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DIASTOLIC BLOOD PRESSURE CARDIOVASCULAR DISEASE and MORTALITY Sidney Port Linda Demer Robert Jennrich Noel Boyle Alan Garfinkel Departments of Mathematics S Port PhD Statistics S Port R Jennrich PhD Medicine Cardiology L Demer MD PhD N Boyle MD PhD A Garfinkel PhD Physiology L Demer and Physiological Science A Garfinkel University of California Los Angeles CA Prepublication management Noel Boyle UCLA School of Medicine Division of Cardiology Room 47 123 CHS 10833 LeConte Avenue Los Angeles CA 90095 1679 Phone 310 794 2165 Fax 310 206 9133 Email nboyle mednet ucla edu Correspondence to professor Sidney Port Department of Mathematics University of California Los Angeles Los Angeles CA 90025 e mail sport ucla edu 1 Summary Background There are two views of the relation between diastolic blood pressure and risks of cardiovascular disease and death The most widely accepted is that risk is steadily rising with diastolic blood pressure The other is that the relation follows a Jcurve in which risks are also increased at low as well as high pressures This view is controversial because it is not consistently found We reanalyzed data from the Framingham study to determine the nature of the relation of cardiovascular risk to diastolic blood pressure to see why the J curve is elusive to seek justification for taking 90 mm Hg as the cut point for diastolic hypertension and to determine if the systolic or diastolic pressure is a better predictor of risk Methods Reanalysis of the Framingham data on diastolic blood pressure using logistic splines Results Interpretations 1 The Framingham data rejects the linear logistic model the risk diastolic blood pressure relation is not continuous and strictly increasing 2 The basic relation of risk to diastolic blood pressure is the same as we previously found for systolic blood pressure namely risk is constant to a threshold at the 70th percentile pressure about 90 mm Hg and steadily increases thereafter 3 Rather than being arbitrary 90 mm Hg is a natural threshold for hypertension 4 The J curve phenomenon is a barely detectable effect hovering on the boundary of statistical significance that may cause a rise in risk for pressures less than 70 mm Hg The weakness of this effect may explain why its detection is so elusive 5 Systolic blood pressure and diastolic blood pressure are equivalent predictors of risk 6 For the soft endpoint of cardiovascular disease incidence there is a sharp jump at 90 mm Hg with risk being constant to the left of 90 mm Hg again constant between 90 mm Hg and 104 mm Hg and a increasing thereafter The difference between this outcome and the hard outcomes suggests that the presence or absence of hypertension may influence the diagnosis of cardiovascular disease 2 INTRODUCTION Both JNC VI 1 and WHO ISH 2 support the view that the relation of both diastolic and systolic blood pressure to endpoints such as death due to cardiovascular disease is continuous strictly increasing and with no lower bound Fig 1 This they say is based on the preponderance of eveidence from epidemiological studies and randomized trials Previous analysis from the Framingham study 3 4 was instrumental in propagating this view of the risk blood pressure relation primarily by the use of linear logistic smoothing of the data We previously reported that the relation between cardiovascular risks and systolic blood pressure was not linear but has a threshold level 5 Risk was not increased at any pressures except for those in the upper 30 of pressure for their age and sex We also found that the use of 140 mm Hg as the universal division between normal and elevated systolic blood pressure was unjustified it needed to be replaced by age and sex dependent cut points Unlike the case with systolic pressure there has long been some opposition to the strictly increasing model of cardiovascular risk with diastolic blood pressure Starting with Andersen s observation 6 that the same Framingham data considered here apparently showed that the risk of cardiovascular disease decreases rather than increases with increasing diastolic pressure to 89 mm hg this controversy has centered on the J curve effect That effect is an apparent increase in risk at low as well as high diastolic blood pressure 6 21 This view is controversial primarily because it is not consistently found We reassessed the diastolic pressure data from the Framingham 18 year follow up report 4 for the following outcomes cardiovascular disease incidence death due to cardiovascular disease and overall mortality Our primary goal was to determine the nature of the relation of these risks to diastolic blood pressure Secondary goals were to explain why the J curve effect was so elusive to see what justification there was for 90 mm Hg being a universal cut point for diastolic hypertension and to find whether the diastolic blood pressure or systolic blood pressure was a better predictor of risk We chose to reanalyze the Framingham 18 year follow up data because 10 it was accurately gathered 2 it was unconfounded by antihypertensive drug intervention 3 it contained women 4 it contained older people 3 Methods The Framingham data 4 are presented separately for each sex divided into three age groups 45 54 55 64 and 65 74 years There are 10 blood pressure categories Table 1 Unlike the systolic pressures for persons aged 45 74 the distribution of diastolic pressures does not change much with age and is about the same in both sexes For practical purposes we can take these distributions to be the same We first determined if the relation of overall and cardiovascular disease death to diastolic blood pressure is homogeneous across the six groups i e if the additive model holds In that model the risk for a person is simply the sum of two effects one based on the group the person is in and the other from the person s blood pressure Our analyses were carried out using both the specific rates and the direct group adjusted rate Models for the specific rates were viewed as sub models of the additive model We modeled the relation of both overall and cardiovascular death to diastolic blood pressure by curves known as logistic splines A logistic spline is a curve that results from continuously joining two or more logistic curves The points where two curves join are called knots and the curves are called segments A logistic spline is parameterized by the location of the knots an intercept term and a slope term for each segment A segment with slope


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