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Temporal dynamics in laboratory medicine: cosinor analysis and real-world data (RWD) approaches to population chronobiology

  • Fernando Marques-Garcia ORCID logo EMAIL logo , Cristina Martinez-Bravo , Xavier Tejedor-Ganduxe and Ruben Fossion
Published/Copyright: February 19, 2025

Abstract

Objectives

Chronobiology is the science that studies biological rhythms based on direct methods and empirical time series of individual subjects. In laboratory medicine, the factor of time is often underestimated, and no methods currently exist to study biological rhythms in population databases of point-like, real-world data (RWD).

Methods

Retrospective databases (24 months, 2022–2023) were extracted for four measurands (sodium, potassium, chloride and leukocytes) from the emergency laboratory. Two different strategies for data grouping were applied: data clouds (with or without outliers) and population-averaged profiles. Cosinor regression analysis was performed on the grouped data to derive circadian parameters. The parameters obtained here were compared to results from the literature, using direct methods and time series.

Results

A total of 409,719 data points were analyzed. All measurands exhibited symmetrical data distributions, except for leukocytes. The data clouds did not visually display rhythmicity, but cosinor analysis revealed a significant circadian rhythm. The removal of outliers had minimal impact on the results. In contrast, population-averaged profiles showed visible rhythmicity, which was confirmed by cosinor analysis with a better goodness-of-fit compared to the data clouds.

Conclusions

Population-averaged profiles have advantages over data clouds in characterizing circadian rhythms and deriving circadian parameters. Population chronobiology, based on RWD, is presented as an alternative to classical individual chronobiology, based on time series and overcomes the limitations of direct methods. Utilizing RWD provides new insights into the relationship between chronobiology and clinical laboratory practice.


Corresponding author: Fernando Marques-Garcia, PhD, Clinical Biochemistry Department, Metropolitan Nord Clinical Laboratory (LUMN), Germans Trias i Pujol University Hospital, 08916, Badalona, Barcelona, Spain, E-mail:

Funding source: DGAPA-UNAM

Award Identifier / Grant number: PAPIIT IN115124

  1. Research ethics: Protocol approved PI-21-034.

  2. Informed consent: Not applicable.

  3. Author contributions: FMG: Conceived and designed the analysis; Contributed data or analysis tools; Performed the analysis; Wrote the paper. CMB: Conceived and designed the analysis; Contributed data or analysis tools; Performed the analysis; Wrote the paper. XTG: Contributed data or analysis tools; Wrote the paper. RF: Conceived and designed the analysis; Contributed data or analysis tools; Performed the analysis; Wrote the paper. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: Financial funding for this work was supplied by the Dirección General de Asuntos del Personal Académico (DGAPA) from the Universidad Nacional Autónoma de México (UNAM) with grant PAPIIT IN115124.

  7. Data availability: Not applicable.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2024-1198).


Received: 2024-10-14
Accepted: 2025-01-29
Published Online: 2025-02-19
Published in Print: 2025-06-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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