Obesity dependent metabolic signatures associated with nonalcoholic fatty liver disease progression

Our understanding of the mechanisms by which nonalcoholic fatty liver disease (NAFLD) progresses from simple steatosis to steatohepatitis (NASH) is still very limited. Despite the growing number of studies linking the disease with altered serum metabolite levels, an obstacle to the development of metabolome-based NAFLD predictors has been the lack of large cohort data from biopsy-proven patients matched for key metabolic features such as obesity. We studied 467 biopsied individuals with normal liver histology (n=90) or diagnosed with NAFLD (steatosis, n=246; NASH, n=131), randomly divided into estimation (80% of all patients) and validation (20% of all patients) groups. Qualitative determinations of 540 serum metabolite variables were performed using ultra-performance liquid chromatography coupled to mass spectrometry (UPLCMS). The metabolic profile was dependent on patient body-mass index (BMI), suggesting that the NAFLD pathogenesis mechanism may be quite different depending on an individual’s level of obesity. A BMI-stratified multivariate model based on the NAFLD serum metabolic profile was used to separate patients with and without NASH. The area under the receiver operating characteristic curve was 0.87 in the estimation and 0.85 in the validation group. The cutoff (0.54) corresponding to maximum average diagnostic accuracy (0.82) predicted NASH with a sensitivity of 0.71 and a specificity of 0.92 (negative/positive predictive values = 0.82/0.84). The present data, indicating that a BMI-dependent serum metabolic profile may be able to reliably distinguish NASH from steatosis patients, have significant implications for the development of NASH biomarkers and potential novel targets for therapeutic intervention

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 stratificatio

A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients

Liver fibrosis must be evaluated in patients with hepatitis C virus (HCV) after liver transplantation because its severity affects their prognosis and the recurrence of HCV. Since invasive biopsy is still the gold standard to identify patients at risk of graft loss from rapid fibrosis progression, it becomes crucial the development of new accurate, non-invasive methods that allow repetitive examination of the patients. Therefore, we have developed a non-invasive, accurate model to distinguish those patients with different liver fibrosis stages. Two hundred and three patients with HCV were histologically classified (METAVIR) into five categories of fibrosis one year after liver transplantation. In this cross-sectional study, patients at fibrosis stages F0-F1 (n = 134) were categorised as "slow fibrosers" and F2-F4 (n = 69) as "rapid fibrosers". Chloroform/methanol serum extracts were analysed by reverse ultra-high performance liquid chromatography coupled to mass spectrometry. A diagnostic model was built through linear discriminant analyses. An algorithm consisting of two sphingomyelins and two phosphatidylcholines accurately classifies rapid and slow fibrosers after transplantation. The proposed model yielded an AUROC of 0.92, 71% sensitivity, 85% specificity, and 84% accuracy. Moreover, specific bile acids and sphingomyelins increased notably along with liver fibrosis severity, differentiating between rapid and slow fibrosers

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.Supported by the National Institutes of Health Blueprint for Neuroscience Research (R01AT001576 to S.C.L., J.M.M.), Agencia Estatal de Investigacion of the Ministerio de Economia, Industria y Competitividad (SAF2014-52097R to J.M.M.), CIBER Hepatic and Digestive Diseases and Instituto de Salud Carlos III (PIE14/0003 to J.M.M.), Etorgai 2015-Gobierno Vasco (ER-2015/00015 to R.M., I.M.A., C.A., A.C.), Plan de Promocion de la Innovacion 2015-Diputacion Foral de Bizkaia (6/12/IN/2015/00131 to A.C., C.A.), National Institute of Diabetes and Digestive and Kidney Diseases (RO1DK81410 to A.J.S.), and Czech Ministry of Health (RVO VFN64165 to L.V.)

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.National Institutes of Health (R01DK123763, R01DK119437, HL151328, P30DK52574, P30DK56341, and UL1TR002345); Ministerio de Economía y Competitividad de España (SAF2017-88041-R); Ministerio de Economía y Competitividad de España for the Severo Ochoa Excellence Accreditation (SEV-2016-0644); CIBERehd (Biomedical Research Center in Hepatic and Digestive Diseases) and Netherlands Organization for Applied Scientific Research Program (PMC13 and PMC15); Spanish Carlos III Health Institute (PI15/01132 and PI18/01075); Miguel Servet Program (CON14/00129 and CPII19/00008); Fondo Europeo de Desarrollo Regional, CIBERehd, Department of Industry of the Basque Country (Elkartek: KK-2020/00008); La Caixa Scientific Foundation (HR17-00601); Liver Investigation: Testing Marker Utility in Steatohepatitis consortium funded by the Innovative Medicines Initiative Program of the European Union (777377), which receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA; Newcastle NIHR Biomedical Research Center; Czech Ministry of Health (RVO-VFN64165/2020); Fondo Nacional De Ciencia y Tecnología de Chile (1191145); and the Comisión Nacional de Investigación, Ciencia y Tecnología (AFB170005, CARE Chile UC); Agencia Nacional de Investigación y Desarrollo (ANID ACE 210009); European Union's Horizon 2020 Research and Innovation Program (825510)

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

Metabolomics Discloses a New Non-invasive Method for the Diagnosis and Prognosis of Patients with Alcoholic Hepatitis

Introduction and aims. Alcoholic hepatitis is the most severe manifestation of alcoholic liver disease. Unfortunately, there are still some unresolved issues in the diagnosis and management of this disease, such as the need of histological diagnosis, an accurate prognostic stratification, and the development of novel targeted therapies. The present study aimed at addressing these issues by means of metabolomics, a novel high-throughput approach useful in other liver diseases.Material and methods. 64 patients with biopsy-proven alcoholic hepatitis were included and compared with 26 patients with decompensated alcoholic cirrhosis without superimposed alcoholic hepatitis, which was ruled out by liver biopsy.Results. The comparison of the metabolic profiles of patients with alcoholic hepatitis and decompensated cirrhosis showed marked differences between both groups. Importantly, metabolic differences were found among alcoholic hepatitis patients when subjects were stratified according to 90-day survival. Based on these findings, two non-invasive signatures were developed. The first one allowed an accurate non-invasive diagnosis of alcoholic hepatitis (AUROC 0.932; 95% CI 0.901-0.963). The second signature showed a good performance in the prognostic stratification of patients with alcoholic hepatitis (AUROC 0.963; 95% CI 0.895-1.000).Conclusions. Signatures based on metabolomics allowed an accurate non-invasive diagnosis and prognostic stratification of alcoholic hepatitis. The differences observed in the metabolic profile of the patients according to the presence and severity of alcoholic hepatitis are related with different mechanisms involved in the pathophysiology of alcoholic hepatitis such as peroxisomal activity, synthesis of inflammatory mediators or oxidation. This information could be useful for the development of novel targeted therapies