Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Emerging Themes in Epidemiology
Published in: Emerging Themes in Epidemiology 1/2017

Open Access 01-12-2017 | Research article

Exploring diurnal variation using piecewise linear splines: an example using blood pressure

Authors: Jamie M. Madden, Xia Li, Patricia M. Kearney, Kate Tilling, Anthony P. Fitzgerald

Published in: Emerging Themes in Epidemiology | Issue 1/2017

Login to get access

Abstract

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.

Conclusion

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).
Appendix
Available only for authorised users
Literature
1.
Salgado-Delgado R, Tapia Osorio A, Saderi N, Escobar C. Disruption of circadian rhythms: a crucial factor in the etiology of depression. Depress Res Treat. 2011;2011:839743.PubMedPubMedCentral
2.
Sihota R, Saxena R, Gogoi M, Sood A, Gulati V, Pandey RM. A comparison of the circadian rhythm of intraocular pressure in primary phronic angle closure glaucoma, primary open angle glaucoma and normal eyes. Indian J Ophthalmol. 2005;53(4):243–7.CrossRefPubMed
3.
Zee PC, Attarian H, Videnovic A. Circadian rhythm abnormalities. Continuum (Minneap Minn). 2013;19(1 Sleep Disorders):132–47.
4.
5.
Hansen TW, Thijs L, Li Y, Boggia J, Kikuya M, Bjorklund-Bodegard K, et al. Prognostic value of reading-to-reading blood pressure variability over 24 hours in 8938 subjects from 11 populations. Hypertension. 2010;55(4):1049–57.CrossRefPubMed
6.
Kikuya M, Hozawa A, Ohokubo T, Tsuji I, Michimata M, Matsubara M, et al. Prognostic significance of blood pressure and heart rate variabilities: the Ohasama study. Hypertension. 2000;36(5):901–6.CrossRefPubMed
7.
Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlof B, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet. 2010;375(9718):895–905.CrossRefPubMed
8.
Schillaci G, Verdecchia P, Borgioni C, Ciucci A, Porcellati C. Lack of association between blood pressure variability and left ventricular mass in essential hypertension. Am J Hypertens. 1998;11(5):515–22.CrossRefPubMed
9.
Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Bohm M, et al. 2013 ESH/ESC Guidelines for the management of arterial hypertension: the Task Force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens. 2013;31(7):1281–357.CrossRefPubMed
10.
O’Brien E, Parati G, Stergiou G, Asmar R, Beilin L, Bilo G, et al. European Society of Hypertension position paper on ambulatory blood pressure monitoring. J Hypertens. 2013;31(9):1731–68.CrossRefPubMed
11.
Roman MJ, Pickering TG, Schwartz JE, Pini R, Devereux RB. Relation of blood pressure variability to carotid atherosclerosis and carotid artery and left ventricular hypertrophy. Arterioscler Thromb Vasc Biol. 2001;21(9):1507–11.CrossRefPubMed
12.
Pierdomenico SD, Di Nicola M, Esposito AL, Di Mascio R, Ballone E, Lapenna D, et al. Prognostic value of different indices of blood pressure variability in hypertensive patients. Am J Hypertens. 2009;22(8):842–7.CrossRefPubMed
13.
Lambert PC, Abrams KR, Jones DR, Halligan AW, Shennan A. Analysis of ambulatory blood pressure monitor data using a hierarchical model incorporating restricted cubic splines and heterogeneous within-subject variances. Stat Med. 2001;20(24):3789–805.CrossRefPubMed
14.
Portaluppi F, Montanari L. Consistency of circadian blood pressure pattern assessed by non-invasive monitoring and cosinor analysis in hospitalized hypertensive patients. Acta Cardiol. 1988;43(5):605–13.PubMed
15.
Portaluppi F, Bagni B, degli Uberti E, Montanari L, Cavallini R, Trasforini G, et al. Circadian rhythms of atrial natriuretic peptide, renin, aldosterone, cortisol, blood pressure and heart rate in normal and hypertensive subjects. J Hypertens. 1990;8(1):85–95.CrossRefPubMed
