Introduction: Metabolic dysfunction–associated steatotic liver disease (MASLD) is common among patients with type 2 diabetes (T2D) and increases the risk of cirrhosis, yet fibrosis often remains clinically silent until hepatic decompensation occurs. Current diabetes guidelines recommend noninvasive fibrosis risk stratification, such as the fibrosis-4 index (FIB-4), during routine diabetes care, but real-world adoption remains unclear. This study aimed to evaluate the implementation of FIB-4 as a risk stratification tool in patients with T2D. Methodology: This was a retrospective study of consecutive patients with T2D who were seen in the Diabetes Clinic of University of Malaya Medical Centre in 2023. Patients were identified as having a higher risk of future cirrhosis based on elevated FIB-4 ≥1.3. Results: The data for 1,009 patients were analyzed, median age 62 (52–71) years, 40.7% male. Elevated FIB-4 was seen in 28.8% (291/1,009). Only 12.0% (35/291) with elevated FIB-4 were referred for further hepatology evaluation, whereas 3.3% (24/718) with low FIB-4 were referred. Among the patients with elevated FIB-4, those referred were more likely known to have hepatic steatosis, had higher alanine aminotransferase, aspartate aminotransferase, gammaglutamyl transferase, and lower low-density lipoprotein cholesterol and platelet count. Over a median follow-up of 1.71 (0.99–1.95) years, totaling 1,440 person-years, two liverrelated events (0.2%) occurred (one each in the elevated and low FIB-4 group, respectively). Sixteen patients (4.0%) experienced cardiovascular events, including one patient (0.6%) in the elevated FIB-4 group and 15 (2.9%) in the low FIB-4 group. Conclusion Despite automated FIB-4 reporting, this risk stratification tool was underutilized in T2D, resulting in missed opportunities for early identification and management of more severe liver disease. Integration of FIB-4 into diabetes care workflows, alongside structured referral pathways and clinician education, may improve early detection and reduce long-term hepatic complications in T2D.
Diabetes Mellitus, Type 2 ; Risk Assessment ; Liver Diseases ; Referral and Consultation

Introduction: The global rise in paediatric obesity has led to an increasing burden of metabolic comorbidities, including metabolic dysfunction–associated steatotic liver disease (MASLD). Obstructive sleep apnea (OSA), characterized by chronic intermittent hypoxia, has been implicated in the pathogenesis of liver injury. This study aimed to evaluate the prevalence of MASLD and its association with OSA among children and adolescents in a tertiary centre. Methodology: A retrospective cross-sectional study was conducted involving children aged 6–15 years who were admitted for polysomnography at Sarawak General Hospital between 2024 and 2025. Clinical, biochemical, and lifestyle data were obtained from medical records and parental questionnaires. Hepatic steatosis was assessed using hepatobiliary ultrasound in a subset of patients. Results: A total of 132 children (mean age 10.8 ± 2.7 years; 87.5% male) were included. The median BMI SDS was 2.50 (IQR: 2.29– 2.93). The mean apnea–hypopnea index (AHI) was 12.0 ± 7.9, with 60% classified as severe OSA. Among the 16 children who underwent liver ultrasound, MASLD was identified in 81.2%, including five cases of grade 1 and eight cases of grade 2 steatosis. Liver transaminitis was present in six patients. Metabolic comorbidities were common, with 43.8% requiring metformin for insulin resistance or impaired glucose tolerance, and four patients requiring antihypertensive therapy. Unhealthy lifestyle behaviors were prevalent: 64.2% consumed sugary drinks ≥3 times weekly, 42.9% consumed fast food ≥3 times weekly, and only 24.3% reported daily fruit or vegetable intake. Physical inactivity (≤2 times/week) was observed in 71.4%, while 64.3% had screen time exceeding 2 hours daily. Conclusion Children and adolescents with moderate-to-severe OSA exhibit a high burden of metabolic dysfunction, including MASLD. The findings support a potential contributory role of OSA-related hypoxia in paediatric fatty liver disease and highlight the need for integrated screening and early intervention in this high-risk population.
Adolescent ; Child ; Malaysia ; Liver Diseases

