A multicohort proteomics-based machine learning score (MASHPredict) for noninvasive diagnosis and long-term prognostication of metabolic dysfunction-associated steatohepatitis

Metabolic dysfunction-associated steatohepatitis (MASH) can progress to cirrhosis and hepatocellular carcinoma, yet the invasiveness of liver biopsy limits its clinical utility. Accurate noninvasive diagnostic tools are essential for improved patient risk stratification.

A machine learning study was conducted using plasma proteomics from 142 participants (83 MASH cases, 59 controls) recruited from a Chinese hospital, randomly divided into training and validation cohorts (7:3 ratio). Key proteins were identified via feature selection using Least Absolute Shrinkage and Selection Operator regression and the Boruta algorithm. Ten machine learning models were developed to create the MASHPredict score, with external validation in two test cohorts. Prognostic evaluation focused on liver-related events, atherosclerotic cardiovascular disease (ASCVD)-related mortality, and all-cause mortality.

MASHPredict demonstrated robust diagnostic performance, with Area Under the Curve (AUC) values of 0.83 (95% CI: 0.74-0.92) in the training cohort, 0.80 (0.66-0.94) in the validation cohort, 0.79 (0.67-0.91) in test cohort 1, and 0.79 (0.76-0.83) in test cohort 2. It outperformed established noninvasive tests, including the Fibrosis-4 Index, AST to Platelet Ratio Index, AST/ALT Ratio, and NAFLD Fibrosis Score. High-risk individuals exhibited significantly increased hazards for liver-related mortality (HR = 8.18), MASH (HR = 12.07), cirrhosis (HR = 3.37), liver cancer (HR = 3.11), ASCVD-related mortality (HR = 1.64), and all-cause mortality (HR = 1.42) (all p < 0.001).

The MASHPredict score is a validated, noninvasive diagnostic tool with broad applicability. Its superior performance in diagnosing MASH and prognosticating liver-related outcomes supports its potential role in early detection, risk stratification, and clinical decision-making.