Is stimulation of browning of human adipose tissue a relevant therapeutic target?
Bibliographic record
Abstract
Brown adipose tissue (BAT) and beige adipose tissues are important contributors to cold-induced whole body thermogenesis in rodents. The documentation in humans of cold- and ß-adrenergic receptor agonist-stimulated BAT glucose uptake using positron emission tomography (PET) and of a decrease of this response in individuals with cardiometabolic disorders led to the suggestion that BAT/beige adipose tissues could be relevant targets for prevention and treatment of these conditions. In this brief review, we will critically assess this question by first describing the basic rationale for this affirmation, second by examining the evidence in human studies, and third by discussing the possible means to activate the thermogenic response of these tissues in humans. Les tissus adipeux bruns (TAB) et beiges sont d’importants effecteurs de la réponse thermogène au froid chez les rongeurs. La description chez l’homme de la stimulation du captage du glucose par les TAB lors de l’exposition au froid ou après l’administration d’agonistes des récepteurs ß-adrénergiques ainsi que de la réduction de cette réponse chez les personnes souffrant de problèmes cardiométaboliques ont mené à la suggestion que les TAB et tissus adipeux beiges pourraient constituer des cibles thérapeutiques pour ces conditions. Dans cette brève revue, nous allons examiner de manière critique cette question premièrement en décrivant les fondements de cette affirmation, deuxièmement en examinant les résultats des études chez l’humain, et troisièmement en discutant les moyens applicables pour activer la réponse thermogène de ces tissus chez l’humain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".