Noninvasive tests maintain high accuracy for advanced fibrosis in chronic hepatitis B patients with different nomenclatures of steatotic liver disease

Journal of Medical Virology, Год журнала: 2024, Номер 96(4)

Опубликована: Апрель 1, 2024

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a new nomenclature proposed in 2023. We aimed to compare the diagnostic efficacy of noninvasive tests (NITs) for advanced fibrosis under different nomenclatures patients with chronic hepatitis B (CHB). A total 844 diagnosed CHB and concurrent (SLD) by biopsy were retrospectively enrolled divided into four groups. The performances fibrosis-4 (FIB-4), gamma-glutamyl transpeptidase platelet ratio index (GPRI), aspartate aminotransferase (APRI), stiffness measurement (LSM) compared among NITs showed similar nonalcoholic fatty (NAFLD), MASLD, metabolic (MAFLD) fibrosis. LSM most stable accuracy NAFLD (AUC = 0.842), MASLD 0.846), MAFLD 0.863) other (p < 0.05). Among NITs, APRI 0.841) GPRI 0.844) performed best & MetALD cutoff value was higher than that three groups, while further comparisons at stages median (1.113) F3-4 group (0.508) Current perform adequately SLD; however, alterations values need be noted.

Язык: Английский

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Regional gray matter changes in steatotic liver disease provide a neurobiological link to depression: A cross-sectional UK biobank cohort study

Metabolism, Год журнала: 2024, Номер 159, С. 155983 - 155983

Опубликована: Июль 30, 2024

Steatotic liver disease (SLD) is characterized by excessive accumulation of lipids in the liver. It associated with elevated risk hepatic and cardiometabolic diseases, as well mental disorders such depression. Previous studies revealed global gray matter reduction SLD. To investigate a possible shared neurobiology depression, we examined fat-related regional alterations SLD its most significant clinical subgroup metabolic dysfunction-associated steatotic (MASLD).

Язык: Английский

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Diagnostic Accuracy of Non-Invasive Diagnostic Tests for Nonalcoholic Fatty Liver Disease: A Systematic Review and Network Meta-Analysis

Clinical Epidemiology, Год журнала: 2025, Номер Volume 17, С. 53 - 71

Опубликована: Янв. 1, 2025

In recent decades, numerous non-invasive tests (NITs) for diagnosing nonalcoholic fatty liver disease (NAFLD) have been developed, however, a comprehensive comparison of their relative diagnostic accuracies is lacking. We aimed to assess and compare the accuracy various NITs NAFLD using network meta-analysis (NMA). conducted systematic search in seven databases up April 2024 identify studies evaluating values NITs, with biopsy as gold standard. The participants included patients suspected or confirmed NAFLD, irrespective age, sex, ethnicity. Statistical analysis was R 4.0.3 Bayesian NMA STATA 17.0 pairwise meta-analysis. Sensitivity, specificity, odds ratio (DOR), area under receiver operating characteristic curve (AUC), superiority index were calculated. calculations performed Rstan package, specifying parameters like MCMC chain count, iteration operational cycles. methodological quality assessed QUADAS-2 tool. Out 15,877 studies, 180 quantitative synthesis, 102 used head-to-head meta-analyses. For steatosis stage 1, Hydrogen Magnetic Resonance Spectroscopy (H-MRS, DOR 15,745,657.6, 95% CI 17.2-1,014,063.59) proved be most accurate. significant fibrosis, HRI leading (DOR 80.94, 6.46-391.41), advanced CK-18 showed highest performance 102654.16, 1.6-134,059.8). high-risk NASH, Real-Time Elastography showing 18.1, 0.7-96.33). Meta-regression analyses suggested that variability may result from differences study design, thresholds, populations, indicators. rank these tests. While some results are promising, not all demonstrate substantial accuracy, highlighting need validation larger datasets. Future research should concentrate on studying thresholds enhancing clarity reporting.

Язык: Английский

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Untargeted lipidomic analysis of metabolic dysfunction-associated steatohepatitis in women with morbid obesity

PLoS ONE, Год журнала: 2025, Номер 20(3), С. e0318557 - e0318557

Опубликована: Март 4, 2025

Metabolic Dysfunction-Associated Steatohepatitis (MASH) represents the severe condition of Steatotic Liver Disease (MASLD). Currently, there is a need to identify non-invasive biomarkers for an accurate diagnosis MASH. Previously, omics studies identified alterations in lipid metabolites involved MASLD. However, these require validation other cohorts. In this sense, our aim was perform lipidomics circulating metabolite profile We assessed liquid chromatography coupled mass spectrometer-based untargeted lipidomic assay serum samples 216 women with morbid obesity that were stratified according their hepatic into Normal (NL, n = 44), Simple Steatosis (SS, 66) and MASH (n 106). First, we are increased MASLD, composed ceramides, triacylglycerols (TAG) some phospholipids. Then, patients SS have characteristic levels diacylglycerols DG (36:2) (36:4), TAG few phospholipids such as PC (32:1), PE (38:3), (40:6), PI (32:0) (32:1). Later, patients, found deoxycholic acid, set TAG, PC, PE, LPI; while decreased (36:0). Finally, reported panel might be used differentiate from made up 9-HODE LPI (16:0) To conclude, investigation has suggested associated MASLD Specifically, seems discriminatory subjects compared individuals. Thus, could diagnostic tool.

Язык: Английский

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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 11, 2025

Early and accurate identification of patients at high risk metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent improve prognosis potentially. We aimed develop validate an explainable prediction model based on machine learning (ML) approaches for MASLD among the adult population. The national cross-sectional study collected data from National Health Nutrition Examination Survey 2017 2020, consisting 13,436 participants, who were randomly split into 70% training, 20% internal validation, 10% external validation cohorts. was defined transient elastography cardiometabolic factors. With 50 medical characteristics easily obtained, six ML algorithms used models. Several evaluation parameters compare predictive performance, including area under receiver-operating-characteristic curve (AUC) precision-recall (P-R) curve. recursive feature elimination method applied select optimal subset. Shapley Additive exPlanations offered global local explanations model. random forest (RF) performed best in discriminative ability 6 models, 10-feature RF finally chosen. final could accurately predict cohorts (AUC: 0.928, 0.918; P-R curve: 0.876, 0.863, respectively). better than each traditional indicators MASLD. An with excellent discrimination calibration performance successfully developed validated clinical extracted using algorithm.

Язык: Английский

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