One-step non-invasive diagnosis of metabolic dysfunction-associated steatohepatitis and fibrosis in high-risk population.

BACKGROUND AND AIM: Type 2 Diabetes mellitus (T2DM), age, and obesity are risk factors for metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to assess the performance of non-invasive tests (NITs) for the diagnosis of metabolic dysfunction-associated steatohepatitis (MASH) and fibrosis in high-risk subjects. METHODS: Multicentre cross-sectional study that included 124 biopsy-proven MASLD in more than 50 years-old patients with overweight/obesity and T2DM. Vibration-controlled transient elastography, Fibrosis-4 index (FIB-4), Non-alcoholic fatty liver disease fibrosis score (NFS), OWLiver Panel (OWLiver DM2 + Metabolomics-Advanced Steatohepatitis Fibrosis Score -MASEF) and FibroScan-AST were performed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC) were calculated. NITs were assessed individually and in sequential/parallel combinations. RESULTS: 35 (28.2%) patients had early MASH and 66 (53.2%) had MASH with significant fibrosis (at-risk MASH). The OWLiver Panel correctly classified 86.1% as MASH, showing an accuracy, sensitivity, specificity, PPV, and NPV of 0.77, 0.86, 0.35, 0.85, and 0.36, respectively. Class III obesity, diabetes control, or gender did not impact on the performance of the OWLiver Panel (p > 0.1). NITs for at-risk MASH showed an AUC > 0.70 except for NFS. MASEF showed the highest accuracy and NPV for at-risk MASH (AUC 0.77 [0.68-0.85], NPV 72%) and advanced fibrosis (AUC 0.80 [0.71-0.88], NPV 92%). Combinations of NITs for the identification of at-risk MASH did not provide any additional benefit over using MASEF alone. CONCLUSION: One-step screening strategy with the OWLiver Panel has high accuracy to detect MASH and at-risk MASH in high-risk subjects for MASLD.

Genetic algorithms applied to translational strategy in metabolic-dysfunction associated steatohepatitis (MASH). Learning from mouse models.

BACKGROUND & AIMS: We previously identified subsets of patients with metabolic (dysfunction)-associated steatotic liver disease (MASLD) with different metabolic phenotypes. Here, we aimed to refine this classification based on genetic algorithms implemented in a Python package. The use of these genetic algorithms can help scientists to solve problems which cannot be solved with other methods. We present this package and its capabilities with specific problems. The name, PyGenMet, comes from its main goal, solving problems in Python with Genetic Algorithms and Metabolomics data. METHODS: We collected serum from methionine adenosyltransferase 1a knockout (Mat1a-KO) mice, which have chronically low level of hepatic S-adenosylmethionine (SAMe) and the metabolomes of all samples were determined. We also analyzed serum metabolomes of 541 patients with biopsy proven MASLD (182 with simple steatosis and 359 with metabolic (dysfunction)-associated steatohepatitis or MASH) and compared them with the serum metabolomes of this specific MASLD mouse model using Genetic Algorithms in order to select patients with a specific phenotype. RESULTS: By applying genetic algorithms, we have found a subgroup of patients with a lipid profile similar to that observed in the mouse model. When analyzing the two groups of patients, we have seen that patients with a lipid profile reflecting the mouse model characteristics show significant differences in lipoproteins, especially in LDL-4, LDL-5, and LDL-6 associated with atherogenic risk. CONCLUSION: The results show that the application of genetic algorithms to subclassify patients with MASLD (or other metabolic disease) give consistent results and are a good approximation for the treatment of large volumes of data such as those from omics sciences and patient classification.

Serum identification of at-risk MASH: The metabolomics-advanced steatohepatitis fibrosis score (MASEF).

