Ketohexokinase inhibition improves NASH by reducing fructose-induced steatosis and fibrogenesis
Bibliographic record
Abstract
Background & Aims Increasing evidence highlights dietary fructose as a major driver of non-alcoholic fatty liver disease (NAFLD) pathogenesis, the majority of which is cleared on first pass through the hepatic circulation by enzymatic phosphorylation to fructose-1-phosphate via the ketohexokinase (KHK) enzyme. Without a current approved therapy, disease management emphasises lifestyle interventions, but few patients adhere to such strategies. New targeted therapies are urgently required. Methods We have used a unique combination of human liver specimens, a murine dietary model of NAFLD and human multicellular co-culture systems to understand the hepatocellular consequences of fructose administration. We have also performed a detailed nuclear magnetic resonance-based metabolic tracing of the fate of isotopically labelled fructose upon administration to the human liver. Results Expression of KHK isoforms is found in multiple human hepatic cell types, although hepatocyte expression predominates. KHK knockout mice show a reduction in serum transaminase, reduced steatosis and altered fibrogenic response on an Amylin diet. Human co-cultures exposed to fructose exhibit steatosis and activation of lipogenic and fibrogenic gene expression, which were reduced by pharmacological inhibition of KHK activity. Analysis of human livers exposed to 13 C-labelled fructose confirmed that steatosis, and associated effects, resulted from the accumulation of lipogenic precursors (such as glycerol) and enhanced glycolytic activity. All of these were dose-dependently reduced by administration of a KHK inhibitor. Conclusions We have provided preclinical evidence using human livers to support the use of KHK inhibition to improve steatosis, fibrosis, and inflammation in the context of NAFLD. Lay summary We have used a mouse model, human cells, and liver tissue to test how exposure to fructose can cause the liver to store excess fat and become damaged and scarred. We have then inhibited a key enzyme within the liver that is responsible for fructose metabolism. Our findings show that inhibition of fructose metabolism reduces liver injury and fibrosis in mouse and human livers and thus this may represent a potential route for treating patients with fatty liver disease in the future.
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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 | 0.000 | 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) | 0.003 | 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".