16.
Munakata M, Imai Y, Hashimoto J, Sakuma H, Sekino H, Abe K, et al. The influence of antihypertensive agents on circadian rhythms of blood pressure and heart rate in patients with essential hypertension. Tohoku J Exp Med. 1992;166(2):217–27.CrossRefPubMed
17.
Ayala DE, Hermida RC, Mojon A, Fernandez JR, Iglesias M. Circadian blood pressure variability in healthy and complicated pregnancies. Hypertension. 1997;30(3 Pt 2):603–10.CrossRefPubMed
18.
Somes GW, Harshfield GA, Arheart KL, Miller ST. A Fourier series approach for comparing groups of subjects on ambulatory blood pressure patterns. Stat Med. 1994;13(12):1201–10.CrossRefPubMed
19.
Staessen JA, Fagard R, Thijs L, Amery A. Fourier analysis of blood pressure profiles. Am J Hypertens. 1993;6(6 Pt 2):184s–7s.PubMed
20.
Selwyn MR, Difranco DM. The application of large Gaussian mixed models to the analysis of 24 hour ambulatory blood pressure monitoring data in clinical trials. Stat Med. 1993;12(18):1665–82.CrossRefPubMed
21.
Edwards LJ, Simpson SL. An analysis of 24-h ambulatory blood pressure monitoring data using orthonormal polynomials in the linear mixed model. Blood Press Monit. 2014;19(3):153–63.CrossRefPubMedPubMedCentral
22.
Macdonald-Wallis C, Tilling K, Fraser A, Nelson SM, Lawlor DA. Associations of blood pressure change in pregnancy with fetal growth and gestational age at delivery: findings from a prospective cohort. Hypertension. 2014;64(1):36–44.CrossRefPubMedPubMedCentral
23.
Kearney PM, Harrington JM, Mc Carthy VJ, Fitzgerald AP, Perry IJ. Cohort profile: the Cork and Kerry diabetes and heart disease study. Int J Epidemiol. 2013;42(5):1253–62.CrossRefPubMed
24.
Madden JM, O’Flynn AM, Dolan E, Fitzgerald AP, Kearney PM. Short-term blood pressure variability over 24 h and target organ damage in middle-aged men and women. J Hum Hypertens. 2015;29:719–25.CrossRefPubMed
25.
Hermida RC, Smolensky MH, Ayala DE, Portaluppi F. 2013 ambulatory blood pressure monitoring recommendations for the diagnosis of adult hypertension, assessment of cardiovascular and other hypertension-associated risk, and attainment of therapeutic goals. Chronobiol Int. 2013;30(3):355–410.CrossRefPubMed
26.
Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073–81.CrossRefPubMedPubMedCentral
27.
28.
Fitzmaurice G, Laird NM, Ware JH. Applied longitudinal analysis. Hoboken: Wiley; 2011.
29.
Parati G, Bilo G, Redon J. Morning and smooth 24-h ambulatory blood pressure control is not achieved in general practice: results from the SURGE observational study. J Hypertens. 2013;31(3):616–23 (discussion 23).CrossRefPubMed
30.
Zuur A, Ieno EN, Walker NJ, Saveliev AA, Smith GM. Mixed effects models and extensions in ecology with R. Berlin: Springer; 2009.CrossRef
31.
Pinheiro JCD, Bates DM. Mixed-effects models in S and S-PLUS. New York: Springer; 2000.CrossRef
32.
Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evolution. 2013;4(2):133–42.CrossRef
33.
34.
Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011;13(2):143–51.CrossRefPubMedPubMedCentral
35.
36.
Pinheiro JBD, DebRoy S, Sarkar D, R Core Team. nlme: Linear and nonlinear mixed effects models. R package version 31-119. 2015.
37.
Armitage P, Berry G, Matthews JNS. Statistical methods in medical research. Oxford: Blackwell; 2009.
38.
39.
Streitberg B, Meyer-Sabellek W, Baumgart P. Statistical analysis of circadian blood pressure recordings in controlled clinical trials. J Hypertens Suppl. 1989;7(3):S11–7.PubMed
40.
41.
Wang Y, Ke C, Brown MB. Shape-invariant modeling of circadian rhythms with random effects and smoothing spline ANOVA decompositions. Biometrics. 2003;59(4):804–12.CrossRefPubMed
42.