BACKGROUND: Early identification of those with NAFLD activity score ≥ 4 and significant fibrosis (≥F2) or at-risk metabolic dysfunction-associated steatohepatitis (MASH) is a priority as these patients are at increased risk for disease progression and may benefit from therapies. We developed and validated a highly specific metabolomics-driven score to identify at-risk MASH. METHODS: We included derivation (n = 790) and validation (n = 565) cohorts from international tertiary centers. Patients underwent laboratory assessment and liver biopsy for metabolic dysfunction-associated steatotic liver disease. Based on 12 lipids, body mass index, aspartate aminotransferase, and alanine aminotransferase, the MASEF score was developed to identify at-risk MASH and compared to the FibroScan-AST (FAST) score. We further compared the performance of a FIB-4 + MASEF algorithm to that of FIB-4 + liver stiffness measurements (LSM) by vibration-controlled transient elastography (VCTE). RESULTS: The diagnostic performance of the MASEF score showed an area under the receiver-operating characteristic curve, sensitivity, specificity, and positive and negative predictive values of 0.76 (95% CI 0.72-0.79), 0.69, 0.74, 0.53, and 0.85 in the derivation cohort, and 0.79 (95% CI 0.75-0.83), 0.78, 0.65, 0.48, and 0.88 in the validation cohort, while FibroScan-AST performance in the validation cohort was 0.74 (95% CI 0.68-0.79; p = 0.064), 0.58, 0.79, 0.67, and 0.73, respectively. FIB-4+MASEF showed similar overall performance compared with FIB-4 + LSM by VCTE ( p = 0.69) to identify at-risk MASH. CONCLUSION: MASEF is a promising diagnostic tool for the assessment of at-risk MASH. It could be used alternatively to LSM by VCTE in the algorithm that is currently recommended by several guidance publications.

Metabolic subtypes of patients with NAFLD exhibit distinctive cardiovascular risk profiles.

BACKGROUND AND AIMS: We previously identified subsets of patients with NAFLD with different metabolic phenotypes. Here we align metabolomic signatures with cardiovascular disease (CVD) and genetic risk factors. APPROACH AND RESULTS: We analyzed serum metabolome from 1154 individuals with biopsy-proven NAFLD, and from four mouse models of NAFLD with impaired VLDL-triglyceride (TG) secretion, and one with normal VLDL-TG secretion. We identified three metabolic subtypes: A (47%), B (27%), and C (26%). Subtype A phenocopied the metabolome of mice with impaired VLDL-TG secretion; subtype C phenocopied the metabolome of mice with normal VLDL-TG; and subtype B showed an intermediate signature. The percent of patients with NASH and fibrosis was comparable among subtypes, although subtypes B and C exhibited higher liver enzymes. Serum VLDL-TG levels and secretion rate were lower among subtype A compared with subtypes B and C. Subtype A VLDL-TG and VLDL-apolipoprotein B concentrations were independent of steatosis, whereas subtypes B and C showed an association with these parameters. Serum TG, cholesterol, VLDL, small dense LDL5,6 , and remnant lipoprotein cholesterol were lower among subtype A compared with subtypes B and C. The 10-year high risk of CVD, measured with the Framingham risk score, and the frequency of patatin-like phospholipase domain-containing protein 3 NAFLD risk allele were lower in subtype A. CONCLUSIONS: Metabolomic signatures identify three NAFLD subgroups, independent of histological disease severity. These signatures align with known CVD and genetic risk factors, with subtype A exhibiting a lower CVD risk profile. This may account for the variation in hepatic versus cardiovascular outcomes, offering clinically relevant risk stratification.

Metabolomic-based noninvasive serum test to diagnose nonalcoholic steatohepatitis: Results from discovery and validation cohorts.

Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease worldwide and includes a broad spectrum of histologic phenotypes, ranging from simple hepatic steatosis or nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH). While liver biopsy is the reference gold standard for NAFLD diagnosis and staging, it has limitations due to its sampling variability, invasive nature, and high cost. Thus, there is a need for noninvasive biomarkers that are robust, reliable, and cost effective. In this study, we measured 540 lipids and amino acids in serum samples from biopsy-proven subjects with normal liver (NL), NAFL, and NASH. Using logistic regression analysis, we identified two panels of triglycerides that could first discriminate between NAFLD and NL and second between NASH and NAFL. These noninvasive tests were compared to blinded histology as a reference standard. We performed these tests in an original cohort of 467 patients with NAFLD (90 NL, 246 NAFL, and 131 NASH) that was subsequently validated in a separate cohort of 192 patients (7 NL, 109 NAFL, 76 NASH). The diagnostic performances of the validated tests showed an area under the receiver operating characteristic curve, sensitivity, and specificity of 0.88 ± 0.05, 0.94, and 0.57, respectively, for the discrimination between NAFLD and NL and 0.79 ± 0.04, 0.70, and 0.81, respectively, for the discrimination between NASH and NAFL. When the analysis was performed excluding patients with glucose levels >136 mg/dL, the area under the receiver operating characteristic curve for the discrimination between NASH and NAFL increased to 0.81 ± 0.04 with sensitivity and specificity of 0.73 and 0.80, respectively. Conclusion: The assessed noninvasive lipidomic serum tests distinguish between NAFLD and NL and between NASH and NAFL with high accuracy. (Hepatology Communications 2018;2:807-820).

Metabolomic Identification of Subtypes of Nonalcoholic Steatohepatitis.

BACKGROUND & AIMS: Nonalcoholic fatty liver disease (NAFLD) is a consequence of defects in diverse metabolic pathways that involve hepatic accumulation of triglycerides. Features of these aberrations might determine whether NAFLD progresses to nonalcoholic steatohepatitis (NASH). We investigated whether the diverse defects observed in patients with NAFLD are caused by different NAFLD subtypes with specific serum metabolomic profiles, and whether these can distinguish patients with NASH from patients with simple steatosis. METHODS: We collected liver and serum from methionine adenosyltransferase 1a knockout (MAT1A-KO) mice, which have chronically low levels of hepatic S-adenosylmethionine (SAMe) and spontaneously develop steatohepatitis, as well as C57Bl/6 mice (controls); the metabolomes of all samples were determined. We also analyzed serum metabolomes of 535 patients with biopsy-proven NAFLD (353 with simple steatosis and 182 with NASH) and compared them with serum metabolomes of mice. MAT1A-KO mice were also given SAMe (30 mg/kg/day for 8 weeks); liver samples were collected and analyzed histologically for steatohepatitis. RESULTS: Livers of MAT1A-KO mice were characterized by high levels of triglycerides, diglycerides, fatty acids, ceramides, and oxidized fatty acids, as well as low levels of SAMe and downstream metabolites. There was a correlation between liver and serum metabolomes. We identified a serum metabolomic signature associated with MAT1A-KO mice that also was present in 49% of the patients; based on this signature, we identified 2 NAFLD subtypes. We identified specific panels of markers that could distinguish patients with NASH from patients with simple steatosis for each subtype of NAFLD. Administration of SAMe reduced features of steatohepatitis in MAT1A-KO mice. CONCLUSIONS: In an analysis of serum metabolomes of patients with NAFLD and MAT1A-KO mice with steatohepatitis, we identified 2 major subtypes of NAFLD and markers that differentiate steatosis from NASH in each subtype. These might be used to monitor disease progression and identify therapeutic targets for patients.

Data in support of enhancing metabolomics research through data mining.

Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this 'omic' discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics workflow. This work has been designed in support of the research article entitled "Enhancing metabolomics research through data mining", which proposed a methodological data handling guideline. An aging research in healthy population was used as a guiding thread to illustrate this process. Here we provide a further interpretation of the obtained statistical results. We also focused on the importance of graphical visualization tools as a clue to understand the most common univariate and multivariate data analyses applied in metabolomics.

Enhancing metabolomics research through data mining.

Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable. BIOLOGICAL SIGNIFICANCE: Metabolomics is a discipline involving the analysis of a large amount of data to gather relevant information. Researchers in this field have to overcome the challenges of complex data processing and statistical analysis issues. A wide range of tasks has to be executed, from the minimization of batch-to-batch/systematic variations in pre-processing, to the application of common data analysis techniques relying on statistical assumptions. In this work, a real-data metabolic profiling research on aging was used to illustrate the proposed workflow and suggest a set of guidelines for analyzing metabolomics data. This article is part of a Special Issue entitled: HUPO 2014.