Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective

Machine Learning-Based Non-Invasive Prediction of Metabolic Dysfunction-Associated Steatohepatitis in Obese Patients: A Retrospective Study

Objectives: We aimed to develop and validate machine learning (ML) models that integrate clinical and laboratory data for the non-invasive prediction of metabolic dysfunction-associated steatohepatitis (MASH) in an obese population. Methods: In this retrospective study, clinical and laboratory data were collected from obese patients undergoing bariatric surgery. The cohort was divided using stratified random sampling, and optimal features were selected with SHapley Additive exPlanations (SHAP). Various ML models, including K-nearest neighbors, linear support vector machine, radial basis function support vector machine, Gaussian process, random forest, multilayer perceptron, adaptive boosting, and naïve Bayes, were developed through cross-validation and hyperparameter tuning. Diagnostic performance was assessed via the area under the curve (AUC) in both training and validation sets. Results: A total of 558 patients were analyzed, with 390 in the training set and 168 in the validation set. In the training cohort, the median age was 35 years, the median body mass index (BMI) was 39.8 kg/m2, 39.0% were male, 37.9% had diabetes mellitus, and 62.8% were diagnosed with MASH. The validation cohort had a median age of 34.1 years, a median BMI of 42.5 kg/m2, 41.7% male, 32.7% with diabetes, and 39.9% with MASH. Among the models, the random forest achieved the highest performance among the models with AUC values of 0.94 in the training set and 0.88 in the validation set. The Gaussian process model attained an AUC of 0.97 in the training cohort but 0.79 in the validation cohort, while the other models achieved AUC values ranging from 0.63 to 0.88 in the training cohort and 0.62 to 0.75 in the validation set. Conclusions: ML models, particularly the random forest, effectively predict MASH using readily available data, offering a promising non-invasive alternative to conventional serological scoring. Prospective studies and external validations are needed to further establish clinical utility.

Unveiling NLR pathway signatures: EP300 and CPN60 markers integrated with clinical data and machine learning for precision NASH diagnosis

BACKGROUND

Given the increasing prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) and non-alcoholic steatohepatitis (NASH), there is a critical need for accurate non-invasive early diagnostic markers.

OBJECTIVE

This study aimed to validate NLRP3-related RNA signatures (EP300, CPN60, and ITGB1 mRNAs, miR-6881-5p, and LncRNA-RABGAP1L-DT-206) using an integrated molecular approach and advanced machine-learning algorithms to identify robust biomarkers for early diagnosis of NASH.

METHODS

A cohort of 237 participants (117 Healthy controls, 60 MAFLD, 120 NASH) was utilized. Twenty-five demographic, clinical, and molecular features were collected from each participant. Various machine learning models were trained on the dataset.

RESULTS

The Random Forest algorithm emerged as the most effective classifier. The model identified nine key features: EP300 mRNA, CPN60 mRNA, AST, D. bilirubin, Albumin, GGT, HbA1c, HOMA-IR, and BMI, achieving an impressive 97 % accuracy in distinguishing NASH from non-NASH cases.

CONCLUSION

The integration of molecular, clinical, and demographic data with machine learning algorithms provides a highly accurate method for the early diagnosis of NASH. This model holds promise for early detection in individuals at risk of progressing to cirrhosis or liver cancer and may aid in identifying new therapeutic targets for managing NASH.

Estimation of non-alcoholic steatohepatitis (NASH) disease using clinical information based on the optimal combination of intelligent algorithms for feature selection and classification

The early diagnosis of NASH disease can decrease the risk of proceeding elements and treatment costs for patients. This study aims to present an optimal combination of intelligent algorithms using advanced machine learning methods, including different feature selections and classifications based on clinical data and blood factors. In this work, collected data were from 176 patients to investigate NASH disease, and 19 features were extracted. We then sought to find the best combination of features based on different feature selection algorithms such as Feature Forward Selection (FFS), Minimum Redundancy Maximum Relevance (MRMR), and Mutual Information (MI). Finally, we used nine classifier frameworks with different mathematical mechanisms, including random forest (RF), logistic regression (LR), Linear Discriminant Analysis (LDA), AdaBoost, K nearest neighbors (KNN), multilayer perceptron model (MLP), support vector machine (SVM), and decision tree (DT) to estimate NASH disease. Our investigation revealed that the combination of dominant features, namely body mass index (BMI), glutamic pyruvic transaminase (GPT), total cholesterol (TC), high-density lipoprotein (HDL), Ezetimibe, lipoprotein level Lp(a), Loge(Lp(a)), total triglyceride (TG), Creatinine (Cre), HbA1c, Fibrate, and Sex, selected by the MRMR algorithm and classified by the RF method can provide the most appropriate performance based on less computation effort and maximum performance with accuracy, AUC, precision, and recall indices, which are 81.51 ± 9.35 , 82.53 ± 11.24 , 85.28 ± 9.68 , and 89.49 ± 7.92 , respectively. This study investigated the configuration of feature selection and classifier that is most suitable for classifying NASH disease based on clinical data and blood factors. The proposed intelligent algorithm based on MRMR and RF classifier can automatically diagnose NASH disease with appropriate performance and present an initial report without any further invasive methods. It also clarifies the diagnostic process and, as a result, the continuation of their prevention and treatment cycle.

Liver Diseases: Science, Fiction and the Foreseeable Future

This Editorial precedes the Special Issue entitled "Novel Challenges and Therapeutic Options for Liver Diseases". Following a historical outline of the roots of hepatology, we provide a brief insight into our colleagues' contributions in this issue on the current developments in this discipline related to the prevention of liver diseases, the metabolic dysfunction-associated steatotic liver disease (or non-alcoholic fatty liver disease, respectively), liver cirrhosis, chronic viral hepatitides, acute-on-chronic liver failure, liver transplantation, the liver-microbiome axis and microbiome transplantation, and telemedicine. We further add some topics not covered by the contributions herein that will likely impact future hepatology. Clinically, these comprise the predictive potential of organokine crosstalk and treatment options for liver fibrosis. With regard to promising developments in basic research, some current findings on the genetic basis of metabolism-associated chronic liver diseases, chronobiology, metabolic zonation of the liver, aspects of the aging liver against the background of demography, and liver regeneration will be presented. We expect machine learning to thrive as an overarching topic throughout hepatology. The largest study to date on the early detection of liver damage-which has been kicked off on 1 March 2024-is highlighted, too.

