Abstract 366: Inhibition of Interleukin-1ß by a Monoclonal Antibody Therapy Reduces Vascular Calcification in Ldlr-/- Mice
Bibliographic record
Abstract
Objective: Given the link between cholesterol and activation of inflammation via interleukin-1β, we thus tested the effects of IL-1β inhibition on atherosclerotic calcification in mice. Methods and Results: A mouse monoclonal antibody (mAB) against IL-1β or placebo was administered subcutaneously to Ldlr-/- and Tg(Pcsk9) models fed a Western diet. Drug level, anthropometric, lipid and glucose profiles were determined. PCSK9, SAA1 and cytokine expressions were measured by ELISA. Aortic calcification was determined by micro-CT and X-Ray densitometry and aortic flow velocity was assessed by ultrasound. Circulating levels of IL-1β in Ldlr-/- mice were significant twice that observed in Tg(Pcsk9) mice. Both mAb and placebo treated mice did not differ in their growth, lipid, glucose profiles and other cytokines while plasma SAA1 levels were lower in mAb-treated mice. Calcifications were significantly diminished in mAb-treatment Ldlr-/- mice (a reduction of 75% by X-ray and 96% by micro-CT) and reduced insignificantly in mAb-treatment Tg(Pcsk9) mice, whereas aortic flow velocity was unchanged in both models. Conclusions: Herein we demonstrate that aortic calcifications can be inhibited by IL-1β mAb in LDL-receptor deficient mice. These results have a translational component to prevent vascular calcification in human and represent new evidence to rationalize targeting inflammation in cardiovascular disease.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".