Diurnal variations in the expression of core-clock genes correlate with resting muscle properties and predict fluctuations in exercise performance across the day

Abstract

ObjectivesIn this study, we investigated daily fluctuations in molecular (gene expression) and physiological (biomechanical muscle properties) features in human peripheral cells and their correlation with exercise performance.Methods21 healthy participants (13 men and 8 women) took part in three test series: for the molecular analysis, 15 participants provided hair, blood or saliva time-course sampling for the rhythmicity analysis of core-clock gene expression via RT-PCR. For the exercise tests, 16 participants conducted strength and endurance exercises at different times of the day (9h, 12h, 15h and 18h). Myotonometry was carried out using a digital palpation device (MyotonPRO), five muscles were measured in 11 participants. A computational analysis was performed to relate core-clock gene expression, resting muscle tone and exercise performance.ResultsCore-clock genes show daily fluctuations in expression in all biological samples tested for all participants. Exercise performance peaks in the late afternoon (15–18 hours for both men and women) and shows variations in performance, depending on the type of exercise (eg, strength vs endurance). Muscle tone varies across the day and higher muscle tone correlates with better performance. Molecular daily profiles correlate with daily variation in exercise performance.ConclusionTraining programmes can profit from these findings to increase efficiency and fine-tune timing of training sessions based on the individual molecular data. Our results can benefit both professional athletes, where a fraction of seconds may allow for a gold medal, and rehabilitation in clinical settings to increase therapy efficacy and reduce recovery times.

A mathematical model of the circadian clock and drug pharmacology to optimize irinotecan administration timing in colorectal cancer

“…It is also important to notice that each patient (or healthy individuals) and each cancer are unique. Accordingly, different cell lines, patients or healthy individuals [62] have specific clock phenotypes, and this requires personalization of treatment. Thus, any clinical application requires that our model is fitted to the individual patient, or to groups of patients with similar clock molecular profiles.…”

“…Several patient-friendly methods for measuring clock-gene expression in saliva or blood samples have been recently validated in the clinics (see [12] for a review). Furthermore, strong oscillations, clearly above noise level, are expected in core-clock gene expression, which facilitates the characterisation of their circadian profiles and reduces the number of needed time points to do so [62] . In addition, a newly available statistical algorithm offers to derive clock gene mRNA circadian rhythms from a single-time-point measurement of 10 clock genes [68] .…”

“…In addition, instead of simplifying a patient’s complex circadian profile by an estimate of a value associated with their circadian time, our model has the potential to fit the circadian rhythms of the patient based on their personal gene expression data from peripheral tissues (e.g. saliva [62] ). Thus, in a clinical application, the model can be fitted both to the tumour clock and to the healthy peripheral clock of the patient.…”

Diurnal variation of fat oxidation rate and energy expenditure in an acute bout of endurance exercise by young healthy males

“…Basti et al . [ 33 ] reported that the exercise performance correlates with daily fluctuation in core-clock genes and peaks in the late-afternoon (15:00–18:00) hours irrespective of gender, but it is always better to keep individual chronotype in perspective for achieving better results of the exercise. An acute bout, participant characteristics and sample size limit the application of our study results to the general population.…”

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

“…It is well documented that there is intramuscular temperature dependency on muscle force generation [67,147,194], and the core body temperat- [53,194] and athletic performance in general [495,644].…”