Propranolol Normalizes Metabolomic Signatures Thereby Improving Outcomes After Burn
Bibliographic record
Abstract
OBJECTIVE AND BACKGROUND: Propranolol, a nonselective beta-receptor blocker, improves outcomes of severely burned patients. While the clinical and physiological benefits of beta-blockade are well characterized, the underlying metabolic mechanisms are less well defined. We hypothesized that propranolol improves outcomes after burn injury by profoundly modulating metabolic pathways. METHODS: In this phase II randomized controlled trial, patients with burns ≥20% of total body surface area were randomly assigned to control or propranolol (dose given to decrease heart rate <100 bpm). Outcomes included clinical markers, inflammatory and lipidomic profiles, untargeted metabolomics, and molecular pathways. RESULTS: Fifty-two severely burned patients were enrolled in this trial (propranolol, n=23 and controls, n=29). There were no significant differences in demographics or injury severity between groups. Metabolomic pathway analyses of the adipose tissue showed that propranolol substantially alters several essential metabolic pathways involved in energy and nucleotide metabolism, as well as catecholamine degradation ( P <0.05). Lipidomic analysis revealed that propranolol-treated patients had lower levels of proinflammatory palmitic acid ( P <0.05) and saturated fatty acids ( P <0.05) with an increased ratio of polyunsaturated fatty acids ( P <0.05), thus shifting the lipidomic profile towards an anti-inflammatory phenotype after burn ( P <0.05). These metabolic effects were mediated by decreased activation of hormone-sensitive lipase at serine 660 ( P <0.05) and significantly reduced endoplasmic reticulum stress by decreasing phospho-JNK ( P <0.05). CONCLUSION: Propranolol's ability to mitigate pathophysiological changes to essential metabolic pathways results in significantly improved stress responses.
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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 | 0.000 |
| 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".