Folate Deficiency and/or the Genetic Variant <i>Mthfr<sup>677C >T</sup></i> Can Drive Hepatic Fibrosis or Steatosis in Mice, in a Sex‐Specific Manner
Bibliographic record
Abstract
Scope Disturbances in one‐carbon metabolism contribute to nonalcoholic fatty liver disease (NAFLD) which encompasses steatosis, steatohepatitis, fibrosis, and cirrhosis. The goal is to examine impact of folate deficiency and the Mthfr677C >T variant on NAFLD. Methods and results This study uses the new Mthfr677C >T mouse model for the human MTHFR677C >T variant. Mthfr677CC and Mthfr677TT mice were fed control diet (CD) or folate‐deficient (FD) diets for 4 months. FD and Mthfr677TT alter choline/methyl metabolites in liver and/or plasma (decreased S‐adenosylmethionine (SAM):S‐adenosylhomocysteine (SAH) ratio, methyltetrahydrofolate, and betaine; increased homocysteine [Hcy]). FD, with contribution from Mthfr677TT, provokes fibrosis in males. Studies of normal livers reveal alterations in plasma markers and gene expression that suggest an underlying predisposition to fibrosis induced by FD and/or Mthfr677TT in males. These changes are absent or reverse in females, consistent with the sex disparity of fibrosis. Sex‐based differences in methylation potential, betaine, sphingomyelin, and trimethylamine‐N‐oxide (TMAO) levels may prevent fibrogenesis in females. In contrast, Mthfr677TT alters choline metabolism, dysregulates expression of lipid metabolism genes, and promotes steatosis in females. Conclusion This study suggests that folate deficiency predisposes males to fibrosis, which is exacerbated by Mthfr677TT, whereas Mthfr677TT predisposes females to steatosis, and reveal novel contributory mechanisms for these NAFLD‐related disorders.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".