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Neurology. 2011 Oct 25;77(17):1619-28. doi: 10.1212/WNL.0b013e3182343314. Epub 2011 Oct 12.

Predicting MCI outcome with clinically available MRI and CSF biomarkers.

Collaborators (204)

Jagust W, Trojanowki J, Toga AW, Beckett L, Green RC, Gamst A, Saykin AJ, Morris J, Potter WZ, Montine T, Thomas RG, Donohue M, Walter S, Dale A, Bernstein M, Felmlee J, Fox N, Thompson P, Alexander G, DeCarli C, Bandy D, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Cairns NJ, Taylor-Reinwald L, Shaw L, Lee VM, Korecka M, Crawford K, Neu S, Harvey D, Kornak J, Saykin AJ, Foroud TM, Potkin S, Shen L, Kachaturian Z, Frank R, Snyder PJ, Molchan S, Kaye J, Dolen S, Quinn J, Schneider L, Pawluczyk S, Spann BM, Brewer J, Vanderswag H, Heidebrink JL, Lord JL, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Stern Y, Honig LS, Bell KL, Mintun MA, Schneider S, Marson D, Griffith R, Clark D, Grossman H, Tang C, Marzloff G, deToledo-Morrell L, Shah RC, Duara R, Varon D, Roberts P, Albert MS, Kozauer N, Zerrate M, Rusinek H, de Leon MJ, De Santi SM, Doraiswamy PM, Petrella JR, Aiello M, Arnold S, Karlawish JH, Wolk D, Smith CD, Given CA 2nd, Hardy P, Lopez OL, Oakley M, Simpson DM, Ismail MS, Brand C, Richard J, Mulnard RA, Thai G, McAdams-Ortiz C, Diaz-Arrastia R, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Laubinger MM, Apostolova L, Silverman DH, Lu PH, Graff-Radford NR, Parfitt F, Johnson H, Farlow M, Herring S, Hake AM, van Dyck CH, MacAvoy MG, Benincasa AL, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung GY, Feldman H, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Wu CK, Johnson N, Mesulam M, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Rentz DM, Johnson KA, Rosen A, Tinklenberg J, Ashford W, Sabbagh M, Connor D, Jacobson S, Killiany R, Norbash A, Nair A, Obisesan TO, Jayam-Trouth A, Wang P, Lerner A, Hudson L, Ogrocki P, DeCarli C, Fletcher E, Carmichael O, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Hendin BA, Scharre DW, Kataki M, Zimmerman EA, Celmins D, Brown AD, Pearlson G, Blank K, Anderson K, Saykin AJ, Santulli RB, Englert J, Williamson JD, Sink KM, Watkins F, Ott BR, Stopa E, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Flynn Longmire C, Spicer K.

Author information

  • 1Department of Radiology, University of California, San Diego, La Jolla, CA 92093-0841, USA.

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.

PMID:
21998317
[PubMed - indexed for MEDLINE]
PMCID:
PMC3198979
Free PMC Article

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