Prohibitin-induced, obesity-associated insulin resistance and accompanying low-grade inflammation causes NASH and HCC
Bibliographic record
Abstract
Obesity increases the risk for nonalcoholic steatohepatitis (NASH) and hepatocarcinogenesis. However, the underlying mechanisms involved in the disease process remain unclear. Recently, we have developed a transgenic obese mouse model (Mito-Ob) by prohibitin mediated mitochondrial remodeling in adipocytes. The Mito-Ob mice develop obesity in a sex-neutral manner, but obesity-associated adipose inflammation and metabolic dysregulation in a male sex-specific manner. Here we report that with aging, the male Mito-Ob mice spontaneously develop obesity-linked NASH and hepatocellular carcinoma (HCC). In contrast, the female Mito-Ob mice maintained normal glucose and insulin levels and did not develop NASH and HCC. The anti-inflammatory peptide ghrelin was significantly upregulated in the female mice and down regulated in the male mice compared with respective control mice. In addition, a reduction in the markers of mitochondrial content and function was found in the liver of male Mito-Ob mice with NASH/HCC development. We found that ERK1/2 signaling was significantly upregulated whereas STAT3 signaling was significantly down regulated in the tumors from Mito-Ob mice. These data provide a proof-of-concept that the metabolic and inflammatory status of the adipose tissue and their interplay at the systemic and hepatic level play a central role in the pathogenesis of obesity-linked NASH and HCC.
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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 | 0.000 |
| 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".