Abdominal and gluteofemoral size and risk of liver cancer: The liver cancer pooling project
Bibliographic record
Abstract
Obesity is known to be associated with primary liver cancer (PLC), but the separate effects of excess abdominal and gluteofemoral size are unclear. Thus, we examined the association between waist and hip circumference with risk of PLC overall and by histologic type—hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The Liver Cancer Pooling Project is a consortium of prospective cohort studies that include data from 1,167,244 individuals (PLC n = 2,208, HCC n = 1,154, ICC n = 335). Multivariable‐adjusted hazard ratios (HRs) and 95% confidence intervals (CI) were estimated using proportional hazards regression. Waist circumference, per 5 cm increase, was associated with an 11% increased PLC risk (HR = 1.11, 95%CI: 1.09–1.14), including when adjusted for hip circumference (HR = 1.12, 95%CI: 1.08–1.17) and also when restricted to individuals in a normal body mass index (BMI) range (18.5 to <25 kg/m 2 ; HR = 1.14, 95%CI: 1.07–1.21). Hip circumference, per 5 cm increase, was associated with a 9% increased PLC risk (HR = 1.09, 95%CI: 1.06–1.12), but no association remained after adjustment for waist circumference (HR = 0.99, 95%CI: 0.94–1.03). HCC and ICC results were similar. These findings suggest that excess abdominal size is associated with an increased risk of liver cancer, even among individuals considered to have a normal BMI. However, excess gluteofemoral size alone confers no increased risk. Our findings extend prior analyses, which found an association between excess adiposity and risk of liver cancer, by disentangling the separate effects of excess abdominal and gluteofemoral size through utilization of both waist and hip circumference measurements.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".