Prenatal Environmental Exposures and Child Growth/Development
Joe M. Braun, RN, MSPH, PhD
September 18th, 2017
Disclaimers
I have no financial conflicts of interest
Acknowledgements
Brown University: Karl Kelsey, George Papandonatos, Samantha Kingsley, Melissa Eliott, & Rondi Butler
CCHMC: Kimberly Yolton & HOME Study Staff
SFU: Bruce Lanphear
University of Cincinnati: Aimin Chen
US CDC: Antonia Calafat
Funding: R00 ES020346, R01 ES024381, R01 ES025214, P01 ES11261, R01 ES014575, & R01 ES020349
Study Participants
Outline
Rationale to study early life exposures and
growth & development
The HOME Study
PFAS and adiposity trajectories
PBDEs and neurodevelopment
Future directions and conclusions
RATIONALE
Developmental Origins of Health
Do our early life experiences
shape the rest of our lives?
Does nutrition, pollution, or
stress affect the risk of disease
during child- or adulthood?
Is our trajectory of development
important in determining disease
risk?
Barker hypothesis (DOHaD)
Why Trajectories Matter
Ong et al., Ped Res, 2002; Oken et al., 2005
Rationale to Study Chemicals
Prior studies show adverse child health associated with prenatal or childhood exposures
– DES and reproductive disorders and obesity
– Pb/Hg and neurobehavior
Universe of >80,000 chemicals
– 1,000s produced in quantities in excess of 1M pounds per year
– Little or no pre-market testing of any
Potentially high impact of exposure on fetal and child health
Children Are Not Little Adults
Environmental agents can cross the
placenta and into specific organs
Less efficient detoxification systems
Children have higher exposure to
some chemicals due to their
behavior, physiology, and anatomy
Rapidly developing organ systems
tend to be more sensitive to
environmental inputs
THE HOME STUDY
The HOME Study: Design, Recruitment, & Eligibility
Pregnancy/birth cohort
Enrolled pregnant women from 7 clinics in 5-
county region around Cincinnati, OH
Recruited from March 2003-January 2006
Eligibility
Braun et al. Cohort Profile: The HOME Study,
Int J. Epid, 2016
HOME Study Data Collection
Prenatal 1-5Y
• Blood
• Urine
• Hair
• Environmental
samples
• Exposure ques.
• SES
• Chart review
8Y 12Y (Ongoing) • Blood
• Urine
• Hair
• Environmental
samples
• Anthropometry
• Behavior
• Executive Function
• Mental/Motor
Development
• IQ
• Language
• Exposure ques.
• Breastfeeding
• Blood
• Urine
• Hair
• Teeth
• Anthropometry
• Body fat
• IQ
• Behavior
• Executive function
• Anxiety
• Academic
achievement
• Exposure ques.
• Fasting Blood
• Urine
• Hair
• Teeth
• Stool
• Cardio-met
• Anthropometry
• DXA
• Diet
• Accelerometry
• Eating behaviors
• Behavior
• Memory
• Brain MRI
• Exposure ques.
Delivery • Cord blood
• Meconium
• Vernix
• Blood
• Urine
• Hair
• Chart review
Characteristic N (%) or Value
Maternal Race
Non-Hispanic White 237 (62)
Non-Hispanic Black 121 (31)
Other 26 (7)
Maternal Education (years)
<12 95 (25)
>12 289 (75)
Parity
0 171 (44)
1+ 216 (56)
Median Household Income ($) 55,000
Mean Age at Delivery (years) 29
Mean BMI at 16W (kg/m2) 27
Characteristics of Participants at Delivery and 8 Years • 389 singleton
deliveries
• 228 singletons at 8 years (59%)
• Those who completed follow-up at 8 years of age comparable to baseline cohort
• 31 vs. 34% NHB
• 25 vs. 27% < 12 years education
• 14 vs. 13% smoked during pregnancy
PFAS AND CHILD ADIPOSITY
Perfluoroalkyl Substances (PFAS)
Persistent chemical compound
used in commercial products and
industrial applications
– Carpet, textiles, leather, paper,
cardboard, food packaging
materials, electronic devices,
cleaning agents, cosmetics,
