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Record W4411491205 · doi:10.54254/2753-8818/2025.24207

Impact of High-Fat Diet on Obesity-Related Diseases: The Role of Gut Microbiota-Derived Short-Chain Fatty Acids

2025· article· en· W4411491205 on OpenAlexaff
Zhe Kan, Lei Yan, Yuting Tang

Bibliographic record

VenueTheoretical and Natural Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGut floraButyrateIntestinal permeabilityDiseaseObesityBiologyPropionateProbioticFatty liverPrebioticFlora (microbiology)Diabetes mellitusBioinformaticsPhysiologyMedicineImmunologyEndocrinologyInternal medicineBiochemistryBacteria

Abstract

fetched live from OpenAlex

The popularity of fast food has led to a rapid global increase in high-fat diets (HFD) recently. The prevalence of HFD has raised public concerns about metabolic health. Animal studies and clinical trials have implied the alternations of gut microbiota components when HFD, thereby influencing their metabolites abundances, specifically short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate, which play important roles in host physiological activities. Alternations in intestinal flora abundance and components may also exacerbate gut permeability, potentially initiating inflammation which is a start of various chronic diseases. This review primarily explores mechanisms by which HFD induces obesity-related diseases, including metabolic dysfunction-associated steatotic liver disease, atherosclerosis, and type 2 diabetes mellitus. Additionally, this review demonstrates the role and effectiveness of intestinal flora, especially probiotics, and their derived SCFAs in preventing disease progression and promoting tissue regeneration in HFD-induced disorders. An intensive study on the significance of intestinal flora and their derived SCFAs to disease progression and therapeutic targets can help supplement the loop between microbial and host homeostasis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.002
GPT teacher head0.253
Teacher spread0.251 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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