Development of language composites for enhanced sensitivity to multiple plasma biomarkers

Area of Science:

• Neuroscience
• Linguistics
• Biomarker Research

Background:

• Language deficits are early indicators of Alzheimer's disease (AD), but current assessments lack connected speech analysis, hindering early detection and personalized interventions.
• Existing composite measures do not fully capture real-world communication, limiting their utility in identifying subtle cognitive changes associated with AD.

Purpose of the Study:

• To develop and validate an empirical language composite integrating connected speech for sensitive detection of early AD.
• To compare the longitudinal sensitivity of different language composites to plasma biomarkers of neurodegeneration.

Main Methods:

• Analysis of cohort data from 824 non-demented adults.
• Construction of three language composites: theoretical, connected speech-specific, and empirical.
• Longitudinal comparison of composite sensitivity to plasma p-tau217, NfL, and GFAP biomarkers.

Main Results:

• The empirical language composite demonstrated superior and consistent sensitivity to all three plasma biomarkers (p-tau217, NfL, GFAP).
• Specific language decline patterns emerged: proper name recall correlated with p-tau217, while animal fluency correlated with NfL and GFAP.
• The empirical composite, including measures like words/minute and filled pauses, effectively captured holistic cognitive-communicative decline.

Conclusions:

• An empirical language composite offers a sensitive tool for detecting and monitoring cognitive-communicative decline in early AD.
• Individual language measures may reflect biomarker-specific profiles, suggesting a nuanced relationship between language and AD pathology.
• Findings support the clinical utility of plasma biomarkers and language-specific composites for early AD detection and tracking disease progression.