Adipose browning response to burn trauma is impaired with aging
Bibliographic record
Abstract
BACKGROUNDThe incidence of burn injuries in older patients is dramatically increasing as the population of older people grows. Despite the increased demand for elderly burn care, the mechanisms that mediate increased morbidity and mortality in older trauma patients are unknown. We recently showed that a burn injury invokes white adipose tissue browning that leads to a substantially increased hypermetabolic response associated with poor outcomes. Therefore, the aim of this study was to determine the effect of age on the metabolic adipose response of browning after a burn injury.METHODOne hundred and seventy patients with burn injury admitted to the Ross Tilley Burn Centre were prospectively enrolled and grouped by age as older (≥50 years) and young (≤35 years). Adipose tissue and sera were collected and analyzed for browning markers and metabolic state via histology, gene expression, and resting energy expenditure assays.RESULTSWe found that older patients with burn injury lacked the adipose browning response, as they showed significant reductions in uncoupling protein 1 (UCP1) expression. This failure of the browning response was associated with reduced whole-body metabolism and decreased survival in older patients with burn injury. Mechanistically, we found that the adipose of both aged patients after burn trauma and aged mice after a burn showed impairments in macrophage infiltration and IL-6, key immunological regulators of the browning process after a severe trauma.CONCLUSIONTargeting pathways that activate the browning response represents a potential therapeutic approach to improve outcomes after burn trauma for elderly patients.FUNDINGNIH (R01-GM087285-01), Canadian Institutes of Health Research (grant no. 123336), and Canada Foundation for Innovation Leaders Opportunity Fund (no. 25407).
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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".