Non-invasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis: using Machine Learning Methods (Preprint)

Novel insights into autophagy in gastrointestinal pathologies, mechanisms in metabolic dysfunction-associated fatty liver disease and acute liver failure

In this editorial, we comment on three articles published in a recent issue of World Journal of Gastroenterology. There is a pressing need for new research on autophagy's role in gastrointestinal (GI) disorders, and also novel insights into some liver conditions, such as metabolic dysfunction-associated fatty liver disease (MAFLD) and acute liver failure (ALF). Despite advancements, understanding autophagy's intricate mechanisms and implications in these diseases remains incomplete. Moreover, MAFLD's pathogenesis, encompassing hepatic steatosis and metabolic dysregulation, require further elucidation. Similarly, the mechanisms underlying ALF, a severe hepatic dysfunction, are poorly understood. Innovative studies exploring the interplay between autophagy and GI disorders, as well as defined mechanisms of MAFLD and ALF, are crucial for identifying therapeutic targets and enhancing diagnostic and treatment strategies to mitigate the global burden of these diseases.

NAIF: A novel artificial intelligence-based tool for accurate diagnosis of stage F3/F4 liver fibrosis in the general adult population, validated with three external datasets

OBJECTIVE

The purpose of this study was to determine the effectiveness of a new AI-based tool called NAIF (NAFLD-AI-Fibrosis) in identifying individuals from the general population with advanced liver fibrosis (stage F3/F4). We compared NAIF's performance to two existing risk score calculators, aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (Fib4).

METHODS

To set up the algorithm for diagnosing severe liver fibrosis (defined as Fibroscan® values E ≥ 9.7 KPa), we used 19 blood biochemistry parameters and two demographic parameters in a group of 5,962 individuals from the NHANES population (2017-2020 pre-pandemic, public database). We then assessed the algorithm's performance by comparing its accuracy, precision, sensitivity, specificity, and F1 score values to those of APRI and Fib4 scoring systems.

RESULTS

In a kept-out sub dataset of the NHANES population, NAIF achieved a predictive precision of 72 %, a sensitivity of 61 %, and a specificity of 77 % in correctly identifying adults (aged 18-79 years) with severe liver fibrosis. Additionally, NAIF performed well when tested with two external datasets of Italian patients with a Fibroscan® score E ≥ 9.7 kPa, and with an external dataset of patients with diagnosis of severe liver fibrosis through biopsy.

CONCLUSIONS

The results of our study suggest that NAIF, using routinely available parameters, outperforms in sensitivity existing scoring methods (Fib4 and APRI) in diagnosing severe liver fibrosis, even when tested with external validation datasets. NAIF uses routinely available parameters, making it a promising tool for identifying individuals with advanced liver fibrosis from the general population. Word count abstract: 236.

An explainable machine learning model for prediction of high-risk nonalcoholic steatohepatitis

Early identification of high-risk metabolic dysfunction-associated steatohepatitis (MASH) can offer patients access to novel therapeutic options and potentially decrease the risk of progression to cirrhosis. This study aimed to develop an explainable machine learning model for high-risk MASH prediction and compare its performance with well-established biomarkers. Data were derived from the National Health and Nutrition Examination Surveys (NHANES) 2017-March 2020, which included a total of 5281 adults with valid elastography measurements. We used a FAST score ≥ 0.35, calculated using liver stiffness measurement and controlled attenuation parameter values and aspartate aminotransferase levels, to identify individuals with high-risk MASH. We developed an ensemble-based machine learning XGBoost model to detect high-risk MASH and explored the model's interpretability using an explainable artificial intelligence SHAP method. The prevalence of high-risk MASH was 6.9%. Our XGBoost model achieved a high level of sensitivity (0.82), specificity (0.91), accuracy (0.90), and AUC (0.95) for identifying high-risk MASH. Our model demonstrated a superior ability to predict high-risk MASH vs. FIB-4, APRI, BARD, and MASLD fibrosis scores (AUC of 0.95 vs. 0.50, 0.50, 0.49 and 0.50, respectively). To explain the high performance of our model, we found that the top 5 predictors of high-risk MASH were ALT, GGT, platelet count, waist circumference, and age. We used an explainable ML approach to develop a clinically applicable model that outperforms commonly used clinical risk indices and could increase the identification of high-risk MASH patients in resource-limited settings.

