This document summarizes a presentation on the contributions of epidemiology to systems biology and medicine. It discusses how epidemiology takes a population-based approach to health and disease, and how post-genome epidemiology examines genes, gene-environment interactions, and new insights into disease mechanisms. It also reviews several ongoing research initiatives that aim to understand the complexity of conditions like asthma and COPD using a systems approach. Finally, it outlines how epidemiology can integrate different levels of complexity, study diverse patient samples, apply bioinformatics tools, lead multidisciplinary efforts, and provide a public health framework for systems medicine.

1. 1st International BRN SYMPOSIUM
From Systems Biology to Personalized
Medicine: an epidemiological view
Josep M. Antó
Centre for Research in Environmental Epidemiology (CREAL)
IMIM, UPF, CIBERESP
Barcelona, 13th June 2014

2. Systems Respiratory Medicine: the epidemiological
view
(1) Epidemiology: a population based approach to health & disease.
(2) The post-genome respiratory epidemiology.
(3) Complexity and reductionism: systems biology approach.
(4) Epidemiology and systems medicine.

3. Association between smoking and lung cancer (RR)
Non-smokers 1.0 1.0 1.0 1.0
1-24 11.0 7.0 8.1 7.8
15-24 13.9 9.5 19.9 12.7
25+ 27.0 16.3 32.4 25.1
Publication date 1950 1952 1964 1976
(1) (2) (3) (4)
(1) Doll R et al. Br Med J 1950. Case-control
(2) Doll R et al. Br Med J 1952. Case-control
(3) Doll R et al. Br Med J 1964. Cohort
(4) Doll R et al. Br Med J 1976. Cohort

4. The essence of the observational epidemiological
approach
(1) Observational design:
 No randomization / prone to bias.
(1) Computational model of exposure-disease association:
 Regression.
 Main effects and interaction.
 Adjusted for confounding.
(1) Criteria of causality:
Temporality.
 Dose-response.
Causal mechanism?
…….

5. Updated model for molecular epidemiology (Vineis P)

6. Epidemiology faces its limits
The search for subtle inks between diet, life style or
environmental factors and disease is an unending
cause of fear-but often yields little certainty (Taubes G.
Science 1995).
(1) Confounding and measurement error of small effects.
(2) Reverse causation.
(3) Single risk factors vs combined effects (interactions).
(4) Phenotype overlap (comorbidity).
(5) Phenotype heterogeneity (sub-phenotypes).
(6)Discordance between randomized (RCT) and observational
studies.

7. Genome and post-genome
(1) Post-genome respiratory epidemiology (Kauffmann F ERJ
2005).
(2) Causes / Risks:
 Genes as causal factors: GWAS epidemiology.
 GxE interactions.
(1) Diseases: new insights on causal mechanisms.
(2) Predictions: new opportunity for screening and personal
genomics.

8. Genome-wide associations reported through March 2010
Manolio TA. N Engl J Med 2010;363:166-76

9. Genes/loci associated with spirometric indices of lung
function
Hall IP. Eur Respir Rev 2013; 22: 127, 53–57
Those genes/loci shown in bold have also been shown to be associated with chronic obstructive
lung disease per se.

10. Risk of developing life-course-persistent asthma according to
generic risk score
Belsky DW et al. Lancet Respir Med 2013;1:453-61.

11. Predictive performance with several common genetic
variants, traditional risk factors, and both
Ioannidis JPA. Ann Intern Med 2009

12. Challenges in the understanding of asthma
 There is no satisfactory causal model of asthma.
 There is no satisfactory definition of asthma.
 Poor understanding of the course of asthma.
 Poor understanding of the allergic and non allergic
mechanisms.
 Lack of satisfactory preventive strategies.
We still don’t understand THE ASTHMA EPIDEMIC
(and its levelling off in some areas).

