Urinary heavy metal burden and overactive bladder risk: a cross-sectional study based on NHANES 2005–2018
Bibliographic record
Abstract
OBJECTIVE: We aimed to explore the relationship between urinary heavy metals and overactive bladder (OAB) among US adults. METHOD: Data were obtained from the National Health and Nutrition Examination Survey 2005-2018. Concentrations of heavy metals in urine were determined by laboratory tests and corrected for urinary creatinine using natural logarithm (ln) transformation. OAB was assessed using questionnaires. The association between each urinary heavy metal and OAB was first explored using multivariate logistic regression, followed by nonlinear correlation analyses and smoothed curve fitting, with stratified analyses and interaction tests for age and sex. In addition, weighted quantile sum (WQS) regression and quantile-based g-computation (qgcomp) analyses were implemented to explore the relationship between mixed heavy metal and OAB. RESULTS: A total of 9086 participants were included in the final study. Multifactorial logistic regression analysis showed that cobalt (Co), lead (Pb), cadmium (Cd), and uranium (Ur) were positively associated with the risk of developing OAB, whereas barium (Ba) and thallium (Tl) were negatively associated with OAB. Nonlinear correlation analysis showed that the associations between the concentrations of Ba and Tl and OAB risk were nonlinear, and Pb, antimony (Sb), tungsten (Tu), Ur, and Cd showed significant positive correlations with OAB. WQS regression showed that OAB risk increased with increasing quartiles of the WQS index [odds ratio (OR) 1.202, 95% confidence interval (CI) 1.064-1.357], with Cd showing the strongest positive correlation with OAB. When the mixture effect was constrained to be negative, an increase in the WQS index quartile corresponded to a 14.3% reduction in OAB risk (OR 0.857, 95% CI 0.778-0.943), with Ba standing out as the most dominant. The results of qgcomp analysis showed that Cd had the largest positive weight of 0.5098, and Ba had the largest negative weight of 0.5976. CONCLUSION: Urinary heavy metals are significantly associated with OAB risk among US adults. The metal contributing most to the positive correlation with OAB risk is Cd, while the metal contributing most to the negative correlation with OAB risk is Ba.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".