Global, Regional, and National Burden of Primary Liver Cancer Attributable to Metabolic Risks: An Analysis of the Global Burden of Disease Study 1990–2021
Bibliographic record
Abstract
INTRODUCTION: The global burden of metabolic diseases is increasing, but estimates of their impact on primary liver cancer are uncertain. We aimed to assess the global burden of primary liver cancer attributable to metabolic risk factors, including high body mass index (BMI) and high fasting plasma glucose (FPG) levels, between 1990 and 2021. METHODS: The total number and age-standardized rates of deaths and disability-adjusted life years (DALYs) from primary liver cancer attributable to each metabolic risk factor were extracted from the Global Burden of Disease Study 1990-2021. The metabolic burden trends of liver cancer across regions and countries by sociodemographic index (SDI) and sex were estimated. The annual percentage changes in age-standardized DALYs rate were also calculated. RESULTS: Globally, in 2021, primary liver cancer attributable to high BMI and/or high FPG was estimated to have caused 59,970 deaths (95% uncertainty interval [UI] 20,567-104,103) and 1,540,437 DALYs (95% UI 540,922-2,677,135). The age-standardized rates of death and DALYs were 0.70 (95% UI 0.24-1.21) and 17.64 (95% UI 6.19-30.65) per 100,000 person-years. A consistent global rise in liver cancer attributable to metabolic risks was observed from 1990 to 2021, with high BMI identified as the major contributing risk factor. The highest burden of deaths and DALYs of liver cancer consistently occurred in high SDI countries, while the fastest growth trends were observed in low-middle SDI countries. The burdens of high levels of BMI and FPG were higher in men than in women. DISCUSSION: Primary liver cancer attributable to high BMI and/or high FPG imposes an increasingly substantial clinical burden on global public health, particularly in high SDI countries. Rapid growth trends are also found in middle SDI countries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".