Defining the serum proteomic signature of hepatic steatosis, inflammation, ballooning and fibrosis in non-alcoholic fatty liver disease

Abstract

Background & aims: Despite recent progress, non-invasive tests for the diagnostic assessment and monitoring of non-alcoholic fatty liver disease (NAFLD) remain an unmet need. Herein, we aimed to identify diagnostic signatures of the key histological features of NAFLD.

Methods: Using modified-aptamer proteomics, we assayed 5,220 proteins in each of 2,852 single serum samples from 636 individuals with histologically confirmed NAFLD. We developed and validated dichotomized protein-phenotype models to identify clinically relevant severities of steatosis (grade 0 vs. 1-3), hepatocellular ballooning (0 vs. 1 or 2), lobular inflammation (0-1 vs. 2-3) and fibrosis (stages 0-1 vs. 2-4).

Results: The AUCs of the four protein models, based on 37 analytes (18 not previously linked to NAFLD), for the diagnosis of their respective components (at a clinically relevant severity) in training/paired validation sets were: fibrosis (AUC 0.92/0.85); steatosis (AUC 0.95/0.79), inflammation (AUC 0.83/0.72), and ballooning (AUC 0.87/0.83). An additional outcome, at-risk NASH, defined as steatohepatitis with NAFLD activity score ≥4 (with a score of at least 1 for each of its components) and fibrosis stage ≥2, was predicted by multiplying the outputs of each individual component model (AUC 0.93/0.85). We further evaluated their ability to detect change in histology following treatment with placebo, pioglitazone, vitamin E or obeticholic acid. Component model scores significantly improved in the active therapies vs. placebo, and differential effects of vitamin E, pioglitazone, and obeticholic acid were identified.

Conclusions: Serum protein scanning identified signatures corresponding to the key components of liver biopsy in NAFLD. The models developed were sufficiently sensitive to characterize the longitudinal change for three different drug interventions. These data support continued validation of these proteomic models to enable a "liquid biopsy"-based assessment of NAFLD.

Clinical trial number: Not applicable.

Impact and implications: An aptamer-based protein scan of serum proteins was performed to identify diagnostic signatures of the key histological features of non-alcoholic fatty liver disease (NAFLD), for which no approved non-invasive diagnostic tools are currently available. We also identified specific protein signatures related to the presence and severity of NAFLD and its histological components that were also sensitive to change over time. These are fundamental initial steps in establishing a serum proteome-based diagnostic signature of NASH and provide the rationale for using these signatures to test treatment response and to identify several novel targets for evaluation in the pathogenesis of NAFLD.

Keywords: NAFLD activity score (NAS); Nonalcoholic fatty liver disease (NAFLD); Nonalcoholic steatohepatitis (NASH); aptamers; cirrhosis; fibrosis; fibrosis stage; hepatocellular ballooning; lobular inflammation; proteomics; steatohepatitis; steatosis.

Fig. 1.

Cohort derivation, validation and longitudinal assessment.

Fig. 2.. Model predictions vs. observed biopsy results in the training, hold-out validation, and paired validation data sets.

Models were trained on dichotomized variables (left and right of vertical yellow lines). Probability outputs of the models (probability of any given sample being in the positive class) are displayed by the original biopsy grade. The decision threshold for all models was greater than or equal to 0.5 (horizontal gray lines). Boxes show medians, 25^th and 75^th centiles. By random chance there were no zero inflammation scores in the paired validation set. Training: left panels; Hold-out validation: center panels; Paired validation: Right panels.

Fig. 3.. Model predictions for at-risk NASH vs. biopsy-based composite outcome of NAS ≥4 and fibrosis ≥2 in training, hold-out validation, and paired validation data sets.

At-risk NASH predictions are calculated by multiplying the predicted probability of the models. The decision threshold was set at 0.0625 (0.5⁴, the equivalent of multiplying the decision thresholds for each model). Yellow vertical lines indicate the binary class threshold and gray horizontal lines indicate the model decision threshold. Boxes show medians, 25^th and 75^th centiles. Prevalence of at-risk NASH was 31%, 38% and 39% for training/holdout/validation data sets respectively. Training: left panels; Hold-out validation: center panels; Paired validation: Right panels.

Fig. 4.. Predictions of protein models in longitudinal serum samples.

Results are for each component for the mixed effects models using continuous predicted probability (logit-transformed) for each study. Higher scores reflect greater probability of being in the positive class. The black dashed line corresponds to the decision cut-off at 0.5. The placebo groups are shown in gray. The active groups are shown in blue and teal. The 95% CIs of the mean predicted probabilities across all samples is shown for each group at each single time point. Confidence intervals were calculated using the standard error estimated from the mixed effects models with week and treatment group fixed effects and a random subject effect.