Metabolic dysfunction-associated steatotic liver disease and the heart.

The prevalence and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) are increasing. Physicians who treat patients with MASLD may acknowledge the strong coincidence with cardiometabolic disease, including atherosclerotic cardiovascular disease (asCVD). This raises questions on co-occurrence, causality, and the need for screening and multidisciplinary care for MASLD in patients with asCVD, and vice versa. Here, we review the interrelations of MASLD and heart disease and formulate answers to these matters. Epidemiological studies scoring proxies for atherosclerosis and actual cardiovascular events indicate increased atherosclerosis in patients with MASLD, yet no increased risk of asCVD mortality. MASLD and asCVD share common drivers: obesity, insulin resistance and type 2 diabetes mellitus (T2DM), smoking, hypertension, and sleep apnea syndrome. In addition, Mendelian randomization studies support that MASLD may cause atherosclerosis through mixed hyperlipidemia, while such evidence is lacking for liver-derived procoagulant factors. In the more advanced fibrotic stages, MASLD may contribute to heart failure with preserved ejection fraction by reduced filling of the right ventricle, which may induce fatigue upon exertion, often mentioned by patients with MASLD. Some evidence points to an association between MASLD and cardiac arrhythmias. Regarding treatment and given the strong co-occurrence of MASLD and asCVD, pharmacotherapy in development for advanced stages of MASLD would ideally also reduce cardiovascular events, as has been demonstrated for T2DM treatments. Given the common drivers, potential causal factors and especially given the increased rate of cardiovascular events, comprehensive cardiometabolic risk management is warranted in patients with MASLD, preferably in a multidisciplinary approach.

Diagnostic accuracy of non-invasive tests to screen for at-risk MASH-An individual participant data meta-analysis.

BACKGROUND & AIMS: There is a need to reduce the screen failure rate (SFR) in metabolic dysfunction-associated steatohepatitis (MASH) clinical trials (MASH+F2-3; MASH+F4) and identify people with high-risk MASH (MASH+F2-4) in clinical practice. We aimed to evaluate non-invasive tests (NITs) screening approaches for these target conditions. METHODS: This was an individual participant data meta-analysis for the performance of NITs against liver biopsy for MASH+F2-4, MASH+F2-3 and MASH+F4. Index tests were the FibroScan-AST (FAST) score, liver stiffness measured using vibration-controlled transient elastography (LSM-VCTE), the fibrosis-4 score (FIB-4) and the NAFLD fibrosis score (NFS). Area under the receiver operating characteristics curve (AUROC) and thresholds including those that achieved 34% SFR were reported. RESULTS: We included 2281 unique cases. The prevalence of MASH+F2-4, MASH+F2-3 and MASH+F4 was 31%, 24% and 7%, respectively. Area under the receiver operating characteristics curves for MASH+F2-4 were .78, .75, .68 and .57 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F2-3 were .73, .67, .60, .58 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F4 were .79, .84, .81, .76 for FAST, LSM-VCTE, FIB-4 and NFS. The sequential combination of FIB-4 and LSM-VCTE for the detection of MASH+F2-3 with threshold of .7 and 3.48, and 5.9 and 20 kPa achieved SFR of 67% and sensitivity of 60%, detecting 15 true positive cases from a theoretical group of 100 participants at the prevalence of 24%. CONCLUSIONS: Sequential combinations of NITs do not compromise diagnostic performance and may reduce resource utilisation through the need of fewer LSM-VCTE examinations.

Anaerobutyricum soehngenii Reduces Hepatic Lipogenic Pathways and Increases Intestinal Gluconeogenic Gene Expression in Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD) Mice.

Metabolic-dysfunction-associated steatotic liver disease (MASLD) is a growing health problem for which no therapy exists to date. The modulation of the gut microbiome may have treatment potential for MASLD. Here, we investigated Anaerobutyricum soehngenii, a butyrate-producing anaerobic bacterium with beneficial effects in metabolic syndrome, in a diet-induced MASLD mouse model. Male C57BL/6J mice received a Western-type high-fat diet and water with 15% fructose (WDF) to induce MASLD and were gavaged with A. soehngenii (108 or 109 colony-forming units (CFU) 3 times per week) or a placebo for 6 weeks. The A. soehngenii gavage increased the cecal butyrate concentrations. Although there was no effect on histological MASLD scores, A. soehngenii improved the glycemic response to insulin. In the liver, the WDF-associated altered expression of three genes relevant to the MASLD pathophysiology was reversed upon treatment with A. soehngenii: Lipin-1 (Lpin1), insulin-like growth factor binding protein 1 (Igfbp1) and Interleukin 1 Receptor Type 1 (Il1r1). A. soehngenii administration also increased the intestinal expression of gluconeogenesis and fructolysis genes. Although these effects did not translate into significant histological improvements in MASLD, these results provide a basis for combined gut microbial approaches to induce histological improvements in MASLD.

