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- Anstee, Quentin M., et al.
(author)
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Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically-characterised cohort
- 2020
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In: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:3, s. 505-515
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Journal article (peer-reviewed)abstract
- BACKGROUND AND AIMS: Genetic factors associated with non-alcoholic fatty liver disease (NAFLD) remain incompletely understood. To date, most GWAS studies have adopted radiologically assessed hepatic triglyceride content as reference phenotype and so cannot address steatohepatitis or fibrosis. We describe a genome-wide association study (GWAS) encompassing the full spectrum of histologically characterized NAFLD.METHODS: The GWAS involved 1483 European NAFLD cases and 17781 genetically-matched population controls. A replication cohort of 559 NAFLD cases and 945 controls was genotyped to confirm signals showing genome-wide or close to genome-wide significance.RESULTS: Case-control analysis identified signals showing p-values ≤ 5 x 10-8 at four locations (chromosome (chr) 2 GCKR/C2ORF16; chr4 HSD17B13; chr19 TM6SF2; chr22 PNPLA3) together with two other signals with p<1 x10-7 (chr1 near LEPR and chr8 near IDO2/TC1). Case-only analysis of quantitative traits steatosis, disease activity score, NAS and fibrosis showed that the PNPLA3 signal (rs738409) was genome-wide significantly associated with steatosis, fibrosis and NAS score and identified a new signal (PYGO1 rs62021874) with close to genome-wide significance for steatosis (p=8.2 x 10-8). Subgroup case-control analysis for NASH confirmed the PNPLA3 signal. The chr1 LEPR SNP also showed genome-wide significance for this phenotype. Considering the subgroup with advanced fibrosis (≥F3), the signals on chromosomes 2, 19 and 22 remained genome-wide significant. With the exception of GCKR/C2ORF16, the genome-wide significant signals replicated.CONCLUSIONS: This study confirms PNPLA3 as a risk factor for the full histological spectrum of NAFLD at genome-wide significance levels, with important contributions from TM6SF2 and HSD17B13. PYGO1 is a novel steatosis modifier, suggesting relevance of Wnt signalling pathways in NAFLD pathogenesis.
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- Boesch, Markus, et al.
(author)
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Adipose tissue macrophage dysfunction is associated with a breach of vascular integrity in NASH
- 2024
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In: Journal of Hepatology. - 0168-8278 .- 1600-0641. ; 80:3, s. 397-408
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Journal article (peer-reviewed)abstract
- Background & Aims: In non-alcoholic fatty liver disease (NAFLD), monocytes infiltrate visceral adipose tissue promoting local and hepatic inflammation. However, it remains unclear what drives inflammation and how the immune landscape in adipose tissue differs across the NAFLD severity spectrum. We aimed to assess adipose tissue macrophage (ATM) heterogeneity in a NAFLD cohort. Methods: Visceral adipose tissue macrophages from lean and obese patients, stratified by NAFLD phenotypes, underwent single-cell RNA sequencing. Adipose tissue vascular integrity and breaching was assessed on a protein level via immunohistochemistry and immunofluorescence to determine targets of interest. Results: We discovered multiple ATM populations, including resident vasculature-associated macrophages (ResVAMs) and distinct metabolically active macrophages (MMacs). Using trajectory analysis, we show that ResVAMs and MMacs are replenished by a common transitional macrophage (TransMac) subtype and that, during NASH, MMacs are not effectively replenished by TransMac precursors. We postulate an accessory role for MMacs and ResVAMs in protecting the adipose tissue vascular barrier, since they both interact with endothelial cells and localize around the vasculature. However, across the NAFLD severity spectrum, alterations occur in these subsets that parallel an adipose tissue vasculature breach characterized by albumin extravasation into the perivascular tissue. Conclusions: NAFLD-related macrophage dysfunction coincides with a loss of adipose tissue vascular integrity, providing a plausible mechanism by which tissue inflammation is perpetuated in adipose tissue and downstream in the liver. Impact and implications: Our study describes for the first time the myeloid cell landscape in human visceral adipose tissue at single-cell level within a cohort of well-characterized patients with non-alcoholic fatty liver disease. We report unique non-alcoholic steatohepatitis-specific transcriptional changes within metabolically active macrophages (MMacs) and resident vasculature-associated macrophages (ResVAMs) and we demonstrate their spatial location surrounding the vasculature. These dysfunctional transcriptional macrophage states coincided with the loss of adipose tissue vascular integrity, providing a plausible mechanism by which tissue inflammation is perpetuated in adipose tissue and downstream in the liver. Our study provides a theoretical basis for new therapeutic strategies to be directed towards reinstating the endogenous metabolic, homeostatic and cytoprotective functions of ResVAMs and MMacs, including their role in protecting vascular integrity.
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- Hardy, Timothy, et al.
