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Record W4214835283 · doi:10.1053/j.gastro.2022.01.058

Nonalcoholic Fatty Liver Disease and the Gut-Liver Axis: Exploring an Undernutrition Perspective

2022· review· en· W4214835283 on OpenAlexafffund
Kylynda C. Bauer, Paula Littlejohn, Victòria Ayala, Anna Creus-Cuadros, B. Brett Finlay

Bibliographic record

VenueGastroenterology · 2022
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsNonalcoholic fatty liver diseaseOvernutritionMalnutritionFatty liverMedicineMetabolic syndromeMicrobiomeMicronutrientDiseaseInternal medicineObesityPopulationGastroenterologyBioinformaticsPathologyBiologyEnvironmental health

Abstract

fetched live from OpenAlex

Nonalcoholic fatty liver disease (NAFLD) is a chronic condition affecting one quarter of the global population. Although primarily linked to obesity and metabolic syndrome, undernutrition and the altered (dysbiotic) gut microbiome influence NAFLD progression. Both undernutrition and NAFLD prevalence are predicted to considerably increase, but how the undernourished gut microbiome contributes to hepatic pathophysiology remains far less studied. Here, we present undernutrition conditions with fatty liver features, including kwashiorkor and micronutrient deficiency. We then review the gut microbiota-liver axis, highlighting key pathways linked to NAFLD progression within both overnutrition and undernutrition. To conclude, we identify challenges and collaborative possibilities of emerging multiomic research addressing the pathology and treatment of undernourished NAFLD.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.141
GPT teacher head0.337
Teacher spread0.195 · 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 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

Citations119
Published2022
Admission routes2
Has abstractno

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