ArticleThe Journal of nutrition2013
Among 10 sociodemographic and lifestyle variables, smoking is strongly associated with biomarkers of acrylamide exposure in a representative sample of the U.S. Population.
Article in The Journal of nutrition, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Prenatal exposure to acrylamide and metabolic health at 20 years of age A biomarker-based Danish cohort study.Environment international · 2026Article
- Reconstructing population exposures to acrylamide from human monitoring data using a pharmacokinetic framework.Chemosphere · 2023Article
- Effectiveness and determinants of smoking cessation in the Saudi Arabian Region of Jazan: A cross-sectional study.Tobacco induced diseases · 2023Article
- The Association Between Exposure to Acrylamide and Mortalities of Cardiovascular Disease and All-Cause Among People With Hyperglycemia.Frontiers in cardiovascular medicine · 2022Article
- Associations of acrylamide with non-alcoholic fatty liver disease in American adults: a nationwide cross-sectional study.Environmental health : a global access science source · 2021Article
- Article
- Dietary and lifestyle determinants of acrylamide and glycidamide hemoglobin adducts in non-smoking postmenopausal women from the EPIC cohort.European journal of nutrition · 2017Article
- Challenges and Lessons Learned in Generating and Interpreting NHANES Nutritional Biomarker Data.Advances in nutrition (Bethesda, Md.) · 2017Review
- Sociodemographic and lifestyle variables are compound- and class-specific correlates of urine phytoestrogen concentrations in the U.S. population.The Journal of nutrition · 2013Article
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Authors and funding
4 authors.
Funding
Abstract
Hemoglobin adducts of acrylamide (HbAA) and glycidamide (HbGA) have been measured as biomarkers of acrylamide exposure and metabolism in a nationally representative sample of the U.S. population in the NHANES 2003-2004. We assessed the association of sociodemographic (age, sex, race-ethnicity, education, and income) and lifestyle (smoking, alcohol consumption, BMI, physical activity, and dietary supplement use) variables with these biomarkers in U.S. adults (aged ≥ 20 y). We used bivariate and multiple regression models and assessed the magnitude of an estimated change in biomarker concentration with change in a covariable for 2 biomarkers of acrylamide exposure. Smoking was strongly and significantly correlated with HbAA and HbGA concentrations (rs = 0.51 and 0.42, respectively), with biomarker concentrations being 126 and 101% higher in smokers compared with nonsmokers after adjusting for sociodemographic and lifestyle covariates. Age was moderately and significantly correlated with both biomarkers (rs = -0.21 and -0.22, respectively). BMI (rs = -0.11) and alcohol consumption (rs = 0.13) were weakly yet significantly correlated with HbAA concentrations only. The estimated percentage change in biomarker concentration was ≤ 20% for all variables other than smoking after adjusting for sociodemographic and lifestyle covariates. Using multiple regression models, the sociodemographic variables explained 9 and 7% whereas the sociodemographic and lifestyle variables together explained 46 and 25% of the variability in HbAA and HbGA, respectively, showing the importance of considering and adequately controlling for these variables in future studies. Our findings will be useful in the design and analysis of future studies that assess and evaluate exposure to acrylamide and its metabolism to glycidamide.
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