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Craig J. Newschaffer, Ph.D.Director, A.J. Drexel Autism Institute

Professor, Epidemiology and BiostatisticsDrexel University

Philadelphia, Pennsylvaniacjn32@drexel.edu

Novel (epidemiologic) perspectives on environmental contributions to autism risk

Outline Background issues Opportunistic research on ASD and the environment (secondary

data) Retrospective research on ASD and the environment (primary

data) Prospective research on ASD and the environment (primary data) The “iceberg and the lamppost”… Discovery approaches to ASD and the environment

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Background issues

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Autism twin studies

EG

CausalMechanism

DietPAH

PKU

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SECULAR TRENDSIN PREVALENCE

ENVIRONMENTALRISK FACTORS

A prenatal critical period…

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Source: Courchesne et al. JAMA; 2003Source: Amaral et al. Trends Nuerosci, 2008

Dev Med Child Neurol, 1994

Opportunistic research on ASD and the environment (secondary data)

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Most common study type last five years Rely on existing sources for data on environmental exposures

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Pesticides

Source: Roberts et al EHP 2007

Organochlorine pesticides applied w/in 500m of residence

AdjustedOR (95% CI)

Zero 1.0

1st quartile 0.6 (0.1 – 4.3)

2nd quartile 0.8 (0.1 – 6.3)

3rd quartile 2.1 (0.6 – 7.3)

4th quartile 7.6 (3.1 – 18.6)

Exposure 26-81 days after estimated date of conception

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Air pollution

Sources: Windham et al EHP 2006Kalkbrenner et al. Epidemiol 2010

HAPWindham et al (2006)

Kalkbrenner et al (2010)

Arsenic 1.3 (0.9 – 1.8) 1.0 (0.8 – 1.3)

Lead 1.1 (0.8 – 1.5) 0.7 (0.4 – 1.1)

Manganese 1.1 (0.8 – 1.6) 1.1 (0.8 – 1.6)

Mercury 1.9 (1.4 – 2.7) 1.2 (0.7 – 2.1)

Pooled Metals

1.5 (1.1 – 2.1) ---

Diesel 1.4 (1.0 – 2.0) 1.1 (0.8 – 1.5)

Methlyene Chloride 1.4 (1.0 – 2.0) 1.4 (0.7 – 2.5)

Vinyl chloride 1.8 (1.3 – 2.4) 0.9 (0.4 – 2.0)

* Significant test for dose-response

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Air pollution

Sources: Windham et al EHP 2006Kalkbrenner et al. Epidemiol 2010Weiskopf ISEE presentation 2012

HAPWindham et al (2006)

Kalkbrenner et al (2010)

Weisskopf (2012)

Arsenic 1.3 (0.9 – 1.8) 1.0 (0.8 – 1.3) 1.3 (0.9 – 2.0)

Lead 1.1 (0.8 – 1.5) 0.7 (0.4 – 1.1) 1.6 (1.1 – 2.3)*

Manganese 1.1 (0.8 – 1.6) 1.1 (0.8 – 1.6) 1.5 (1.1 – 2.2)*

Mercury 1.9 (1.4 – 2.7) 1.2 (0.7 – 2.1) 2.0 (1.2 – 3.3)*

Pooled Metals

1.5 (1.1 – 2.1) --- 1.5 (1.3 – 1.7)*

Diesel 1.4 (1.0 – 2.0) 1.1 (0.8 – 1.5) 2.0 (1.0 – 3.9)*

Methlyene Chloride 1.4 (1.0 – 2.0) 1.4 (0.7 – 2.5) 1.8 (1.2 – 2.6)*

Vinyl chloride 1.8 (1.3 – 2.4) 0.9 (0.4 – 2.0) 1.2 (0.8 – 1.8)

* Significant test for dose-response

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Maternal prenatal medication use

Source: Gidaya et al IMFAR poster 2012

B2AR agonistprescription

Any exposureAdjustedOR (95% CI)

1-45 daysAdjustedOR (95% CI)

>45 daysAdjustedOR (95% CI)

Any 1.3 (1.1 – 1.5) ---- ----

1st triimester 1.1 (0.9 – 1.4) 1.1 (0.8 – 1.1) 1.2 (0.8 – 1.7)

2nd trimester 1.4 (1.1 – 1.7) 1.2 (0.9 – 1.6) 1.5 (1.1 – 2.0)