(author)
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The European NAFLD Registry : A real-world longitudinal cohort study of nonalcoholic fatty liver disease
- 2020
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In: Contemporary Clinical Trials. - : Elsevier. - 1551-7144 .- 1559-2030. ; 98
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Journal article (peer-reviewed)abstract
- Non-Alcoholic Fatty Liver Disease (NAFLD), a progressive liver disease that is closely associated with obesity, type 2 diabetes, hypertension and dyslipidaemia, represents an increasing global public health challenge. There is significant variability in the disease course: the majority exhibit only fat accumulation in the liver but a significant minority develop a necroinflammatory form of the disease (non-alcoholic steatohepatitis, NASH) that may progress to cirrhosis and hepatocellular carcinoma. At present our understanding of pathogenesis, disease natural history and long-term outcomes remain incomplete. There is a need for large, well characterised patient cohorts that may be used to address these knowledge gaps and to support the development of better biomarkers and novel therapies. The European NAFLD Registry is an international, prospectively recruited observational cohort study that aims to establish a large, highly-phenotyped patient cohort and linked bioresource. Here we describe the infrastructure, data management and monitoring plans, and the standard operating procedures implemented to ensure the timely and systematic collection of high-quality data and samples. Already recruiting subjects at secondary/tertiary care centres across Europe, the Registry is supporting the European Union IMI2-funded LITMUS Liver Investigation: Testing Marker Utility in Steatohepatitis consortium, which is a major international effort to robustly validate biomarkers that diagnose, risk stratify and/or monitor NAFLD progression and liver fibrosis stage. The European NAFLD Registry has the demonstrable capacity to support research and biomarker development at scale and pace.
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- Lee, Jenny, et al.
(author)
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Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study
- 2023
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In: Hepatology. - : LIPPINCOTT WILLIAMS & WILKINS. - 0270-9139 .- 1527-3350. ; 78:1, s. 258-271
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Journal article (peer-reviewed)abstract
- 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.
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- Mcteer, Matthew, et al.
(author)
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Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information
- 2024
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In: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 19:2
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Journal article (peer-reviewed)abstract
- 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.
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| 8. |
- Orešič, Matej, 1967-, et al.
(author)
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Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids
- 2013
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In: Diabetologia. - : Springer. - 0012-186X .- 1432-0428. ; 56:10, s. 2266-2274
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Journal article (peer-reviewed)abstract
- AIMS/HYPOTHESIS: We examined whether analysis of lipids by ultra-performance liquid chromatography (UPLC) coupled to MS allows the development of a laboratory test for non-alcoholic fatty-liver disease (NAFLD), and how a lipid-profile biomarker compares with the prediction of NAFLD and liver-fat content based on routinely available clinical and laboratory data.METHODS: We analysed the concentrations of molecular lipids by UPLC-MS in blood samples of 679 well-characterised individuals in whom liver-fat content was measured using proton magnetic resonance spectroscopy ((1)H-MRS) or liver biopsy. The participants were divided into biomarker-discovery (n = 287) and validation (n = 392) groups to build and validate the diagnostic models, respectively.RESULTS: Individuals with NAFLD had increased triacylglycerols with low carbon number and double-bond content while lysophosphatidylcholines and ether phospholipids were diminished in those with NAFLD. A serum-lipid signature comprising three molecular lipids ('lipid triplet') was developed to estimate the percentage of liver fat. It had a sensitivity of 69.1% and specificity of 73.8% when applied for diagnosis of NAFLD in the validation series. The usefulness of the lipid triplet was demonstrated in a weight-loss intervention study.CONCLUSIONS/INTERPRETATION: The liver-fat-biomarker signature based on molecular lipids may provide a non-invasive tool to diagnose NAFLD, in addition to highlighting lipid molecular pathways involved in the disease.
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- Vali, Yasaman, et al.
