Blood Pressure Changes in Relation to Arsenic Exposure in a US Pregnancy Cohort
Bibliographic record
Abstract
Inorganic arsenic exposure has been related to the risk of increased blood pressure. However, available data are based largely on cross-sectional studies conducted in highly exposed populations. Pregnancy is a period of particular vulnerability to environmental insults and little is known about the cardiovascular impacts of arsenic exposure during pregnancy. We conducted a prospective longitudinal study to evaluate the association between prenatal arsenic exposure and blood pressure changes over pregnancy in 514 women enrolled in the New Hampshire Birth Cohort Study, an ongoing study located in New Hampshire, US, where over 10% of participant household wells exceed the arsenic maximum contaminant level of 10 µg/L established by the US EPA. We considered urinary arsenic as our measure of exposure, as drinking water and diet may contribute to overall arsenic burden in US pregnant women. Using linear mixed effects models adjusted for potential confounders, we found that each 10 µg/L increase in total urinary arsenic was associated with a 0.31 mmHg (95% CI: 0.04 to 0.57, p = 0.02) greater increase in systolic blood pressure per month and a 0.28 mmHg (95% CI: 0.05 to 0.52, p = 0.02) greater increase in pulse pressure per month over the course of pregnancy. Our observation that increased arsenic exposure was related to more rapid increases in blood pressure over the course of pregnancy may have important implications as even modest increases in blood pressure impact CVD risk. To our knowledge, our study is among the first longitudinal study of arsenic exposure and blood pressure, and the first to examine cardiovascular effects of arsenic exposure during pregnancy. As cardiovascular morbidity and mortality rise worldwide, the potential risk of later life cardiovascular diseases in mothers and children who are exposed to arsenic during pregnancy makes this a critical area of investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".