Elevated serum receptor activator of nuclear factor kappa B ligand and osteoprotegerin levels in late-onset male hypogonadism
Bibliographic record
Abstract
PURPOSE: Studies that analyze the levels of osteoprotegerin (OPG) or receptor activator of nuclear factor kappa B ligand (RANKL) in hypogonadal men, the majority of whom have prostate cancer or are undergoing androgen-deprivation therapy, are few and inconclusive. METHODS: 81 men aged 69.3 ± 0.8 years (39 men with late-onset hypogonadism and 42 age-matched controls) were recruited. Serum levels of OPG, total soluble RANKL (sRANKL), total and free testosterone (FT), estradiol (E2), sex hormone-binding globulin (SHBG), follicle-stimulating hormone, luteinizing hormone (LH), prolactin, bone-specific alkaline phosphatase (BAP) and β-Cross Laps were assessed. RESULTS: Compared with controls, both OPG (p = 0.023) and sRANKL (p = 0.010) serum levels were increased in men with late-onset hypogonadism; however, when expressed as a ratio, sRANKL/OPG, the two groups were not significantly different. Simple and age-adjusted analyses showed that OPG was inversely related to FT and positively related to SHBG, E2 and BAP. In the patient population, LH demonstrated statistically significant correlations with both OPG (r = 0.274, p = 0.013) and sRANKL (r = 0.276, p = 0.018). Multiple regression analysis retained age, SHBG, E2 and BAP as independent predictors of OPG, explaining 27.71% of serum OPG variability. CONCLUSIONS: Late-onset hypogonadism is associated with enhanced RANKL activity. Increased bone turnover-related OPG levels may act as a coupling factor between bone resorption and formation. The results suggest anti-RANKL-agents as therapeutic tools in osteoporotic men with late-onset hypogonadism.
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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) | 0.001 | 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".