TNF-alpha and hyperandrogenism: a clinical, biochemical, and molecular genetic study
Bibliographic record
Abstract
To evaluate the role of TNF-alpha in the pathogenesis of hyperandrogenism, we have evaluated the serum TNF-alpha levels, as well as several polymorphisms in the promoter region of the TNF-alpha gene, in a group of 60 hyperandrogenic patients and 27 healthy controls matched for body mass index. Hyperandrogenic patients presented with mildly increased serum TNF-alpha levels as compared with controls (mean[median] +/- SD: 7.2[7.0] +/- 3.3 pg/ml vs. 5.6[4.4] +/- 4.0 pg/ml, P < 0.02). Although no differences in body mass index and insulin resistance indexes were observed between patients and controls, when subjects were classified by body weight, serum TNF-alpha was increased only in lean patients as compared with lean controls, but this difference was not statistically significant when comparing obese patients with obese controls. The TNF-alpha gene polymorphisms studied here (-1196C/T, -1125G/C, -1031T/C, -863C/A, -857C/T, -316G/A, -308G/A, -238G/A, and -163G/A) were equally distributed in hyperandrogenic patients and controls. However, carriers of the -308A variant presented with increased basal and leuprolide-stimulated serum androgens and 17-hydroxyprogesterone levels when considering patients and controls as a group. No differences were observed in serum TNF-alpha levels, body mass index, and insulin resistance indexes, depending on the presence or absence of these variants. In conclusion, our present results suggest that the TNF-alpha system might contribute to the pathogenesis of hyperandrogenism, independent of obesity and insulin resistance. However, elucidation of the precise mechanisms underlying the relationship between the TNF-alpha system and androgen excess is needed before considering TNF-alpha as a significant contributing factor to the development of hyperandrogenism.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".