In recent years, gut microbiota has been increasingly recognized as a key player in ethanol metabolism and the development of related diseases. On one hand, ethanol intake directly affects the gut, leading to significant alterations in microbial diversity and composition. On the other hand, gut microbiota influences ethanol-induced damage to various organs, especially the liver, through multiple metabolic byproducts (such as short-chain fatty acids like butyrate, propionate, and acetate), modulation of immune responses, alteration of intestinal barrier function, and regulation of ethanol-metabolizing enzymes. Given the close association between gut microbiota and ethanol metabolism, the gut microbiome presents a promising therapeutic target for alcohol-related liver diseases. This review summarizes recent advances in understanding how gut microbiota affects ethanol metabolism, aiming to elucidate its role in the onset and progression of ethanol-related diseases and to provide a theoretical basis and novel targets for microbiota-based interventions.
Gastrointestinal Microbiome/physiology* ; Ethanol/metabolism* ; Humans ; Fatty Acids, Volatile/metabolism* ; Liver Diseases, Alcoholic/metabolism* ; Animals ; Alcohol Drinking/metabolism*

The gut microbiota is an indispensable symbiotic entity within the human holobiont, serving as a critical regulator of host lipid metabolism homeostasis. Therefore, it has emerged as a central subject of research in the pathophysiology of metabolic disorders. This microbial consortium orchestrates key aspects of host lipid dynamics-including absorption, metabolism, and storage-through multifaceted mechanisms such as the enzymatic processing of dietary polysaccharides, the facilitation of long-chain fatty acid uptake by intestinal epithelial cells (IECs), and the bidirectional modulation of adipose tissue functionality. Mounting evidence underscores that gut microbiota-derived metabolites not only directly mediate canonical lipid metabolic pathways but also interface with host immune pathways, epigenetic machinery, and circadian regulatory systems, thereby establishing an intricate crosstalk that coordinates systemic metabolic outputs. Perturbations in microbial composition (dysbiosis) drive pathological disruptions to lipid homeostasis, serving as a pathogenic driver for conditions such as obesity, hyperlipidemia, and non-alcoholic fatty liver disease (NAFLD). This review systematically examines the emerging mechanistic insights into the gut microbiota-mediated regulation of intestinal lipid metabolism, while it elucidates its translational implications for understanding metabolic disease pathogenesis and developing targeted therapies.
Humans ; Gastrointestinal Microbiome/physiology* ; Lipid Metabolism ; Animals ; Intestinal Mucosa/metabolism* ; Homeostasis ; Dysbiosis ; Obesity/metabolism* ; Intestines/microbiology* ; Non-alcoholic Fatty Liver Disease/metabolism* ; Metabolic Diseases/metabolism*

Nuclear factor erythroid 2-related factor 2 (Nrf2) is an intracellular transcription factor that helps protect against oxidative stress in different types of cells under pathological conditions. Mitochondria are vital organelles that function in diverse metabolic processes in the body, including redox reactions, lipid metabolism, and cell death. Mitophagy, a specific form of autophagy for damaged mitochondria, plays a critical role in the pathophysiology of liver diseases. In this review, we explain in detail the roles of the Nrf2 signaling pathway and mitophagy, and the relationship between them, in various hepatic diseases (nonalcoholic fatty liver disease, viral hepatitis, alcoholic liver disease, drug-induced liver injury, autoimmune hepatitis, hepatic ischemia‒reperfusion injury, and liver cancer). We also offer some potential insights and treatments relevant to clinical applications.
Humans ; NF-E2-Related Factor 2/metabolism* ; Mitophagy/physiology* ; Kelch-Like ECH-Associated Protein 1/metabolism* ; Signal Transduction ; Liver Diseases/etiology* ; Animals ; Oxidative Stress ; Mitochondria/metabolism* ; Non-alcoholic Fatty Liver Disease ; Liver Neoplasms

Food-derived bioactive peptides (FBPs), particularly those with ten or fewer amino acid residues and a molecular weight below 1300 Da, have gained increasing attention for their safe, diverse structures and specific biological activities. The development of FBP-based functional foods and potential medications depends on understanding their structure‒activity relationships (SARs), stability, and bioavailability properties. In this review, we provide an in-depth overview of the roles of FBPs in treating various diseases, including Alzheimer's disease, hypertension, type 2 diabetes mellitus, liver diseases, and inflammatory bowel diseases, based on the literature from July 2017 to Mar. 2023. Subsequently, attention is directed toward elucidating the associations between the bioactivities and structural characteristics (e.g., molecular weight and the presence of specific amino acids within sequences and compositions) of FBPs. We also discuss in silico approaches for FBP screening and their limitations. Finally, we summarize recent advancements in formulation techniques to improve the bioavailability of FBPs in the food industry, thereby contributing to healthcare applications.
Humans ; Peptides/therapeutic use* ; Structure-Activity Relationship ; Functional Food ; Diabetes Mellitus, Type 2/drug therapy* ; Biological Availability ; Alzheimer Disease/drug therapy* ; Inflammatory Bowel Diseases/drug therapy* ; Hypertension/drug therapy* ; Liver Diseases/drug therapy* ; Bioactive Peptides, Dietary

