Selenium, antioxidants, cardiovascular disease, and all-cause mortality: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Antioxidants have been promoted for cardiovascular disease (CVD) risk reduction and for the prevention of cancer. Our preliminary analysis suggested that only when selenium was present were antioxidant mixtures associated with reduced all-cause mortality. OBJECTIVE: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to determine the effect of selenium supplementation alone and of antioxidant mixtures with or without selenium on the risk of CVD, cancer, and mortality. METHODS: We identified studies using the Cochrane Library, Medline, and Embase for potential CVD outcomes, cancer, and all-cause mortality following selenium supplementation alone or after antioxidant supplement mixtures with and without selenium up to June 5, 2020. RCTs of ≥24 wk were included and data were analyzed using random-effects models and classified by the Grading of Recommendations, Assessment, Development, and Evaluation approach. RESULTS: The meta-analysis identified 9423 studies, of which 43 were used in the final analysis. Overall, no association of selenium alone or antioxidants was seen with CVD and all-cause mortality. However, a decreased risk with antioxidant mixtures was seen for CVD mortality when selenium was part of the mix (RR: 0.77; 95% CI: 0.62, 0.97; P = 0.02), with no association when selenium was absent. Similarly, when selenium was part of the antioxidant mixture, a decreased risk was seen for all-cause mortality (RR: 0.90; 95% CI: 0.82, 0.98; P = 0.02) as opposed to an increased risk when selenium was absent (RR: 1.09; 95% CI: 1.04, 1.13; P = 0.0002). CONCLUSION: The addition of selenium should be considered for supplements containing antioxidant mixtures if they are to be associated with CVD and all-cause mortality risk reduction. This trial was registered at https://www.crd.york.ac.uk/PROSPERO/ as CRD42019138268.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".