Browning of white adipose tissue after a burn injury promotes hepatic steatosis and dysfunction
Bibliographic record
Abstract
Burn patients experiencing hypermetabolism develop hepatic steatosis, which is associated with liver failure and poor outcomes after the injury. These same patients also undergo white adipose tissue (WAT) browning, which has been implicated in mediating post-burn cachexia and sustained hypermetabolism. Despite the clinical presentation of hepatic steatosis and WAT browning in burns, whether or not these two pathological responses are linked remains poorly understood. Here, we show that the burn-induced WAT browning and its associated increased lipolysis leads to the accelerated development of hepatic steatosis in mice. Deletion of interleukin 6 (IL-6) and the uncoupling protein 1 (UCP1), regulators of burn-induced WAT browning completely protected mice from hepatic steatosis after the injury. Treatment of post-burn mice with propranolol or IL-6 receptor blocker attenuated burn-induced WAT browning and its associated hepatic steatosis pathology. Lipidomic profiling in the plasma of post-burn mice and burn patients revealed elevated levels of damage-inducing lipids (palmitic and stearic acids), which induced hepatic endoplasmic reticulum (ER) stress and compromised hepatic fat oxidation. Mechanistically, we show that hepatic ER stress after a burn injury leads to a greater ER-mitochondria interaction, hepatocyte apoptosis, oxidative stress, and impaired fat oxidation. Collectively, our findings uncover an adverse "cross-talk" between the adipose and liver tissue in the context of burn injury, which is critically mediated by WAT browning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".