MASLD/MetALD and mortality in individuals with any cardio-metabolic risk factor: a population based study with 26.7 years of follow-up.

BACKGROUND AIMS: A new term, metabolic dysfunction-associated steatotic liver disease(MASLD), has been proposed by a multi-society expert panel. However, it remains unclear whether hepatic steatosis per se in MASLD contributes to an increased risk of mortality in individuals with any cardio-metabolic risk factor(CMRF), which are also significant risk factors for increased mortality. This study aimed to compare all-cause and cause-specific mortality between the 'MASLD/MetALD' and 'no steatotic liver disease(SLD)' groups in individuals with any CMRF. APPROACH AND RESULTS: A population-based cohort study was conducted using 10,750 participants of NHANES III. All-cause and cause-specific(cardiovascular, cancer, diabetes, and liver) mortality risks were compared between the 'MASLD', 'MetALD', and 'no SLD' groups using the Cox proportional hazards model with complex survey design weights, adjusted for confounders. Over 26 years, the 'MASLD' group did not show significantly increased all-cause(adjusted hazard ratio 1.04[95% confidence interval 0.95-1.14], p=0.413), cardiovascular(0.88[0.75-1.04], p=0.139), or cancer(1.06[0.84-1.33], p=0.635) mortality risk compared to the 'no SLD' group in individuals with any CMRF. The MetALD group was associated with increased all-cause(1.41 [1.05-1.89], p=0.022), cancer(2.35[1.33-4.16], p=0.004) and liver(15.04[2.96-76.35], p=0.002) mortality risk compared with the no SLD group. This trend was more pronounced in MetALD group with advanced fibrosis assessed by FIB-4. CONCLUSION: In individuals with CMRF, the presence of steatotic liver disease (MASLD) alone did not increase the risk of mortality, except in cases with more alcohol consumption (MetALD). Therefore controlling metabolic risk factors and reducing alcohol consumption in people with MASLD or MetALD will be crucial steps to improve long-term health outcomes.

Alpha-fetoprotein: Past, present, and future.

Alpha-fetoprotein (AFP) is a glycoprotein that plays an important role in immune regulation with critical involvement in early human development and maintaining the immune balance during pregnancy. Postfetal development, the regulatory mechanisms controlling AFP undergo a shift and AFP gene transcription is suppressed. Instead, these enhancers refocus their activity to maintain albumin gene transcription throughout adulthood. During the postnatal period, AFP expression can increase in the setting of hepatocyte injury, regeneration, and malignant transformation. It is the first oncoprotein discovered and is routinely used as part of a screening strategy for HCC. AFP has been shown to be a powerful prognostic biomarker, and multiple HCC prognosis models confirmed the independent prognostic utility of AFP. AFP is also a useful predictive biomarker for monitoring the treatment response of HCC. In addition to its role as a biomarker, AFP plays important roles in immune modulation to promote tumorigenesis and thus has been investigated as a therapeutic target in HCC. In this review article, we aim to provide an overview of AFP, encompassing the discovery, biological role, and utility as an HCC biomarker in combination with other biomarkers and how it impacts clinical practice and future direction.

A Multidisciplinary Assessment of ChatGPT's Knowledge of Amyloidosis: Observational Study.

BACKGROUND: Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients. OBJECTIVE: We performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis. METHODS: In total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing). RESULTS: ChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to "general questions" (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4's responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9). CONCLUSIONS: Large language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPT's responses exceeded the American Medical Association's recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technology's limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.

Large language models and bariatric surgery patient education: a comparative readability analysis of GPT-3.5, GPT-4, Bard, and online institutional resources.

