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

Role of the anterior Hox genes in segment-specific NB7-3 lineage development in the embryonic central nervous system of Drosophila melanogaster

The homeotic (Hox) genes specify the segmental identities along the anterior-posterior body axis. During Drosophila ventral nerve cord development, the Hox genes play a major role in many aspects of neurogenesis like neural cell fate specification, cell number, apoptosis and proper differentiation of the neurons at embryonic and post-embryonic development in a segmental manner. This study informed that Antennapedia complex (ANT-C) genes Deformed (Dfd), Sex combs reduced (Scr) and Antennapedia (Antp) control the number and size of NB7-3 lineages in a segment-specific manner. Analysis of NB7-3 lineages in these mutants shows a two-step Hox gene control at early and late embryonic phases of development. They exhibit prolonged survival of early dying neurons within this lineage until late developmental stages and importantly complete lineage duplication phenotype in a segment-specific way. Detailed study of the duplication phenotype in Antp mutants reveals that it arises from two sequentially delaminating neural stem cells (neuroblasts, NBs). In early Antp mutant embryos the neuroectodermal precursor (NEP) of NB7-3 performs an additional cell division before it attains neural identity. Both daughter cells subsequently delaminate as NB7-3 in a segment-specific manner due to prolonged proneural gene expression in the mutant. The study reveals that a Hox mediated simultaneous cell division and specification program can intersect in the neuroectoderm to control the number of delaminating neural stem cells. The Hox genes encode transcription factors that control the expression of downstream target genes through a common 60 amino acid domain, the homeodomain, which is involved in DNA-binding. The results in this study revealed that a combination of novel in vivo DNA-binding activity-dependent and -independent functions of ANT-C proteins are involved in regulating the neural stem cells arising from the NB7-3 proneural cluster. Regulation of cell division within the 7-3 NEP does not require the Hox DNA-binding function, but this function is necessary for prompt repression of proneural gene expression after NB delamination and, subsequently, for execution of proper apoptosis of NB7-3 progeny cells

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