S2890 A Global Perspective on Liver Cancer From Hyperglycemia: National and Regional Burden Based on the 2021 GBD Study
Bibliographic record
Abstract
Introduction: Hyperglycemia has emerged as a significant and potentially modifiable risk factor for liver cancer, contributing to the rising global cancer burden. However, the extent of its impact at global, regional, and national levels remains insufficiently characterized. This study aims to comprehensively quantify the burden of liver cancer attributable to hyperglycemia by analyzing Disability-Adjusted Life Years (DALYs), mortality, and Years of Life Lost (YLLs) using data from the 2021 Global Burden of Disease (GBD) Study. Methods: We analyzed GBD 2021 data on liver cancer attributable to hyperglycemia from 1990 to 2021. Trends in age-standardized mortality, DALYs, and YLLs were assessed using Average Annual Percent Change (AAPC) with 95% confidence intervals (CIs). Results: Globally, hyperglycemia as a risk factor for liver cancer led to a total of 6.05 million DALYs). DALY rates increased from 1.73 per 100,000 in 1990 to 4.05 in 2021 (Average Annual Percentage Change [AAPC]: 2.82). A total of 265,302 deaths were attributed to hyperglycemia-related liver cancer, with mortality rates rising from 0.07 to 0.18 per 100,000 (AAPC: 3.17). Hyperglycemia contributed to 5.98 million YLLs, with YLL rates increasing from 1.71 to 4.01 per 100,000 over the study period (AAPC: 2.81). Regionally, the fastest rise in DALYs, mortality rates, and YLLs occurred in Australasia (AAPC: 6.38, 6.71, and 6.37, respectively), followed by Southern Latin America (AAPC: 5.87, 6.08, and 5.86) and High-income North America (AAPC: 5.73, 5.82, and 5.72). Nationally, the highest burden of DALYs, mortality rates, and YLLs was observed in Canada (7.11, 7.73, and 7.09 per 100,000, respectively), followed by Australia and Chile. In contrast, the greatest decline across all 3 metrics was documented in Mauritius. Conclusion: The global burden of liver cancer attributable to hyperglycemia has risen substantially over the past 3 decades, with the steepest increases observed in Australasia, Southern Latin America, and High-income North America. Nationally, Canada, Australia, and Chile faced the highest disease burden, while Mauritius showed a notable decline. These findings highlight critical geographic disparities and underscore the urgent need for targeted metabolic risk reduction strategies to curb the rising impact of hyperglycemia on liver cancer outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".