Targeting Skeletal Muscle in Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Duchenne Muscular Dystrophy (DMD) is a severe X-linked disorder with progressive myofiber degeneration and fibrosis from dystrophin deficiency. Current therapies are largely supportive with limited anti-fibrotic benefit, prompting new strategies. Sodium-Glucose Co-Transporter-2 inhibitors (SGLT2i) show emerging anti-fibrotic and anti-inflammatory effects. We integrated open-access proteomic and transcriptomic datasets for in-silico analyses, including differential gene expression (DE), Weighted Gene Co-Expression Network Analysis (WGCNA), and pathway enrichment to identify dysregulated pathways potentially reversible by SGLT2i. Immune cell composition was estimated using CIBERSORTx in human and murine datasets. Therapeutic effects were tested with empagliflozin (EMPA) in mdx mice (30 mg/kg/day for 4 weeks starting at 12 weeks) and DMD mdx rats (10 mg/kg/day for 4 months starting at 5 months), with vehicle controls. Validation used RT-qPCR, grip-strength testing, and histological fibrosis staining. Analyses highlighted dysregulated extracellular matrix organization, cytokine signaling, and immune responses. Forty overlapping genes were identified; hub genes included COL3A1, COL5A2, and TGF-β1. EMPA reduced Tgfb1 expression in DMD rats and significantly decreased collagen deposition in skeletal muscle. Functional testing showed longer grip duration in EMPA-treated mice. Immune profiling revealed shifts in T cells and macrophages, indicating immunomodulation. Findings were consistent across species and data modalities analyzed. These results demonstrate that EMPA modulates fibrosis, inflammation, and muscle endurance in DMD models. These data support repurposing SGLT2i as a promising therapeutic strategy for DMD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".