ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2025
Cross-Species Modeling Identifies Gene Signatures in Type 2 Diabetes Mouse Models Predictive of Inflammatory and Estrogen Signaling Pathways Associated with Alzheimer's Disease Outcomes in Humans.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Computational translation of mouse models of osteoarthritis predicts human disease.Osteoarthritis and cartilage · 2026Article
- Integrated cross-species translation and biophysical multi-scale modeling links molecular signatures and locomotory phenotypes in spaceflight-induced sarcopenia.NPJ microgravity · 2026Article
- Metabolites associated with type 2 diabetes and Alzheimer's disease trigger differential intracellular signaling responses in mouse primary neurons.Brain research · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Integrated Cross-Species Translation and Biophysical Multi-Scale Modeling Links Molecular Signatures and Locomotory Phenotypes in Spaceflight-Induced Sarcopenia.bioRxiv : the preprint server for biology · 2025Article
- Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease.NPJ systems biology and applications · 2025Article
- Computational Translation of Mouse Models of Osteoarthritis Predicts Human Disease.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Alzheimer's disease (AD), the predominant form of dementia, is influenced by several risk factors, including type 2 diabetes (T2D), a metabolic disorder characterized by the dysregulation of blood sugar levels. Despite mouse and human studies reporting this connection between T2D and AD, the mechanism by which T2D contributes to AD pathobiology is not well understood. A challenge in understanding mechanistic links between these conditions is that evidence between mouse and human experimental models must be synthesized, but translating between these systems is difficult due to evolutionary distance, physiological differences, and human heterogeneity. To address this, we employed a computational framework called translatable components regression (TransComp-R) to overcome discrepancies between pre-clinical and clinical studies using omics data. Here, we developed a novel extension of TransComp-R for multi-disease modeling to analyze transcriptomic data from brain samples of mouse models of AD, T2D, and simultaneous occurrence of both disease (ADxT2D) and postmortem human brain data to identify enriched pathways predictive of human AD status. Our TransComp-R model identified inflammatory and estrogen signaling pathways encoded by mouse principal components derived from models of T2D and ADxT2D, but not AD alone, predicted with human AD outcomes. The same mouse PCs predictive of human AD outcomes were able to capture sex-dependent differences in human AD biology, including significant effects unique to female patients, despite the TransComp-R being derived from data from only male mice. We demonstrated that our approach identifies biological pathways of interest at the intersection of the complex etiologies of AD and T2D which may guide future studies into pathogenesis and therapeutic development for patients with T2D-associated AD.
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