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SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline

Toledo, Jon B ; Rashid, Tanweer ; Liu, Hangfan ; Launer, Lenore ; Shaw, Leslie M ; Heckbert, Susan R ; Weiner, Michael ; Seshadri, Sudha ; Habes, Mohamad Kasuga, Kensaku

PloS one, 2022-11, Vol.17 (11), p.e0276392-e0276392 [Periódico revisado por pares]

United States: Public Library of Science

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  • Título:
    SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline
  • Autor: Toledo, Jon B ; Rashid, Tanweer ; Liu, Hangfan ; Launer, Lenore ; Shaw, Leslie M ; Heckbert, Susan R ; Weiner, Michael ; Seshadri, Sudha ; Habes, Mohamad
  • Kasuga, Kensaku
  • Assuntos: Aged ; Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - pathology ; Alzheimer's disease ; Amyloid beta-Peptides ; Biomarkers ; Brain ; Care and treatment ; Cerebrospinal fluid ; Cognitive ability ; Cognitive Dysfunction - diagnostic imaging ; Cognitive Dysfunction - pathology ; Cross-Sectional Studies ; Dementia ; Diagnosis ; Humans ; Learning algorithms ; Machine Learning ; Magnetic resonance imaging ; Medical imaging ; Methods ; Multivariate analysis ; Neurodegenerative diseases ; Neuroimaging ; Neuropsychology ; PET imaging ; Positron emission ; Positron emission tomography ; Positron-Emission Tomography - methods ; Regression analysis ; Regression models ; Support vector machines ; Tau protein ; tau Proteins ; Tracers
  • É parte de: PloS one, 2022-11, Vol.17 (11), p.e0276392-e0276392
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    Competing Interests: Dr. Shaw provides quality control oversight for the Roche Electrosys immunoassay platform as part of the ADNI-3 study. Dr. Weiner has served on the Scientific Advisory Boards for Alzheon, Inc., Accera, Merck, Nestle (Nolan), PCORI (PPRN), Eli Lilly, Delfino Logic Ltd. (for Merck), Dolby Ventures, Brain Health Registry, and ADNI. He served on the editorial boards for Alzheimer’s & Dementia and MRI. He has provided consulting and/or acted as a speaker/lecturer to Synarc, Pfizer, Accera, Inc., Alzheimer’s Drug Discovery Foundation (ADDF), Merck, BioClinica, Eli Lilly, Howard University, Guidepoint, Denali Therapeutics, Nestle/Nestec, GLG Research, Atheneum Partners, BIONEST Partners, American Academy of Neurology (AAN), and Society for Nuclear Medicine and Molecular Imaging (SNMMI). This does not alter our adherence to PLOS ONE policies on sharing data and materials. Other authors report no competing interests.
    Membership of the Alzheimer’s Disease Neuroimaging Initiative Group is listed in the Acknowledgments.
  • Descrição: Recently, tau PET tracers have shown strong associations with clinical outcomes in individuals with cognitive impairment and cognitively unremarkable elderly individuals. flortaucipir PET scans to measure tau deposition in multiple brain areas as the disease progresses. This information needs to be summarized to evaluate disease severity and predict disease progression. We, therefore, sought to develop a machine learning-derived index, SPARE-Tau, which successfully detects pathology in the earliest disease stages and accurately predicts progression compared to a priori-based region of interest approaches (ROI). 587 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort had flortaucipir scans, structural MRI scans, and an Aβ biomarker test (CSF or florbetapir PET) performed on the same visit. We derived the SPARE-Tau index in a subset of 367 participants. We evaluated associations with clinical measures for CSF p-tau, SPARE-MRI, and flortaucipir PET indices (SPARE-Tau, meta-temporal, and average Braak ROIs). Bootstrapped multivariate adaptive regression splines linear regression analyzed the association between the biomarkers and baseline ADAS-Cog13 scores. Bootstrapped multivariate linear regression models evaluated associations with clinical diagnosis. Cox-hazards and mixed-effects models investigated clinical progression and longitudinal ADAS-Cog13 changes. The Aβ positive cognitively unremarkable participants, not included in the SPARE-Tau training, served as an independent validation group. Compared to CSF p-tau, meta-temporal, and averaged Braak tau PET ROIs, SPARE-Tau showed the strongest association with baseline ADAS-cog13 scores and diagnosis. SPARE-Tau also presented the strongest association with clinical progression in cognitively unremarkable participants and longitudinal ADAS-Cog13 changes. Results were confirmed in the Aβ+ cognitively unremarkable hold-out sample participants. CSF p-tau showed the weakest cross-sectional associations and longitudinal prediction. Flortaucipir indices showed the strongest clinical association among the studied biomarkers (flortaucipir, florbetapir, structural MRI, and CSF p-tau) and were predictive in the preclinical disease stages. Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker. The combination of different biomarker modalities better predicted cognitive performance.
  • Editor: United States: Public Library of Science
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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