The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Combining Early Markers Strongly Predicts Conversion from Mild Cognitive Impairment to Alzheimer's Disease
Open accessAbstract
Background The utility of combining early markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer’s Disease (AD) remains uncertain. Methods 148 outpatients with MCI, broadly defined, were followed at 6-month intervals. Hypothesized baseline predictors for follow-up conversion to AD (entire sample: 39/148 converters) were cognitive test performance, informant report of functional impairment, apolipoprotein E genotype, olfactory identification deficit, MRI hippocampal and entorhinal cortex volumes. Results In the 3-year follow-up patient sample (33/126 converters), five of eight hypothesized predictors were selected by backward and stepwise logistic regression: FAQ (informant report of functioning), UPSIT (olfactory identification), SRT immediate recall (verbal memory), MRI hippocampal volume, MRI entorhinal cortex volume. For 10% false positives (90% specificity), this five-predictor combination showed 85.2% sensitivity, combining age and MMSE showed 39.4% sensitivity, and combining age, MMSE, and the three clinical predictors (SRT immediate recall, FAQ, and UPSIT) showed 81.3% sensitivity. Area under ROC curve was greater for the five-predictor combination (0.948) than age plus MMSE (0.821; p =.0009), and remained high in sub-samples with MMSE ≥ 27/30 and amnestic MCI. For the entire patient sample, based on dichotomizing estimated risk at 0.5, positive likelihood ratio was 16.8 (95% CI 6.4, 44.3) and negative likelihood ratio was 0.2 (95% CI 0.1, 0.4). Conclusions The five-predictor combination strongly predicted conversion to AD and was markedly superior to combining age and MMSE. Combining only clinically administered measures also led to strong predictive accuracy. If independently replicated, the findings have potential utility for early detection of AD.
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.
Integrity signals
Registry
Cites
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.
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
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. ‐., Nordberg, A., Bäckman, L., Albert, M., Almkvist, O., et al. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01380.x · Cited by 4,946
Doty, R. L., Shaman, P., & Dann, M. (1984) · Physiology & Behavior · doi:10.1016/0031-9384(84)90269-5 · Cited by 1,854
Self-citationHippocampal formation glucose metabolism and volume losses in MCI and AD
De Santi, S., de Leon, M. J., Rusinek, H., Convit, A., Tarshish, C. Y., Roche, A., Tsui, W. H., Kandil, E., Boppana, M., Daisley, K., et al. (2001) · Neurobiology of Aging · doi:10.1016/s0197-4580(01)00230-5 · Cited by 548
Utility of the Apolipoprotein E Genotype in the Diagnosis of Alzheimer's Disease
Mayeux, R., Saunders, A. M., Shea, S., Mirra, S., Evans, D., Roses, A. D., Hyman, B. T., Crain, B., Tang, M.-X., & Phelps, C. H. (1998) · New England Journal of Medicine · doi:10.1056/nejm199802193380804 · Cited by 547
Lopez, O. L., Jagust, W. J., Dulberg, C., Becker, J. T., DeKosky, S. T., Fitzpatrick, A., Breitner, J., Lyketsos, C., Jones, B., Kawas, C., et al. (2003) · Archives of Neurology · doi:10.1001/archneur.60.10.1394 · Cited by 477
Tervo, S., Kivipelto, M., Hänninen, T., Vanhanen, M., Hallikainen, M., Mannermaa, A., & Soininen, H. (2004) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000076356 · Cited by 298
Transitions to mild cognitive impairments, dementia, and death: findings from the Nun Study
Tyas, S. L., Salazar, J. C., Snowdon, D. A., Desrosiers, M. F., Riley, K. P., Mendiondo, M. S., & Kryscio, R. J. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm085 · Cited by 163
Cited by
At the interface of sensory and motor dysfunctions and Alzheimer’s disease.
Albers, M. W., Gilmore, G. C., Kaye, J., Murphy, C., Wingfield, A., Bennett, D. A., Boxer, A. L., Buchman, A. S., Cruickshanks, K. J., Devanand, D. P., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.04.514 · Cited by 589
Self-citationBaseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort
Risacher, S., Saykin, A., Wes, J., Shen, L., Firpi, H., & McDonald, B. (2009) · Current Alzheimer Research · doi:10.2174/156720509788929273 · Cited by 538
Ward, A., Tardiff, S., Dye, C., & Arrighi, H. M. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000354370 · Cited by 426
The olfactory bulb as the entry site for prion-like propagation in neurodegenerative diseases
Rey, N. L., Wesson, D. W., & Brundin, P. (2018) · Neurobiology of Disease · doi:10.1016/j.nbd.2016.12.013 · Cited by 257
Brown, P. J. (2011) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.57 · Cited by 242
Metadata
- Authors
- Devanand, D. P., Liu, X., Tabert, M. H., Pradhaban, G., Cuasay, K., Bell, K., de Leon, M. J., Doty, R. L., Stern, Y., & Pelton, G. H.
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2008-11
- Volume
- 64
- Issue
- 10
- Pages
- 871-879
- ISSN
- 0006-3223 (ISSN-L)
- OpenAlex
- W2097122549
- Cited by
- 397
- References
- 11
- FWCI
- 8.77
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Combining Early Markers Strongly Predicts Conversion from Mild Cognitive Impairment to Alzheimer's Disease. https://app.cochat.ai/papers/10.1016/j.biopsych.2008.06.020
- The paper
- Devanand, D. P., Liu, X., Tabert, M. H., Pradhaban, G., Cuasay, K., Bell, K., de Leon, M. J., Doty, R. L., Stern, Y., & Pelton, G. H. (2008). Combining Early Markers Strongly Predicts Conversion from Mild Cognitive Impairment to Alzheimer's Disease. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2008.06.020
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.
