Overproduction of endothelin-1 impairs glucose tolerance but does not promote visceral adipose tissue inflammation or limit metabolic adaptations to exercise
Bibliographic record
Abstract
Endothelin-1 (ET-1) is a potent vasoconstrictor and proinflammatory peptide that is upregulated in obesity. Herein, we tested the hypothesis that ET-1 signaling promotes visceral adipose tissue (AT) inflammation and disrupts glucose homeostasis. We also tested if reduced ET-1 is a required mechanism by which exercise ameliorates AT inflammation and improves glycemic control in obesity. We found that 1) diet-induced obesity, AT inflammation, and glycemic dysregulation were not accompanied by significantly increased levels of ET-1 in AT or circulation in wild-type mice and that endothelial overexpression of ET-1 and consequently increased ET-1 levels did not cause AT inflammation yet impaired glucose tolerance; 2) reduced AT inflammation and improved glucose tolerance with voluntary wheel running was not associated with decreased levels of ET-1 in AT or circulation in obese mice nor did endothelial overexpression of ET-1 impede such exercise-induced metabolic adaptations; 3) chronic pharmacological blockade of ET-1 receptors did not suppress AT inflammation in obese mice but improved glucose tolerance; and 4) in a cohort of human subjects with a wide range of body mass indexes, ET-1 levels in AT, or circulation were not correlated with markers of inflammation in AT. In aggregate, we conclude that ET-1 signaling is not implicated in the development of visceral AT inflammation but promotes glucose intolerance, thus representing an important therapeutic target for glycemic dysregulation in conditions characterized by hyperendothelinemia. Furthermore, we show that the salutary effects of exercise on AT and systemic metabolic function are not contingent on the suppression of ET-1 signaling.
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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) | 0.000 | 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".