Metabolic dysfunction-associated fatty liver disease and low muscle strength: A comment

The diagnosis of non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated fatty liver disease only on the basis of laboratory parameter score such as Hepatic Steatosis Index which includes liver enzymes, gender, basal metabolic index, and presence of diabetic mellitus is not sufficient to exclude other causes of deranged liver enzymes especially medications and autoimmune related liver diseases. As the guideline suggests ultrasound is the preferred first-line diagnostic procedure for imaging of NAFLD, as it provides additional diagnostic information and the combination of biomarkers/scores and transient elastography might confer additional diagnostic accuracy and evident from previous similar studies too.

Limitations of Noninvasive Tests-Based Population-Level Risk Stratification Strategy for Nonalcoholic Fatty Liver Disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed.

AIMS

We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis.

METHODS

Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged [Formula: see text] 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites.

RESULTS

Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range.

CONCLUSIONS

We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups.

Relationship between diet quality and nonalcoholic fatty liver disease predictor indices in Iranian patients with metabolic syndrome: A cross-sectional study

The present study aimed to assess the association between diet quality and nonalcoholic fatty liver disease (NAFLD) predictor indices in patients with metabolic syndrome (MetS). This cross-sectional study was carried out among 344 adult patients with MetS. The diet quality of patients was calculated by Healthy Eating Index-2015 (HEI-2015). NAFLD predictor indices (Hepatic Steatosis Index [HSI], Triglyceride-Glucose Index [TyG], and Fatty Liver Index [FLI]) were calculated and compared according to the HEI-2015 quartiles. The relationship between the HEI-2015 score and HSI, FLI, and TyG Index was estimated using multiple linear regression analysis. The findings of the present study revealed that patients with the highest HEI score had the lowest FLI score (p = .003) and HSI score (p = .05). There was an inverse relationship between the HEI-2015 score and FLI (β = -0.49; p < .001), HSI (β = -0.05; p = .25), and TyG Index (β = -0.002; p = .34). According to our result, after adjusting for possible confounding factors, there was a statistically significant inverse association between HEI-2015 and FLI.

Non-alcoholic Fatty Liver Disease and COVID-19 Susceptibility and Outcomes: a Korean Nationwide Cohort

BACKGROUND

Evidence for the association between underlying non-alcoholic fatty liver disease (NAFLD), the risk of testing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive, and the clinical consequences of coronavirus disease 2019 (COVID-19) is controversial and scarce. We aimed to investigate the association between the presence of NAFLD and the risk of SARS-CoV-2 infectivity and COVID-19-related outcomes.

METHODS

We used the population-based, nationwide cohort in South Korea linked with the general health examination records between January 1, 2018 and July 30, 2020. Data for 212,768 adults older than 20 years who underwent SARS-CoV-2 testing from January 1 to May 30, 2020, were obtained. The presence of NAFLDs was defined using three definitions, namely hepatic steatosis index (HSI), fatty liver index (FLI), and claims-based definition. The outcomes were SARS-CoV-2 test positive, COVID-19 severe illness, and related death.

RESULTS

Among 74,244 adults who completed the general health examination, there were 2,251 (3.0%) who were SARS-CoV-2 positive, 438 (0.6%) with severe COVID-19 illness, and 45 (0.06%) COVID-19-related deaths. After exposure-driven propensity score matching, patients with pre-existing HSI-NAFLD, FLI-NAFLD, or claims-based NAFLD had an 11-23% increased risk of SARS-CoV-2 infection (HSI-NAFLD 95% confidence interval [CI], 1-28%; FLI-NAFLD 95% CI, 2-27%; and claims-based NAFLD 95% CI, 2-31%) and a 35-41% increased risk of severe COVID-19 illness (HSI-NAFLD 95% CI, 8-83%; FLI-NAFLD 95% CI, 5-71%; and claims-based NAFLD 95% CI, 1-92%). These associations are more evident as liver fibrosis advanced (based on the BARD scoring system). Similar patterns were observed in several sensitivity analyses including the full-unmatched cohort.

CONCLUSION

Patients with pre-existing NAFLDs have a higher likelihood of testing SARS-CoV-2 positive and severe COVID-19 illness; this association was more evident in patients with NAFLD with advanced fibrosis. Our results suggest that extra attention should be given to the management of patients with NAFLD during the COVID-19 pandemic.

Prediction of Cardiovascular Risk Using Nonalcoholic Fatty Liver Disease Scoring Systems

This study aimed to determine whether nonalcoholic fatty liver disease (NAFLD) is an independent risk factor for CVD and to identify the most useful NAFLD diagnostic tool for predicting CVD. Data from a total of 23,376 Korean adults without established CVD were analyzed. Cardiovascular risk was calculated using the Framingham Risk Score (FRS) 2008. The presence of NAFLD was defined as moderate-to-severe fatty liver disease diagnosed by ultrasonography. Scores for fatty liver were calculated using four NAFLD scoring systems (Fatty Liver Index, FLI; Hepatic Steatosis Index, HSI; Simple NAFLD Score, SNS; Comprehensive NAFLD Score, CNS), and were compared and analyzed according to cardiovascular risk group. Using the FRS, 67.4% of participants were considered to be at low risk of CVD, 21.5% at intermediate risk, and 11.1% at high risk. As the risk of CVD increased, both the prevalence of NAFLD and the score from each NAFLD scoring system increased significantly (p < 0.001). In the unadjusted analysis, the CNS had the strongest association with high CVD risk; in the adjusted analysis, the FLI score was most strongly associated with high CVD risk. Fatty liver is an important independent risk factor for CVD. Therefore, the available NAFLD scoring systems could be utilized to predict CVD.

A novel model for predicting fatty liver disease by means of an artificial neural network

Background

The artificial neural network (ANN) emerged recently as a potent diagnostic tool, especially for complicated systemic diseases. This study aimed to establish a diagnostic model for the recognition of fatty liver disease (FLD) by virtue of the ANN.

Methods

A total of 7,396 pairs of gender- and age-matched subjects who underwent health check-ups at the First Affiliated Hospital, College of Medicine, Zhejiang University (Hangzhou, China) were enrolled to establish the ANN model. Indices available in health check-up reports were utilized as potential input variables. The performance of our model was evaluated through a receiver-operating characteristic (ROC) curve analysis. Other outcome measures included diagnostic accuracy, sensitivity, specificity, Cohen's k coefficient, Brier score, and Hosmer-Lemeshow test. The Fatty Liver Index (FLI) and the Hepatic Steatosis Index (HSI), retrained using our training-group data with its original designated input variables, were used as comparisons in the capability of FLD diagnosis.

Results

Eight variables (age, gender, body mass index, alanine aminotransferase, aspartate aminotransferase, uric acid, total triglyceride, and fasting plasma glucose) were eventually adopted as input nodes of the ANN model. By applying a cut-off point of 0.51, the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908 [95% confidence interval (CI), 0.901-0.915]-significantly higher (P < 0.05) than that of the FLI model (0.881, 95% CI, 0.872-0.891) and that of the HSI model (0.885; 95% CI, 0.877-0.893). Our ANN model exhibited higher diagnostic accuracy, better concordance with ultrasonography results, and superior capability of calibration than the FLI model and the HSI model.

Conclusions

Our ANN system showed good capability in the diagnosis of FLD. It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.