Existing Cohort - CLU-IN
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Transcript Existing Cohort - CLU-IN
Welcome to the CLU-IN Internet Seminar
Early-life Exposures - Long-term Health Consequences: Session 2,
Metals and Metal Mixtures
Sponsored by: NIEHS Superfund Research Program
Delivered: March 28, 2012, 1:00 PM - 3:00 PM, EDT (17:00-19:00 GMT)
Instructors:
Robert Wright, Associate Professor of Pediatrics & Environmental Health, Director, Superfund Research Program,
Harvard Medical School and School of Public Health ([email protected])
Rebecca Fry, Assistant Professor, Environmental Sciences & Engineering, University of North Carolina Gillings
School of Global Public Health ([email protected])
Moderator:
Bill Hagel, U.S. EPA, Region 3 ([email protected])
Visit the Clean Up Information Network online at www.cluin.org
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Chemical Mixtures
and
Neurodevelopment
Robert O. Wright MD MPH
Director, Harvard SRP
Associate Professor of
Pediatrics
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Why should we study Mixtures?
Real life exposure scenario
Most Superfund sites are mixtures
Can
guide which chemicals to assess
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Why should we study Mixtures?
Mixed Exposures can be thought of as an
extension of the “2-hit” hypothesis
1st
hit leaves brain in vulnerable state
2nd hit needed to produce toxicity
Fits with developmental theories of plasticity
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Mixtures may be most relevant
to the general population
High vs low doses of chemicals
Mixtures may be irrelevant at “high” doses
If
blood lead is >100 ug/dL, can a low dose of
Mn make any difference?
If blood Pb is 10 ug/dL perhaps a second hit
by Mn then becomes relevant
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Chemical Mixtures and Brain
Development
Metals
Pb,
Mn, As, Hg, methyl Hg,
Organic chemicals
PCBs, DDT,
Solvents
Pesticides
Organophosphates,
Carbamates, pyrethroids
Drugs of abuse
GHB,
cocaine, benzodiazepine, ketamine
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Neurodevelopment-Review
How do chemicals produce neurotoxicity in
the developing brain?
High
dose
Neurodegeneration, damage, cell death
Low
dose
May be no signs of damage
Interferes with network formation
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Developmental Neurotoxicology
Vulnerable periods
Childhood
Neurodevelopment
Elderly
Neurodegeneration
Critical Developmental Windows
Developmental
life stages at which processes
occur (i.e. gene expression) which may not occur
at other life stages.
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Neuronal Cells
Myelin (Schwann cell)
Synapse
Axon
Dendrite
Nucleus
Cell Body
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Biological VulnerabilityNeurodevelopment
Construction of the central nervous system
(CNS) begins in utero,
Continues throughout childhood and
involves the production of 100 billion nerve
cells and 1 trillion glial cells.
Cell migrate, differentiate, and form
synapses
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Synapses
Transmit signals between neurons
Environmental
stimuli will cause neurons to fire
Neuronal/synaptic firing is a signaling process to
mold the synaptic architecture of the brain
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How does the Brain Build this
Network?
Some of it is stochastic
Synapses
are made by the billions, and in
some respects randomly, between neurons.
We make a net gain in synapses from fetal life
till about age 2 years
Then the number of synapses in our brain
starts to decrease
Why?
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Synaptic Network
Environmental Stimuli cause nerves to fire:
When they fire, neurotransmitters are
released into synaptic junctions
This
releases growth factors
Signals that this is an important neuronal
connection (i.e. it gets used)
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Synaptic Pruning
Environmental stimuli mold the CNS.
Synapses
that produce function are repeatedly fired
and kept
Synapses that are dormant are deleted
In other words there is a “natural selection”
process
Functional
synapses release growth factors
Nonfunctional synapses do not release the growth
factors
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Hebb Synapses
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Weisel and Hubel
Newborn kittens
Patch
one eye for one month
Retinal development (specifically the development of
neuronal connections) in the patched eye would not
occur.
Patch Adult cat eye for one month
Compare
neuronal networks between patched and
unpatched eye
No difference than comparing unpatched cats
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Implication
Natural Selection is not just a process by
which genetic variants are selected.
Neuronal Cells and synaptic networks may
also undergo a process of natural
selection
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So how do Chemicals affect
Development?
