The association between diverse serum folate with MAFLD and liver fibrosis based on NHANES 2017-2020.

Background: Metabolically Associated Fatty Liver Disease (MAFLD) marks a progression from the previous paradigm of Non-Alcoholic Fatty Liver Disease (NAFLD), presenting a redefined diagnostic framework that accentuates metabolic factors while recognizing non-alcoholic contributors. In our investigation, our principal aim was to scrutinize the conceivable correlation between diverse serum folate levels and the prevalence of MAFLD and liver fibrosis. Methods: In our investigation, we conducted an extensive analysis utilizing data derived from the National Health and Nutrition Examination Survey (NHANES) across the years 2017-2020. We aimed to investigate the association between different serum folate concentrations and the prevalence of MAFLD and liver fibrosis by comprehensive multivariate analysis. This analytical approach considered various variables, encompassing sociodemographic characteristics, lifestyle factors, hypertension, and diabetes. By including these potential confounders in our analysis, we aimed to ensure the stability of the findings regarding the association between different serum folate concentrations and the development of MAFLD and liver fibrosis. Results: In our investigation, we utilized multiple linear regression models to thoroughly analyze the data, revealing noteworthy insights. Evidently, elevated levels of both total folate and 5-MTHF exhibited a distinct negative correlation with CAP, while 5-MTHF demonstrated a notable negative correlation with LSM. Furthermore, multiple logistic regression models were employed for an in-depth examination of the data. As the concentrations of total folate and 5-MTHF in the serum increased, a substantial decrease in the likelihood of MAFLD and liver fibrosis occurrence was observed. Conclusion: The findings of this investigation robustly suggest the prevalence of MAFLD and liver fibrosis decreased significantly with the increase of serum concentrations of total folate and 5-MTHF.

Effect of circadian rhythm change on gut microbiota and the development of nonalcoholic fatty liver disease in mice.

BACKGROUND: This study was to investigate the effect and possible mechanism of circadian rhythm change on the development of nonalcoholic fatty liver disease (NAFLD) in mice. METHODS: A total of 80 male SPF-grade 4-week C57BL/6J mice were randomly divided into normal diet normal light/dark cycle (ND-LD) and high-fat diet all dark (HFD-DD) groups. Weight measurements were taken weekly, and after 24 weeks of intervention, 24 mice from both groups were randomly selected and analyzed. Additionally, the remaining mice in the HFD-DD group were divided into two groups: one group continued the high-fat all-dark treatment (HFD-DD-DD), and the other group was restored to normal light/dark cycle treatment (HFD-DD-LD). Mice were euthanized after a total of 48 weeks of intervention. Measurements were taken for each mouse including liver function serum indicators, liver tissue pathological sections, rhythm-related proteins, and determination of the gut microbiota community. RESULTS: The HFD induced NAFLD in mice, exhibiting symptoms such as obesity, lipid and glucose metabolism disorders, elevated liver enzymes, and decreased gut microbiota diversity. The composition of the gut microbiota was significantly different from that of the normal diet group, with a significant increase in the ratio of Firmicutes to Bacteroides. Restoration of normal light/dark cycles exacerbated the disorder of lipid metabolism, liver steatosis, and the expression of BMAL1 in mice and significantly reduced the diversity of gut microbiota. CONCLUSIONS: Circadian rhythm changes aggravate the development of NAFLD induced by a high-fat diet by affecting glucose metabolism, liver steatosis, and gut microbiota diversity. Restoration of normal circadian rhythm did not improve NAFLD. Our findings open up new avenues for the prevention, diagnosis, and treatment of NAFLD.

Identification of disease-related genes and construction of a gene co-expression database in non-alcoholic fatty liver disease.

