Inherited Genetic Risk of Liver Fibrosis in Lean Versus Nonlean Metabolic Dysfunction–Associated Steatotic Liver Disease ( <scp>MASLD</scp> )
Bibliographic record
Abstract
INTRODUCTION: Previous studies have revealed conflicting results regarding liver fibrosis risk in lean metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to compare the risk of significant fibrosis in lean versus nonlean MASLD and identify fibrosis-associated factors in lean MASLD. METHODS: The study was a cross-sectional analysis of prospectively enrolled adults with MASLD. Individuals with lean MASLD were age- and sex-matched with nonlean MASLD. Fibrosis assessment included vibration-controlled transient elastography, magnetic resonance elastography and liver biopsy. A genetic risk score (GRS), summating the effect alleles of PNPLA3 and TM6SF2 minus the protective HSD17B13 genotype, was estimated to consider inherited genetic risk across BMI categories. Results were validated in an external Latin American cohort. RESULTS: 11.6 years and 69.2% were female. 44 (14.1%) individuals were lean, 90 (28.9%) were overweight, 90 (28.9%) had class I obesity and 88 (28.1%) had class II or greater obesity. The prevalence of significant fibrosis was 27.3% in lean and 31.1% in nonlean (p = 0.653). Individuals with a high GRS had a higher prevalence of significant fibrosis compared to patients with low GRS (36.5% vs. 25.2%, p = 0.043) and the prevalence of significant fibrosis was similar in lean and nonlean patients with high GRS (31.3% vs. 37.1%, p = 0.645). The Latin American cohort exhibited similar results. CONCLUSIONS: The prevalence of significant fibrosis and the effect of GRS were similar in lean and nonlean MASLD, highlighting that lean MASLD patients may have a comparable risk to overweight and obese MASLD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".