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Record W3011653330 · doi:10.1186/s12981-020-00267-2

Metformin effect on gut microbiota: insights for HIV-related inflammation

2020· review· en· W3011653330 on OpenAlexafffund
Jing Ouyang, Stéphane Isnard, John Lin, Brandon Fombuena, André Marette, Bertrand Routy, Yaokai Chen, Jean‐Pierre Routy

Bibliographic record

VenueAIDS Research and Therapy · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversité LavalCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversité de MontréalMcGill University Health Centre
FundersNational Drug Abuse Treatment Clinical Trials NetworkCanadian Institutes of Health ResearchCanadian Foundation for AIDS ResearchNational Science and Technology Major ProjectStyrelsen för Internationellt UtvecklingssamarbetePfizer
KeywordsMetforminDysbiosisGut floraMedicineInflammationDiabetes mellitusImmunologyHuman immunodeficiency virus (HIV)Immune systemBioinformaticsBiologyEndocrinology

Abstract

fetched live from OpenAlex

The gut microbiota is emerging as a prominent player in maintaining health through several metabolic and immune pathways. Dysregulation of gut microbiota composition, also known as dysbiosis, is involved in the clinical outcome of diabetes, inflammatory bowel diseases, cancer, aging and HIV infection. Gut dysbiosis and inflammation persist in people living with HIV (PLWH) despite receiving antiretroviral therapy, further contributing to non-AIDS comorbidities. Metformin, a widely used antidiabetic agent, has been found to benefit microbiota composition, promote gut barrier integrity and reduce inflammation in human and animal models of diabetes. Inspired by the effect of metformin on diabetes-related gut dysbiosis, we herein critically review the relevance of metformin to control inflammation in PLWH. Metformin may improve gut microbiota composition, in turn reducing inflammation and risk of non-AIDS comorbidities. This review will pave the way towards innovative strategies to counteract dysregulated microbiota and improve the lives of PLWH.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.059
GPT teacher head0.405
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations60
Published2020
Admission routes2
Has abstractyes

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