Nonalcoholic steatohepatitis: A comprehensive updated review of risk factors, symptoms, and treatment

Non-alcoholic steatohepatitis (NASH) is a subtype of nonalcoholic fatty liver disease and a progressive and chronic liver disorder with a significant risk for the development of liver-related morbidity and mortality. The complex and multifaceted pathophysiology of NASH makes its management challenging. Early identification of symptoms and management of patients through lifestyle modification is essential to prevent the development of advanced liver disease. Despite the increasing prevalence of NASH, there is no FDA-approved treatment for this disease. Currently, medications targeting metabolic disease risk factors and some antifibrotic medications are used for NASH patients but are not sufficiently effective. The beneficial effects of different drugs and phytochemicals represent new avenues for the development of safer and more effective treatments for NASH. In this review, different risk factors, clinical symptoms, diagnostic methods of NASH, and current treatment strategies for the management of patients with NASH are reviewed.

Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease - The Gut and Obesity in Asia (GO-ASIA) Study

BACKGROUND

The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non-alcoholic fatty liver disease (NAFLD/MASLD).

AIMS

We evaluated the performance of machine learning (ML) and non-patented scores for ruling out SF among NAFLD/MASLD patients.

METHODS

Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria).

RESULTS

Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p < 0.001, each). ML models showed 7%-12% better discrimination than FIB-4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB-4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set).

CONCLUSIONS

ML with clinical, anthropometric data and simple blood investigations perform better than FIB-4 for ruling out SF in biopsy-proven Asian NAFLD/MASLD patients.

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information

AIMS

Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints.

METHODS

Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable.

RESULTS

Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance.

CONCLUSIONS

This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023)

BACKGROUND AND OBJECTIVES

Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data.

METHODS

We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information".

RESULTS

We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches.

CONCLUSION

AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.

Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequential forward selection (SFS), chi-square, analysis of variance (ANOVA), and mutual information (MI). The selected features were used to train machine learning classifiers including SVM, random forest, AdaBoost, LightGBM, and XGBoost. Hyperparameter tuning was done for each classifier using randomized search. Model evaluation was performed using leave-one-out cross-validation over 100 repetitions. Among the classifiers, random forest, combined with SFS feature selection and 10 features, obtained the best performance: Accuracy: 81.32% ± 6.43%, Sensitivity: 86.04% ± 6.21%, Specificity: 70.49% ± 8.12% Precision: 81.59% ± 6.23%, and F1-score: 83.75% ± 6.23% percent. Our findings highlight the promise of machine learning in enhancing early diagnosis of NASH and provide a compelling alternative to conventional diagnostic techniques. Consequently, this study highlights the promise of machine learning techniques in enhancing early and non-invasive diagnosis of NASH based on readily available clinical and blood data. Our findings provide the basis for developing scalable approaches that can improve screening and monitoring of NASH progression.

Development and validation of machine learning models for nonalcoholic fatty liver disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) had become the most prevalent liver disease worldwide. Early diagnosis could effectively reduce NAFLD-related morbidity and mortality. This study aimed to combine the risk factors to develop and validate a novel model for predicting NAFLD.

METHODS

We enrolled 578 participants completing abdominal ultrasound into the training set. The least absolute shrinkage and selection operator (LASSO) regression combined with random forest (RF) was conducted to screen significant predictors for NAFLD risk. Five machine learning models including logistic regression (LR), RF, extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and support vector machine (SVM) were developed. To further improve model performance, we conducted hyperparameter tuning with train function in Python package 'sklearn'. We included 131 participants completing magnetic resonance imaging into the testing set for external validation.

RESULTS

There were 329 participants with NAFLD and 249 without in the training set, while 96 with NAFLD and 35 without were in the testing set. Visceral adiposity index, abdominal circumference, body mass index, alanine aminotransferase (ALT), ALT/AST (aspartate aminotransferase), age, high-density lipoprotein cholesterol (HDL-C) and elevated triglyceride (TG) were important predictors for NAFLD risk. The area under curve (AUC) of LR, RF, XGBoost, GBM, SVM were 0.915 [95% confidence interval (CI): 0.886-0.937], 0.907 (95% CI: 0.856-0.938), 0.928 (95% CI: 0.873-0.944), 0.924 (95% CI: 0.875-0.939), and 0.900 (95% CI: 0.883-0.913), respectively. XGBoost model presented the best predictive performance, and its AUC was enhanced to 0.938 (95% CI: 0.870-0.950) with further parameter tuning.

CONCLUSIONS

This study developed and validated five novel machine learning models for NAFLD prediction, among which XGBoost presented the best performance and was considered a reliable reference for early identification of high-risk patients with NAFLD in clinical practice.

Identification of Fast Progressors Among Patients With Nonalcoholic Steatohepatitis Using Machine Learning

Background and Aims

There is a high unmet need to develop noninvasive tools to identify nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) patients at risk of fast progression to end-stage liver disease (ESLD). This study describes the development of a machine learning (ML) model using data around the first clinical evidence of NAFLD/NASH to identify patients at risk of future fast progression.

Methods

Adult patients with ESLD (cirrhosis or hepatocellular carcinoma) due to NAFLD/NASH were identified in Optum electronic health records (2007-2018 period). Patients were stratified into fast (0.5 and 3 years) and standard progressor (6-10 years) cohorts based on retrospectively established progression time between ESLD and the earliest observable disease, and characteristics were reported using descriptive statistics. Two ML models predicting fast progression were created, performance was compared, and top predictive features from the final model were compared between cohorts.

