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 3

Intersubject variabilities in rest-activity and chest surface temperature.

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Intersubject variabilities in rest-activity and chest surface temperatur...
(A) Representative examples of chronograms of chest surface temperature (top) and rest-activity (bottom) of 2 participants (blue represents a female, 71 years old; gray, a male, 34 years old). Hourly aggregated data are shown with dots, with solid curves corresponding to Fourier fitting with harmonics estimated using Spectrum Resampling algorithm (28). The dark bars represent the participants’ respective sleeping spans. (B) Top: Circadian activity state probability plot from harmonic HMM for a 78-year-old male participant illustrating the computation method of the center-of-rest time. Three activity states were assumed in the HMM, i.e., inactive state (blue), moderately active state (pink), and highly active state (red). The 3 states’ probabilities sum up to 1. The center-of-rest time was computed as the gravity center of the inactive state probability profile (blue), as indicated with a dashed, vertical black line. Bottom: Box plot (5th–95th percentiles) of the center-of-rest times in the 33 participants. The dark bar represents the mean sleep span of all 33 participants. (C) Representative examples of the chest surface temperature of the same participants as in A. Five-minute aggregated data are shown as dots, and solid curves represent the averaged 24-hour profiles using cosinor fitting. The dark bar represents the mean sleep span of both participants. (D) Range of chest surface temperature acrophases (and 90% confidence limits estimated by bootstrap method) of the 24 participants displaying a 24-hour rhythm (left) and the 9 participants with a dominant 12-hour rhythm (right). The dark bar represents the mean sleep span of the corresponding participants.