Myostatin Inhibition as an Adjuvant Therapy in Type 1 Diabetes Mellitus
Bibliographic record
Abstract
While Type 1 Diabetes Mellitus (T1DM) is characterized by hypoinsulinemia and hyperglycemia, persons with T1DM also develop insulin resistance and recent studies have demonstrated that insulin resistance is a primary mediator of the micro and macrovascular complications that invariably develop in this chronic disease. Therefore, we hypothesized that a reduction in circulating myostatin (a TGF‐beta family member known to regulate muscle mass) in a T1DM mouse model would improve skeletal muscle health, resulting in an increased insulin sensitivity and a reduction in blood glucose. To that end, we crossed Akita diabetic mice with the Myostatin Ln/Ln mouse line (which displays a ~60% reduction in myostatin levels) to generate a novel mouse line (Myostatin Ln/Ln ; Akita +/− ). Our data support the hypothesis that reducing circulating myostatin levels prevented the loss of skeletal muscle mass observed in T1DM, as well as significantly increasing Glut1 and Glut4 transporter densities with the end result being an increased glucose uptake in response to an insulin tolerance test (ITT). These positive changes in T1DM skeletal muscle health resulted in significant reductions in resting blood glucose levels and other diabetic symptoms (hyperphagia, polydypsia), even in the absence of exogenous insulin. Taken together, these studies provide a foundation for considering pharmacological myostatin inhibitors as an adjuvant therapy in T1DM as a means to delay the development of diabetic complications. Support or Funding Information This work is funded by a grant from Natural Sciences and Engineering Research Council of Canada to TJH
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".