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Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease
Ball BK, Park JH, Bergendorf AM, Proctor EA, Brubaker DK.. NPJ systems biology and applications, 2025-05-29
Topic: Amyloid, tau and blood-test diagnosis
Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. We next assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Finally, we validated our findings using single cell RNA-sequencing blood data of AD and healthy individuals and found erythroid cells contained the most gene expression signatures to the T2D PC. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.
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APA 7th
Ball, B. K., Park, J. H., Bergendorf, A. M., Proctor, E. A., & Brubaker, D. K. (2025). Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease. NPJ systems biology and applications. https://doi.org/10.1038/s41540-025-00539-5
Vancouver
Ball BK, Park JH, Bergendorf AM, Proctor EA, Brubaker DK. Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease. NPJ systems biology and applications. 2025. doi:10.1038/s41540-025-00539-5. PMID: 40442087.
BibTeX
@article{ball2025-40442087,
author = {Ball, B. K. and Park, J. H. and Bergendorf, A. M. and Proctor, E. A. and Brubaker, D. K.},
title = {{Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease}},
journal = {NPJ systems biology and applications},
year = {2025},
doi = {10.1038/s41540-025-00539-5},
pmid = {40442087},
url = {https://doi.org/10.1038/s41540-025-00539-5}
}
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Key findings
- Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD).
- Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD.
- We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state.
- Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples.
- Finally, we validated our findings using single cell RNA-sequencing blood data of AD and healthy individuals and found erythroid cells contained the most gene expression signatures to the T2D PC.
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Study details
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people 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 26 of the 58 points we could judge it on, which is 45 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NPJ systems biology and applications) |
| 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) |
| 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 40442087) |
| Free to read | 2 / 2 | Free to read |
| Points | 26 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100. |
| Total | 45 / 100 | — |
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