Background: The mechanism of NAFLD progression remains incompletely understood. Current gene-centric analysis methods lack reproducibility in transcriptomic studies. Methods: A compendium of NAFLD tissue transcriptome datasets was analyzed. Gene co-expression modules were identified in the RNA-seq dataset GSE135251. Module genes were analyzed in the R gProfiler package for functional annotation. Module stability was assessed by sampling. Module reproducibility was analyzed by the ModulePreservation function in the WGCNA package. Analysis of variance (ANOVA) and Student's t-test was used to identify differential modules. The receiver operating characteristic (ROC) curve was used to illustrate the classification performance of modules. Connectivity Map was used to mine potential drugs for NAFLD treatment. Results: Sixteen gene co-expression modules were identified in NAFLD. These modules were associated with multiple functions such as nucleus, translation, transcription factors, vesicle, immune response, mitochondrion, collagen, and sterol biosynthesis. These modules were stable and reproducible in the other 10 datasets. Two modules were positively associated with steatosis and fibrosis and were differentially expressed between non-alcoholic steatohepatitis (NASH) and non-alcoholic fatty liver (NAFL). Three modules can efficiently separate control and NAFL. Four modules can separate NAFL and NASH. Two endoplasmic reticulum related modules were both upregulated in NAFL and NASH compared to normal control. Proportions of fibroblasts and M1 macrophages are positively correlated with fibrosis. Two hub genes Aebp1 and Fdft1 may play important roles in fibrosis and steatosis. m6A genes were strongly correlated with the expression of modules. Eight candidate drugs for NAFLD treatment were proposed. Finally, an easy-to-use NAFLD gene co-expression database was developed (available at https://nafld.shinyapps.io/shiny/). Conclusion: Two gene modules show good performance in stratifying NAFLD patients. The modules and hub genes may provide targets for disease treatment.

The clinical characteristics of pancreatic colloid carcinoma and the development and validation of its cancer-specific survival prediction nomogram.

Background: Pancreatic colloid carcinoma (CC) is a subtype of pancreatic ductal adenocarcinoma (DAC) with low incidence but high malignancy. Unfortunately, there is no consensus regarding the clinical features and prognostic factors associated with CC, and the prognosis is unpredictable. We aimed to assess the clinicopathological characteristics of this rare disease and develop a nomogram for predicting cancer-specific survival (CSS) in CC. Methods: We gathered comprehensive clinicopathological data from the Surveillance, Epidemiology, and End Results (SEER) database on 17,617 patients with DAC and 561 individuals with CC. Kaplan-Meier was used to plot each survival curve. Subsequently, we split the 561 patients with CC in a 7:3 split ratio between an internal training cohort (n=393) and an external validation cohort (n=168). The independent prognostic factors for CC patients in the training cohort were discovered using univariate and multivariate Cox regression analyses, and a nomogram was created. We assessed the nomogram's performance by using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results: The median for follow-up of CC patients was 15 months (range: 1-163 months), and the 1-, 3-, and 5-year CSS were 58.4%, 30.2% and 22.6%. For CC patients in the training cohort, age [hazard ratio (HR) =1.29; 95% confidence interval (CI): 1.00-1.65], sex (HR =0.64; 95% CI: 0.51-0.81), T3 stage (HR =2.21; 95% CI: 1.26-3.88), T4 stage (HR =2.76; 95% CI: 1.47-5.18), N1 stage (HR =1.29; 95% CI: 1.02-1.63), M1 stage (HR =1.60; 95% CI: 1.17-2.18), surgery (HR =0.30; 95% CI: 0.22-0.42), and radiotherapy (HR =0.76; 95% CI: 0.58-1.01) were the main predictors of the nomogram. The C-indexes of the training cohort and the validation cohort were 0.734 and 0.732, respectively. The 1-, 3-, and 5-year AUC values of the nomogram were predicted to be 0.827, 0.816, and 0.831 in the training cohort, 0.801, 0.841, and 0.835 in the validation cohort, respectively. Conclusions: Based on several clinical features, we established the first predictive model of CC. This nomogram could be used to guide treatment decisions in patients with CC.

