Towards the Exposome
Manchester 10 September 2013
Environmental PAFs for cancer (Global
burden of disease, WHO)
Data from Ezzati et al., “Comparative Quantification of Mortality and Burden of Disease Attributable to
Selected Risk Factors,” Global Burden of Disease and Risk Factors, Chapter 4, WHO, 2006.
A self‐fulfilling prophecy: are we underestimating the role
of the environment in gene‐environment interaction
( P Vineis Int J Epidemiol 2004)
According to estimates, the common genotyping method Taqman
has 96% sensitivity and 98% specificity, thus allowing little error in
classification. On the contrary, sensitivity in environmental
exposure assessment is quite often lower than 70% and specificity
Genotype is stable, measured accurately (sens, spec=90-100%),
frequency of alleles is high
Environmental exposures are changing (life-course events), often
measured inaccurately, frequency may be too low
Some environmental exposures can be studied by epidemiology with
confidence , i.e. measurement error is relatively low and has little
impact on estimates (e.g. smoking). Advancement in exposure
assessment due e.g. to GIS techniques for air pollution.
When measurement error is too high we need biomarkers (e.g. number
of sexual partners, OR for cervical cancer around 2; HPV strains, OR
Discoveries that support the original model of molecular epidemiology
Marker linked to exposure or disease
Urinary metabolites (NNK, NNN)
Biologically effective dose
Vineis and Perera, 2007
Nitrosocompounds in tobacco
PAHs , aromatic compounds
Exposure and/or cancer
DNA repair capacity in head
and neck cancer
Exposome - definition
The exposome concept refers to the totality of environmental
exposures from conception onwards, The internal exposome is based
on measurements in biological material of complete sets of biomarkers
of exposure, using repeated biological samples especially during
critical life stages.
Biomarkers which can be measured in this context cover a wide range
of molecules, ranging from xenobiotics and their metabolites in blood
(metabolomics) to covalent complexes with DNA and proteins
The term omics generally refers to the rigorous study of a complete set
of biological and non-biological molecules with high-throughput
techniques (Rappaport and Smith 2010).
Prospective cohorts provide an ideal context in which to bring
together the best laboratory science, epidemiology, biostatistics
and bioinformatics to investigate cancer risk factors.
To realize this potential, however, requires a commitment to
develop and adapt laboratory tools for application to the bio‐
specimens being collected.
In addition, emphasis is needed on the collection and processing
of biological specimens in a manner, as far as is predictable,
consistent with the future laboratory analyses and avoid biases
at the time of sampling.
1. precious and limited biobanked material, not easily released
2. single (spot) biological samples
3. usually blood, not urine (which may be better e.g. for
4. no cohorts allow life‐course epidemiology
5. in‐depth exposure assessment is limited by feasibility (for
cancer you need large sample sizes)
6. lab measurements and omics have the same limitations
related to sample size and feasibility
7. biostatistical approaches and causal interpretation
8. ethical issues
Schematic representation of the implementation of the ‘meet-in-the middle’ approach
(Chadeau-Hyam et al, Biomarkers 2011).
Main finding in pilot study from EPIC-Italy on colon
cancer - Role of gut microbiota? Concept of “gut
No markers found in association with breast cancer, 8 signals
found in association with colon cancer (Chadeau-Hyam et al,
Dietary fibers intake was found to be associated to four putative
markers out of 235 (with corresponding p-values ranging from
0.003 to 0.02).
One marker indicates a possible link with gut microbial
fermentation of plant phenolics in the colon (Nicholson et al.,
2005, Phipps et al., 1998, Aura, 2007), a process also plausibly
linked to higher dietary fibers exposure and lower colon cancer
Second study nested in EPIC on colon
•194 cases, 194 controls (age and sex‐matched) from EPIC‐Italy
•134 non‐polar metabolites measured using LC‐MS (Imperial
Lab) – e.g. lipids
•Investigation of metabolite levels in relation to colorectal
•Association of metabolites with colorectal cancer risk factors (in
particular obesity and dietary factors)
Metabolomics and Colon Cancer Risk:
OR1 (95% CI)
for 1-log unit change in metabolite level
MI - Validation
MI - Test
Cotinine vs Smoking Status
Exposure marker - AHRR methylation is strongly associated with former smoking
(first marker of past smoking). (Shenker et al, Human Molecular Genetics 2013;
Epidemiology, in press)
Basic components of the EXPOsOMICS project
1. Select and integrate subjects, samples and data from 3 types of existing
studies that collectively reflect all life stages from conception to old age
Experimental Short‐Term Studies (STS) including Oxford Street study
Adult Long‐Term Studies
2. Measure the external exposome component for air and water
contaminants by performing extensive, repeated Personal Exposure
Monitoring (PEM) (WP3‐4).
3. Measure the internal exposome in fresh and archived samples (WP5‐7).
Fresh blood samples will be collected from the individuals undergoing PEM,
i.e. individuals in STS and those from a representative subsamples from MCO
A conceptual model of life-course disease risk
Population studies of chronic diseases have traditionally recruited middleaged subjects. However, there is strong evidence that (a) the risk of disease is
influenced by early exposures, including in utero; (b) life-stages include critical
periods (during which changes in exposure have long-term effects on disease
risks or related, intermediate markers) and sensitive periods (during which an
exposure has stronger effect on development and, hence, disease risk than at
The idea of a sequence of critical and sensitive periods leads to the concept of
"chain of risk", i.e. the interplay of early exposures and late exposures. To use
this concept in practice implies having access to multiple life-stages in
exposure assessment and epidemiological studies, and repeated measurements
of biomarkers at different time windows. This approach requires an intergenerational epidemiological study design and novel statistical analyses.
Critical stages of life
Mid‐ and late‐life
(use 5V o/p.)
‐Accelerometer (user activity)
‐user I/O (questionnaire)
‐Download of logged data (above) via USB
‐ “Dedicated” smart phone in a pouch on sensor pack to enable user input/output (i.e. we do not intend to
“leverage” the user’s personal phone, at this stage).
‐Rechargable Li battery pack supplies power to instruments and smartphone via USB hub for 36 hr autonomy.
‐Each Sensor and Smartphone log data independently (synched in time during initial setup).
gentle walking for two hours (6km)
Oxford Street and Hyde Park
– random order
– separated by >3 weeks
contemporary measurements of exposures
Need for new biostatistical tools and causal interpretation
- repeat samples and intra-individual variation
- validation of omics: Hebels et al, EHP
- quality controls (e.g. nuisance parameters: Chadeau-Hyam et al, submitted)
- longitudinal models of causality
Chadeau-Hyam M, et al. Deciphering the complex: Methodological overview
of statistical models to derive OMICS-based biomarkers.
Environ Mol Mutagen. 2013 Aug;54(7):542-57.
Hebels et al. Performance in omics analyses of blood samples in long-term
storage: opportunities for the exploitation of existing biobanks in
environmental health research. Environ Health Perspect. 2013 Apr;121(4):4807.
Vineis P, et al. Advancing the application of omics-based biomarkers in
environmental epidemiology. Environ Mol Mutagen. 2013 Aug;54(7):461-7.
Longitudinal HMM for smoking‐induced lung cancer (Chadeau‐Hyam et
al., Epidemiology in press)
• Exposome concept: risk of chronic diseases is not only driven
by the exposure level itself, but also by its evolution in time and
by potential temporal patterns in the exposure history.
Include a temporal component in causal inferences
• Definition of a longitudinal compartmental model (SIR-type)
S: healthy; I: growing and undiagnosed tumor; R: diagnosed;
M: other cause mortality. S and I are hidden (SUI is observed)