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Integrative machine learning approach to risk prediction for dementia and Alzheimer’s disease
Amos Stern, Michal Linial. GeroScience, 2025-08-27
Topic: Amyloid, tau and blood-test diagnosis
Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence. With increasing life expectancy, early diagnosis and improved clinical strategies are urgently needed. This study developed and evaluated machine learning (ML) models to predict AD risk using UK Biobank data, integrating health, genetic, and lifestyle factors. The cohort included 2878 AD cases and 72,366 controls. Among several algorithms, CatBoost performed best (ROC-AUC = 0.773), especially in females. Inputs included ICD-10 codes from 5 years pre-diagnosis, ApoE-ε4 genotype, and large collection of modifiable risk factors. Despite fewer cases, the risk predictive models for vascular dementia (VaD) outperformed the unique AD models. ApoE-ε4 was the most predictive genetic marker, while other common variants had limited utility. Key non-genetic predictors included comorbidities (e.g., diabetes, hypertension), education, physical activity, and diet. These findings highlight the value of integrating diverse data sources for dementia risk prediction and emphasize the role of sex-specific modeling and modifiable factors in early, personalized intervention strategies.
Key findings
- These findings highlight the value of integrating diverse data sources for dementia risk prediction and emphasize the role of sex-specific modeling and modifiable factors in early, personalized intervention strategies.
Why it matters
Machine learning models using UK Biobank health, genetic, and lifestyle data predicted Alzheimer's disease risk with a best ROC-AUC of 0.773, with ApoE-ε4 the most predictive genetic marker and comorbidities, education, physical activity, and diet among key non-genetic predictors. This is risk prediction and does not show that any intervention prevents or treats Alzheimer's disease.
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Study details
- Participants2,878
- Effect sizeAUC = 0.773
- Evidence strengthModerate
How this study scored 50/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (GeroScience) |
| Journal's citation record | 1 / 4 | GeroScience: journal h-index 83 (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 | 3 / 5 | Rated “moderate” 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 | 2,878 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 | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Michal Linial: h-index 57 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Hebrew University of Jerusalem: institution h-index 904 (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 40864401) |
| Free to read | 2 / 2 | Free to read |
| Total | 50 / 100 | — |
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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. Check the citation against the original before you use it.
APA 7th
Stern, A., & Linial, M. (2025). Integrative machine learning approach to risk prediction for dementia and Alzheimer’s disease. GeroScience. https://doi.org/10.1007/s11357-025-01828-x
Vancouver
Stern A, Linial M. Integrative machine learning approach to risk prediction for dementia and Alzheimer’s disease. GeroScience. 2025. doi:10.1007/s11357-025-01828-x. PMID: 40864401.
BibTeX
@article{stern2025-40864401,
author = {Stern, Amos and Linial, Michal},
title = {{Integrative machine learning approach to risk prediction for dementia and Alzheimer’s disease}},
journal = {GeroScience},
year = {2025},
doi = {10.1007/s11357-025-01828-x},
pmid = {40864401},
url = {https://doi.org/10.1007/s11357-025-01828-x}
}
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