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Background and Aim: Cytochrome P450 2E1 (CYP2E1) plays a role in lipid metabolism, and by increasing hepatic oxidative stress and inflammation, the upregulation of CYP2E1 is involved in development of nonalcoholic steatohepatitis (NASH). We aimed to explore the relationship between CYP2E1‐333A>T (rs2070673) and the histological severity of nonalcoholic fatty liver disease (NAFLD). Methods: We studied 438 patients with biopsy‐proven NAFLD. NASH was defined as NAFLD Activity Score ≥ 5 with existence of steatosis, ballooning, and lobular inflammation. CYP2E1‐333A>T (rs2070673) was genotyped by matrix‐assisted laser desorption ionization‐time of flight mass spectrometry. Serum cytokines related to inflammation were measured by the Bio‐plex 200 system to investigate possible mediating factors involved in the process. Results: The TA genotype of rs2070673 had a higher prevalence of moderate/severe lobular inflammation (27.6% vs 20.3% vs 13.3%, P < 0.01) and NASH (55.7% vs 42.4% vs 40.5%, P < 0.01) compared with the AA and TT genotypes, respectively. In multivariable regression modeling, the heterozygote state TA was associated with moderate/severe lobular inflammation (adjusted odds ratio: 2.31, 95% confidence interval 1.41–3.78, P < 0.01) or NASH (adjusted odds ratio: 1.82, 95% confidence interval 1.22–2.69, P < 0.01), independently of age, sex, common metabolic risk factors, and presence of liver fibrosis. Compared with no‐NASH, NASH patients had significantly higher levels of serum interleukin‐1 receptor antagonist, interleukin‐18, and interferon‐inducible protein‐10 (IP‐10), whereas only IP‐10 was increased with the rs2070673 TA variant (P = 0.01). Mediation analysis showed that IP‐10 was responsible for ~60% of the association between the rs2070672 and NASH. Conclusions: The TA allele of rs2070673 is strongly associated with lobular inflammation and NASH, and this effect appears to be largely mediated by serum IP‐10 levels. [ABSTRACT FROM AUTHOR]

Background: Metabolic dysfunction‐associated fatty liver disease (MAFLD) is an emerging risk factor for chronic kidney disease (CKD). N‐terminal propeptide of collagen type 3 (PRO‐C3) is a biomarker of advanced fibrosis in MAFLD and PRO‐C3 may be involved in renal fibrosis. We aimed to use PRO‐C3 measurements to generate a new algorithmic score to test the prediction of MAFLD with chronic kidney disease (MAFLD–CKD). Methods: A derivation and independent validation cohort of 750 and 129 Asian patients with biopsy‐confirmed MAFLD were included. Serum PRO‐C3 concentration was measured and regression analyses were performed to examine associations with MAFLD–CKD. A derivative algorithm for MAFLD–CKD risk prediction was evaluated with receiver operator characteristic (ROC) curve analysis. Results: The study included two Asian cohorts (n = 180 with MAFLD–CKD; mean‐eGFR: 94.93 mL/min/1.73 m2; median‐urinary albumin‐to‐creatinine ratio: 6.58 mg/mmol). PRO‐C3 was associated with the severity of MAFLD‐CKD and independently associated with MAFLD–CKD (adjusted odds ratio = 1.16, 95% confidence interval [CI]: 1.08–1.23, p <.001). A new non‐invasive score (termed PERIOD) including PRO‐C3 efficiently predicted MAFLD‐CKD (AUROC =.842, 95% CI:.805–.875). Accuracy, specificity and negative predictive values were 80.2%, 85.1% and 88.4%, respectively. In the validation cohort, the PERIOD score had good diagnostic performance (AUROC =.807, 95% CI:.691–.893) with similar results in all patient subgroups. In the MAFLD–CKD subgroup, the accuracy for identifying advanced fibrosis was further improved by combining the PRO‐C3‐based ADAPT with the Agile 3+ scores (AUROC =.90, 95% CI:.836–.964). Conclusions: The PERIOD score is helpful for accurately predicting the risk of MAFLD–CKD. PRO‐C3 can also be used to assess liver fibrosis in people with MAFLD–CKD. [ABSTRACT FROM AUTHOR]

