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Clinical Manifestations.

Natasha Clarke1,2, Christophe Bedetti1, Pierre-Briac Metayer1

  • 1Université de Montréal, Montréal, QC, Canada.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
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Summary

Sex differences impact Alzheimer's disease classification. Connected speech analysis shows linguistic markers are more sensitive in women, suggesting sex-stratified approaches are crucial for accurate diagnosis and treatment.

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Gerontology

Background:

  • Connected speech (CS) alterations are observed in Alzheimer's disease (AD) and may serve as early markers for cognitive impairment.
  • Machine learning models show promise in classifying individuals across the AD spectrum using CS features.
  • Subtle sex-based linguistic differences may affect symptom presentation and classification accuracy.

Purpose of the Study:

  • To investigate the impact of sex on connected speech features and classification performance across the Alzheimer's disease spectrum.
  • To determine if sex-stratified machine learning models improve diagnostic accuracy for various stages of cognitive decline.

Main Methods:

  • Analysis of Cookie Theft scene descriptions from 751 participants in the CCNA COMPASS-ND cohort.
  • Extraction of 40 lexical, semantic, and syntactic CS features using a Python pipeline.
  • Training of logistic regression models for classification of AD, vascular-AD, MCI, vascular-MCI, and SCI versus cognitively unimpaired (CU) participants, performed separately for men and women.

Main Results:

  • Women achieved higher classification accuracy (AUC) than men in subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and vascular-AD (v-AD), with MCI remaining significant after correction.
  • Classification performance was above chance for women but below chance for men in SCI and MCI.
  • Analysis of v-AD revealed distinct speech patterns between sexes: men with v-AD showed fluent but less detailed speech with lexical access difficulties, while women exhibited non-fluent, repetitive speech with more filled pauses.

Conclusions:

  • Sex-stratified classification models demonstrate differential performance, indicating the need for sex-specific approaches in AD research and clinical practice.
  • Linguistic markers derived from connected speech may be more sensitive in detecting cognitive decline in women along the AD spectrum.
  • The findings underscore the importance of sex-stratified analyses for accurate diagnosis and potential therapeutic interventions in Alzheimer's disease.