GSK3 inhibition improves skeletal muscle function and whole-body metabolism in male mouse models of Duchenne muscular dystrophy
Bibliographic record
Abstract
Inhibiting glycogen synthase kinase 3 (GSK3) improves muscle function, metabolism, and bone health in many diseases and conditions; however, whether GSK3 should be targeted for Duchenne muscular dystrophy (DMD), a severe muscle wasting disorder with no cure, remains unknown. Here, we show the effects of GSK3 inhibition in male DBA/2J (D2) and C57BL/10 (C57) mdx mice. Treating D2 mdx mice with GSK3 inhibitors alone or in combination with aerobic exercise improves muscle strength, endurance, and morphology, attenuates the hypermetabolic phenotype, and enhances insulin sensitivity. GSK3 inhibition in C57 mdx mice also improves muscle fatigue resistance and increases cage ambulation. Moreover, muscle-specific GSK3 knockdown in mdx mice augments muscle force production and endurance. In both mdx strains, GSK3 inhibition increases bone mineral content and density. Overall, these improvements to muscle, metabolic, and bone health with GSK3 inhibition in mdx mice may have clinical implications for patients with DMD, where the current standard of care, glucocorticoids, delay the loss of ambulation but increase the risk for insulin resistance and osteoporosis. Along with our observation of lowered β-catenin content in DMD myoblasts, a known cellular target for GSK3, this study provides ample evidence in support of inhibiting GSK3 for this disease. Inhibiting glycogen synthase kinase 3 (GSK3) improves muscle function, metabolism, and bone health in many other diseases. Here, Marcella et al. found that inhibiting glycogen synthase kinase 3, alone or combined with exercise, improves muscle health and function in mouse models of Duchenne muscular dystrophy without negatively affecting insulin sensitivity or bone health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".