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Related Experiment Video

Updated: Feb 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting Progression to Mild Cognitive Impairment.

Ronald C Petersen^1,2, Emily S Lundt², Terry M Therneau²

¹Department of Neurology, Mayo Clinic, Rochester, MN.

Annals of Neurology

|December 7, 2018

Summary

This summary is machine-generated.

Biomarker abnormalities increase Alzheimer disease risk. A 75-year-old with abnormal amyloid and cortical thinning biomarkers has a 20% chance of cognitive impairment by age 80, versus less than 10% with normal biomarkers.

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Area of Science:

Neurology
Biomarkers
Cognitive Impairment

Background:

Alzheimer disease prediction often focuses on group trends, lacking individual-level risk data.
Biomarkers show promise for Alzheimer disease prediction, but individual risk assessment is underexplored.

Purpose of the Study:

To estimate the absolute risk of cognitive impairment based on biomarker status at an individual level.
To analyze how age and specific biomarker abnormalities influence cognitive impairment risk.

Main Methods:

Utilized data from the population-based Mayo Clinic Study of Aging.
Categorized participants by biomarker groups (normal vs. abnormal amyloid and cortical thinning).
Calculated absolute risk of cognitive impairment by age 80 for different biomarker profiles.

Main Results:

Cognitive impairment risk escalates with age and the presence of any biomarker abnormality.
A 75-year-old with abnormal amyloid and cortical thinning biomarkers faces approximately 20% risk of impairment by age 80.
Individuals with normal biomarkers have less than a 10% risk by age 80.
Those with a single abnormal biomarker exhibited intermediate risk levels.

Conclusions:

Biomarker status is a significant predictor of individual cognitive impairment risk.
Combining age and biomarker information refines Alzheimer disease risk prediction.
This study provides crucial individual-level risk data for cognitive impairment.