Abstract 4362463: Association between Toxic Metals exposure and Apolipoprotein among the USA population: A mixture Analysis from 2013-2016
Bibliographic record
Abstract
Background: Toxic metals have been associated with a variety of chronic diseases. Apolipoprotein B (ApoB) is a marker of cardiovascular risk influenced by environmental metal exposures. However, prior studies often evaluated these exposures individually without accounting for their potential combined effects. This study examined the association between individual and combined toxic metal exposure and ApoB levels among the U.S. population. Methods: We analyzed data from the 2013–2016 National Health and Nutrition Examination Survey, aged ≥12 years. Information on demographics, lifestyle factors, and biomarkers is collected. Blood concentrations of lead (µg/dL), cadmium(µg/L), mercury(µg/L), selenium(µg/L), and manganese (µg/L) are measured. ApoB levels, the primary outcome, are assessed via blood samples. Linear regression models estimate associations between individual metals and ApoB levels, both unadjusted and adjusted for covariates. Weighted Quantile Sum (WQS) regression is applied to examine the effect of metal mixtures on ApoB. Results: We included 5,867 participants with the mean (SD) concentration of ApoB 87.69 (26.06). Averaged blood concentrations are lead 1.20 µg/dL, cadmium 0.44 µg/L, mercury 1.47 µg/L, selenium 196.03 µg/L, and manganese 10.41 µg/L. Each 1 µg/L increase in blood cadmium was associated with a 4.41 mg/dL increase in ApoB (95% CI: 2.70, 6.11). Blood lead was also significantly associated with 1.06 mg/dL increase in ApoB (β = 1.06; 95% CI: 0.261, 1.86), as was blood mercury 1.08 mg/dL increase in ApoB (β = 1.08; 95% CI: 0.65, 1.52), and selenium 0.127 mg/dL increase in ApoB (β = 0.127; 95% CI: 0.085, 0.169). Blood manganese was not significantly associated with ApoB in either unadjusted or adjusted models. In the WQS mixture analysis, per quantile increase in combined metal, ApoB levels increase by 8.40 units (Estimate = 8.40, 95% CI: 6.59–10.20). Within the mixture, the height contributors were blood selenium (32%), lead (29%), and mercury (19%), suggesting these metals play a dominant role in influencing ApoB levels. Conclusions: Toxic metal exposures, particularly to selenium, lead, and mercury, are positively associated with increased ApoB levels in the U.S. population, both individually and as part of a mixture. These findings highlight the importance of accounting for co-exposures to environmental metals in cardiovascular risk assessment and underscore the need for further longitudinal research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".