Sleep and liver function biomarkers in relation to risk of incident liver cancer: a nationwide prospective cohort study
Bibliographic record
Abstract
BACKGROUND: To assess the largely undetermined separate and joint effects of sleep and liver function biomarkers on liver cancer. METHODS: Data of 356,894 participants without cancer at baseline in the UK Biobank were analyzed. Sleep score was evaluated using five sleep traits (sleep duration, chronotype, insomnia, snoring, and excessive daytime sleepiness) and dichotomized into healthy or unhealthy sleep. Circulating liver function biomarkers were measured. Cox proportional hazard model was performed to investigate the independent and joint associations of sleep and liver function biomarkers with liver cancer incidence. RESULTS: After a median follow-up time of 13.1 years, 394 cases of incident liver cancer were documented. The multivariable-adjusted hazard ratio (HR) for liver cancer was 1.46 (95% confidence interval: 1.15-1.85) associated with unhealthy sleep (vs. healthy sleep), and was 1.17 (1.15-1.20), 1.20 (1.18-1.22), 1.69 (1.47-1.93), 1.06 (1.06-1.07), 1.08 (1.07-1.09), 1.81 (1.37-2.39), or 0.29 (0.18-0.46) associated with each 10-unit increase in alanine transaminase (ALT), aspartate transaminase (AST), total bilirubin (TBIL), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), total protein (TP), or albumin (ALB), respectively. Individuals with unhealthy sleep and high (≥ median) ALT, AST, TBIL, GGT, ALP, or TP or low (< median) ALB level had the highest HR of 3.65 (2.43-5.48), 4.03 (2.69-6.03), 1.97 (1.40-2.77), 4.69 (2.98-7.37), 2.51 (1.75-3.59), 2.09 (1.51-2.89), or 2.22 (1.55-3.17) for liver cancer, respectively. Significant additive interaction of unhealthy sleep with high TP level on liver cancer was observed with relative excess risk due to an interaction of 0.80 (0.19-1.41). CONCLUSIONS: Unhealthy sleep was associated with an increased risk of liver cancer, especially in participants with lower ALB levels or higher levels of ALT, AST, TBIL, GGT, ALP, or particularly TP.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".