Circulating Progesterone and Obesity in Men
Bibliographic record
Abstract
Progesterone can be detected in male plasma and has been considered to originate mainly from the adrenals. We have examined the association between circulating progesterone and obesity in a sample of thirty-eight lean to morbidly obese men aged 44.5 +/- 9.9 years (BMI: 44.3 +/- 12.8 kg/m (2)). Plasma concentrations of progesterone, 17-OH-progesterone as well as androstenedione, testosterone, DHT and DHEA-S were determined. Negative correlations were observed between plasma progesterone levels and body weight (r = - 0.47, p < 0.05), BMI (r = - 0.56, p < 0.001), waist circumference (r = - 0.58, p < 0.001), as well as subcutaneous adipocyte diameter (r = - 0.50, p < 0.05). Plasma levels of 17-OH-progesterone, DHEA-S, androstenedione, testosterone and DHT were also negatively associated with body weight, BMI and waist circumference. However, the ratio of 17-OH-progesterone-to-progesterone and androstenedione-to-17-OH-progesterone were not related to these variables. A positive correlation was found between circulating progesterone and DHEA-S levels (r = 0.50, p < 0.002 after adjustment for age). Accordingly, using multivariate regression analyses, the best steroid predictor of progesterone level was plasma DHEA-S. Waist circumference was the best predictor of progesterone levels in a multivariate model including steroid concentrations as well as waist circumference, BMI and subcutaneous adipocyte diameter. In conclusion, plasma progesterone was negatively associated with markers of obesity such as BMI, waist circumference and subcutaneous adipocyte diameter in this sample of men. Circulating DHEA-S level was the best steroid correlate of plasma progesterone. We suggest that the low progesterone levels observed in obese men may reflect decreased adrenal C(19) steroid production in the adrenal cortex. Further research is needed to confirm this hypothesis.
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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 | 0.001 | 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".