Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

BackgroundHistologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.MethodsThis was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0–4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0–2 vs F3 vs F4; LSM: 2·67; NFS: 0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226.FindingsOf 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44–63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33–91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62–0·81) for histology, 0·76 (0·70–0·83) for LSM-VCTE, 0·74 (0·64–0·82) for FIB-4, and 0·70 (0·63–0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression.InterpretationSimple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases

An unbiased ranking of murine dietary models based on their proximity to human Metabolic Dysfunction–associated Steatotic Liver Disease (MASLD)

Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), encompasses steatosis and steatohepatitis (NASH/MASH), leading to cirrhosis and hepatocellular carcinoma. Preclinical MASH research is mainly performed in rodents; however, the model that best recapitulates human disease is yet to be defined. We conducted a wide-ranging retrospective review (metabolic phenotype, liver histopathology, transcriptome benchmarked against humans) of murine models (mostly male), and ranked them using an unbiased MASLD “Human Proximity Score” (MHPS) to define their metabolic relevance and ability to induce MASH-fibrosis. Here, we show that Western Diets align closely with human MASH; high cholesterol content, extended study duration, and/or genetic manipulation of disease-promoting pathways are required to intensify liver damage and accelerate significant (F2+) fibrosis development. Choline-deficient models rapidly induce MASH-fibrosis while showing relatively poor translatability. Our ranking of commonly used MASLD models, based on their proximity to human MASLD, helps with selection of appropriate in vivo models to accelerate preclinical research.This study has been conducted as part of the Preclinical work package of the LITMUS (Liver Investigation: Testing Marker Utility in Steatohepatitis) project. The LITMUS study is a large multi-centre study aiming to evaluate Non-Alcoholic Fatty Liver Disease biomarkers. The Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under Grant Agreement 777377, funded the LITMUS study. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA. EMBL-EBI Core funding supported EP and IK through funding and computing resources from EMBL-EBI. Funding from the MRC (Medical Research Council) supported IK. M.V. is supported by the University of Bari (Horizon Europe Seed cod. id. S06-miRNASH), the Foundation for Liver Research (Intramural Funding), Associazione Italiana Ricerca sul Cancro (IG2022 Grant n. 27521) and Ministry of University and Research on Next Generation EU Funds [COD: P202222FCC, CUP: H53D23009960001, D.D. MUR 1366 (01-09-2023), Title: “System Biology” approaches in HCV Patients with Residual Hepatic Steatosis after Viral Eradication; Cod PE00000003, CUP: H93C22000630001, DD MUR 1550, Title: “ON Foods - Research and innovation network on food and nutrition Sustainability, Safety and Security – Working ON Foods”; Cod: CN00000041, CUP: H93C22000430007, Title PNRR “National Center for Gene Therapy and Drugs based on RNA Technology”, M4C2-Investment 1.4; Code: CN00000013, CUP: H93C22000450007, Title PNNR: “National Centre for HPC, Big Data and Quantum Computing”). A.V-P. is funded by MRC MDU, MRC Metabolic Diseases Unit (MC_UU_00014/5): Disease Model Core, Biochemistry Assay Lab, Histology Core and British Heart Foundation. F.O. is funded by UK Medical Research Council Program Grants MR/K0019494/1 and MR/R023026/1. C.M.P.R. is supported by Fundação para a Ciência e Tecnologia (PTDC/MED-FAR/3492/2021) and La Caixa Foundation (LCF/PR/HR21/52410028). Q.M.A. is supported by the Newcastle NIHR Biomedical Research Centre. S.L.F. and W.S. are supported by the NIH (NIH R01 DK128289; NCI 5P30CA196521-08 to S.L.F.; NIH R01 DK136016 to W.S.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript

Characterizing ZC3H18, a Multi-domain Protein at the Interface of RNA Production and Destruction Decisions

