TY - JOUR
T1 - Using non-parametric Bayes shrinkage to assess relationships between multiple environmental and social stressors and neonatal size and body composition in the Healthy Start cohort
AU - Martenies, Sheena E.
AU - Hoskovec, Lauren
AU - Wilson, Ander
AU - Moore, Brianna F.
AU - Starling, Anne P.
AU - Allshouse, William B.
AU - Adgate, John L.
AU - Dabelea, Dana
AU - Magzamen, Sheryl
N1 - This work was funded by grants R01DK076648 (PI: Dabelea) from the National Institute of Diabetes and Digestive and Kidney Diseases, 5UH3OD023248 (PI: Dabelea) from the National Institutes of Health, and RD-839278 (PI: Magzamen) from the US Environmental Protection Agency.
SEM and SM conceived the original study question. SEM conducted the statistical analysis and wrote the initial draft of the manuscript. LH consulted on the statistical methods, wrote the R package used to facilitate the statistical analysis, and drafted sections related to statistical methods. AW contributed to the study design and the interpretation of results. BFM consulted on the birth outcome data and the interpretation of results. APS consulted on the design of the study, the use of birth outcome data, and the interpretation of results. WBA developed spatial data sets used in this work and contributed to the interpretation of results. JLA consulted on the study design and the exposure assessment methods and contributed to the interpretation of results. DD consulted on the study design and interpretation of the results. DD is the principal investigator for grants R01DK076648 and 5UH3OD023248 (National Institutes of Health) which funded this work. SM consulted on the study design and statistical methods and the interpretation of results. SM is the principal investigator for grant RD-839278 (US Environmental Protection Agency) which funded this work in part. All authors reviewed the manuscript and contributed to the final version. The authors read and approved the final manuscript.
PY - 2022/12
Y1 - 2022/12
N2 - Background: Both environmental and social factors have been linked to birth weight and adiposity at birth, but few studies consider the effects of exposure mixtures. Our objective was to identify which components of a mixture of neighborhood-level environmental and social exposures were driving associations with birth weight and adiposity at birth in the Healthy Start cohort. Methods: Exposures were assessed at the census tract level and included air pollution, built environment characteristics, and socioeconomic status. Prenatal exposures were assigned based on address at enrollment. Birth weight was measured at delivery and adiposity was measured using air displacement plethysmography within three days. We used non-parametric Bayes shrinkage (NPB) to identify exposures that were associated with our outcomes of interest. NPB models were compared to single-predictor linear regression. We also included generalized additive models (GAM) to assess nonlinear relationships. All regression models were adjusted for individual-level covariates, including maternal age, pre-pregnancy BMI, and smoking. Results: Results from NPB models showed most exposures were negatively associated with birth weight, though credible intervals were wide and generally contained zero. However, the NPB model identified an interaction between ozone and temperature on birth weight, and the GAM suggested potential non-linear relationships. For associations between ozone or temperature with birth weight, we observed effect modification by maternal race/ethnicity, where effects were stronger for mothers who identified as a race or ethnicity other than non-Hispanic White. No associations with adiposity at birth were observed. Conclusions: NPB identified prenatal exposures to ozone and temperature as predictors of birth weight, and mothers who identify as a race or ethnicity other than non-Hispanic White might be disproportionately impacted. However, NPB models may have limited applicability when non-linear effects are present. Future work should consider a two-stage approach where NPB is used to reduce dimensionality and alternative approaches examine non-linear effects.
AB - Background: Both environmental and social factors have been linked to birth weight and adiposity at birth, but few studies consider the effects of exposure mixtures. Our objective was to identify which components of a mixture of neighborhood-level environmental and social exposures were driving associations with birth weight and adiposity at birth in the Healthy Start cohort. Methods: Exposures were assessed at the census tract level and included air pollution, built environment characteristics, and socioeconomic status. Prenatal exposures were assigned based on address at enrollment. Birth weight was measured at delivery and adiposity was measured using air displacement plethysmography within three days. We used non-parametric Bayes shrinkage (NPB) to identify exposures that were associated with our outcomes of interest. NPB models were compared to single-predictor linear regression. We also included generalized additive models (GAM) to assess nonlinear relationships. All regression models were adjusted for individual-level covariates, including maternal age, pre-pregnancy BMI, and smoking. Results: Results from NPB models showed most exposures were negatively associated with birth weight, though credible intervals were wide and generally contained zero. However, the NPB model identified an interaction between ozone and temperature on birth weight, and the GAM suggested potential non-linear relationships. For associations between ozone or temperature with birth weight, we observed effect modification by maternal race/ethnicity, where effects were stronger for mothers who identified as a race or ethnicity other than non-Hispanic White. No associations with adiposity at birth were observed. Conclusions: NPB identified prenatal exposures to ozone and temperature as predictors of birth weight, and mothers who identify as a race or ethnicity other than non-Hispanic White might be disproportionately impacted. However, NPB models may have limited applicability when non-linear effects are present. Future work should consider a two-stage approach where NPB is used to reduce dimensionality and alternative approaches examine non-linear effects.
KW - Adiposity
KW - Air displacement plethysmography
KW - Birth weight
KW - Environmental mixtures
KW - Non-parametric Bayes shrinkage
KW - Social stressors
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U2 - 10.1186/s12940-022-00934-z
DO - 10.1186/s12940-022-00934-z
M3 - Article
C2 - 36401268
AN - SCOPUS:85142301091
SN - 1476-069X
VL - 21
JO - Environmental Health: A Global Access Science Source
JF - Environmental Health: A Global Access Science Source
IS - 1
M1 - 111
ER -