Chronic alcohol consumption decreases brown adipose tissue mass and disrupts thermoregulation: a possible role for altered retinoid signaling
Bibliographic record
Abstract
Retinoic acid, an active metabolite of dietary vitamin A, acts as a ligand for nuclear receptor transcription factors with more than 500 known target genes. It is becoming increasingly clear that alcohol has a significant impact on cellular retinoic acid metabolism, with resultant effects on its function. Here, we test the hypothesis that chronic alcohol consumption impairs retinoic acid signaling in brown adipose tissue (BAT), leading to impaired BAT function and thermoregulation. All studies were conducted in age-matched, male mice consuming alcohol-containing liquid diets. Alcohol's effect on BAT was assessed by histology, qPCR, HPLC, LC/MS and measures of core body temperature. Our data show that chronic alcohol consumption decreases BAT mass, with a resultant effect on thermoregulation. Follow-up mechanistic studies reveal a decreased triglyceride content in BAT, as well as impaired retinoic acid homeostasis, associated with decreased BAT levels of retinoic acid in alcohol-consuming mice. Our work highlights a hitherto uncharacterized effect of alcohol on BAT function, with possible implications for thermoregulation and energy metabolism in drinkers. Our data indicate that alcohol's effects on brown adipose tissue may be mediated through altered retinoic acid signaling.
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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".