Treatment with a novel agent combining docosahexaenoate and metformin increases protectin DX and IL‐6 production in skeletal muscle and reduces insulin resistance in obese diabetic <i>db/db</i> mice
Bibliographic record
Abstract
AIMS: To compare the therapeutic potential of TP-113, a unique molecular entity linking DHA with metformin, for alleviating insulin resistance in obese diabetic mice through the PDX/IL-6 pathway. MATERIAL AND METHODS: We utilized the generically obese diabetic db/db mouse model for all experiments. Initial studies investigated both a dose and time course response. These results were then utilized to design a long-term (5 week) treatment protocol. Mice were gavaged twice daily with 1 of 3 treatments: 200 mg/kg BW TP113, an equivalent dose of metformin alone (70 mg/kg BW) or water. Whole-body insulin sensitivity was measured using the hyperinsulinaemic-isoglycaemic clamp procedure in awake unrestrained mice. RESULTS: We first confirmed that acute TP-113 treatment raises PDX and IL-6 levels in skeletal muscle. We next tested the long-term glucoregulatory effect of oral TP-113 in obese diabetic db/db mice and compared its effect to an equivalent dose of metformin. A 5-week oral treatment with TP-113 reduced insulin resistance compared to both vehicle treatment and metformin alone, revealed by the determination of whole-body insulin sensitivity for glucose disposal using the clamp technique. This insulin-sensitizing effect was explained primarily by improvement of insulin action to suppress hepatic glucose production in TP-113-treated mice. These effects of TP-113 were greater than that of an equivalent dose of metformin, indicating that TP-113 increases metformin efficacy for reducing insulin resistance. CONCLUSION: We conclude that TP-113 improves insulin sensitivity in obese diabetic mice through activation of the PDX/IL-6 signaling axis in skeletal muscle and improved glucoregulatory action in the liver.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".