What time is it? A tale of three clocks, with implications for personalized medicine

Klerman, Elizabeth B.; Rahman, Shadab A.; Hilaire, Melissa A St

doi:10.1111/jpi.12646

Cited by 9 publications

(8 citation statements)

References 24 publications

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“…Therefore, a single phase estimate may be useful for clinical and other applications. The accuracy required for phase estimation methods to be clinically useful, however, may depend on the application 9 . Errors of approximately one hour may be acceptable to predict cognitive performance, but efficacy of phase‐shifting decreases from about 80% to 60% if errors in the timing of a light pulse increase from 1 to 2 hours, 15 resulting in possible errors in the shifted phase 43 …”

Section: Discussionmentioning

confidence: 99%

“…accuracy required for phase estimation methods to be clinically useful, however, may depend on the application. 9 Errors of approximately one hour may be acceptable to predict cognitive performance, but efficacy of phase-shifting decreases from about 80% to 60% if errors in the timing of a light pulse increase from 1 to 2 hours, 15 resulting in possible errors in the shifted phase. Although our population is larger than in previous studies, training the model on still larger populations could be beneficial for refining the model, as machine learning approaches have greatest success on large datasets because of the large number of parameters that are needed in these models.…”

Section: Notementioning

confidence: 99%

“…Both the magnitude and direction of the phase shift caused by both photic and nonphotic (including pharmaceutical) interventions depends on the phase (ie, time) at which the intervention is delivered. [5][6][7][8] Thus, we must be able to precisely assess an individual's current circadian phase 9 before applying interventions.…”

Section: Introductionmentioning

confidence: 99%

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A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data

Brown

Hilaire

McHill

et al. 2021

Journal of Pineal Research

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The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resourceintensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake schedules, with mean errors reported between 0.5 and 1 hour. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: Mean errors in estimating the time of DLMO are approximately 1.5-1.6 hours. We reframed the problem as a classification problem and estimated whether an individual's current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation-identifying the time at which the switch in classification occurs-to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55%-65% with a range of 20%-80% for a given day; this accuracy was highly dependent upon the phase angle (ie, time) between DLMO and sleep onset, with the highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included 2 of 13 | BROWN et al.

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Section: Discussionmentioning

confidence: 99%

Section: Notementioning

confidence: 99%

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A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data

Brown

Hilaire

McHill

et al. 2021

Journal of Pineal Research

Self Cite

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“…Finally, since patients may suffer from distinct clock misalignments, it will also be important to define with clear biomarkers their circadian state [ 124 ]. Several tools are now available to monitor the internal circadian time of patients thanks to gene expression analysis of blood samples [ 125 , 126 ], or epidermal samples [ 127 ], which will be instrumental for personalized medicine.…”

Section: Current Challenges and Perspectives: How To Define The Most ...mentioning

confidence: 99%

An Overview of the Circadian Clock in the Frame of Chronotherapy: From Bench to Bedside

2022

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Most living organisms in both the plant and animal kingdoms have evolved processes to stay in tune with the alternation of day and night, and to optimize their physiology as a function of light supply. In mammals, a circadian clock relying on feedback loops between key transcription factors will thus control the temporally regulated pattern of expression of most genes. Modern ways of life have highly altered the synchronization of human activities with their circadian clocks. This review discusses the links between an altered circadian clock and the rise of pathologies. We then sum up the proofs of concept advocating for the integration of circadian clock considerations in chronotherapy for health care, medicine, and pharmacotherapy. Finally, we discuss the current challenges that circadian biology must face and the tools to address them.

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“…The influence of the circadian clock on health and disease has been extensively reviewed in recent work [ 37 , 38 , 39 , 40 , 41 ], here we review some of the methods that can be used to determine a personalized circadian time, with a focus on the mammalian system to facilitate clinical applicability. We start with a brief overview on the current developments of chronotherapy ( Section 2 ) and then review the computational strategies for assessing circadian rhythms ( Section 3 ).…”

Section: Introductionmentioning

confidence: 99%

An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling

Hesse

Malhan

Yalҫin

et al. 2020

Cancers

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Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient’s internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.

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What time is it? A tale of three clocks, with implications for personalized medicine

Cited by 9 publications

References 24 publications

A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data

A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data

An Overview of the Circadian Clock in the Frame of Chronotherapy: From Bench to Bedside

An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling

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