Molecular epidemiology, biomarkers, process ontology

1. What kind of thing
is a biomarker?
Federica Russo
Philosophy | Humanities | Amsterdam
russofederica.wordpress.com | @federicarusso

2. Overview
Data and health
From epidemiology to molecular epidemiology
Big health data and big stakes
The promise of the omics data
The aetiological standpoint
From Koch to biomarkers
The ontological status of biomarkers
Towards a process-based ontology for disease causation
2

3. DATA AND HEALTH
3

4. A science approach
to health and disease
Discovering the mechanisms of health and disease
A tradition that starts at least in the 19th C
Experimental medicine, biology, …
Establishing correlations between categories –
disease, exposure, determinants …
A tradition that starts at least in the 17th C, blossoming
in 2nd half of 20th C
Epidemiology: the study how health and disease vary within
and across populations
4

5. From epidemiology to
molecular epidemiology
Environmental exposure and disease
(How) do {air, water, chemicals, …} cause {cancer, asthma,
allergies, ...}?
Traditional epidemiology
Establish correlation between classes of environmental
factors and of disease
Molecular epidemiology
Measurements at molecular level
Identify biomarkers of exposure >> of early clinical changes
>> of disease
5

6. Exposure causes Disease
Measure chemicals in water, air, etc
Identify biomarkers of exposure
Detect biomarkers of early clinical
changes
Match with biomarkers of disease 6
Make categories
of environmental
factors
Match with
categories of
disease

7. Visit:
http://www.exposomicsproject.eu
7

8. BIG HEALTH DATA AND BIG STAKES
8

9. Data- and technology- driven
Big data helps with the generation of evidence
of correlation (statistical analyses) and of
mechanisms (omics analyses)
Specifically, technology is central to: generating,
processing, storing / archiving, curating,
analysing data
9

10. Data collection,
storage, generation
Sensors, smartphones, GPS
Biobanks
Omic technologies
Liquid chromatography
Mass spectometry
Nuclear magnetic
resonance spectroscopy
…
Statistics softwares
10

11. What are we measuring?
What is it that causes disease?

12. THE AETIOLOGICAL STANDPOINT
12

13. The ‘old’ aetiological standpoint
Carter 2003, The rise of causal concepts of disease
Aetiological standpoint from Koch and Pasteur onwards
Disease is defined in terms of its causes
Causes are natural, universal, necessary
Canguilhem 1943, Le normal et le pathologique
“To act, it is at least necessary to localise”
The experimental approach of Bernard
We can localise pathogens!
13

14. Koch’s aetiological standpoint 1.1
From mono-causal to multi-causal frameworks
Social determinants
…
But the idea remains: we search for those
entities that cause disease
Why? To understand and to control disease
14

15. The promise of biomarkers research
Using these ‘-omics’ technologies to inform hypothesis-directed pathway-
based approaches to molecular epidemiology and to help direct genome-
wide exploratory analyses into more promising directions. [...] one might
think of the ‘‘-omics’’ data as providing the missing link among
exposure, genes, and disease. (Thomas 2006, p. 490)
‘-Omics’ tools can be directly applied to samples from an epidemiologic
case-control or cohort study to better characterise intermediate
pathways, potentially providing the ‘missing links’ among
exposures, genes, and diseases. (Vineis et al. 2009)
Omic technologies offer great potential to identify biomarkers. (Vineis
and Chadeau-Hyam 2011)
15

16. The new aetiological standpoint?
Are biomarkers the sought tiny-tiny
entities?

17. THE ONTOLOGICAL STATUS OF
BIOMARKERS
17

18. 18
Traditions
of
Entity
Hunting
Aetiological
standpoint /
infectious disease
model / search
for pathogens
Mechanistic
explanation /
causally relevant
entities
Analytic
metaphysics /
Language and
ontology / focus
on particulars

19. Picking up entities?
While classical statistical models to analyzing -omics data serve the
purpose of identifying signals and separating them
from noise, little has been done in chronic diseases to model
time into the exposure-biomarker-disease continuum.
[Vineis and Chadeau-Hyam 2011]
From these two parallel analyses [statistical analyses], we obtained
lists of putative markers of (i) the disease outcome, and (ii)
exposure. These were compared in a second step in order to
identify possible intersecting signals, therefore defining
potential intermediate biomarkers.
[Chadeau-Hyam et al 2011]
19
We pick up signal!