Summary: Nuclear RNA metabolism is influenced by protein complexes connecting to both RNA-productive and -destructive pathways. The ZC3H18 protein binds the cap-binding complex (CBC), universally present on capped RNAs, while also associating with the nuclear exosome targeting (NEXT) complex, linking to RNA decay. To dissect ZC3H18 function, we conducted interaction screening and mutagenesis of the protein, which revealed a phosphorylation-dependent isoform. Surprisingly, the modified region of ZC3H18 associates with core histone proteins. Further examination of ZC3H18 function, by genome-wide analyses, demonstrated its impact on transcription of a subset of protein-coding genes. This activity requires the CBC-interacting domain of the protein, with some genes being also dependent on the NEXT- and/or histone-interacting domains. Our data shed light on the domain requirements of a protein positioned centrally in nuclear RNA metabolism, and they suggest that post-translational modification may modulate its function. : The ZC3H18 protein is involved in RNA decay mediated by the CBC-NEXT complex. Winczura et al. identify a phosphorylation-dependent interaction of ZC3H18 with histones, and they find separate CBCA-, NEXT-, and histone-binding domains. They suggest a role for ZC3H18 in mRNA biogenesis, which for some genes is independent of its role in RNA decay. Keywords: ZC3H18, NEXT, CBC, RNA exosome, histones, transcription, RNA deca

An unbiased ranking of murine dietary models based on their proximity to human metabolic dysfunction-associated steatotic liver disease (MASLD).

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease, encompasses steatosis and metabolic dysfunction-associated steatohepatitis (MASH), leading to cirrhosis and hepatocellular carcinoma. Preclinical MASLD research is mainly performed in rodents; however, the model that best recapitulates human disease is yet to be defined. We conducted a wide-ranging retrospective review (metabolic phenotype, liver histopathology, transcriptome benchmarked against humans) of murine models (mostly male) and ranked them using an unbiased MASLD 'human proximity score' to define their metabolic relevance and ability to induce MASH-fibrosis. Here, we show that Western diets align closely with human MASH; high cholesterol content, extended study duration and/or genetic manipulation of disease-promoting pathways are required to intensify liver damage and accelerate significant (F2+) fibrosis development. Choline-deficient models rapidly induce MASH-fibrosis while showing relatively poor translatability. Our ranking of commonly used MASLD models, based on their proximity to human MASLD, helps with the selection of appropriate in vivo models to accelerate preclinical research.This study has been conducted as part of the Preclinical work package of the LITMUS (Liver Investigation: Testing Marker Utility in Steatohepatitis) project. The LITMUS study is a large multi-centre study aiming to evaluate Non-Alcoholic Fatty Liver Disease biomarkers. The Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under Grant Agreement 777377, funded the LITMUS study. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA. EMBL-EBI Core funding supported EP and IK through funding and computing resources from EMBL-EBI. Funding from the MRC (Medical Research Council) supported IK. M.V. is supported by the University of Bari (Horizon Europe Seed cod. id. S06-miRNASH), the Foundation for Liver Research (Intramural Funding), Associazione Italiana Ricerca sul Cancro (IG2022 Grant n. 27521) and Ministry of University and Research on Next Generation EU Funds [COD: P202222FCC, CUP: H53D23009960001, D.D. MUR 1366 (01-09-2023), Title: “System Biology” approaches in HCV Patients with Residual Hepatic Steatosis after Viral Eradication; Cod PE00000003, CUP: H93C22000630001, DD MUR 1550, Title: “ON Foods - Research and innovation network on food and nutrition Sustainability, Safety and Security – Working ON Foods”; Cod: CN00000041, CUP: H93C22000430007, Title PNRR “National Center for Gene Therapy and Drugs based on RNA Technology”, M4C2-Investment 1.4; Code: CN00000013, CUP: H93C22000450007, Title PNNR: “National Centre for HPC, Big Data and Quantum Computing”). A.V-P. is funded by MRC MDU, MRC Metabolic Diseases Unit (MC_UU_00014/5): Disease Model Core, Biochemistry Assay Lab, Histology Core and British Heart Foundation. F.O. is funded by UK Medical Research Council Program Grants MR/K0019494/1 and MR/R023026/1. C.M.P.R. is supported by Fundação para a Ciência e Tecnologia (PTDC/MED-FAR/3492/2021) and La Caixa Foundation (LCF/PR/HR21/52410028). Q.M.A. is supported by the Newcastle NIHR Biomedical Research Centre. S.L.F. and W.S. are supported by the NIH (NIH R01 DK128289; NCI 5P30CA196521-08 to S.L.F.; NIH R01 DK136016 to W.S.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Background and aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis