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Schindler SE, Li Y, Buckles VD, et al. Predicting Symptom Onset in Sporadic Alzheimer Disease With Amyloid PET. Neurology. 2021;97(18):e1823-e1834doi: 10.1212/WNL.0000000000012775.
Schindler, S. E., Li, Y., Buckles, V. D., Gordon, B. A., Benzinger, T. L. S., Wang, G., Coble, D., Klunk, W. E., Fagan, A. M., Holtzman, D. M., Bateman, R. J., Morris, J. C., & Xiong, C. (2021). Predicting Symptom Onset in Sporadic Alzheimer Disease With Amyloid PET. Neurology, 97(18), e1823-e1834. https://doi.org/10.1212/WNL.0000000000012775
Schindler, Suzanne E, et al. "Predicting Symptom Onset in Sporadic Alzheimer Disease With Amyloid PET." Neurology vol. 97,18 (2021): e1823-e1834. doi: https://doi.org/10.1212/WNL.0000000000012775
Schindler SE, Li Y, Buckles VD, Gordon BA, Benzinger TLS, Wang G, Coble D, Klunk WE, Fagan AM, Holtzman DM, Bateman RJ, Morris JC, Xiong C. Predicting Symptom Onset in Sporadic Alzheimer Disease With Amyloid PET. Neurology. 2021 Nov 02;97(18):e1823-e1834. doi: 10.1212/WNL.0000000000012775. Epub 2021 Sep 09. PMID: 34504028; PMCID: PMC8610624.
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Neurology. 2021 Nov 02;97(18):e1823-e1834. doi: 10.1212/WNL.0000000000012775. Epub 2021 Sep 09.

Predicting Symptom Onset in Sporadic Alzheimer Disease With Amyloid PET.

Neurology

Suzanne E Schindler, Yan Li, Virginia D Buckles, Brian A Gordon, Tammie L S Benzinger, Guoqiao Wang, Dean Coble, William E Klunk, Anne M Fagan, David M Holtzman, Randall J Bateman, John C Morris, Chengjie Xiong

Affiliations

  1. From the Department of Neurology (S.E.S., Y.L., V.D.B., A.M.F., D.M.H., R.J.B., J.C.M.), Knight Alzheimer Disease Research Center (S.E.S., V.D.B., B.A.G., T.L.S.B., G.W., D.C., A.M.F., D.M.H., R.J.B., J.C.M., C.X.), Division of Biostatistics (Y.L., G.W., D.C., C.X.), Mallinckrodt Institute of Radiology (B.A.G., T.L.S.B.), and Hope Center for Neurological Disorders (A.M.F., D.M.H., R.J.B.), Washington University School of Medicine, St. Louis, MO; and Department of Neurology and Psychiatry (W.E.K.), University of Pittsburgh, PA. [email protected].
  2. From the Department of Neurology (S.E.S., Y.L., V.D.B., A.M.F., D.M.H., R.J.B., J.C.M.), Knight Alzheimer Disease Research Center (S.E.S., V.D.B., B.A.G., T.L.S.B., G.W., D.C., A.M.F., D.M.H., R.J.B., J.C.M., C.X.), Division of Biostatistics (Y.L., G.W., D.C., C.X.), Mallinckrodt Institute of Radiology (B.A.G., T.L.S.B.), and Hope Center for Neurological Disorders (A.M.F., D.M.H., R.J.B.), Washington University School of Medicine, St. Louis, MO; and Department of Neurology and Psychiatry (W.E.K.), University of Pittsburgh, PA.

PMID: 34504028 PMCID: PMC8610624 DOI: 10.1212/WNL.0000000000012775

Abstract

BACKGROUND AND OBJECTIVES: To predict when cognitively normal individuals with brain amyloidosis will develop symptoms of Alzheimer disease (AD).

METHODS: Brain amyloid burden was measured by amyloid PET with Pittsburgh compound B. The mean cortical standardized uptake value ratio (SUVR) was transformed into a timescale with the use of longitudinal data.

RESULTS: Amyloid accumulation was evaluated in 236 individuals who underwent >1 amyloid PET scan. The average age was 66.5 ± 9.2 years, and 12 individuals (5%) had cognitive impairment at their baseline amyloid PET scan. A tipping point in amyloid accumulation was identified at a low level of amyloid burden (SUVR 1.2), after which nearly all individuals accumulated amyloid at a relatively consistent rate until reaching a high level of amyloid burden (SUVR 3.0). The average time between levels of amyloid burden was used to estimate the age at which an individual reached SUVR 1.2. Longitudinal clinical diagnoses for 180 individuals were aligned by the estimated age at SUVR 1.2. In the 22 individuals who progressed from cognitively normal to a typical AD dementia syndrome, the estimated age at which an individual reached SUVR 1.2 predicted the age at symptom onset (

CONCLUSION: The age at symptom onset in sporadic AD is strongly correlated with the age at which an individual reaches a tipping point in amyloid accumulation.

© 2021 American Academy of Neurology.

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