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Constraint Based Metabolic Modeling Identifies Robust Biomarkers and Therapeutic Target Combinations in Alzheimer Disease
Wang F, Li P, Huang RS.. Research Square, 2026-10-15
Topic: Gut, metabolism and diet
Alzheimer’s disease (AD) involves widespread metabolic, mitochondrial, inflammatory, and redox dysfunction. We extended Recon3D with lumped synthesis, degradation, and recycling reactions for AD-associated proteins and inflammatory mediators and integrated brain-region-specific transcriptomic data to reconstruct metabolic models of asymptomatic AD, symptomatic AD, and healthy tissue. Parsimonious multi-objective metabolite-flow variability analysis across five nutrient environments identified medium-robust metabolic alterations, and fuzzy multi-objective optimization prioritized candidate interventions. Model-predicted intracellular production flows and uptake of aromatic and branched-chain amino acids were consistently increased, whereas CoA/itaconate, purine-nucleotide, isocitrate, and glutathione metabolism showed stage- and region-dependent changes. PPA2 upregulation and SUCLG2 downregulation emerged as promising single-enzyme interventions. Two-enzyme combinations (PGM1↓/PPA2↑ and SUCLG2↓/PPA2↑) improved predicted recovery across most disease–medium models. Adding increased uptake of itaconate, omega-3 eicosatetraenoic acid, Mead acid, or LPE20:4 to selected pairs further improved robustness. These findings provide testable hypotheses for metabolic biomarkers and combination interventions in Alzheimer’s disease.
Key findings
- These findings provide testable hypotheses for metabolic biomarkers and combination interventions in Alzheimer’s disease.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. 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 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 13 of the 62 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Research Square) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| 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 | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 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 | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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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. Check the citation against the original before you use it.
APA 7th
Wang, F., Li, P., & Huang, R. S. (2026). Constraint Based Metabolic Modeling Identifies Robust Biomarkers and Therapeutic Target Combinations in Alzheimer Disease. Research Square. https://doi.org/10.21203/rs.3.rs-11324647/v1
Vancouver
Wang F, Li P, Huang RS. Constraint Based Metabolic Modeling Identifies Robust Biomarkers and Therapeutic Target Combinations in Alzheimer Disease. Research Square. 2026. doi:10.21203/rs.3.rs-11324647/v1.
BibTeX
@article{wang2026-13e8938d,
author = {Wang, F. and Li, P. and Huang, R. S.},
title = {{Constraint Based Metabolic Modeling Identifies Robust Biomarkers and Therapeutic Target Combinations in Alzheimer Disease}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-11324647/v1},
url = {https://doi.org/10.21203/rs.3.rs-11324647/v1}
}
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