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| 2. |
- Armandi, Angelo, et al.
(författare)
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Serum ferritin levels can predict long-term outcomes in patients with metabolic dysfunction-associated steatotic liver disease
- 2024
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Ingår i: Gut. - : BMJ PUBLISHING GROUP. - 0017-5749 .- 1468-3288.
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Tidskriftsartikel (refereegranskat)abstract
- Objective Hyperferritinaemia is associated with liver fibrosis severity in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), but the longitudinal implications have not been thoroughly investigated. We assessed the role of serum ferritin in predicting long-term outcomes or death. Design We evaluated the relationship between baseline serum ferritin and longitudinal events in a multicentre cohort of 1342 patients. Four survival models considering ferritin with confounders or non-invasive scoring systems were applied with repeated five-fold cross-validation schema. Prediction performance was evaluated in terms of Harrell's C-index and its improvement by including ferritin as a covariate. Results Median follow-up time was 96 months. Liver-related events occurred in 7.7%, hepatocellular carcinoma in 1.9%, cardiovascular events in 10.9%, extrahepatic cancers in 8.3% and all-cause mortality in 5.8%. Hyperferritinaemia was associated with a 50% increased risk of liver-related events and 27% of all-cause mortality. A stepwise increase in baseline ferritin thresholds was associated with a statistical increase in C-index, ranging between 0.02 (lasso-penalised Cox regression) and 0.03 (ridge-penalised Cox regression); the risk of developing liver-related events mainly increased from threshold 215.5 mu g/L (median HR=1.71 and C-index=0.71) and the risk of overall mortality from threshold 272 mu g/L (median HR=1.49 and C-index=0.70). The inclusion of serum ferritin thresholds (215.5 mu g/L and 272 mu g/L) in predictive models increased the performance of Fibrosis-4 and Non-Alcoholic Fatty Liver Disease Fibrosis Score in the longitudinal risk assessment of liver-related events (C-indices>0.71) and overall mortality (C-indices>0.65). Conclusions This study supports the potential use of serum ferritin values for predicting the long-term prognosis of patients with MASLD.
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| 3. |
- Govaere, Olivier, et al.
(författare)
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A proteo-transcriptomic map of non-alcoholic fatty liver disease signatures
- 2023
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Ingår i: Nature Metabolism. - : NATURE PORTFOLIO. - 2522-5812. ; 5:4, s. 572-578
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Tidskriftsartikel (refereegranskat)abstract
- Govaere et al. integrate circulating protein data from more than 300 patients with non-alcoholic fatty liver disease (NAFLD) with transcriptomics and develop a non-invasive diagnostics tool to identify patients with at-risk NAFLD based on body mass index, type 2 diabetes status and four circulating proteins. Non-alcoholic fatty liver disease (NAFLD) is a common, progressive liver disease strongly associated with the metabolic syndrome. It is unclear how progression of NAFLD towards cirrhosis translates into systematic changes in circulating proteins. Here, we provide a detailed proteo-transcriptomic map of steatohepatitis and fibrosis during progressive NAFLD. In this multicentre proteomic study, we characterize 4,730 circulating proteins in 306 patients with histologically characterized NAFLD and integrate this with transcriptomic analysis in paired liver tissue. We identify circulating proteomic signatures for active steatohepatitis and advanced fibrosis, and correlate these with hepatic transcriptomics to develop a proteo-transcriptomic signature of 31 markers. Deconvolution of this signature by single-cell RNA sequencing reveals the hepatic cell types likely to contribute to proteomic changes with disease progression. As an exemplar of use as a non-invasive diagnostic, logistic regression establishes a composite model comprising four proteins (ADAMTSL2, AKR1B10, CFHR4 and TREM2), body mass index and type 2 diabetes mellitus status, to identify at-risk steatohepatitis.
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| 4. |
- Lee, Jenny, et al.
(författare)
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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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Ingår i: Hepatology. - : LIPPINCOTT WILLIAMS & WILKINS. - 0270-9139 .- 1527-3350. ; 78:1, s. 258-271
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Tidskriftsartikel (refereegranskat)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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| 5. |
- McGlinchey, Aidan J, 1984-, et al.
(författare)
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The Metabolomics of Non-Alcoholic Fatty Liver Disease : Of Networks and Biomarkers
- 2021
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 75:Suppl. 2, s. S579-S580
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background and aims: Non-alcoholic fatty liver disease (NAFLD), the leading cause of chronic liver disease, affects 25%+ of people worldwide. Detailed understanding of the metabolomics of NAFLD, and non-invasive diagnostic techniques for the stages of NAFLD are unavailable. We identify specific serum molecular lipid signatures to these ends.First, we leverage lipidomic and polar metabolomic data (n = 643) subjects, to produce a clear, meaningful interaction map, linking lipids, metabolites, clinical factors and disease outcomes. We find non-spurious associations therein, as features of interest, and for downstream analysis.Third, NAFLD fibrosis biomarker identification was performed using machine learning, with our candidate lipids/metabolites to be forwarded to a successor project; the LITMUS project, towards clinically-applicable, non-invasive, sensitive and specific classification of NAFLD patients.Method: Serum lipids and polar metabolites were measured by mass spectrometry in the EPoS cohort of patients (n = 176 lipids and n = 36 polar metabolites), combined with clinical data from (n = 643 subjects), followed by model-based clustering, giving 10 lipid clusters (LCs).Correlations were calculated pairwise between (1) all LCs, (2) “input” clinical data (height, weight, BMI, blood platelet count) and (3) outcomes (fibrosis, steatosis, NAS score, etc.). Non-rejection rates (NRRs) were calculated for relationships, remove spurious associations (NRR > 0.4). We project the remaining associations as a network; a novel metabolomic overview NAFLD.ANOVA and Tukey’s Honest Significant Differences (Tukey HSDs) revealed detailed metabolic signatures across NAFLD, fibrosis and steatosis stages.Random forest machine learning was used to classify NAFLD patients: LOW (0-1 fibrosis grade) or HIGH (2–4 fibrosis grade), using individual lipids and metabolites, identifying putative biomarkers.Results: In linewith our previous findings, many lipids associate with steatosis and fibrosis in NAFLD. Our novel overview network revealsas sociations between specific LCs and clinical variables, such as TGs (LC3), and a subgroup of TGs of lowest and highest carbon numbers (LC9) along with PC (O)s (LC7) positively associating with NAFLD score and fibrosis. Conversely, LPCs (LC4), particularly sphingomyelins (SMs, LC6), negatively associated with these variables. Many other metabolites changing across NAFLD stages beg further discussion.Conclusion: In addition to generation of a novel metabolomic network of NAFLD, we demonstrate feasibility of lipidomic and metabolomic data to classify NAFLD patients’fibrosis grades (median AUC: 0.765), competitive with gold-standard clinical variables (age, BMI, sex, diabetes, liver AST/ALT, platelet count) (median AUC: 0.778). These biomarkers are being taken forward (LITMUS project) to develop clinical testing.