Results

Among a total of 4013 NAFLD patients with cirrhosis or hepatocellular carcinoma (mean age 58.6 ± 12.5; 65% female), 24% were fast (n = 951) and 25% standard (n = 992) progressors that were used for modeling. The cohorts were comparable for gender, body mass index, type 2 diabetes, and arterial hypertension, but differed significantly for obesity, hyperlipidemia, and age at index. The final model (NASH FASTmap) is a 44 feature light gradient boosting model which performed better (area under the curve [0.77], F1-score [0.74], accuracy [0.71], and precision [0.71]) than eXtreme gradient boosting model to predict fast progression.

Conclusion

Future fast progression to ESLD in NAFLD/NASH patients can be predicted from clinical data using ML. Electronic health record implementation of NASH FASTmap could support clinical assessment for risk stratification and potentially improve disease management.

Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future

Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.

The Aging Human Liver: The Weal and Woe of Evolutionary Legacy

Aging is characterized by the progressive decline of biological integrity and its compensatory mechanisms as well as immunological dysregulation. This goes along with an increasing risk of frailty and disease. Against this background, we here specifically focus on the aging of the human liver. For the first time, we shed light on the intertwining evolutionary underpinnings of the liver's declining regenerative capacity, the phenomenon of inflammaging, and the biotransformation capacity in the process of aging. In addition, we discuss how aging influences the risk for developing nonalcoholic fatty liver disease, hepatocellular carcinoma, and/or autoimmune hepatitis, and we describe chronic diseases as accelerators of biological aging.

Artificial intelligence in nonalcoholic fatty liver disease

Background

Nonalcoholic fatty liver disease (NAFLD) has led to serious health-related complications worldwide. NAFLD has wide pathological spectra, ranging from simple steatosis to hepatitis to cirrhosis and hepatocellular carcinoma. Artificial intelligence (AI), including machine learning and deep learning algorithms, has provided great advancement and accuracy in identifying, diagnosing, and managing patients with NAFLD and detecting squeal such as advanced fibrosis and risk factors for hepatocellular cancer. This review summarizes different AI algorithms and methods in the field of hepatology, focusing on NAFLD.

Methods

A search of PubMed, WILEY, and MEDLINE databases were taken as relevant publications for this review on the application of AI techniques in detecting NAFLD in suspected population

Results

Out of 495 articles searched in relevant databases, 49 articles were finally included and analyzed. NASH-Scope model accurately distinguished between NAFLD and non-NAFLD and between NAFLD without fibrosis and NASH with fibrosis. The logistic regression (LR) model had the highest accuracy, whereas the support vector machine (SVM) had the highest specificity and precision in diagnosing NAFLD. An extreme gradient boosting model had the highest performance in predicting non-alcoholic steatohepatitis (NASH). Electronic health record (EHR) database studies helped the diagnose NAFLD/NASH. Automated image analysis techniques predicted NAFLD severity. Deep learning radiomic elastography (DLRE) had perfect accuracy in diagnosing the cases of advanced fibrosis.

Conclusion

AI in NAFLD has streamlined specific patient identification and has eased assessment and management methods of patients with NAFLD.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

A machine-learning approach for nonalcoholic steatohepatitis susceptibility estimation

BACKGROUND

Nonalcoholic steatohepatitis (NASH), a severe form of nonalcoholic fatty liver disease, can lead to advanced liver damage and has become an increasingly prominent health problem worldwide. Predictive models for early identification of high-risk individuals could help identify preventive and interventional measures. Traditional epidemiological models with limited predictive power are based on statistical analysis. In the current study, a novel machine-learning approach was developed for individual NASH susceptibility prediction using candidate single nucleotide polymorphisms (SNPs).

METHODS

A total of 245 NASH patients and 120 healthy individuals were included in the study. Single nucleotide polymorphism genotypes of candidate genes including two SNPs in the cytochrome P450 family 2 subfamily E member 1 (CYP2E1) gene (rs6413432, rs3813867), two SNPs in the glucokinase regulator (GCKR) gene (rs780094, rs1260326), rs738409 SNP in patatin-like phospholipase domain-containing 3 (PNPLA3), and gender parameters were used to develop models for identifying at-risk individuals. To predict the individual's susceptibility to NASH, nine different machine-learning models were constructed. These models involved two different feature selections including Chi-square, and support vector machine recursive feature elimination (SVM-RFE) and three classification algorithms including k-nearest neighbor (KNN), multi-layer perceptron (MLP), and random forest (RF). All nine machine-learning models were trained using 80% of both the NASH patients and the healthy controls data. The nine machine-learning models were then tested on 20% of both groups. The model's performance was compared for model accuracy, precision, sensitivity, and F measure.

RESULTS

Among all nine machine-learning models, the KNN classifier with all features as input showed the highest performance with 86% F measure and 79% accuracy.

CONCLUSIONS

Machine learning based on genomic variety may be applicable for estimating an individual's susceptibility for developing NASH among high-risk groups with a high degree of accuracy, precision, and sensitivity.

Advance of Serum Biomarkers and Combined Diagnostic Panels in Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) affects approximately 25-30% population worldwide, which progresses from simple steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma, and has complications such as cardiovascular events. Liver biopsy is still the gold standard for the diagnosis of NAFLD, with some limitations, such as invasive, sampling deviation, and empirical judgment. Therefore, it is urgent to develop noninvasive diagnostic biomarkers. Currently, a large number of NAFLD-related serum biomarkers have been identified, including apoptosis, inflammation, fibrosis, adipokines, hepatokines, and omics biomarkers, which could effectively diagnose NASH and exclude patients with progressive fibrosis. We summarized serum biomarkers and combined diagnostic panels of NAFLD, to provide some guidance for the noninvasive diagnosis and further clinical studies.

