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Uncovering stage‐specific neural and molecular progression in Alzheimer's disease: Implications for early screening
Yun Lin, Xuetao Shi, Jia Mu, Huixia Ren, Xiaosen Jiang, Xiaosen Jiang, Lin Zhu, Xingya Cai et al.. Alzheimer s & Dementia, 2025-05-01
Topic: Amyloid, tau and blood-test diagnosis
Key markers and mechanisms were identified for each stage: initial neurophysiological deficits in SCD with compensatory metabolomic responses, gut-brain axis dysregulation in MCI, and extensive metabolic disruption and multisystem breakdown in AD. Using random forest models, we identified specific feature combinations that achieved predictive areas under the curve (AUCs) of 0.78 for SCD, 0.84 for MCI, and 0.98 for AD, highlighting EEG as a particularly effective early screening tool. This study elucidates AD's pathophysiological progression and highlights the potential of machine learning-assisted multimodal strategies for early detection and staging. HIGHLIGHTS: Early electroencephalogram (EEG) changes and compensatory metabolomic responses define subjective cognitive decline (SCD) stage. In mild cognitive impairment (MCI), gut-brain axis dysfunction alters microbial diversity and functional pathways. In Alzheimer's disease (AD), systemic breakdown disruption enables near-perfect machine learning (ML) detection. Random forest models yield predictive areas under the curve (AUCs) of 0.78 (SCD), 0.84 (MCI), 0.98 (AD). EEG is a convenient, cost-efficient marker for early screening.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Lin, Y., Shi, X., Mu, J., Ren, H., Jiang, X., Zhu, L., et al. (2025). Uncovering stage‐specific neural and molecular progression in Alzheimer's disease: Implications for early screening. Alzheimer s & Dementia. https://doi.org/10.1002/alz.70182
Vancouver
Lin Y, Shi X, Mu J, Ren H, Jiang X, Zhu L, et al. Uncovering stage‐specific neural and molecular progression in Alzheimer's disease: Implications for early screening. Alzheimer s & Dementia. 2025. doi:10.1002/alz.70182. PMID: 40386988.
BibTeX
@article{lin2025-40386988,
author = {Lin, Yun and Shi, Xuetao and Mu, Jia and Ren, Huixia and Jiang, Xiaosen and Zhu, Lin and Cai, Xingya and others},
title = {{Uncovering stage‐specific neural and molecular progression in Alzheimer's disease: Implications for early screening}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz.70182},
pmid = {40386988},
url = {https://doi.org/10.1002/alz.70182}
}
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Key findings
- Key markers and mechanisms were identified for each stage: initial neurophysiological deficits in SCD with compensatory metabolomic responses, gut-brain axis dysregulation in MCI, and extensive metabolic disruption and multisystem breakdown in AD.
- Using random forest models, we identified specific feature combinations that achieved predictive areas under the curve (AUCs) of 0.78 for SCD, 0.84 for MCI, and 0.98 for AD, highlighting EEG as a particularly effective early screening tool.
- This study elucidates AD's pathophysiological progression and highlights the potential of machine learning-assisted multimodal strategies for early detection and staging.
- HIGHLIGHTS: Early electroencephalogram (EEG) changes and compensatory metabolomic responses define subjective cognitive decline (SCD) stage.
- In mild cognitive impairment (MCI), gut-brain axis dysfunction alters microbial diversity and functional pathways.
Why it matters
This study identified stage-specific neural and molecular markers and used machine learning to predict stages of Alzheimer's disease, with electroencephalogram highlighted as a potential early screening tool. It is about detection and staging, not about treating, curing, or preventing the disease.
Study details
- Participants277
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (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 | 2 / 4 | 277 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 | 2 / 7 | Cited 14 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 8.9× 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 14 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Yi Chen Guo: h-index 36 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | BGI Group (China): institution h-index 1,147 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 8 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40386988) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
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