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| 6. |
- Mcteer, Matthew, et al.
(författare)
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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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Ingår i: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 19:2
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Tidskriftsartikel (refereegranskat)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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| 7. |
- Ratziu, Vladimir, et al.
(författare)
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Cost of non-alcoholic steatohepatitis in Europe and the USA: The GAIN study
- 2020
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Ingår i: JHEP Reports. - : Elsevier. - 2589-5559 .- 2589-5559. ; 2:5
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundXX1Aims: Non-alcoholic steatohepatitis (NASH) leads to cirrhosis and is associated with a substantial socioeconomic burden, which, coupled with rising prevalence, is a growing public health challenge. However, there are few real-world data available describing the impact of NASH.Methods: The Global Assessment of the Impact of NASH (GAIN) study is a prevalence-based burden of illness study across Europe (France, Germany, Italy, Spain, and the UK) and the USA. Physicians provided demographic, clinical, and economic patient information via an online survey. In total, 3,754 patients found to have NASH on liver biopsy were stratified by fibrosis score and by biomarkers as either early or advanced fibrosis. Per-patient costs were estimated using national unit price data and extrapolated to the population level to calculate the economic burden. Of the patients, 767 (20%) provided information on indirect costs and health-related quality of life using the EuroQOL 5-D (EQ-5D; n = 749) and Chronic Liver Disease Questionnaire - Non-Alcoholic Fatty Liver Disease (CLDQ-NAFLD) (n = 723).Results: Mean EQ-5D and CLDQ-NAFLD index scores were 0.75 and 4.9, respectively. For 2018, the mean total annual per patient cost of NASH was (sic)2,763, (sic)4,917, and (sic)5,509 for direct medical, direct non-medical, and indirect costs, respectively. National per-patient cost was highest in the USA and lowest in France. Costs increased with fibrosis and decompensation, driven by hospitalisation and comorbidities. Indirect costs were driven by work loss.Conclusions: The GAIN study provides real-world data on the direct medical, direct non-medical, and indirect costs associated with NASH, including patient-reported outcomes in Europe and the USA, showing a substantial burden on health services and individuals. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL).
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| 9. |
- Sen, Partho, 1983-, et al.
(författare)
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Genome-scale metabolic modeling of human hepatocytes reveals dysregulation of glycosphingolipid pathways in progressive non-alcoholic fatty liver disease
- 2021
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 75:Suppl. 2, s. S256-S256
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background and aims: Non-alcoholic fatty liver disease (NAFLD) is a spectrum of chronic liver diseases intertwined with the metabolic disorders. The prevalence of NAFLD is rapidly increasing worldwide, while the pathologyand the underlying mechanism driving NAFLD is not fully understood. In NAFLD, a series of metabolic changes takes place in the liver. However, the alteration of the metabolic pathways in the human liver along the progression of NAFLD,i.e., transition from non-alcoholic steatosis (NAFL) to steatohepatitis (NASH) through cirrhosis remains to be discovered. Here, we sought to examine the metabolic pathways of the human liver across the full histological spectrum of NAFLD.Method: We analyzed the whole liver tissue transcriptomic (RNA-Seq)1 and serum metabolomics data obtained from a large cohort of histologically characterized patients derived from the European NAFLD Registry (n = 206), and developed genome-scale metabolic models (GEMs) of human hepatocytes at different stages of NAFLD. The integrative approach employed in this study has enabled us to understand the regulation of the metabolic pathways of human liver in NAFL, and with progressive NASH-associated fibrosis (F0-F4).Results: Our study identified several metabolic signatures in the liver and blood of these patients, specifically highlighting the alteration of vitamins (A, E) and glycosphingolipids, and their link with complex glycosaminoglycans in advanced fibrosis. Furthermore, by applying genome-scale metabolic modeling, we were able to identify the metabolic differences among carriers of widely validated genetic variants associated with NAFLD/NASH disease severity in three genes (PNPLA3,TM6SF2andHSD17B13).Conclusion: The study provides insights into the underlying pathways of the progressive-fibrosing steatohepatitis. Of note, there is a marked dysregulation of the glycosphingolipid metabolism in the liver of the patients with advanced fibrosis.
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