Abstract 4362448: Environmental Exposure to Toxic Metals and Cardiovascular Diseases Outcomes from 2015-2023: A Mixture Analysis
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Toxic metals are widespread environmental contaminants and may play a role in the development of CVD. However, individuals are typically exposed to metal mixtures rather than single elements, and few studies have examined the combined effect of such exposures using advanced mixture modeling. To evaluate the associations between blood levels of five toxic metals and five CVDs using both single and mixture toxic metal exposure. Methods: We used data from the 2015–2023 National Health and Nutrition Examination Survey. Blood concentrations of lead, cadmium, mercury, selenium, and manganese are examined in the presence of congestive heart failure, coronary heart disease, angina, heart attack, and stroke. Multivariable logistic regression models and weighted quantile sum (WQS) regression adjust for sociodemographic, behavioral, and dietary confounders. Results: We included 15,000 U.S. adults aged 20 years and older. 48.33% were male, with 34% identifying as Black and 14% Hispanic. CVD prevalence ranged from 2.5% (angina) to 4.7% (stroke). Mean blood concentrations were 1.24 µg/dL for lead and 0.48 µg/L for cadmium. In adjusted single-metal models, cadmium was consistently and positively associated with greater odds of CVD outcomes, including angina (OR: 1.26, 95% CI: 1.04–1.54), heart attack (OR: 1.43, 95% CI: 1.17–1.76), and stroke (OR: 1.16, 95% CI: 0.99–1.37). Lead and mercury showed varied associations across outcomes, and selenium was marginally protective in some models. In WQS mixture models, significant associations were observed for coronary heart disease (WQS estimate = 0.2585, 95% CI: 0.1066–0.4104, p = 0.0009). For other outcomes (stroke, heart attack), WQS mixture effects were not statistically significant. Cadmium contributed the greatest weight to the metal mixture across most outcomes (e.g., 63.5% for CHF, 67.6% for stroke), with manganese contributing notably to coronary heart disease (weight: 76.9%). Conclusions: Cadmium is a major contributor to the observed associations between metal exposure and cardiovascular diseases in U.S. adults. While mixture modeling revealed a significant effect for coronary heart disease, cadmium dominated the mixture components in most outcomes. Further prospective studies are recommended to understand the role of the toxic metal exposures, especially Cadmium, in CVD prevalence.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".