The effects of the β<sub>1</sub>‐adrenergic receptor antagonist bisoprolol administration on mirabegron‐stimulated human brown adipose tissue thermogenesis
Bibliographic record
Abstract
Abstract Aim Pharmacological stimulation of human brown adipose tissue (BAT) has been hindered by ineffective activation or undesirable off‐target effects. Oral administration of the maximal allowable dose of mirabegron (200 mg), a β3‐adrenergic receptor (β3‐AR) agonist, has been effective in stimulating BAT thermogenesis and whole‐body energy expenditure. However, this has been accompanied by undesirable cardiovascular effects. Therefore, we hypothesized that combining mirabegron with a β1‐AR antagonist could suppress these unwanted effects and increase the stimulation of the β3‐AR and β2‐AR in BAT. Methods We performed a randomized crossover trial (NCT04823442) in 8 lean men. Mirabegron (200 mg) was administered orally with or without the β1‐AR antagonist bisoprolol (10 mg). Dynamic [11C]‐acetate and 2‐deoxy‐2‐[18F]fluoro‐d‐glucose PET/CT scans were performed sequentially after oral administration of mirabegron ± bisoprolol. Results Compared to room temperature, mirabegron alone increased BAT oxidative metabolism (0.84 ± 0.46 vs. 1.79 ± 0.91 min−1, p = 0.0433), but not when combined with bisoprolol. The metabolic rate of glucose in BAT, measured using [18F]FDG PET, was significantly higher with mirabegron than mirabegron with bisoprolol (24 ± 10 vs. 16 ± 8 nmol/g/min, p = 0.0284). Bisoprolol inhibited the mirabegron‐induced increase in systolic blood pressure and heart rate. Conclusion The administration of bisoprolol decreases the adverse cardiovascular effects of mirabegron. However, the provided dose also blunted the mirabegron‐stimulated increase in BAT lipolysis, thermogenesis, and glucose uptake. The attenuation in BAT blood flow induced by the large dose of bisoprolol may have limited BAT thermogenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".