EDC-MixRisk
“Integrating Epidemiology and Experimental Biology to Improve Risk
Assessment of Exposure to Mixtures of Endocrine Disruptive
Compounds”
Joëlle Rüegg, Carl-Gustaf Bornehag
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 634880.
Key concern related to EDCs
• Many more potential EDCs than experimentally indicated
• Many more hormones than yet addressed in research
• Mixture effects yet poorly understood and researched
• Early life exposure of particular concern
• Low dose effects and non-monotonic dose response
• Urgent needs for regulatory actions on EDCs
EDC-MixRisk Utrecht 19.5.2016
Joëlle Rüegg, CG Bornehag
The EDC-MixRisk
EDC-MixRisk Utrecht 19.5.2016
Joëlle Rüegg, CG Bornehag
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Chris Gennings
Mount Sinai
School of Medicine
New York, USA
EDC-MixRisk EDC SYMPOSIUM
• Karolinska Institute
• Karlstad University
• Gothenburg University
• Lund University
• Stockholm University
• Uppsala University
Wieland Kiess
University Hospital
Leipzig,
Leipzig, GE
Barbara Demeneix
Muséum National
d’Histoire Naturelle
Paris, FR
Giuseppe Testa
The European Inst.
Of Oncology
Milan, IT
Hannu Kiviranta
National Institute for
Health and Welfare
Helsinki, FI
Efthymia Kitraki
University of Athens
Athens, GR
EDC-MixRisk will:
1. Identify EDC mixtures associated with adverse
health outcomes in two pregnancy cohort studies
within: growth and metabolism, neurodevelopment
and sexual development
EDC-MixRisk Utrecht 19.5.2016
Joëlle Rüegg, CG Bornehag
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EDC-MixRisk will:
2. Identify molecular mechanisms underlying EDC
exposure and adverse health outcomes using
experimental animal and cell models.
EDC-MixRisk Utrecht 19.5.2016
Joëlle Rüegg, CG Bornehag
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EDC-MixRisk will:
3: Develop an approach for evaluating, integrating
and synthesizing complex datasets for EDCs and
mixtures.
=
Altogether EDC-MixRisk will lead to better risk management!
EDC-MixRisk Utrecht 19.5.2016
Joëlle Rüegg, CG Bornehag
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Scientific project work flow:
EDC-MixRisk Utrecht 19.5.2016
Joëlle Rüegg, CG Bornehag
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Mixtures from epi-studies
to be tested
Mixture 0
Mixture 1
(SELMA)
(SELMA, Life Child)
Prenatal EDC exposure
20 EDCs
(August 2015)
Prenatal EDC exposure
+55 EDCs
(November 2016)
Modifiers
Genetic/Epigenetic information
Developed biomarkers
Health domains (<2.5y)
Sexual development (R) (AGD)
Neurodevelopment (N) (LD)
Metabolism and growth (G) (BW)
Health domains (7y)
Sexual development (R)
Neurodevelopment (N)
Metabolism and growth (G)
Natural
hormones
Estrogen
Testosterone
Thyroids. etc.
Growth and metabolism (Birth Weight)
20 EDCs
Mixture 0
Sexual development (AGD)
Neurodevelopment (Language Delay)
EDC-MixRisk
Mixture 1
+55 EDCs
Prenatal/Postnatal
Three health domains in children
Reproduction (R)
Neurodevelopment (N)
Metabolism and growth (G)
Modifiers
Stress during pregnancy
Genetics/Epigenetics
Sexual dimorphism
Pharmaceuticals
(Paracetamol)
etc.
Group
Phthalates
(urine)
Parent
compound
Metabolite
n>LOD
GM (95% CI)
(ng/mL)
95%
(ng/mL)
DEP
MEP
2,356
67.97 (60.30 - 76.60)
511.49
DBP
MBP
2,356
63.99 (57.72 - 70.94)
236.03
BBzP
MBzP
2,356
16.11 (14.17 - 18.32)
101.59
MEHP
MEHHP
MEOHP
MECCP
7OH-MMeOP
7OXO-MMeOP
7CX-MMeOP
BPA
Triclosan
PFNA
PFDA
PFUnDA
PFDoDA
PFHxS
PFHpA
PFOA
PFOS
2,356
2,356
2,356
2,356
2,356
2,356
2,356
2,356
2,167
2,393
2,393
2,381
1,083
2,393
1,748
2,393
2,393
3.28 (2.98 - 3.61)
14.49 (13.23 - 15.87)
9.73 (8.86 - 10.68)
14.25 (13.03 - 15.58)
6.82 (6.14 - 7.58)
3.06 (2.81 - 3.34)
10.84 (10.00 - 11.75)
17.30
67.57
46.33
65.30
60.18
20.92
78.31
6.37
315.41
1.33
0.60
0.53
0.08
3.66
0.10
3.96
12.00
DEHP
DiNP
Phenols
(urine)
Per- and
polyfluorinated alkyl
substances (serum)
Phenols
PFASs
1.54 (1.48 - 1.91)
0.75 (0.61 - 1.18)
0.54 (0.53-0.55)
0.26 (0.25-0.27)
0.21 (0.20-0.22)
0.05 (0.04-0.06)
1.31 (1.30-1.32)
0.03 (0.01-0.04)
1.61 (1.60-1.62)
5.24 (5.23-5.25)
Analysis strategy in four steps
1. Identify bad actors (R, N, G) by the use of WQSregression in epi-data (SELMA)
2. Estimate the daily intake of bad actors (urinary
compounds) in +2,300 SELMA mothers
3. Transform daily intake estimates to plasma
concentrations on a molar basis
4. Define mixture proportions from geometric
mean in SELMA of these plasma concentrations
to be tested in experimental settings
Daily intake of the phthalate diesters and
phenols estimated from urinary concentrations
in +2,300 SELMA mothers
Koch et al (2007)
Results from the first year..
Mixture G: associated
with low birth weight in
SELMA
1x
10x
In vitro model for
adipogenesis
Birth
weigth
100x
1000x
Thyroid hormone
reporter tadpoles
Maternal blood/urine
levels of about 20
chemicals at
pregnancy week 10
16
Results from the first year..
Mixture G: associated
with low birth weight in
SELMA
1x
10x
In vitro model for
adipogenesis
100x
1000x
Thyroid hormone
reporter tadpoles
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Mixture G induces
adipogenesis
undifferentiate
undifferentiate
dd
differentiated
differentiate
10x
d
Rel. expression
Gene expression of lipoprotein lipase
8
7
6
5
4
3
2
1
0
non treated
differentiate
d
1000x
differentiate
10-6
10-4
10x
1000x
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..the EDC-MixRisk concept seems to
work!
Chemical analyses and heal
examinations at age 7
E.g. Thyroid hormone: are the
associations affected by maternal
TH/iodine levels?
Identification of molecular
mechanisms/key events
Epigenetic marks (DNA
methylation/miRNA) as
biomarkers?
Building up the database
SYRINA
Thank you!
More information: www.edcmixrisk.org
Coordinator: Dr Åke Bergman
+46-70 644 3861
[email protected]
Vice Coordinator: Dr Joëlle Rüegg
+46 73 7121592
[email protected]
Vice Coordinator: Dr CG Bornehag
+46 70 5866565
[email protected]
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