Biologic monitoring (i.e., biomonitoring) is used to assess human exposures to environmental and workplace chemicals. Urinary biomonitoring data typically are adjusted to a constant creatinine concentration to correct for variable dilutions among spot samples. Traditionally, this approach has been used in population groups without much diversity. The inclusion of multiple demographic groups in studies using biomonitoring for exposure assessment has increased the variability in the urinary creatinine levels in these study populations. Our objectives were to document the normal range of urinary creatinine concentrations among various demographic groups, evaluate the impact that variations in creatinine concentrations can have on classifying exposure status of individuals in epidemiologic studies, and recommend an approach using multiple regression to adjust for variations in creatinine in multivariate analyses. We performed a weighted multivariate analysis of urinary creatinine concentrations in 22,245 participants of the Third National Health and Nutrition Examination Survey (1988–1994) and established reference ranges (10th–90th percentiles) for each demographic and age category. Significant predictors of urinary creatinine concentration included age group, sex, race/ethnicity, body mass index, and fat-free mass. Time of day that urine samples were collected made a small but statistically significant difference in creatinine concentrations. For an individual, the creatinine-adjusted concentration of an analyte should be compared with a “reference” range derived from persons in a similar demographic group (e.g., children with children, adults with adults). For multiple regression analysis of population groups, we recommend that the analyte concentration (unadjusted for creatinine) should be included in the analysis with urinary creatinine added as a separate independent variable. This approach allows the urinary analyte concentration to be appropriately adjusted for urinary creatinine and the statistical significance of other variables in the model to be independent of effects of creatinine concentration.

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Recent advances in environmental health research have greatly improved our ability to measure and quantify how individuals are exposed. These advances, however, bring bioethical uncertainties and potential risks that individuals should be aware of before consenting to participate. This study assessed how well participants from two environmental health studies comprehended consent form material. After signing the consent form, participants were asked to complete a comprehension assessment tool. The tool measured whether participants could recognize or recall six elements of the consent form they had just reviewed. Additional data were collected to look for differences in comprehension by gender, age, race, and the time spent reading the original consent form. Seventy-three participants completed a comprehension assessment tool. Scores ranged from 1.91 to 6.00 (mean = 4.66); only three people had perfect comprehension scores. Among the least comprehended material were questions on study-related risks. Overall, 53% of participants were not aware of two or more study-related risks. As environmental public health studies pose uncertainties and potential risks, researchers need to do more to assess participants’ understanding before assuming that individuals have given their ‘informed’ consent.

Residents of a condominium building in Hoboken, New Jersey, were exposed to mercury contamination in indoor air. Elevated levels of mercury were detected in urine samples provided by the residents, and 69% of the urine mercury levels were 20 microg/l or greater. Urine mercury levels were correlated positively with the duration of residency in the building and with the time (i.e., h/d) residents spent in the building. Environmental and biomonitoring data indicated that the residents were being exposed to mercury levels that were cause for health concern. Local health authorities, therefore, declared the building to be unfit for habitation and ordered that the premises be vacated. Health officials monitored the personal belongings of residents for mercury contamination before the items were removed from the building. The residents were offered medical evaluations and support services as part of the relocation effort.

Urinary creatinine is almost universally employed to adjust concentrations of urinary analytes for variations in hydration status. In the February 2005 issue of EHP, Barr et al. used data from the Third National Health and Nutrition Examination Survey (NHANES III) to establish reference ranges for urinary creatinine for specific age and demographic categories (Barr et al. 2005). They reported that the significant predictors of urinary creatinine concentrations include age, sex, race/ethnicity, body mass index, and fat-free mass. Although these indicators have been known for many years, the unintentional adjustment for these covariates when urinary metabolites are expressed per gram creatinine can have profound effects on the interpretation of data. The fact that these effects are often underappreciated or even unnoticed renders this paper highly relevant to exposure assessment and well worth revisiting. In our studies of arsenic methylation and one-carbon metabolism, we have noted several additional complications when expressing urinary arsenic as micrograms per gram creatinine. Note that one-carbon metabolism refers to the folatedependent biochemical pathway responsible for methylation of DNA, arsenic, and hundreds of other substrates.Our study in Bangladesh on 1,650 adults revealed that urinary creatinine concentrations are significantly correlated with plasma folate concentrations-particularly among males, who had a higher prevalence of folate deficiency than females in Bangladesh (Gamble et al. 2005). Although this association had not been previously reported, it is not surprising considering that the formation of creatine from methylation of guanidinoacetate accounts for approximately 75% of all folate-dependent transmethylation reactions (Mudd and Poole 1975) and that creatine is the direct precursor of creatinine.In some analyses, adjusting urinary arsenic for creatinine obscured correlations between folate and arsenic metabolism. In other analyses, correlations between folate and arsenic/creatinine were due in part to the associations between folate and creatinine. Correct interpretation of the data would not be possible without considering the impact of the correlation between urinary creatinine and plasma folate. As did Barr et al. (2005), we decided to include urinary creatinine in the statistical models as a separate independent variable. However, because of the intimate link between creatine metabolism and one-carbon metabolism, inclusion of urinary creatinine in some models resulted in overcontrolling for the effects of folate and homocysteine, our variables of interest. Thus, expression of total urinary arsenic per gram creatinine runs the risk of confounding relationships between total urinary arsenic and arsenic metabolism. Adjusting for the specific gravity of urine was not useful because it is so highly correlated with urinary creatinine.In summary, we concur with Barr et al. (2005) that urinary creatinine should be included in multiple regression models as a separate independent variable; in...