UCP1 expression in human brown adipose tissue is inversely associated with cardiometabolic risk factors
Bibliographic record
Abstract
OBJECTIVE: Brown adipose tissue (BAT) is a therapeutic target for obesity. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is commonly used to quantify human BAT mass and activity. Detectable 18F-FDG uptake by BAT is associated with reduced prevalence of cardiometabolic disease. However, 18F-FDG uptake may not always be a reliable marker of BAT thermogenesis, for example, insulin resistance may reduce glucose uptake. Uncoupling protein 1 (UCP1) is the key thermogenic protein in BAT. Therefore, we hypothesised that UCP1 expression may be altered in individuals with cardiometabolic risk factors. METHODS: We quantified UCP1 expression as an alternative marker of thermogenic capacity in BAT and white adipose tissue (WAT) samples (n = 53) and in differentiated brown and white pre-adipocytes (n = 85). RESULTS: UCP1 expression in BAT, but not in WAT or brown/white differentiated pre-adipocytes, was reduced with increasing age, obesity, and adverse cardiometabolic risk factors such as fasting glucose, insulin, and blood pressure. However, UCP1 expression in BAT was preserved in obese subjects of <40 years of age. To determine if BAT activity was also preserved in vivo, we undertook a case-control study, performing 18F-FDG scanning during mild cold exposure in young (mean age ∼22 years) normal weight and obese volunteers. 18F-FDG uptake by BAT and BAT volume were similar between groups, despite increased insulin resistance. CONCLUSION: 18F-FDG uptake by BAT and UCP1 expression are preserved in young obese adults. Older subjects retain precursor cells with the capacity to form new thermogenic adipocytes. These data highlight the therapeutic potential of BAT mass expansion and activation in obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".