Circulating Selenium Concentration Is Inversely Associated With the Prevalence of Stroke: Results From the Canadian Health Measures Survey and the National Health and Nutrition Examination Survey
Bibliographic record
Abstract
Background Observational studies have suggested that selenium (Se) may have beneficial effects against certain cardiovascular outcomes, with a possible U-shaped association. We assessed the hypothesis that blood Se concentration might be inversely associated with the prevalence of stroke and the relationship would be nonlinear. Methods and Results Data collected from adult participants (aged ≥20 years) in the Canadian Health Measures Survey ( CHMS 2007-2011, n=7065) and the US National Health and Nutrition Examination Survey ( NHANES 2011-2012, n=5030) were analyzed. A total of 82 (1.16%) and 202 (4.02%) stroke cases were identified in CHMS and NHANES . Respondents with stroke had lower Se levels than those without stroke, with a mean difference of 16 μg/L and 12 μg/L for CHMS and NHANES , respectively. Respondents with high blood Se concentration (tertile 3) had a lower prevalence of stroke compared with those with low Se concentration (tertile 1). The adjusted odds ratios were 0.38 (95% CI : 0.15, 0.92) and 0.57 (95% CI : 0.31, 1.03) for CHMS and NHANES , respectively. A continuous decreasing trend of stroke with whole blood selenium was observed in CHMS , whereas the curve plateaued starting at 190 μg/L for NHANES , based on the cubic restricted spline regression. Sensitivity analysis using the serum and urinary Se concentrations demonstrates that our results were consistent across different selenium biomarkers. Conclusions We observed inverse cross-sectional associations between whole blood Se and the prevalence of stroke in representative samples of the Canadian and the US population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".