Cold exposure modulates potential brown adipokines in humans, but only <scp>FGF21</scp> is associated with brown adipose tissue volume
Bibliographic record
Abstract
Abstract Objective The study objective was to investigate the effect of cold exposure on the plasma levels of five potential human brown adipokines (chemokine ligand 14 [CXCL14], growth differentiation factor 15 [GDF15], fibroblast growth factor 21 [FGF21], interleukin 6 [IL6], and bone morphogenic protein 8b [BMP8b]) and to study whether such cold‐induced effects are related to brown adipose tissue (BAT) volume, activity, or radiodensity in young humans. Methods Plasma levels of brown adipokines were measured before and 1 h and 2 h after starting an individualized cold exposure in 30 young adults (60% women, 21.9 ± 2.3 y; 24.9 ± 5.1 kg/m 2 ). BAT volume, 18 F‐fluorodeoxyglucose uptake, and radiodensity were assessed by a static positron emission tomography‐computerized tomography scan after cold exposure. Results Cold exposure increased the concentration of CXCL14 (Δ2h = 0.58 ± 0.98 ng/mL; p = 0.007), GDF15 (Δ2h = 19.63 ± 46.2 pg/mL; p = 0.013), FGF21 (Δ2h = 33.72 ± 55.13 pg/mL; p = 0.003), and IL6 (Δ1h = 1.98 ± 3.56 pg/mL; p = 0.048) and reduced BMP8b (Δ2h = −37.12 ± 83.53 pg/mL; p = 0.022). The cold‐induced increase in plasma FGF21 was positively associated with BAT volume (Δ2h: β = 0.456; R 2 = 0.307; p = 0.001), but not with 18 F‐fluorodeoxyglucose uptake or radiodensity. None of the changes in the other studied brown adipokines was related to BAT volume, activity, or radiodensity. Conclusions Cold exposure modulates plasma levels of several potential brown adipokines in humans, whereas only cold‐induced changes in FGF21 levels are associated with BAT volume. These findings suggest that human BAT might contribute to the circulatory pool of FGF21.
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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) | 0.001 | 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 | 0.001 |
| 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 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".