Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 394

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance

Open access
Ewers, Michael, Walsh, Cathal, Trojanowski, John Q., Shaw, Leslie M., Petersen, Ronald C., Jack, Clifford R., Feldman, Howard H., Bokde, Arun L.W., Alexander, Gene E., Scheltens, Philip, Vellas, Bruno, Dubois, Bruno, Weiner, Michael, Hampel, Harald·2012·Neurobiology of Aging

Abstract

The current study tested the accuracy of primary MRI and cerebrospinal fluid (CSF) biomarker candidates and neuropsychological tests for predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. In a cross-validation paradigm, predictor models were estimated in the training set of AD (N = 81) and elderly control subjects (N = 101). A combination of CSF t-tau/Aβ1-4 ratio and MRI biomarkers or neuropsychological tests (free recall and trail making test B (TMT-B)) showed the best statistical fit in the AD vs. HC comparison, reaching a classification accuracy of up to 64% when applied to the prediction of MCI conversion (3.3-year observation interval, mean = 2.3 years). However, several single-predictor models showed a predictive accuracy of MCI conversion comparable to that of any multipredictor model. The best single predictors were right entorhinal cortex (prediction accuracy = 68.5% (95% CI (59.5, 77.4))) and TMT-B test (prediction accuracy 64.6% (95% CI (55.5, 73.4%))). In conclusion, short-term conversion to AD is predicted by single marker models to a comparable degree as by multimarker models in amnestic MCI subjects.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neurobiolaging.2010.10.019

