Hepatocyte-Specific Disruption of CD36 Attenuates Fatty Liver and Improves Insulin Sensitivity in HFD-Fed Mice
Bibliographic record
Abstract
CD36/FAT (fatty acid translocase) is associated with human and murine nonalcoholic fatty liver disease, but it has been unclear whether it is simply a marker or whether it directly contributes to disease pathogenesis. Mice with hepatocyte-specific deletion of Janus kinase 2 (JAK2L mice) have increased circulating free fatty acids (FAs), dramatically increased hepatic CD36 expression and profound fatty liver. To investigate the role of elevated CD36 in the development of fatty liver, we studied two models of hepatic steatosis, a genetic model (JAK2L mice) and a high-fat diet (HFD)-induced steatosis model. We deleted Cd36 specifically in hepatocytes of JAK2L mice to generate double knockouts and from wild-type mice to generate CD36L single-knockout mice. Hepatic Cd36 disruption in JAK2L livers significantly improved steatosis by lowering triglyceride, diacylglycerol, and cholesterol ester content. The largest differences in liver triglycerides were in species comprised of oleic acid (C18:1). Reduction in liver lipids correlated with an improvement in the inflammatory markers that were elevated in JAK2L mice, namely aspartate aminotransferase and alanine transaminase. Cd36 deletion in mice on HFD (CD36L-HFD) reduced liver lipid content and decreased hepatic 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene-FA uptake as compared with CON-HFD. Additionally, CD36L-HFD mice had improved whole-body insulin sensitivity and reduced liver and serum inflammatory markers. Therefore, CD36 directly contributes to development of fatty liver under conditions of elevated free FAs by modulating the rate of FA uptake by hepatocytes. In HFD-fed animals, disruption of hepatic Cd36 protects against associated systemic inflammation and insulin resistance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".