Endothelin antagonism reduces hemoglobin A1c in patients with pulmonary hypertension
Bibliographic record
Abstract
Our lab recently reported that the blockade of endothelin-1 (ET-1) receptors attenuates insulin resistance in obese mice; therefore, we hypothesized that patients taking ET-1 receptor antagonists (ERAs) will have improved glycemic control. University of Mississippi Medical Center (2013–2020) electronic health record (EPIC) data were extracted from patients ≥18 years old with a clinical diagnosis of pulmonary hypertension (Food and Drug Administration indication for ERA use) and at least two clinical visits within 2 years. Patients prescribed ERAs ( n = 11) were similar in age (61 ± 14 years vs. 60 ± 14 years), body mass index (BMI) (34 ± 8 kg/m 2 vs. 35 ± 11 kg/m2), diabetes prevalence (73% vs. 80%, p = 0.59), and follow-up time (209 ± 74 days vs. 283 ± 180 days) compared with patients not taking ERAs ( n = 137). There was a small but similar decrease in BMI at follow-up in the ERA (−1.9 ± 3 kg/m2) and control patients (−1.6 ± 5 kg/m2). At follow-up, hemoglobin A1c (HbA1c) significantly decreased −12% ± 11% of baseline in patients taking ERAs, while this did not occur in the control patients (2% ± 20% increase in HbA1c). In the whole population, baseline HbA1c and ERA prescription predicted the fall in HbA1c, while there was no significant association with demographics, diabetes prevalence, and diabetic treatment. These data suggest a potential role of ET-1 in promoting insulin resistance and warrant further investigation into using these drugs for glycemic control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".