Prevalence and predictive features of metabolic dysfunction-associated steatotic liver disease in type 1 diabetes.

AIMS/HYPOTHESIS: The prevalence and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) in type 1 diabetes remain unclear. Therefore, we investigated the prevalence and severity of MASLD in type 1 diabetes and assessed which clinical features are most important in predicting MASLD severity. METHODS: A total of 453 individuals with type 1 diabetes (41.6 ± 15.0 years, 64% female, body mass index [BMI] 25.4 ± 4.2 kg/m2, and HbA1c 55.6 ± 12 mmol/mol) underwent vibration-controlled transient elastography (VCTE), with a controlled attenuation parameter (CAP) score for steatosis (≥280.0 dB/m) and a liver stiffness measurement (LMS) for fibrosis (≥8.0 kPa). A machine learning Extra-Trees classification model was performed to assess the predictive power of the clinical features associated with type 1 diabetes with respect to steatosis and fibrosis. RESULTS: The prevalence of hepatic steatosis and fibrosis was 9.5% (95% CI, 6.8-12.2) and 3.5% (95% CI, 1.8-5.2). Higher LMS was associated with a longer duration of type 1 diabetes (median 30.5 [IQR 18.0-39.3] years vs 15.0 [IQR 6.0-27.0] years), and individuals were older, had a higher BMI (mean 27.8 ± 5.2 vs 25.3 ± 4.1 kg/m2), and a higher CAP score (mean 211.4 ± 51.7 dB/m vs 241.4 ± 75.6 dB/m). The most important predictive features of fibrosis were duration of type 1 diabetes, age, and systolic blood pressure, with a mean ± SD area under the curve of 0.73 ± 0.03. CONCLUSION: Individuals with type 1 diabetes and high blood pressure, older age, higher BMI, and longer duration of disease could be considered at high-risk for developing MASLD.

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information.

AIMS: Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints. METHODS: Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable. RESULTS: Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance. CONCLUSIONS: This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD.

Accurate non-invasive biomarkers to diagnose metabolic dysfunction-associated steatotic liver disease (MASLD)-related fibrosis are urgently needed. This study applies a translational approach to develop a blood-based biomarker panel for fibrosis detection in MASLD. A molecular gene expression signature identified from a diet-induced MASLD mouse model (LDLr-/-.Leiden) is translated into human blood-based biomarkers based on liver biopsy transcriptomic profiles and protein levels in MASLD patient serum samples. The resulting biomarker panel consists of IGFBP7, SSc5D and Sema4D. LightGBM modeling using this panel demonstrates high accuracy in predicting MASLD fibrosis stage (F0/F1: AUC = 0.82; F2: AUC = 0.89; F3/F4: AUC = 0.87), which is replicated in an independent validation cohort. The overall accuracy of the model outperforms predictions by the existing markers Fib-4, APRI and FibroScan. In conclusion, here we show a disease mechanism-related blood-based biomarker panel with three biomarkers which is able to identify MASLD patients with mild or advanced hepatic fibrosis with high accuracy.

Implementation of a liver health check in people with type 2 diabetes.

As morbidity and mortality related to potentially preventable liver diseases are on the rise globally, early detection of liver fibrosis offers a window of opportunity to prevent disease progression. Early detection of non-alcoholic fatty liver disease allows for initiation and reinforcement of guidance on bodyweight management, risk stratification for advanced liver fibrosis, and treatment optimisation of diabetes and other metabolic complications. Identification of alcohol-related liver disease provides the opportunity to support patients with detoxification and abstinence programmes. In all patient groups, identification of cirrhosis ensures that patients are enrolled in surveillance programmes for hepatocellular carcinoma and portal hypertension. When considering early detection strategies, success can be achieved from applying ad-hoc screening for liver fibrosis in established frameworks of care. Patients with type 2 diabetes are an important group to consider case findings of advanced liver fibrosis and cirrhosis, as up to 19% have advanced fibrosis (which is ten times higher than the general population) and almost 70% have non-alcoholic fatty liver disease. Additionally, patients with type 2 diabetes with alcohol use disorders have the highest proportion of liver-related morbidity of people with type 2 diabetes generally. Patients with type 2 diabetes receive an annual diabetes review as part of their routine clinical care, in which the health of many organs are considered. Yet, liver health is seldom included in this review. This Viewpoint argues that augmenting the existing risk stratification strategy with an additional liver health check provides the opportunity to detect advanced liver fibrosis, thereby opening a window for early interventions to prevent end-stage liver disease and its complications, including hepatocellular carcinoma.