Effects of Moderate Alcohol Consumption in Non-Alcoholic Fatty Liver Disease

Any alcohol use in NAFLD patients is associated with significant changes to the intestinal virome

BACKGROUND AND AIMS

The prevalence of alcohol use disorder (AUD) and metabolic dysfunction-associated fatty liver disease (MAFLD) are increasing worldwide, leading to the increasing likelihood of both etiologies contributing to a patient's liver disease. However, the effects of modest alcohol use in NAFLD are controversial and more studies are needed. We compared the intestinal viromes of patients with AUD and NAFLD in order to evaluate the effect of alcohol consumption on the intestinal viromes of NAFLD patients by extracting virus-like particles and performing metagenomic sequencing.

APPROACH AND RESULTS

Viral nucleic acids were extracted from fecal samples and subjected to metagenomic sequencing. We demonstrate significant differences in the intestinal viromes of NAFLD and AUD patients, and that alcohol use in NAFLD patients reclassified to MAFLD accounted for significant differences in the intestinal viromes. The relative abundance of several Lactococcus phages was more similar between AUD patients and alcohol-consuming MAFLD patients than non-alcohol-consuming MAFLD patients and control subjects, and multivariate modeling using the most discriminating Lactococcus phages could better predict alcohol use in the MAFLD population than the alcohol-associated liver disease/NAFLD Index. Significant differences in the viral composition and diversity were also seen between MAFLD patients with low and moderate alcohol consumption compared with no alcohol consumption.

CONCLUSIONS

The intestinal virome of MAFLD patients who consume low to moderate amounts of alcohol are significantly different from those who do not, and many features of the intestinal virome of alcohol-consuming MAFLD patients resemble that of AUD patients.

Prediction of Fatty Liver Disease in a Chinese Population Using Machine-Learning Algorithms

BACKGROUND

Fatty liver disease (FLD) is an important risk factor for liver cancer and cardiovascular disease and can lead to significant social and economic burden. However, there is currently no nationwide epidemiological survey for FLD in China, making early FLD screening crucial for the Chinese population. Unfortunately, liver biopsy and abdominal ultrasound, the preferred methods for FLD diagnosis, are not practical for primary medical institutions. Therefore, the aim of this study was to develop machine learning (ML) models for screening individuals at high risk of FLD, and to provide a new perspective on early FLD diagnosis.

METHODS

This study included a total of 30,574 individuals between the ages of 18 and 70 who completed abdominal ultrasound and the related clinical examinations. Among them, 3474 individuals were diagnosed with FLD by abdominal ultrasound. We used 11 indicators to build eight classification models to predict FLD. The model prediction ability was evaluated by the area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, and kappa value. Feature importance analysis was assessed by Shapley value or root mean square error loss after permutations.

RESULTS

Among the eight ML models, the prediction accuracy of the extreme gradient boosting (XGBoost) model was highest at 89.77%. By feature importance analysis, we found that the body mass index, triglyceride, and alanine aminotransferase play important roles in FLD prediction.

CONCLUSION

XGBoost improves the efficiency and cost of large-scale FLD screening.

Ameliorative effects of chlorogenic acid on alcoholic liver injury in mice via gut microbiota informatics

Chlorogenic acid (CGA) is a functional phenolic acid widely used in food and medicine-related fields. It has been proved to be effective in the treatment of alcoholic liver disease (ALD). However, the exact mechanism by which CGA prevents ALD, especially from the crosstalk between gut and liver, has not been previously reported. This work was aimed to explore the protective effects of CGA against ALD and its relationships to gut-liver axis abnormalities. Experimental results showed the increased (p < 0.05) serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), low density lipoprotein (LDL), total cholesterol (TC) and triglyceride (TG) levels of mice fed with ethanol were ameliorated by supplementing with CGA. Moreover, CGA promoted the production of n-butyric acid by nearly 3 times (1.78 vs 0.62 nM, p < 0.01), a short-chain fatty acid that helps maintain the integrity of the intestinal barrier. Furthermore, CGA alleviated microbial dysbiosis, evidenced by the increased relative abundances of beneficial bacteria Muribaculaceae, Bacteroides, Alloprevotella, and Parabacteroides, and decreased that of opportunistic pathogens Eubacterium_nodatum, Eubacterium_ruminantium, and Anaerotruncus. Correlation analysis further elucidated the microbiota altered after CGA intervention was positively correlated with short-chain fatty acids and antioxidant indexes, while negatively correlated with inflammatory cytokines. In summary, these findings suggested the hepatoprotective effect of CGA was ascribed to the modulation of gut-liver axis homeostasis.

Silibinin Schiff Base Derivatives Counteract CCl4-Induced Acute Liver Injury by Enhancing Anti-Inflammatory and Antiapoptotic Bioactivities

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Purpose

Patients and Methods

Results

Conclusion

Triglyceride Glucose-Body Mass Index and Risk of Incident Type 2 Diabetes Mellitus in Japanese People With Normal Glycemic Level: A Population-Based Longitudinal Cohort Study

BACKGROUND

It has been proved that triglyceride glucose-body mass index (TyG-BMI) is a readily available and clinically significant indicator of insulin resistance (IR). Nevertheless, the association between TyG-BMI and incident Type 2 diabetes mellitus (T2DM) remains uncertain. This study aimed to study the relationship between TyG-BMI and T2DM and explore the predictive characteristics of TyG-BMI.

METHODS

Our study was conducted as a longitudinal cohort study. 8,430 men and 7,034 women were enrolled and analyzed. They were both non-diabetic subjects with normal glycemic levels. Follow-up lasted for 13 years, from 1994 to 2016. To make the number of TyG-BMI in each group similar, the subjects were divided into four groups with 3866 subjects in each group.

RESULTS

During the 13-year follow-up period, 373 subjects were diagnosed with incident T2DM. Our multivariate Cox regression analysis revealed that TyG-BMI was an independent predictor of incident T2DM. In addition, our research identified four specific groups, young people (18-44 years old), women, the non-hypertensive population and non-drinkers were at significantly higher risk of developing TyG-BMI-related diabetes (P-interaction< 0.05). The best threshold TyG-BMI for predicting incident T2DM was 197.2987 (area under the curve 0.7738).

CONCLUSIONS

Our longitudinal cohort study demonstrated the positive correlation between baseline TyG-BMI and risk of incident T2DM in Japanese with normal glycemic levels, and this risk was significantly higher in the young people, women, the non-hypertensive population and non-drinkers.