BACKGROUND: The readability of online bariatric surgery patient education materials (PEMs) often surpasses the recommended 6th grade level. Large language models (LLMs), like ChatGPT and Bard, have the potential to revolutionize PEM delivery. We aimed to evaluate the readability of PEMs produced by U.S. medical institutions compared to LLMs, as well as the ability of LLMs to simplify their responses. METHODS: Responses to frequently asked questions (FAQs) related to bariatric surgery were gathered from top-ranked health institutions. FAQ responses were also generated from GPT-3.5, GPT-4, and Bard. LLMs were then prompted to improve the readability of their initial responses. The readability of institutional responses, initial LLM responses, and simplified LLM responses were graded using validated readability formulas. Accuracy and comprehensiveness of initial and simplified LLM responses were also compared. RESULTS: Responses to 66 FAQs were included. All institutional and initial LLM responses had poor readability, with average reading levels ranging from 9th grade to college graduate. Simplified responses from LLMs had significantly improved readability, with reading levels ranging from 6th grade to college freshman. When comparing simplified LLM responses, GPT-4 responses demonstrated the highest readability, with reading levels ranging from 6th to 9th grade. Accuracy was similar between initial and simplified responses from all LLMs. Comprehensiveness was similar between initial and simplified responses from GPT-3.5 and GPT-4. However, 34.8% of Bard's simplified responses were graded as less comprehensive compared to initial. CONCLUSION: Our study highlights the efficacy of LLMs in enhancing the readability of bariatric surgery PEMs. GPT-4 outperformed other models, generating simplified PEMs from 6th to 9th grade reading levels. Unlike GPT-3.5 and GPT-4, Bard's simplified responses were graded as less comprehensive. We advocate for future studies examining the potential role of LLMs as dynamic and personalized sources of PEMs for diverse patient populations of all literacy levels.

Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.

Background and aims: With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer. Materials and Methods: We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application. Results: We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were "medical image analysis for diagnosis" (37%), "diagnostic or prognostic biomarkers modeling & bioinformatics" (19%), and "genomic or molecular analysis" (18%). Conclusion: Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.

Perceptions and Expectations Regarding the Step 1 Score Change Among Surgical and Non-Surgical Program Directors: A Systematic Review and Meta-Analysis.

There are currently no studies examining differences in perceptions and expected impact of the Step 1 score change to pass/fail between surgical and non-surgical program directors (PDs). We conducted a systematic review in May 2023 of PubMed, Scopus, Web of Science, and PSYCInfo to evaluate studies examining PDs' perspectives regarding the Step 1 score change. We performed random-effects meta-analyses to determine differences in perspectives among surgical and non-surgical PDs. Surgical PDs (76.8% [95% CI, 72.1%-82.0%], I2 = 52%) reported significantly greater rates of disagreement with the score change compared to non-surgical (65.1% [95% CI, 57.9%-73.1%], I2 = 69.7%) (P = .01). Surgical PDs also reported significantly greater rates of agreement that the score change will increase the difficulty in objectively comparing applicants (88.1% [95% CI, 84.6%-91.7%], I2 = 16.4%), compared to non-surgical (81.0% [95% CI, 75.6%-86.8%], I2 = 72.6%) (P = .04). There was less heterogeneity among non-surgical PDs (88.7% [95% CI, 86.2%-91.2%], I2 = 0%), compared to surgical (84.7% [95% CI, 79.0%-90.8%], I2 = 67.3%), regarding expected increases in emphasis on Step 2, although the difference in rates of agreement was not statistically significant. Overall, there is significant heterogeneity in the literature regarding expected changes in the residency application review process. Most PDs reported significant disagreement with the score change, greater expected difficulty in objectively evaluating applicants, and greater emphasis on Step 2, with surgical PDs reporting greater rates of disagreement, greater expected difficulty, and heterogeneity regarding expected increases in emphasis on Step 2, compared to non-surgical. Additionally, there is significant heterogeneity in the overall literature regarding expected changes in the residency application review process. Further research is needed to establish evidence-based guidelines that improve the overall residency application process for all stakeholders.

Machine learning versus multivariate logistic regression for predicting severe COVID-19 in hospitalized children with Omicron variant infection.

