The DOI matches its Crossref record.
Rice consumption contributes to arsenic exposure in US women
Open accessAbstract
Emerging data indicate that rice consumption may lead to potentially harmful arsenic exposure. However, few human data are available, and virtually none exist for vulnerable periods such as pregnancy. Here we document a positive association between rice consumption and urinary arsenic excretion, a biomarker of recent arsenic exposure, in 229 pregnant women. At a 6-mo prenatal visit, we collected a urine sample and 3-d dietary record for water, fish/seafood, and rice. We also tested women's home tap water for arsenic, which we combined with tap water consumption to estimate arsenic exposure through water. Women who reported rice intake ( n = 73) consumed a median of 28.3 g/d, which is ∼0.5 cup of cooked rice each day. In general linear models adjusted for age and urinary dilution, both rice consumption (g, dry mass/d) and arsenic exposure through water (μg/d) were significantly associated with natural log-transformed total urinary arsenic ( , , both P < 0.0001), as well as inorganic arsenic, monomethylarsonic acid, and dimethylarsinic acid (each P < 0.005). Based on total arsenic, consumption of 0.56 cup/d of cooked rice was comparable to drinking 1 L/d of 10 μg As/L water, the current US maximum contaminant limit. US rice consumption varies, averaging ∼0.5 cup/d, with Asian Americans consuming an average of >2 cups/d. Rice arsenic content and speciation also vary, with some strains predominated by dimethylarsinic acid, particularly those grown in the United States. Our findings along with others indicate that rice consumption should be considered when designing arsenic reduction strategies in the United States.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Naujokas, M. F., Anderson, B., Ahsan, H., Aposhian, H. V., Graziano, J. H., Thompson, C., & Suk, W. A. (2013) · Environmental Health Perspectives · doi:10.1289/ehp.1205875 · Cited by 1,372
Heavy Metals and Neurological Disorders: From Exposure to Preventive Interventions
Rehman, Q., Rehman, K., & Akash, M. S. H. (2021) · Emerging Contaminants and Associated Treatment Technologies · doi:10.1007/978-3-030-66376-6_4 · Cited by 39
Heat Impacts on Work, Human Performance, and Daily Life
Kjellstrom, T., Lemke, B., Matthias Otto, P., Hyatt, O. M., Briggs, D. J., & Freyberg, C. A. (2015) · Climate Change and Public Health · doi:10.1093/med/9780190202453.003.0004 · Cited by 9
Metadata
- Authors
- Gilbert-Diamond, D., Cottingham, K. L., Gruber, J. F., Punshon, T., Sayarath, V., Gandolfi, A. J., Baker, E. R., Jackson, B. P., Folt, C. L., & Karagas, M. R.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-12-05
- Volume
- 108
- Issue
- 51
- Pages
- 20656-20660
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2063754654
- Cited by
- 346
- References
- 0
- FWCI
- 15.44
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Rice consumption contributes to arsenic exposure in US women. https://app.cochat.ai/papers/10.1073/pnas.1109127108
- The paper
- Gilbert-Diamond, D., Cottingham, K. L., Gruber, J. F., Punshon, T., Sayarath, V., Gandolfi, A. J., Baker, E. R., Jackson, B. P., Folt, C. L., & Karagas, M. R. (2011). Rice consumption contributes to arsenic exposure in US women. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1109127108
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.