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

Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis

Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major cause of liver fibrosis, but there are no non-invasive tests for diagnosing or preventing it. In our study, we analyzed data from multiple sources to find crucial genes linked to NASH fibrosis. We built diagnostic models using 103 machine learning algorithms and validated them with two external datasets. All models performed well, with the best one (RF + Enet[alpha = 0.6]) achieving an average AUC of 0.822. This model used five key genes: LUM, COL1A2, THBS2, COL5A2, and NTS. Our findings show that these genes are important in collagen and extracellular matrix pathways, shedding light on how NASH progresses to liver fibrosis. We also found that certain immune cells, like M1 macrophages, are involved in this process. This study provides a reliable diagnostic tool for assessing fibrosis risk in NASH patients and suggests potential for immunotherapy, laying a foundation for future treatments.

Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3

AIMS

Nonalcoholic fatty liver disease (NAFLD) is a multisystem disease that can trigger the metabolic syndrome. Early prevention and treatment of NAFLD is still a huge challenge for patients and clinicians. The aim of this study was to develop and validate machine learning (ML)-based predictive models. The model with optimal performance would be developed as a set of simple arithmetic tools for predicting the risk of NAFLD individually.

METHODS

Statistical analyses were performed in 2428 individuals extracted from the National Health and Nutrition Examination Survey (NHANES, cycle 2017-2020.3) database. Feature variables were selected by the least absolute shrinkage and selection operator (LASSO) regression. Seven ML algorithms, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), K-nearest neighbor (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to construct models based on the feature variables and evaluate their performance. The model with the best performance was transformed into a diagnostic predictive nomogram (DPN). The DPN was developed into an online calculator and an Excel algorithm tool. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and subgroup analyses were used to compare and assess the predictive abilities of the DPN and six existing NAFLD predictive models, including the ZJU index, the hepatic steatosis index (HSI), the triglyceride-glucose index (TyG), the Framingham steatosis index (FSI), the fatty liver index (FLI), and the visceral adiposity index (VAI).

RESULTS

Among the 2428 participants, the prevalence of NAFLD was 47.45%. LASSO regression identified eight variables from 39 variables, including body mass index (BMI), waist circumference (WC), alanine aminotransferase (ALT), triglyceride (TG), diabetes, hypertension, uric acid (UA), and race. Among the models constructed by the seven algorithms mentioned above, the LR-based model performed the best, demonstrating outstanding performance in terms of area under the curve (AUC, 0.823), accuracy (0.754), precision (0.768), specificity (0.804), and positive predictive value (0.768). It was then transformed into the DPN, which was successfully developed as an online calculator and an Excel algorithm tool. The diagnostic accuracy (AUC 0.856, 95% confidence interval (CI) 0.839-0.874, and AUC 0.823, 95% CI 0.793-0.854, respectively) and net clinical benefit of DPN in the training and validation sets were superior to those of the ZJU, HSI, TyG, FSI, FLI, and VAI. The results were maintained in subgroup analyses.

CONCLUSIONS

The LR model based on ML was developed, exhibiting good performance. DPN can be used as an individualized tool for rapid detection of NAFLD.

The impact of a low-calorie, reduced-fat diet on liver attenuation imaging: a randomized clinical trial

PURPOSE

To investigate whether a low-calorie, reduced-fat diet affects liver attenuation imaging (ATI) measurements.

METHODS

A total of 320 participants were enrolled in this prospective study. They were randomly assigned to four groups: a fasting group, a postprandial 0.5-hour examination group, a postprandial 2-hour examination group, and a postprandial 4-hour examination group. All participants first underwent liver ATI examination in a fasting state. Those in the postprandial groups then consumed a low-calorie, reduced-fat diet before undergoing a second ATI examination at 0.5 h, 2 h, or 4 h after the meal. The ATI values were compared among the groups. The differences between postprandial and fasting ATI values were also analyzed for the postprandial groups. Additionally, the consistency of the grading diagnosis of hepatic steatosis between the postprandial and fasting states was evaluated in the postprandial groups.

RESULTS

The ATI values for the 0.5 h postprandial group, 2 h postprandial group, and 4 h postprandial group were not significantly different from those of the fasting group (P = 0.576, 0.471, and 0.992, respectively). No significant differences were noted in the ATI values recorded during the postprandial and fasting states within each of the postprandial groups (P = 0.573, 0.076, and 0.805, respectively). The kappa values for diagnostic consistency between the postprandial and fasting states across the three divergent criteria were 0.833-0.951, 0.812-0.855, and 0.737-0.862, respectively.

CONCLUSION

A low-calorie, reduced-fat diet does not significantly affect liver ATI measurements or the grading of hepatic steatosis. However, the lack of representation of older adults and populations with higher BMIs in this study may limit its generalizability, with the lack of external validation as a limitation. These issues should be tested and confirmed in further studies.

CLINICAL TRIAL NUMBER

(ChiCTR2200062314, August 2022).

Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation

Introduction

Non-alcoholic fatty liver disease (NAFLD) represents a major global health challenge, often undiagnosed because of suboptimal screening tools. Advances in machine learning (ML) offer potential improvements in predictive diagnostics, leveraging complex clinical datasets.

Methods

We utilized a comprehensive dataset from the Dryad database for model development and training and performed external validation using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020 cycles. Seven distinct ML models were developed and rigorously evaluated. Additionally, we employed the SHapley Additive exPlanations (SHAP) method to enhance the interpretability of the models, allowing for a detailed understanding of how each variable contributes to predictive outcomes.

Results

A total of 14,913 participants were eligible for this study. Among the seven constructed models, the light gradient boosting machine achieved the highest performance, with an area under the receiver operating characteristic curve of 0.90 in the internal validation set and 0.81 in the external NHANES validation cohort. In detailed performance metrics, it maintained an accuracy of 87%, a sensitivity of 92.9%, and an F1 score of 0.92. Key predictive variables identified included alanine aminotransferase, gammaglutamyl transpeptidase, triglyceride glucose-waist circumference, metabolic score for insulin resistance, and HbA1c, which are strongly associated with metabolic dysfunctions integral to NAFLD progression.

Conclusions

The integration of ML with SHAP interpretability provides a robust predictive tool for NAFLD, enhancing the early identification and potential management of the disease. The model's high accuracy and generalizability across diverse populations highlight its clinical utility, though future enhancements should include longitudinal data and lifestyle factors to refine risk assessments further.

Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features

Objective

This study aimed to investigate the use of radiomics features and clinical information by four machine learning algorithms for predicting the prognosis of patients with hepatocellular carcinoma (HCC) who have been treated with transarterial chemoembolization (TACE).

Methods

A total of 105 patients with HCC treated with TACE from 2002 to 2012 were enrolled retrospectively and randomly divided into two cohorts for training (n = 74) and validation (n = 31) according to a ratio of 7:3. The Spearman rank, random forest, and univariate Cox regression were used to select the optimal radiomics features. Univariate Cox regression was used to select clinical features. Four machine learning algorithms were used to develop the models: random survival forest, eXtreme gradient boosting (XGBoost), gradient boosting, and the Cox proportional hazard regression model. The area under the curve (AUC) and C-index were devoted to assessing the performance of the models in predicting HCC prognosis.

Results

A total of 1,834 radiomics features were extracted from the computed tomography images of each patient. The clinical risk factors for HCC prognosis were age at diagnosis, TNM stage, and metastasis, which were analyzed using univariate Cox regression. In various models, the efficacy of the combined models generally surpassed that of the radiomics and clinical models. Among four machine learning algorithms, XGBoost exhibited the best performance in combined models, achieving an AUC of 0.979 in the training set and 0.750 in the testing set, demonstrating its strong prognostic prediction capability.

Conclusion

The superior performance of the XGBoost-based combined model underscores its potential as a powerful tool for enhancing the precision of prognostic assessments for patients with HCC.

A machine learning model predicts stroke associated with blood cadmium level

Stroke is the leading cause of death and disability worldwide. Cadmium is a prevalent environmental toxicant that may contribute to cardiovascular disease, including stroke. We aimed to build an effective and interpretable machine learning (ML) model that links blood cadmium to the identification of stroke. Our data exploring the association between blood cadmium and stroke came from the National Health and Nutrition Examination Survey (NHANES, 2013-2014). In total, 2664 participants were eligible for this study. We divided these data into a training set (80%) and a test set (20%). To analyze the relationship between blood cadmium and stroke, a multivariate logistic regression analysis was performed. We constructed and tested five ML algorithms including K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), multilayer perceptron (MLP), and random forest (RF). The best-performing model was selected to identify stroke in US adults. Finally, the features were interpreted using the Shapley Additive exPlanations (SHAP) tool. In the total population, participants in the second, third, and fourth quartiles had an odds ratio of 1.32 (95% CI 0.55, 3.14), 1.65 (95% CI 0.71, 3.83), and 2.67 (95% CI 1.10, 6.49) for stroke compared with the lowest reference group for blood cadmium, respectively. This blood cadmium-based LR approach demonstrated the greatest performance in identifying stroke (area under the operator curve: 0.800, accuracy: 0.966). Employing interpretable methods, we found blood cadmium to be a notable contributor to the predictive model. We found that blood cadmium was positively correlated with stroke risk and that stroke risk from cadmium exposure could be effectively predicted by using ML modeling.

