Background The triglyceride and glucose index (TyG) and triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) are substitute markers of insulin resistance (IR). In a retrospective cross-sectional study, the authors aimed to compare the efficacy of the two indicators in diagnosing metabolic-associated fatty liver disease (MAFLD) to construct a novel disease diagnosis model. Methods Overall, 229 patients (97 MAFLD and 132 Non-MAFLD at West China Hospital of Sichuan University were included. MAFLD was diagnosed using ultrasonography. Biochemical indexes were collected and analyzed by logistic regression to screen out indicators that were expressed differently in MAFLD patients and healthy controls, which were incorporated into a diagnostic model. Results After adjusting for age, sex, and body mass index (BMI), serum alanine transaminase (ALT), aspartate transaminase (AST), AST/ALT (A/A), fasting plasma glucose (FPG), cystatin C (Cys-C), uric acid (URIC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), non-HDL-C, LDL-C/HDL-C, non-HDL-C/HDL-C, TG/HDL-C, TC/HDL-C, TyG, and TyG-BMI were risk factors for MAFLD. The odds ratio of TG/HDL-C and TyG were 5.629 (95%CI: 3.039–10.424) and 182.474 (95%CI: 33.518–993.407), respectively. In identifying MAFLD, TyG, TyG-BMI, TG, and TG/HDL-C were found to be the most vital indexes based on the random forest method, with the area under the curve (AUC) greater than 0.9. In addition, the combination of BMI, ALT, and TyG had a high diagnostic efficiency for MAFLD. Conclusions TyG and TG/HDL-C were potential risk factors for MAFLD, and the former performed better in diagnosing MAFLD. The combination of BMI, ALT, and TyG improved the diagnostic capability for MAFLD.

Introduction We aimed to confirm the association between some single nucleotide polymorphisms (SNPs) and metabolic dysfunction‐associated fatty liver disease (MAFLD) in western China. Methods A total of 286 cases and 250 healthy controls were enrolled in our study. All samples were genotyped for patatin‐like phospholipase domain containing 3 ( PNPLA3 ) rs738409, transmembrane 6 superfamily member 2 ( TM6SF2 ) rs58542926, membrane‐bound O‐acyltransferase domain containing 7 ( MBOAT7 ) rs641738, glucokinase regulator ( GCKR ) rs1260326 and rs780094, and GATA zinc finger domain containing 2A ( GATAD2A ) rs4808199. Using logistic regression analysis, we evaluated the association between MAFLD and each SNP under different models. Multiple linear regression was used to find the association between SNPs and laboratory characteristics. Multifactor dimensionality reduction was applied to test SNP–SNP interactions. Results The recessive model and additive model of PNPLA3 rs738409 variant were related to MAFLD (odds ratio [OR] = 1.791 and 1.377, respectively, p = 0.038 and 0.027, respectively). However, after Benjamini‐Hochberg adjustment for multiple tests, all associations were no longer statistically significant. PNPLA3 rs738409 correlated with AST levels. GCKR rs780094 and rs1260326 negatively correlated with serum glucose but positively correlated with triglycerides in MAFLD. Based on MDR analysis, the best single‐locus and multilocus models for MAFLD risk were rs738409 and six‐locus models, respectively. Conclusions In the Han population in western China, no association was found between these SNPs and the risk of MAFLD. PNPLA3 rs738409 was associated with aspartate aminotransferase levels in MAFLD patients. GCKR variants were associated with increased triglyceride levels and reduced serum fasting glucose in patients with MAFLD.

