Environ Pollut

Environmental research often relies on urinary biomarkers which require dilution correction to accurately measure exposures. Specific gravity (SG) and creatinine (UCr) are commonly measured urinary dilution factors. Epidemiologic studies may assess only one of these measures, making it difficult to pool studies that may otherwise be able to be combined. Participants from the National Health and Nutrition Examination Survey 2007-2008 cycle were used to perform k-fold validation of a nonlinear model estimating SG from UCr. The final estimated model was applied to participants from the School Inner-City Asthma Intervention Study, who submitted urinary samples to the Children's Health Exposure Analysis Resource. Model performance was evaluated using calibration metrics to determine how closely the average estimated SG was to the measured SG. Additional models, with interaction terms for age, sex, body mass index, race/ethnicity, relative time of day when sample was collected, log transformed 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), and asthma status were estimated and assessed for improvement. The association between monobenzyl phthalate (MBZP) and asthma symptom days, controlling for measured UCr, measured SG, and each estimated SG were compared to assess validity of the estimated SG. The model estimating SG from UCr alone, resulted in a beta estimate of 1.10 (95% CI: 1.01, 1.19), indicating agreement between model-predicted SG and measured SG. Inclusion of age and sex in the model improved estimation (\u3b2\ua0=\ua01.06, 95% CI: 0.98, 1.15). The full model accounting for all interaction terms with UCr resulted in the best agreement (\u3b2\ua0=\ua01.01, 95% CI: 0.93,1.09). Associations between MBZP and asthma symptoms days, controlling for each estimated SG, were within the range of effect estimates when controlling for measured SG and measured UCr (Rate ratios\ua0=\ua01.28-1.34). Our nonlinear modeling provides opportunities to estimate SG in studies that measure UCr or vice versa, enabling data pooling despite differences in urine dilution factors.U2C ES026561/ES/NIEHS NIH HHSUnited States/U2C ES026553/ES/NIEHS NIH HHSUnited States/R01 AI073964/AI/NIAID NIH HHSUnited States/P30 ES000002/ES/NIEHS NIH HHSUnited States/K23 ES031663/ES/NIEHS NIH HHSUnited States/P30 ES023515/ES/NIEHS NIH HHSUnited States/R00 ES027508/ES/NIEHS NIH HHSUnited States/R01 TS000296/TS/ATSDR CDC HHSUnited States/K24 AI106822/AI/NIAID NIH HHSUnited States/U01 AI110397/AI/NIAID NIH HHSUnited States/U2C ES026555/ES/NIEHS NIH HHSUnited States/R01TS000296/ACL/ACL HHSUnited States/P30 ES017885/ES/NIEHS NIH HHSUnited States