Predictability of individual circadian phase during daily routine for medical applications of circadian clocks
Sandra Komarzynski, … , Bärbel Finkenstädt, Francis Lévi
Sandra Komarzynski, … , Bärbel Finkenstädt, Francis Lévi
Published August 20, 2019
Citation Information: JCI Insight. 2019;4(18):e130423. https://doi.org/10.1172/jci.insight.130423.
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Clinical Research and Public Health Neuroscience Therapeutics

Predictability of individual circadian phase during daily routine for medical applications of circadian clocks

Abstract

BACKGROUND Circadian timing of treatments can largely improve tolerability and efficacy in patients. Thus, drug metabolism and cell cycle are controlled by molecular clocks in each cell and coordinated by the core body temperature 24-hour rhythm, which is generated by the hypothalamic pacemaker. Individual circadian phase is currently estimated with questionnaire-based chronotype, center-of-rest time, dim light melatonin onset (DLMO), or timing of core body temperature (CBT) maximum (acrophase) or minimum (bathyphase).METHODS We aimed at circadian phase determination and readout during daily routines in volunteers stratified by sex and age. We measured (a) chronotype, (b) every minute (q1min) CBT using 2 electronic pills swallowed 24 hours apart, (c) DLMO through hourly salivary samples from 1800 hours to bedtime, and (d) q1min accelerations and surface temperature at anterior chest level for 7 days, using a teletransmitting sensor. Circadian phases were computed using cosinor and hidden Markov modeling. Multivariate regression identified the combination of biomarkers that best predicted core temperature circadian bathyphase.RESULTS Among the 33 participants, individual circadian phases were spread over 5 hours, 10 minutes (DLMO); 7 hours (CBT bathyphase); and 9 hours, 10 minutes (surface temperature acrophase). CBT bathyphase was accurately predicted, i.e., with an error less than 1 hour for 78.8% of the subjects, using a new digital health algorithm (INTime), combining time-invariant sex and chronotype score with computed center-of-rest time and surface temperature bathyphase (adjusted R2 = 0.637).CONCLUSION INTime provided a continuous and reliable circadian phase estimate in real time. This model helps integrate circadian clocks into precision medicine and will enable treatment timing personalization following further validation.FUNDING Medical Research Council, United Kingdom; AP-HP Foundation; and INSERM.

Authors

Sandra Komarzynski, Matei Bolborea, Qi Huang, Bärbel Finkenstädt, Francis Lévi

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Figure 2

Intersubject variabilities in main circadian biomarkers.

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Intersubject variabilities in main circadian biomarkers. (A) Salivary me...
(A) Salivary melatonin profiles in 2 female participants (28 years old shown in gray and 30 years old shown in blue); the vertical dashed lines represent DLMO, which differed by 1 hour and 11 minutes between both subjects. The dark bar represents the mean sleep spans of both participants. (B) DLMO variations among 24 subjects. DLMO could not be computed for 6 participants because of improper or lacking information on sampling times (n = 5) or exposure to light greater than 50 lux within 30 minutes of sampling (n = 1). The dark bar represents the mean sleep span of the 24 participants. (C) Core body temperature patterns in the 2 same participants shown in A. Five-minute aggregated data are displayed as dots; the solid curves illustrate the averaged 24-hour profiles according to 2-harmonic cosinor fitting. Bathyphases with 90% CIs estimated by the bootstrap method are indicated with dashed lines and color bands. The dark bar represents the mean sleep span of both participants. (D) Core body temperature bathyphase (and 90% CI) variations among the 33 participants. The dark bar represents the mean sleep span of all participants. (E) Scatter plots and dashed regression line, with results from both Pearson’s and Spearman’s correlations between DLMO and core body temperature bathyphase for the 24 subjects.