1 Welcome to the CLU-IN Internet Seminar Early-life Exposures - Long-term Health Consequences:...

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1 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

Transcript of 1 Welcome to the CLU-IN Internet Seminar Early-life Exposures - Long-term Health Consequences:...

Page 1: 1 Welcome to the CLU-IN Internet Seminar Early-life Exposures - Long-term Health Consequences: Session 2, Metals and Metal Mixtures Sponsored by: NIEHS.

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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

Page 2: 1 Welcome to the CLU-IN Internet Seminar Early-life Exposures - Long-term Health Consequences: Session 2, Metals and Metal Mixtures Sponsored by: NIEHS.

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Page 3: 1 Welcome to the CLU-IN Internet Seminar Early-life Exposures - Long-term Health Consequences: Session 2, Metals and Metal Mixtures Sponsored by: NIEHS.

Chemical Mixtures and NeurodevelopmentRobert 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” hypothesis1st hit leaves brain in vulnerable state2nd 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

Axon

Myelin (Schwann cell)

Synapse

Dendrite

Cell Body

Nucleus

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Biological Vulnerability-Neurodevelopment 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 fireNeuronal/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 factorsSignals 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 evidentFunctionally, 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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Additive Effects of sequential exposure

Multiplicative Effects of sequential exposure

Child with low prenatal &High post natal exposure

Child with high prenatal &low post natal exposure

Child with high prenatal &High post natal exposure

Bay

ley

Sca

les

MD

I

0 6 12 18 24

Age in Months

90

1

00

110

Effects of Sequential Toxic Metal Exposure on Neurodevelopment

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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 mother2nd, 3rd trimester, delivery

Post natal exposure biomarkers in child1 and 2 years of age

Bayley Scales of infant development1 and 2 years of age

Either Pooled across cohortsOr as a meta-regression

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Outline of Cohort Resource Utilization For this Program

Proposed Cohort 1000 Bangladesh Children (2 yr old)Blood Pb/Mn/As

umbilical cord1 year2 year

Bayley 6 months12 months18 months24 months

DSA 12 monthsCovariates Maternal IQ, gender education, hemoglobin,

DNA, Mother, Father, Infant

Existing Cohort1000 Mexico children (2 yr old)Blood Pb/Mn/As

umbilical cord1 year2 year

Bayley 6 months12 months18 months24 months

DSA 12 monthsCovariates Maternal IQ, gender, education, hemoglobin,

DNA, Mother, Father, Infant

Existing Cohort600 Tar Creek (2 yr old)Blood Pb/Mn/As

umbilical cord1 year2 year

Bayley 12 months24 months

Covariates Maternal IQ, gender, education, hemoglobin,

DNA- Mothers/Infants

Metal Mixtures in NeurodevelopmentProject 1

Gene-Metal interactions in NeurodevelopmentProject 2

DSA=Delayed SpatialAlternation

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40

60

80

10

01

20

MD

I_2

4/F

itte

d v

alu

es

0 5 10 15bloodpb12

MDI_24 Fitted values

40

60

80

10

01

20

MD

I_2

4/F

itte

d v

alu

es

0 5 10 15 20bloodpb12

MDI_24 Fitted values

Lowest 4 Quintiles of Blood Mn Highest Quintile of Blood Mn

Plots of MDI scores vs Blood Lead by Quintiles of Blood Mn

40

60

80

10

01

20

MD

I_2

4/F

itte

d v

alu

es

0 5 10 15bloodpb12

MDI_24 Fitted values

40

60

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01

20

MD

I_2

4/F

itte

d v

alu

es

0 5 10 15 20bloodpb12

MDI_24 Fitted values

Lowest 4 Quintiles of Blood Mn Highest Quintile of Blood Mn

Plots of MDI scores vs Blood Lead by Quintiles of Blood Mn

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Current workFinishing Year 2

Pooling vs meta-regressionMeta data issues

BiomarkersAll bloodAvoids issues that come up if using biomarkers

from different matricesUrine vs blood vs hair

Different half lives

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Other complexities

Different doses in different cohortsBangladesh>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 As2 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 requiresLarge sample sizesValidation in multiple populationsComplex analytical approaches

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