The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification
Open accessAbstract
BACKGROUND: Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. METHODS: Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. RESULTS: Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. CONCLUSIONS: We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.
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“Importantly, our results provide empirical support to the most recently revised clinical criteria for MCI, where the concept of “MCI due to AD” is proposed to include “impairment in one or more cognitive domains” and an allowance for “mild problems performing complex functional tasks” [70].”
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Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
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The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
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Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336
Bias in error estimation when using cross-validation for model selection
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Two Phase 3 Trials of Bapineuzumab in Mild-to-Moderate Alzheimer's Disease
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Cited by
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622
Early neuronal accumulation of DNA double strand breaks in Alzheimer’s disease
Shanbhag, N. M., Evans, M. D., Mao, W., Nana, A. L., Seeley, W. W., Adame, A., Rissman, R. A., Masliah, E., & Mucke, L. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0723-5 · Cited by 214
Lombardi, G., Crescioli, G., Cavedo, E., Lucenteforte, E., Casazza, G., Bellatorre, A.-G., Lista, C., Costantino, G., Frisoni, G., Virgili, G., et al. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009628.pub2 · Cited by 214
Bhagwat, N., Viviano, J. D., Voineskos, A. N., Chakravarty, M. M., & Initiative, A. D. N. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006376 · Cited by 157
Self-citationContributions of DNA Damage to Alzheimer’s Disease
Lin, X., Kapoor, A., Gu, Y., Chow, M. J., Peng, J., Zhao, K., & Tang, D. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21051666 · Cited by 117
“Using two independent cohorts (n = 13 and n = 23), significant increases in γH2AX were recently demonstrated in astrocytes and neurons in the hippocampus and frontal cortex of AD brains [44]; the increase occurred in brains with mild cognitive impairment (MCI) [44], a preclinical stage of AD [45,46], suggesting an imp…”
Background
Metadata
- Authors
- Korolev, I. O., Symonds, L. L., Bozoki, A. C., & Initiative, A. D. N.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-02-22
- Volume
- 11
- Issue
- 2
- Pages
- e0138866
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2281498324
- Cited by
- 194
- References
- 27
- FWCI
- 18.63
- Type
- journal article
- Language
- en
- Open access
- gold
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- Korolev, I. O., Symonds, L. L., Bozoki, A. C., & Initiative, A. D. N. (2016). Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. PLOS ONE. https://doi.org/10.1371/journal.pone.0138866
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
