Subcutaneous adipose tissue sclerostin is reduced and Wnt signaling is enhanced following 4‐weeks of sprint interval training in young men with obesity
Bibliographic record
Abstract
Sclerostin is a Wnt/β-catenin antagonist, mainly secreted by osteocytes, and most known for its role in reducing bone formation. Studies in rodents suggest sclerostin can also regulate adipose tissue mass and metabolism, representing bone–adipose tissue crosstalk. Exercise training has been shown to reduce plasma sclerostin levels; but the effects of exercise on sclerostin and Wnt/β-catenin signaling specifically within adipose tissue has yet to be examined. The purpose of this study was to examine subcutaneous WAT (scWAT) sclerostin content and Wnt signaling in response to exercise training in young men with obesity. To this end, 7 male participants (BMI = 35 ± 4; 25 ± 4 years) underwent 4 weeks of sprint interval training (SIT) involving 4 weekly sessions consisting of a 5-min warmup, followed by 8 × 20 s intervals at 170% of work rate at VO2peak, separated by 10 s of rest. Serum and scWAT were sampled at rest both pre- and post-SIT. Despite no changes in serum sclerostin levels, we found a significant decrease in adipose sclerostin content (−37%, p = 0.04), an increase in total β-catenin (+52%, p = 0.03), and no changes in GSK3β serine 9 phosphorylation. There were also concomitant reductions in serum TNF-α (−0.36 pg/ml, p = 0.03) and IL-6 (−1.44 pg/ml, p = 0.05) as well as an increase in VO2peak (+5%, p = 0.03) and scWAT COXIV protein content (+95%, p = 0.04). In conclusion, scWAT sclerostin content was reduced and β-catenin content was increased following SIT in young men with excess adiposity, suggesting a role of sclerostin in regulating human adipose tissue in response to exercise training.
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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) | 0.003 | 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".