ArticleScientific reports2025
Association between urinary cadmium levels and increased gallstone disease in US adults.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Heavy metal exposure is acknowledged as a risk factor for poor health. However, the effect of heavy metal exposure on the prevalence of gallstones is still unknown. Therefore, we investigated the relationship between heavy metal concentrations and the prevalence of gallstones among US adults. Multivariate logistic regression indicated that only urinary cadmium was an independent risk factor for gallstones. Compared to the low urine cadmium group, the high cadmium group had a elevated increased risk of gallstone formation. Furthermore, the weighted quantile sum model showed that heavy metal mixtures were not associated with gallstone prevalence. Additionally, urinary cadmium levels were associated with an increased risk of gallstone formation in young individuals, males, Mexican Americans, Non-Hispanic Whites, as well as smokers and drinkers. Moreover, nine machine learning methods were utilized to construct an interpretable predictive model for gallstone prevalence. Among these models, the XGBoost model exhibited the highest performance and was selected for further investigation. Subsequently, shapely additive explanations was used for model interpretation. The results also indicated that urinary cadmium concentrations were the most important variable for gallstones. Thus, our results indicated that long-term chronic cadmium exposure is a risk factor for gallstone prevalence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.