitations.
Our approach uncovers meaningful character-level signatures and enables visualization of subtle cognitive disruptions through recurrence plots,
researchers noted in the study, suggesting that character-level temporal patterns offer a promising new direction for digital biomarker discovery that complements traditional word-level analyses.
Clinical Implications for Precision Health and Monitoring
The convergence of motor performance data, serial blood biomarker dynamics, and digital linguistic profiling points toward a more integrated framework for early disease detection. Blood-based assays offer minimal invasiveness compared to cerebrospinal fluid collection, and growing reliability with technologies like Simoa supports using serial plasma total tau as a prognostic tool for longitudinal monitoring.
Patients exhibiting rising or fluctuating biomarker levels could become prime candidates for further clinical assessment or early intervention. Meanwhile, recognizing that motor changes often precede measurable cognitive decline underscores the necessity of combining physical performance assessments with biomarker evaluation to track neurodegeneration across aging populations.
