Impaired Arteriolar Dilation in a Mouse Model of Familial Hypercholesterolemia: Impact of Chronic Exercise and Anti‐Cholesterol Therapy
Bibliographic record
Abstract
Hypercholesterolemia is a strong risk factor for negative cardiovascular outcomes and, while mechanistic benefits of ameliorative therapies have not been fully elucidated, it has been clearly demonstrated that evolution of familial hypercholesterolemia (FH) can culminate in profound impairments to microvascular function. To interrogate thus, we hypothesized that FH‐induced increases in oxidant stress and inflammation will impair skeletal muscle resistance arteriolar dilation due to a decreased NO bioavailability. Vascular responses to NO‐dependent stimuli were attenuated with FH, and responses were improved by antioxidant treatment, suggesting oxidant scavenging of NO. Chronic ingestion of ezetimibe/simvastatin did not restore vascular reactivity with FH, but improved inflammation through reductions to TNF‐α, IL‐6, and CRP. Chronic swimming exercise caused minor improvements to vascular dilation, but improved antioxidant protection through increased SOD‐1 and catalase expression. Combination therapy improved dilator reactivity, and lowered inflammation and oxidant stress. Taken together, these observations suggest that genetic dyslipidemia creates a condition wherein endothelial function is compromised, negatively impacting dilator responses. Interventions which can reduce both oxidant stress and chronic inflammation appear to be effective in improving endothelial function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".