Key result
Searching for new causes of cardiovascular disease remains highly relevant, as the conclusion that conventional risk factors explain most disease often relies on inappropriate statistical methodology.
The search for new cardiovascular risk determinants remains highly relevant, and current assessments of risk prediction improvement often use inappropriate statistical methodology.
Can we improve our understanding of cardiovascular disease (CVD) causality and prediction? Intuitively, we can. Recent publications, however, could be misinterpreted as suggesting the opposite. First, the Interheart study, which concluded that nine conventional risk factors explain >90% of premature myocardial infarction, is at risk for being interpreted as saying that other, 'new' cardiovascular risk factors can only cause a small remaining fraction of disease of at most 10%. Secondly, papers addressing the predictive value of new risk factors or markers of early CVD risk have concluded that risk prediction does not improve by adding these variables to risk models. In this paper, we will explain that searching for 'new causes' of CVD is still highly relevant, and that improvement of risk prediction is often assessed using inappropriate statistical methodology.
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Smulders et al. (2008) conducted an editorial in Cardiovascular disease (CVD). New cardiovascular risk determinants vs. Conventional risk factors was evaluated. Searching for new causes of cardiovascular disease remains highly relevant, as the conclusion that conventional risk factors explain most disease often relies on inappropriate statistical methodology.
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