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Record W3211894860 · doi:10.3390/nu13113999

Impact of Exercise on Gut Microbiota in Obesity

2021· review· en· W3211894860 on OpenAlexaff
Jerónimo Aragón‐Vela, Patricio Solis‐Urra, Francisco Javier Ruiz‐Ojeda, Ana I. Álvarez‐Mercado, Jorge Olivares-Arancibía, Julio Plaza‐Díaz

Bibliographic record

VenueNutrients · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersCHIST-ERAAgencia Estatal de InvestigaciónAgencia Nacional de Investigación y DesarrolloAgenția Națională pentru Cercetare și DezvoltareFundación Ramón ArecesFundación Alfonso Martín Escudero
KeywordsGut floraObesityDysbiosisPhysical activityFirmicutesMedicineBioinformaticsPhysical therapyBiologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Physical activity, exercise, or physical fitness are being studied as helpful nonpharmacological therapies to reduce signaling pathways related to inflammation. Studies describing changes in intestinal microbiota have stated that physical activity could increase the microbial variance and enhance the ratio of Firmicutes/Bacteroidetes, and both actions could neutralize the obesity progression and diminish body weight. The aim of this review is to provide an overview of the literature describing the relationship between physical activity profiles and gut microbiota and in obesity and some associated comorbidities. Promoting physical activity could support as a treatment to maintain the gut microbiota composition or to restore the balance toward an improvement of dysbiosis in obesity; however, these mechanisms need to be studied in more detail. The opportunity to control the microbiota by physical activity to improve health results and decrease obesity and related comorbidities is very attractive. Nevertheless, several incompletely answered questions need to be addressed before this strategy can be implemented.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.899
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.027
GPT teacher head0.364
Teacher spread0.337 · 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 designNot applicable
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

Citations80
Published2021
Admission routes1
Has abstractyes

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