13. Early lung development
JM Antó adapted from Stock J et al. Lancet Resp Med 2013
Endotoxins
Parental smoke
Vitamine D
BHR
Atopy
Pre-conceptional Pre-natal
Asthma
COPD
Breastfeeding
Physical activity
Air pollution
RV
RSV
Smoking
Growth
COMPLEXITY

14. Diagram illustrating the different levels of complexity of
airways disease
Vanfleteren LEGW et al. Thorax 2014

16. Systems Biology as an alternative to reductionism
Winslow RL et al. Sci Transl Med 2012
Wolkenhauer O. Systems
biology: the reincarnation of
systems theory applied in
biology? Brief Bioinform
2001.
Ideker T et al. A new
approach to decoding life:
systems biology. Annu Rev
Genomics Hum Genet 2001.

17. Multiscale and multilevel influences in cancer prevention and
control
Morrissey JP et al. J Natl Cancer Inst Monogr 2012;44:56-66

18. DNA sequencing opens up the possibility of analyzing a large number of individual genomes and transcriptomes,
and complete reference proteomes and metabolomes are within reach using powerful analytical techniques based
on chromatography, mass spectrometry and nuclear magnetic resonance.
Computational and mathematical tools have enabled the development of systems approaches for deciphering the
functional and regulatory networks underlying the behavior of complex biological systems.
Iterative systems approaches are starting to provide deeper insights into the mechanisms of human diseases, and
to facilitate the development of better diagnostic and prognostic biomarkers for cancer and many other diseases.
Systems approaches will transform the way drugs are developed through academy-industry partnerships that will
target multiple components of networks and pathways perturbed in diseases.
They will enable medicine to become predictive, personalized, preventive and participatory.
Systems medicine should be developed through an international network of systems biology and medicine
centers dedicated to inter-disciplinary training and education, to help reduce the gap in healthcare between
developed and developing countries.

19. The elements that will allow systems medicine to tackle
deciphering biological complexity
Hood L. RMMJ 2013.
Social sciences
Behavioral sciences

20. Associations between poverty, non-communicable diseases
(NCDs), and development goals
Beaglehole R. Lancet 2011; 377:1438-47

21. In 10 years each individual will be surrounded by a virtual
cloud of billions of data points—P4 medicine
Hood L. RMMJ 2013;4 (2):e0012.

22. Rose G. Sick Individuals, Sick Populations. IJE 1985
 The largest number of cases comes from the lowest risk groups.
o High risk and Population risk approaches complement.
 The determinants (causes) of cases are not necessarily the
determinants of the disease population rates:
o Multilevel (both individual and population) studies are
necessary to integrate both identify both types of causes.
 Medicine should be social and group oriented as well as individual.

23. Translational research framework influenced by four
“drivers” of epidemiology
Lam TK et al. Cancer Epidemiol Biomarkers Prev 2013;22 (2):181-188

24. Ongoing research initiatives, in Europe/USA, aiming at understanding
the complexity of asthma & COPD
Vanfleteren LEGW et al. Thorax 2014
1. U-BIOPRED: Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes.
http://www.ubiopred.eu
2. EvA: Emphysema versus Airway study.
http://www.eva-copd.eu
3. MeDALL: Mechanisms of the Development of Allergy
http://www.medall-fp7.eu
4. Synergy-COPD project
http://www.synergy-copd.eu
5. AirPROM: Airway Disease Predicting Outcomes through Patient Specific Computational Modelling
http://www.airprom.eu
6. ESCAPE: European Study of Cohorts for Air Pollution Effects
http://www.escapeproject.eu
7. CHANCES: Consortium on Health and Ageing: Network of Cohorts in Europe and the USA
http://www.chancesfp7.eu
8. COPDgene study
http://www.copdgene.org
1. SPIROMICS: Subpopulations and intermediate outcome measures in COPD Study
http://www.cscc.unc.edu/spir/
2. MESA: Multi-Ethnic Study of Atherosclerosis
http://www.mesa-nhlbi.org

25. How epidemiology contributes to systems biology and
systems medicine
 Integrating different level of complexity: social,
environmental and behavioural.
 Studying population based samples and unbiased samples
of patients.
 Broadening the traditional statistical approaches to the
broader range of tools in bioinformatics.
 Participating /Leading multidisciplinary consortia.
 Developing a wide public health /population based
framework for Systems Medicine (Evaluation and Evidence
Based).