Smoking primes the metabolomic response in trauma
Bibliographic record
Abstract
INTRODUCTION: Smoking is a public health threat because of its well-described link to increased oxidative stress-related diseases including peripheral vascular disease and coronary artery disease. Tobacco use has been linked to risk of inpatient trauma morbidity including acute respiratory distress syndrome; however, its mechanistic effect on comprehensive metabolic heterogeneity has yet to be examined. METHODS: Plasma was obtained on arrival from injured patients at a Level 1 trauma center and analyzed with modern mass spectrometry-based metabolomics. Patients were stratified by nonsmoker, passive smoker, and active smoker by lower, interquartile, and upper quartile ranges of cotinine intensity peaks. Patients were substratified by high injury/high shock (Injury Severity Score, ≥15; base excess, <-6) and compared with healthy controls. p Value of <0.05 following false discovery rate correction of t test was considered significant. RESULTS: Forty-eight patients with high injury/high shock (7 nonsmokers [15%], 25 passive smokers [52%], and 16 active smokers [33%]) and 95 healthy patients who served as controls (30 nonsmokers [32%], 43 passive smokers [45%], and 22 active smokers [23%]) were included. Elevated metabolites in our controls who were active smokers include enrichment in chronic inflammatory and oxidative processes. Elevated metabolites in active smokers in high injury/high shock include enrichment in the malate-aspartate shuttle, tyrosine metabolism, carnitine synthesis, and oxidation of very long-chain fatty acids. CONCLUSION: Smoking promotes a state of oxidative stress leading to mitochondrial dysfunction, which is additive to the inflammatory milieu of trauma. Smoking is associated with impaired mitochondrial substrate utilization of long-chain fatty acids, aspartate, and tyrosine, all of which accentuate oxidative stress following injury. This altered expression represents an ideal target for therapies to reduce oxidative damage toward the goal of personalized treatment of trauma patients. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".