Patient-centered care: Key elements applicable to chronic liver disease

Chronic liver disease (CLD) is a progressive illness with high symptom burden and functional and cognitive impairment, often with comorbid mental and substance use disorders. These factors lead to significant deterioration in quality of life, with immense burden on patients, caregivers, and healthcare. The current healthcare system in the United States does not adequately meet the needs of patients with CLD or control costs given the episodic, reactive, and fee-for-service structure. There is also a need for clinical and financial accountability for CLD care. In this context, we describe the key elements required to shift the CLD care paradigm to a patient-centered and value-based system built upon the Porter model of value-based health care. The key elements include (1) organization into integrated practice units, (2) measuring and incorporating meaningful patient-reported outcomes, (3) enabling technology to allow innovation, (4) bundled care payments, (5) integrating palliative care within routine care, and (6) formalizing centers of excellence. These elements have been shown to improve outcomes, reduce costs, and improve overall patient experience for other chronic illnesses and should have similar benefits for CLD. Payers need to partner with providers and systems to build upon these elements and help align reimbursements with patients' values and outcomes. The national organizations such as the American Association for Study of Liver Diseases need to guide key stakeholders in standardizing these elements to optimize patient-centered care for CLD.

Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review

Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.

Towards harnessing the value of organokine crosstalk to predict the risk for cardiovascular disease in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Importantly, NAFLD increases the risk for cardiovascular disease (CVD). A causal relationship has been substantiated. Given the pandemic proportions of NAFLD, a reliable scoring system for predicting the risk of NAFLD-associated CVD is an urgent medical need. We here review cumulative evidence suggesting that systemically released organokines - especially certain adipokines, hepatokines, and cardiokines - may serve this purpose. The underlying rationale is that these signalers directly communicate between white adipose tissue, liver, and heart as key players in the pathogenesis of NAFLD and resultant CVD events. Moreover, evidence suggests that these organ-specific cytokines are secreted in a biologically predetermined, cascade-like pattern. Consequently, upon pinpointing organokines of relevance, we sketch requirements to establish an algorithm predictive of the CVD risk in patients with NAFLD. Such an algorithm, as to be consolidated in the form of an applicable equation, may be improved continuously by machine learning. To the best of our knowledge, such an option has not yet been considered. Establishing and implementing a reliable algorithm for determining the NAFLD-associated CVD risk has the potential to save many NAFLD patients from life-threatening CVD events.

Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH)

Objectives

To develop and evaluate machine learning models to detect patients with suspected undiagnosed non-alcoholic steatohepatitis (NASH) for diagnostic screening and clinical management.

Methods

In this retrospective observational non-interventional study using administrative medical claims data from 1 463 089 patients, gradient-boosted decision trees were trained to detect patients with likely NASH from an at-risk patient population with a history of obesity, type 2 diabetes mellitus, metabolic disorder or non-alcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-risk patients or in the subset without a prior NAFL diagnosis (at-risk non-NAFL patients). Models were trained and validated using retrospective medical claims data and assessed using area under precision recall curves and receiver operating characteristic curves (AUPRCs and AUROCs).

Results

The 6-month incidences of NASH in claims data were 1 per 1437 at-risk patients and 1 per 2127 at-risk non-NAFL patients. The model trained to detect NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 to 0.0110) and an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60× above NASH incidence. The model trained to detect NASH in the non-NAFL cohort had an AUPRC of 0.0030 (95% CI 0.0029 to 0.0031) and an AUROC of 0.78. At 10% recall, model precision was 1%, which is 20× above NASH incidence.

Conclusion

The low incidence of NASH in medical claims data corroborates the pattern of NASH underdiagnosis in clinical practice. Claims-based machine learning could facilitate the detection of patients with probable NASH for diagnostic testing and disease management.

MAFLD/NAFLD Biopsy-Free Scoring Systems for Hepatic Steatosis, NASH, and Fibrosis Diagnosis

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as nonalcoholic fatty liver disease, is the most prevalent liver disorder worldwide. Historically, its diagnosis required biopsy, even though the procedure has a variable degree of error. Therefore, new non-invasive strategies are needed. Consequently, this article presents a thorough review of biopsy-free scoring systems proposed for the diagnosis of MAFLD. Similarly, it compares the severity of the disease, ranging from hepatic steatosis (HS) and nonalcoholic steatohepatitis (NASH) to fibrosis, by contrasting the corresponding serum markers, clinical associations, and performance metrics of these biopsy-free scoring systems. In this regard, defining MAFLD in conjunction with non-invasive tests can accurately identify patients with fatty liver at risk of fibrosis and its complications. Nonetheless, several biopsy-free scoring systems have been assessed only in certain cohorts; thus, further validation studies in different populations are required, with adjustment for variables, such as body mass index (BMI), clinical settings, concomitant diseases, and ethnic backgrounds. Hence, comprehensive studies on the effects of age, morbid obesity, and prevalence of MAFLD and advanced fibrosis in the target population are required. Nevertheless, the current clinical practice is urged to incorporate biopsy-free scoring systems that demonstrate adequate performance metrics for the accurate detection of patients with MAFLD and underlying conditions or those with contraindications of biopsy.

A Potential Role for Bile Acid Signaling in Celiac Disease-Associated Fatty Liver

Celiac disease (CeD) is a chronic autoimmune disorder characterized by an intolerance to storage proteins of many grains. CeD is frequently associated with liver damage and steatosis. Bile acid (BA) signaling has been identified as an important mediator in gut-liver interaction and the pathogenesis of non-alcoholic fatty liver disease (NAFLD). Here, we aimed to analyze BA signaling and liver injury in CeD patients. Therefore, we analyzed data of 20 CeD patients on a gluten-free diet compared to 20 healthy controls (HC). We furthermore analyzed transaminase levels, markers of cell death, BA, and fatty acid metabolism. Hepatic steatosis was determined via transient elastography, by MRI and non-invasive scores. In CeD, we observed an increase of the apoptosis marker M30 and more hepatic steatosis as compared to HC. Fibroblast growth factor 19 (FGF19) was repressed in CeD, while low levels were associated with steatosis, especially in patients with high levels of anti-tissue transglutaminase antibodies (anti-tTG). When comparing anti-tTG-positive CeD patients to individuals without detectable anti-tTG levels, hepatic steatosis was accentuated. CeD patients with significant sonographic steatosis (defined by CAP ≥ 283 db/m) were exclusively anti-tTG-positive. In summary, our results suggest that even in CeD patients in clinical remission under gluten-free diet, alterations in gut-liver axis, especially BA signaling, might contribute to steatotic liver injury and should be further addressed in future studies and clinical practice.