With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019 (COVID-19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID-19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1-3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778-5.733], comorbidity (OR: 1.993, 95% CI:1.154-3.443), cough (OR: 0.409, 95% CI:0.236-0.709), and baseline neutrophil-to-lymphocyte ratio (OR: 1.108, 95% CI: 1.023-1.200), lactate dehydrogenase (OR: 1.993, 95% CI: 1.154-3.443), blood urea nitrogen (OR: 1.002, 95% CI: 1.000-1.003) and total bilirubin (OR: 1.178, 95% CI: 1.005-3.381) were independent risk factors for severe COVID-19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID-19 in children with Omicron variant infection.

Upper limit of normal ALT levels in health and metabolic diseases: Pooled analysis of 423,355 individuals with bootstrap modelling.

INTRODUCTION: Given the global rise in obesity-related metabolic diseases, the upper limit of normal (ULN) alanine aminotransferase (ALT) in individuals with and without metabolic diseases may have changed. We performed a meta-analysis combined with bootstrap modelling to estimate the ALT ULN levels for individuals with and without metabolic diseases. METHODS AND RESULTS: Two separate searches of the PubMed, Embase and Cochrane databases were performed, one to identify healthy individuals which yielded 12 articles (349,367 individuals); another to include those with potential metabolic diseases but without known liver disease which yielded 35 articles (232,388 individuals). We estimated the mean ALT using a random-effects mixed model and the ULN level (95th-percentile value) via a bootstrap model with 10,000 resamples. In individuals without metabolic diseases and known liver disease, the ALT ULN levels were 32 U/L overall; 36 U/L in males and 28 U/L in females. In analyses that included individuals with metabolic diseases, the ALT ULN levels were 40 U/L among the overweight/obese (29 U/L if normal weight) and 36 U/L among those with type 2 diabetes mellitus (T2DM) (33 U/L if no T2DM). On meta-regression of study-level factors, body mass index (coefficient 1.49, 95% CI 0.11-2.86, p = 0.03), high-density lipoprotein (coefficient -0.47, 95% CI -0.85-(-0.08), p = 0.02) and triglycerides (coefficient 0.19, 95% CI 0.12-0.25, p < 0.0001) correlated with ALT. CONCLUSION: We provide expected ranges of ALT ULN levels for individuals without known liver disease without metabolic diseases and those with or without T2DM and/or are normal weight or overweight/obese. These data may have implications for clinical care and screening.

Global incidence of adverse clinical events in non-alcoholic fatty liver disease: A systematic review and meta-analysis.

BACKGROUND/AIMS: Nonalcoholic fatty liver disease (NAFLD) is associated with a multitude of adverse outcomes. We aimed to estimate the pooled incidence of NAFLD-related adverse events. METHODS: We performed a systematic review and meta-analysis of cohort studies of adults with NAFLD to evaluate the pooled incidence of adverse events. RESULTS: 19,406 articles were screened, 409 full-text articles reviewed, and 79 eligible studies (1,377,466 persons) were included. Mean age was 51.47 years and body mass index 28.90 kg/m2. Baseline comorbidities included metabolic syndrome (41.73%), cardiovascular disease (CVD) (16.83%), cirrhosis (21.97%), and nonalcoholic steatohepatitis (NASH) (58.85%). Incidence rate per 1,000 person-years for mortality included: all-cause (14.6), CVD-related (4.53), non-liver cancer-related (4.53), and liver-related (3.10). Incidence for liver-related events included overall (24.3), fibrosis progression (49.0), cirrhosis (10.9), liver transplant (12.0), and hepatocellular carcinoma (HCC) (3.39). Incidence for non-liver events included metabolic syndrome (25.4), hypertension (25.8), dyslipidemia (26.4), diabetes (19.0), CVD (24.77), renal impairment (30.3), depression/anxiety (29.1), and non-liver cancer (10.5). Biopsy-proven NASH had higher incidence of HCC (P=0.043) compared to non-NASH. Higher rates of CVD and mortality were observed in North America and Europe, hypertension and non-liver cancer in North America, and HCC in Western Pacific/Southeast Asia (P<0.05). No significant differences were observed by sex. Time-period analyses showed decreasing rates of cardiovascular and non-liver cancer mortality and increasing rates of decompensated cirrhosis (P<0.05). CONCLUSION: People with NAFLD have high incidence of liver and non-liver adverse clinical events, varying by NASH, geographic region, and time-period, but not sex.

