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Combining early markers strongly predicts conversion from mild cognitive impairment to Alzheimer's disease

Davangere P Devanand1, Xinhua Liu, Matthias H Tabert

  • 1Division of Geriatric Psychiatry, New York State Psychiatric Institute, College of Physicians and Surgeons, Columbia University, New York, New York 10032, USA. dpd3@columbia.edu

Biological Psychiatry
|August 30, 2008
PubMed
Abstract

Insights

Predicting Alzheimer's Disease (AD) conversion from mild cognitive impairment (MCI) is improved by combining five key markers. This novel combination significantly outperforms traditional methods like age and Mini-Mental State Examination (MMSE) for early AD detection.

Area of Science:

  • Neuroscience
  • Gerontology
  • Biomarkers

Background:

  • Predicting conversion from mild cognitive impairment (MCI) to Alzheimer's Disease (AD) is crucial for timely intervention.
  • The efficacy of combining early predictive markers for MCI to AD conversion remains under investigation.

Purpose of the Study:

  • To evaluate the utility of combining multiple early markers for predicting conversion from MCI to AD.
  • To compare the predictive accuracy of a multi-marker combination against traditional predictors.

Main Methods:

  • A cohort of 148 MCI outpatients was followed for 3 years.
  • Hypothesized predictors included cognitive tests, informant reports, APOE genotype, olfactory function, and MRI volumes (hippocampal, entorhinal cortex).
  • Logistic regression was used to identify significant predictors of AD conversion.

Main Results:

  • A five-predictor model (Pfeffer Functional Activities Questionnaire, University of Pennsylvania Smell Identification Test, Selective Reminding Test immediate recall, hippocampal volume, entorhinal cortex volume) achieved 85.2% sensitivity for AD conversion.
  • This multi-marker model demonstrated significantly higher predictive accuracy (AUC=.948) compared to age and MMSE (AUC=.821).
  • A combination of clinical measures (SRT, FAQ, UPSIT) also showed strong predictive accuracy (81.3% sensitivity).

Conclusions:

  • A five-predictor combination effectively predicts conversion from MCI to AD, significantly outperforming age and MMSE.
  • The findings suggest potential utility for early AD detection if independently replicated.
  • Combining clinically administered measures offers a robust approach to predicting AD conversion.

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