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

Stefanie A Busgang1, Syam S Andra1, Paul Curtin1

  • 1Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Environmental Research
|November 22, 2022
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Summary
This summary is machine-generated.

This study developed a nonlinear model to estimate urinary specific gravity (SG) from creatinine (UCr) levels, enabling better data pooling across environmental health studies. The model improves accuracy when only one dilution factor is measured, enhancing exposure assessment.

Keywords:
Calibration metricsData poolingDilution factors

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Area of Science:

  • Environmental Health
  • Biomarker Research
  • Epidemiology

Background:

  • Urinary biomarkers are crucial for environmental exposure assessment but require dilution correction using specific gravity (SG) or creatinine (UCr).
  • Inconsistent measurement of urinary dilution factors across studies hinders data pooling and comprehensive analysis.
  • Standardizing or estimating these factors is essential for robust epidemiological research.

Purpose of the Study:

  • To develop and validate a nonlinear model for estimating urinary specific gravity (SG) from urinary creatinine (UCr) measurements.
  • To assess the model's performance and its ability to improve data integration in environmental health studies.
  • To evaluate the validity of the estimated SG by comparing exposure-outcome associations with those derived from measured values.

Main Methods:

  • K-fold validation was used to develop a nonlinear model estimating SG from UCr using data from the National Health and Nutrition Examination Survey (2007-2008).
  • The model was applied to samples from the School Inner-City Asthma Intervention Study.
  • Model performance was evaluated using calibration metrics, and additional models incorporating interaction terms (age, sex, BMI, race/ethnicity, time of day, NNAL, asthma status) were assessed.

Main Results:

  • A nonlinear model estimating SG from UCr alone showed good agreement (beta = 1.10).
  • Inclusion of age and sex improved estimation (beta = 1.06), with the full model including all interaction terms showing the best agreement (beta = 1.01).
  • Associations between monobenzyl phthalate (MBZP) and asthma symptom days were consistent whether controlling for measured SG/UCr or estimated SG.

Conclusions:

  • Nonlinear modeling effectively estimates urinary SG from UCr, facilitating data pooling across studies with different dilution factor measurements.
  • This approach enhances the ability to combine datasets, improving the power and scope of environmental exposure research.
  • The validated model offers a practical solution for harmonizing urinary biomarker data in epidemiological studies.