FGF21 underlies a hormetic response to metabolic stress in methylmalonic acidemia
Bibliographic record
Abstract
Methylmalonic acidemia (MMA), an organic acidemia characterized by metabolic instability and multiorgan complications, is most frequently caused by mutations in methylmalonyl-CoA mutase (MUT). To define the metabolic adaptations in MMA in acute and chronic settings, we studied a mouse model generated by transgenic expression of Mut in the muscle. Mut-/-;TgINS-MCK-Mut mice accurately replicate the hepatorenal mitochondriopathy and growth failure seen in severely affected patients and were used to characterize the response to fasting. The hepatic transcriptome in MMA mice was characterized by the chronic activation of stress-related pathways and an aberrant fasting response when compared with controls. A key metabolic regulator, Fgf21, emerged as a significantly dysregulated transcript in mice and was subsequently studied in a large patient cohort. The concentration of plasma FGF21 in MMA patients correlated with disease subtype, growth indices, and markers of mitochondrial dysfunction but was not affected by renal disease. Restoration of liver Mut activity, by transgenesis and liver-directed gene therapy in mice or liver transplantation in patients, drastically reduced plasma FGF21 and was associated with improved outcomes. Our studies identify mitocellular hormesis as a hepatic adaptation to metabolic stress in MMA and define FGF21 as a highly predictive disease biomarker.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".