A Systematic Review of Oxidative Stress Markers and Risk of Coronary Artery Calcification
Bibliographic record
Abstract
Background: Early diagnosis of atherosclerosis, particularly in its subclinical phase, is crucial for reducing mortality and morbidity associated with cardiovascular diseases. This study aims to investigate the relationship between oxidative stress markers and coronary artery calcification (CAC), enhancing our understanding of the pathophysiology of CAC. Methods: In October 2022, we conducted a systematic search of the Web of Science, Scopus, PubMed, and Embase databases without language or time restrictions, screening a total of 557 records. We excluded studies involving animals, in vitro experiments, reviews, case reports, clinical trials, editorials, and clinical guidelines. Eligible human observational studies (cohort and cross-sectional) that examined the link between CAC and oxidative stress markers were included. The Newcastle-Ottawa Scale was employed to assess the quality of the included studies. Results: Our systematic review encompassed 40 studies, all of which included both male and female participants, predominantly using cross-sectional designs. Participants included individuals at low, intermediate, or high risk of coronary artery disease, patients with type 2 diabetes, those with existing cardiovascular disease, and asymptomatic individuals. The studies investigated various oxidative stress markers, including serum uric acid and 8-isoprostane, both of which showed strong correlations with CAC incidence and severity. Conclusion: Oxidative stress markers may positively correlate with CAC scores, indicating a potential avenue for identifying individuals at heightened risk. This review underscores the need for further studies to facilitate early diagnosis of cardiovascular complications and the establishment of novel pharmacological targets.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".