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 13 references

Research works printed but not linked: 34

  1. Blennow, 2003, CSF markers for incipient Alzheimer's disease, Lancet Neurol, 2, 605, 10.1016/S1474-4422(03)00530-1 · DOI · Paper page
  2. Bouwman, 2007, CSF biomarkers and medial temporal lobe atrophy predict dementia in mild cognitive impairment, Neurobiol. Aging, 28, 1070, 10.1016/j.neurobiolaging.2006.05.006 · DOI
  3. Brys, 2009, Magnetic resonance imaging improves cerebrospinal fluid biomarkers in the early detection of Alzheimer's disease, J. Alzheimers Dis, 16, 351, 10.3233/JAD-2009-0968 · DOI
  4. Brys, 2009, Prediction and longitudinal study of CSF biomarkers in mild cognitive impairment, Neurobiol. Aging, 30, 682, 10.1016/j.neurobiolaging.2007.08.010 · DOI
  5. Csernansky, 2000, Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus, Neurology, 55, 1636, 10.1212/WNL.55.11.1636 · DOI · Paper page
  6. de Leon, 2006, Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment, Neurobiol. Aging, 27, 394, 10.1016/j.neurobiolaging.2005.07.003 · DOI · Paper page
  7. Devanand, 2008, Combining early markers strongly predicts conversion from mild cognitive impairment to Alzheimer's disease, Biol. Psychiatry, 64, 871, 10.1016/j.biopsych.2008.06.020 · DOI · Paper page
  8. Devanand, 2007, Hippocampal and entorhinal atrophy in mild cognitive impairment: Prediction of Alzheimer disease, Neurology, 68, 828, 10.1212/01.wnl.0000256697.20968.d7 · DOI · Paper page
  9. Du, 2001, Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease, J. Neurol. Neurosurg. Psychiatry, 71, 441, 10.1136/jnnp.71.4.441 · DOI · Paper page
  10. Dubois, 2007, Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria, Lancet Neurol, 6, 734, 10.1016/S1474-4422(07)70178-3 · DOI · Paper page
  11. Ewers, 2007, Multicenter assessment of CSF-phosphorylated tau for the prediction of conversion of MCI, Neurology, 69, 2205, 10.1212/01.wnl.0000286944.22262.ff · DOI
  12. Fleisher, 2008, Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment, Neurology, 70, 191, 10.1212/01.wnl.0000287091.57376.65 · DOI
  13. Frank, 2003, Biological markers for therapeutic trials in Alzheimer's disease, Neurobiol. Aging, 24, 521, 10.1016/S0197-4580(03)00002-2 · DOI · Paper page
  14. Hansson, 2006, Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study, Lancet Neurol, 5, 228, 10.1016/S1474-4422(06)70355-6 · DOI · Paper page
  15. Herukka, 2005, CSF A{beta}42 and tau or phosphorylated tau and prediction of progressive mild cognitive impairment, Neurology, 64, 1294, 10.1212/01.WNL.0000156914.16988.56 · DOI
  16. Herukka, 2007, CSF Abeta42, Tau and phosphorylated Tau, APOE epsilon4 allele and MCI type in progressive MCI, Neurobiol. Aging, 28, 507, 10.1016/j.neurobiolaging.2006.02.001 · DOI
  17. Kantarci, 2003, Neuroimaging in Alzheimer disease: an evidence-based review, Neuroimaging Clin. N Am, 13, 197, 10.1016/S1052-5149(03)00025-X · DOI
  18. McKhann, 1984, Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease, Neurology, 34, 939, 10.1212/WNL.34.7.939 · DOI · Paper page
  19. Pennanen, 2004, Hippocampus and entorhinal cortex in mild cognitive impairment and early AD, Neurobiol. Aging, 25, 303, 10.1016/S0197-4580(03)00084-8 · DOI · Paper page
  20. Ritchie, 2001, Classification criteria for mild cognitive impairment: a population-based validation study, Neurology, 56, 37, 10.1212/WNL.56.1.37 · DOI · Paper page
  21. Rozzini, 2007, Conversion of amnestic Mild Cognitive Impairment to dementia of Alzheimer type is independent to memory deterioration, Int. J. Geriatr. Psychiatry, 22, 1217, 10.1002/gps.1816 · DOI
  22. Sarazin, 2007, Amnestic syndrome of the medial temporal type identifies prodromal AD: a longitudinal study, Neurology, 69, 1859, 10.1212/01.wnl.0000279336.36610.f7 · DOI · Paper page
  23. Schemper, 2003, Predictive accuracy and explained variation, Stat. Med, 22, 2299, 10.1002/sim.1486 · DOI
  24. Schmand, 2010, Meta-analysis of CSF and MRI biomarkers for detecting preclinical Alzheimer's disease, Psychol. Med, 40, 135, 10.1017/S0033291709991516 · DOI
  25. Schuff, 2009, the, A.s., Disease, Brain, 132, 1067, 10.1093/brain/awp007 · DOI · Paper page
  26. Tabert, 2006, Neuropsychological Prediction of Conversion to Alzheimer Disease in Patients With Mild Cognitive Impairment, Arch. Gen. Psychiatry, 63, 916, 10.1001/archpsyc.63.8.916 · DOI · Paper page
  27. Tapiola, 2008, MRI of hippocampus and entorhinal cortex in mild cognitive impairment: a follow-up study, Neurobiol. Aging, 29, 31, 10.1016/j.neurobiolaging.2006.09.007 · DOI
  28. Tierney, 1996, Prediction of probable Alzheimer's disease in memory-impaired patients: A prospective longitudinal study, Neurology, 46, 661, 10.1212/WNL.46.3.661 · DOI · Paper page
  29. Tierney, 2005, Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years, Neurology, 64, 1853, 10.1212/01.WNL.0000163773.21794.0B · DOI · Paper page
  30. Vemuri, 2008, Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies, Neuroimage, 39, 1186, 10.1016/j.neuroimage.2007.09.073 · DOI · Paper page
  31. Vemuri, 2009, MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change, Neurology, 73, 294, 10.1212/WNL.0b013e3181af79fb · DOI · Paper page
  32. Visser, 2009, Prevalence and prognostic value of CSF markers of Alzheimer's disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study, Lancet Neurol, 8, 619, 10.1016/S1474-4422(09)70139-5 · DOI · Paper page
  33. Wang, 2009, Fully-automated, multi-stage hippocampus mapping in very mild Alzheimer disease, Hippocampus, 19, 541, 10.1002/hipo.20616 · DOI
  34. Wimo, 1997, Costs of dementia and dementia care: a review, Int. J. Geriatr. Psychiatry, 12, 841, 10.1002/(SICI)1099-1166(199708)12:8<841::AID-GPS652>3.0.CO;2-R · DOI

Cited by

Metadata

Authors
Ewers, M., Walsh, C., Trojanowski, J. Q., Shaw, L. M., Petersen, R. C., Jack, C. R., Feldman, H. H., Bokde, A. L. W., Alexander, G. E., Scheltens, P., Vellas, B., Dubois, B., Weiner, M., & Hampel, H.
Venue
Neurobiology of Aging
Publisher
Elsevier BV
Published
2012-07
Volume
33
Issue
7
Pages
1203-1214.e2
ISSN
0197-4580 (ISSN-L)
OpenAlex
W1970684162
Cited by
394
References
13
FWCI
16.35
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • Science Foundation Ireland (SFI) investigator program award (08/IN.1/B1846)
  • National Institute on Aging (AG19610, AG024904, AG025526, AG11378)
  • NIH Foundation
  • Evelyn F. McKnight Brain Institute of the University of Arizona
  • state of Arizona and Arizona Department of Health Services
  • Alexander Family Alzheimer's Research Professorship, Mayo Clinic
  • Pfizer
  • Eisai
  • Janssen
  • Novartis
  • Lilly
  • AstraZeneca
  • Sanofi
  • NIH
  • CIHR
  • Alzheimer Society of Canada
  • Michael Smith Health Research Foundation
  • NIA (UO1 AG-024904)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.