The association between sleep duration, quality, and nonalcoholic fatty liver disease: A cross-sectional study.

Sleep can affect nonalcoholic fatty liver disease (NAFLD). We investigated the association between sleep duration, sleep quality, and NAFLD. From January to December 2018, 1,073 patients (age: 37.94 ± 10.88, Body Mass Index (BMI): 22.85 ± 3.27) were enrolled. Pittsburgh Sleep Quality Index Questionnaire and Munich Chronotype Questionnaire were used to assess sleep duration, quality, and habits. Ultrasonography was used to diagnose NAFLD. Multivariate logistic regression models were used to calculate the odds ratio (OR) and 95% confidence interval (CI) of the risk of NAFLD by different types of sleep duration and sleep quality. No significant differences in sleep time, sleep quality, and sleep habits between the NAFLD and the non-NAFLD groups were observed (P > 0.05). There was no correlation between sleep duration and NAFLD in the whole cohort. After adjusting for age, exercise, fasting plasma glucose, and BMI, the group with long sleep duration showed a decreased risk of NAFLD in men (OR = 0.01, 95% CI: 0.001-0.27, P = 0.032). However, in all four adjusted models, no correlation between sleep duration, quality, and NAFLD was found in women. In conclusion, sleep duration was significantly and negatively associated with NAFLD in men but not women. Prospective studies are required to confirm this association.

Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance.

BACKGROUND: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. HCC transcriptome has been extensively studied; however, the progress in disease mechanisms, prognosis, and treatment is still slow. METHODS: A rank-based module-centric workflow was introduced to analyze important modules associated with HCC development, prognosis, and drug resistance. The currently largest HCC cell line RNA-Seq dataset from the LIMORE database was used to construct the reference modules by weighted gene co-expression network analysis. RESULTS: Thirteen reference modules were identified with validated reproducibility. These modules were all associated with specific biological functions. Differentially expressed module analysis revealed the crucial modules during HCC development. Modules and hub genes are indicative of patient survival. Modules can differentiate patients in different HCC stages. Furthermore, drug resistance was revealed by drug-module association analysis. Based on differentially expressed modules and hub genes, six candidate drugs were screened. The hub genes of those modules merit further investigation. CONCLUSION: We proposed a reference module-based analysis of the HCC transcriptome. The identified modules are associated with HCC development, survival, and drug resistance. M3 and M6 may play important roles during HCV to HCC development. M1, M3, M5, and M7 are associated with HCC survival. High M4, high M9, low M1, and low M3 may be associated with dasatinib, doxorubicin, CD532, and simvastatin resistance. Our analysis provides useful information for HCC diagnosis and treatment.

Gene co-expression analysis identifies modules related to insufficient sleep in humans.

BACKGROUND: Insufficient sleep and circadian rhythm disruption may cause cancer, obesity, cardiovascular disease, and cognitive impairment. The underlying mechanisms need to be elucidated. METHOD: Weighted gene co-expression network analysis (WGCNA) was used to identify co-expressed modules. Connectivity Map tool was used to identify candidate drugs based on top connected genes. R ptestg package was utilized to detected module rhythmicity alteration. A hypergeometric test was used to test the enrichment of insomnia SNP signals in modules. Google Scholar was used to validate the modules and hub genes by literature. RESULTS: We identified a total of 45 co-expressed modules. These modules were stable and preserved. Eight modules were correlated with sleep restriction duration. Module rhythmicity was disrupted in sleep restriction subjects. Hub genes that involve in insufficient sleep also play important roles in sleep disorders. Insomnia GWAS signals were enriched in six modules. Finally, eight drugs associated with sleep disorders were identified. CONCLUSION: Systems biology method was used to identify sleep-related modules, hub genes, and candidate drugs. Module rhythmicity was altered in sleep insufficient subjects. Thiamphenicol, lisuride, timolol, and piretanide are novel candidates for sleep disorders.