Association between low blood selenium concentrations and poor hand grip strength in United States adults participating in NHANES (2011–2014)
Bibliographic record
Abstract
The trace element selenium, which is found in selenoproteins, plays an antioxidant role in preventing muscle tissue injury. A positive association between selenium concentrations and hand grip strength has been reported in older adults; however, the evidence of this association is scarce in general adults. In this study, we aimed to evaluate the association between blood selenium concentrations and low hand grip strength using the data from the National Health and Nutrition Examination Survey 2011–2012 and 2013–2014 in the United States (US). Logistic regression was used to calculate the odds ratio (OR) of low hand grip strength, with blood selenium level adjusted for potential confounders. Among 8158 adults (women: 51.59%) with a mean age of 47 (range: 18–80) years, women and non-Hispanic Blacks tended to have low blood selenium concentrations. Notably, participants with high blood selenium concentrations (range, 178.1–192.5 µg/L) were more likely to have a low risk of low hand grip strength after adjusting for the potential covariates (OR: 0.60, 95% confidence interval (CI): 0.38–0.95) than those with low blood selenium concentrations. After excluding participants with chronic diseases, high blood selenium concentrations were found to be associated with a low risk of low hand grip strength (OR: 0.30, 95% CI: 0.14–0.65). A J-shaped relationship was found between selenium concentrations and low hand grip strength ( P for nonlinear trend <0.0001). Subgroup analyses revealed a significantly consistent relationship among women, non-Hispanic Whites and others, and individuals with overweight or obesity ( P < 0.05). Our study suggests that blood selenium concentrations are inversely associated with hand grip strength in general US adults. However, further prospective studies are required to confirm the causality between selenium concentrations and hand grip strength.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".