Changes in biomarkers of endothelial function, oxidative stress, inflammation and lipids after smoking cessation: A cohort study
Bibliographic record
Abstract
BACKGROUND: Tobacco use is known to be involved in the development of cardiovascular diseases, which leads to premature mortality. Endothelial dysfunction, the first step in this process, was shown induced by smoking. It is reported that quitting smoking could reduce the risk of diseases, but the implied mechanisms are still unclear. This study aimed to evaluate the biological markers of endothelial function in smokers when actively smoking and after cessation. METHODS: Quantification of several biomarkers reflecting inflammation, endothelium activation, oxidative stress, and lipids was performed in 65 smokers when actively smoking and after cessation (median abstinence duration of 70 days). RESULTS: A possible decrease of inflammation was observed through the concentration reduction of a proinflammatory cytokine (interleukine-6) on quitting. A decrease of endothelium activation was visible by the reduced level of the soluble intercellular adhesion molecule. Two antioxidants, uric acid and vitamin C, were found at higher concentration than before the cessation, potentially reflecting the decrease of oxidative stress on quitting. Lipid profile was improved post-quit since HDL level was increased and LDL level was decreased. All these effects were visible at short term with abstinence duration less than 70 days. No sex-specific difference was observed and no additional changes were observed for longer abstinence duration. CONCLUSION: These observations suggest that some adverse effects of smoking on endothelial function could be reversible on quitting smoking. It could encourage smokers to enter a cessation program to reduce the risk for cardiovascular diseases development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".