Defining imaging biomarker cut points for brain aging and Alzheimer's disease

Clifford R. Jack, Heather J. Wiste, Stephen D. Weigand, Terry M. Therneau, Val J. Lowe, David S. Knopman, Jeffrey L. Gunter, Matthew L. Senjem, David T. Jones, Kejal Kantarci, Mary M. Machulda, Michelle M. Mielke, Rosebud O. Roberts, Prashanthi Vemuri, Denise A. Reyes, Ronald C. Petersen

Research output: Contribution to journalArticle

165 Scopus citations

Abstract

Introduction Our goal was to develop cut points for amyloid positron emission tomography (PET), tau PET, flouro-deoxyglucose (FDG) PET, and MRI cortical thickness. Methods We examined five methods for determining cut points. Results The reliable worsening method produced a cut point only for amyloid PET. The specificity, sensitivity, and accuracy of cognitively impaired versus young clinically normal (CN) methods labeled the most people abnormal and all gave similar cut points for tau PET, FDG PET, and cortical thickness. Cut points defined using the accuracy of cognitively impaired versus age-matched CN method labeled fewer people abnormal. Discussion In the future, we will use a single cut point for amyloid PET (standardized uptake value ratio, 1.42; centiloid, 19) based on the reliable worsening cut point method. We will base lenient cut points for tau PET, FDG PET, and cortical thickness on the accuracy of cognitively impaired versus young CN method and base conservative cut points on the accuracy of cognitively impaired versus age-matched CN method.

Original languageEnglish (US)
Pages (from-to)205-216
Number of pages12
JournalAlzheimer's and Dementia
Volume13
Issue number3
DOIs
StatePublished - Mar 1 2017

Keywords

  • Alzheimer's MRI
  • Alzheimer's biomarkers
  • Alzheimer's disease
  • Alzheimer's imaging
  • Amyloid PET
  • FDG PET
  • Quantitative imaging
  • Tau PET

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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