Lower Sex hormone-binding Globulin is a Potential Marker for the Insulin Resistance in non-obese Untreated Taiwanese Women with Polycystic Ovary Syndrome
Bibliographic record
Abstract
Background: Polycystic ovary syndrome (PCOS) is considered to share similar features with metabolic syndrome (MBS). Useful clinical diagnostics is necessary to evaluate the early manifestations of worsening health illustrated in non-obese women with PCOS. Objective: We investigated the association of serum biochemical and hormonal factors of non-obese patients with PCOS. Methods: Thirty qualified premenopausal, non-obese Taiwanese women with PCOS were enrolled in this study while another 15 non-obese premenopausal women were enrolled and served as controls. Anthropometric measures and select essential biochemical and hormonal measures were measured. The regressions between selected biochemical measures and the anthropometric measures were assessed between these groups. Results: When the PCOS group was compared with the control, the levels of high-density lipoprotein cholesterol (HDL-C and sex hormone-binding globulin SHBG) levels were significantly lower (56.3 ± 13.3 mg/dL and 49.9 ± 22.9 nmol/L, respectively, p＜0.05) and while high-sensitivity C-reactive protein (hs-CRP) was significantly higher 0.18 ± 0.21 mg/L). SHBG was significantly correlated with androgen profiles, HDL-C and triglycerides levels after being adjusted for age and BMI, hormone levels and anthropometric variables. HDL-C is highly inversely associated with waist circumference (WC) and waist-to-hip ratio (WHR). The fasting insulin, homeostasis model assessment (HOMA) of insulin resistance and results from 2-h glucose challenge test significantly but negatively correlated with SHBG levels after being adjusting for age and BMI in the PCOS group. Conclusion: Lower SHBG is a useful clinical marker to help detecting the insulin resistance in patients of non-obese Taiwanese women with 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 | 0.001 | 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) | 0.002 | 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".