3rd trimester 1.4 (1.1 – 1.7) 1.4 (1.1 – 1.8) 1.4 (1.0 – 2.0)

Odds ratios adjusted for parental age, sex of the child, history of maternal asthma (matching: child birth month and year)

Retrospective research on ASD and the environment (primary data)

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Most results over the next five years will come from studies with this design

Advantages: – Outcome:

• Confirms and subtypes cases– Exposure:

• Individual level information– Integration of biosampling (exposure biomarkers; genetic

data to address GxE) Major limitation: measuring exposure during critical periods

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Source: Hirtz-Picciotto et al Environ Hlth 2011

PBDE exposure

Effect of periconception vitamin intake modified by maternal MTHFR genotype

Larger than expected risk seen for no vitamin use and TT (low enzyme activity) genotype

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Prenatal vitamin exposure (GxE)

Source: Schmidt et al Epidemiol 2011

Prospective research on ASD and the environment (primary data)

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Emergent results over the next five years – more findings over the following five years

Advantages: – Outcome:

• Confirms and subtypes cases• Longitudinal outcomes• Quantitative outcomes

– Exposure: • Individual level information• Real-time exposure data collection in critical windows

– Integration of biosampling (exposure biomarkers; genetic data to address GxE)

Major limitation: time and cost

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PREGNANCY

DELIVERYPre-pregnancy

follow-up

POST-PARTUM / EARLY CHILDHOOD

Sibling @ 36 mos

Mom• Medical records• Self-report• Biological samples

Home• Environmental samples• Environmental surveys

• Medical records• Biological

samples

Mom• Biological samples

Sibling • Medical records• Behavioral assessments• Physical examinations• Biological samples• Maternal report

Eligibilityinterview Home

• Environmental samples

• Environmental surveys

Proband • Medical records• Behavioral

assessments• Physical examinations• Biological samples

Dad• Medical records• Self-report• Biological samples

The EARLI study

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The “iceberg and the lamppost”

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80,000 new synthesized chemicals

3,000 high volume

chemicals

1,000 neurotoxic in animal models

200 neurotoxic in humans

8 identified developmental neurotoxicants

Sources: Landrigan and Grandjean Lancet 2006Landrigan. Curr Opin Pediat 2010Landrigan and Goldman Health Aff 2012

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Source: Rappaport and Smith Science 2010

Proof-of-principal work completed comparing smokers and non-smokers

Source: Li et al Molec Cell Proteomics 2008

Exposomics“Adductomics”

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Genome-wide SNP and environment interaction

Identify SNPs whose effects on ASD risk are heterogeneous by particular prenatal exposures:- Infection- Medication use

(B2AR and SSRIs)- Smoking - Alcohol use

Study toExploreEarly Development

Source: Ladd-Acosta et al. IMFAR presentation 2012

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Genotype * Maternal prenatal infectionGenotype * Active:Passive Smoking

Source: Ladd-Acosta et al. IMFAR presentation 2012

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Genome-wide DNA methylation

No indication of differences in methylation over the course of pregnancy

Methylation pattern in some regions is different in pregnancy than post partum (post-partum patterns in pregnant women similar to controls)

Methylation patterns in some regions during pregnancy were found to differ by exposure status (alcohol)

Source: Feinberg et al. IMFAR poster 2012

Recap Strong rationale exists for investigating environmental risk

factors and ASD Transitioning from opportunistic research relying on secondary

data to studies involving primary data collection Prospective and retrospective approaches are now underway -omics approaches being integrated into epidemiologic samples

– Will provide a platform for discovery work on environmental risk factors

– Will also support targeted, hypothesis driven work

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AcknowledgementsCollaboratorsM. Dani FallinChristine Ladd-AcostaJason FeinbergNicole GidayaEARLI and SEED investigators

ColleaguesWilliam FunkMarc WeiskopffEric Roberts

Funding5R01 ES016443 (NIEHS, NINDS, NICHD,

NIMH)5R01 ES016443 S1 S2 (NIEHS)5938 (Autism Speaks)1R01 ES017646 (NIEHS, OD, NICHD) R01 ES017646 (Fallin)

Staff at EARLI and SEED field sites, DCC, and repositoriesAll the EARLI and SEED participating families!!!

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