Integrated liver-secreted and plasma proteomics identify a predictive model that stratifies MASH
Bibliographic record
Abstract
Obesity is a major risk factor for metabolic-associated steatotic liver disease (MASLD), which can progress to metabolic-associated steatohepatitis (MASH). There are no validated non-invasive tests to stratify persons with obesity with a greater risk for MASH. Herein, we assess plasma and liver from 266 obese individuals spanning the MASLD spectrum. Ninety-six human livers were precision-cut, and mass spectrometry-based proteomics identifies 3,333 proteins in the liver-secretion medium, of which 107 are differentially secreted in MASH compared with no pathology. The plasma proteome is markedly remodeled in MASH but is not different between patients with steatosis and no pathology. The APASHA model, comprising plasma apolipoprotein F (APOF), proprotein convertase subtilisin/kexin type 9 (PCSK9), afamin (AFM), S100 calcium-binding protein A6 (S100A6), HbA1c, and zinc-alpha-2-glycoprotein (AZGP1), stratifies MASH (area under receiver operating characteristic [AUROC] = 0.88). Our investigations detail the evolution of liver-secreted and plasma proteins with MASLD progression, providing a rich resource defining human liver-secreted proteins and creating a predictive model to stratify patients with obesity at risk of MASH. • Catalog of liver-secreted and plasma proteins with MASLD progression in humans • Mass spectrometry analysis identifies remodeling of plasma proteins in human MASH • Protein secretion from the liver is altered in human MASH • The APASHA model effectively stratifies patients with obesity at risk of MASH De Nardo et al. profile the plasma and liver-secreted proteome from obese individuals spanning the spectrum of MASLD. They identify biomarkers for MASH in persons with obesity and develop and validate a biologically plausible non-invasive risk prediction model to predict or exclude MASH.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".