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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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- Hyysalo, Jenni, et al.
(author)
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Genetic variation in PNPLA3 but not APOC3 influences liver fat in non-alcoholic fatty liver disease
- 2012
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In: Journal of Gastroenterology and Hepatology. - : Wiley. - 0815-9319. ; 27:5, s. 951-956
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Journal article (peer-reviewed)abstract
- Background and Aim: A recent study in Indian subjects suggested common variants in apolipoprotein C3 (APOC3) (T-455C at rs2854116 and C-482T at rs2854117) to contribute to non-alcoholic fatty liver disease (NAFLD), plasma apoC3 and triglyceride concentrations. Our aim was to determine the contribution of genetic variation in APOC3 on liver fat content and plasma triglyceride and apoC3 concentrations in a larger European cohort. Methods: Atotal of 417 Finnish individuals were genotyped for rs2854116 and rs2854117 in APOC3 and the known rs738409 in patatin-like phospholipase domain-containing protein 3 (PNPLA3) influencing liver fat. Plasma apoC3 concentration was measured enzymatically, and liver fat by proton magnetic resonance spectroscopy. Results: APOC3 wild-type homozygotes and variant allele (T-455C or C-482T or both) carriers did not differ with regard to liver fat, apoC3 concentrations, triglyceride-, high density lipoprotein-, fasting plasma glucose, insulin-, alanine aminotransferase-and aspartate aminotransferase-concentrations, nor was there a difference in prevalence of NAFLD. In contrast, carriers of the PNPLA3 GG genotype at rs738409 had a 2.7-fold (median 11.3%) higher liver fat than those with the CC (median 4.2%) genotype. The PNPLA3 rs738409 was also an independent predictor of liver fat, together with age, gender, and body mass index. Conclusion: Genetic variants in PNPLA3 but not APOC3 contribute to the variance in liver fat content due to NAFLD.
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- Kotronen, Anna, et al.
(author)
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Genetic variation in the ADIPOR2 gene is associated with liver fat content and its surrogate markers in three independent cohorts
- 2009
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In: European Journal of Endocrinology. - 1479-683X. ; 160:4, s. 593-602
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Journal article (peer-reviewed)abstract
- Aims: We investigated whether polymorph isms in candidate genes involved in lipid metabolism and type 2 diabetes are related to liver I, at content. Methods: Liver fat content was measured using proton magnetic resonance spectroscopy (H-1-MRS) in 302 Finns, in whom single nucleotide polymorphisms (SNPs) in acyl-CoA synthetase long-chain family member 4 (ACSL4). acliponectin receptors 1 and 2 (ADIPOR1 and ADIPOR2), and the three peroxisome proliferator-activated receptors (PPARA, PPARD, and PPARG) were analyzed. To validate our findings, SNPs significantly associated with liver fat content were Studied in two independent cohorts and related to surrogate markers of liver fat content. Results: In the Finnish subjects, polymorphisms in ACSL4 (rs7887981), ADIPOR2 (rs767870), and PPARG (rs3856806) were significantly associated with liver fat content measured with H-1-MRS after adjusting for age, gender, and BMI, Anthropometric and circulating parameters were comparable between genotypes. In the first validation cohort of similar to 600 Swedish men, ACSL4 rs7887981 was related to fasting insulin and triglyceride concentrations, and ADIPOR2 rs767870 to serum gamma glutamyltransfer concentrations after adjusting for BMI. The SNP in PPARG (rs3856806) was not significantly associated with any relevant metabolic parameter in this cohort. In the second validation cohort of similar to 3000 subjects from Western Finland, ADIPOR2 rs767870, but not ACSL4 rs7887981 was related to fasting triglyceride concentrations. Conclusions: Genetic variation, particularly in the ADIPOR2 gene, contributes to variation in hepatic fat accumulation in humans.
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- Kotronen, Anna, et al.
(author)
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Prediction of Non-Alcoholic Fatty Liver Disease and Liver Fat Using Metabolic and Genetic Factors
- 2009
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In: Gastroenterology. - : Elsevier BV. - 1528-0012 .- 0016-5085. ; 137:3, s. 865-872
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Journal article (peer-reviewed)abstract
- BACKGROUND & AIMS: Our aims were to develop a method to accurately predict non-alcoholic fatty liver disease (NAFLD) and liver fat content based on routinely available clinical and laboratory data and to test whether knowledge of the recently discovered genetic variant in the PNPLA3 gene (rs738409) increases accuracy of the prediction. METHODS: Liver fat content was measured using proton magnetic resonance spectroscopy in 470 subjects, who were randomly divided into estimation (two thirds of the subjects, n = 313) and validation (one third of the subjects, n = 157) groups. Multivariate logistic and linear regression analyses were used to create an NAFLD liver fat score to diagnose NAFLD and liver fat equation to estimate liver fat percentage in each individual. RESULTS: The presence of the metabolic syndrome and type 2 diabetes, fasting serum (fS) insulin, FS-aspartate aminotransferase (AST), and the AST/alanine aminotransferase ratio were independent predictors of NAFLD. The score had an area under the receiver operating characteristic curve of 0.87 in the estimation and 0.86 in the validation group. The optimal cut-off point of -0.640 predicted increased liver fat content with sensitivity of 86% and specificity of 71%. Addition of the genetic information to the score improved the accuracy of the prediction by only <1%. Using the same variables, we developed a liver fat equation from which liver fat percentage of each individual could be estimated. CONCLUSIONS: The NAFLD liver fat score and liver fat equation provide simple and noninvasive tools to predict NAFLD and liver fat content.
