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Landau SM, Lee J, Murphy A, Ward TJ, Harrison TM, Baker SL, DeCarli C, Harvey D, Tosun D, Weiner MW, Koeppe RA, Jagust WJ. Individuals with Alzheimer's disease and low tau burden: Characteristics and implications. Alzheimers Dement 2024; 20:2113-2127. [PMID: 38241084 PMCID: PMC10984443 DOI: 10.1002/alz.13609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 01/21/2024]
Abstract
INTRODUCTION Abnormal amyloid-beta (Aβ) and tau deposition define Alzheimer's Disease (AD), but non-elevated tau is relatively frequent in patients on the AD pathway. METHODS We examined characteristics and regional patterns of 397 Aβ+ unimpaired and impaired individuals with low tau (A+T-) in relation to their higher tau counterparts (A+T+). RESULTS Seventy-one percent of Aβ+ unimpaired and 42% of impaired Aβ+ individuals were categorized as A+T- based on global tau. In impaired individuals only, A+T- status was associated with older age, male sex, and greater cardiovascular risk. α-synuclein was linked to poorer cognition, particularly when tau was low. Tau burden was most frequently elevated in a common set of temporal regions regardless of T+/T- status. DISCUSSION Low tau is relatively common in patients on the AD pathway and is linked to comorbidities that contribute to impairment. These findings have implications for the selection of individuals for Aβ- and tau-modifying therapies.
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Affiliation(s)
- Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - JiaQie Lee
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Alice Murphy
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Tyler J. Ward
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Theresa M. Harrison
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Suzanne L. Baker
- Molecular Biophysics and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Charles DeCarli
- School of MedicineUniversity of California, DavisSacramentoCaliforniaUSA
| | - Danielle Harvey
- School of MedicineUniversity of California, DavisSacramentoCaliforniaUSA
| | - Duygu Tosun
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)Center for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of MedicineDepartment of Psychiatry and Behavioral SciencesDepartment of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Robert A. Koeppe
- Department of RadiologyUniversity of MichiganAnn ArborMichiganUSA
| | - William J. Jagust
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
- Molecular Biophysics and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
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Kang SH, Cheon BK, Kim JS, Jang H, Kim HJ, Park KW, Noh Y, Lee JS, Ye BS, Na DL, Lee H, Seo SW. Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2021; 80:143-157. [PMID: 33523003 DOI: 10.3233/jad-201092] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cost and safety issues. OBJECTIVE We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers. METHODS We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers). RESULTS Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity. CONCLUSION Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.
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Affiliation(s)
- Sung Hoon Kang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Bo Kyoung Cheon
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Ji-Sun Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Kyung Won Park
- Department of Neurology, Dong-A University Medical Center, Dong-A University College of Medicine, Busan, Korea
| | - Young Noh
- Department of Neurology, Gachon University Gil Medical Center, Incheon, Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Seoul, Korea
| | - Byoung Seok Ye
- Department of Neurology, Severance hospital, Yonsei University School of Medicine, Seoul, Korea
| | - Duk L Na
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyejoo Lee
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.,Samsung Alzheimer Research Center and Center for Clinical Epidemiology Medical Center, Seoul, Korea.,Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, Korea
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