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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;77:1619–1628

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Journal ArticleDOI

Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers.

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.
Journal ArticleDOI

Mild cognitive impairment due to Alzheimer disease in the community

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.
Journal ArticleDOI

Associations between Alzheimer disease biomarkers, neurodegeneration, and cognition in cognitively normal older people.

TL;DR: Accumulation of neurodegenerative abnormalities was related to poor memory and executive functions as well as larger WML volumes but not elevated Pittsburgh compound B retention, confirming that a substantial proportion of cognitively normal older adults harbor neurodegenersation, without Aβ burden.
References
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Journal ArticleDOI

Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade

TL;DR: This work proposes a model that relates disease stage to AD biomarkers in which Abeta biomarkers become abnormal first, before neurodegenerative biomarkers and cognitive symptoms, and neurodegnerative biomarker become abnormal later, and correlate with clinical symptom severity.
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