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Predictability of individual circadian phase during daily routine for medical applications of circadian clocks
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. In...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Clinical Investigation
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795290/ https://www.ncbi.nlm.nih.gov/pubmed/31430260 http://dx.doi.org/10.1172/jci.insight.130423 |
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author | Komarzynski, Sandra Bolborea, Matei Huang, Qi Finkenstädt, Bärbel Lévi, Francis |
author_facet | Komarzynski, Sandra Bolborea, Matei Huang, Qi Finkenstädt, Bärbel Lévi, Francis |
author_sort | Komarzynski, Sandra |
collection | PubMed |
description | 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 R(2) = 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. |
format | Online Article Text |
id | pubmed-6795290 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Society for Clinical Investigation |
record_format | MEDLINE/PubMed |
spelling | pubmed-67952902019-10-30 Predictability of individual circadian phase during daily routine for medical applications of circadian clocks Komarzynski, Sandra Bolborea, Matei Huang, Qi Finkenstädt, Bärbel Lévi, Francis JCI Insight Clinical Medicine 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 R(2) = 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. American Society for Clinical Investigation 2019-09-19 /pmc/articles/PMC6795290/ /pubmed/31430260 http://dx.doi.org/10.1172/jci.insight.130423 Text en © 2019 Komarzynski et al. http://creativecommons.org/licenses/by/4.0/ This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Clinical Medicine Komarzynski, Sandra Bolborea, Matei Huang, Qi Finkenstädt, Bärbel Lévi, Francis Predictability of individual circadian phase during daily routine for medical applications of circadian clocks |
title | Predictability of individual circadian phase during daily routine for medical applications of circadian clocks |
title_full | Predictability of individual circadian phase during daily routine for medical applications of circadian clocks |
title_fullStr | Predictability of individual circadian phase during daily routine for medical applications of circadian clocks |
title_full_unstemmed | Predictability of individual circadian phase during daily routine for medical applications of circadian clocks |
title_short | Predictability of individual circadian phase during daily routine for medical applications of circadian clocks |
title_sort | predictability of individual circadian phase during daily routine for medical applications of circadian clocks |
topic | Clinical Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795290/ https://www.ncbi.nlm.nih.gov/pubmed/31430260 http://dx.doi.org/10.1172/jci.insight.130423 |
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