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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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- Llaurado, Gemma, et al.
(author)
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Liver Fat Content and Hepatic Insulin Sensitivity in Overweight Patients With Type 1 Diabetes
- 2015
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In: Journal of Clinical Endocrinology and Metabolism. - : The Endocrine Society. - 1945-7197 .- 0021-972X. ; 100:2, s. 607-616
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Journal article (peer-reviewed)abstract
- Objectives: Patients with type 1 diabetes mellitus (T1DM) lack the portal/peripheral insulin gradient, which might diminish insulin stimulation of hepatic lipogenesis and protect against development of nonalcoholic fatty liver disease (NAFLD). We compared liver fat content and insulin sensitivity of hepatic glucose production and lipolysis between overweight T1DM patients and nondiabetic subjects. Materials and Methods: We compared 32 overweight adult T1DM patients and 32 nondiabetic subjects matched for age, body mass index (BMI), and gender. Liver fat content was measured using proton magnetic resonance spectroscopy (H-1-MRS), body composition by magnetic resonance imaging, and insulin sensitivity using the euglycemic-hyperinsulinemic clamp technique (insulin 0.4 mU/kg.min combined with infusion of D-[3-H-3] glucose). We also hypothesized that low liver fat might protect from obesity-associated increases in insulin requirements and, therefore, determined insulin requirements across BMI categories in 3164 T1DM patients. Results: Liver fat content was significantly lower in T1DM patients than in nondiabetic subjects (0.6% [25th-75th quartiles, 0.3%-1.1%] vs 9.0% [ 3.0%-18.0%]; P<.001). The endogenous rate of glucose production (R-a) during euglycemic hyperinsulinemia was significantly lower (0.4 [-0.7 to 0.8] mg/kg fat-free mass.min vs 0.9 [0.2-1.6] fat-free mass.min; P=.012) and the percent suppression of endogenous R-a by insulin was significantly greater (89% [78%-112%] vs 77% [50%-94%]; p=.009) in T1DM patients than in nondiabetic subjects. Serum nonesterified fatty acid concentrations during euglycemic hyperinsulinemia were significantly lower (78.5 [33.0-155.0] vs 306 [200.0-438.0] mu mol/L; P<.001) and the percent suppression of nonesterified fatty acids significantly higher (89.1% [78.6%-93.3%] vs 51.4% [36.5%-71.1%]; P<.001) in T1DM patients than in nondiabetic subjects. Insulin doses were similar across BMI categories. Conclusions: T1DM patients might be protected from steatosis and hepatic insulin resistance. Obesity may not increase insulin requirements in T1DM.
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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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| 10. |
- Pavlides, Michael, et al.
(author)
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Liver investigation: Testing marker utility in steatohepatitis (LITMUS): Assessment & validation of imaging modality performance across the NAFLD spectrum in a prospectively recruited cohort study (the LITMUS imaging study): Study protocol
- 2023
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In: Contemporary Clinical Trials. - : ELSEVIER SCIENCE INC. - 1551-7144 .- 1559-2030. ; 134
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Journal article (peer-reviewed)abstract
- Non-alcoholic fatty liver disease (NAFLD) is the liver manifestation of the metabolic syndrome with global prevalence reaching epidemic levels. Despite the high disease burden in the population only a small proportion of those with NAFLD will develop progressive liver disease, for which there is currently no approved pharmacotherapy. Identifying those who are at risk of progressive NAFLD currently requires a liver biopsy which is problematic. Firstly, liver biopsy is invasive and therefore not appropriate for use in a condition like NAFLD that affects a large proportion of the population. Secondly, biopsy is limited by sampling and observer dependent variability which can lead to misclassification of disease severity. Non-invasive biomarkers are therefore needed to replace liver biopsy in the assessment of NAFLD. Our study addresses this unmet need. The LITMUS Imaging Study is a prospectively recruited multi-centre cohort study evaluating magnetic resonance imaging and elastography, and ultrasound elastography against liver histology as the reference standard. Imaging biomarkers and biopsy are acquired within a 100-day window. The study employs standardised processes for imaging data collection and analysis as well as a real time central monitoring and quality control process for all the data submitted for analysis. It is anticipated that the high-quality data generated from this study will underpin changes in clinical practice for the benefit of people with NAFLD. Study Registration: clinicaltrials.gov: NCT05479721
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