Summary: Background: Liver fibrosis is the strongest histological risk factor for liver‐related complications and mortality in metabolic dysfunction‐associated fatty liver disease (MAFLD). Second harmonic generation/two‐photon excitation fluorescence (SHG/TPEF) is a powerful tool for label‐free two‐dimensional and three‐dimensional tissue visualisation that shows promise in liver fibrosis assessment. Aim: To investigate combining multi‐photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. Methods: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy‐confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre‐processed images and test data sets. Multi‐layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. Results: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3‐4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3‐4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. Conclusion: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD. [ABSTRACT FROM AUTHOR]

Hepatocytic ballooning is a key histological feature in the diagnosis of non‐alcoholic steatohepatitis (NASH) and is an essential component of the two most widely used histological scoring systems for diagnosing and staging non‐alcoholic fatty liver disease (NAFLD) [namely, the NAFLD activity score (NAS), and the steatosis, activity and fibrosis (SAF) scoring system]. As a result of the increasing incidence of NASH globally, the diagnostic challenges of hepatocytic ballooning are unprecedented. Despite the clear pathological concept of hepatocytic ballooning, there are still challenges in assessing hepatocytic ballooning in 'real life' situations. Hepatocytic ballooning can be confused with cellular oedema and microvesicular steatosis. Significant inter‐observer variability does exist in assessing the presence and severity of hepatocytic ballooning. In this review article, we describe the underlying mechanisms associated with hepatocytic ballooning. Specifically, we discuss the increased endoplasmic reticulum stress and the unfolded protein response, as well as the rearrangement of the intermediate filament cytoskeleton, the appearance of Mallory‐Denk bodies and activation of the sonic Hedgehog pathway. We also discuss the use of artificial intelligence in the detection and interpretation of hepatocytic ballooning, which may provide new possibilities for future diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is a usual chronic liver disease and lacks non-invasive biomarkers for the clinical diagnosis and prognosis. Extracellular vesicles (EVs), a group of heterogeneous small membrane-bound vesicles, carry proteins and nucleic acids as promising biomarkers for clinical applications, but it has not been well explored on their lipid compositions related to NAFLD studies. Here, we investigate the lipid molecular function of urinary EVs and their potential as biomarkers for non-alcoholic steatohepatitis (NASH) detection. Methods: This work includes 43 patients with non-alcoholic fatty liver (NAFL) and 40 patients with NASH. The EVs of urine were isolated and purified using the EXODUS method. The EV lipidomics was performed by LC-MS/MS. We then systematically compare the EV lipidomic profiles of NAFL and NASH patients and reveal the lipid signatures of NASH with the assistance of machine learning. Results: By lipidomic profiling of urinary EVs, we identify 422 lipids mainly including sterol lipids, fatty acyl lipids, glycerides, glycerophospholipids, and sphingolipids. Via the machine learning and random forest modeling, we obtain a biomarker panel composed of 4 lipid molecules including FFA (18:0), LPC (22:6/0:0), FFA (18:1), and PI (16:0/18:1), that can distinguish NASH with an AUC of 92.3%. These lipid molecules are closely associated with the occurrence and development of NASH. Conclusion: The lack of non-invasive means for diagnosing NASH causes increasing morbidity. We investigate the NAFLD biomarkers from the insights of urinary EVs, and systematically compare the EV lipidomic profiles of NAFL and NASH, which holds the promise to expand the current knowledge of disease pathogenesis and evaluate their role as non-invasive biomarkers for NASH diagnosis and progression. [ABSTRACT FROM AUTHOR]