Development of language composites for enhanced sensitivity to multiple plasma biomarkers.

BACKGROUND

While language deficits are among the earliest detectable signs of Alzheimer`s disease (AD), no existing composites integrate connected speech - essential for capturing real-world communication - limiting precise detection and personalized interventions.

METHODS

We analyzed cohort data from 824 non-demented adults and constructed three language composites: a theoretical composite based on expert knowledge, a connected speech-specific composite derived from the theoretical composite, and an empirical language composite optimized for predicting cognitive progression. We compared their longitudinal sensitivity to plasma tau phosphorylated at threonine 217 (p-tau217), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP).

RESULTS

The empirical language composite showed superior and consistent sensitivity (faster decline rate) with all three biomarkers. Proper name recall declined uniquely with increased p-tau217, while animal fluency declined selectively with higher NfL and GFAP.

DISCUSSION: An empirical language composite captures holistic cognitive-communicative decline, while individual measures may imply a biomarker-specific language profile. Our findings support the clinical utility of plasma biomarkers and language-specific composites as sensitive early indicators of disease progression.

HIGHLIGHTS: Empirical language composite had optimal sensitivity to plasma p-tau217, NfL, GFAP. It comprises words/minute, filled pauses, proper name recall, and animal fluency. It offers potential for language-dominant ADRD precise detection and monitoring.