Month: February 2016

I’m speaking at the EBM+ project meeting at UCL on Monday on a topic that I’ve been working on for a couple of months now. Very briefly, the talk is about Wigmore charts, and ways that we might use them to support clinical decision-making.

Wigmore chart
Wigmore chart

Wigmore charts (like the example here that I’ve borrowed from Anderson, Schum and Twining’s excellent 2005 book) were originally designed to support complex legal arguments. Imagine that you are trying to build a complex legal case: trying to convict someone of fraud, say. Wigmore charts are a tool for showing how these complicated legal argument works. Here, the “ultimate probandum” is the legal verdict that you are trying to reach (in this case, something like “x knowingly defrauded y”). The chart shows the steps of the legal argument that support this final verdict, all the way down to the many pieces (often, in court cases, many thousands of them) on which the case is build.

My current thought is that these inferential networks would be useful in medicine too, particularly when dealing with complex decisions about evidence. I think that we might use Wigmore charts, or something similar, as an heuristic (see Chow’s recent BJPS paper for a cracking introduction). But to say more would give the game away.

You can have a look at my slides here – [2mB .pdf].

Molecular epidemiology is advancing our understanding of the mechanisms of health and disease. It does so by changing the scale of measurement. Traditional epidemiological methods, including environmental epidemiology, gather information about the association between exposure and disease by studying ‘macro-variables’. Pollution, types of diseases, socio-economic factors, etc. Molecular epidemiology studies pretty much the same thing, but measures both exposure and disease at the level of molecules. So for instance, we want to know how much of certain chemicals we breathe in busy city centres, or how much of a given disinfectant is used in a swimming pool.   The intuition is that we have to plot how much of these hazards may induce changes in our bodies, until disease develops. And so we need to measure what happens in our bodies, once we are exposed to them. But exposure may work on different time spans. We want to understand what effect it makes in the very short term – say a few hours – to walk up and down in busy Oxford Street in London, and what effect it makes longer term, until we detect the first signals of disease development, and finally of disease. This approach to the study of health and disease is based on the concept of exposome and on a methodology called ‘meeting-in-the-middle’. (See e.g. REF 1-4 below)

But why is this interesting? For several reasons. One such reason is that, while we might be able to crack soon the deepest bio-chemical mechanisms of several diseases (see e.g. the ones studied by the project ‘EXPOsOMICS’ http://www.exposomicsproject.eu ), we still need to understand how these map onto socio-economic, behavioural, psychological, and demographic characteristics of individuals. The reason is twofold. On the one hand, it is difficult to see how we can design effective public health interventions by targeting molecules. Instead, we must target individuals – more precisely their behaviours. And we cannot properly target their behaviours until the understand ‘who’ these people are. On the other hand, the fact that we search for the molecular basis of disease (and of health) does not imply reducing disease (or health) to a mere bio-chemical phenomenon. There are several arguments to support this, as mentioned by Kelly et al (ref 5). But, it seems to me, a very interesting reason come from within molecular biology (and epidemiology) itself: the study of the epigenome suggests that there is indeed a relation between the social and the biological sphere. And this relation is not one reduction, but of causation. Agreed, the type of causation here involved hasn’t been understood in its full extent yet, and much work is still needed both from the scientific side and the philosophical side. But the seeds are there.

This, and other reasons why molecular epidemiology is an interesting place where science and philosophy can and should meet, is discussed in a forthcoming paper co-authored by Prof. Paolo Vineis and myself. Stay tuned!

References:

  1.   Chadeau-Hyam M, Athersuch TJ, Keun HC, Iorio MD, Ebbels TM, Jenab M, Sacerdote C, Bruce SJ, Holmes E, Vineis P (2011) Meeting-in-the-middle using metabolic profiling—a strategy for the identification of intermediate biomarkers in cohort studies. Biomarkers 16(1):83–88
  2. Rappaport, S.M., Smith, M.T. 2010. Environment and disease risks. Science 330:460–461.
  3. Vineis P, Chadeau-Hyam M (2011) Integrating biomarkers into molecular epidemiological studies. Curr Opin Oncol 23(1):100–105
  4. Wild CP (2005) Complementing the genome with an ‘‘exposome’’: the outstanding challenge of environmental exposure measure- ment in molecular epidemiology. Cancer Epidemiol Biomarkers Prev 14:1847–1850
  5. Kelly M.P, Kelly R.S., Russo F. (2014) The integration of social, behavioural, and biological mechanisms in models of pathogenesis. Perspectives in Biology and Medicine, 57(3), 308-328.

The Reasoner is a monthly digest highlighting exciting new research on reasoning, inference and method broadly construed. It is interdisciplinary, covering research in, e.g., philosophy, logic, AI, statistics, cognitive science, law, psychology, mathematics and the sciences. Each month, there is a column on Evidence-Based Medicine. Here is this month’s column:

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