.svg){kind=logo}
MULTI-MODEL APPROACH FOR MODELLING CIRCADIAN RHYTHM PHYSIOLOGICAL PARAMETERS
Keywords:
cosinor model, splines, Fourier series, ARIMA, cortisol, melatonin, circadian rhythmDOI:
https://doi.org/10.17654/0973514325003Abstract
Present study focuses on modelling four circadian rhythmic physiological parameters (body temperature, heart rate, cortisol levels, and melatonin levels) using a multi-model approach. Four distinct models were employed: cosinor model, spline models, Fourier series (Fourier decomposition), and ARIMA models (a traditional statistical tool for modelling times series data). A convenience sample of twenty internee doctors was selected from four public sector hospitals located in Jeddah. Data were collected over a 24-hour period, ensuring the inclusion of typical circadian patterns for the four physiological parameters. The models were evaluated based on their fit and predictive accuracy, with Mean Squared Error (MSE) used as the primary metric. For extracting relevant results, R software was used and codes developed for the proposed study are given in Appendix ‘A’. Outcomes of the present study demonstrated that each model effectively captured different aspects of the circadian rhythms, with Fourier series (Fourier decomposition) and spline models providing the most flexible fits and excelled in performance accuracy. The multi-model approach enabled a comprehensive understanding of the circadian patterns, offering insights into the complex dynamics of physiological rhythms. This study highlights the importance of using diverse modelling techniques to capture the multifaceted nature of circadian rhythms, with implications for improving health outcomes through better understanding and management of biological cycles.
Received: August 3, 2024
Revised: September 23, 2024
Accepted: October 1, 2024
References
M. H. Hastings, Central clocking, Biological Rhythms and Medicine: Cellular, Metabolic, Physiopathologic, and Pharmacologic Aspects, C. P. Kopp and G. S. Oxford, eds., Springer, US, 1997, pp. 10-18. DOI: 10.1007/978-1-4615-5407-7-2.
J. S. Takahashi, H. K. Hong, C. H. Ko and E. L. McDearmon, The genetics of mammalian circadian order and disorder: implications for physiology and disease, Nature Reviews Genetics 9(10) (2008), 764-775. DOI: 10.1038/nrg2430.
R. Refinetti, Circadian Physiology, CRC Press, 2019.
G. Cornélissen, Cosinor-based rhythmometry, Theoretical Biology and Medical Modelling 11(1) (2014), 1-24.
B. S. B. Gonçalves, T. Adamowicz, F. M. Louzada, C. R. C. Moreno and J. F. Araujo, A fresh look at the use of nonparametric analysis in actimetry, Sleep Medicine Reviews 18(6) (2014), 494-499.
J. O. Ramsay and B. W. Silverman, Functional Data Analysis, 2nd ed., Springer, 2005.
H. Wu, X. Wang and T. Gasser, Statistical methods for analyzing circadian rhythms in biological time series, Statistical Methods in Medical Research 21(4) (2012), 337-353.
W. Wang, J. O. Berger and M. J. Brewer, Bayesian analysis of circadian rhythms, J. Roy. Statist. Soc. Ser. C 63(3) (2014), 467-484.
G. Delyon, M. Lavielle and E. Moulines, Splines for modeling circadian rhythms: a review, J. Statist. Plan. Inference 199 (2019), 90-101.
G. E. P. Box, G. M. Jenkins, G. C. Reinsel and G. M. Ljung, Time Series Analysis: Forecasting and Control, John Wiley and Sons, 2015.
L. Glass, A. Beuter and D. Larocque, Time series analysis of complex dynamics in physiology: a case study of circadian rhythms, Biol. Cybernet. 108(3) (2013), 273-281.
X. Zhong, T. Hilton, G. J. Gates, Y. Jiao and M. Millar, Circadian rhythm and ARIMA model of heart rate, Physiological Measurement 37(4) (2016), 599-614.
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, OTexts, 2018.
P. Bloomfield, Fourier analysis of Time Series: An Introduction, John Wiley and Sons, 2000.
R. Refinetti, Circadian Physiology, CRC Press, 2016.
G. Cornélissen and F. Halberg, Introduction to chronobiology, Biological Rhythm Research 32(2) (2001), 203-210.
R. Refinetti, The circadian rhythm of body temperature, Frontiers in Bioscience 15 (2010), 564-594.
K. Krauchi and A. Wirz-Justice, Circadian rhythm of heat production, heart rate, and skin and core temperature under unmasking conditions in men, The American Journal of Physiology 267(3 Pt 2) (1994), R819-R829.
M. H. Smolensky, R. C. Hermida, F. Portaluppi and R. Manfredini, Twenty-four-hour pattern in blood pressure and heart rate: physiological and therapeutic implications, Chronobiology International 24(6) (2007), 1129-1151.
Y. Zhang, J. Sunderram and J. Refuerzo, A Fourier-based approach to modeling and predicting the circadian rhythm of core body temperature, Journal of Biological Rhythms 22(3) (2007), 238-245.
R. Refinetti and M. Menaker, The circadian rhythm of body temperature, Physiology and Behavior 51(3) (1992), 613-637.
E. D. Weitzman, D. Fukushima, C. Nogeire, H. Roffwarg, T. F. Gallagner and L. Hellman, Twenty-four hour pattern of the episodic secretion of cortisol in normal subjects, The Journal of Clinical Endocrinology and Metabolism 33(1) (1971), 14-22.
J. Arendt, Melatonin and the Mammalian Pineal Gland, Springer Science and Business Media, 1998.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 PUSHPA PUBLISHING HOUSE, PRAYAGRAJ, INDIA
This work is licensed under a Creative Commons Attribution 4.0 International License.
_________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
Non-Commercial Use: For non-commercial purposes only. No commercial activities without explicit permission.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Puspha Publishing House for more info or permissions.
Journal Impact Factor: 
Publication count: 330
Google h-index: 10
Downloads: 87500+