Conjunctional Relationship between Serum Uric Acid and Serum Nickel with Non-Alcoholic Fatty Liver Disease in Men: A Cross-Sectional Study

Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning

BACKGROUND

Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steatohepatitis (MASH), is the most prevalent chronic liver disease. Currently, the management of MASLD is impeded by the lack of reliable diagnostic biomarkers and effective therapeutic strategies.

METHODS

We analyzed eight independent clinical MASLD datasets from the GEO database. Differential expression and weighted gene co-expression network analyses (WGCNA) were used to identify 23 genes related to inflammation. Five hub genes were selected using machine learning techniques (SVM-RFE, LASSO, and RandomForest) combined with a literature review. Nomograms were created to predict MASLD incidence, and the diagnostic potential of the hub genes was evaluated through receiver operating characteristic (ROC) curves. Additionally, Protein-Protein Interaction (PPI) networks, functional enrichment, and immune infiltration analyses were performed. Potential transcription factors and therapeutic agents were also explored. Finally, the expression and biological significance of these hub genes were validated using MASLD animal model, histological examination and transcriptomic profiles.

RESULTS

We identified five hub genes-UBD/FAT10, STMN2, LYZ, DUSP8, and GPR88-that are potential biomarkers for MASLD. These genes exhibited strong diagnostic potential, either individually or in combination.

CONCLUSION

This study highlights five key biomarkers as promising candidates for understanding MASLD. These findings offer new insights into the disease's pathophysiology and may contribute to the development of better diagnostic and therapeutic approaches.

Associations of Urinary Nickel with NAFLD and Liver Fibrosis in the USA: A Nationwide Cross‑Sectional Study

Despite the robust correlation between metabolic disorders and heavy metals, there has been limited research on the associations between nickel levels and non-alcoholic fatty liver disease (NAFLD) as well as liver fibrosis. This study aimed to examine the associations among urinary nickel, NAFLD, and liver fibrosis. The data utilized in this study were obtained from the National Health and Nutrition Examination Survey 2017-2020. A comprehensive screening process was conducted, resulting in the inclusion of a total of 3169 American adults in the analysis. The measurement of urinary nickel was conducted through inductively coupled-plasma mass spectrometry. Vibration-controlled transient elastography was employed to assess the controlled attenuation parameter and liver stiffness measurement as indicators for NAFLD and liver fibrosis, respectively. Multivariable logistic regression models were employed to evaluate the associations among urinary nickel, NAFLD, and liver fibrosis. Restricted cubic splines were employed to explored the nonlinear associations. After adjusting for all covariates, the correlation between the highest quartile of urinary nickel and NAFLD was found to be significant (OR = 1.65; 95% CI, 1.19-2.27). Subgroup analysis revealed that the correlation was significant only in men. A significant association occurred between the second quartile of urinary nickel and liver fibrosis (OR 1.88; 95% CI, 1.22-2.90). Restricted cubic spline showed that the relationship was linear between urinary nickel and NAFLD and non-monotonic, inverse U-shaped between urinary nickel and liver fibrosis. This cross-sectional study indicated that the risk of NAFLD is associated with urinary nickel, and this correlation was only present among males.

Association between multiple metals exposure and metabolic dysfunction-associated fatty liver disease among Chinese adults

BACKGROUND

Previous research has investigated the hepatotoxicity of single metal exposure. However, there is limited evidence about metal mixture and their association with metabolic dysfunction-associated fatty liver disease (MAFLD), particularly in the Chinese population.

OBJECTIVE

To investigate the individual and combine effects of 20 metals on MAFLD in a large population in China.

METHODS

This study included 3651 participants from the Medical Physical Examination Center of Tongji Hospital, Wuhan, China. MAFLD was identified based on ultrasonic graphic evidence of hepatic steatosis and the presence of overweight/obese, diabetes mellitus, or metabolic dysregulation. Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine urinary concentrations of 20 metals. Logistic regression was used to assess the relationship between individual metal and MAFLD, with results presented as odds ratios (ORs) and 95 % confidence intervals (CIs). Weighted quantile sum (WQS) regression was performed to evaluate the combine effect of metals.

RESULTS

The prevalence of MAFLD among the participants was 32.1 % (1173/3651). In singe-metal analysis, high exposure to zinc (OR =1.42; 95 % CI = 1.27, 1.59) and selenium (OR = 1.23; 95 % CI = 1.10, 1.39) were positively associated with MAFLD. No significant association was found for other metals. WQS regression analysis showed that urinary metal mixture was positively associated with MAFLD (OR = 1.32, 95 % CI: 1.15, 1.51), with zinc (50.4 %) being the largest contributor, followed by barium (10.8 %).

CONCLUSIONS

In conclusion, our finding suggested that exposure to the mixture of metals was positively correlated with MAFLD, with zinc being the major contributor.

