Anti-Atherogenic Effect of Insulin in vivo
Bibliographic record
Abstract
Metabolic syndrome is a risk factor for atherosclerosis and restenosis. In metabolic syndrome, insulin resistance coexists with hyperinsulinemia and hyperlipidemia. Hyperlipidemia has growth-promoting effects, whereas insulin has both growth-promoting and growth-inhibitory effects on vascular smooth muscle cells in vitro. The objective of this study was to investigate the effects of hyperlipidemia and hyperinsulinemia on vascular cell growth in vivo after arterial injury. Rats fed a low-fat diet were treated with either subcutaneous blank (LFC) or insulin (LFI) implants. Rats fed a high-fat diet also received blank (HFC) or insulin (HFI) implants. After 3 days, rats received balloon carotid injury, and 14 days later they were sacrificed to measure neointimal area and proliferation. Hyperinsulinemia was present in LFI and HFI and hyperlipidemia was present in HFC and HFI. Neointimal area was higher in HFC (0.153 +/- 0.009 mm(2), p < 0.05) but lower in LFI (0.098 +/- 0.005, p < 0.01) than LFC (0.127 +/- 0.005). In HFI (0.142 +/- 0.008, p < 0.05) neointimal area was not different from HFC or LFC. In conclusion, insulin reduced neointimal growth, but the effect of insulin was diminished by the high-fat diet. Thus, our results demonstrate an anti-atherogenic effect of insulin in vivo and suggest that in metabolic syndrome insulin resistance rather than hyperinsulinemia is the atherogenic risk factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".