Selective Versus Nonselective β-Adrenergic Receptor Blockade in Chronic Heart Failure: Differential Effects on Myocardial Energy Substrate Utilization
Bibliographic record
Abstract
BACKGROUND: Non-selective and selective beta-blockers have been shown to improve outcomes in chronic heart failure (CHF). Recent data suggests the non-selective beta-blockers have a more favourable effect on outcomes than beta(1)-selective agents. We sought to examine the differential effects of non-selective versus selective beta-blockade on myocardial substrate utilization in patients with CHF. METHODS AND RESULTS: Twenty-two patients with CHF were randomised to the non-selective beta-blocker carvedilol or the selective beta-blocker metoprolol (double-blind). Measurement of hemodynamics, arterial and coronary sinus free fatty acid (FFA) and lactate levels, and cardiac norepinephrine spillover (CANESP) were made before and after 4 months of therapy. In the carvedilol group (n=11), there was a significant reduction in myocardial FFA uptake (0.12+/-0.02 to 0.1+/-0.02 mmol/l, P<0.03). By contrast, in the metoprolol group (n=11) there was no change in myocardial FFA extraction. Carvedilol therapy tended to increase myocardial lactate extraction (0.24+/-0.05 to 0.35+/-0.08 mmol/l, P=0.08) while metoprolol therapy resulted in a trend in the opposite direction (0.18+/-0.03 to 0.11+/-0.04 mmol/l, P=0.09). The change in lactate extraction in the carvedilol group was significantly different from that in the metoprolol group (+0.11+/-0.06 vs. -0.09+/-0.04 mmol/l, P<0.01). Carvedilol treatment caused a significant reduction in CANESP while metoprolol had a neutral effect (-95+/-27 vs. 25+/-42 pmol/min, carvedilol vs. metoprolol P<0.03). CONCLUSION: Carvedilol treatment caused a 20% reduction in myocardial free fatty acid extraction while metoprolol had a neutral effect. These differences are most probably related to the differential effects of these two agents on efferent cardiac sympathetic activity and may be relevant to the reported differential effects of these drugs on clinical outcomes.
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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) | 0.000 | 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".