1. Hex-Tox 논문초독회
이 장 우

2. 1. Introduction
Increasing interest in whether environmental chemical exposures may contribute to the rising prevalence of obesity.
But, there are few epidemiologic data on early-life chemical exposures that may be obesogenic.
Bisphenol A
is used extensively worldwide, polycarbonate plastics, epoxy resins and consumer products
is detected urine, placenta, amniotic fluids and breast milk
Main route for human exposure is considered to be dietary ingestion

3. 1. Introduction Evidence from animal studies
Alter early adipogenesis and increase fat mass later in life
Effect on weight homeostasis
Human studies
Two previous cross-sectional analyses both showed increasing body mass index (BMI) with increasing BPA levels in children and adolescents (Trasande et al., 2012; Harley et al., 2013)
There is substantial evidence that obesity risk may begin very early in life (Druet et al., 2012; Stettler et al., 2012; Monteiro et al., 2005).

4. 1. Introduction Objective
- We examined the effects of prenatal BPA exposure on rapid growth in the first 6 months of life and on other obesity-related anthropometric measurements (ie, waist circumference and BMI) later in infancy and early childhood.

5. 2. Methods Study population Birth cohort study INMA
Main analysis 402 children born at term
Questionnaires (1st and 3rd trimesters, at delivery, child was 6, 14 months and 4 years)
maternal characteristics, maternal and paternal weight and height
Maternal diet (101 item foods)
Postnatal questionnaires (feeding practices and sedentary activities)

6. 2. Methods Child anthropometry
Repeated weight measures from birth to 6 months of age were extracted from medical records (without data : measurement within ±14 days)
sex-specific growth models : 2nd-order Reed model
Age- and sex-specific Z scores for weight at birth and at 6 months of age were calculated using the World Health Organization (WHO) referent.
Rapid growth from birth to 6 months of age was defined as a Z score weight gain greater than 0.67 standard deviation (SD)
BMI z score equal to or above the 85 percentile
At 14 months and 4 years of age, waist circumference was measured at the nearest 0.1 cm using an inelastic tape
Standardizing waist circumference
(waist circumference SD = [child waist circumference − sex and age-specific population waist circumference mean]/sex and age-specific population waist circumference SD).
Analyses using the waist-to-height ratio (ie, waist circumference in cm/height in cm) versus the waist circumference Z scores adjusting models for height were also compared.

7. 2. Methods Prenatal BPA exposure Statistical analysis
Total BPA (ie, free plus conjugated) was quantified using liquid chromatography mass spectrometry
used the average of the creatinine-adjusted BPA concentrations measured in the 1st and 3rd trimester
Statistical analysis
Linear regression models (weight change, waist circumference, and BMI Z scores)
Generalized linear models for relative risks (rapid growth and overweight)
Generalized additive models for the shape of the relationships
GAMs indicated that the associations between BPA and the outcomes did not deviate from linearity (P-gain >0.10).
Final models were adjusted for child’s sex, exact age at the time the outcome was measured, exact time of day of urine sample collection, and the following maternal characteristics: country of origin, age at delivery, education, parity, prepregnancy BMI, and smoking during pregnancy. Models for waist circumference Z score were additionally adjusted for child’s height.

8. 3. Results

9. 3. Results

10. 3. Results

11. 3. Results

12. 3. Results Mother who had smoked : β = 0.66 (95% CI : 0.08-1.30)2 3
Mother who had smoked : β = 0.66 (95% CI : )
Mother who had smoked : association was stronger
Excluding outlier : waist (β = 0.35, 95% CI : ), BMI (β = 0.42, 95% CI : )
RR (1.70, 95% CI : )

13. 3. Results

14. 3. Results Similar result Single spot urine
Excluding <0.2g/L creatinine
Complete follow up data (n=344)
Using weight-for-length age- and sex-specific Z scores and waist-to-height ratio

15. 4. Discussion
Weakly associated with increased waist circumference and BMI in children at the age of 4 years and somewhat stronger in children of mothers who smoked during pregnancy
Urinary mean BPA concentrations measured in this study are similar to those reported previously (European, American, Latina population)
Cross-sectional studies
Positive association with elevated BMI in aged 6 to 19 years (Trasnade et al., 2012).
Positive association with elevated BMI, waist circumference, and fat mass at the age of 9 years (Harley et al., 2013)
Needed to evaluate observed effects at prepubertal and adolescent ages.

16. 4. Discussion Methodological aspects
BPA has a short-half life (ICC=0.05, within-subject variability was greater)
Thus, use the average of two spot urine concentrations (throughout pregnancy)
Classified subjects according to tertiles : findings for the obesity-related outcomes were similar
Reliability as an indicator (BMI and waist circumference)
In recent study, aim of reducing BMI, small reduction of BMI are assocated with an improvement in cardiovascular risk factors including a reduction in insulin and low-density lipoprotein
BMI increases observed in our study could be an important indicator of physiologic changes related to later disease risk

17. 4. Discussion Potential limitation
Information only for indirect indicators of adiposity
use of more direct measurements (fat mass, lipid and hormone)
The obesogenic effects of perinatal BPA exposure at low doses have been further suggested to be enhanced and more persistent in female rats (Rubin et al., 2001; Somm et al., 2001)
we did not find evidence that child’s sex modifies
Effect heterogeneity by child sex should be further explored in larger populations.
Lower maternal education and smoking during pregnancy are associated with increased maternal BPA concentrations in this cohort and overweight in infancy in this population.
underestimation of risk estimates

18. 4. Discussion
Strength
Repeated anthropometric measurements, postnatal growth predictors at various ages
Considering potential confounder
Found evidence that some risk factors (age, smoking)