Different minimal alcohol consumption in male and female individuals with metabolic dysfunction-associated fatty liver disease.

BACKGROUND AND AIMS: The relationship between moderate alcohol intake and health outcomes among individuals with metabolic dysfunction-associated fatty liver disease (MAFLD) is complex. Our aim was to investigate the association of minimal alcohol consumption with all-cause and cause-specific mortality among MAFLD individuals of different genders. METHODS: Our study included 2630 MAFLD individuals from the Third National Health and Nutrition Examination Survey. Cox regression analysis was performed to assess the association between alcohol use measures and all-cause and cause-specific mortality. Restricted cubic spline curves were used to evaluate the relationship between alcohol consumption per week and all-cause mortality. RESULTS: In the entire MAFLD cohort, we observed significant disparities in clinical characteristics between male and female individuals with MAFLD. Higher weekly alcohol consumption was significantly associated with all-cause and cause-specific mortality (male, hazard ratios [HRs]: 1.009, 95% CIs: 1.004-1.014; female, HRs: 1.032, 95% CIs: 1.022-1.042). In males with MAFLD, a linear association with all-cause mortality was observed for weekly alcohol consumption (p for non-linearity = .21). Conversely, in females with MAFLD, the risk of all-cause mortality remained relatively stable until 2 drinks per week, after which it rapidly increased with each additional drink consumed, and the increase in mortality risk was higher than that observed in males (p for non-linearity < .05). CONCLUSIONS: Our findings indicate that any increase in weekly alcohol consumption was associated with increased all-cause mortality in men with MAFLD. Conversely, consuming less than 2 drinks per week had minimal impact on the risk of mortality among female.

The impact of an increased Fibrosis-4 index and the severity of hepatic steatosis on mortality in individuals living with diabetes.

BACKGROUND AND AIMS: Data on the effects of liver fibrosis and hepatic steatosis on outcomes in individuals living with diabetes are limited. Therefore, we investigated the predictive value of the fibrosis and the severity of hepatic steatosis for all-cause mortality in individuals living with diabetes. METHODS: A total of 1903 patients with diabetes from the Third National Health and Nutrition Examination Survey (NHANES III) dataset were enrolled. Presumed hepatic fibrosis was evaluated with Fibrosis-4 index (FIB-4). The mortality risk and corresponding hazard ratio (HR) were analyzed with the Kaplan-Meier method and multivariable Cox proportional hazard models. RESULTS: Over a median follow-up of 19.4 years, all-cause deaths occurred in 69.6%. FIB-4 ≥ 1.3 was an independent predictor of mortality in individuals living with diabetes (HR 1.219, 95% confidence interval [CI]: 1.067-1.392, p = 0.004). Overall, FIB-4 ≥ 1.3 without moderate-severe steatosis increased the mortality risk (HR 1.365; 95%CI 1.147-1.623, p < 0.001). The similar results were found in individuals living with diabetes with metabolic dysfunction-associated fatty liver disease (MAFLD) (HR 1.499; 95%CI 1.065-2.110, p = 0.020), metabolic syndrome (MetS) (HR 1.397; 95%CI 1.086-1.796, p = 0.009) or abdominal obesity (HR 1.370; 95%CI 1.077-1.742, p = 0.010). CONCLUSIONS: Liver fibrosis, as estimated by FIB-4, may serve as a more reliable prognostic indicator for individuals living with diabetes than hepatic steatosis. Individuals living with diabetes with FIB-4 ≥ 1.3 without moderate-severe steatosis had a significantly increased all-cause mortality risk. These findings highlight the importance of identifying and monitoring those individuals, as they may benefit from further evaluation and risk stratification.

Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study.

Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).

Incidence and predictors of hepatocellular carcinoma in NAFLD without diagnosed cirrhosis: a nationwide real-world U.S. study.

BACKGROUND: A substantial proportion of patients with nonalcoholic fatty liver disease (NAFLD)-related hepatocellular carcinoma (HCC) do not have cirrhosis. Data regarding the incidence and predictors of HCC development in NAFLD without cirrhosis are limited. We conducted a large, national study of NAFLD patients without documented cirrhosis to examine the incidence and predictors for HCC development. METHODS: This retrospective study included 751,603 NAFLD patients (54% female) without documented cirrhosis derived from the deidentified Optum Clinformatics® Data Mart Database. Patients with cirrhosis, platelets < 120,000/µL or FIB-4 values > 2.67 were excluded. RESULTS: The mean age was 53.7 ± 15.0 years, 45.9% were male, 39.5% had diabetes, 57.6% were White, 18.4% Hispanic, 8.2% Black and 4.9% were Asian. The mean platelet count was 264,000 ± 72,000/µL, and 96.3% of patients had a FIB-4 < 1.30. Over 1,686,607 person-years of follow-up, there were 76 incident cases of HCC, resulting in an HCC incidence rate of 0.05 per 1000 person-years. There was a higher HCC incidence rate among patients with platelets ≤ 150,000/µL, versus those with platelets > 150,000/µL (0.23 per 1000 person-years, vs. 0.04 per 1000 person-years, p = 0.02) but not in subgroup analyses for age, sex, race/ethnicity or diabetes. Using multivariable Cox proportional hazards model adjusted multiple confounders, platelet count ≤ 150,000/µL remained an independent predictor of HCC development (adjusted HR 5.80, 95% CI 1.67-20.1, p = 0.006). CONCLUSION: HCC incidence in NAFLD without documented cirrhosis was below the threshold for cost-effective HCC surveillance in overall and multiple subgroup analyses. Platelet count < 150,000/µL may be a useful predictor of HCC development in this population.

Prevalence of and associated factors for sarcopenia in patients with liver cirrhosis: A systematic review and meta-analysis.

BACKGROUND: Sarcopenia is associated with poor outcomes in patients with cirrhosis. However, the prevalence of and associated factors for developing sarcopenia in this population remain to be determined. OBJECTIVES: This study aimed to summarize the prevalence, characteristics, and associated factors of sarcopenia in patients with cirrhosis. METHODS: Electronic searches were performed from inception to June 9, 2022 to identify the eligible studies. We meta-analyzed the prevalence of sarcopenia in overall patients with cirrhosis and subgroups. Both crude and adjusted odds ratios (ORs) were pooled using the random effects model. RESULTS: A total of 55 studies involving 13,158 patients from 17 countries were included. The overall prevalence of sarcopenia was 40.1 % (95 % CI 35.4%-44.9 %) in patients with cirrhosis. The pooled prevalence was higher in males, Child-Pugh class C cirrhosis, decompensated stage, ascites, subjective global assessment class C cirrhosis, and when sarcopenia was defined by L3-SMI (third lumbar-skeletal muscle index) at a higher cutoff. In multivariate analysis, older age (adjusted OR 1.04, 95 % CI 1.00-1.07), male (adjusted OR 4.75, 95 % CI 2.72-8.28), lower body mass index (BMI) (adjusted OR 0.78, 95 % CI 0.73-0.83), alcoholic liver disease (ALD) (adjusted OR 1.43, 95 % CI 1.19-1.72), but not ascites and hepatic encephalopathy, were significantly associated with an increased risk of sarcopenia in patients with cirrhosis. CONCLUSION: Sarcopenia is a prevalent complication, and older age, male patients, lower BMI, and patients with ALD are associated with an increased risk of sarcopenia in patients with cirrhosis.