Identification of 90 NAFLD GWAS loci and establishment of NAFLD PRS and causal role of NAFLD in coronary artery disease

The prevalence of non-alcoholic fatty liver disease (NAFLD), now also known as metabolic dysfunction-associated fatty liver disease (MAFLD), is rapidly increasing worldwide due to the ongoing obesity epidemic. However, currently the NALFD diagnosis requires non-readily available imaging technologies or liver biopsy, which has drastically limited the sample sizes of NAFLD studies and hampered the discovery of its genetic component. Here we utilized the large UK Biobank (UKB) to accurately estimate the NAFLD status in UKB based on common serum traits and anthropometric measures. Scoring all individuals in UKB for NAFLD risk resulted in 28,396 NAFLD cases and 108,652 healthy individuals at a >90% confidence level. Using this imputed NAFLD status to perform the largest NAFLD genome-wide association study (GWAS) to date, we identified 94 independent (R2 < 0.2) NAFLD GWAS loci, of which 90 have not been identified before; built a polygenic risk score (PRS) model to predict the genetic risk of NAFLD; and used the GWAS variants of imputed NAFLD for a tissue-aware Mendelian randomization analysis that discovered a significant causal effect of NAFLD on coronary artery disease (CAD). In summary, we accurately estimated the NAFLD status in UKB using common serum traits and anthropometric measures, which empowered us to identify 90 GWAS NAFLD loci, build NAFLD PRS, and discover a significant causal effect of NAFLD on CAD.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, affecting over 30% of the United States population. Early patient identification using a simple method is highly desirable.

AIM

To create machine learning models for predicting NAFLD in the general United States population.

METHODS

Using the NHANES 1988-1994. Thirty NAFLD-related factors were included. The dataset was divided into the training (70%) and testing (30%) datasets. Twenty-four machine learning algorithms were applied to the training dataset. The best-performing models and another interpretable model (i.e., coarse trees) were tested using the testing dataset.

RESULTS

There were 3235 participants (n = 3235) that met the inclusion criteria. In the training phase, the ensemble of random undersampling (RUS) boosted trees had the highest F1 (0.53). In the testing phase, we compared selective machine learning models and NAFLD indices. Based on F1, the ensemble of RUS boosted trees remained the top performer (accuracy 71.1% and F1 0.56) followed by the fatty liver index (accuracy 68.8% and F1 0.52). A simple model (coarse trees) had an accuracy of 74.9% and an F1 of 0.33.

CONCLUSION

Not every machine learning model is complex. Using a simpler model such as coarse trees, we can create an interpretable model for predicting NAFLD with only two predictors: fasting C-peptide and waist circumference. Although the simpler model does not have the best performance, its simplicity is useful in clinical practice.

Development and validation of a neural network for NAFLD diagnosis

Non-Alcoholic Fatty Liver Disease (NAFLD) affects about 20–30% of the adult population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Liver ultrasound (US) is widely used as a noninvasive method to diagnose NAFLD. However, the intensive use of US is not cost-effective and increases the burden on the healthcare system. Electronic medical records facilitate large-scale epidemiological studies and, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. Our goal was to develop and validate a simple Neural Network (NN)-based web app that could be used to predict NAFLD particularly its absence. The study included 2970 subjects; training and testing of the neural network using a train–test-split approach was done on 2869 of them. From another population consisting of 2301 subjects, a further 100 subjects were randomly extracted to test the web app. A search was made to find the best parameters for the NN and then this NN was exported for incorporation into a local web app. The percentage of accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall and f1-score were verified. After that, Explainability (XAI) was analyzed to understand the diagnostic reasoning of the NN. Finally, in the local web app, the specificity and sensitivity values were checked. The NN achieved a percentage of accuracy during testing of 77.0%, with an area under the ROC curve value of 0.82. Thus, in the web app the NN evidenced to achieve good results, with a specificity of 1.00 and sensitivity of 0.73. The described approach can be used to support NAFLD diagnosis, reducing healthcare costs. The NN-based web app is easy to apply and the required parameters are easily found in healthcare databases.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Transaminase Concentrations Cannot Separate Non-Alcoholic Fatty Liver and Non-Alcoholic Steatohepatitis in Morbidly Obese Patients Irrespective of Histological Algorithm

BACKGROUND

In current general practice, elevated serum concentrations of liver enzymes are still regarded as an indicator of non-alcoholic fatty liver disease (NAFLD) or non-alcoholic steatohepatitis (NASH). In this study, we analyzed if an adjustment of the upper limit of normal (ULN) for serum liver enzymes can improve their diagnostic accuracy.

METHODS

Data from 363 morbidly obese patients (42.5 ± 10.3 years old; mean BMI: 52 ± 8.5 kg/m2), who underwent bariatric surgery, was retrospectively analyzed. NAFL and NASH were defined histologically according to non-alcoholic fatty liver activity score (NAS) and according to steatosis activity fibrosis (SAF) score for 2 separate analyses, respectively.

RESULTS

In 121 women (45%) and 45 men (46%), elevated values for at least one serum parameter (ALT, AST, γGT) were present. The serum concentrations of ALT (p < 0.0001), AST (p < 0.0001) and γGT (p = 0.0023) differed significantly between NAFL and NASH, irrespective of the applied histological classification method. Concentrations of all 3 serum parameters correlated significantly positively with the NAS and the SAF score, with correlation coefficients between 0.33 (ALT/NAS) and 0.40 (γGT/SAF). The area under the curves to separate NAFL and NASH by liver enzymes achieved a maximum of 0.70 (ALT applied to NAS-based classification). For 95% specificity, the ULN for ALT would be 47.5 U/L; for 95% sensitivity, the ULN for ALT would be 17.5 U/L, resulting in 62% uncategorized patients.

CONCLUSION

ALT, AST, and γGT are unsuitable for non-invasive screening or diagnosis of NAFL or NASH. Utilizing liver enzymes as an indicator for NAFLD or NASH should generally be questioned.

