L-arginine impact on inflammatory and cardiac markers in patients undergoing coronary artery bypass graft: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Numerous studies have explored the effects of L-arginine, whether administered in the form of a supplement or through infusion during cardioplegia, on cardiac and inflammatory markers in individuals undergoing coronary artery bypass grafting (CABG). However, these studies presented contradictory findings. Consequently, the objective of this study was to investigate the impact of l-arginine on these markers by analyzing available randomized controlled trials (RCTs). METHODS: We performed an extensive search across various databases, including Embase, Medline/PubMed, Web of Science, Scopus, Cochrane Library, and Google Scholar, covering research published until December 2023. To analyze the mean changes in inflammatory and cardiac markers between the L-arginine and control groups, we calculated the weighted mean difference (WMD) along with the corresponding 95% confidence interval (CI) using a random-effects model. RESULTS: A total of 393 RCTs were identified during the initial search. After screening and selection, 7 trials were included. In a meta-analysis of three trials that reported troponin T levels, we found a significant impact of L-arginine on reducing troponin T levels (WMD = -0.61 ng/ml; 95% CI: -1.07, -0.15). Our analysis also showed that L-arginine had a noticeable impact on decreasing interleukin-6 (IL-6) levels (WMD = -7.72 pg/ml; 95% CI: -15.05, -0.39). However, we found no considerable impact of L-arginine treatment on creatine phosphokinase-MB (CPK-MB), tumor necrosis factor-alpha (TNF-α), and troponin I compared to the placebo groups. CONCLUSIONS: Our findings suggest that L-arginine may benefit patients undergoing CABG, as it helps reduce inflammatory reactions and limits myocardial ischemia. This study registered in the PROSPERO database (Registration No. CRD42024508341).
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".