Lead exposure, B-vitamins, and plasma homocysteine in older men
Bibliographic record
Abstract
Background: Risks for cardiovascular and neurodegenerative disease known to be associated with homocysteine, a one-carbon metabolite, may be influenced by lead exposure. Longitudinal studies and studies of the interplay of lead exposure and dietary factors are lacking. Aims: To examine the longitudinal association of recent and cumulative lead exposure with homocysteine concentrations and the potential modifying effect of dietary intake of nutrients involved in one-carbon metabolism. Methods: In a subcohort of the VA Normative Aging Study (1,056 men with 2,301 total observations between 1993 and 2011), we used mixed effects models to estimate differences in repeated measures of total plasma homocysteine across concentrations of lead in blood and tibia bone, assessing recent and cumulative exposure, respectively. We also estimated differences in rate of change in homocysteine over time associated with blood and bone lead concentrations. Results: Higher exposure to lead was associated with higher homocysteine. An interquartile range (IQR) increment in recent blood lead concentration (3-µg/dl) was associated with 6.3% higher homocysteine concentration (95% confidence interval (CI): 4.8 to 7.8). An IQR increment in cumulative tibia bone lead concentration (14-µg/g) was associated with 3.7% higher homocysteine (95% CI, 1.6 to 5.6), which diminished when we accounted for blood lead. For context, in our data, homocysteine levels increased by 5.7% with 5 years of aging. The association between blood lead and homocysteine was significantly stronger among participants with lower v. higher dietary intakes of vitamin B6 and folate. Conclusions: Higher levels of lead exposure were associated with elevated homocysteine. Increasing intake of folate and B6 may be an effective way to mitigate lead’s effects on homocysteine, and, in turn, cardiovascular disease and neurodegeneration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".