BACKGROUND: Chronic liver disease (CLD), mainly non-alcoholic fatty liver disease (NAFLD), is a significant public health concern worldwide. This study aims to quantify the burden of NAFLD in CLD globally and within China, using data from the Global Burden of Disease (GBD) Study 2021, providing crucial insights for global and local health policies. METHODS: The study used comprehensive data from the GBD study 2021. It included estimates of prevalence, incidence, mortality, and disability-adjusted life years (DALYs). Age-standardized rates and average annual percent change (AAPC) from 2011 to 2021 were reported. A meticulous decomposition analysis was conducted. RESULTS: In 2021, there were 1582.5 million prevalent cases, 47.6 million incident cases, 1.4 million deaths, and 44.4 million DALYs attributable to CLD, globally. Among these, NAFLD has emerged as the predominant cause, accounting for 78.0% of all prevalent CLD cases (1234.7 million) and 87.2% of incident cases (41.5 million). Correspondingly, NAFLD had the highest age-standardized prevalence (15,017.5 per 100,000 population) and incidence (876.5 per 100,000 population) rates among CLDs. In addition, China's CLD age-standardized prevalence rate was 21,659.5 per 100,000 population, and the age-standardized incidence rate was 752.6 per 100,000 population, higher than the global average. From 2011 to 2021, the global prevalence rate of CLD increased slowly (AAPC = 0.17), consistent with the trend in China (AAPC = 0.23). Furthermore, the prevalence rate of NAFLD rose significantly in China (AAPC = 1.30) compared with the global average (AAPC = 0.91). Decomposition analysis also showed the worldwide increase in deaths and DALYs for NAFLD, which were primarily attributable to population growth and aging. CONCLUSIONS The burden of CLD and NAFLD remains substantial globally and within China in terms of high prevalence and incidence. As such, this underscores the need for targeted prevention and treatment strategies. These findings emphasize the importance of continued surveillance and research to mitigate the growing impact of liver diseases on global and Chinese health systems.
Humans ; Non-alcoholic Fatty Liver Disease/mortality* ; Global Burden of Disease ; China/epidemiology* ; Prevalence ; Male ; Disability-Adjusted Life Years ; Female ; Incidence ; Middle Aged ; Chronic Disease ; Adult ; Quality-Adjusted Life Years ; Liver Diseases/epidemiology* ; Aged

Liver fibrosis is a key histologic marker of long-term outcome in chronic liver disease. Non-invasive tests (NITs) have been shown to have predictive value, but the superiority of "dynamic" versus "static" assessment remains controversial. This article systematically reviews the latest evidence to elucidate the association between longitudinal changes in NITs and hepatic adverse events and assess the incremental contribution of dynamic monitoring to the model. Additionally, it reveals that the dynamic monitoring of NITs is truly superior to single evaluation, but the evidence is limited and the heterogeneity is significant. Dynamic modeling approaches for NITs require a shift from traditional parameter estimation to time-series machine learning. Future studies should make breakthroughs in disease stratification, modeling method innovation, data quality improvement, and prediction ability assessment so as to promote the transition of NITs from "static risk label" to "dynamic individualized engine," which can truly serve clinical decision-making.

Liver fibrosis is a key histologic marker of long-term outcome in chronic liver disease. Non-invasive tests (NITs) have been shown to have predictive value, but the superiority of "dynamic" versus "static" assessment remains controversial. This article systematically reviews the latest evidence to elucidate the association between longitudinal changes in NITs and hepatic adverse events and assess the incremental contribution of dynamic monitoring to the model. Additionally, it reveals that the dynamic monitoring of NITs is truly superior to single evaluation, but the evidence is limited and the heterogeneity is significant. Dynamic modeling approaches for NITs require a shift from traditional parameter estimation to time-series machine learning. Future studies should make breakthroughs in disease stratification, modeling method innovation, data quality improvement, and prediction ability assessment so as to promote the transition of NITs from "static risk label" to "dynamic individualized engine," which can truly serve clinical decision-making.

Objective To investigate the association of sleep disorders with the development and progression of nonalcoholic fatty liver disease(NAFLD).Methods A total of 1 868 participants from the health examination cohort and fatty liver cohort of Beijing Friendship Hospital from June 2022 to June 2023 were enrolled as subjects.Related data were collected from all subjects,including age,sex,education level,chronic medical history,and biochemical parameters,and all subjects completed Pittsburgh Sleep Quality Index(PSQI)scale independently.According to the diagnostic criteria,the subjects were divided into non-NAFLD group with 1 122 subjects and NAFLD group with 746 subjects,and according to the stage of progression,the patients in the NAFLD group were further divided into simple fatty liver group(SFL group with 624 subjects)and nonalcoholic steatohepatitis(NASH)group with 122 subjects.A one-way analysis of variance or the Kruskal-Wallis H test was used for comparison of continuous data between three groups.The chi-square test was used for comparison of categorical data between the three groups.The binary Logistic regression analysis was used to investigate the association between sleep factors and NAFLD,and the multinomial Logistic regression analysis was used to investigate the association between sleep factors and the different stages of NAFLD;two multivariate models were constructed for adjustment of potential confounding factors,i.e.,an age-sex adjustment model and a multivariate adjustment model,and the multivariate adjustment model adjusted the factors of age,sex,education level,smoking,diabetes,hypertension,body mass index(BMI),triglyceride(TG),and high-density lipoprotein cholesterol(HDL-C).Results There were significant differences in age,sex,BMI,education level,smoking,diabetes,hypertension,alanine aminotransferase,TG,and HDL-C between the non-NAFLD,SFL,and NASH groups(all P<0.05).There were also significant differences between the three groups in the total score of PSQI scale and the proportion of subjects with a score of 0—3 points for the 7 sleep components(all P<0.05).The multivariate adjustment model showed no significant association between sleep disorders and SFL,while long sleep latency(odds ratio[OR]=4.04,95%confidence interval[CI]:2.33—7.03,P<0.001),short sleep duration(OR=3.53,95%CI:1.83—6.82,P<0.001),and severe sleep disorders(OR=2.96,95%CI:1.48—5.93,P=0.002)were closely associated with the risk of NASH.Conclusion Overall sleep condition and its components of sleep disorders are not significantly associated with the simple fatty liver;however,long sleep latency,short sleep duration,and severe sleep disorders can increase the risk of NASH,which should be taken seriously in clinical practice.

Low-level viremia after antiviral therapy has gradually attracted attention due to its relation to liver fibrosis progression, hepatocellular carcinoma occurrence and long-term survival rate reduction in patients with chronic hepatitis B. In addition, it should be used as a risk factor for intervention during antiviral therapy in order to achieve complete virological response and improve the long-term prognosis of patients.