firefighting foams
Perfluorooctanoate (PFOA) &
perfluorooctane sulfonate (PFOS)
commonly detected in serum
– Half-life of 3-7 years
Being phased out of use
Fromme et al. 2010, Olsen et al. 2007, EFSA 2008, Buck et al. 2011, Calafat et al. 2007
PFAS as Obesogens
PFAS interact with biological systems involved in obesity
– Increase cortisol levels
– Activate PPAR a and g
– Changes in DNA methylation
PFOA associated with reduced birth weight in animals
and humans
– 19 gram decrease in BW per ng/mL increase (CI: -30, -8)
Prenatal exposure associated with increased BMI and
early childhood growth
– Not all studies
Zhao et al. 2011, Taxvig et al. 2012, Vanden Heuvel et al. 2006, Watkins et al. 2014, Koustas et al. 2014,
Halldorsson et al. 2012, Maisonet et al. 2012, Andersen et al. 2013, Barry et al. 2014; Johnson et al. 2014
Research Questions
Is prenatal PFOA exposure associated with:
– Changes in child adiposity from age 2-8 years
– Child adiposity at age 8 years
Methods: The HOME Study
Prenatal •Serum PFOA
•SES & demographics
•Serum cotinine
•Maternal BMI
2-5 Years •Repeated anthropometry:
weight and height
8 Years •Anthropometry: weight,
height, body fat, & waist
circumference
Median: 5.3 vs. 2.3 ng/mL
Median: 13.3 vs. 10.1 ng/mL
Median: 1.5 vs. 1.0 ng/mL
Median: 0.9 vs. 0.7 ng/mL
Non-linearity p-value: 0.002
2nd vs. 1st tercile: 3.6%; 95% CI: 1.8, 5.5
3rd vs. 1st tercile: 1.5%; 95% CI: -0.4, 3.4
Braun et al., Obesity, 2015
Difference in BMI from 2 to 8 Years (n=285, 1,012 visits) T1: 0.12; CI: -0.08, 0.32 T2: 0.44; CI: 0.23, 0.64 T3: 0.37; CI: 0.14, 0.60 T2 x age int p=0.03 T3 x age int p=0.11
2nd Tercile
1st Tercile
3rd Tercile
Braun et al., Obesity, 2015
Discussion
Higher prenatal serum PFOA concentrations
associated with:
– Accelerated gains in BMI from 2-8 years
– Non-linear associations with adiposity at age 8
years
PBDES AND TRAJECTORIES OF CHILD NEURODEVELOPMENT
Sensitivity and Plasticity of Developing Brain
Developing brain is sensitive to
environmental stressors
Does plasticity allow for adaptation
to prior stressors?
Are alterations permanent?
– Example of lead
Andersen et al., 2011; Wright et al., 2008; Cecil et al., 2008
Polybrominated Diphenyl Ethers (PBDEs)
PBDE and Neurodevelopment
PBDEs affect many hormonal systems important for
neurodevelopment
PBDEs associated with
– Reduced mental development and IQ
– Behavior problems
– Poorer executive function
No studies examining persistence of these
associations
Dingemans et al., 2011; Cowell et al., 2017; Eskenazi et al., 2013, Chen et al., 2014; Sagiv et al., 2015; Herbstman et al., 2010
Research Question
Do associations between prenatal PBDE
exposure and child neurobehavior from age 1-
8 years persist, attenuate, or emerge?
Methods: The HOME Study
BASC-2: 274 children, 1,024 repeated measures
BSID-II: 291 children, 752 repeated measures
WPPSI-III/WISC-IV: 219 children, 373 repeated measures
Prenatal •Serum PBDE-47
•SES & demographics
•Depressive sx
•Serum cotinine
1-3 Years •Maternal IQ
•Caregiving
•Neurobehavior
•BASC-2:
Externalizing
•BSID-II: MDI
8 Years •Neurobehavior
•BASC-2:
Externalizing
•WISC-IV:
FSIQ
4-5 Years •Neurobehavior
•BASC-2:
Externalizing
•WPPSI-III:
FSIQ
Longitudinal PBDE Concentrations
0.1
1
10
100
Co
nce
ntr
ati
on
(n
g/g
lip
id)
. . . . . .
1
10
100
1000
.. . . . .
0.1
1
10
100
1000
.. . . . .
Prenatal 1 Year 2 Years 3 Years 5 Years 8 Years0.1
1
10
100
1000
Co
nce
ntr
ati
on
(n
g/g
lip
id)
. . . . . .
Prenatal 1 Year 2 Years 3 Years 5 Years 8 Years0.1
1
10
100
1000
. . . . . .
Prenatal 1 Year 2 Years 3 Years 5 Years 8 Years1
10
100
1000
. . . . .
