Increased Ratio of Total Testosterone to Dihydrotestosterone May Predict an Adverse Metabolic Outcome in Polycystic Ovary Syndrome
Bibliographic record
Abstract
Background: Androgen excess may correlate with metabolic risk in polycystic ovary syndrome (PCOS). The aim of the study was to determine the role of total testosterone to dihydrotestosterone (TT/DHT) ratio in assessing the adverse metabolic outcome in PCOS. Methods: This cross-sectional study encompassed 40 PCOS women recruited on the basis of revised Rotterdam criteria 2003, and 40 age-matched control subjects. TT, sex hormone binding globulin (SHBG) and insulin levels were measured by chemiluminescent microparticle immunoassay (CMIA) while DHT by enzyme-linked immunosorbent assay (ELISA). In addition, TT/DHT ratio, free androgen index (FAI), and insulin resistance (IR) by homeostatic model of assessment of insulin resistance (HOMA-IR) were calculated. Results: TT/DHT ratio was significantly higher in PCOS group than control group (P < 0.001). No significant difference was found for DHT (P = 0.261). PCOS patients had significantly higher TT (0.69 ± 0.26 vs. 0.30 ± 0.13 ng/mL; P < 0.001), FAI (P < 0.001) and low SHBG (P = 0.004) compared to controls. TT/DHT ratio was significantly higher in PCOS with impaired glucose tolerance (IGT) (P = 0.037) and metabolic syndrome (MetS) (P = 0.041). The best cutoff value for TT/DHT ratio to diagnose PCOS was observed to be 2.38 (sensitivity: 70%, specificity: 32.5% and area under the curve (AUC): 0.753). TT/DHT ratio also showed positive correlation with weight (r = 0.323, P = 0.042), waist circumference (WC) (r = 0.372, P = 0.018), HOMA-IR (r = 0.385, P = 0.014), 2-h post 75-g glucose (2h-PG), (r = 0.413, P = 0.008) and triglyceride (TG) level (r = 0.402, P = 0.010) in PCOS. Conclusions: There is a close relation between the TT/DHT ratio and adverse metabolic outcome in PCOS. Therefore, TT/DHT ratio may be considered as a predictor of adverse metabolic findings in PCOS. J Endocrinol Metab. 2019;9(6):186-192 doi: https://doi.org/10.14740/jem601
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".