Exploring diurnal variation using piecewise linear splines: an example using blood pressure.
BACKGROUND
There are many examples of physiological processes that follow a circadian cycle and researchers are interested in alternative methods to illustrate and quantify this diurnal variation. Circadian blood pressure (BP) deserves additional attention given uncertainty relating to the prognostic significance of BP variability in relation to cardiovascular disease. However, the majority of studies exploring variability in ambulatory blood pressure monitoring (ABPM) collapse the data into single readings ignoring the temporal nature of the data. Advanced statistical techniques are required to explore complete variation over 24 h.


METHODS
We use piecewise linear splines in a mixed-effects model with a constraint to ensure periodicity as a novel application for modelling daily blood pressure. Data from the Mitchelstown Study, a cross-sectional study of Irish adults aged 47-73 years (n = 2047) was utilized. A subsample (1207) underwent 24-h ABPM. We compared patterns between those with and without evidence of subclinical target organ damage (microalbuminuria).


RESULTS
We were able to quantify the steepest rise and fall in SBP, which occurred just after waking (2.23 mmHg/30 min) and immediately after falling asleep (-1.93 mmHg/30 min) respectively. The variation about an individual's trajectory over 24 h was 12.3 mmHg (standard deviation). On average those with microalbuminuria were found to have significantly higher SBP (7.6 mmHg, 95% CI 5.0-10.1) after adjustment for age, sex and BMI. Including an interaction term between each linear spline and microalbuminuria did not improve model fit.


CONCLUSIONS
We have introduced a practical method for the analysis of ABPM where we can determine the rate of increase or decrease for different periods of the day. This may be particularly useful in examining chronotherapy effects of antihypertensive medication. It offers new measures of short-term BP variability as we can quantify the variation about an individual's trajectory but also allows examination of the variation in slopes between individuals (random-effects).
RCSI Population and Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland.
Jamie M
JM
Madden
Department of Mathematics and Statistics, La Trobe University, Melbourne, Australia.
Xia
X
Li
Department of Epidemiology and Public Health, University College Cork, Cork, Ireland.
Patricia M
PM
Kearney
School of Social and Community Medicine, University of Bristol, Bristol, UK.
Kate
K
Tilling
Department of Epidemiology and Public Health, University College Cork, Cork, Ireland.|Department of Statistics, University College Cork, Cork, Ireland.
Anthony P
AP
Fitzgerald
Biostatistics; Blood pressure patterns; Blood pressure variability; Circadian modeling; Mixed-effects models