Biomarkers.

Area of Science:

• Neurology
• Cognitive Science
• Biomarker Discovery

Background:

• Early detection of amyloid positivity is crucial for timely Alzheimer's disease (AD) and Mild Cognitive Impairment due to AD (MCI-AD) diagnosis.
• Current methods for detecting amyloid positivity often rely on expensive biomarkers, leading to diagnostic delays and missed treatment or clinical trial opportunities.

Purpose of the Study:

• To develop and validate a rapid, cost-effective screening paradigm for MCI-AD and AD using cognitive assessment.
• To evaluate the ability of Cognivue Clarity and the derived Cognivue Amyloid Risk Measure (CARM) to identify individuals with amyloid positivity.

Main Methods:

• 887 participants from the Bio-Hermes Study underwent Cognivue Clarity testing, amyloid PET scans, and pTau217 measurement.
• A machine learning approach was used to derive the 4-level CARM from Cognivue Clarity data.
• A 2x2 screening paradigm combining Cognivue Clarity global scores and CARM was created.

Main Results:

• Cognivue Clarity thresholds and CARM levels significantly correlated with amyloid PET SUVR and plasma pTau217 levels (p < .001).
• Amyloid positivity increased across CARM thresholds, with the majority of MCI-AD and AD dementia cases falling into the Impaired/High-risk category.
• True Controls were predominantly in the Not-Impaired/Low-risk category, while non-AD cases were distributed across all categories.

Conclusions:

• Cognivue Clarity, a 10-minute computerized test, can detect cognitive impairment and, with CARM, predict amyloid positivity.
• A staged screening approach using Cognivue Clarity and CARM, potentially followed by plasma pTau217, offers a cost-effective strategy for early AD detection.
• This approach enhances the identification of individuals for early AD treatment and clinical trial enrollment, bypassing the need for costly PET scans.