Using the Montreal cognitive assessment to identify individuals with subtle cognitive decline.

Area of Science:

• Neurology
• Cognitive Science
• Gerontology

Background:

• Dementia diagnosis can be improved with early detection of subclinical neurodegenerative changes.
• Subtle cognitive decline and mild cognitive impairment are early indicators of potential neurological issues.
• The Montreal Cognitive Assessment (MoCA) can be utilized to identify individuals with subtle cognitive decline.

Purpose of the Study:

• To identify individuals with subtle cognitive decline using item-level performance on the MoCA.
• To group individuals based on cognitive performance patterns.
• To characterize these cognitive groups using demographic and clinical factors.

Main Methods:

• K-modes cluster analysis was applied to individual MoCA item data from the Alzheimer's Disease Neuroimaging Initiative.
• Clusters were validated using convergent neuropsychological tests.
• Multinomial logistic regression was used to compare and characterize the identified clusters.

Main Results:

• A three-cluster solution demonstrated 77.3% precision, identifying high-performing, memory-deficit, and compound-deficit (memory and executive function) groups.
• Significant demographic differences were observed: older age in compound deficits, more females in memory deficits, and fewer married individuals in compound deficits.
• Age at MoCA was not associated with increased odds of membership in the high-performing cluster.

Conclusions:

• Cluster analysis successfully identified three distinct cognitive profiles within the cognitively unimpaired population.
• Individuals in the compound deficits cluster were older and less often married, suggesting a combination of cognitive and clinical factors.
• Early identification of at-risk individuals using MoCA performance can facilitate timely interventions to slow cognitive decline.