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Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease
Al-Hammadi Noor, Abouelyazid Mahmoud, Brown David C, Lalwani Pooja, Devos Hannes, Carr David B, Babulal Ganesh M. The journals of gerontology. Series B, Psychological sciences and social sciences, 2025-02-19
Topic: Amyloid, tau and blood-test diagnosis
This study classified preclinical Alzheimer's disease (AD) using cognitive screening, neighborhood deprivation via the area deprivation index (ADI), and sociodemographic and genetic risk factors. Additionally, it compared the predictive accuracy of multiple machine learning algorithms and examined model performance with two bootstrapping procedures. Data were drawn from a longitudinal cohort that required participants to be age 65 or older, cognitively normal at baseline, and active drivers, defined as taking at least one trip a week. Naturalistic driving data were collected using a commercial datalogger. Biomarker positivity was determined via amyloid pathology using cerebrospinal fluid and positron emission tomography imaging. ADI was captured based on geocoding latitude and longitude to derive a national ranking for the specific location (home or unique destination). Machine learning algorithms classified preclinical AD. Each individual model's predictive ability was confirmed in a 20% testing dataset with 100 rounds of resampling with and without replacement. Among 292 participants (n = 2,792 observations), including ADI of trip destinations, participants' home ADI, and frequency of trips to the same ADI led to a slight but notable improvement in predicting preclinical AD. The ensemble model demonstrated superior predictive performance, highlighting the potential of integrating multiple models for early AD detection. Our findings underscore the importance of incorporating socioeconomic and environmental variables, such as neighborhood deprivation, in predicting preclinical AD. Addressing socioeconomic disparities through public health strategies is crucial for mitigating AD risk and enhancing the quality of life for older adults.
Key findings
- The ensemble model demonstrated superior predictive performance, highlighting the potential of integrating multiple models for early AD detection.
- Our findings underscore the importance of incorporating socioeconomic and environmental variables, such as neighborhood deprivation, in predicting preclinical AD.
Why it matters
Using cognitive screening, neighborhood disadvantage, and genetic and social factors, machine learning models slightly improved prediction of preclinical Alzheimer's disease, with a combined model performing best. This is about early detection, not treatment or cure, and the improvement from neighborhood factors was described as slight.
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Study details
- Study designlongitudinal study
- Participants2,792
- Evidence strengthModerate
How this study scored 65/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here and still being cited, so those were left out: it earned 47 of the 72 points we could judge it on, which is 65 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (The journals of gerontology. Series B, Psychological sciences and social sciences) |
| Journal's citation record | 2 / 4 | The Journals of Gerontology Series B: journal h-index 225 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| 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 | 2,792 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | — / 7 | Citation count not trusted: 12 of 31 library studies citing it were published more than a year before it - a sign OpenAlex merged several works under this DOI — left out of this score |
| Citations per year | — / 5 | Citation count not trusted (see above) — left out of this score |
| Impact for its field and year | — / 6 | Citation count not trusted (see above) — left out of this score |
| Cited by studies here | — / 6 | Library citations not trusted (see above) — left out of this score |
| Still being cited | — / 4 | Citation count not trusted (see above) — left out of this score |
| Senior author's track record | 2 / 4 | Last-listed author Ganesh Muneshwar Babulal: h-index 31 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Washington University in St. Louis: institution h-index 1,947 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39969227) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 47 / 72 | Scored on 15 of 20 parts. We have no data for the rest, so they are left out: 47 of the 72 points we could judge it on is 65 out of 100. |
| Total | 65 / 100 | — |
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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 names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Noor, A.-H., Mahmoud, A., Brown David, C., Pooja, L., Hannes, D., Carr David, B., et al. (2025). Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease. The journals of gerontology. Series B, Psychological sciences and social sciences. https://doi.org/10.1093/geronb/gbaf023
Vancouver
Noor A.-H, Mahmoud A, Brown David C, Pooja L, Hannes D, Carr David B, et al. Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease. The journals of gerontology. Series B, Psychological sciences and social sciences. 2025. doi:10.1093/geronb/gbaf023. PMID: 39969227.
BibTeX
@article{noor2025-39969227,
author = {Noor, Al-Hammadi and Mahmoud, Abouelyazid and Brown David, C. and Pooja, Lalwani and Hannes, Devos and Carr David, B. and Babulal Ganesh, M.},
title = {{Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease}},
journal = {The journals of gerontology. Series B, Psychological sciences and social sciences},
year = {2025},
doi = {10.1093/geronb/gbaf023},
pmid = {39969227},
url = {https://doi.org/10.1093/geronb/gbaf023}
}
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