OBJECTIVE: To develop and validate a nomogram model for predicting 30-day mortality among elderly patients with sepsis-associated liver dysfunction (SALD), to identify high-risk patients and improve prognosis. METHODS: A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database for elderly patients with SALD who were first admitted to the intensive care unit (ICU) of Beth Israel Deaconess Medical Center between 2008 and 2019, including basic characteristics, severity scores, underlying diseases, infection foci, 24-hour vital signs, initial laboratory indicators, 24-hour complications, and prognosis related indicators. Patients were randomly assigned to training group and validation group in a ratio of 7 : 3. The training group used the LASSO regression analysis, as well as multivariate Logistic regression analysis to screen for independent risk factors for 30-day mortality. A nomogram prediction model was constructed, and receiver operator characteristic curve (ROC curve), calibration curves, and decision curve analysis (DCA) were used to evaluate the model, and validate the model using the validation cohort. RESULTS: A total of 630 elderly patients with SLAD were included in the study, including 441 in the training group and 189 in the validation group. Oxford acute severity of illness score (OASIS) for training group [odds ratio (OR) = 1.060, 95% confidence interval (95%CI) was 1.034-1.086], 24-hour pulse oxygen saturation (SpO₂; OR = 0.876, 95%CI was 0.797-0.962), initial mean corpuscular volume (MCV; OR = 1.043, 95%CI was 1.009-1.077), initial red blood cell distribution width (RDW; OR = 1.237, 95%CI was 1.123-1.362), initial blood glucose (OR = 1.008, 95%CI was 1.004-1.013), and initial aspartate aminotransferase (AST; OR = 1.000, 95%CI was 1.000-1.001) were independent risk factors for 30-day mortality in patients (all P < 0.05). Based on the above variables, a nomogram model was constructed, and the ROC curve showed that the area under the curve (AUC) of the model in the training group was 0.757 (95%CI was 0.712-0.803), with a sensitivity of 65.05% and a specificity of 74.90%; the AUC of the model in the validation group was 0.712 (95%CI was 0.631-0.792), with a sensitivity of 58.67% and a specificity of 81.58%. The calibration curves of the training and validation groups show that both the fitted curves were close to the standard curves. The Hosmer-Lemeshow test: the training group (χ ² = 6.729, P = 0.566), the validation group (χ ² = 13.889, P = 0.085), indicating that the model can fit the observed data well. The DCA curve shows that when the threshold probability of the training group was 16% to 94% and the threshold probability of the validation group was 27% to 99%, the net benefit of the model was good. CONCLUSIONS OASIS, 24-hour SpO₂, initial MCV, initial RDW, initial blood glucose and initial AST are independent risk factors for 30-day mortality in elderly patients with SALD. The nomogram based on these six variables demonstrates good predictive performance.
Humans ; Sepsis/complications* ; Retrospective Studies ; Nomograms ; Aged ; Prognosis ; Risk Factors ; Liver Diseases/mortality* ; Intensive Care Units ; ROC Curve ; Male ; Female ; Logistic Models

Efferocytosis refers to the process of phagocytes engulfing and clearing the cells after programmed cell death. In recent years, an increasing number of studies have shown that the mechanisms of efferocytosis are closely related to drug-induced liver injury, hepatic ischemia-reperfusion injury, viral hepatitis, cholestatic liver diseases, metabolic-associated fatty liver disease, alcoholic liver disease, and other liver disorders. This review summarized the research progress on the role of efferocytosis in liver diseases, with the hope of providing new targets for the prevention and treatment of liver diseases.
Humans ; Liver Diseases/metabolism* ; Animals ; Phagocytosis/physiology* ; Phagocytes ; Efferocytosis