BDE-28 BDE-47 BDE-99
BDE-100BDE-153 BDE-209
Vuong et al., 2016
PBDE-47 and Externalizing Scores
2nd vs. 1st: 0.1 (95% CI: -1.6, 1.8) 3rd vs. 1st: 2.0 (95% CI: 0, 4.0) Per 10-fold: 2.0 (95% CI: -0.2, 4.2)
Age x tercile EMM p-value=0.67
Braun et al., Neurotoxicology, 2017
PBDE-47 and MDI
Rate, 1st: 1.7 (95% CI: 0.2, 3.2) Rate, 2nd: 0.5 (95% CI: -0.9, 1.9) Rate, 3rd: -1.4 (95% CI: -3.3, 0.4)
Diff 2nd vs. 1st: -1.2 (95% CI: -3.3, 0.8) Diff 3rd vs. 1st: -3.1 (95% CI: -5.5, -0.8)
Age x tercile EMM p-value=0.04
Braun et al., Neurotoxicology, 2017
PBDE-47 and FSIQ
2nd vs. 1st: -4.3 (95% CI: -8.1, -0.5) 3rd vs. 1st: -5.0 (95% CI: -9.1, -0.9) Per 10-fold: -4.1 (-8.3, 0.1)
Age x tercile EMM p-value=0.56
Braun et al., Neurotoxicology, 2017
Summary
Serum PBDE-47 during pregnancy was
associated with:
– Persistent increases in externalizing behaviors
from age 2-8 years
– Declining mental development from age 1-3 years
– Persistent decrements in cognitive abilities from
age 5-8 years
FUTURE DIRECTIONS
Windows of Heightened Vulnerability
Chemical exposures and development continue into infancy and childhood
Statistical methods to identify susceptible periods using repeated exposure measures (Stacy et al., Environ Int, 2017; Sanchez et al, EHP, 2009)
Do paternal exposures matter? (Braun et al., Current Epi Reports, 2017)
Biological Mechanisms
Oxidative stress,
epigenetics, and –omics
Mechanistic outcomes
may be more sensitive
outcomes
Identifying mechanisms
may lead to tertiary
treatments Kingsley et al., Environ Res, 2017
Chemical Mixtures
Identify “bad actors” among a suite of agents
and pursue most promising leads
Identify synergism/ antagonism (e.g.,
interaction) between multiple pollutants
Quantify the net effect of a class or multiple
classes of exposure(s) using summary metric
NIEHS workshop to identify most promising
statistical methods (Taylor et al., EHP, 2016)
Braun et al., EHP, 2016
Conclusions
Trajectories of health may be altered by early
life chemical exposures
Longitudinal studies offer the ability to assess
absolute and relative changes in health
Opportunity to integrate trajectories, mixtures,
biological mechanisms, and windows of
susceptibility
Thank You!
Extra Slides
SES by Follow-Up at Age 8 Years
Characteristic N Full Cohort (%) N 8-Year Follow-Up (%)
Race
Non-Hispanic White 244 (62) 141 (60)
Non Hispanic Black 121 (31) 79 (34)
Other 27 (7) 13 (6)
Maternal Education
< 12 Years 95 (25) 59 (26)
Some College/Tech School 99 (25) 65 (29)
>Bachelor’s 198 (50) 109 (48)
Income
< $40,000 156 (40) 97 (42)
>$40,000 236 (60 136 (58)
Smoking During Pregnancy
Yes 344 (86) 202 (87)
No 54 (14) 31 (13)
Exposure Biomarkers Measured
Chemical, Nutrient, or Hormone 16W 26W Delivery 1Y 2Y 3Y 4Y 5Y 8Y
Lead B B,C B B B B B B
Mercury B B,C B B B B B B
Cadmium U B B B B
Arsenic (speciated) U
Nicotine and Cotinine S S S,M,C S S S S
Phenols U U U U U U U U U
Phthalates U U U U U U U U U,T
Herbicides U U U
Organophosphorous Pesticides U U U U U U U U
Pyrethroid Pesticides U U U U U U U U
Polybrominated Diphenyl Ethers S S S,C S S S S S
Perfluoroalkyl Substances S S S,C S S
Organochlorine Pesticides S S S, C, M S S S S S
Abbreviations – B: Blood, U: Urine, S: Serum, M: Meconium, C: Cord Blood, T: Teeth
Median: 5.3 vs. 2.3 ng/mL
Median: 13.3 vs. 10.1 ng/mL
Median: 1.5 vs. 1.0 ng/mL
Median: 0.9 vs. 0.7 ng/mL
Source of High PFOA Exposure
DuPont fluoropolymer plant
located on Ohio River 250
miles upstream of
Cincinnati, OH
Cincinnati draws drinking
water from Ohio River
Investigating tap water as a
potential source of exposure
in HOME Study women
Parkersburg, WV
Cincinnati, OH
Dietary Confounding
Romano, Savitz, and Braun 2014 and Sharpe 2012
Diet is predominant
source of PFAS and diet
quality is associated with
child BMI
Most diet measures (e.g.,
FFQ) do not capture
packaging or chemicals
Potential for both negative
and positive confounding
Packaged food or fish intake
Chemical exposure
Child BMI
Diet quality
Health Behaviors
Maternal or Household Lifestyle and Behavioral Factors
Maternal PFAS
Child Adiposity
Child Diet
Child Physical Activity
Child Sleep
Breast- feeding
Child PFAS
Directed acyclic graph for the relationship between PFAS exposure and child adiposity
0 1 2 3 4 5
Age in Years
0
1
2
3
Me
an
BM
I Z
-Sco
re
ObeseOverweightLean
Statistical Methods-I
Linear mixed models with random intercept
and slope for age, unstructured covariance
matrix Do associations between PBDE-47 and neurobehavior
emerge or attenuate as children age?
