Non-invasive diagnosis of non-alcoholic fatty liver disease: Current status and future perspective

Identification of hub gene for the pathogenic mechanism and diagnosis of MASLD by enhanced bioinformatics analysis and machine learning

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous disease caused by multiple etiologies. It is characterized by excessive fat accumulation in the liver. Without intervention, MASLD can progress from steatosis to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis and even to cirrhosis and hepatocellular carcinoma. However, the pathogenesis of MASH and the mechanism underlying the development of fibrosis remain poorly understood, posing challenges for accurate diagnosis of MASH and fibrosis. In this study, we analyzed tissue RNA-seq data and clinical information of healthy individuals and MASLD patients from multiple datasets, the key genes and pathways involved in the occurrence and progression of MASLD, MASH, and fibrosis were screened respectively. Our findings reveal that the development of MASLD, MASH and fibrosis is associated with lipid metabolism processes. Based on the RNA expression profiles of identified hub genes, we established three alternative diagnostic models for MASLD, MASH, and fibrosis. These models demonstrated excellent performance in the diagnosis of MASLD, MASH, and fibrosis, with AUC values exceeding 0.9, implicating its potential clinical values in disease diagnosis.

Non-Invasive Tests as a Replacement for Liver Biopsy in the Assessment of MASLD

Metabolic dysfunction-associated steatotic fatty liver disease (MASLD) is a worsening global health issue, affecting over one-third of the adult population and representing the leading cause of liver-related morbidity and mortality. MASLD is not only a key precursor to chronic liver disease, but also a systemic condition that leads to numerous extrahepatic complications, increasing the risk of cardiovascular diseases, chronic kidney disease, type 2 diabetes, and certain cancers. The primary reference method for assessing liver fibrosis, allowing for precise determination of its location and severity, remains liver biopsy. However, it is an invasive procedure and involves certain risks. In recent years, the importance of MASLD diagnosis using noninvasive diagnostic methods has been increasing, including serological markers, methods based on multi-omics, and imaging techniques such as liver elastography. This review presents data on the diagnosis and evaluation of this disease that may find application in future clinical practice. The focus is on presenting both currently used and newly identified noninvasive diagnostic methods that open up the prospect of gradually replacing biopsy in the diagnosis of MASLD.

Construction and evaluation of a diagnostic model for metabolic dysfunction-associated steatotic liver disease based on advanced glycation end products and their receptors

Background

Effective biomarkers for the diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) remain limited. This study aims to evaluate the potential of advanced glycation end products (AGEs) and their endogenous secretory receptor (esRAGE) as non-invasive biomarkers for diagnosing MASLD, to explore differences between obese and non-obese MASLD patients, and to develop a novel diagnostic model based on these biomarkers.

Methods

This study enrolled 341 participants, including 246 MASLD patients (118 non-obese, 128 obese) and 95 healthy controls. Serum AGEs and esRAGE levels were measured by ELISA. Key predictors were identified using the Lasso algorithm, and a diagnostic model was developed with logistic regression and visualized as nomograms. Diagnostic accuracy and utility were evaluated through the area under the curve (AUC), bootstrap validation, calibration curves, and decision curve analysis (DCA).

Results

Serum AGEs and esRAGE levels were significantly higher in MASLD patients compared to controls. Moreover, obese MASLD patients had higher esRAGE levels than non-obese ones, but no significant difference in AGEs levels was found. A diagnostic model incorporating age, WC, BMI, ALT, TG, HDL, AGEs, and esRAGE achieved an AUC of 0.963, with 94.3% sensitivity and 85.3% specificity. The AUC for bootstrap internal validation was 0.963 (95% CI: 0.944-0.982). Calibration curves showed strong predictive accuracy, and DCA demonstrated high net clinical benefit.

Conclusion

Serum AGEs and esRAGE serve as non-invasive biomarkers for distinguishing MASLD patients. We developed and validated diagnostic models for MASLD, offering valuable tools to identify at-risk populations and improve prevention and treatment strategies.

Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT

PURPOSE

This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.

MATERIALS AND METHODS

The study retrospectively enrolled 840 individuals who underwent non-contrast abdominal CT and quantitative CT (QCT) examinations at the First Affiliated Hospital of Zhengzhou University from July 2022 to May 2023. Subsequently, these participants were divided into a training set (n = 539) and a testing set (n = 301) in a 9:5 ratio. The liver fat content measured by experienced radiologists using QCT technology served as the reference standard. The liver images from the non-contrast abdominal CT scans were then segmented as regions of interest (ROI) from which radiomics features were extracted. Two-dimensional (2D) and three-dimensional (3D) radiomics models, as well as 2D and 3D deep learning models, were developed, and machine learning models based on clinical data were constructed for the four-category diagnosis of fatty liver. The characteristic curves for each model were plotted, and area under the receiver operating characteristic curve (AUC) were calculated to assess their efficacy in the classification and diagnosis of fatty liver.

RESULTS

A total of 840 participants were included (mean age 49.1 years ± 11.5 years [SD]; 581 males), of whom 610 (73%) had fatty liver. Among the patients with fatty liver, there were 302 with mild fatty liver (CT fat fraction of 5%-14%), 155 with moderate fatty liver (CT fat fraction of 14%-28%), and 153 with severe fatty liver (CT fat fraction >28%). Among all models used for diagnosing fatty liver, the 2D radiomics model based on the random forest algorithm achieved the highest AUC (0.973), while the 2D radiomics model based on the Bagging decision tree algorithm showed the highest sensitivity (0.873), specificity (0.939), accuracy (0.864), precision (0.880), and F1 score (0.876).

CONCLUSION

A systematic comparison was conducted on the performance of 2D and 3D radiomics models, as well as deep learning models, in the diagnosis of four-category fatty liver. This comprehensive model comparison provides a broader perspective for determining the optimal model for liver fat diagnosis. It was found that the 2D radiomics models based on the random forest and Bagging decision tree algorithms show high consistency with the QCT-based classification diagnosis of fatty liver used by experienced radiologists.

The relationship between nonalcoholic fatty liver disease and frailty: A systematic review and meta-analysis

BACKGROUND AND AIM

Frailty is frequently observed in end-stage liver disease of various etiologies, but its role in nonalcoholic fatty liver disease (NAFLD) remains incompletely understood. We aimed to conduct a systematic review and meta-analysis to assess the association and prevalence of frailty in NAFLD.

METHODS

A systematic review of PubMed/MEDLINE, EMBASE, Web of Science, and Scopus was performed. The random-effects model was used to estimate the pooled prevalence of frailty. Meta-analyzed odds ratios (OR) were calculated to examine the association between frailty and NAFLD.

RESULTS

Among the initial 430 articles identified, 18 studies were included. Three studies involving 3673 participants had a pooled OR of 2.03 (95% CI: 1.51-2.72; Iˆ2 = 1.1%; p < 0.0001) for the association between frailty and NAFLD. The pooled prevalence of frailty in individuals with NAFLD was 23% (95% CI: 13%-38%; Iˆ2 = 93.5%) using the liver frailty index (LFI) and 8% (95% CI: 3%-21%; Iˆ2 = 98.1%) using the Fried frailty index (FFI). NAFLD patients' mean grip strength and balance time were 26.4 kg (95% CI: 23.0-29.8) and 23s (95% CI: 10-35), respectively. Among studies that also included individuals with liver cirrhosis, grip strength was lower in those with cirrhosis vs. the broader population of those with NAFLD.

CONCLUSIONS

Our study suggests that frailty is highly prevalent in individuals with NAFLD, with a significantly higher prevalence compared to those without NAFLD. Individuals with NAFLD have more than two-fold increased odds of frailty. Assessing frailty in NAFLD patients enables targeted management to improve outcomes.

Diagnostic Accuracy of Non-Invasive Diagnostic Tests for Nonalcoholic Fatty Liver Disease: A Systematic Review and Network Meta-Analysis

PURPOSE

In recent decades, numerous non-invasive tests (NITs) for diagnosing nonalcoholic fatty liver disease (NAFLD) have been developed, however, a comprehensive comparison of their relative diagnostic accuracies is lacking. We aimed to assess and compare the diagnostic accuracy of various NITs for NAFLD using network meta-analysis (NMA).

MATERIALS AND METHODS

We conducted a systematic search in seven databases up to April 2024 to identify studies evaluating the diagnostic values of NITs, with liver biopsy as the gold standard. The participants included patients with suspected or confirmed NAFLD, irrespective of age, sex, ethnicity. Statistical analysis was conducted using R 4.0.3 for Bayesian NMA and STATA 17.0 for pairwise meta-analysis. Sensitivity, specificity, diagnostic odds ratio (DOR), area under the receiver operating characteristic curve (AUC), and superiority index were calculated. Bayesian calculations were performed using the Rstan package, specifying parameters like MCMC chain count, iteration count, and operational cycles. The methodological quality of included studies was assessed using the QUADAS-2 tool.

RESULTS

Out of 15,877 studies, 180 were included in the quantitative synthesis, and 102 were used in head-to-head meta-analyses. For diagnosing steatosis stage 1, Hydrogen Magnetic Resonance Spectroscopy (H-MRS, DOR 15,745,657.6, 95% CI 17.2-1,014,063.59) proved to be the most accurate. For significant fibrosis, HRI leading (DOR 80.94, 95% CI 6.46-391.41), For advanced fibrosis, CK-18 showed the highest performance (DOR 102654.16, 95% CI 1.6-134,059.8). For high-risk NASH, Real-Time Elastography showing the highest performance (DOR 18.1, 95% CI 0.7-96.33). Meta-regression analyses suggested that variability in the diagnostic accuracy of NITs for NAFLD may result from differences in study design, thresholds, populations, and performance indicators.

