Fibroblast growth factor 21: update on genetics and molecular biology
Bibliographic record
Abstract
PURPOSE OF REVIEW: Since its discovery, most research on fibroblast growth factor 21 (FGF21) has focused on its antihyperglycemia properties. However, attention has recently shifted towards elucidating the ability of FGF21 to lower circulating lipid levels and ameliorate liver inflammation and steatosis. We here discuss the physiology of FGF21 and its role in lipid metabolism, with a focus on genetics, which has up until now not been fully appreciated. RECENT FINDINGS: New developments have uncovered associations of common small-effect variants of the FGF21 gene, such as the single nucleotide polymorphisms rs2548957 and rs838133, with numerous physiological, biochemical and behavioural phenotypes linked to energy metabolism and liver function. In addition, rare loss-of-function variants of the cellular receptors for FGF21 have been recently associated with severe endocrine and metabolic phenotypes. These associations corroborate the findings from basic studies and preliminary clinical investigations into the therapeutic potential of FGF21 for the treatment of metabolic dysfunction-associated steatotic liver disease (MASLD) and hypertriglyceridemia. Furthermore, recent breakthrough research has begun to dissect mechanisms of a potential FGF21 brain-adipose axis. Such inter-organ communication would be comparable to that seen with other potent metabolic hormones. A deeper understanding of FGF21 could prove to be further beneficial for drug development. SUMMARY: FGF21 is a potent regulator of lipid and energy homeostasis and its physiology is currently at the centre of investigative efforts to develop agents targeting hypertriglyceridemia and MASLD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".