Background: Investigators measuring exposure biomarkers in urine typically adjust for creatinine to account for dilution-dependent sample variation in urine concentrations. Similarly, it is standard to adjust for serum lipids when measuring lipophilic chemicals in serum. However, there is controversy regarding the best approach, and existing methods may not effectively correct for measurement error.

Objectives: We compared adjustment methods, including novel approaches, using simulated case–control data.

Methods: Using a directed acyclic graph framework, we defined six causal scenarios for epidemiologic studies of environmental chemicals measured in urine or serum. The scenarios include variables known to influence creatinine (e.g., age and hydration) or serum lipid levels (e.g., body mass index and recent fat intake). Over a range of true effect sizes, we analyzed each scenario using seven adjustment approaches and estimated the corresponding bias and confidence interval coverage across 1,000 simulated studies.

Results: For urinary biomarker measurements, our novel method, which incorporates both covariate-adjusted standardization and the inclusion of creatinine as a covariate in the regression model, had low bias and possessed 95% confidence interval coverage of nearly 95% for most simulated scenarios. For serum biomarker measurements, a similar approach involving standardization plus serum lipid level adjustment generally performed well.

Conclusions: To control measurement error bias caused by variations in serum lipids or by urinary diluteness, we recommend improved methods for standardizing exposure concentrations across individuals.

Citation: O’Brien KM, Upson K, Cook NR, Weinberg CR. 2016. Environmental chemicals in urine and blood: improving methods for creatinine and lipid adjustment. Environ Health Perspect 124:220–227; http://dx.doi.org/10.1289/ehp.1509693

Address correspondence to C.R. Weinberg, 111 T.W. Alexander Dr., Research Triangle Park, NC 27709. Telephone: (919) 541-4927. E-mail: weinber2@niehs.nih.gov

We thank A.M. Jukic and A. Wilcox for providing data for this analysis, and A.M. Jukic, D. Baird, and M. Longnecker for helpful comments on the paper.

This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences (grant Z01ES049003-23).

The authors declare they have no actual or potential competing financial interests.

Received: 8 January 2015 Accepted: 21 July 2015 Advance Publication: 24 July 2015 Final Publication: 1 February 2016

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