Background & Aims Dysregulated iron homeostasis plays an important role in the hepatic manifestation of metabolic‐associated fatty liver disease (MAFLD). We investigated the causal effects of five iron metabolism markers, regular iron supplementation and MAFLD risk. Methods Genetic summary statistics were obtained from open genome‐wide association study databases. Two‐sample bidirectional Mendelian randomization analysis was performed to estimate the causal effect between iron status and MAFLD, including Mendelian randomization inverse‐variance weighted, weighted median methods and Mendelian randomization‐Egger regression. The Mendelian randomization‐PRESSO outlier test, Cochran's Q test and Mendelian randomization‐Egger regression were used to assess outliers, heterogeneity and pleiotropy respectively. Results Mendelian randomization inverse‐variance weighted results showed that the genetically predicted per standard deviation increase in liver iron (Data set 2: odds ratio 1.193, 95% confidence interval [CI] 1.074–1.326, p = .001) was associated with an increased MAFLD risk, consistent with the weighted median estimates and Mendelian randomization–Egger regression, although Data set 1 was not significant. Mendelian randomization inverse‐variance weighted analysis showed that genetically predicted MAFLD was significantly associated with increased serum ferritin levels in both datasets (Dataset 1: β = .038, 95% CI = .014 to .062, p = .002; Dataset 2: β = .081, 95% CI = .025 to .136, p = .004), and a similar result was observed with the weighted median methods for Dataset 2 instead of Mendelian randomization‐Egger regression. Conclusions This study uncovered genetically predicted causal associations between iron metabolism status and MAFLD. These findings underscore the need for improved guidelines for managing MAFLD risk by emphasizing hepatic iron levels as a risk factor and ferritin levels as a prognostic factor.

ObjectiveMetabolic associated fatty liver disease (MAFLD) affects nearly a quarter of the world’s population. Our study aimed to characterize the gut microbiome and overall changes in the fecal and serum metabolomes in MAFLD patients.MethodsThirty-two patients diagnosed with MAFLD and 30 healthy individuals (control group, CG) were included in this study, the basic clinical characteristics and laboratory test results including routine biochemistry, etc. were recorded for all, and their serum and fecal samples were collected. A portion of the fecal samples was subjected to 16S rDNA sequencing, and the other portion of the fecal samples and serum samples were subjected to non-targeted metabolomic detection based on liquid chromatography-mass spectrometry (LC–MS). Statistical analysis of clinical data was performed using SPSS software package version 25.0 (SPSS Inc., Chicago, IL, United States). The analysis of 16S rDNA sequencing results was mainly performed by R software (V. 2.15.3), and the metabolomics data analysis was mainly performed by CD 3.1 software. Two-tailed p value < 0.05 was considered statistically significant.ResultsThe 16S sequencing data suggested that the species richness and diversity of MAFLD patients were reduced compared with controls. At the phylum level, the relative abundance of Bacteroidota, Pseudomonadota, and Fusobacteriota increased and Bacillota decreased in MAFLD patients. At the genus level, the relative abundances of Prevotella, Bacteroides, Escherichia-Shigella, etc. increased. 2,770 metabolites were detected in stool samples and 1,245 metabolites were detected in serum samples. The proportion of differential lipid metabolites in serum (49%) was higher than that in feces (21%). There were 22 differential metabolites shared in feces and serum. And the association analysis indicated that LPC 18:0 was positively correlated with Christensenellaceae_R-7_group, Oscillospiraceae_UCG-002; neohesperidin was also positively correlated with Peptoniphilus, Phycicoccus, and Stomatobaculum.ConclusionMicrobial sequencing data suggested decreased species richness and diversity and altered β-diversity in feces. Metabolomic analysis identified overall changes in fecal and serum metabolites dominated by lipid molecules. And the association analysis with gut microbes provided potentially pivotal gut microbiota-metabolite combinations in MAFLD patients, which might provide new clues for further research on the disease mechanism and the development of new diagnostic markers and treatments.

Tongue squamous cell carcinoma is highly malignant and has a poor prognosis. In this study, we aimed to combine whole-genome sequencing, whole-genome methylation, and whole-transcriptome analyses to understand the molecular mechanisms of tongue squamous cell carcinoma better. Oral tongue squamous cell carcinoma and adjacent normal tissues from five patients with tongue squamous cell carcinoma were included as five paired samples. After multi-omics sequencing, differentially methylated intervals, methylated loop sites, methylated promoters, and transcripts were screened for variation in all paired samples. Correlations were analyzed to determine biological processes in tongue squamous cell carcinoma. We found five mutated methylation promoters that were significantly associated with mRNA and lncRNA expression levels. Functional annotation of these transcripts revealed their involvement in triggering the mitogen-activated protein kinase cascade, which is associated with cancer progression and the development of drug resistance during treatment. The prognostic signature models constructed based on WDR81 and HNRNPH1 and combined clinical phenotype–gene prognostic signature models showed high predictive efficacy and can be applied to predict patient prognostic risk in clinical settings. We identified biological processes in tongue squamous cell carcinoma that are initiated by mutations in the methylation promoter and are associated with the expression levels of specific mRNAs and lncRNAs. Collectively, changes in transcript levels affect the prognosis of tongue squamous cell carcinoma patients.