Intestinal microbiota promoted NiONPs-induced liver fibrosis via effecting serum metabolism

Nickel oxide nanoparticles (NiONPs) are toxic heavy metal compounds that induce liver fibrosis and metabolic disorders. Current research shows that the intestinal microbiota regulates liver metabolism through the gut-liver axis. However, it is unclear whether NiONPs affect the intestinal microbiota and the relationship between microbiota and liver metabolic disorders. Therefore, in this study, we established liver fibrosis model by administering 0.015, 0.06 and 0.24 mg/mL NiONPs through tracheal instillation twice a week for 9 weeks in rats, then we collected serum and fecal sample for whole metabolomics and metagenomic sequencing. As the result of sequencing, we screened out seven metabolites (beta-D-glucuronide, methylmalonic acid, linoleic acid, phosphotidylcholine, lysophosphatidylinositol, docosapentaenoic acid and progesterone) that related to functional alterations (p < 0.05), and obtained a decrease of probiotics abundances (p < 0.05) as well as a variation of the microbiota enzyme activity (p < 0.05), indicating that NiONPs inhibited the proliferation of probiotics. As the result of correlation analysis, we found a positive correlation between differential metabolites and probiotics, such as lysophosphatidylinositol was positively correlated with Desulfuribacillus, Jeotgallibacillus and Rummeliibacillus (p < 0.05). We also found that differential metabolites had correlations with differential proteins and enzymes of intestinal microbiota, such as glucarate dehydratase, dihydroorotate dehydrogenase and acetyl-CoA carboxylase (p < 0.05). Finally, we screened six metabolic pathways with both differential intestinal microbiota enzymes and metabolites were involved, such as pentose and glucuronate interconversions, and linoleic acid metabolism. In vitro experiments showed that NiONPs increased the transcriptional expression of Col1A1 in LX-2 cells, while reducing the mRNA expression of serine/threonine activators, acetyl coenzyme carboxylase, and lysophosphatidylinositol synthase, and short chain fatty acid sodium butyrate can alleviate these variation trends. The results proved that the intestinal microbiota enzyme systems were associated with serum metabolites, suggesting that the disturbance of intestinal microbiota and reduction of probiotics promoted the occurrence and development of NiONPs-induced liver fibrosis by affecting metabolic pathways.

Association Between Non-alcoholic Fatty Liver Disease and Heavy Metal Exposure: a Systematic Review

Non-alcoholic fatty liver disease (NAFLD) is a debilitating disease with adverse effects including cirrhosis and hepatocellular carcinoma. Heavy metals can cause severe dysfunction in different body organs including the liver. This review offers the study regarding the positive or negative association between heavy metals exposure and non-alcoholic fatty liver disease. The method used in this study is a systematic review based on searching in the PubMed, Scopus, and Science direct databases with the keywords of fatty liver, non-alcohol fatty liver, heavy metal, mercury, cadmium, arsenic, chromium, thallium, lead, iron, zinc, and nickel. There were 2200 articles searched in databases, and after assessment, 28 articles were selected. Positive association is established between arsenic, cadmium, iron, lead, mercury, and fatty liver disease. A negative relationship is found between zinc, copper, and progressive fatty liver disease. Furthermore, laboratory methods for NAFLD diagnosis were examined according to the obtained manuscripts. Among the different diagnostic methods, magnetic resonance imaging (MRI) is a sensitive method.

Association Between Serum Uric Acid and Non-Alcoholic Fatty Liver Disease: An Updated Systematic Review and Meta-Analysis

Objective

Recent epidemiological evidence shows that there is an association between serum uric acid (SUA) levels and nonalcoholic fatty liver disease (NAFLD). The purpose of this meta-analysis is to summarize all available evidence and assess the associations between SUA levels and NAFLD.

Methods

Using two databases, Web of Science and PubMed, observational studies were applied from the establishment of the databases to June 2022. We used a random effect model to construct the pooled odds ratio (OR) and 95% confidence interval (CI) to appraise the association between SUA levels and NAFLD. The Begg's test was conducted to appraise publication bias.

Results

A total of 50 studies were included, involving 2,079,710 participants (719,013 NAFLD patients). The prevalence and incidence rates (95% CIs) of NAFLD in the patients with hyperuricemia were 65% (57-73%) and 31% (20-41%), respectively. Compared to participants with lower levels of SUA, the pooled OR (95% CI) of NAFLD in those with higher levels of SUA was 1.88 (95% CI: 1.76-2.00). In the subgroup analyses, we found that SUA levels were positively associated with NAFLD in all subgroups, according to study design, study quality, sample size, sex, comparison, age, or country.

Conclusion

This meta-analysis shows that increased SUA levels are positively associated with NAFLD. The results suggested that reducing SUA levels can be a potential strategy for the prevention of NAFLD.

Registration Number

PROSPERO-CRD42022358431.

Recent advances in the application of ionomics in metabolic diseases

Trace elements and minerals play a significant role in human health and diseases. In recent years, ionomics has been rapidly and widely applied to explore the distribution, regulation, and crosstalk of different elements in various physiological and pathological processes. On the basis of multi-elemental analytical techniques and bioinformatics methods, it is possible to elucidate the relationship between the metabolism and homeostasis of diverse elements and common diseases. The current review aims to provide an overview of recent advances in the application of ionomics in metabolic disease research. We mainly focuses on the studies about ionomic or multi-elemental profiling of different biological samples for several major types of metabolic diseases, such as diabetes mellitus, obesity, and metabolic syndrome, which reveal distinct and dynamic patterns of ion contents and their potential benefits in the detection and prognosis of these illnesses. Accumulation of copper, selenium, and environmental toxic metals as well as deficiency of zinc and magnesium appear to be the most significant risk factors for the majority of metabolic diseases, suggesting that imbalance of these elements may be involved in the pathogenesis of these diseases. Moreover, each type of metabolic diseases has shown a relatively unique distribution of ions in biofluids and hair/nails from patients, which might serve as potential indicators for the respective disease. Overall, ionomics not only improves our understanding of the association between elemental dyshomeostasis and the development of metabolic disease but also assists in the identification of new potential diagnostic and prognostic markers in translational medicine.