Inflammaging is a process of cellular dysfunction associated with chronic inflammation, which plays a significant role in the onset and progression of liver diseases. Research on its mechanisms has become a hotspot. In viral hepatitis, inflammaging primarily involve oxidative stress, cell apoptosis and necrosis, as well as gut microbiota dysbiosis. In non-alcoholic fatty liver disease, inflammaging is more complex, involving insulin resistance, fat deposition, lipid metabolism disorders, gut microbiota dysbiosis, and abnormalities in NAD⁺ metabolism. In liver tumors, inflammaging is characterized by weakening of tumor suppressive mechanisms, remodeling of the liver microenvironment, metabolic reprogramming, and enhanced immune evasion. Therapeutic strategies targeting inflammaging have been developing recently, and antioxidant therapy, metabolic disorder improvement, and immunotherapy are emerging as important interventions for liver diseases. This review focuses on the mechanisms of inflammaging in liver diseases, aiming to provide novel insights for the prevention and treatment of liver diseases.
Humans ; Liver Diseases/pathology* ; Inflammation ; Oxidative Stress ; Non-alcoholic Fatty Liver Disease ; Liver Neoplasms ; Gastrointestinal Microbiome

OBJECTIVES: To analyze the multimorbidity patterns and core diseases among hospitalized patients in different age groups and to explore the impacts of multimorbidity patterns on hospitalization costs. METHODS: Electronic medical records of adult inpatients (aged ≥18 years) from Ningbo Medical Center Lihuili Hospital between January 1, 2018, and June 30, 2023 were collected. The multimorbidity status involving 53 specific diseases was analyzed across different age groups. Association rule mining was used to identify common multimorbidity patterns. Complex network analysis was used to identify core diseases within the multimorbidity networks. Generalized estimating equations (GEE) were used to analyze the impact of different multimorbidity patterns on hospitalization costs. RESULTS: The prevalence of multimorbidity among the 359 402 adult inpatients was 38.51%, with higher rates observed in males (43.60%) and elderly patients (58.29%). Association rule mining identified 15 common multimorbidity patterns, which exhibited differences across age groups. The most prevalent multimorbidity pattern overall was "diabetes→hypertension" (support=7.04%, confidence=62.17%, lift=2.17). In the young adult group, the most prevalent pattern was "dyslipidemia→chronic liver disease" (support=1.19%, confidence=53.17%, lift=6.04). In the middle-aged group, it was "diabetes→hypertension" (support=4.84%, confidence=50.28%, lift=2.15). In the elderly group, it was "coronary heart disease, diabetes→hypertension" (support=2.38%, confidence=77.43%, lift=1.63). Complex network analysis revealed that the core diseases within multimorbidity networks differed across age groups. The core disease identified in the young adult group was chronic liver disease (degree centrality=50, betweenness centrality=0.055, closeness centrality=0.963). Core diseases in the middle-aged group included hypertension, chronic liver disease, and diabetes (all with degree centrality=52, betweenness centrality=0.022, closeness centrality=1.000). Core diseases in the elderly group comprised hypertension, diabetes, malignant tumors, chronic liver disease, thyroid disease, anemia, and arrhythmia (all with degree centrality=52, betweenness centrality=0.009, closeness centrality=1.000). Generalized estimating equations analysis indicated that, most multimorbidity patterns were significantly associated with increased hospitalization costs. However, the magnitude of cost increase varied across different multimorbidity patterns. Specifically, hospitalization costs for patients with patterns such as "heart failure→hypertension", "stroke→hypertension", "malignant tumor, diabetes→hypertension", "stroke, diabetes→hypertension", and "diabetes, heart failure→hypertension" were more than double those of patients without any target diseases. CONCLUSIONS Multimorbidity patterns and core diseases among hospitalized patients differ significantly across age groups, and different patterns exert varying impacts on hospitalization costs. These findings underscore the necessity for age-stratified and multimorbidity pattern specific management strategies.
Humans ; Multimorbidity ; Male ; Hospitalization/economics* ; Female ; Aged ; Middle Aged ; Adult ; Age Factors ; Young Adult ; Adolescent ; Diabetes Mellitus/epidemiology* ; Electronic Health Records ; Aged, 80 and over ; Hospital Costs ; China/epidemiology* ; Hypertension/economics* ; Liver Diseases/epidemiology*