The clinical antiprotozoal drug halofuginone promotes weight loss by elevating GDF15 and FGF21
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".