Application of Risk Scores for Hepatocellular Carcinoma in Patients with Chronic Hepatitis B: Current Status and Future Perspective

Accurate risk prediction for hepatocellular carcinoma (HCC) among patients with chronic hepatitis B (CHB) may guide treatment strategies including initiation of antiviral therapy and also inform implementation of HCC surveillance. There have been 26 risk scores developed to predict HCC in CHB patients with (n = 14) or without (n = 12) receiving antiviral treatment; all of them invariably include age in the scoring formula. Virological biomarkers of replicative activities (i.e., hepatitis B virus DNA level or hepatitis B envelope antigen status) are frequently included in the scores derived from patients with untreated CHB, whereas measurements that gauge severity of liver fibrosis and/or reserve of hepatic function (i.e., cirrhosis diagnosis, liver stiffness measurement, platelet count, or albumin) are essential components in the scores developed from treated patients. External validation is a prerequisite for clinical application but not yet performed for all scores. For the future, higher predictive accuracy may be achieved with machine learning based on more comprehensive data.

Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases

Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.

Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis

Objective

To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML).

Materials and Methods

This retrospective study utilized two databases: a) the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease (NAFLD) adult database (2004-2009), and b) the Optum^® de-identified Electronic Health Record dataset (2007-2018), a real-world dataset representative of common electronic health records in the United States. We developed an ML model to predict NASH, using confirmed NASH and non-NASH based on liver histology results in the NIDDK dataset to train the model.

Results

Models were trained and tested on NIDDK NAFLD data (704 patients) and the best-performing models evaluated on Optum data (~3,000,000 patients). An eXtreme Gradient Boosting model (XGBoost) consisting of 14 features exhibited high performance as measured by area under the curve (0.82), sensitivity (81%), and precision (81%) in predicting NASH. Slightly reduced performance was observed with an abbreviated feature set of 5 variables (0.79, 80%, 80%, respectively). The full model demonstrated good performance (AUC 0.76) to predict NASH in Optum data.

Discussion

The proposed model, named NASHmap, is the first ML model developed with confirmed NASH and non-NASH cases as determined through liver biopsy and validated on a large, real-world patient dataset. Both the 14 and 5-feature versions exhibit high performance.

Conclusion

The NASHmap model is a convenient and high performing tool that could be used to identify patients likely to have NASH in clinical settings, allowing better patient management and optimal allocation of clinical resources.

Artificial intelligence in precision medicine in hepatology

The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline-specific therapy toward patient-specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi-omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non-alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real-world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.

Nine Markers to Predict Nonalcoholic Fatty Liver Disease for a Chinese Diabetic Population

Background: Nonalcoholic fatty liver disease (NAFLD) increases the possibility to suffer from liver or cardiovascular disease. Although hepatic biopsy is well acknowledged as the standard diagnosis, it is difficult to implement because of its intrusiveness and cost concerns. Moreover, overweight people or diabetic patients are always NAFLD-positive, but not absolute. Therefore, to distinguish whether a diabetic case has NAFLD via nonintrusive indicators is of great significance for further interventions. Objective: With 8499 diabetic patients hosted by Shanghai Sixth People’s Hospital, we try to rank the impacts of multiple routine indicators (features) on NAFLD, and further predict NAFLD within this diabetic population. Methods: We first rank dozens of related features according to their contributions in NAFLD prediction, and then we prune several trivial features to simplify the prediction. Additionally, three classification algorithms are considered and compared, e.g., C4.5, Naïve Bayes and Random Forest. Results: The experiment shows that Random Forest outperforms the rest (accuracy 85.1%, recall 90.98% and AUC 0.631). Conclusions: We find that the top nine markers together can effectively tell NAFLD out of this diabetic population. They are triglyceride (TG), low density lipoprotein (LDL), insulin (INS), hbA1C, high-density lipoprotein (HDL), fasting plasma glucose (FPG), age, total cholesterol (TC) and duration.

Hepatokines and adipokines in NASH-related hepatocellular carcinoma

The incidence of hepatocellular carcinoma (HCC) is increasing in industrialised societies; this is likely secondary to the increasing burden of non-alcoholic fatty liver disease (NAFLD), its progressive form non-alcoholic steatohepatitis (NASH), and the metabolic syndrome. Cumulative studies suggest that NAFLD-related HCC may also develop in non-cirrhotic livers. However, prognosis and survival do not differ between NAFLD- or virus-associated HCC. Thus, research has increasingly focused on NAFLD-related risk factors to better understand the biology of hepatocarcinogenesis and to develop new diagnostic, preventive, and therapeutic strategies. One important aspect thereof is the role of hepatokines and adipokines in NAFLD/NASH-related HCC. In this review, we compile current data supporting the use of hepatokines and adipokines as potential markers of disease progression in NAFLD or as early markers of NAFLD-related HCC. While much work must be done to elucidate the mechanisms and interactions underlying alterations to hepatokines and adipokines, current data support the possible utility of these factors - in particular, angiopoietin-like proteins, fibroblast growth factors, and apelin - for detection or even as therapeutic targets in NAFLD-related HCC.

Performance of non-invasive fibrosis scores in non-alcoholic fatty liver disease with and without morbid obesity

BACKGROUND

Non-invasive scores, such as the non-alcoholic fatty liver disease (NAFLD) Fibrosis Score (NFS), are increasingly used for liver fibrosis assessment in patients with NAFLD. The aim of this study was to assess the applicability and reliability of non-invasive fibrosis scores in NAFLD patients with and without morbid obesity.

METHODS

Three hundred sixty-eight patients with biopsy-proven NAFLD identified between January 2012 and December 2015 were studied; 225 with morbid obesity (biopsy obtained during bariatric surgery) and 143 patients without (termed as "conventional").

RESULTS

Median age was 47 years, 57% were female. Median body mass index (BMI) was 42.9 kg/m² with significant differences between our conventional and morbidly obese patients (BMI 29.0 vs. 50.8 kg/m², p < 0.001). Overall, 42% displayed mild/moderate and 16% advanced liver fibrosis (stage III/IV). All tested scores were significantly linked to fibrosis stage (p < 0.001 for all). FIB-4 (AUROC 0.904), APRI (AUROC 0.848), and NFS (AUROC 0.750) were identified as potent predictors of advanced fibrosis, although NFS overestimated fibrosis stage in morbid obesity. Limiting BMI to a maximum of 40 kg/m² improved NFS' overall performance (AUROC 0.838). FIB-4 > 1.0 indicated high probability of advanced fibrosis (OR = 29.1). FIB-4 predicted advanced fibrosis independently from age, sex, BMI, and presence of morbid obesity.

