Total testosterone, sex hormone‐binding globulin, and free testosterone concentrations and risk of primary liver cancer: A prospective analysis of 200,000 men and 180,000 postmenopausal women
Bibliographic record
Abstract
Abstract In most countries, males have ~2–3 times higher incidence of primary liver cancer than females. Sex hormones have been hypothesized to contribute to these differences, but the evidence remains unclear. Using data from the UK Biobank, which included ~200,000 males and ~180,000 postmenopausal females who provided blood samples at recruitment, we estimated hazard ratios (HR 2 ) and 95% confidence intervals (CI) for a doubling in hormone concentration from multivariable adjusted Cox regression for circulating total testosterone, sex‐hormone binding globulin (SHBG), and free testosterone concentrations and risk of primary liver cancer. After a median of 11.8 years of follow‐up, 531 cases of primary liver cancer were observed, of which 366 occurred in males and 165 occurred in females. Total testosterone and SHBG were shown to be positively associated with liver cancer risk in both males and females (Total testosterone HR 2 : 3.42, 95% CI:2.42–4.84 and 1.29, 0.97–1.72, respectively; SHBG HR 2 : 5.44, 4.42–6.68 and 1.52, 1.09–2.12, respectively). However, free testosterone was inversely associated with primary liver cancer in males (HR 2 : 0.42, 0.32–0.55) and no association was observed in females. When analyses compared two main liver cancer subtypes, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), there was evidence of heterogeneity; associations for total testosterone and SHBG concentrations were only positively associated with HCC in both males (HR 2 : 3.56, 2.65–4.79 and 7.72, 6.12–9.73, respectively) and females (HR 2 : 1.65, 1.20–2.27 and 6.74, 3.93–11.5, respectively) but not with ICC. Further research understanding the mechanisms of how sex‐steroids may influence liver cancer risk is needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".