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Record W4408865253 · doi:10.1126/sciadv.adt3142

The clinical antiprotozoal drug halofuginone promotes weight loss by elevating GDF15 and FGF21

2025· article· en· W4408865253 on OpenAlexaff
Suowen Xu, Wenqi Zhao, Zhihua Wang, Monan Liu, Mengyun Xu, Fanshun Zhang, Meijie Chen, Yanjun Yin, Meiming Su, Shiyong Liu, Min‐Dian Li, Danielle Kamato, Song‐Yang Zhang, Dongdong Wang, Stefan Offermanns, John R. Speakman

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsMcMaster UniversityToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsFGF21MedicineObesityInsulin resistanceMetabolic syndromeGDF15PharmacologyWeight lossInternal medicineFibroblast growth factor

Abstract

fetched live from OpenAlex

Obesity is a debilitating global pandemic with a huge cost on health care due to it being a major underlying risk factor for several diseases. Therefore, there is an unmet medical need for pharmacological interventions to curb obesity. Here, we report that halofuginone, a Food and Drug Administration–approved anti-scleroderma and antiprotozoal drug, is a promising anti-obesity agent in preclinical mouse and pig models. Halofuginone suppressed food intake, increased energy expenditure, and resulted in weight loss in diet-induced obese mice while also alleviating insulin resistance and hepatic steatosis. Using molecular and pharmacological tools with transcriptomics, we identified that halofuginone increases fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15) levels via activating integrated stress response. Using Gdf15 and Fgf21 knockout mice, we show that both hormones are necessary to elicit anti-obesity changes. Together, our study reports the beneficial metabolic effects of halofuginone and underscores its utility in treating obesity and its associated metabolic complications, which merits clinical assessment.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
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 head0.008
GPT teacher head0.341
Teacher spread0.333 · 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 designObservational
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

Citations12
Published2025
Admission routes1
Has abstractyes

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