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

Steatotic Liver Disease: Pathophysiology and Emerging Pharmacotherapies

Steatotic liver disease (SLD) displays a dynamic and complex disease phenotype. Consequently, the metabolic dysfunction-associated steatotic liver disease (MASLD)/metabolic dysfunction-associated steatohepatitis (MASH) therapeutic pipeline is expanding rapidly and in multiple directions. In parallel, noninvasive tools for diagnosing and monitoring responses to therapeutic interventions are being studied, and clinically feasible findings are being explored as primary outcomes in interventional trials. The realization that distinct subgroups exist under the umbrella of SLD should guide more precise and personalized treatment recommendations and facilitate advancements in pharmacotherapeutics. This review summarizes recent updates of pathophysiology-based nomenclature and outlines both effective pharmacotherapeutics and those in the pipeline for MASLD/MASH, detailing their mode of action and the current status of phase 2 and 3 clinical trials. Of the extensive arsenal of pharmacotherapeutics in the MASLD/MASH pipeline, several have been rejected, whereas other, mainly monotherapy options, have shown only marginal benefits and are now being tested as part of combination therapies, yet others are still in development as monotherapies. Although the Food and Drug Administration (FDA) has recently approved resmetirom, additional therapeutic approaches in development will ideally target MASH and fibrosis while improving cardiometabolic risk factors. Due to the urgent need for the development of novel therapeutic strategies and the potential availability of safety and tolerability data, repurposing existing and approved drugs is an appealing option. Finally, it is essential to highlight that SLD and, by extension, MASLD should be recognized and approached as a systemic disease affecting multiple organs, with the vigorous implementation of interdisciplinary and coordinated action plans. SIGNIFICANCE STATEMENT: Steatotic liver disease (SLD), including metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis, is the most prevalent chronic liver condition, affecting more than one-fourth of the global population. This review aims to provide the most recent information regarding SLD pathophysiology, diagnosis, and management according to the latest advancements in the guidelines and clinical trials. Collectively, it is hoped that the information provided furthers the understanding of the current state of SLD with direct clinical implications and stimulates research initiatives.

Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach

Objective

Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most prevalent liver diseases and is associated with pre-hypertension and hypertension. Our research aims to develop interpretable machine learning (ML) models to accurately identify MASLD in hypertensive and pre-hypertensive populations.

Methods

The dataset for 4722 hypertensive and pre-hypertensive patients is from subjects in the NAGALA study. Six ML models, including the decision tree, K-nearest neighbor, gradient boosting, naive Bayes, support vector machine, and random forest (RF) models, were used in this study. The optimal model was constructed according to the performances of models evaluated by K-fold cross-validation (k = 5), the area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, sensitivity, specificity, and F1. Shapley additive explanation (SHAP) values were employed for both global and local interpretation of the model results.

Results

The prevalence of MASLD in hypertensive and pre-hypertensive patients was 44.3% (362 cases) and 28.3% (1107 cases), respectively. The RF model outperformed the other five models with an AUC of 0.889, AP of 0.800, accuracy of 0.819, sensitivity of 0.816, specificity of 0.821, and F1 of 0.729. According to the SHAP analysis, the top five important features were alanine aminotransferase, body mass index, waist circumference, high-density lipoprotein cholesterol, and total cholesterol. Further analysis of the feature selection in the RF model revealed that incorporating all features leads to optimal model performance.

Conclusions

ML algorithms, especially RF algorithm, improve the accuracy of MASLD identification, and the global and local interpretation of the RF model results enables us to intuitively understand how various features affect the chances of MASLD in patients with hypertension and pre-hypertension.

Automated machine learning models for nonalcoholic fatty liver disease assessed by controlled attenuation parameter from the NHANES 2017-2020

Background

Nonalcoholic fatty liver disease (NAFLD) is recognized as one of the most common chronic liver diseases worldwide. This study aims to assess the efficacy of automated machine learning (AutoML) in the identification of NAFLD using a population-based cross-sectional database.

Methods

All data, including laboratory examinations, anthropometric measurements, and demographic variables, were obtained from the National Health and Nutrition Examination Survey (NHANES). NAFLD was defined by controlled attenuation parameter (CAP) in liver transient ultrasound elastography. The least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection. Six algorithms were utilized on the H2O-automated machine learning platform: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost), and Deep Learning (DL). These algorithms were selected for their diverse strengths, including their ability to handle complex, non-linear relationships, provide high predictive accuracy, and ensure interpretability. The models were evaluated by area under receiver operating characteristic curves (AUC) and interpreted by the calibration curve, the decision curve analysis, variable importance plot, SHapley Additive exPlanation plot, partial dependence plots, and local interpretable model agnostic explanation plot.

Results

A total of 4177 participants (non-NAFLD 3167 vs NAFLD 1010) were included to develop and validate the AutoML models. The model developed by XGBoost performed better than other models in AutoML, achieving an AUC of 0.859, an accuracy of 0.795, a sensitivity of 0.773, and a specificity of 0.802 on the validation set.

Conclusions

We developed an XGBoost model to better evaluate the presence of NAFLD. Based on the XGBoost model, we created an R Shiny web-based application named Shiny NAFLD (http://39.101.122.171:3838/App2/). This application demonstrates the potential of AutoML in clinical research and practice, offering a promising tool for the real-world identification of NAFLD.