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Predicting brain amyloid load with digital and blood-based biomarkers
Chen W, Liao Y, Shi X, Su F, Kong H, Fang Y, Zheng Y, Zhou J et al.. Alzheimer's research & therapy, 2025-07-05
Topic: Amyloid, tau and blood-test diagnosis
Background With the recent approval of anti-β-amyloid (Aβ) treatment for Alzheimer's disease (AD), a demand has emerged for scalable, convenient and accurate estimations of brain Aβ burden for the detection of AD that would enable timely, accurate and reliable diagnosis in one's primary care physician's (PCPs) office as called for recently by World Health Organization (WHO). Methods MemTrax, a 2-minute online memory test, was selected as the digital biomarker of cognitive impairment, and blood-based biomarkers (BBMs) including Aβ42, Aβ40, P-tau181, GFAP and NfL were used to estimate AD-related metrics in different groups of elderly individuals (n = 349) for comparison with Aβ PET scans of brain Aβ burden. The correlations between MemTrax, MoCA, BBMs and brain Aβ burden, expressed in centiloid (CL) values, were analyzed for predicting CL value alone or in combinations using machine-learning (ML). Results Both MemTrax and the MoCA were able to differentiate Aβ status similarly. Integration of MemTrax and BBMs using ML, however, significantly improved the AUCs (over the same with MoCA) for differentiating Aβ status. MemTrax and p-Tau181/Aβ42 composite showed the strongest relationship with CL value among other BBMs. Most importantly, regression analyses of MemTrax and p-Tau181/Aβ42 aptly predicted CL values. Conclusion The combination of MemTrax and BBMs provides an accurate, convenient, non-invasive, cost-effective and scalable way to estimate Aβ load, which provides an opportunity for mass screening and timely and accurate diagnosis of AD. Our findings could also facilitate more effective AD clinical management in the PCPs office worldwide for more equitable access to current standard of care.
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APA 7th
Chen, W., Liao, Y., Shi, X., Su, F., Kong, H., Fang, Y., et al. (2025). Predicting brain amyloid load with digital and blood-based biomarkers. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-025-01801-y
Vancouver
Chen W, Liao Y, Shi X, Su F, Kong H, Fang Y, et al. Predicting brain amyloid load with digital and blood-based biomarkers. Alzheimer's research & therapy. 2025. doi:10.1186/s13195-025-01801-y. PMID: 40618175.
BibTeX
@article{chen2025-40618175,
author = {Chen, W. and Liao, Y. and Shi, X. and Su, F. and Kong, H. and Fang, Y. and Zheng, Y. and Zhou, J. and others},
title = {{Predicting brain amyloid load with digital and blood-based biomarkers}},
journal = {Alzheimer's research \& therapy},
year = {2025},
doi = {10.1186/s13195-025-01801-y},
pmid = {40618175},
url = {https://doi.org/10.1186/s13195-025-01801-y}
}
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Key findings
- Results Both MemTrax and the MoCA were able to differentiate Aβ status similarly.
- Integration of MemTrax and BBMs using ML, however, significantly improved the AUCs (over the same with MoCA) for differentiating Aβ status.
- Our findings could also facilitate more effective AD clinical management in the PCPs office worldwide for more equitable access to current standard of care.
Study details
- Participants349
- Evidence strengthLow
How this study scored 53/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 31 of the 58 points we could judge it on, which is 53 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer's research & therapy) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 349 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 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40618175) |
| Free to read | 2 / 2 | Free to read |
| Points | 31 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 31 of the 58 points we could judge it on is 53 out of 100. |
| Total | 53 / 100 | — |
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