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Record W194131811 · doi:10.1093/sleep/33.7.861

Sleep and Metabolic Fitness

2010· letter· en· W194131811 on OpenAlexaff
Angelo Tremblay, Jean‐Philippe Chaput

Bibliographic record

VenueSLEEP · 2010
Typeletter
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSleep restrictionGhrelinAppetiteContext (archaeology)Sleep (system call)Metabolic syndromeLeptinMedicineEndocrinologySleep deprivationObesityInternal medicinePsychologyHormoneCircadian rhythmBiology

Abstract

fetched live from OpenAlex

The authors have indicated no financial conflicts of interest. WHO WOULD HAVE GUESSED IN 1988, WHEN REAVEN1 DESCRIBED THE CONCEPT OF SYNDROME X (SUBSEQUENTLY NAMED METABOLIC SYNDROME), THAT sleep would eventually be perceived as one of its main related environmental factors? Almost nobody, and this would have been understandable. Indeed, in a context where regular physical activity participation was already recognized as a key player in the prevention of the syndrome, it would have been difficult to anticipate that an increase of the most sedentary activity (i.e., sleep) would be a prerequisite to optimal metabolic fitness. The experimental evidence provided by Van Cauter's group2–4 can be viewed as a cornerstone in the global demonstration of a role of adequate sleep duration as a determinant of metabolic health. Specifically, partial sleep restriction tested in their laboratory decreased glucose tolerance, elevated cortisol concentrations, decreased the satiety hormone leptin, increased the appetite-stimulating hormone ghrelin, and increased hunger and appetite.2–4 In this issue of SLEEP, Gangwisch and colleagues5 contribute in an important manner to the proof of concept in this field. Their study, performed in a large sample of adolescents (n = 14,257) in the United States, showed that for each additional hour of sleep, a significant decrease is observed in the risk of being diagnosed with high cholesterol in young adulthood, particularly in females. In other words, short sleep durations in adolescence seem to leave a print that may increase the long-term predisposition to hypercholesterolemia. As mentioned by the authors, “interventions that lengthen sleep could potentially serve as treatments and as primary preventative measures for hypercholesterolemia.” This argument will however need to be supported by experimental evidence (the only method that can definitively prove causality), because the present observational study provides no insight into the putative underlying mechanistic relationship(s) between adolescent sleep duration and risk of hypercholesterolemia. Interestingly, a randomized clinical trial is currently under way to evaluate the efficacy of a behavioral intervention to extend sleep duration in obese short sleepers.6 Beyond the demonstration of the short sleep-hypercholesterolemia relationship, the study by Gangwisch et al.5 reminds us that there may be unintended consequences to our hectic lifestyle that values voluntary restriction of sleep. The National Sleep Foundation7 claims that sleep duration has decreased by more than one hour over the last few decades. Hopefully, the results of the Gangwisch et al.5 study could help in health education programs to promote a better sleep-related lifestyle in adolescents. After all, few would argue that the reduction of sleep time in adolescents is healthy, and therefore there is minimal risk in taking a pragmatic approach and encouraging a good night's sleep as an adjunct to other health promotion measures in teenagers.8–10

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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