Delivery of adiponectin gene to skeletal muscle using ultrasound targeted microbubbles improves insulin sensitivity and whole body glucose homeostasis
Bibliographic record
Abstract
Numerous studies have shown that adiponectin confers antidiabetic effects via both insulin-like and insulin-sensitizing actions. The majority of adiponectin in circulation is derived from adipocytes; however, other tissues such as skeletal muscle can produce adiponectin. This study was designed to investigate the functional significance of adiponectin produced by skeletal muscle. We encapsulated the adiponectin gene in lipid-coated microspheres filled with octafluoropropane gas that were injected into the systemic circulation and destroyed within the microvasculature of skeletal muscle using ultrasound. We first demonstrated safe and successful targeting of luciferase and green fluorescent protein reporter genes to skeletal muscle using this approach and then confirmed efficient overexpression of adiponectin mRNA and oligomeric protein forms. Glucose tolerance test indicated that overexpression of adiponectin in skeletal muscle was able to improve glucose intolerance induced by feeding mice a high-fat diet (HFD), and this correlated with improved skeletal muscle insulin signaling. We then performed hyperinsulinemic-euglycemic clamp studies and demonstrated that adiponectin overexpression attenuated the decreases in glucose infusion rate, glucose disposal, and increase in glucose appearance induced by HFD. Ultrasound-targeted microbubble destruction (UTMD) delivery of adiponectin to skeletal muscle also enhanced serum adiponectin levels and improved hepatic insulin sensitivity. In conclusion, our data show that UTMD efficiently delivers adiponectin to skeletal muscle and that this improves insulin sensitivity and glucose homeostasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".