Overweight Impacts Histological Disease Activity of De Novo Metabolic Dysfunction‐Associated Steatotic Liver Disease After Liver Transplantation
Bibliographic record
Abstract
BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading indication for liver transplantation (LT), but also occurs after LT. The prevalence of de novo MASLD (dnMASLD) after LT, based on both surveillance (svLbx) and indication biopsies (indLbx), is unknown. Furthermore, the impact of the distinct cardiometabolic risk factors on histological disease activity has not been assessed. We aimed to evaluate the prevalence of dnMASLD and the association between the cardiometabolic risk factors and histological disease activity. METHODS: We performed a retrospective single-center study in a LT cohort with indLbx and svLbx. Patients with NAFLD before LT were excluded. RESULTS: We analyzed 249 patients who underwent either svLbx or indLbx. Forty-eight (19.2%) had either dnMASLD (n = 26/249, 10.4%) or metabolic dysfunction associated steatohepatitis (dnMASH) (n = 22/249, 8.8%). Although dnMASLD/dnMASH was more frequent in indLbx (35.1%, p < 0.01), still 16.5% of patients with svLbx had dnMASLD/dnMASH. While overweight (p < 0.01) and diabetes (p = 0.01) were more frequent in patients with dnMASH, only overweight was associated with histological disease activity in the multivariate analysis. No impact of dnMASLD on the overall survival was observed. CONCLUSION: While dnMASLD is more frequent in patients with indLBX, it also occurs in 16.5% of patients without signs of graft dysfunction. Overweight has the strongest impact on histological disease activity and should be monitored carefully after LT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".