Predicting MCI outcome with clinically available MRI and CSF biomarkers
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TLDR
In this paper, the ability of clinically available volumetric MRI (vMRI) and CSF biomarkers, alone or in combination with a quantitative learning measure, to predict conversion to Alzheimer disease (AD) in patients with mild cognitive impairment (MCI).Abstract:
Objective: To determine the ability of clinically available volumetric MRI (vMRI) and CSF biomarkers, alone or in combination with a quantitative learning measure, to predict conversion to Alzheimer disease (AD) in patients with mild cognitive impairment (MCI). Methods: We stratified 192 MCI participants into positive and negative risk groups on the basis of 1) degree of learning impairment on the Rey Auditory Verbal Learning Test; 2) medial temporal atrophy, quantified from Food and Drug Administration–approved software for automated vMRI analysis; and 3) CSF biomarker levels. We also stratified participants based on combinations of risk factors. We computed Cox proportional hazards models, controlling for age, to assess 3-year risk of converting to AD as a function of risk group and used Kaplan-Meier analyses to determine median survival times. Results: When risk factors were examined separately, individuals testing positive showed significantly higher risk of converting to AD than individuals testing negative (hazard ratios [HR] 1.8– 4.1). The joint presence of any 2 risk factors substantially increased risk, with the combination of greater learning impairment and increased atrophy associated with highest risk (HR 29.0): 85% of patients with both risk factors converted to AD within 3 years, vs 5% of those with neither. The presence of medial temporal atrophy was associated with shortest median dementia-free survival (15 months). Conclusions: Incorporating quantitative assessment of learning ability along with vMRI or CSF biomarkers in the clinical workup of MCI can provide critical information on risk of imminent conversion to AD. Neurology ® 2011;77:1619–1628read more
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Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers.
Jennifer L. Shaffer,Jeffrey R. Petrella,Forrest Sheldon,Kingshuk Roy Choudhury,Vince D. Calhoun,R. Edward Coleman,P. Murali Doraiswamy +6 more
TL;DR: Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing, and FDG PET appears to add the greatest prognostic information.
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Ronald C. Petersen,Paul S. Aisen,Bradley F. Boeve,Yonas E. Geda,Robert J. Ivnik,David S. Knopman,Michelle M. Mielke,Vernon S. Pankratz,Rosebud O. Roberts,Walter A. Rocca,Stephen D. Weigand,Michael W. Weiner,Heather J. Wiste,Clifford R. Jack +13 more
TL;DR: The newly proposed National Institute on Aging–Alzheimer's Association (NIA‐AA) criteria for mild cognitive impairment (MCI) due to Alzheimer's disease suggest a combination of clinical features and biomarker measures, but their performance in the community is not known.
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TL;DR: The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom‐based monitoring of all scanners could be used as a model for other multisite trials.
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Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.
Leslie M. Shaw,Hugo Vanderstichele,Malgorzata Knapik-Czajka,Christopher M. Clark,Paul S. Aisen,Ronald C. Petersen,Kaj Blennow,Holly Soares,Adam J. Simon,Piotr Lewczuk,Robert A. Dean,Eric Siemers,William Z. Potter,Virginia M.-Y. Lee,John Q. Trojanowski +14 more
TL;DR: Develop a cerebrospinal fluid biomarker signature for mild Alzheimer's disease (AD) in Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects.
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