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Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data
Linda Karlsson, Jacob Vogel, Ida Arvidsson, Kalle Åström, Olof Strandberg, Jakob Seidlitz, Richard A I Bethlehem, Erik Stomrud et al.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025-02
Topic: Plasma and spinal-fluid biomarkers
Models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared = 0.66-0.69), with especially high contribution from plasma phosphorylated tau-217 (p-tau217). MRI variables were the best predictors of asymmetric tau load between the two hemispheres (best model: R-squared = 0.28-0.42). The models showed high generalizability to external test cohorts with data collected at multiple sites. Through a proof-of-concept two-step classification workflow, we also demonstrated possible model translations to a clinical setting. This study highlights the promising and limiting aspects of using machine learning to predict tau-PET from scalable cost-effective variables, with findings relevant for clinical settings and future research. HIGHLIGHTS: Accessible variables showed potential in estimating tau tangle load and distribution. Plasma phosphorylated tau-217 (p-tau217) and magnetic resonance imaging (MRI) were the best predictors of different tau-PET (positron emission tomography) composites. Machine learning models demonstrated high generalizability across AD cohorts.
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APA 7th
Karlsson, L., Vogel, J., Arvidsson, I., Åström, K., Strandberg, O., Seidlitz, J., et al. (2025). Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.14600
Vancouver
Karlsson L, Vogel J, Arvidsson I, Åström K, Strandberg O, Seidlitz J, et al. Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2025. doi:10.1002/alz.14600. PMID: 39985487.
BibTeX
@article{karlsson2025-39985487,
author = {Karlsson, Linda and Vogel, Jacob and Arvidsson, Ida and Åström, Kalle and Strandberg, Olof and Seidlitz, Jakob and Bethlehem, Richard A I and Stomrud, Erik and others},
title = {{Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2025},
doi = {10.1002/alz.14600},
pmid = {39985487},
url = {https://doi.org/10.1002/alz.14600}
}
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.
Key findings
- Models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared = 0.66-0.69), with especially high contribution from plasma phosphorylated tau-217 (p-tau217).
- MRI variables were the best predictors of asymmetric tau load between the two hemispheres (best model: R-squared = 0.28-0.42).
- The models showed high generalizability to external test cohorts with data collected at multiple sites.
- Through a proof-of-concept two-step classification workflow, we also demonstrated possible model translations to a clinical setting.
- This study highlights the promising and limiting aspects of using machine learning to predict tau-PET from scalable cost-effective variables, with findings relevant for clinical settings and future research.
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Study details
- Evidence strengthLow
How this study scored 46/100
Published in a leading specialty journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / 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, multicenter collaboration and free to read, so those were left out: it earned 26 of the 56 points we could judge it on, which is 46 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia : the journal of the Alzheimer's Association) |
| 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 | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39985487) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 26 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 26 of the 56 points we could judge it on is 46 out of 100. |
| Total | 46 / 100 | — |
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