journal articles
SPEECH-BASED DIGITAL BIOMARKERS FOR EARLY ETIOLOGICAL STRATIFICATION OF ALZHEIMER’S DISEASE AND FRONTOTEMPORAL DEGENERATION: A BIOMARKER-CONFIRMED PROSPECTIVE STUDY
Eloïse Da Cunha, Valeria Manera, Frédéric Chorin, Justine Lemaire, Alexandra Plonka, Aurélie Mouton, Raphaël Zory, Auriane Gros
BACKGROUND: Early differentiation between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is a prerequisite for secondary prevention and targeted trial enrollment, yet remains challenging at disease onset. We investigated whether automated speech analysis could serve as a digital biomarker for early etiological stratification across clinically heterogeneous presentations.
METHODS: In this prospective biomarker-confirmed prognostic study, 172 participants (108 patients with biomarker-confirmed AD or FTLD and 64 controls) completed a standardized speech protocol at initial clinical assessment. Acoustic, temporal, and phonatory features were automatically extracted. Machine learning models and a stacking ensemble were trained using stratified, repeated 5-fold cross-validation to discriminate between AD and FTLD pathology, with exploratory analysis extending to atypical and rare phenotypes crossed with physiopathology, including primary progressive aphasia (PPA) variants.
RESULTS: Speech-based models achieved high sensitivity and specificity in distinguishing physiopathology independently (mean area under the curve (AUC)=0.986) and crossed phenotype and physiopathological diagnostic association (mean AUC=0.966).The ensemble identified 82% of cases with clinicopathological discordance. Interpretability analyses revealed distinct speech signatures: AD was associated with global speech slowing and phonatory instability, while FTLD was characterized by reduced verbal output and acoustic hypo-expressivity.
CONCLUSIONS: Automated speech analysis provides a promising non-invasive digital biomarker for the early etiological stratification of AD and FTLD, including atypical phenotypes, with high accuracy in a monocentric biomarker-confirmed cohort. These findings support the feasibility of speech-based etiological stratification and its potential to complement existing biomarker frameworks, particularly in cases of clinicopathological discordance. External validation is required before clinical deployment can be considered.
CITATION:
Eloïse Da Cunha ; Valeria Manera ; Frédéric Chorin ; Justine Lemaire ; Alexandra Plonka ; Aurélie Mouton ; Raphaël Zory ; Auriane Gros (2025): Speech-based digital biomarkers for early etiological stratification of Alzheimer’s disease and frontotemporal degeneration: a biomarker-confirmed prospective study. The Journal of Prevention of Alzheimer’s Disease (JPAD). https://doi.org/10.1016/j.tjpad.2026.100573