CONCLUSION

We conducted a network meta-analysis to rank the accuracy of these tests. While some results are promising, not all NITs demonstrate substantial accuracy, highlighting the need for validation with larger datasets. Future research should concentrate on studying the thresholds of NITs and enhancing the clarity of methodological reporting.

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.

The Metabolomic Footprint of Liver Fibrosis

Both experimental and clinical liver fibrosis leave a metabolic footprint that can be uncovered and defined using metabolomic approaches. Metabolomics combines pattern recognition algorithms with analytical chemistry, in particular, 1H and 13C nuclear magnetic resonance spectroscopy (NMR), gas chromatography-mass spectrometry (GC-MS) and various liquid chromatography-mass spectrometry (LC-MS) platforms. The analysis of liver fibrosis by each of these methodologies is reviewed separately. Surprisingly, there was little general agreement between studies within each of these three groups and also between groups. The metabolomic footprint determined by NMR (two or more hits between studies) comprised elevated lactate, acetate, choline, 3-hydroxybutyrate, glucose, histidine, methionine, glutamine, phenylalanine, tyrosine and citrate. For GC-MS, succinate, fumarate, malate, ascorbate, glutamate, glycine, serine and, in agreement with NMR, glutamine, phenylalanine, tyrosine and citrate were delineated. For LC-MS, only β-muricholic acid, tryptophan, acylcarnitine, p-cresol, valine and, in agreement with NMR, phosphocholine were identified. The metabolomic footprint of liver fibrosis was upregulated as regards glutamine, phenylalanine, tyrosine, citrate and phosphocholine. Several investigators employed traditional Chinese medicine (TCM) treatments to reverse experimental liver fibrosis, and a commentary is given on the chemical constituents that may possess fibrolytic activity. It is proposed that molecular docking procedures using these TCM constituents may lead to novel therapies for liver fibrosis affecting at least one-in-twenty persons globally, for which there is currently no pharmaceutical cure. This in-depth review summarizes the relevant literature on metabolomics and its implications in addressing the clinical problem of liver fibrosis, cirrhosis and its sequelae.

Chinese medicine in the treatment of non-alcoholic fatty liver disease based on network pharmacology: a review

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by abnormalities in hepatic fat deposition, the incidence of which has been increasing year by year in recent years. It has become the largest chronic liver disease globally and one of the important causes of cirrhosis and even primary liver cancer formation. The pathogenesis of NAFLD has not yet been fully clarified. Modern medicine lacks targeted clinical treatment protocols for NAFLD, and most drugs lack efficacy and have high side effects. In contrast, Traditional Chinese Medicine (TCM) has significant advantages in the treatment and prevention of NAFLD, which have been widely recognized by scholars around the world. In recent years, through the establishment of a "medicine-disease-target-pathway" network relationship, network pharmacology can explore the molecular basis of the role of medicines in disease prevention and treatment from various perspectives, predicting the pharmacological mechanism of the corresponding medicines. This approach is compatible with the holistic view and treatment based on pattern differentiation of TCM and has been widely used in TCM research. In this paper, by searching relevant databases such as PubMed, Web of Science, and Embase, we reviewed and analyzed the relevant signaling pathways and specific mechanisms of action of single Chinese medicine, Chinese medicine combinations, and Chinese patent medicine for the treatment of NAFLD in recent years. These related studies fully demonstrated the therapeutic characteristics of TCM with multi-components, multi-targets, and multi-pathways, which provided strong support for the exact efficacy of TCM exerted in the clinic. In conclusion, we believe that network pharmacology is more in line with the TCM mindset of treating diseases, but with some limitations. In the future, we should eliminate the potential risks of false positives and false negatives, clarify the interconnectivity between components, targets, and diseases, and conduct deeper clinical or experimental studies.

Berberine Effects in Pre-Fibrotic Stages of Non-Alcoholic Fatty Liver Disease-Clinical and Pre-Clinical Overview and Systematic Review of the Literature

Non-alcoholic fatty liver disease (NAFLD) is the predominant cause of chronic liver conditions, and its progression is marked by evolution to non-alcoholic steatosis, steatohepatitis, cirrhosis related to non-alcoholic steatohepatitis, and the potential occurrence of hepatocellular carcinoma. In our systematic review, we searched two databases, Medline (via Pubmed Central) and Scopus, from inception to 5 February 2024, and included 73 types of research (nine clinical studies and 64 pre-clinical studies) from 2854 published papers. Our extensive research highlights the impact of Berberine on NAFLD pathophysiology mechanisms, such as Adenosine Monophosphate-Activated Protein Kinase (AMPK), gut dysbiosis, peroxisome proliferator-activated receptor (PPAR), Sirtuins, and inflammasome. Studies involving human subjects showed a measurable reduction of liver fat in addition to improved profiles of serum lipids and hepatic enzymes. While current drugs for NAFLD treatment are either scarce or still in development or launch phases, Berberine presents a promising profile. However, improvements in its formulation are necessary to enhance the bioavailability of this natural substance.