Transl Clin Pharmacol.  2019 Mar;27(1):24-32. 10.12793/tcp.2019.27.1.24.

Characterization of circadian blood pressure patterns using non-linear mixed effects modeling

Affiliations
  • 1Department of Pharmacology, Yonsei University College of Medicine, Seoul 03722, Korea. kspark@yuhs.ac
  • 2Brain Korea 21 Plus Project for Medical Science, Yonsei University, Seoul 03722, Korea.
  • 3Ministry of Food and Drug Safety, Cheongju 28159, Korea.

Abstract

Characterizing the time course of baseline or pre-drug blood pressure is important in acquiring unbiased estimates of antihypertensive drug effect. In this study, we recruited 23 healthy male volunteers and measured systolic (SBP) and diastolic blood pressure (DBP) over 24 hours on an hourly basis. Using a non-linear mixed effects model, circadian rhythm observed in blood pressure measurements was described by incorporating two cosine functions with periods 24 and 12 hours. A mixture model was applied to identify subgroups exhibiting qualitatively different circadian rhythms. Our results suggested that 78% of the study population, defined as "˜dippers', demonstrated a typical circadian profile with a morning rise and a nocturnal dip. The remaining 22% of the subjects defined as "˜non-dippers', however, were not adequately described using the typical profile and demonstrated an elevation of blood pressure during night-time. Covariate search identified weight as being positively correlated with mesor of SBP. Visual predictive checks using 1,000 simulated datasets were performed for model validation. Observations were in agreement with predicted values in "˜dippers', but deviated slightly in "˜non-dippers'. Our work is expected to serve as a useful reference in assessing systematic intra-day blood pressure fluctuations and antihypertensive effects as well as assessing drug safety of incrementally modified drugs.

Keyword

Circadian blood pressure fluctuation; Nonlinear mixed effects model; Non-dipper; NONMEM

MeSH Terms

Blood Pressure*
Circadian Rhythm
Dataset
Humans
Male
Volunteers

Figure

  • Figure 1 Typical circadian profiles of ‘dippers’ (blue) and ‘non-dippers’ (red) subject groups in SBP (left) and DBP (right) (SBP, Systolic blood pressure; DBP, Diastolic blood pressure).

  • Figure 2 Goodness of fit plots showing smoothed predictions (blue line), superimposed on smoothed observations (red line) and original observations (green dots). (SBP, Systolic blood pressure; DBP, Diastolic blood pressure).

  • Figure 3 Conditional weighted residuals (CWRES) plotted against TIME (TOP) and typical predictions (PRED) (BOTTOM). Blue and red colors denote SBP and DBP, respectively (SBP, Systolic blood pressure; DBP, Diastolic blood pressure).

  • Figure 4 Individual predictions of SBP (red), superimposed on observations (blue), for dippers (TOP) and non-dippers (BOTTOM) (SBP, Systolic blood pressure).

  • Figure 5 Individual predictions of DBP (red), superimposed on observations (blue), for dippers (TOP) and non-dippers (BOTTOM) (DBP, Diastolic blood pressure).

  • Figure 6 VPC of SBP (LEFT) and DBP (RIGHT) for dippers (TOP) and non-dippers (BOTTOM). The colored bands correspond to 95% confidence interval around 5%, 50%, and 95% percentiles of the predictions, and the lines correspond to the median values of the corresponding percentiles of the observations (SBP, Systolic blood pressure; DBP, Diastolic blood pressure; DV, Dependent variable).


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