4.7 Article

Predicting MCI outcome with clinically available MRI and CSF biomarkers

期刊

NEUROLOGY
卷 77, 期 17, 页码 1619-1628

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1212/WNL.0b013e3182343314

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资金

  1. NIH [NIA K01AG029218, NINDS K02NS067427, P30 AG010129, K01 AG030514]
  2. Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
  3. Scripps Mercy Hospital, San Diego, CA
  4. National Institute on Aging
  5. National Institute of Biomedical Imaging and Bioengineering
  6. Abbott
  7. AstraZeneca
  8. Bayer Schering Pharma
  9. Bristol-Myers Squibb
  10. Eisai Inc.
  11. Elan Corporation
  12. Genentech, Inc.
  13. GE Healthcare
  14. GlaxoSmithKline
  15. Innogenetics
  16. Johnson Johnson
  17. Eli Lilly and Co.
  18. Medpace, Inc.
  19. Merck and Co., Inc.
  20. Novartis
  21. Pfizer Inc
  22. Roche
  23. Schering-Plough Corp.
  24. Synarc, Inc.
  25. Wyeth
  26. Alzheimer's Association
  27. Alzheimer's Drug Discovery Foundation
  28. US Food and Drug Administration
  29. Dana Foundation
  30. NIH/NINDS
  31. Janssen Alzheimer Immunotherapy
  32. General Electric Medical Foundation
  33. NIH (NINDS, NIA)
  34. Research Council, Sweden
  35. LUA/ALF
  36. Vastra Gotalandsregionen, Sweden
  37. Swedish Alzheimer Foundation
  38. Stiftelsen for Gamla Tjanarinnor
  39. King Gustaf V and Queen Victoria Foundation
  40. Swedish Brain Power project
  41. Swedish Council for Working Life and Social Research

向作者/读者索取更多资源

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 (R) 2011;77:1619-1628

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