Cross‐sectional associations of acylation stimulating protein (<scp>ASP</scp>) and adipose tissue gene expression with estradiol and progesterone in pre‐ and postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: Sex steroid hormones play an important regulatory role in fat metabolism and obesity. We hypothesized involvement of interactions between ovarian hormones with acylation stimulating protein (ASP). DESIGN, PATIENTS AND MEASUREMENTS: In 392 women with wide age (18-69 years) and body size (BMI: 17 to 90 kg/m(2) ) ranges, fasting plasma levels of ASP, ovarian hormones, glucose, adiponectin and lipids/apolipoproteins were assessed, along with determination of metabolic syndrome (MS) features. Gene expression of C3 (ASP precursor) and related receptors C5L2, C3aR and C5aR in subcutaneous and omental adipose tissues was measured in a subset. RESULTS: Acylation stimulating protein correlated negatively with concentrations of estradiol (P < 0·0001), adiponectin (P < 0·001) and apolipoprotein A1 (P < 0·001) and positively with apolipoprotein B levels (P < 0·001), systolic blood pressure (P < 0·001), waist circumference (P < 0·001), and triglyceride concentrations (P < 0·01). In age-matched groups of lean, overweight, metabolically healthy obese (MHO) and obese with metabolic syndrome (MSO), there was a stepwise increase in ASP levels (P < 0·001) while concentrations of adiponectin (P < 0·0001) and estradiol (P < 0·001) but not those of progesterone decreased. Progesterone but not estradiol levels correlated positively with C3 gene expression in omental adipose tissue (P < 0·05) and negatively with C5L2 expression in both omental (P < 0·01) and subcutaneous (P < 0·05) adipose tissues. CONCLUSION: Our results are consistent with the concept that sex hormones differentially influence circulating ASP and adipose tissue gene expression of its related proteins in a depot-specific manner. ASP may play a role in the regulation of regional fat metabolism through interactions with sex hormones in women.
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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 | 0.000 |
| 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) | 0.001 | 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".