prenatal environmental exposures and child growth/development › sites › default › files ›...

Prenatal Environmental Exposures and Child Growth/Development

Joe M. Braun, RN, MSPH, PhD

[email protected]

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




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)



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)



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

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

48

50

52

54

56

58

60

62

1 2 3 4 5 6 7 8 9

Me

an N

eu

ro O

utc

om

e

Child Age

Attenuating

Low

High

48

50

52

54

56

58

60

62

1 2 3 4 5 6 7 8 9

Me

an N

eu

ro O

utc

om

e

Child Age

Emerging

Low

High

48

50

52

54

56

58

60

62

1 2 3 4 5 6 7 8 9

Me

an N

eu

ro O

utc

om

e

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

ela

tio

n C

oe

ffic

ien

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

prenatal environmental exposures and child growth/development › sites › default › files ›...

Documents