Inducible hepatic expression of CREBH mitigates diet-induced obesity, insulin resistance, and hepatic steatosis in mice
Bibliographic record
Abstract
Cyclic AMP-responsive element-binding protein H (CREBH encoded by Creb3l3 ) is a transcription factor that regulates the expression of genes that control lipid and glucose metabolism as well as inflammation. CREBH is upregulated in the liver under conditions of overnutrition, and mice globally lacking the gene ( CREBH −/− ) are highly susceptible to diet-induced obesity, insulin resistance, and hepatic steatosis. The net protective effects of CREBH have been attributed in large part to the activities of fibroblast growth factor (Fgf)-21 (Fgf21), a target gene that promotes weight loss, improves glucose homeostasis, and reduces hepatic lipid accumulation. To explore the possibility that activation of the CREBH–Fgf21 axis could ameliorate established effects of high-fat feeding, we generated an inducible transgenic hepatocyte-specific CREBH overexpression mouse model ( Tg-rtTA ). Acute overexpression of CREBH in livers of Tg-rtTA mice effectively reversed diet-induced obesity, insulin resistance, and hepatic steatosis. These changes were associated with increased activities of thermogenic brown and beige adipose tissues in Tg-rtTA mice, leading to reductions in fat mass, along with enhanced insulin sensitivity and glucose tolerance. Genetically silencing Fgf21 in Tg-rtTA mice abrogated the CREBH-mediated reductions in body weight loss, but only partially reversed the observed improvements in glucose metabolism. These findings reveal that the protective effects of CREBH activation may be leveraged to mitigate diet-induced obesity and associated metabolic abnormalities in both Fgf21-dependent and Fgf21-independent pathways.
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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) | 0.000 | 0.000 |
| 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".