Lead as a “paradigm” toxicant
At “low” doses (blood lead around 5-10 ug/dL)
Lead will interact with Protein Kinase C
Stimulate neurotransmitter release
Neurons fire in the absence of an appropriate environmental
stimuli
Lead mimics calcium
Calcium is critical to nerve signal transmission
Calcium enters neurons during depolarization
Lead blocks calcium channels
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Lead and the Brain
Net effect
Lead stimulates nerves to fire in a more stochastic
fashion
Lead also inhibits neurotransmission (both
appropriate neurotransmission and inappropriate
neurotransmission)
Makes it hard to think/concentrate
Changes the underlying synaptic architecture, making it
less efficient
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Childhood Lead Poisoning
Lead
exposure introduces noise to the
process of synaptic pruning
Which
synapses are chosen for survival
and which regress becomes more random
Net
effect if prolonged- is that the
underlying neuronal networks are less
efficient.
Structurally no damage is evident
Functionally, deficits are measurable.
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Plasticity
The brain’s capacity to diminish the effects of
toxic insults through structural/functional
changes
This
occurs through the same processes as synaptic
selection
In other words plasticity allows for new connections to
be made which improve function following an insult
Maladaptive vs adaptive plasticity
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110
Child with low prenatal &
High post natal exposure
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Child with high prenatal &
low post natal exposure
Child with high prenatal &
High post natal exposure
Additive Effects of sequential exposure
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Bayley Scales MDI
Effects of Sequential Toxic Metal Exposure on
Neurodevelopment
Multiplicative Effects of sequential exposure
0
6
12
18
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Age in Months
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Harvard SRP Project 1: Epidemiology of
Developmental Windows, Metal
Mixtures and Neurodevelopment
Uses existing infrastructure/data in 2 ongoing
cohorts of neurodevelopment and metals
Mexico
City, Tar Creek
Measure As, Mn, Pb
Use existing infrastructure on a 3rd cohort
designed to assess reproductive health study in
Bangladesh on Arsenic
Add
follow-up and neurodevelopment measures
Add Pb and Manganese measure
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Design
Prenatal exposure biomarkers in mother
2nd,
Post natal exposure biomarkers in child
1
and 2 years of age
Bayley Scales of infant development
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3rd trimester, delivery
and 2 years of age
Either Pooled across cohorts
Or
as a meta-regression
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Outline of Cohort Resource Utilization For this Program
Metal Mixtures in
Neurodevelopment
Project 1
Gene-Metal interactions in
Neurodevelopment
Project 2
DSA=Delayed Spatial
Alternation
Proposed Cohort
1000 Bangladesh Children (2 yr old)
BloodPb/Mn/As
umbilical cord
1 year
2 year
Bayley
6 months
12 months
18 months
24 months
DSA
12 months
Covariates Maternal IQ, gender
education, hemoglobin,
Existing Cohort
600 Tar Creek (2 yr old)
BloodPb/Mn/As
umbilical cord
1 year
2 year
Bayley
12 months
24 months
Covariates Maternal IQ, gender,
education, hemoglobin,
Existing Cohort
1000 Mexico children (2 yr old)
BloodPb/Mn/As
umbilical cord
1 year
2 year
Bayley
6 months
12 months
18 months
24 months
DSA
12 months
Covariates Maternal IQ, gender,
education, hemoglobin,
DNA- Mothers/Infants
DNA, Mother, Father, Infant
DNA, Mother, Father, Infant
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Plots of MDI scores vs Blood Lead by Quintiles of Blood Mn
Highest Quintile of Blood Mn
80
60
40
40
60
80
MDI_24/Fitted values
100
100
120
120
Lowest 4 Quintiles of Blood Mn
0
5
10
bloodpb12
MDI_24
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Fitted v alues
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0
5
10
15
bloodpb12
MDI_24
Fitted v alues
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Current work
Finishing
Pooling
Meta
Year 2
vs meta-regression
data issues
Biomarkers
All
blood
Avoids issues that come up if using
biomarkers from different matrices
Urine
vs blood vs hair
Different half lives
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Other complexities
Different doses in different cohorts
Bangladesh>Mexico>Tar
Creek
Which developmental windows are
important for mixtures?
Repeated
measures of exposure at different
life stages
Interactions may occur across time
Prenatal
may modify 1 year blood Metal
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Summary
Low level chemical exposures may be
more relevant in children
Low level chemical mixed exposures may
also be more relevant in children
Our program is designed to test 2 and 3
way interactions among Pb, Mn and As
2
way Mn-Pb interactions already
demonstrated
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Summary
Chemical mixtures reflect real life
While complex, understanding the
variance in dose response curves requires
understanding mixed exposures
Ignoring mixed exposures will lead to
biased effect estimates
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Summary
Like understanding G X E interactions
mixtures research requires
Large
sample sizes
Validation in multiple populations
Complex analytical approaches
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