Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures.
Background: The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort.Methods: We developed a multimodal biomarker classifier for predicting brain amyloidosis using co...
| Publicado en: | Neurology Vol. 84; no. 7; pp. 729 - 738 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
2/17/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103757236&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103757236 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283878 NRO jtl: Neurology issn: 00283878 maglogo: N pubinfo: dt: 2/17/2015 vid: 84 iid: 7 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 103757236 103757236 NLM25609767 2012907604 10.1212/WNL.0000000000001231 NLM25609767 PMC4336101 103757236 ppf: 729 ppct: 9 formats: tig: atl: Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures. aug: au: Apostolova, Liana G Hwang, Kristy S Avila, David Elashoff, David Kohannim, Omid Teng, Edmond Sokolow, Sophie Jack, Clifford R Jagust, William J Shaw, Leslie Trojanowski, John Q Weiner, Michael W Thompson, Paul M affil: From the Departments of Neurology (L.G.A., K.S.H., D.A., O.K., E.T., P.M.T.), Medicine Statistics Core (D.E.), and School of Nursing (S.S.), David Geffen School of Medicine at University of California, Los Angeles; Institute for Neuroinformatics (P.M.T.), Keck School of Medicine, University of Southern California, Los Angeles; Veterans Affairs Greater Los Angeles Healthcare System (E.T.); Department of Diagnostic Radiology (C.R.J.), Mayo Clinic, Rochester, MN; Department of Public Health and Neuroscience (W.J.J.), University of California, Berkeley; Department of Pathology and Laboratory Medicine (L.S., J.Q.T.), University of Pennsylvania School of Medicine, Philadelphia; Department of Radiology (M.W.W.), University of California, San Francisco; and Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA. lapostolova@mednet.ucla.edu. sug: subj: Amyloidosis Diagnosis Amyloidosis Pathology Brain Pathology Cognition Cognition Disorders Cognition Disorders Blood Aged Algorithms Alzheimer's Disease Alzheimer's Disease Blood Alzheimer's Disease Pathology Alzheimer's Disease Psychosocial Factors Amines Biological Markers Blood Biological Markers Cerebrospinal Fluid Clinical Assessment Tools Disease Progression Female Human Information Science Male Neuropsychological Tests Peptides Cerebrospinal Fluid Prospective Studies Resource Databases Sensitivity and Specificity Thiazoles Tomography, Emission-Computed Funding Source Aged: 65+ years Female Male ab: Background: The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort.Methods: We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1-42 (Aβ42) ≤ 192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥ 1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia.Results: The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%.Conclusions: Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future.Classification Of Evidence: This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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