Acylation-stimulating protein increases and correlates with increased progesterone levels during the luteal phase of the menstrual cycle
Bibliographic record
Abstract
OBJECTIVE: The menstrual cycle represents a continuous state of change in terms of female sex steroid environment. Progesterone is linked to increased fat storage while estrogen exerts anti-lipogenic effects. This study investigated variations in the potent lipogenic factor acylation-stimulating protein (ASP), and examined its association with hormonal and lipid profile alterations across the menstrual cycle. METHODS AND DESIGN: Nineteen non-obese women with regular menstrual cycles were investigated in a longitudinal study during the follicular, ovulatory, and mid-luteal phases (ML) of the cycle. Fasting ASP, LH, FSH, progesterone, estradiol, insulin, lipid profile, and apoproteins were evaluated during different phases of the cycle. RESULTS: ASP levels changed significantly throughout the menstrual cycle (K-related Friedman test: P=0.013). Interestingly, these changes coincide with variations in progesterone levels across the cycle as no significant change in the ASP levels was seen across the follicular phases of the cycle, followed by a significant increase in the ovulatory phase, which continued to elevate toward the ML. The ASP levels correlated positively with the progesterone levels normally elevated in the ML. No significant correlation was seen between ASP and estrogen or any other measured female hormone. Multiple regression analysis including all measured parameters and body mass index showed that progesterone was the only significant predictor of the ASP levels. CONCLUSION: Our findings suggest that during the menstrual cycle of normal women, the ASP levels coincidentally fluctuate with the progesterone levels, possibly reflecting cooperation between them in fat storage enhancement.
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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) | 0.000 | 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) | 0.000 | 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".