A Machine Learning Based Approach for Estimating Specific Gravity in Elementary-School Aged Children

23 Pages Posted: 21 Sep 2022

See all articles by Stefanie A. Busgang

Stefanie A. Busgang

Mount Sinai Health System - Icahn School of Medicine; Mount Sinai Health System - Department of Environmental Medicine and Public Health

Syam S. Andra

Mount Sinai Health System - Icahn School of Medicine

Paul Curtin

Mount Sinai Health System - Icahn School of Medicine

Elena Colicino

Mount Sinai Health System - Institute for Translational Epidemiology

Matthew J. Mazzella

Mount Sinai Health System - Icahn School of Medicine

Moira Bixby

Mount Sinai Health System - Icahn School of Medicine

Alison Sanders

University of Pittsburgh

John D. Meeker

University of Michigan at Ann Arbor - Department of Environmental Health Sciences

Marissa Hauptman

Harvard University - Harvard Medical School

Shirisha Yelamanchili

Mount Sinai Health System - Icahn School of Medicine

Wanda Phipatanakul

Harvard University - Harvard Medical School

Chris Gennings

Mount Sinai Health System - Department of Environmental Medicine and Public Health

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 combine.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, and relative time of day when sample was collected were estimated and assessed for improvement. The association between 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 1.11 (95% CI: 1.03, 1.20), indicating agreement between model-predicted SG and measured SG. The full model accounting for all interaction terms with UCr resulted in the best agreement (β= 1.05, 95% CI: 0.97,1.13). 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.

Note:

Funding Information: This work was supported in part by funding from NIH/NIEHS: U2CES026561, U2CES026553, U2CES026555, R00ES027508, R01AI073964, R01AI073964-02S1, K24AI106822, K23ES031663, U01AI110397, and P30ES000002. Dr. Hauptman was also supported by the American Academy of Pediatrics (AAP) and funded in part by cooperative agreement award with the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) FAIN:NU61TS000296. The U.S. Environmental Protection Agency (U.S.EPA) supports the Pediatric Environmental Health Specialty Units (PEHSUs) by providing partial funding to the ATSDR under Inter-Agency Agreement DW-75-95877701.

Declaration of Interests: The authors declare that they have no competing interests.

Ethics Approval Statement: Data for this project was obtained from the publicly available data in the Human Health Exposure Resource (HHEAR) Data Repository, which has been approved under Icahn School of Medicine at Mount Sinai IRB Protocol # 16-00947. The National center for Health Statistics Research Ethics Review Board approved documented consent for all NHANES participants.

Keywords: Keywords: dilution factors, data pooling, calibration metrics

Suggested Citation

Busgang, Stefanie A. and Andra, Syam S. and Curtin, Paul and Colicino, Elena and Mazzella, Matthew J. and Bixby, Moira and Sanders, Alison and Meeker, John D. and Hauptman, Marissa and Yelamanchili, Shirisha and Phipatanakul, Wanda and Gennings, Chris, A Machine Learning Based Approach for Estimating Specific Gravity in Elementary-School Aged Children. Available at SSRN: https://ssrn.com/abstract=4195941 or http://dx.doi.org/10.2139/ssrn.4195941

Stefanie A. Busgang (Contact Author)

Mount Sinai Health System - Icahn School of Medicine ( email )

Mount Sinai Health System - Department of Environmental Medicine and Public Health ( email )

Syam S. Andra

Mount Sinai Health System - Icahn School of Medicine ( email )

United States

Paul Curtin

Mount Sinai Health System - Icahn School of Medicine ( email )

United States

Elena Colicino

Mount Sinai Health System - Institute for Translational Epidemiology ( email )

Matthew J. Mazzella

Mount Sinai Health System - Icahn School of Medicine ( email )

United States

Moira Bixby

Mount Sinai Health System - Icahn School of Medicine ( email )

United States

Alison Sanders

University of Pittsburgh ( email )

John D. Meeker

University of Michigan at Ann Arbor - Department of Environmental Health Sciences ( email )

1415 Washington Heights
Ann Arbor, MI 48109-2800
United States

Marissa Hauptman

Harvard University - Harvard Medical School ( email )

25 Shattuck St
Boston, MA 02115
United States

Shirisha Yelamanchili

Mount Sinai Health System - Icahn School of Medicine ( email )

United States

Wanda Phipatanakul

Harvard University - Harvard Medical School

25 Shattuck St
Boston, MA 02115
United States

Chris Gennings

Mount Sinai Health System - Department of Environmental Medicine and Public Health ( email )

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