CONCLUSIONS

Our data suggest that FIB-4 score is an accurate predictor of advanced fibrosis in NAFLD throughout all BMI stages. Without adjustment, NFS tends to overestimate fibrosis in morbidly obese NAFLD patients. This problem may be solved by implementation of an upper BMI limit (for NFS) or adjustment of diagnostic thresholds.

Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study

Background & aims

Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best of these as a predictor of NAFLD.

Methods

The study included 2970 subjects, 2920 constituting the training set and 50, randomly selected, used in the test phase, performing cross-validation. The best predictors were combined to create three models: 1) FLI plus GLUCOSE plus SEX plus AGE, 2) AVI plus GLUCOSE plus GGT plus SEX plus AGE, 3) BRI plus GLUCOSE plus GGT plus SEX plus AGE. Eight machine learning algorithms were trained with the predictors of each of the three models created. For these algorithms, the percent accuracy, variance and percent weight were compared.

Results

The SVM algorithm performed better with all models. Model 1 had 68% accuracy, with 1% variance and an algorithm weight of 27.35; Model 2 had 68% accuracy, with 1% variance and an algorithm weight of 33.62 and Model 3 had 77% accuracy, with 1% variance and an algorithm weight of 34.70. Model 2 was the most performing, composed of AVI plus GLUCOSE plus GGT plus SEX plus AGE, despite a lower percentage of accuracy.

Conclusion

A Machine Learning approach can support NAFLD diagnosis and reduce health costs. The SVM algorithm is easy to apply and the necessary parameters are easily retrieved in databases.

Lipoprotein and Metabolic Profiles Indicate Similar Cardiovascular Risk of Liver Steatosis and NASH

BACKGROUND AND AIM

Nonalcoholic fatty liver disease (NAFLD) affects about 25% of the global population, with no reliable noninvasive tests to diagnose nonalcoholic steatohepatitis (NASH) and to differentiate between NASH and nonalcoholic fatty liver (NAFL) (steatosis alone). It is unclear if NAFL and NASH differ in cardiovascular risk for patients. Here, we compared obese NAFLD patients with a healthy cohort to test whether cholesterol compounds could represent potential noninvasive markers and to estimate associated risks.

METHOD

Serum samples of 46 patients with histologically confirmed NAFLD (17 NAFL, 29 NASH) who underwent bariatric surgery were compared to 32 (9 males, 21 females) healthy controls (HCs). We analyzed epidemiological data, liver enzymes, cholesterol and lipid profile, and amino acids. The latter were analyzed by nuclear magnetic resonance spectroscopy.

RESULTS

Total serum and high-density lipoprotein (HDL) cholesterol were significantly lower in the NAFLD group than in HCs, with a stronger reduction in NASH. Similar observations were made for sub-specification of HDL-p, HDL-s, SHDL-p, and LHDL-p cholesterols. Low-density lipoprotein (LDL)-s and LLDL-p cholesterol were significantly reduced in NAFLD groups. Interestingly, SLDL-p cholesterol was significantly higher in the NAFL group with a stronger elevation in NASH than in HCs. The amino acids alanine, leucin, and isoleucine were significantly higher in the NAFL and NASH groups than in HCs.

CONCLUSION

We show in this study that cholesterol profiles, apolipoproteins, and amino acids could function as a potential noninvasive test to screen for NAFLD or even NASH in larger populations. However, few differences in cholesterol profiles were identified between the NAFL and NASH groups, indicating similar cardiovascular risk profiles.

Accuracy of cytokeratin 18 (M30 and M65) in detecting non-alcoholic steatohepatitis and fibrosis: A systematic review and meta-analysis

Introduction

Association between elevated cytokeratin 18 (CK-18) levels and hepatocyte death has made circulating CK-18 a candidate biomarker to differentiate non-alcoholic fatty liver from non-alcoholic steatohepatitis (NASH). Yet studies produced variable diagnostic performance. We aimed to provide summary estimates with increased precision for the accuracy of CK-18 (M30, M65) in detecting NASH and fibrosis among non-alcoholic fatty liver disease (NAFLD) adults.

Methods

We searched five databases to retrieve studies evaluating CK-18 against a liver biopsy in NAFLD adults. Reference screening, data extraction and quality assessment (QUADAS-2) were independently conducted by two authors. Meta-analyses were performed for five groups based on the CK-18 antigens and target conditions, using one of two methods: linear mixed-effects multiple thresholds model or bivariate logit-normal random-effects model.

Results

We included 41 studies, with data on 5,815 participants. A wide range of disease prevalence was observed. No study reported a pre-defined cut-off. Thirty of 41 studies provided sufficient data for inclusion in any of the meta-analyses. Summary AUC [95% CI] were: 0.75 [0.69–0.82] (M30) and 0.82 [0.69–0.91] (M65) for NASH; 0.73 [0.57–0.85] (M30) for fibrotic NASH; 0.68 (M30) for significant (F2-4) fibrosis; and 0.75 (M30) for advanced (F3-4) fibrosis. Thirteen studies used CK-18 as a component of a multimarker model.

Conclusions

For M30 we found lower diagnostic accuracy to detect NASH compared to previous meta-analyses, indicating a limited ability to act as a stand-alone test, with better performance for M65. Additional external validation studies are needed to obtain credible estimates of the diagnostic accuracy of multimarker models.

