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There is a non-linear relationship between mortality and blood pressure

Stamler stated ‘the relation of SBP [systolic blood pressure] to risk of death is continuous, graded, and strong, and there is no evidence of a threshold . . .’. The formulation of this ‘lower the better’ principle, in terms of the linear logistic model (often referred to simply as the linear model) is the paradigm for the relationship of all cardiovascular risks to blood pressure and forms the foundation for the current guidelines for hypertension. But it is often forgotten that when a study reports a linear (or any other) relationship between two variables it is not the data itself, but the model used to interpret the data, that is yielding the relationship. Almost universally, studies that report a linear relationship of risk to blood pressure, do so via the linear models such as the Cox model or the linear logistic model. Formally, that model can be applied to any bivariate data and, independently of the data, will always show that there is a linear relationship between the two variables. Before one can have confidence that the stated linearity correctly reflects the behaviour of the data, and is not just an artifact of the model, it is necessary to carefully examine the data in relation to the proposed model. At a minimum, it must be demonstrated that the model actually ‘fits’ the data and that it does not ‘smooth away’ important features of the data. Twenty years ago Keys, using simple graphical methods, concluded that the linear model, in terms of the relationship of overall and coronary heart disease death to blood pressure was unjustified. Could Keys be correct? To see, we reexamined data from the Framingham Heart Study. That carefully conducted and most widely cited study played a seminal role in firmly entrenching the current linear thinking on blood pressure. Although ‘soft’ end-points, such as cardiovascular risk, are certainly of interest, they