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Record W3024899165 · doi:10.1113/jp279913

Can you out‐exercise a bad sleep? A muscle‐centric view

2020· letter· en· W3024899165 on OpenAlexaff
Stephanie Estafanos, Carolyn Adams, Cassidy T. Tinline‐Goodfellow, Nathan Hodson

Bibliographic record

VenueThe Journal of Physiology · 2020
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSleep restrictionSleep deprivationSkeletal muscleSleep (system call)EndocrinologyMedicineInternal medicinePhysiologyPsychologyCircadian rhythm

Abstract

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A lack of sleep, both in terms of quantity and ‘quality’, can alter normal physiological and cognitive processes, including the regulation of energy balance, metabolism and memory. Despite the critical role of sleep in a host of physiological processes, its importance is often overlooked in biomedical research. Recently, sleep deprivation was shown to induce skeletal muscle atrophy in rodents (de Sá Souza et al. 2016). In humans, factors such as ageing, excess adiposity (obesity) and inactivity can also have deleterious effects on muscle mass, mainly through reductions in myofibrillar protein synthesis (MyoPS), the more highly regulated variable of muscle protein turnover. However, the effect of sleep restriction on skeletal muscle protein turnover is yet to be explicitly investigated in humans. A recent article in The Journal of Physiology by Saner et al. (2020) investigated the impact of 5 nights of sleep restriction on mechanisms governing skeletal muscle mass in humans. The findings provide great insight into the protein synthetic response to sleep restriction, and also highlight the potential therapeutic effect of exercise in this regard. The study by Saner et al. (2020) followed a parallel group design in which the effect of 5 nights of sleep restriction (4 h of sleep per night), with or without daily high-intensity interval exercise (HIIE), were compared with a normal sleep (8 h) no exercise control group in healthy men. Deuterated water (D2O) was used to measure free-living rates of MyoPS throughout the study. Fasted muscle biopsies were taken at baseline (pre-intervention), after 2 nights of regular sleep, and after the 5 night intervention. Molecular pathways regulating muscle protein synthesis and breakdown were also examined. The study objectively measured sleep, defined as ‘time in bed’, with a wrist-watch activity device and also standardized daily physical activity and diet throughout the intervention. Moreover, stringent recruitment guidelines were used to ensure participant homogeneity (e.g. all participants habitually slept 6–9 h per night). It was found that rates of MyoPS were significantly lower in participants undergoing 5 nights of sleep restriction compared to participants sleeping 8 h per night. However, the group that performed HIIE during 5 nights of sleep restriction had rates of MyoPS similar to the well-rested controls. Because MyoPS is considered to be a predominant contributor to the maintenance of skeletal muscle mass across the lifespan, these novel findings suggest that sleep restriction may reduce skeletal muscle quality in humans (via less new proteins being synthesized), although performing HIIE regularly could prevent this. At the cellular level, alterations in muscle mass are co-ordinated by net muscle protein balance (NPB), the algebraic difference between rates of MyoPS and muscle protein breakdown (MPB). As such, it is possible for MyoPS to be altered without alterations in NPB if MPB is similarly modified by a stimulus. For example, resistance exercise in the fasted state elevates MyoPS and MPB and, as such, the increase in NPB is less than the increase in MyoPS. Although Saner et al. (2020) eloquently displayed reductions in MyoPS with sleep restriction, rates of MPB were unfortunately not measured, and animal models suggest that 96 h of total sleep deprivation, albeit a more severe model, may increase rates of MPB (de Sá Souza et al. 2016). Thus, the complete effect of sleep restriction on skeletal muscle protein turnover or potential chronic changes in muscle mass cannot be concluded from the present study. It's important to note, however, that MPB is very difficult to measure in vivo and accurate measurement remains problematic. Future work may therefore opt to measure whole-body NPB in an effort to obtain a more comprehensive picture of protein turnover. Nonetheless, we commend Saner et al. (2020) for using a range of molecular techniques, including a quantitative reverse transcriptase-polymerase chain reaction and immunoblotting, to investigate gene expression (mRNA) and post-translational modifications/protein content of purported MPB regulators. For the majority of targets investigated, neither gene expression, nor post-translational modifications/protein content were altered by experimental conditions. However, this may reflect the rested/fasted time points chosen for muscle biopsies because phosphorylation of signalling proteins typically occurs shortly after exercise or protein feeding, usually returning to baseline several hours post exercise. For example, it is possible that markers of MPB measured by Saner et al. (2020) (e.g. p62/SQSTM mRNA expression) could have been altered with sleep restriction, although the timing of biopsies may not have captured these transient changes. Indeed, it was recently reported that 96 h of paradoxical sleep deprivation in rodents increased MPB, as reflected by increased ubiquitinated proteins, 26S proteasomal enzyme activity and markers of autophagy (LC3 and p62/SQSTM protein content) (de Sá Souza et al. 2016). Furthermore, recent work conducted in humans by Lamon et al. (2020) observed a reduction in feeding-induced MyoPS following 1 night of total sleep-deprivation in male subjects, although the cellular mechanisms of protein turnover were not investigated. Taken together, it is evident that sleep