English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this edition of Thrombosis and Hemostasis, Zakai et al present data from the REGARDS study assessing the interplay between twowell-established thrombotic risk factors in relation to both stroke and coronary heart disease (CHD) risk.1 Their data show that high levels of coagulation factor VIII (FVIII) increase the risk of both stroke and CHD, seemingly more pronounced for the latter. Low levels of protein C (PC) only seem to increase the risk of CHD as the risk of stroke does not seem to be affected. This in itself is already an interesting find, as it is in contrast with a recent review and analyses of the literature that suggested that an increased coagulation propensity was strongly associated with the risk of stroke than with myocardial infarction, especially in the young.2But the paper by Zakai et al takes the analyses further and puts a focus on the synergistic inter-play between FVIII and PC. FVIII and PC, pars pro toto, can be seen as markers of the haemostatic balance which is the result of all thrombotic and anti-thrombotic traits. Given the direct effect of activated PC on FVIII, it is not inconceivable that a combination of these two risk factors play a synergistic role in developing arterial thrombosis.3 Basically, they ask themselves, is the combination of their effects more than the sum of their parts? Such an inter-play of two risk factors is also known as interaction of which two types can be discerned, being statistical and biological interaction. Each concept of interaction comes with its own applications and interpretations. Testing for statistical interaction is classically done by including a cross-product term in a regression model and testing for statistical significance. The main goal of such an addition is to optimize the way the model is fitted to the underlying data and is therefore often a good idea when optimizing model fit is the goal of the analyses. When the cross-product term is statistically significantly different from the null hypothesis, there is said to be statistical interaction. However, it is important to note that this interaction is modelled on the scale of the regression model in which the cross-product term is entered. For linear regression, this means that the cross-product term shows the increase of the outcome parameter if one is exposed to both risk factors on top of the sum of the individual effects. However, when relative risks aremodelled (e.g. hazard ratios from a Cox proportional hazards model), the scale is multiplicative and therefore the cross-product term shows the increase of the risk on top of the multiplication of the two individual effects. This is why statistical significance of a positive cross-product term is formally referred to as supramultiplicative interaction. This is distinct fromassessing biological interaction, which looks for interaction on an additive scale. The easiest way to assess biological interaction between two risk factors (A and B) is to estimate and compare the risk of disease for those who are exposed to no risk factor ( / ), risk factor A (þ/ ), risk factor B(–/þ) or the combination of both (þ/þ). This way, it is possible to model the additional risk conferred by being exposed to both risk factors on top of the risk of the two individual risk factors in itself, also known as supra-additive interaction. The presence of supra-additive interaction helps to understand the biology of the disease. It indicates that some individuals only develop the disease because they are exposed to the combination of both risk factors. Sometimes it could also hint towards a clinical opportunity to reduce the risk in specific sub-group of patients. This is, however, not always the case as can be learned from the interaction between oral contraceptive (OC) use and high levels of kallikrein C1 (KAL-C1) inhibitor antigen levels.4OC use alone increases the risk of ischaemic stroke in young women by approximately twofold, whereas KAL-C1 alone increases the stroke risk by fivefold. The combination of both increases the ischaemic stroke risk by approximately 23-fold, which is much more than can be expected based on the increase in riskof the two individual risk factors alone. Given that OC use

FVIII, Protein C and the Risk of Arterial Thrombosis: More than the Sum of Its Parts