Association Between Depression and Urinary Heavy Metal Levels
Bibliographic record
Abstract
Introduction: Growing concerns about heavy metal pollution due to urbanization and industrialization have highlighted potential links between heavy metal levels and neurological disorders, including depression. This project aims to investigate the relationship between urinary heavy metal levels and depression status. Methods: The US National Health and Nutrition Examination Survey (NHANES) 2011-2018 data were used. Depression was assessed using a nine-item version of the Patient Health Questionnaire (PHQ-9), with a cut-off point of 10 for depression cases. 13 urinary heavy metals were included. Both univariate analyses, the weighted Wilcoxon test and weighted logistic regression with heavy metal variables transformed into quintiles, and multivariate analyses, Classification and Regression Tree (CART) and random forest, were conducted to investigate the association. Results: The weighted Wilcoxon test found higher levels of cadmium (Cd), antimony (Sb), tin (Sn) and tungsten (Tu) and lower levels of mercury (Hg) and arsenic (As) in the depression group. Weighted logistic regression revealed higher depression risks in the fifth quintile of Cd, the third, fourth and fifth quintiles of Sb, and the third and fifth quintiles of Tu levels. Lower risk was detected in the fifth quintile of As levels. Multivariate analysis identified Sn, Cd, As, cesium (Cs), and thallium (Tl) as crucial metals for classifying depression. Conclusion: In conclusion, this project reveals the complex relationship between urinary heavy metals and depression. Depression was associated with different sets of metals depending on the testing method used, and additional investigation is required to explore the potential interactions.
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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".