高解像度テレマティクスと感覚運動プロファイリングを活用した前臨床アルツハイマー病の診断への推進

Early detection of preclinical Alzheimer's disease (AD) could expand preventative care. Current biomarkers are costly, invasive, or lack generalizability. Driving and sensorimotor performance may reveal prodromal changes.

We tested whether features from high-frequency driving trips detect preclinical AD and whether demographic, genetic, or sensorimotor data improve accuracy.

Drivers aged ≥ 65 (n = 254) from Driving Real-World In-Vehicle Evaluation System (DRIVES) completed cerebrospinal fluid Aβ₄₂/Aβ₄₀ and amyloid Positron emission tomography (PET) to label amyloid positive (preclinical AD) or negative. A GPS datalogger recorded location (1 Hz) and accelerometer/gyroscope (20 Hz) data between June 2022 and January 2024. Eleven driving features (e.g., average speed, jerk, idle time, turns) were extracted per trip. Vision, hearing, olfaction, gait, and grip strength were assessed. TabNet models classified amyloid status using (1) driving only, (2) driving plus age and APOE ε4, and (3) driving plus age, APOE ε4, sex, and education. LightGBM models evaluated sensorimotor features. Performance was measured on a 20% held-out test set (AUC, accuracy, precision, recall, F1).

The top-performing model (driving, age, APOE ε4, sex, education) achieved an AUC of 0.84, accuracy of 0.85, and F1 score of 0.85. Key predictors were idle time, turns, and average jerk. Sensorimotor models performed modestly (AUCs of 0.66 [sensory alone] and 0.67 [sensory and sociodemographic]), with grip strength and word-in-noise scores as the top contributors.

A high-frequency trip's driving telemetry, combined with age and APOE ε4 status, discriminates preclinical AD, outperforming multisensory measures. Driving offers a scalable, digital biomarker to complement conventional testing. Monitoring may enable population-level screening for older adults at risk.