Background: The triglyceride and glucose index (TyG) and triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) are substitute markers of insulin resistance (IR). In a retrospective cross-sectional study, the authors aimed to compare the efficacy of the two indicators in diagnosing metabolic-associated fatty liver disease (MAFLD) to construct a novel disease diagnosis model. Methods: Overall, 229 patients (97 MAFLD and 132 Non-MAFLD at West China Hospital of Sichuan University were included. MAFLD was diagnosed using ultrasonography. Biochemical indexes were collected and analyzed by logistic regression to screen out indicators that were expressed differently in MAFLD patients and healthy controls, which were incorporated into a diagnostic model. Results: After adjusting for age, sex, and body mass index (BMI), serum alanine transaminase (ALT), aspartate transaminase (AST), AST/ALT (A/A), fasting plasma glucose (FPG), cystatin C, uric acid, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), non-HDL-C, LDL-C/HDL-C, non-HDL-C/HDL-C, TG/HDL-C, TC/HDL-C, TyG, and TyG-BMI were risk factors for MAFLD. The odds ratio of TG/HDL-C and TyG were 5.387 (95%CI: 2.986-9,718) and 107.945 (95%CI: 25.824-451.222), respectively. In identifying MAFLD, TyG, TG/HDL-C, and TG were found to be the most vital indexes based on the random forest method, with the area under the curve (AUC) greater than 0.9. In addition, the combination of gender, BMI, ALT, TG, HDL-C, TyG, and TyG-BMI had a high diagnostic efficiency for MAFLD. Conclusions: TyG and TG/HDL-C were potential risk factors for MAFLD, and the former performed better in diagnosing MAFLD. The combination of gender, BMI, ALT, TG, HDL-C, TyG, and TyG-BMI improved the diagnostic capability for MAFLD.

Background The triglyceride and glucose index (TyG) and triglyceride-to-high density lipoprotein cholesterol ratio (TG/HDL-C) were found to be substitute markers of insulin resistance (IR). We aimed to compare the efficacy of the two indicators in the diagnosis of Metabolic-Associated Fatty Liver Disease (MAFLD), which was rarely covered in the literature, and to construct a novel disease diagnosis model.Methods A retrospective cross-sectional study was carried out in West China Hospital of Sichuan University and 229 people (97 MAFLD and 132 Non-MAFLD) were included. Biochemical indexes were collected and analyzed by logistic regression to screen out indicators that expressed differently in MAFLD patients and healthy controls and incorporate them into a diagnostic model. MAFLD was diagnosed by Ultrasound.Results After adjusting for age, gender and BMI, Serum ALT, AST, AST/ALT (A/A), FPG, Cys-C, URIC, TG, HDL-C, ALP, GGT, nonHDL-C, LDL-C/HDL-C, nonHDL-C/HDL-C, TG/HDL-C, TC/HDL-C, TyG and TyG-BMI were risk factors of MAFLD through binary logistics regression analysis. The odds ratio of TG/HDL-C and TyG were 5.387 (95%CI: 2.986-9,718) and 107.945 (95% CI: 25.824-451.222). In identifying MAFLD, TyG, TG/HDL-C and TG were found to be the most vital indexes by the random forest method and the area under the curve (AUC) of them are all greater than 0.9 respectively. In addition, the combination of gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI had a great diagnostic efficiency for MAFLD.Conclusions TyG and TG/HDL-C were potential risk factors for MAFLD and the former made a better performance in diagnosing MAFLD. The combination of gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI improved the diagnostic capability of MAFLD.

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