S2587 Mapping the Burden of Hyperglycemia-Associated NAFLD: A Global Burden of Disease (GBD) 2021-Based Analysis
Bibliographic record
Abstract
Introduction: Hyperglycemia is a well-established metabolic risk factor for non-alcoholic fatty liver disease (NAFLD), yet its population-level burden remains underexplored across global, regional, and national levels. This study aims to quantify trends in disability-adjusted life years (DALYs), mortality, and years of life lost (YLLs) due to NAFLD attributable to hyperglycemia from 1990 to 2021 using data from the Global Burden of Disease (GBD) Study. Methods: We extracted NAFLD-related data attributable to hyperglycemia from the 2021 GBD database. Age-standardized DALYs, mortality rates, and YLLs were analyzed globally, regionally, and nationally. Annual Percentage Change (APC) and Average APC (AAPC) with 95% confidence intervals (CI) were calculated to assess trends. Results: Globally, hyperglycemia as a risk factor for NAFLD contributed to 3.83 million DALYs. The DALY rate increased from 1.01 in 1990 to 2.71 in 2021 (AAPC: 3.26). A total of 176,335 deaths were attributed to hyperglycemia-related NAFLD, with mortality rates rising from 0.04 to 0.12 per 100,000 population (AAPC: 3.55). Additionally, hyperglycemia led to 3.79 million YLLs, increasing from a rate of 1.00 in 1990 to 2.67 in 2021 (AAPC: 3.25). Nationally, the highest DALY rates were observed in Canada (7.59), followed by Australia (7.14) and Chile (7.07), while the lowest burden was reported in Mauritius (–0.42). These same countries also had the highest mortality and YLL rates: Canada (mortality 8.19; YLL 7.96), Australia (mortality 7.81; YLL 7.61), and Chile (mortality 7.66; YLL 7.45). Mauritius consistently recorded the lowest rates (mortality –0.43; YLL –0.42). Regionally, the fastest increases in DALY rates, mortality, and YLLs were observed in Australasia (AAPC: 6.69, 6.98, and 6.69, respectively), followed by Southern Latin America and High-income North America. Conclusion: The global burden of NAFLD attributable to hyperglycemia has more than doubled over the past 3 decades, with the steepest rises observed in high-income regions such as Australasia, Southern Latin America, and North America. Notably, Canada, Australia, and Chile faced the highest national burdens. These findings highlight the urgent need for integrated strategies targeting hyperglycemia to curb the growing liver disease burden, particularly in high-risk regions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".