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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

Mild cognitive impairment due to Alzheimer disease in the community

Ronald C Petersen1, Paul Aisen, Bradley F Boeve

  • 1Department of Neurology, Mayo Clinic and Foundation, Rochester, MN; Alzheimer's Disease Research Center, Mayo Clinic and Foundation, Rochester, MN; Department of Health Sciences Research, Mayo Clinic and Foundation, Rochester, MN.

Annals of Neurology
|May 21, 2013
PubMed
Abstract

Insights

The National Institute on Aging-Alzheimer's Association (NIA-AA) criteria for mild cognitive impairment (MCI) are applicable in community and clinical settings. However, conflicting biomarkers in some individuals require further investigation for Alzheimer's disease (AD) pathways.

Area of Science:

  • Neurology
  • Biomarkers
  • Neurodegenerative Diseases

Background:

  • The National Institute on Aging-Alzheimer's Association (NIA-AA) proposed new criteria for mild cognitive impairment (MCI) due to Alzheimer's disease (AD).
  • These criteria combine clinical features with biomarker measures.
  • The performance of these criteria in community settings remains unknown.

Purpose of the Study:

  • To assess the applicability of the NIA-AA criteria for MCI due to AD in both community and clinical trial settings.
  • To evaluate the outcomes of MCI subjects based on these criteria.

Main Methods:

  • Comparison of 154 MCI subjects from the population-based Mayo Clinic Study of Aging (MCSA) with 58 amnestic MCI subjects from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1).
  • Assessment of biomarker status (amyloid deposition, neurodegeneration) in relation to the proposed NIA-AA criteria.
  • Evaluation of subject progression and outcomes.

Main Results:

  • The NIA-AA criteria were applicable to most MCI subjects across both MCSA and ADNI-1 cohorts.
  • A significant proportion of subjects (14-16%) were biomarker-negative.
  • Many subjects (29% MCSA, 17% ADNI-1) showed neurodegeneration without amyloid deposition, suggesting potential non-AD pathways. Neurodegeneration was a key predictor of progression.

Conclusions:

  • The NIA-AA criteria for MCI due to AD demonstrate applicability in both community-based studies and clinical trials.
  • A notable percentage of MCI subjects exhibit biomarker profiles inconsistent with the proposed AD model, highlighting the need for further research into these discrepancies.
  • Conflicting biomarker results in a sizeable proportion of subjects warrant deeper exploration to refine understanding of AD progression.

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