Kinematic Correlates of Early Speech Motor Changes in Cognitively Intact APOE-ε4 Carriers: A Preliminary Study Using...

Alzheimer's disease (AD) is the most prevalent form of dementia and a major public health challenge. In the absence of a cure, accurate and innovative early diagnostic methods are essential for proactive life and healthcare planning. Speech metrics have shown promising potential for identifying individuals with mild cognitive impairment (MCI) and AD, prompting investigation into whether speech motor features can detect elevated risk even prior to cognitive decline. This study examined whether speech kinematic features measured during a color-word interference task could distinguish cognitively normal APOE-ε4 carriers (E4+) from non-carriers (E4-). Lip movement properties were extracted across pre-, during-, and post-interference sentence segments. Descriptive statistics and independent t-tests were conducted to examine group-level trends in lip movement duration, average speed, and range. Although no group differences reached statistical significance, several features showed moderate effect sizes, suggesting potential neuromotor differences. A support vector machine model with a degree-2 polynomial kernel achieved 87.5% accuracy in classifying APOE-ε4 status using three features: lip movement duration prior to interference, average lip speed during interference, and the change in lip movement range from pre- to during-interference segments. These features reflect subtle differences between the two groups in baseline motor planning, susceptibility to cognitive-motor interference, and articulatory adaptability. The model's high precision (88.90%), sensitivity (88.90%), and specificity (85.70%) underscore the potential clinical utility of speech kinematics for early risk identification. These findings support the use of non-invasive, low-burden speech analysis as a promising digital biomarker for AD risk in cognitively intact individuals and highlight its potential for scalable, remote screening and longitudinal monitoring in diverse populations.