(author)
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Biomarkers for staging fibrosis and non-alcoholic steatohepatitis in non-alcoholic fatty liver disease (the LITMUS project) : a comparative diagnostic accuracy study
- 2023
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In: The Lancet Gastroenterology & Hepatology. - : Elsevier Ltd. - 2468-1253. ; 8:8, s. 714-725
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Journal article (peer-reviewed)abstract
- Background: The reference standard for detecting non-alcoholic steatohepatitis (NASH) and staging fibrosis—liver biopsy—is invasive and resource intensive. Non-invasive biomarkers are urgently needed, but few studies have compared these biomarkers in a single cohort. As part of the Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS) project, we aimed to evaluate the diagnostic accuracy of 17 biomarkers and multimarker scores in detecting NASH and clinically significant fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) and identify their optimal cutoffs as screening tests in clinical trial recruitment. Methods: This was a comparative diagnostic accuracy study in people with biopsy-confirmed NAFLD from 13 countries across Europe, recruited between Jan 6, 2010, and Dec 29, 2017, from the LITMUS metacohort of the prospective European NAFLD Registry. Adults (aged ≥18 years) with paired liver biopsy and serum samples were eligible; those with excessive alcohol consumption or evidence of other chronic liver diseases were excluded. The diagnostic accuracy of the biomarkers was expressed as the area under the receiver operating characteristic curve (AUC) with liver histology as the reference standard and compared with the Fibrosis-4 index for liver fibrosis (FIB-4) in the same subgroup. Target conditions were the presence of NASH with clinically significant fibrosis (ie, at-risk NASH; NAFLD Activity Score ≥4 and F≥2) or the presence of advanced fibrosis (F≥3), analysed in all participants with complete data. We identified thres holds for each biomarker for reducing the number of biopsy-based screen failures when recruiting people with both NASH and clinically significant fibrosis for future trials. Findings: Of 1430 participants with NAFLD in the LITMUS metacohort with serum samples, 966 (403 women and 563 men) were included after all exclusion criteria had been applied. 335 (35%) of 966 participants had biopsy-confirmed NASH and clinically significant fibrosis and 271 (28%) had advanced fibrosis. For people with NASH and clinically significant fibrosis, no single biomarker or multimarker score significantly reached the predefined AUC 0·80 acceptability threshold (AUCs ranging from 0·61 [95% CI 0·54–0·67] for FibroScan controlled attenuation parameter to 0·81 [0·75–0·86] for SomaSignal), with accuracy mostly similar to FIB-4. Regarding detection of advanced fibrosis, SomaSignal (AUC 0·90 [95% CI 0·86–0·94]), ADAPT (0·85 [0·81–0·89]), and FibroScan liver stiffness measurement (0·83 [0·80–0·86]) reached acceptable accuracy. With 11 of 17 markers, histological screen failure rates could be reduced to 33% in trials if only people who were marker positive had a biopsy for evaluating eligibility. The best screening performance for NASH and clinically significant fibrosis was observed for SomaSignal (number needed to test [NNT] to find one true positive was four [95% CI 4–5]), then ADAPT (six [5–7]), MACK-3 (seven [6–8]), and PRO-C3 (nine [7–11]). Interpretation: None of the single markers or multimarker scores achieved the predefined acceptable AUC for replacing biopsy in detecting people with both NASH and clinically significant fibrosis. However, several biomarkers could be applied in a prescreening strategy in clinical trial recruitment. The performance of promising markers will be further evaluated in the ongoing prospective LITMUS study cohort. Funding: The Innovative Medicines Initiative 2 Joint Undertaking. © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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| 11. |
- Zhou, You, et al.
(author)
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Noninvasive Detection of Nonalcoholic Steatohepatitis Using Clinical Markers and Circulating Levels of Lipids and Metabolites
- 2016
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In: Clinical Gastroenterology and Hepatology. - Maryland Heights, MO, USA : Elsevier. - 1542-3565 .- 1542-7714. ; 14:10, s. 1463-1472.e6
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Journal article (peer-reviewed)abstract
- BACKGROUND & AIMS: Use of targeted mass spectrometry (MS)-based methods is increasing in clinical chemistry laboratories. We investigate whether MS-based profiling of plasma improves noninvasive risk estimates of nonalcoholic steatohepatitis (NASH) compared with routinely available clinical parameters and patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotype at rs738409.METHODS: We used MS-based analytic platforms to measure levels of lipids and metabolites in blood samples from 318 subjects who underwent a liver biopsy because of suspected NASH. The subjects were divided randomly into estimation (n = 223) and validation (n = 95) groups to build and validate the model. Gibbs sampling and stepwise logistic regression, which fulfilled the Bayesian information criterion, were used for variable selection and modeling.RESULTS: Features of the metabolic syndrome and the variant in PNPLA3 encoding I148M were significantly more common among subjects with than without NASH. We developed a model to identify subjects with NASH based on clinical data and PNPLA3 genotype (NASH Clin Score), which included aspartate aminotransferase (AST), fasting insulin, and PNPLA3 genotype. This model identified subjects with NASH with an area under the receiver operating characteristic of 0.778 (95% confidence interval, 0.709-0.846). We then used backward stepwise logistic regression analyses of variables from the NASH Clin Score and MS-based factors associated with NASH to develop the NASH ClinLipMet Score. This included glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6, AST, and fasting insulin, along with PNPLA3 genotype. It identified patients with NASH with an area under the receiver operating characteristic of 0.866 (95% confidence interval, 0.820-0.913). The NASH ClinLipMet score identified patients with NASH with significantly higher accuracy than the NASH Clin Score or MS-based profiling alone.CONCLUSIONS: A score based on MS (glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6) and knowledge of AST, fasting insulin, and PNPLA3 genotype is significantly better than a score based on clinical or metabolic profiles alone in determining the risk of NASH.
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