Predicting progression to Alzheimer's disease dementia using cognitive measures

Area of Science:

• Neurology
• Cognitive Science
• Geriatrics

Background:

• Cognitive measures prognostic in dementia research may have clinical utility in memory clinics.
• Identifying predictive cognitive measures is crucial for early Alzheimer's disease (AD) dementia detection, especially when biomarkers are unavailable.

Purpose of the Study:

• To identify cognitive measures with the highest predictive power for future Alzheimer's disease (AD) dementia in a memory clinic setting.
• To evaluate the utility of cognitive measures in predicting AD dementia progression in a real-world clinical environment.

Main Methods:

• Utilized 18 years of routine Memory Clinic data from 328 patients with subjective cognitive decline or mild cognitive impairment.
• Compared statistical models using baseline cognitive test scores versus models including both baseline and 1-year follow-up cognitive change scores.
• Assessed prediction of conversion to Alzheimer's disease (AD) dementia.

Main Results:

• Baseline scores on global cognitive function (Mini-mental state examination, Cambridge Cognitive Examination-Revised), verbal episodic memory, and psychomotor speed were the strongest predictors of AD dementia.
• Including cognitive change scores from 1-year follow-up significantly improved the accuracy of AD dementia prediction compared to baseline scores alone.
• The most effective cognitive predictors in a clinical setting align with those identified in research cohorts.

Conclusions:

• Cognitive measures effective in research cohorts are also valuable in memory clinic settings for predicting Alzheimer's disease (AD) dementia.
• Incorporating changes in cognitive function over time enhances the accuracy of predicting the onset of AD dementia.
• Routine cognitive assessments in memory clinics can aid in the early identification of individuals at risk for AD dementia.