Global burden of metabolic dysfunction-associated steatotic liver disease, 2010 to 2021
Bibliographic record
Abstract
Background & Aims: This study used the Global Burden of Disease data (2010-2021) to analyze the rates and trends of point prevalence, annual incidence, and years lived with disability (YLDs) for metabolic dysfunction-associated steatotic liver disease (MASLD) in 204 countries. Methods: Total numbers and age-standardized rates per 100,000 population for MASLD prevalence, annual incidence, and YLDs were compared across regions and countries by age, sex, and sociodemographic index (SDI). Smoothing spline models were used to evaluate the relationship between the burden of MASLD and SDI. Estimates were reported with uncertainty intervals (UI). Results: 14,310.6 cases per 100,000 population). Prevalence peaked at ages 45-49 for men and 50-54 for women. Kuwait (32,312.2 cases per 100,000 people; 95% UI: 29,947.1-34,839.0), Egypt (31,668.8 cases per 100,000 people; 95% UI: 29,272.5-34,224.7), and Qatar (31,327.5 cases per 100,000 people; 95% UI: 29,078.5-33,790.9) had the highest prevalence rates in 2021. The largest increases in age-standardized point prevalence estimates from 2010 to 2021 were in China (16.9%, 95% UI 14.7%-18.9%), Sudan (13.3%, 95% UI 9.8%-16.7%) and India (13.2%, 95% UI 12.0%-14.4%). MASLD incidence varied with SDI, peaking at moderate SDI levels. Conclusions: MASLD is a global health concern, with the highest prevalence reported in Kuwait, Egypt, and Qatar. Raising awareness about risk factors and prevention is essential in every country, especially in China, Sudan and India, where disease incidence and prevalence are rapidly increasing. Impact and implications: This research provides a comprehensive analysis of the global burden of MASLD, highlighting its rising prevalence and incidence, particularly in countries with varying sociodemographic indices. The findings are significant for both clinicians and policymakers, as they offer critical insights into the regional disparities in MASLD burden, which can inform targeted prevention and intervention strategies. However, the study's reliance on modeling and available data suggests cautious interpretation, and further research is needed to validate these findings in clinical and real-world settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".