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Estimation of Machine Learning–Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study
Z. N. Gu, Shuang Liu, Huijuan Ma, Yifan Long, Xuehao Jiao, Xin Gao, Bingying Du, Xiaoying Bi et al.. JMIR Aging, 2025-01-20
Topic: Risk, prevention and care in population studies
Background The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASCVD remain unclear, necessitating the development of accurate prediction models. Objective The aim of the study is to develop a machine learning model for use in patients with ASCVD to predict dementia risk using available clinical and sociodemographic data. Methods This prognostic study included patients with ASCVD between 2006 and 2010, with registration of follow-up data ending on April 2023 based on the UK Biobank. We implemented a data-driven strategy, identifying predictors from 316 variables and developing a machine learning model to predict the risk of incident dementia, Alzheimer disease, and vascular dementia within 5, 10, and longer-term follow-up in patients with ASCVD. Results A total of 29,561 patients with ASCVD were included, and 1334 (4.51%) developed dementia during a median follow-up time of 10.3 (IQR 7.6-12.4) years. The best prediction model (UK Biobank ASCVD risk prediction model) was light gradient boosting machine, comprising 10 predictors including age, time to complete pairs matching tasks, mean time to correctly identify matches, mean sphered cell volume, glucose levels, forced expiratory volume in 1 second z score, C-reactive protein, forced vital capacity, time engaging in activities, and age first had sexual intercourse. This model achieved the following performance metrics for all incident dementia: area under the receiver operating characteristic curve: mean 0.866 (SD 0.027), accuracy: mean 0.883 (SD 0.010), sensitivity: mean 0.637 (SD 0.084), specificity: mean 0.914 (SD 0.012), precision: mean 0.479 (SD 0.031), and F1-score: mean 0.546 (SD 0.043). Meanwhile, this model was well-calibrated (Kolmogorov-Smirnov test showed goodness-of-fit P value>.99) and maintained robust performance across different temporal cohorts. Besides, the model had a beneficial potential in clinical practice with a decision curve analysis. Conclusions The findings of this study suggest that predictive modeling could inform patients and clinicians about ASCVD at risk for dementia.
Key findings
- Background The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia.
- Results A total of 29,561 patients with ASCVD were included, and 1334 (4.51%) developed dementia during a median follow-up time of 10.3 (IQR 7.6-12.4) years.
- Conclusions The findings of this study suggest that predictive modeling could inform patients and clinicians about ASCVD at risk for dementia.
Why it matters
In UK Biobank data from 29,561 patients with atherosclerotic cardiovascular disease, a machine learning model using clinical and sociodemographic variables predicted risk of incident dementia. The authors suggest this could inform patients and clinicians about risk, but it is a prediction tool, not a treatment, cure, or prevention.
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Study details
- Participants29,561
- Effect size4.51%
- Evidence strengthLow
How this study scored 46/100
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 (JMIR Aging) |
| Journal's citation record | 0 / 4 | JMIR Aging: journal h-index 49 (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 | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 4 / 4 | 29,561 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 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 | 1 / 4 | Last-listed author Xingjie Shi: h-index 17 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Fudan University: institution h-index 954 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40009844) |
| Free to read | 2 / 2 | Free to read |
| Total | 46 / 100 | — |
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Read the original
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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
Gu, Z. N., Liu, S., Ma, H., Long, Y., Jiao, X., Gao, X., et al. (2025). Estimation of Machine Learning–Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study. JMIR Aging. https://doi.org/10.2196/64148
Vancouver
Gu ZN, Liu S, Ma H, Long Y, Jiao X, Gao X, et al. Estimation of Machine Learning–Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study. JMIR Aging. 2025. doi:10.2196/64148. PMID: 40009844.
BibTeX
@article{gu2025-40009844,
author = {Gu, Z. N. and Liu, Shuang and Ma, Huijuan and Long, Yifan and Jiao, Xuehao and Gao, Xin and Du, Bingying and Bi, Xiaoying and others},
title = {{Estimation of Machine Learning–Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study}},
journal = {JMIR Aging},
year = {2025},
doi = {10.2196/64148},
pmid = {40009844},
url = {https://doi.org/10.2196/64148}
}
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