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Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification
Korolev IO, Symonds LL, Bozoki AC, Alzheimer's Disease Neuroimaging Initiative.. PloS one, 2016-02-22
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD).
- Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility.
- 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.
Why it matters
This study developed a model using baseline data from 259 patients with mild cognitive impairment to predict progression to Alzheimer's dementia, with the best model combining cognitive and functional markers with MRI measures reaching 80% accuracy. Cognitive and functional markers were most predictive, while plasma proteomic markers had limited predictive utility, so this concerns prediction rather than treatment.
Study details
- Participants139
- Effect sizeAUC = 0.87
- Evidence strengthLow
How this study scored 55/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 53 of the 96 points we could judge it on, which is 55 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (PloS one) |
| Journal's citation record | 4 / 4 | PLoS ONE: journal h-index 594 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2016 — within 20 year(s) of the newest study here |
| Sample size | 2 / 4 | 139 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 196 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.3 citations a year over 12 year(s) since 2016 |
| Impact for its field and year | 4 / 6 | Cited 18.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 13 other studies in this library |
| Still being cited | 2 / 4 | Cited 40 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 1 / 3 | Michigan State University: institution h-index 1,089 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 26901338) |
| Free to read | 2 / 2 | Free to read |
| Points | 53 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 53 of the 96 points we could judge it on is 55 out of 100. |
| Total | 55 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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APA 7th
Korolev, I. O., Symonds, L. L., Bozoki, A. C., & Alzheimer's Disease Neuroimaging Initiative. (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
Vancouver
Korolev IO, Symonds LL, Bozoki AC, Alzheimer's Disease Neuroimaging Initiative. Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. PloS one. 2016. doi:10.1371/journal.pone.0138866. PMID: 26901338.
BibTeX
@article{korolev2016-26901338,
author = {Korolev, I. O. and Symonds, L. L. and Bozoki, A. C. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification}},
journal = {PloS one},
year = {2016},
doi = {10.1371/journal.pone.0138866},
pmid = {26901338},
url = {https://doi.org/10.1371/journal.pone.0138866}
}
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