Lead-Related Genetic Loci, Cumulative Lead Exposure and Incident Coronary Heart Disease: The Normative Aging Study
Bibliographic record
Abstract
BACKGROUND: Cumulative exposure to lead is associated with cardiovascular outcomes. Polymorphisms in the δ-aminolevulinic acid dehydratase (ALAD), hemochromatosis (HFE), heme oxygenase-1 (HMOX1), vitamin D receptor (VDR), glutathione S-transferase (GST) supergene family (GSTP1, GSTT1, GSTM1), apolipoprotein E (APOE),angiotensin II receptor-1 (AGTR1) and angiotensinogen (AGT) genes, are believed to alter toxicokinetics and/or toxicodynamics of lead. OBJECTIVES: We assessed possible effect modification by genetic polymorphisms in ALAD, HFE, HMOX1, VDR, GSTP1, GSTT1, GSTM1, APOE, AGTR1 and AGT individually and as the genetic risk score (GRS) on the association between cumulative lead exposure and incident coronary heart disease (CHD) events. METHODS: We used K-shell-X-ray fluorescence to measure bone lead levels. GRS was calculated on the basis of 22 lead-related loci. We constructed Cox proportional hazard models to compute adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CHD. We applied inverse probability weighting to account for potential selection bias due to recruitment into the bone lead sub-study. RESULTS: Significant effect modification was found by VDR, HMOX1, GSTP1, APOE, and AGT genetic polymorphisms when evaluated individually. Further, the bone lead-CHD associations became larger as GRS increases. After adjusting for potential confounders, a HR of CHD was 2.27 (95%CI: 1.50-3.42) with 2-fold increase in patella lead levels, among participants in the top tertile of GRS. We also detected an increasing trend in HRs across tertiles of GRS (p-trend = 0.0063). CONCLUSIONS: Our findings suggest that lead-related loci as a whole may play an important role in susceptibility to lead-related CHD risk. These findings need to be validated in a separate cohort containing bone lead, lead-related genetic loci and incident CHD data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".