Brown adipose tissue thermogenesis in polycystic ovary syndrome
Bibliographic record
Abstract
OBJECTIVE: Polycystic ovary syndrome (PCOS) is associated with increased obesity with a greater propensity to weight gain and a lack of sustainable lifestyle interventions. Altered brown adipose tissue (BAT) thermogenesis is a potential contributor to obesity in PCOS. BAT activity and modulation have not been studied in PCOS. This observational study explored BAT thermogenesis and its associations in women with and without PCOS. PARTICIPANTS AND METHODS: Cutaneous temperature was recorded from supraclavicular (indicator of BAT activity) and upper arm regions using dataloggers (SubCue, Calgary, Canada) in a cross-sectional substudy, nested within a randomized control trial, of community-recruited premenopausal women with (n = 47, Rotterdam diagnostic criteria) and without (n = 11) PCOS. RESULTS: Complete temperature data were available in 44 PCOS (mean age: 30.0 ± 6.2, mean BMI: 29.3 ± 5.5) and 11 non-PCOS (mean age: 33.0 ± 7.0, mean BMI: 25 ± 3) women. Women with PCOS had lower supraclavicular skin temperature compared to controls overall (33.9 ± 0.7 vs 34.5 ± 1, P < 0.05) and during sleep (34.5 ± 0.6 vs 35.2 ± 0.9, P < 0.001). In the PCOS group, supraclavicular skin temperature overall and over sleep and waking hours correlated inversely with testosterone (r = -0.41 P < 0.05, r = -0.485 P < 0.01 and r = -0.450 P < 0.01 respectively). Testosterone levels explained approximately 15%, 30% and 20% of the variability in supraclavicular skin temperature overall and over sleep and waking hours in women with PCOS, respectively. CONCLUSION: Women with PCOS have lower BAT activity compared to controls. BAT thermogenesis is negatively associated with androgen levels in PCOS.
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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 | 0.001 |
| 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) | 0.001 | 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".