Early Health Technology Assessment during Nonalcoholic Steatohepatitis Drug Development: A Two-Round, Cross-Country, Multicriteria Decision Analysis

Background. The assessment of value along the clinical development of new biopharmaceutical compounds is a challenging task. Complex and uncertain evidence has to be analyzed, considering a multitude of value preferences from different stakeholders. Objective. To investigate the use of multicriteria decision analysis (MCDA) to support decision making during drug development while considering payer and health technology assessment (HTA) value concerns, by applying the Advance Value Framework in nonalcoholic steatohepatitis (NASH) and testing for the consistency of the results. Design. A multiattribute value theory methodology was applied and 2 rounds of decision conferences (DCs) were organized in 3 countries (England, France, and Germany), with the participation of national key experts and stakeholders using the MACBETH questioning protocol and algorithm. A total of 51 health care professionals, patient advocates, and methodologists, including (ex-) committee members or assessors from national HTA bodies, participated in 6 DCs in the study countries. Target Population. NASH patients in fibrosis stages F2 to 3 were considered. Interventions. The value of a hypothetical product profile was assessed against 3 compounds under development using their phase 2 results. Outcome Measures. DC participants' value preferences were elicited involving criteria selection, options scoring, and criteria weighting. Results. Highly consistent valuation rankings were observed in all DCs, always favoring the same compound. Highly consistent rankings of criteria clusters were observed, favoring therapeutic benefit criteria, followed by safety profile and innovation level criteria. Limitations. There was a lack of comparative treatment effects, early evidence on surrogate endpoints was used, and stakeholder representativeness was limited in some DCs. Conclusions. The use of MCDA is promising in supporting early HTA, illustrating high consistency in results across countries and between study rounds.

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.

METHODS AND FINDINGS

We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one.

CONCLUSIONS

In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03814915.

Evaluation of the Effect Derived from Silybin with Vitamin D and Vitamin E Administration on Clinical, Metabolic, Endothelial Dysfunction, Oxidative Stress Parameters, and Serological Worsening Markers in Nonalcoholic Fatty Liver Disease Patients

Nowadays, the nonalcoholic fatty liver disease represents the main chronic liver disease in the Western countries, and the correct medical therapy remains a big question for the scientific community. The aim of our study was to evaluate the effect derived from the administration for six months of silybin with vitamin D and vitamin E (RealSIL 100D®) on metabolic markers, oxidative stress, endothelial dysfunction, and worsening of disease markers in nonalcoholic fatty liver disease patients. We enrolled 90 consecutive patients with histological diagnosis of nonalcoholic fatty liver disease and 60 patients with diagnosis of reflux disease (not in therapy) as healthy controls. The nonalcoholic fatty liver disease patients were randomized into two groups: treated (60 patients) and not treated (30 patients). We performed a nutritional assessment and evaluated clinical parameters, routine home tests, the homeostatic model assessment of insulin resistance, NAFLD fibrosis score and fibrosis-4, transient elastography and controlled attenuation parameter, thiobarbituric acid reactive substances, tumor necrosis factor α, transforming growth factor β, interleukin-18 and interleukin-22, matrix metalloproteinase 2, epidermal growth factor receptor, insulin growth factor-II, cluster of differentiation-44, high mobility group box-1, and Endocan. Compared to the healthy controls, the nonalcoholic fatty liver disease patients had statistically significant differences for almost all parameters evaluated at baseline (p < 0.05). Six months after the baseline, the proportion of nonalcoholic fatty liver disease patients treated that underwent a statistically significant improvement in metabolic markers, oxidative stress, endothelial dysfunction, and worsening of disease was greater than not treated nonalcoholic fatty liver disease patients (p < 0.05). Even more relevant results were obtained for the same parameters by analyzing patients with a concomitant diagnosis of metabolic syndrome (p < 0.001). The benefit that derives from the use of RealSIL 100D could derive from the action on more systems able to advance the pathology above all in that subset of patients suffering from concomitant metabolic syndrome.

Exercise regulates lipid droplet dynamics in normal and fatty liver

Lipids droplets (LD) are dynamics organelles that accumulate neutral lipids during nutrient surplus. LD alternates between periods of growth and consumption through regulated processes including as de novo lipogenesis, lipolysis and lipophagy. The liver is a central tissue in the regulation of lipid metabolism. Non-Alcoholic Fatty Liver Diseases (NAFLD) is result of the accumulation of LD in liver. Several works have been demonstrated a positive effect of exercise on reduction of liver fat. However, the study of the exercise on liver LD dynamics is far from being understood. Here we investigated the effect of chronic exercise in the regulation of LD dynamics using a mouse model of high fat diet-induced NAFLD. Mice were fed with a high-fat diet or control diet for 12 weeks; then groups were divided into chronic exercise or sedentary for additional 8 weeks. Our results showed that exercise reduced fasting glycaemia, insulin and triacylglycerides, also liver damage. However, exercise did not affect the intrahepatic triacylglycerides levels and the number of LD but reduced their size. In addition, exercise decreased the SREBP-1c levels, without changes in lipolysis, mitochondrial proteins or autophagy/lipophagy markers. Unexpectedly in the control mice, exercise increased the number of LD, also PLIN2, SREBP-1c, FAS, ATGL, HSL and MTTP levels. Our findings show that exercise rescues the liver damage in a model of NAFLD reducing the size of LD and normalizing protein markers of de novo lipogenesis and lipolysis. Moreover, exercise increases proteins associated to LD dynamics in the control mice.

Current Status in Testing for Nonalcoholic Fatty Liver Disease (NAFLD) and Nonalcoholic Steatohepatitis (NASH)

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in Western countries with almost 25% affected adults worldwide. The growing public health burden is getting evident when considering that NAFLD-related liver transplantations are predicted to almost double within the next 20 years. Typically, hepatic alterations start with simple steatosis, which easily progresses to more advanced stages such as nonalcoholic steatohepatitis (NASH), fibrosis and cirrhosis. This course of disease finally leads to end-stage liver disease such as hepatocellular carcinoma, which is associated with increased morbidity and mortality. Although clinical trials show promising results, there is actually no pharmacological agent approved to treat NASH. Another important problem associated with NASH is that presently the liver biopsy is still the gold standard in diagnosis and for disease staging and grading. Because of its invasiveness, this technique is not well accepted by patients and the method is prone to sampling error. Therefore, an urgent need exists to find reliable, accurate and noninvasive biomarkers discriminating between different disease stages or to develop innovative imaging techniques to quantify steatosis.