Reilly T, Atkinson G, Waterhouse J. Biological rhythms and exercise. New York: Oxford; 1997.
43.
Hardy R. Commentary: are piecewise mixed effects models useful in epidemiology? Int J Epidemiol. 2001;30(6):1341–2.CrossRefPubMed
44.
Kario K. Essential manual of 24 hour blood pressure management: from morning to nocturnal hypertension. New York: Wiley-Blackwell; 2015.CrossRef
45.
Kario K. Morning surge in blood pressure and cardiovascular risk: evidence and perspectives. Hypertension. 2010;56(5):765–73.CrossRefPubMed
46.
Kario K, Pickering TG, Umeda Y, Hoshide S, Hoshide Y, Morinari M, et al. Morning surge in blood pressure as a predictor of silent and clinical cerebrovascular disease in elderly hypertensives: a prospective study. Circulation. 2003;107(10):1401–6.CrossRefPubMed
47.
Parati G, Vrijens B, Vincze G. Analysis and interpretation of 24-h blood pressure profiles: appropriate mathematical models may yield deeper understanding. Am J Hypertens. 2008;21(2):123–5 (discussion 7–9).CrossRefPubMed
48.
Howe LD, Tilling K, Matijasevich A, Petherick ES, Santos AC, Fairley L, et al. Linear spline multilevel models for summarising childhood growth trajectories: a guide to their application using examples from five birth cohorts. Stat Methods Med Res. 2016;25(5):1854–74.CrossRefPubMed
49.
Taylor KS, Heneghan CJ, Stevens RJ, Adams EC, Nunan D, Ward A. Heterogeneity of prognostic studies of 24-hour blood pressure variability: systematic review and meta-analysis. PLoS ONE. 2015;10(5):e0126375.CrossRefPubMedPubMedCentral
50.
Bilo G, Giglio A, Styczkiewicz K, Caldara G, Maronati A, Kawecka-Jaszcz K, et al. A new method for assessing 24-h blood pressure variability after excluding the contribution of nocturnal blood pressure fall. J Hypertens. 2007;25(10):2058–66.CrossRefPubMed
51.
Bamberger KT. The application of intensive longitudinal methods to investigate change: stimulating the field of applied family research. Clin Child Fam Psychol Rev. 2016;19(1):21–38.CrossRefPubMed
52.
Diehl M, Hooker K, Sliwinski MJ. Handbook of intraindividual variability across the life span. New York, NY: Routledge, Taylor & Francis Group; 2015.
53.
Boker SM, Molenaar PC, Nesselroade JR. Issues in intraindividual variability: individual differences in equilibria and dynamics over multiple time scales. Psychol Aging. 2009;24(4):858–62.CrossRefPubMedPubMedCentral
54.
Liu JH, Zhang X, Kripke DF, Weinreb RN. Twenty-four-hour intraocular pressure pattern associated with early glaucomatous changes. Invest Ophthalmol Vis Sci. 2003;44(4):1586–90.CrossRefPubMed
Metadata
Title
Exploring diurnal variation using piecewise linear splines: an example using blood pressure
Authors
Jamie M. Madden
Xia Li
Patricia M. Kearney
Kate Tilling
Anthony P. Fitzgerald
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Emerging Themes in Epidemiology / Issue 1/2017
Electronic ISSN: 1742-7622
DOI
https://doi.org/10.1186/s12982-017-0055-5

Other articles of this Issue 1/2017

Emerging Themes in Epidemiology 1/2017 Go to the issue

Research article

Estimating the prevalence of dementia using multiple linked administrative health records and capture–recapture methodology

Research article

Flexible semiparametric joint modeling: an application to estimate individual lung function decline and risk of pulmonary exacerbations in cystic fibrosis

Analytic perspective

Randomised and non-randomised studies to estimate the effect of community-level public health interventions: definitions and methodological considerations

Review

Spatial analysis of cluster randomised trials: a systematic review of analysis methods

Analytic perspective

Decision trees in epidemiological research

Commentary

Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation