Disentangling the Progression of Non-alcoholic Fatty Liver Disease in the Human Gut Microbiota

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

Gulf War Illness Is Associated with Host Gut Microbiome Dysbiosis and Is Linked to Altered Species Abundance in Veterans from the BBRAIN Cohort

Gulf War Illness (GWI) is a debilitating condition marked by chronic fatigue, cognitive problems, pain, and gastrointestinal (GI) complaints in veterans who were deployed to the 1990-1991 Gulf War. Fatigue, GI complaints, and other chronic symptoms continue to persist more than 30 years post-deployment. Several potential mechanisms for the persistent illness have been identified and our prior pilot study linked an altered gut microbiome with the disorder. This study further validates and builds on our prior preliminary findings of host gut microbiome dysbiosis in veterans with GWI. Using stool samples and Multidimensional Fatigue Inventory (MFI) data from 89 GW veteran participants (63 GWI cases and 26 controls) from the Boston biorepository, recruitment, and integrative network (BBRAIN) for Gulf War Illness, we found that the host gut bacterial signature of veterans with GWI showed significantly different Bray-Curtis beta diversity than control veterans. Specifically, a higher Firmicutes to Bacteroidetes ratio, decrease in Akkermansia sp., Bacteroides thetaiotamicron, Bacteroides fragilis, and Lachnospiraceae genera and increase in Blautia, Streptococcus, Klebsiella, and Clostridium genera, that are associated with gut, immune, and brain health, were shown. Further, using MaAsLin and Boruta algorithms, Coprococcus and Eisenbergiella were identified as important predictors of GWI with an area under the curve ROC predictive value of 74.8%. Higher self-reported MFI scores in veterans with GWI were also significantly associated with an altered gut bacterial diversity and species abundance of Lachnospiraceae and Blautia. These results suggest potential therapeutic targets for veterans with GWI that target the gut microbiome and specific symptoms of the illness.

Implications of Microbiota and Immune System in Development and Progression of Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent type of liver disease worldwide. The exact pathophysiology behind MASLD remains unclear; however, it is thought that a combination of factors or "hits" act as precipitants for disease onset and progression. Abundant evidence supports the roles of diet, genes, metabolic dysregulation, and the intestinal microbiome in influencing the accumulation of lipids in hepatocytes and subsequent progression to inflammation and fibrosis. Currently, there is no cure for MASLD, but lifestyle changes have been the prevailing cornerstones of management. Research is now focusing on the intestinal microbiome as a potential therapeutic target for MASLD, with the spotlight shifting to probiotics, antibiotics, and fecal microbiota transplantation. In this review, we provide an overview of how intestinal microbiota interact with the immune system to contribute to the pathogenesis of MASLD and metabolic dysfunction-associated steatohepatitis (MASH). We also summarize key microbial taxa implicated in the disease and discuss evidence supporting microbial-targeted therapies in its management.

Gut microbiota induced epigenetic modifications in the non-alcoholic fatty liver disease pathogenesis

Non-alcoholic fatty liver disease (NAFLD) represents a growing global health concern that can lead to liver disease and cancer. It is characterized by an excessive accumulation of fat in the liver, unrelated to excessive alcohol consumption. Studies indicate that the gut microbiota-host crosstalk may play a causal role in NAFLD pathogenesis, with epigenetic modification serving as a key mechanism for regulating this interaction. In this review, we explore how the interplay between gut microbiota and the host epigenome impacts the development of NAFLD. Specifically, we discuss how gut microbiota-derived factors, such as lipopolysaccharides (LPS) and short-chain fatty acids (SCFAs), can modulate the DNA methylation and histone acetylation of genes associated with NAFLD, subsequently affecting lipid metabolism and immune homeostasis. Although the current literature suggests a link between gut microbiota and NAFLD development, our understanding of the molecular mechanisms and signaling pathways underlying this crosstalk remains limited. Therefore, more comprehensive epigenomic and multi-omic studies, including broader clinical and animal experiments, are needed to further explore the mechanisms linking the gut microbiota to NAFLD-associated genes. These studies are anticipated to improve microbial markers based on epigenetic strategies and provide novel insights into the pathogenesis of NAFLD, ultimately addressing a significant unmet clinical need.

Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis

BACKGROUND

Invasive detection methods such as liver biopsy are currently the gold standard for diagnosing liver cirrhosis and can be used to determine the degree of liver fibrosis and cirrhosis. In contrast, non-invasive diagnostic methods, such as ultrasonography, elastography, and clinical prediction scores, can prevent patients from invasiveness-related discomfort and risks and are often chosen as alternative or supplementary diagnostic methods for liver fibrosis or cirrhosis. However, these non-invasive methods cannot specify the pathological grading and early diagnosis of the lesions. Recent studies have revealed that gut microbiome-based machine learning can be utilized as a non-invasive diagnostic technique for liver cirrhosis or fibrosis, but there is no evidence-based support. Therefore, this study conducted a systematic review and meta-analysis for the first time to investigate the accuracy of machine learning based on the gut microbiota in the prediction of liver fibrosis and cirrhosis.

METHODS

A comprehensive and systematic search of publications published before April 2th, 2023 in PubMed, Cochrane Library, Embase, and Web of Science was conducted for relevant studies on the application of gut microbiome-based metagenomic sequencing modeling technology to the diagnostic prediction of liver cirrhosis or fibrosis. A bivariate mixed-effects model and Stata software 15.0 were adopted for the meta-analysis.

RESULTS

Ten studies were included in the present study, involving 11 prediction trials and 838 participants, 403 of whom were fibrotic and cirrhotic patients. Meta-analysis showed the pooled sensitivity (SEN) = 0.81 [0.75, 0.85], specificity (SEP) = 0.85 [0.77, 0.91], positive likelihood ratio (PLR) = 5.5 [3.6, 8.7], negative likelihood ratio (NLR) = 0.23 [0.18, 0.29], diagnostic odds ratio (DOR) = 24 [14, 41], and area under curve (AUC) = 0.86 [0.83-0.89]. The results demonstrated that machine learning methods had excellent potential to analyze gut microbiome data and could effectively predict liver cirrhosis or fibrosis. Machine learning provides a powerful tool for non-invasive prediction and diagnosis of liver cirrhosis or liver fibrosis, with broad clinical application prospects. However, these results need to be interpreted with caution due to limited clinical data.

CONCLUSION

Gut microbiome-based machine learning can be utilized as a practical, non-invasive technique for the diagnostic prediction of liver cirrhosis or fibrosis. However, most of the included studies applied the random forest algorithm in modeling, so a diversified prediction system based on microorganisms is needed to improve the non-invasive detection of liver cirrhosis or fibrosis.

Association between gut microbiota and hepatocellular carcinoma from 2011 to 2022: Bibliometric analysis and global trends

Background

Hepatocellular carcinoma (HCC) is a primary malignant tumor responsible for approximately 90% of all liver cancers in humans, making it one of the leading public health problems worldwide. The gut microbiota is a complex microbial ecosystem that can influence tumor formation, metastasis, and resistance to treatment. Therefore, understanding the potential mechanisms of gut microbiota pathogenesis is critical for the prevention and treatment of HCC.

Materials and methods

A search was conducted in the Web of Science Core Collection (WoSCC) database for English literature studies on the relationship between gut microbiota and HCC from 2011 to 2022. Bibliometric analysis tools such as VOSviewer, CiteSpace, and R Studio were used to analyze global trends and research hotspots in this field.

Results

A total of 739 eligible publications, comprising of 383 articles and 356 reviews, were analyzed. Over the past 11 years, there has been a rapid increase in the annual number of publications and average citation levels, especially in the last five years. The majority of published articles on this topic originated from China (n=257, 34.78%), followed by the United States of America (n=203, 27.47%), and Italy (n=85, 11.50%). American scholars demonstrated high productivity, prominence, and academic environment influence in the research of this subject. Furthermore, the University of California, San Diego published the most papers (n=24) and had the highest average citation value (value=152.17) in the study of the relationship between gut microbiota and HCC. Schnabl B from the USA and Ohtani N from Japan were the authors with the highest number of publications and average citation value, respectively.

