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Record W4295528343 · doi:10.1515/mr-2022-0019

Fibroblast growth factor 21 and dietary interventions: what we know and what we need to know next

2022· review· en· W4295528343 on OpenAlexafffund
Tianru Jin

Bibliographic record

VenueMedical Review · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersBanting and Best Diabetes Centre, University of TorontoCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsNeed to knowFibroblast growth factorPsychological interventionMedicineInternal medicineComputer scienceNursingComputer security

Abstract

fetched live from OpenAlex

Dietary interventions include the change of dietary styles, such as fasting and dietary or nutrient restrictions; or the addition of plant-derived compounds (such as polyphenols known as curcumin, resveratrol, or anthocyanin, or other nutraceuticals) into the diet. During the past a few decades, large number of studies have demonstrated therapeutic activities of these dietary interventions on metabolic and other diseases in human subjects or various animal models. Mechanisms underlying those versatile therapeutic activities, however, remain largely unclear. Interestingly, recent studies have shown that fibroblast growth factor 21 (FGF21), a liver-derived hormone or hepatokine, mediates metabolic beneficial effects of certain dietary polyphenols as well as protein restriction. Here I have briefly summarized functions of FGF21, highlighted related dietary interventions, and presented literature discussions on role of FGF21 in mediating function of dietary polyphenol intervention and protein restriction. This is followed by presenting my perspective view, with the involvement of gut microbiota. It is anticipated that further breakthroughs in this field in the near future will facilitate conceptual merge of classical medicine and modern medicine.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.383
Teacher spread0.305 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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