If not, what is the association between PBDE-47 and
neurobehavior at all ages (i.e., persistence)?
Statistical Methods-II
To estimate change over time, the following model was
used
𝑌𝑖𝑗 = 𝛽𝑜𝑖 + 𝛽1𝑖𝐴𝑔𝑒𝑖𝑗 + 𝛽2𝑃𝐵𝐷𝐸𝑡2 + 𝛽3𝑃𝐵𝐷𝐸𝑡3+ 𝛽4𝐴𝑔𝑒𝑖𝑗 × 𝑃𝐵𝐷𝐸𝑡2 + 𝛽5𝐴𝑔𝑒𝑖𝑗 × 𝑃𝐵𝐷𝐸𝑡3
If b4 = b5 =0, then we estimated the following to
estimate average association across all visits 𝑌𝑖𝑗 = 𝛽𝑜𝑖 + 𝛽1𝑖𝐴𝑔𝑒𝑖𝑗 + 𝛽2𝑃𝐵𝐷𝐸𝑡2 + 𝛽3𝑃𝐵𝐷𝐸𝑡3
Graphical Example of Associations
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1 2 3 4 5 6 7 8 9
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Child Age
Attenuating
Low
High
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1 2 3 4 5 6 7 8 9
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Child Age
Emerging
Low
High
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Child Age
Persistent
Low
High
PBDE-47 Concentrations by Follow-Up at Age 8 Years
Follow-Up N Median (25th, 75th) [ng/g]
Yes 228 20 (12, 36)
No 160 17 (10, 34)
Neurobehavioral Scores at 1st Visit
Score N Mean (SD)
Behavioral Symptom Index 323 51 (7)
Externalizing 323 48 (8)
Internalizing 323 46 (8)
Mental Development Index 328 92 (10)
Psychomotor Development Index 328 90 (13)
Full Scale IQ 251 101 (15)
Performance IQ 252 102 (16)
Verbal IQ 251 100 (15)
Variability of Neuro: ICCs
BSI
Externalizing
Internalizing
Mental
Psychom
otor
Full Scale IQ
Verbal IQ
Perform
ance IQ
Neurobehavioral Scale
0.0
0.2
0.4
0.6
0.8
1.0
Intr
ac
las
s C
orr
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tio
n C
oe
ffic
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tIntraclass Correlation Coefficients of Neurobehavioral Scales
Relative Risk of At-Risk Scores
Neurobehavioral Measure (Age, Years) Administered
N (%) with At-Risk Score at Baseline
RR of At-Risk
Score at Follow-up Visits (CI)
BASC-2 (2, 3, 4, 5, & 8)
Behavioral Symptom Index 23 (8) 5.8 (3.5, 9.6)
Externalizing 23 (8) 4.6 (2.8, 7.5)
Internalizing 11 (4) 5.7 (3.2, 10)
BSID-II (1, 2, &3)
MDI 67 (24) 1.7 (1.3, 2.3)
PDI 97 (35) 2.2 (1.6, 3.1)
WPPSI-III/WISC-IV (5 & 8)
FSIQ 24 (14) 17 (7.4, 39)
VIQ 24 (14) 7.4 (3.9, 14)
PIQ 24 (14) 5.3 (2.4, 12)
Mixtures: We’re Exposed to Them
Woodruff et al. (EHP, 2011)
examined concentrations of
163 chemicals in 268
pregnant women from
NHANES
In a subset of 54 women with
52 chemicals measured,
median number of detects was
37 (28-45)
Detectable levels of multiple
chemical classes in individual
women
Many Methods, No One Correct
FDP and sensitivity of methods varied (Agier EHP, 2016) – Traditional EWAS
approach had high FDP (Patel et al., 2010)
– No method 'perfect'
Future directions: – Apply these methods to
real-world data
– Better characterize EDC mixtures experienced across the lifespan
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