This document summarizes research from the MeDALL project investigating the mechanisms of allergy development. It discusses: 1) Analyzing data from 12 birth cohorts involving over 16,000 children to identify classical and novel phenotypes of asthma, rhinitis, and eczema, finding high levels of comorbidity between the conditions. 2) Developing a new microarray to improve detection of allergen sensitization across Europe. 3) Conducting the first epigenome-wide analysis of asthma and allergy combining multiple countries and time points, identifying several genome-wide significant hits. 4) Using targeted proteomics to identify protein biomarkers associated with asthma/allergy and integrating these findings with genetic

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MeDALL (Mechanisms of the
Development of ALLergy): an integrated
approach from phenotypes to systems
medicine
Judith Garcia-Aymerich
Centre for Research in Environmental Epidemiology (CREAL)
Barcelona, June 12 2014

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The Burden of Asthma
 A chronic inflammatory disorder of the airways with many cellular
mechanisms involved, associated with bronchial hyper
responsiveness and episodes of wheezing and breathlessness.
 It affects the whole life cycle.
 7th cause of DALYs in developed countries.
 Both in urban and rural environments.
 Worldwide increase since 1970.

3.  There is no satisfactory causal model of asthma.
 The definition of asthma is a mess.
 We don’t understand the course of asthma.
 We don’t understand the allergic mechanism (Treg/Th1/Th2).
 There are no satisfactory preventive strategies.
 We still don’t understand THE ASTHMA EPIDEMIC.
Challenges in the understanding of asthma

4. Barabasi A. NEJM 2007
Changing the way of thinking
• Complex diseases (e.g., asthma) are heterogeneous (different
entities?) and overlap (with other diseases).
• Causal relationships are complex.
• Disease mechanisms consist in multiple interrelated pathways.

5. MeDALL: Mechanisms of the Development of
ALLergies

6. MeDALL aims
Generate novel knowledge on the mechanisms of allergy from
early childhood to young adulthood.
To propose a novel phenotype definition based on an integrative
translational approach (network of molecular and environmental
factors)
To identify relevant mechanisms (-omics)
To translate results into health care (early diagnosis, prevention
and therapy).
Bousquet J et al. Allergy 2011.
Antó JM et al. JACI 2012.

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Phenotypes identification: classical and novel (JM Antó, CREAL)
Birth cohorts: infancy and childhood (J Sunyer, CREAL)
Birth cohorts: children and adolescents (T Kiel, Charité-Berlin)
Characterization of inhalant and food allergens (R Valenta, Vienna)
Epigenetics (D Postma, UMMG Groningen)
Proteomics (S Guerra, CREAL)
Systems biology (C Auffray, CNRS, Lyon)
Confirmatory animal models (B Lambretch, Univ Gent, Gent)
In vitro human immunology (M Adkis, SIAK, Davos)
Translational integration into health care (T Haathela, HUCH, Helsinki)
MeDALL Work Packages

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Birth cohorts in MeDALL
Birth
cohort
Country Recruitment
(birth)
Age in
2012-13
# available # followed
in MeDALL
BIB UK 2007-2009 4-6 y
>22,000 6,000
EDEN France 2003-2005 8-10 y
INMA Spain 2004-2008 5-8 y
PARIS France 2005 8 y
RHEA Greece 2007-2009 7-8 y
ROBIC Italy 2003-2004 9-10 y
AMICS Spain 1997-1998 15-16 y
>21,000 12,000
BAMSE Sweden 1994-1996 16-18 y
DARC Denmark 1998-1999 14-15 y
ECA Norway 1992-1993 16-17 y
GINI Germany 1995-1998 16-18 y
LISA Germany 1997-1998 15-16 y
PIAMA Netherlands 1996-1997 16-17 y
Younger
birth
cohorts
Older
birth
cohorts

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12 birth cohorts; 16147 children at 4 y, 11080 children at 8 y
Classical definitions: asthma, rhinitis and eczema (systematic
review, experts meeting)
Classical phenotypes
Lancet Respir Med 2014

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Statistical analysis
 Cross-sectional analyses (at 4 and at 8y):
 Prevalence of diseases (alone and in combination)
 Expected frequencies assuming independence of diseases
 relative (O/E) and absolute excess comorbidity (%obs-%exp)
 Longitudinal analyses (10107 children 4 & 8 y):
 Log-linear regression models (pooled and meta-analysis)
 Population attributable risk
 All models stratified by IgE sensitization.
 Extensive sensitivity analyses.

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comorbiditysingle disease
Prevalence of diseases distribution in children at 4 and 8 years
Results

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Excess risk comorbidity in children at 4 and 8 years

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Excess risk comorbidity in children at 4 and 8 years

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Risk of any comorbidity at age 8 years, according to presence of
disease at 4 y

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Risk of any comorbidity at age 8 years, according to presence of
disease at 4 y, by IgE sensitisation

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Risk of any comorbidity at age 8 years, according to presence of
disease at 4 y, by IgE sensitisation
IgE sensitisation accounts only for 38% of development of any
comorbidity at 8 y.

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Interpretation
 Coexistence of eczema, rhinitis, and asthma in the same child is
common.
 Chance - this is the first study to exclude chance
 Selection bias - unlikely in population-based birth cohort studies
 Causation (true comorbidity) - Comorbidity likely to result
from causal mechanisms.
 IgE sensitisation plays an important role but can not be considered
the only dominant causal mechanism of comorbidity for these
diseases.