20. Early theorisations of biomarkers
A must read
Molecular epidemiologist Paul Schulte (1993)
Biomarkers
Biological markers: ‘’technologically powerful
measures of biological variables’’
Indicate events in the continuum [E----D] at different
levels
20

21. What do biomarkers stand for?
It is vital to understand the nature of the relationship between the
mark and the event
“Does the marker represent an event, is it an event itself, is it a
correlate of the event, or is it a predictor of the event?
The answers to these questions may affect who is sampled, how
and when they are sampled, and what confounders or effect
modifiers are considered. […] Thus, a biologic marker often
refers to the use made of a piece of biologic
information rather than to a specific type of
information.”
Schulte, 1993, p.14-15.
21

22. Process-based disease causation?
Biomarkers are not causes
They are not even markers for causes, per se
Biomarkers mark salient points of a process (=
disease) to be understood in causal terms
22

23. Process ontologies
Latest insights from phil biology
To say what an individual is we must look at
processes, rather than entities
Dupré, Pradeau, Seibt, …
We can conceive as disease as a (causal) process
Sharp distinction between normal and pathological
collapses
23

24. Theoretical foundations wanted!
An epistemology based on levels of abstractions
See Phil Information approach
A process ontology
See recent Phil Bio and Ontology
An informational account of causality
See recent Phil Causality
24

25. Now back to business
Health and disease as processes
Biomarkers of disease
Biomarkers of health
When / How to intervene?
At biological level or at social level?
Overdiagnosing?
…
25

26. TO SUM UP AND CONCLUDE
26

27. Molecular epidemiology is paradigmatic of the
datification of science
Big health data and omics create opportunities and
challenges
Reflecting on technology for data collection, generation,
etc. forces us to rethink the received ‘aetiological
standpoint’ of disease
The new standpoint is still aetiological, but the causal unit
is an ontology not entity-based, but a process-based
Molecular entities do exist, but a level of abstraction to
be specified
27

28. (SUPER) SELECTED REFERENCES
28

29. Molecular epidemiology
P.A. Schulte and F. Perera (eds) Molecular Epidemiology. Principles and Methods.
Academic press
Vineis P, Perera F (2007) Molecular epidemiology and biomarkers in etiologic cancer
research: the new in light of the old. Cancer Epidemiol Biomarkers Prev 16(10):1954–
1965
National Academies of Sciences, Engineering, and Medicine. 2017. Using 21st Cen- tury
Science to Improve Risk-Related Evaluations. Washington, DC: The National Academies
Press. doi: 10.17226/24635.
Russo F. and Vineis P. (2017) Opportunities and challenges of molecular epidemiology. In
G. Boniolo and M. J. Nathan (eds) Philosophy of Molecular Medicine: Foundational
Issues in Research and Practice. Routledge.
http://www.exposomicsproject.eu
Evidential pluralism and causality in molecular epidemiology
Russo F, Williamson J (2012) EnviroGenomarkers: The Interplay Between Mechanisms and
Difference Making in Establishing Causal Claims, Medicine Studies, DOI
10.1007/s12376-012-0079-7
Vineis P. and Russo F. Epigenetics and the Exposome. In Oxford Research Encyclopaedia of
Environmental Science. Oxford University Press. In press.
Russo F. and Vineis P. (2017) Opportunities and challenges of molecular epidemiology. In
G. Boniolo and M. J. Nathan (eds) Philosophy of Molecular Medicine: Foundational
Issues in Research and Practice. Routledge, ch.12.
Technology in molecular epidemiology
Russo F. (2016) On the poietic character of technology. Humana.Mente Journal of
Philosophical Studies, 30, 147-174. 29