Early exposure to animals and childhood body mass index percentile and percentage fat mass

ABSTRACT

Introduction

A few studies have identified childhood animal exposure as associated with adiposity, but results are inconsistent and differ in timing.

Methods

We conducted an observational cohort study of children ages 4–8 in the Environmental Influences on Child Health Outcomes [ECHO] study. The main exposure was having a dog in the home and/or regular contact with farm animals during the first year of life. Outcomes of interest were child BMI percentile (adjusted for gender and age) categorized as normal/underweight (<85^th percentile), overweight (85^th to <95^th), and obese (≥95^th), and percent fat mass (continuous). Associations were analyzed using multinomial logistic regression and multivariable linear regression, respectively, with and without multiple imputation.

Results

First-year animal exposure occurred in 245 of 770 (31.8%) children. Children with early animal exposure had 0.53 (95% CI: 0.28, 0.997) times the odds of being in the obese BMI category compared to those exposed to animals after controlling for covariates: maternal pre-pregnancy BMI, race/ethnicity, reported child activity level, receiving food assistance, age child began daycare (<1 year vs 1+), exclusively breastfed x6 months, and NICU admission (n = 721). Children with early animal exposure had, on average, 1.5% (95% CI: −3.0, −0.1) less fat mass than exposed children after adjustment for maternal BMI, race/ethnicity, activity, food assistance, breastfeeding, and maternal education (n = 548). Multiple imputation did not alter either result.

Conclusion

These results provide evidence that exposure to dogs or farm animals in the first year of life is associated with lower odds of obesity and lower percent fat mass in childhood.

KEYWORDS:

• Obesity
• child
• dogs
• African Americans
• Asian Americans
• Hispanic Americans

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

Please contact Dr. Kelly J. Hunt, [email protected]

Additional information

Funding

This work was supported by the National Institutes of Health [UG3OD023316].NICHD-Fetal Growth Studies:The research referred to in this presentation was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Intramural Funding and included American Recovery and Reinvestment Act funding via contract numbers HHSN275200800013C;HHSN275200800002I;HHSN27500006;HHSN275200800003IC;HHSN275200800014C;HHSN275200800012C;HHSN275200800028C;HHSN275201000009Cand the authors acknowledge the research teams at all participating clinical centers including Christina Care Health Systems, University of California, Irvine, Long Beach Memorial Medical Center, Northwestern University, Medical University of South Carolina, Columbia University, New York Presbyterian Queens, St Peters’ University Hospital, University of Alabama at Birmingham, Women and Infants Hospital of Rhode Island, Fountain Valley Regional Hospital and Medical Center and Tufts University.ECHO:Research reported in this publication was supported by the Environmental Influences on Child Health Outcomes (ECHO) program, Office of The Director, National Institutes of Health, under Award Numbers UG3OD023316. Project Title: Exposome Contributors to Child Health Originating from National Fetal Growth Study (ECCHO-NFGS). We wish to thank the mothers and children who participated in ECHO for their time and effort.Study data were collected and managed using REDCap electronic data capture tools* hosted by the South Carolina Clinical and Translational Science (SCTR) Institute at the Medical University of South Carolina. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.REDCap at SCTR is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Grant Number UL1 TR001450.*PA Harris, R Taylor, R Thielke, J Payne, N Gonzalez, JG. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42 (2):377-81.PA Harris, R Taylor, BL Minor, V Elliott, M Fernandez, L O’Neal, L McLeod, G Delacqua, F Delacqua, J Kirby, SN Duda, REDCap Consortium, The REDCap consortium: Building an international community of software partners, J Biomed Inform. 9 May 2019 [doi: 10.1016/j.jbi.2019.103208].