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Unsupervised classification of asthma,
rhinitis, and eczema in MeDALL
population-based birth cohorts
Judith Garcia-Aymerich; Marta Benet; Yvan Saeys; Mariona Pinart; Xavier Basagaña; Henriette A Smit;
Valérie Siroux; Jocelyne Just; Isabelle Momas; Fanny Rancière; Thomas Keil; Cynthia Hohmann;
Susanne Lau; Ulrich Wahn; Joachim Heinrich; Christina G Tischer; Maria Pia Fantini; Jacopo Lenzi;
Daniela Porta; Gerard H Koppelman; Dirkje S Postma; Dietrich Berdel; Sibylle Koletzko; Marjan
Kerkhof; Ulrike Gehring; Magnus Wickman; Erik Melén; Carsten Bindslev–Jensen; Esben Eller; Inger
Kull; Karin C Lodrup Carlsen; Kai-Hakon Carlsen; Bart N Lambrecht; Manolis Kogevinas; Jordi Sunyer;
Francine F Kauffmann; Jean Bousquet; Josep M Antó
7 birth cohorts; 17209 children at 4 y, 14585 children at 8 y
Distribution of 23 variables (phenotypic traits), covering asthma,
rhinitis, dermatitis, food allergy, specific IgE levels, and child
characteristics.
Novel phenotypes
Manuscript submitted

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Statistical analysis
• Multiple (20) imputations
• Cluster using k-means: groups according to variables distances
Maximizing between cluster- distances
Minimizing within cluster – distances
• # groups: stopping rules (C-H, ASW)
+ consensus measures + significance
• Longitudinal stability (from 4 to 8 y)
• Role of IgE:
•Including/excluding
•Stratification
• Sensitivity analyses:
•Alternative clustering methods
•Alternative selection variables / children
•Test model misspecification
( ) ( )kn
)k(W
1k
B(k)
−−

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Distribution of values (20 imputations) of the Calinsky-Harabasz
stopping rule across 2 to 10 cluster groups at 4 and 8 years
4 years 8 years
Results

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Prevalence of symptoms of asthma, rhinitis and eczema, in the
2 groups identified in cluster analysis, at 4 and 8 years

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Distribution of classical definitions of current asthma, rhinitis,
eczema, and their comorbidity, according to the two groups identified
in cluster analysis, at 4 and 8 years

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Interpretation
 At the population level, asthma, rhinitis and eczema in children are
better classified as a single group than as three different diseases.
 The classification is not influenced by IgE sensitization.
 99% of children with any comorbidity were classified in the
symptomatic group.
 The results support the concept of a unifying phenotype.

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Integrated interpretation of classical and novel
phenotypes
 Eczema, rhinitis and asthma are highly associated likely as a result of
both IgE and non-IgE mechanisms.
 Defining, classifying and investigating these diseases as separate
entities neglects their important interrelationships. Emphasis in
searching for cellular sub-phenotypes should be balanced with
increased efforts to assess unifying phenotypes.
 MeDALL ongoing research provides a unique opportunity to
understand the complex network of risk factors and pathways that
explain the close interrelationships between asthma, rhinitis and
eczema.

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Development of the MeDALL micro-array (based on previous
ImmunoCAP ISAC)
- Increase number of allergens – 176 allergen molecules
- Covering European allergens  European map of sensitization
- Detection of pathogenic (IgE) and protective (IgG) responses
- Improved sensitivity of the test, high specificity
- Under conditions of low allergen
amount  more close to biological
responses in real life exposures
The MeDALL allergen-chip (R Valenta)
Lupinek 2014, Methods

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 The first Epigenome Wide Analysis (EWAS) in Asthma and Allergic
Disease; combining different European countries and multiple time
points.
 Aim: To identify CpG sites on the genome that are significantly
associated with asthma and allergic diseases
 809 paired measures in children at 0/4 and 4/8 y (about 200
asthmatics).
 Detailed information on longitudinal exposures, and disease
development.
Epigenetics (D Postma & G Koppelman)

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Epigenetics - DNA methylation
Illumina Infinium Array, >450 000 CpG sites
• 2 controls on every chip to model chip to chip variation
• Correction for technical variables and cell type.
• Effect of age, gender, and center.
Preliminary results: 7 genome wide significant hits for
asthma/allergy.

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Targeted proteomics (S Guerra)
Goals:
 To identify protein biomarkers associated to asthma/allergic
diseases
 To conduct targeted integrated analyses with genetic-epigenetic-
protein data (associations of protein levels with genetic variation in
their encoding genes)

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Multiplexing
Assessing multiple biomarkers
Targeted proteomics (S Guerra)

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 Large multiplex panel in 125 INMA/124 BAMSE: >50 proteins
detectable on a continuous scale.
 YKL-40 (ELISA) in 312 INMA/252 BAMSE.
 IL1RL1a (ELISA) in 309 INMA / 250 BAMSE.
 CC16 (ELISA) in 297 INMA serum samples.

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MeDALL 2nd STAGE OMICS STUDIES
 On going on thousands of samples
 Validation (replication) of Stage 1 signals
 Longitudinal analysis
 Relate molecular profiles with classical & novel phenotypes
 Integration genomics/transcriptomics/proteomics is a priority.

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MeDALL Challenges
 Large-scale pooling and harmonization efforts.
 Assessing the phenotypes in a unsupervised way.
 Developing a Knowledge Management Infrastructure (Biomax)
 Integrating mechanisms: IgE, epigenetics, biomarkers,
transcriptomics.
 Establishing close links in vitro immunology and animal models.
 Exploiting the translational opportunities.

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Acknowledgements
 CREAL group (JM Antó, M Bustamante, S Guerra, JR Gonzalez, M
Kogevinas, J Sunyer, M Benet...)
 MeDALL PIs (J Bousquet, T Keil, R Valenta, D Postma, Ch Auffray,
M Wickman, E Melen, G Koppelman...)

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