A cross-validation based approach for estimating specific gravity in elementary-school aged children using a nonlinear model

Abstract

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 (β = 1.06, 95% CI: 0.98, 1.15). The full model accounting for all interaction terms with UCr resulted in the best agreement (β = 1.01, 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 = 1.28-1.34). 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.

Keywords: Calibration metrics; Data pooling; Dilution factors.

Figure 1:

SG and UCr were measured in 1530 elementary school aged children from NHANES cycle 2007–2008.

Figure 2:

Linear relationship between natural log-transformed UCr and SG in the training sample, showing the correlation between measures. A smoothing line (in red) indicates a possible sigmoidal relationship between log transformed UCr and SG.

Figure 3:

Calibration plots showing the linear relationship between estimated SG and measured SG using a nonlinear model accounting for UCr, using NHANES as a training dataset (N=1530). Parameter estimates can be found in Table 3. Data estimated for model 1 is shown in (A) for the training cohort and (B) for the validation cohort. Data estimated from model 2 is shown in (C) for validation cohort and model 3 in (D) for validation cohort and model 4 in (E) for validation cohort.