Cumulative effects of excess high-normal alanine aminotransferase levels in relation to new-onset metabolic dysfunction-associated fatty liver disease in China

BACKGROUND

Within the normal range, elevated alanine aminotransferase (ALT) levels are associated with an increased risk of metabolic dysfunction-associated fatty liver disease (MAFLD).

AIM

To investigate the associations between repeated high-normal ALT measurements and the risk of new-onset MAFLD prospectively.

METHODS

A cohort of 3553 participants followed for four consecutive health examinations over 4 years was selected. The incidence rate, cumulative times, and equally and unequally weighted cumulative effects of excess high-normal ALT levels (ehALT) were measured. Cox proportional hazards regression was used to analyse the association between the cumulative effects of ehALT and the risk of new-onset MAFLD.

RESULTS

A total of 83.13% of participants with MAFLD had normal ALT levels. The incidence rate of MAFLD showed a linear increasing trend in the cumulative ehALT group. Compared with those in the low-normal ALT group, the multivariate adjusted hazard ratios of the equally and unequally weighted cumulative effects of ehALT were 1.651 [95% confidence interval (CI): 1.199-2.273] and 1.535 (95%CI: 1.119-2.106) in the third quartile and 1.616 (95%CI: 1.162-2.246) and 1.580 (95%CI: 1.155-2.162) in the fourth quartile, respectively.

CONCLUSION

Most participants with MAFLD had normal ALT levels. Long-term high-normal ALT levels were associated with a cumulative increased risk of new-onset MAFLD.

HOXC8/TGF-β1 positive feedback loop promotes liver fibrosis and hepatic stellate cell activation via activating Smad2/Smad3 signaling

Liver fibrosis occurs in any chronic liver disease, where extraordinary increase of extracellular matrix components is caused by the hepatic stellate cell (HSC) activation. HOXC8 has been disclosed to participate inregulating cell proliferation and fibrosis in tumors. However, the role of HOXC8 in liver fibrosis and the underlying molecular mechanisms has not yet been investigated. In this study, we founded that HOXC8 mRNA and protein was elevated in a carbon tetrachloride (CCl₄)-induced liver fibrosis mouse model and transforming growth factor-β (TGF-β)-treated human (LX-2) HSC cells. Importantly, we observed that downregulating HOXC8 alleviates liver fibrosis and suppressed the fibrogenic gene induction induced by CCl₄ in vivo. In addition, inhibition of HOXC8 suppressed the HSC activation and the expression of fibrosis-associated genes (α-SMA and COL1a1) induced by TGF-β1 in LX-2 cells in vitro, while HOXC8 overexpression had the opposite effects. Mechanistically, we demonstrated HOXC8 activates TGFβ1 transcription and enhanced the phosphorylated Smad2/Smad3 levels, suggesting a positive feedback loop between HOXC8 and TGF-β1 that facilitates TGF-β signaling and subsequent HSCs activation. Collectively, our data strongly indicated that a HOXC8/TGF-β1 positive feedback loop plays as a critical role in controlling the HSC activation and in the liver fibrosis process, suggesting that inhibition of HOXC8 may serve as a promoting therapeutic strategy for diseases characterized by liver fibrosis.

Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort

BACKGROUND

The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population.

MATERIALS AND METHODS

Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population).

RESULTS

For the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs.

CONCLUSIONS

We provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in the future, it might be implemented and potentially applied to program broad screenings for liver fibrosis.