restriction alters one aspect of muscle protein turnover, MyoPS; however, the relative contribution of MPB, and associated signalling pathways to skeletal muscle homeostasis in this context is unclear. Future studies should build off these findings by directly measuring post-feeding MyoPS and MPB, in addition to measuring a more comprehensive panel of signalling targets, aiming to determine whether NPB is altered in response to sleep restriction and exercise. Nonetheless, the characterization of MyoPS provides valuable information and is a good first step in our understanding of how an important physiological process, sleep, can affect skeletal muscle tissue. The finding that only three sessions of HIIE can mitigate the consequences of 5 days of sleep restriction is quite remarkable. However, the lack of a positive control (i.e. normal sleep + HIIE) did not allow us to determine whether the exercise-induced stimulation of MyoPS following HIIE was attenuated by sleep restriction. Nevertheless, this finding adds to the myriad of ways that exercise serves as ‘medicine’, rescuing the detrimental effects on a range of metabolic outcomes. For individuals who frequently report inadequate sleep, it is plausible to assume that a ‘lack of time’ would be a barrier to regular exercise participation. Because HIIE is largely credited as being a time-efficient exercise strategy, the inclusion of this exercise modality and the novel observation of its protective effect on MyoPS can be applauded for its clinical relevance. Saner et al. (2020) selected a demanding HIIE protocol involving 10 × 60 s intervals on a cycle ergometer at 90% of Wpeak, with a total time commitment of ∼25–30 min. Although this protocol is commonly used in the HIIE literature, recent studies have also demonstrated the efficacy of modified HIIE protocols involving as little as a 10 min time commitment, as well HIIE protocols modified to not include equipment (Gibala & Little 2019). To optimize real-world translation, future research may benefit from exploring the therapeutic potential of more ‘practical’ HIIE protocols on MyoPS during sleep restriction. It would also be interesting to investigate whether the metabolic benefits of HIIE on sleep restriction are sex-specific because previous work suggests a blunted MyoPS response to acute HIIE in females compared to males (Scalzo et al. 2014). A recent preprint article also revealed decreased feeding-induced MyoPS following 1 day of sleep deprivation in males but not females (Lamon et al. 2020). Taken together, these data implicate potential sexual dimorphisms in the protein synthetic response to anabolic stimuli (i.e. contraction and feeding) and sleep-restriction. Further exploration of the mechanisms underlying sex-specific responses in the sleep-muscle interaction would represent a fruitful area of future study. The sleep-restriction model used by Saner et al. (2020) was a potent stimulus for provoking acute physiological changes; however, it is fairly severe and may not be common in real life. Participants underwent 5 continuous days of sleep restriction with 4 h ‘time-in-bed’ each night. These findings are of high relevance to select populations including those with certain sleep disorders, military personnel and/or frequent travellers crossing multiple time zones. However, shift workers, for example, frequently undergo only 1 d of total sleep deprivation when shifting from overnight to daytime shifts (or vice versa) or 2–3 ‘sleep-deprived’ nights within a 7-day period. Also, many individuals habitually sleep for 6–7 h per night instead of the recommended 8–9 h. Therefore, the use of sleep-restriction models that mimic other common sleep patterns are warranted to optimize applicability of the findings for different populations. Furthermore, despite the groups being well matched at baseline, the parallel group design does not account for between-subject variability in basal MyoPS rates. Smith et al. (2011) found that there is considerable normal physiological variation in muscle protein turnover during basal, postabsorptive conditions in healthy young and middle-aged adults. As a result, it is unclear to what extent differences in basal MyoPS rates may have contributed to the reduced MyoPS in the sleep-restricted group. Future work should aim to build off the findings of Saner et al. (2020) by utilizing a within-subject design to consolidate the effects of acute sleep restriction and exercise on MyoPS. In summary, the observations of Saner et al. (2020) highlight the detrimental effects of sleep restriction on MyoPS and identify HIIE as a potential therapeutic strategy. Because adequate muscle mass and function throughout the lifespan is a major predictor of longevity, the findings from the present study have important implications for the health of populations who frequently undergo periods of reduced sleep. This research will inform the design of future investigations that further examine the molecular pathways underpinning the response to acute sleep restriction, as well as the utility of different exercise and/or nutritional interventions that aim to combat the sleep-associated loss of muscle mass. No competing interests declared. All authors have approved the final version of the manuscript submitted for publication and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship and all those who qualify for authorship are listed. NH is supported by a Mitacs Accelerate Postdoctoral Fellowship. We apologize for not citing all relevant articles as a result of reference limitations. We appreciate the insightful feedback of Dr Jenna Gillen and Dr Daniel Moore during the preparation of the manuscript.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.007
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0060.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.231
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
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