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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study
Bingsheng Wang, Ruihan Xie, Wenhao Qi, Jiani Yao, Yankai Shi, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu et al.. BMJ Open, 2025-02-01
Topic: Amyloid, tau and blood-test diagnosis
11 variables were critical in the training phase, including educational level, depression, insomnia, age, Body Mass Index (BMI), medication count, gender, stenting, systolic blood pressure (sbp), neurosis and rapid eye movement. The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%. The most influential predictors were educational level, total medication count, age, sbp and BMI. The proposed classifier can help guide preclinical screening of AD in the elderly population.
Key findings
- 11 variables were critical in the training phase, including educational level, depression, insomnia, age, Body Mass Index (BMI), medication count, gender, stenting, systolic blood pressure (sbp), neurosis and rapid eye movement.
- The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%.
- The most influential predictors were educational level, total medication count, age, sbp and BMI.
- The proposed classifier can help guide preclinical screening of AD in the elderly population.
Why it matters
This cross-sectional study developed a machine learning model using routine clinical variables to help screen for Alzheimer's disease risk in older people. It is a risk-screening tool only and does not test any treatment, cure or prevention.
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Study details
- Study designcross-sectional
- Participants2,379
- Effect size76.2%; 92.9%
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (BMJ Open) |
| Journal's citation record | 2 / 4 | BMJ Open: journal h-index 240 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| 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 | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 3 / 4 | 2,379 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 2 / 7 | Cited 9 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 5.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 9 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Shihua Cao: h-index 12 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Chinese University of Hong Kong: institution h-index 1,100 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39922598) |
| Free to read | 2 / 2 | Free to read |
| Total | 52 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Wang, B., Xie, R., Qi, W., Yao, J., Shi, Y., Lou, X., et al. (2025). Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study. BMJ Open. https://doi.org/10.1136/bmjopen-2024-092293
Vancouver
Wang B, Xie R, Qi W, Yao J, Shi Y, Lou X, et al. Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study. BMJ Open. 2025. doi:10.1136/bmjopen-2024-092293. PMID: 39922598.
BibTeX
@article{wang2025-39922598,
author = {Wang, Bingsheng and Xie, Ruihan and Qi, Wenhao and Yao, Jiani and Shi, Yankai and Lou, Xiajing and Dong, Chaoqun and Zhu, Xiaohong and others},
title = {{Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study}},
journal = {BMJ Open},
year = {2025},
doi = {10.1136/bmjopen-2024-092293},
pmid = {39922598},
url = {https://doi.org/10.1136/bmjopen-2024-092293}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 13.