The increase in the use of the term biomarker is a recent one.  When one looks back at the use of this term in the literature over the last fifty years, there was an explosive increase in its use in the 1980s and 1990s, and it continues to grow today.  However, biomarker research as we now know it has a much deeper history.
 

Here we are going to focus on just one paper, published in 1965, twelve years before the term “biomarker” appeared in either the title or abstract of any paper in the PubMed database[i].  This is a paper by Sir Austin Bradford Hill, which appeared in the Proceedings of the Royal Society of Medicine entitled “The Environment and Disease:  Association or Causation?”.
 

Sir Austin neatly and eloquently describes nine factors that he feels should be taken into account when assessing the relationship between an environmental factor and disease.  These are:

1. Strength
2. Consistency
3. Specificity
4. Temporality
5. Biological gradient
6. Plausibility
7. Coherence
8. Experiment
9. Analogy

In this blog we discuss the applicability of each of these factors to biomarker research today.  However, before we do, it is important to note that the aims of biomarker research today are much broader than the primary aim of Sir Austin’s paper – which was to discuss the ways in which an observed association between the environment and some disease may be assessed for the degree of causality involved.  However, only a very few biomarkers lie directly on this causal path (some biomarkers change in response to the disease itself, others are only indirectly associated with the disease and its causes), but crucially their utility does not depend upon a causal association.  However, particularly when biomarkers are used to aid the identification of disease, there are clear parallels between Sir Austin Bradford Hill’s assessment of causality and our current need to assess utility.
 

1.  Strength. Sir Austin’s primary factor to consider in the interpretation of causality was the strength of the association.  He argues that the stronger the association between two factors, the more likely it is that they are causally related.  However, he cautions against the obverse interpretation – that a weak association implies a lack of causality.  In fact, the strength of an association depends on the proportion of the variance in one factor that explained by the other over the relevant sampling timescale.  In other words, there may be a completely causal relationship between X and Y, but X may be only one factor (possibly a small factor) controlling Y.  The remaining variance in Y may even be random fluctuations (so X is the only factor causally associated with Y), yet the …

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