Modeling Blood Pressure Allostasis and Adaptation
As articulated above, studies regarding the morbidity and mortality risk of arterial
blood pressure variation have focused on summary measures of variation over 24 h,
such as the standard deviation of the 24-h arithmetic average of the intermittent
measurements (Hansen et al. 2010 ), the coefficient of variation (24-h standard
deviation/24-h mean) (e.g., Kikuya et al. 2000 ; Flores 2013 ) or the difference in
pressure associated with the transitions from waking to sleep (e.g., Fagard et al.
2009 ), or sleep to waking (e.g., Kario 2010 ). It has long been known that there is
heteroscedasticity in the relationship between the mean 24-h blood pressure and its
variance, such that as the mean pressure increases, so does the variance (Pickering
1991 ). Thus, it is not surprising that when the mean and variability measures are
forced into the same predictive model in populations containing large numbers of
both hypertensives and normotensives, variability appears to add little to the pre-
diction of cardiovascular risk beyond the mean 24-h blood pressure, but is pre-
dictive when variability alone, without the 24-h mean, is considered (e.g., Hansen
et al. 2010 ). While efforts have been made to create standardized variation indexes
for clinical purposes, from these types of variation assessment there is no capacity
for examining what is actually driving the variation, nor any ability to assess
whether the observed variation is appropriate for the circumstances, which in my
opinion, are really the relevant characteristics of variation when it comes to car-
diovascular disease risk. Bluntly, examining gross blood pressure variability does
not really tell you why the variation is adaptive or how it leads to pathology. To
understand why blood pressure varies, it is critical to have information regarding
the conditions of each blood pressure measurement. That is, if blood pressure is
responding to the ambient conditions in an allostatic fashion and thus facilitating
adaptation, in order to evaluate the adaptive process, there needs to be a means of
defining the constantly changing conditions (James 2013 ).
While direct observation of subjects wearing the monitor has been used (e.g., Ice
et al. 2003 ), for most studies of circadian blood pressure variation, subjects have
self-reported their waking conditions at each blood pressure measurement in a
diary, which have taken on a variety of forms, from pencil and paper in the earliest
studies, to handheld computers in later assessments (James2007b, 2013 ). Most
studies that examine the sources of variation in intermittently taken blood pressure
measurements have not been conducted with a focus toward allostasis, or even
understanding cardiovascular adaptation for that matter. Rather, the interest has
either been in simply defining the sources of diurnal blood pressure variation, or
evaluating whether people with specific characteristics differ in their responses to
similar stimuli (James 2013 ).
As I have previously noted, to assess the sources of diurnal ambulatoryblood
pressure variation, two general approaches have been used (James2007a, 2013 ).
Thefirst is a“natural experimental”approach in which there are a priori design
elements that define predictable changing behaviors or situations that occur during a
typical day. This quasi-experimental approach has its roots in laboratory reactivity
150 G.D. James