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Record W2898755363 · doi:10.1038/s41467-018-07146-5

Long term but not short term exposure to obesity related microbiota promotes host insulin resistance

2018· article· en· W2898755363 on OpenAlexafffund
Kevin P. Foley, Soumaya Zlitni, Emmanuel Denou, Brittany M. Duggan, Rebecca W. Chan, Jennifer C. Stearns, Jonathan D. Schertzer

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsInsulin resistanceTerm (time)Host (biology)ObesityResistance (ecology)BiologyBioinformaticsMedicineGeneticsEndocrinologyEcology

Abstract

fetched live from OpenAlex

The intestinal microbiota and insulin sensitivity are rapidly altered after ingestion of obesogenic diets. We find that changes in the composition of the fecal microbiota precede changes in glucose tolerance when mice are fed obesogenic, low fiber, high fat diets (HFDs). Antibiotics alter glycemia during the first week of certain HFDs, but antibiotics show a more robust improvement in glycemic control in mice with protracted obesity caused by long-term feeding of multiple HFDs. Microbiota transmissible dysglycemia and glucose intolerance only occur when germ-free mice are exposed to obesity-related microbes for more than 45 days. We find that sufficient host exposure time to microbiota derived from HFD-fed mice allows microbial factors to contribute to insulin resistance, independently from increased adiposity in mice. Our results are consistent with intestinal microbiota contributing to chronic insulin resistance and dysglycemia during prolonged obesity, despite rapid diet-induced changes in the taxonomic composition of the fecal microbiota.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.294
Teacher spread0.281 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations85
Published2018
Admission routes2
Has abstractyes

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