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Metabolic flexibility and obesity in children and youth

2010· review· en· W1568848466 on OpenAlexafffund
Julien Aucouturier, Pascale Duché, Brian W. Timmons

Bibliographic record

VenueObesity Reviews · 2010
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsMcMaster UniversityMcMaster Children's Hospital
FundersMcMaster University
KeywordsObesityFlexibility (engineering)Insulin resistanceEndocrinologyInternal medicineMetabolic syndromeSkeletal muscleType 2 diabetesMedicineDiabetes mellitusBiology

Abstract

fetched live from OpenAlex

The concept of metabolic flexibility describes the ability of skeletal muscle to switch between the oxidation of lipid as a fuel during fasting periods to the oxidation of carbohydrate during insulin stimulated period. Alterations in energy metabolism in adults with obesity, insulin resistance and/or type 2 diabetes induce a state of impaired metabolic flexibility, or metabolic inflexibility. Despite the increase in the prevalence of type 2 diabetes in obese children and youth, less is known about the factors involved in the development of metabolic inflexibility in the paediatric population. Metabolic flexibility is conditioned by nutrient partitioning in response to feeding, substrate mobilization and delivery to skeletal muscle during fasting or exercising condition, and skeletal muscle oxidative capacity. Our aim in this review was to identify among these factors those making obese children at risk of metabolic inflexibility. The development of ectopic rather than peripheral fat storage appears to be a factor strongly linked with a reduced metabolic flexibility. Tissue growth and maturation are determinants of impaired energy metabolism later in life but also as a promising way to reverse metabolic inflexibility given the plasticity of many tissues in youth. Finally, we have attempted to identify perspectives for future investigations of metabolic flexibility in obese children that will improve our understanding of the genesis of metabolic diseases associated with obesity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.344
Teacher spread0.285 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations44
Published2010
Admission routes2
Has abstractyes

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