Abstract 692: Therapeutic Inhibition of miR-33 Does Not Promote Obesity, Insulin Resistance or Hepatic Lipid Accumulation
Bibliographic record
Abstract
Inhibition of miR-33, a critical post-transcriptional regulator of cholesterol homeostasis, prevents atherosclerosis progression both dependently and independently of its effects on circulating HDL levels. However, recent studies suggest that genetic deletion or inhibition of miR-33 may adversely affect metrics of insulin resistance, lipid metabolism and hepatic steatosis. Given the therapeutic interest in miR-33 inhibitors for treating atherosclerosis, we sought to determine the effects of long-term pharmacological inhibition of miR33 in a mouse model of diet-induced obesity (DIO). Results: C57BL6/J mice were fed a high-fat diet in conjunction with anti-miR therapy (10mg/kg of control anti-miR or anti-miR33) for 20 weeks. There was significant but equivalent weight gain in all 3 groups. While anti-miR33 therapy increased total plasma cholesterol compared to control anti-miR treated mice (p≤0.01), there was no differences relative to PBS treated mice. Anti-miR33 treatment also resulted in a marked decrease in serum triglycerides relative to control anti-miR (29% decrease, p≤0.05) but once again not compared to PBS treated mice. Metrics of insulin resistance (GTT, ITT) were not altered in anti-miR33 treated mice relative to controls. Interestingly, indirect calorimetric measurements demonstrated that anti-miR33 treated mice had reduced respiratory exchange ratios (RER) compared to both control anti-miR and PBS treated mice (18% decrease, p≤0.05), suggesting an increase in fatty acid versus carbohydrate utilization in anti-miR33 treated mice. In addition, hepatic protein expression of known miR-33 target gene ABCA1 was de-repressed upon miR-33 inhibition, indicating that miR-33 was efficiently inhibited in this model. In contrast, putative miR-33 target gene SREBP-1 protein expression was not altered, nor were SREBP-1 downstream target genes FASN and ACC. Finally, anti-miR33 treated mice did not accumulate more lipids in their livers relative to control. In conclusion, we show in a DIO model that therapeutic silencing of miR-33 does not promote hepatic steatosis nor does it increase metrics of insulin resistance, suggesting that pharmacological inhibition of miR-33 may be a safe therapeutic for the treatment of atherosclerosis.
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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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".