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Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics
Varatharajah Y, Ramanan VK, Iyer R, Vemuri P, Alzheimer’s Disease Neuroimaging Initiative.. Scientific reports, 2019-02-19
Topic: Risk, prevention and care in population studies
In the Alzheimer's disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression of CR1 (complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.
Key findings
- In the Alzheimer's disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management.
- Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years.
- Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression of CR1 (complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value.
- This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.
Why it matters
This study built models that identified people with mild cognitive impairment who progressed to Alzheimer's disease within 3 years with about 93% accuracy, with amyloid, tau and neuronal injury markers accounting for large shares of the variance. It is a prediction study and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 50/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (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 | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 101 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 11.2 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 7.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 44 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Valory Pavlik: h-index 44 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | University of Washington: institution h-index 3,540 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 37 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30783207) |
| Free to read | 2 / 2 | Free to read |
| Total | 50 / 100 | — |
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Read the original
- DOI: 10.1038/s41598-019-38793-3
- PubMed: 30783207
- Original source →
- Free full text: Scientific Reports →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Varatharajah, Y., Ramanan, V. K., Iyer, R., Vemuri, P., & Alzheimer’s Disease Neuroimaging Initiative. (2019). Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics. Scientific reports. https://doi.org/10.1038/s41598-019-38793-3
Vancouver
Varatharajah Y, Ramanan VK, Iyer R, Vemuri P, Alzheimer’s Disease Neuroimaging Initiative. Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics. Scientific reports. 2019. doi:10.1038/s41598-019-38793-3. PMID: 30783207.
BibTeX
@article{varatharajah2019-30783207,
author = {Varatharajah, Y. and Ramanan, V. K. and Iyer, R. and Vemuri, P. and {Alzheimer’s Disease Neuroimaging Initiative}},
title = {{Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics}},
journal = {Scientific reports},
year = {2019},
doi = {10.1038/s41598-019-38793-3},
pmid = {30783207},
url = {https://doi.org/10.1038/s41598-019-38793-3}
}
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