Concentrations of Lead, Mercury, Selenium, and Manganese in Blood and Hand Grip Strength among Adults Living in the United States (NHANES 2011–2014)
Bibliographic record
Abstract
Exposure to lead and mercury can cause deficits in neuromotor function. Selenium and manganese are essential elements, hence both deficiency and excess could result in decreased neuromotor function. We aimed to examine hand grip strength, a marker of neuromotor function, and blood concentrations of lead, mercury, selenium, and manganese in the general U.S. population. We used data from the National Health and Nutrition Examination Survey (NHANES, 2011-2014) on 6199 participants ages 20-79 years. We assessed associations of blood concentration for these elements and grip strength with generalized regression models, and cubic splines to detect possible nonlinear relations, adjusting for confounders. The results showed that mercury and manganese were not associated with grip strength. Lead was associated with weaker grip strength in women (for 10-fold increase in lead, -2.4 kg; 95% CI: -4.2, -0.5), but not in men. Higher selenium was associated with stronger grip strength in women (8.5 kg; 95% CI: 1.9, 15.1) and men (4.6; 95% CI: -11.9, 21.0), although the association was not significant in the latter. In conclusion, lead exposure was associated with weaker grip strength in women, even at the low exposure levels in the population. Furthermore, low blood selenium level was associated with weaker grip strength, suggesting that some individuals might have selenium deficiency manifesting with poorer neuromotor function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".