1. Biomarkers and measuring
the internal exposome
John Cherrie
PDC Session: The exposome and exposure in the workplace
2. Let’s take a minute to think…
Ÿ Work with the person next to you.
Ÿ How can you distinguish biomarkers of effect
from biomarkers of exposure?
Ÿ Why is this important?
3. Summary…
Ÿ Conventional biomonitoring of single
substances
Ÿ Targeted biomonitoring of multiple
substances
Ÿ Omics and untargeted studies
Ÿ Half-life and toxicologically-relevant window
Ÿ Timing of sample collection
Ÿ Biobanking
4. Isocyanates…
Ÿ A study to assess the changes in control of
exposure to hexamethylene diisocyanate
(HDI) based paints used in vehicle spraying
after an intervention
Ÿ Paint sprayers and managers invited to one of 32
Safety and Health Awareness Days (SHADs)
Ÿ Urinary biomonitoring for
Hexamethylene diamine (HDA)
Ÿ Before and after the intervention
Jones, K., Cocker, J., & Piney, M. (2013). Isocyanate exposure control in
motor vehicle paint spraying: evidence from biological monitoring. The
Annals of Occupational Hygiene, 57(2), 200–209.
7. Multiple biomarker studies…
Ÿ Phthalates, perfluoroalkyl acids, metals
and organochlorines and reproductive
function
Ÿ a priori concerns
Ÿ cross-sectional study (n=602) of male partners of
pregnant women
Ÿ Fifteen contaminants were detected in more than
70% of blood samples
Ÿ Twenty-two reproductive biomarkers assessed
Lenters et al. (2014). Phthalates, perfluoroalkyl acids, metals and
organochlorines and reproductive function: a multipollutant assessment in
Greenlandic, Polish and Ukrainian men. OEM, 1–10.
9. Outcomes…
Ÿ Over 300 exposure–outcome associations
Ÿ 10 associations encompassing 8 outcomes
Ÿ Several associations were consistent across
the three study populations
Ÿ Positive associations between mercury and inhibin
B and cadmium and testosterone
Ÿ Inverse associations between DiNP metabolites
and testosterone and polychlorinated biphenyl-153
and progressive sperm motility
10. Omics…
Ÿ Omics is biomonitoring for multiple analytes
Ÿ Exposure
Ÿ Health status
Ÿ Disease occurrence and progression related
Ÿ Evaluated for…
Ÿ Individual genes, RNA expression
(transcriptomics), protienomics, metabolomics,
epigenomics and more!
Coughlin, S. S. (2014). Toward a road map for global -omics: a primer on -omic
technologies. American Journal of Epidemiology, 180(12), 1188–1195.
12. Methodologies
Ÿ Non–targeted approaches
Ÿ Targeted / Semi–targeted
approaches – list of potentially
significant metabolites
Ÿ Analytical tools:
Ÿ Nuclear Magnetic Spectroscopy (NMR)
Ÿ Liquid Chromatography Mass
Spectrometry (LC-MS)
Ÿ Gas Chromatography MS (GC-MS)
Ÿ And more…
13. EWAS…
Ÿ Patel and colleagues carried out an
Environment-Wide Association Study on
Type 2 diabetes
Ÿ 266 environmental factors
Ÿ Four NHANES cohorts each involving 500 – 3,300
subjects
Ÿ Identified they had a potential false-discovery rate
of 10 to 30%
Patel, C. J., Bhattacharya, J., & Butte, A. J. (2010). An Environment-Wide
Association Study (EWAS) on Type 2 Diabetes Mellitus. PLoS ONE, 5(5), e10746.
15. Agnostic approaches to
identify disease
associations…
Ÿ Genome-wide association studies (GWAS)
Ÿ Steve Rappaport has suggested a similar
Exposome-wide association (EWAS)
paradigm
Ÿ Assumes we start with cases of disease and
compare the omics profile (or the exposome) with
controls (case-control study design)
Ÿ How stable are the biomarkers?
Ÿ Are these biomarkers of effect or exposure?
16. “Meet-in-the-middle”
approaches to understanding
causality…
Ÿ With biomarkers of effect and exposure, associations
strengthen judgments of causality
Vineis et al (2013). Advancing the application of omics-based biomarkers
in environmental epidemiology. Environmental and Molecular
Mutagenesis, 54(7), 461–467.
Prospective study
Assess links between environment and risk-
predictive biomarkers
Nested case-control study of
intermediate biomarkers biomarkers
Exposure
Biomarkers of
Exposure
Intermediate
omic markers
Disease
18. Timing of sample collection…
Ÿ Preconception in parents
Ÿ During pregnancy, particularly the first trimester
Ÿ Childhood, before and after 3 years of age
Ÿ Puberty
Ÿ Adulthood, particularly middle age
Ÿ During menopause in women (between the age
of 45 years and 55 years)
Ÿ After 65 years
Ÿ Post 80 – 85 years
19. Biobanking samples…
Ÿ Omics analysis needs samples, which must
be collected prospectively and stored
indefinitely
Ÿ Often requires storage at very low temperature
Ÿ Along with associated metadata
Ÿ Rigorous protocols for sample handling
Ÿ Industrial scale processing
Ÿ Informed consent and research governance
Elliott et al (2008). The UK Biobank sample handling and storage protocol for
the collection, processing and archiving of human blood and urine. International
Journal of Epidemiology, 37(2), 234–244.
20. The future!
Ÿ We don’t need to have all the answers
today…
Ÿ Analytical technology constantly improving
Ÿ Providing we have good long-term storage
we should be able to analyse samples in
the future