Conclusion

In recent years, research on the gut microbiota's role in HCC has made rapid progress. Through a review of published literature, it has been found that the gut microbiota is crucial in the pathogenesis of HCC and in oncotherapy.

Yinchen Linggui Zhugan decoction ameliorates high fat diet-induced nonalcoholic fatty liver disease by modulation of SIRT1/Nrf2 signaling pathway and gut microbiota

Yinchen Linggui Zhugan decoction (YLZD) is an effective and classical traditional herbal prescription for treating the nonalcoholic fatty liver disease (NAFLD) and has been proven to be effective in the regulation of lipid metabolism disorder and attenuate inflammation for a NAFLD rat model. However, the exact underlying mechanism has not been elucidated. In the current study, a NAFLD rat model was established using a high-fat diet (HFD) for 10 weeks, followed by YLZD treatment with 1.92 g/kg/day for 4 weeks to explore the mechanisms of YLZD. Our results showed that YLZD decreased the hepatic lipid deposition, restored the liver tissue pathological lesions, inhibited the expression of oxidative stress, and decreased the inflammatory cytokines levels. Meanwhile, the genes and proteins expressions of SIRT1/Nrf2 signaling pathway together with downstream factors including HO-1 and NQO1 were elevated in the YLZD treated NAFLD rats. For further elaborating the upstream mechanism, short-chain fatty acids (SCFAs) in serum and feces were measured by liquid chromatograph mass spectrometer and gas chromatograph mass spectrometer, and the differences in gut microbiota of rats in each group were analyzed through high-throughput sequencing of 16S rRNA. The results demonstrated that the contents of butyric acid (BA) and total SCFAs in YLZD-treated NAFLD rats were significantly increased in serum and feces. 16S rRNA sequencing analysis illustrated that YLZD intervention led to a modification of the gut microbiota composition, with a decrease of Oribacterium, Lactobacillus and the ratio of Firmicutes/Bacteroides, as well as the increase in SCFAs-producing bacteria such as Christensenellaceae, Clostridia, Muribaculaceae, and Prevotellaceae. Spearman rank correlation analysis indicated that BA and total SCFAs were negatively co-related with oxidative stress-related factors and inflammatory cytokines, while they were positively co-related with SIRT1/Nrf2 pathway related genes and proteins. Furthermore, in vitro study confirmed that BA effectively reduced oxidative stress by activating SIRT1/Nrf2 signaling pathway in L02 cells. Together, the present data revealed YLZD could ameliorate HFD-induced NAFLD in rats by the modulation of SIRT1/Nrf2 signaling pathway and gut microbiota.

Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions

Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793-0.8838 compared to 0.6759-0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.

Anti-Inflammatory Dietary Approach to Prevent the Development and Progression of Non-Alcoholic Fatty Liver Diseases

Non-alcoholic fatty liver disease (NAFLD) is an increasing health problem worldwide and is associated with insulin resistance, increased visceral fat mass, and cardiovascular problems. Lifestyle factors such as sedentary lifestyle, chronic stress, obesogenic environment as well as a Western pattern diet are main contributors to the development and progression of this disease. In particular, the diet plays a pivotal role. An unhealthy diet including high consumption of red and processed meats, refined carbohydrates, simple sugars, highly processed foods with food additives and conservatives are lighting the fire for a low-grade inflammation. If other risk factors come into play, metabolic and hormonal derangement may occur, leading to the increase in visceral fat, gut dysbiosis and leaky gut, which stoke the inflammatory fire. Thus, lifestyle interventions are the most effective approach to quell the inflammatory processes. An anti-inflammatory and low-glycemic diet named the GLykLich diet, which includes whole and unprocessed foods, may reduce the risk of increased morbidity and mortality. The GLykLich diet suggests a meal consisting of complex carbohydrates (fiber), good quality of protein and healthy fats (DHA/EPA), and is rich in secondary plant products. There is no single nutrient to prevent the progression of NAFLD, rather, it is the complexity of substances in whole unprocessed foods that reduce the inflammatory process, improve metabolic state, and thus reverse NAFLD.