Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia

the Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

Abstract

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge-based cut-offs for each component of ATN and compared it to two data-driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data-driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia-like and normal groups. Both data-driven classification methods performed better than the empirical cut-offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data-driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

Original languageEnglish (US)
Pages (from-to)1855-1867
Number of pages13
JournalAlzheimer's and Dementia
Volume17
Issue number11
DOIs
StatePublished - Nov 2021

Keywords

  • Alzheimer's disease
  • amyloid
  • biomarker profile
  • machine learning
  • mild cognitive impairment
  • neurodegeneration
  • predictive analytics
  • tau

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

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