Literature DB >> 31256127

Quantitative Brain Amyloid Measures Predict Time-to-Progression from Amnestic Mild Cognitive Impairment to Alzheimer's Disease.

Sungmin Jun1, Heeyoung Kim1, Bum Soo Kim1, Bong-Goo Yoo2, Won Gu Lee2.   

Abstract

BACKGROUND: This study was designed to investigate factors that predict progression from amnestic mild cognitive impairment (aMCI) to probable Alzheimer's disease (AD).
OBJECTIVE: We studied the usefulness of quantitative assessment of amyloid burden measured by Florbetapir PET scan.
METHODS: The study cohort consisted of aMCI participants older than 65 and those with available Florbetapir PET scan at diagnosis from the ADNI database (http://adni.loni.usc.edu). To assess the prognostic impact of amyloid burden, a staging system based on the global SUVr of the PET scan was applied. We defined the stages as: stage I, negative amyloid scan; stage II, positive amyloid in 1st tertile; stage III, positive amyloid in 2nd tertile; and stage IV, positive amyloid in 3rd tertile.
RESULTS: Of 250 eligible aMCI subjects (age 74.1±5.4, female n = 105), 71 (28.4%) were diagnosed with probable AD within 3 years. Higher amyloid stages showed faster cognitive decline by Kaplan-Meier analysis. In multivariate Cox analysis, with stage I as a reference, the hazard ratio (HR) increased as the stage increased: stage II (HR, 4.509; p = 0.015), stage III (HR, 7.616; p = 0.001), and stage IV (HR, 9.421; p < 0.001). Along with amyloid stage, ApoE ɛ4 (HR, 1.943; p = 0.031), score of CDR-SB (HR, 1.845; p < 0.001) and ADAS 11 (HR, 1.144; p < 0.001), and hippocampal volume (HR, 0.002; p = 0.005) were also identified as predictors of dementia progression in aMCI subjects.
CONCLUSIONS: Large amyloid burden measured from amyloid PET scan could be a predictor of faster cognitive decline in aMCI patients.

Entities:  

Keywords:  Alzheimer’s disease; Florbetapir; amyloid; mild cognitive impairment; positron emission tomography

Year:  2019        PMID: 31256127     DOI: 10.3233/JAD-190070

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  2 in total

1.  Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes.

Authors:  Jun Pyo Kim; Jeonghun Kim; Yeshin Kim; Seung Hwan Moon; Yu Hyun Park; Sole Yoo; Hyemin Jang; Hee Jin Kim; Duk L Na; Sang Won Seo; Joon-Kyung Seong
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12-28       Impact factor: 9.236

2.  Unique regional patterns of amyloid burden predict progression to prodromal and clinical stages of Alzheimer's disease.

Authors:  Julia Pfeil; Merle C Hoenig; Elena Doering; Thilo van Eimeren; Alexander Drzezga; Gérard N Bischof
Journal:  Neurobiol Aging       Date:  2021-06-21       Impact factor: 4.673

  2 in total

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