Lean non-alcoholic fatty liver disease and the risk of all-cause mortality: An updated meta-analysis
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Cohort studies reported controversial results regarding the long-term prognosis of patients with lean non-alcoholic fatty liver disease (NAFLD) compared to non-lean NAFLD patients. This updated meta-analysis aimed to estimate the magnitude of the association between lean body mass index and all-cause mortality risk in NAFLD patients. MATERIALS AND METHODS: We systematically searched the EMBASE and MEDLINE databases from inception to March 2023 to identify observational studies that reported hazard ratio (HR) for all-cause mortality of patients with lean NAFLD versus those with non-lean, overweight, or obese NAFLD. Multivariable-adjusted hazard ratios (HRs) for all-cause mortality were pooled using a random effects model. RESULTS: =77 %). The magnitude of this risk remained unchanged even after stratified analysis by measures of NAFLD diagnosis, study country, cohort setting, length of follow-up, adjustment with fibrosis stage/cirrhosis, and the Newcastle-Ottawa Scale. The risk was independent of age, sex, and cardiometabolic risk factors. Sensitivity analyses did not alter these findings. The funnel plot and Egger's test revealed no significant publication bias. CONCLUSIONS: This meta-analysis revealed that lean NAFLD is associated with an approximately 1.6-fold increased mortality risk. Further studies are needed to unravel the existing but complex link between lean NAFLD and an increased risk of death.
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 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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".