Blood Lead and Other Metal Biomarkers as Risk Factors for Cardiovascular Disease Mortality
Bibliographic record
Abstract
Analyses of the Third National Health and Nutrition Examination Survey (NHANES III) in 1988 to 1994 found an association of increasing blood lead levels < 10 μg/dL with a higher risk of cardiovascular disease (CVD) mortality. The potential need to correct blood lead for hematocrit/hemoglobin and adjust for biomarkers for other metals, for example, cadmium and iron, had not been addressed in the previous NHANES III-based studies on blood lead-CVD mortality association. We analyzed 1999 to 2010 NHANES data for 18,602 participants who had a blood lead measurement, were ≥ 40 years of age at the baseline examination and were followed for mortality through 2011. We calculated the relative risk for CVD mortality as a function of hemoglobin- or hematocrit-corrected log-transformed blood lead through Cox proportional hazard regression analysis with adjustment for serum iron, blood cadmium, serum C-reactive protein, serum calcium, smoking, alcohol intake, race/Hispanic origin, and sex. The adjusted relative risk for CVD mortality was 1.44 (95% confidence interval = 1.05, 1.98) per 10-fold increase in hematocrit-corrected blood lead with little evidence of nonlinearity. Similar results were obtained with hemoglobin-corrected blood lead. Not correcting blood lead for hematocrit/hemoglobin resulted in underestimation of the lead-CVD mortality association while not adjusting for iron status and blood cadmium resulted in overestimation of the lead-CVD mortality association. In a nationally representative sample of U.S. adults, log-transformed blood lead was linearly associated with increased CVD mortality. Correcting blood lead for hematocrit/hemoglobin and adjustments for some biomarkers affected the association.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".