Competing Factors Link to Bone Health in Polycystic Ovary Syndrome: Chronic Low-Grade Inflammation Takes a Toll
Bibliographic record
Abstract
Abstract Chronic inflammation predisposes to poor bone health. Women with polycystic ovary syndrome (PCOS) experience androgen excess, ovulatory disturbances, insulin resistance, abdominal adiposity and chronic inflammation. Our objective was to investigate the relationships among bone health parameters, chronic subclinical inflammation and anthropometric measures in premenopausal women with and without PCOS. In 61 premenopausal women, 22 women with PCOS and 39 controls, we assessed bone parameters (total hip bone mineral density [BMD] by dual-energy X-ray absorptiometry and radius strength-strain index [SSI] by peripheral quantitative computed tomography), inflammation (C-reactive protein/albumin), oxidative stress (leukocyte telomere length, urinary 8-hydroxydeoxyguanosine); hemoglobin A1c; anthropometric measures (body mass index, waist-to-height ratio, cross-sectional muscle area). A diagnosis of PCOS negatively predicted (beta = −0.251, p = 0.022) hip BMD in a regression model including weight. In women with PCOS, inflammation, which was predicted by increased waist-to-height ratio and current use of oral contraceptives, attenuated the positive influences of increased weight and muscle mass on bone strength and was inversely associated with radial SSI (R 2 = 0.25, p = 0.018). In conclusion, chronic subclinical inflammation may negatively impact bone physiology in women with PCOS. Strategies focused on reducing abdominal adiposity and avoiding medications that increase inflammation may counter this effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".