Predictors of fibrosis, clinical events, and mortality in MASLD: Data from the Global-MASLD study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Advanced histologic fibrosis is a major predictor of mortality in metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to identify advanced fibrosis clinical determinants across diverse MASLD populations and to assess the prognostic value of noninvasive markers (NITs) of fibrosis for adverse outcomes. APPROACH AND RESULTS: The Global MASLD (G-MASLD) enrolled biopsy-confirmed MASLD patients with clinical, histologic, and noninvasive test (NIT) data. Factors associated with the presence of advanced histologic fibrosis (F3-F4) in MASLD and clinical outcomes were assessed. There were 17,792 patients with MASLD. Advanced fibrosis (≥F3) was present in 35%. The prevalence of type 2 diabetes (T2D) increased stepwise with fibrosis stage, from 28% in F0 to 70% in F4 (trend p <0.0001). Independent predictors of advanced fibrosis included older age, T2D, and obesity, although the association with obesity varied by region. Among patients with follow-up (mean 6.6 y), 6.5% died and 10.1% experienced a clinical event. Older age, male sex, T2D, and obesity were independent predictors of both mortality and clinical events ( p <0.05). Fibrosis severity, whether defined histologically or by NITs, was strongly associated with higher risks of death and liver-related outcomes (all adjusted HR>1.0, p <0.001). Five-year mortality was 2.1% overall, rising to 8.3% in patients with cirrhosis, and exceeded 10% among those with high-risk NIT score values. CONCLUSIONS: In this large global biopsy-based MASLD cohort, advanced fibrosis was highly prevalent and strongly linked to T2D. Both histologic fibrosis and NITs were independent predictors of mortality and clinical outcomes, underscoring the prognostic value of fibrosis assessment with NITs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".