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.

Inhibition of carnitine palmitoyltransferase 1A in hepatic stellate cells protects against fibrosis.

BACKGROUND & AIMS: The pathogenesis of liver fibrosis requires activation of hepatic stellate cells (HSCs); once activated, HSCs lose intracellular fatty acids but the role of fatty acid oxidation and carnitine palmitoyltransferase 1A (CPT1A) in this process remains largely unexplored. METHODS: CPT1A was found in HSCs of patients with fibrosis. Pharmacological and genetic manipulation of CPT1A were performed in human HSC cell lines and primary HCSs. Finally, we induced fibrosis in mice lacking CPT1A specifically in HSCs. RESULTS: Herein, we show that CPT1A expression is elevated in HSCs of patients with non-alcoholic steatohepatitis, showing a positive correlation with the fibrosis score. This was corroborated in rodents with fibrosis, as well as in primary human HSCs and LX-2 cells activated by transforming growth factor ß1 (TGFß1) and fetal bovine serum (FBS). Furthermore, both pharmacological and genetic silencing of CPT1A prevent TGFß1- and FBS-induced HSC activation by reducing mitochondrial activity. The overexpression of CPT1A, induced by saturated fatty acids and reactive oxygen species, triggers mitochondrial activity and the expression of fibrogenic markers. Finally, mice lacking CPT1A specifically in HSCs are protected against fibrosis induced by a choline-deficient high-fat diet, a methionine- and choline-deficient diet, or treatment with carbon tetrachloride. CONCLUSIONS: These results indicate that CPT1A plays a critical role in the activation of HSCs and is implicated in the development of liver fibrosis, making it a potentially actionable target for fibrosis treatment. LAY SUMMARY: We show that the enzyme carnitine palmitoyltransferase 1A (CPT1A) is elevated in hepatic stellate cells (HSCs) in patients with fibrosis and mouse models of fibrosis, and that CPT1A induces the activation of these cells. Inhibition of CPT1A ameliorates fibrosis by preventing the activation of HSCs.

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.

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.

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.

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.

Liquid chromatography-mass spectrometry-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in most western countries. Current NAFLD diagnosis methods (e.g., liver biopsy analysis or imaging techniques) are poorly suited as tests for such a prevalent condition, from both a clinical and financial point of view. The present work aims to demonstrate the potential utility of serum metabolic profiling in defining phenotypic biomarkers that could be useful in NAFLD management. A parallel animal model/human NAFLD exploratory metabolomics approach was employed, using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) to analyze 42 serum samples collected from nondiabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-methyltransferase knockout (GNMT-KO) NAFLD mouse model. Multivariate statistical analysis of the data revealed a series of common biomarkers that were significantly altered in the NAFLD (GNMT-KO) subjects in comparison to their normal liver counterparts (WT). Many of the compounds observed could be associated with biochemical perturbations associated with liver dysfunction (e.g., reduced Creatine) and inflammation (e.g., eicosanoid signaling). This differential metabolic phenotyping approach may have a future role as a supplement for clinical decision making in NAFLD and in the adaption to more individualized treatment protocols.

Mass Spectrometry Applied to Human Cerebrospinal Fluid Lipidome.

Lipidomics aims at characterizing lipid profiles and their biological role with respect to protein expression involved in lipid metabolism. Specifically, cerebrospinal fluid (CSF) lipidomics is offering a new perspective in the search for surrogate biomarkers to facilitate early diagnosis of psychiatric and neurodegenerative diseases. In this chapter, we describe a nontargeted approach to profile lipid molecular species present in human CSF using ultrahigh-performance liquid chromatography-electrospray ionization-time-of-flight mass spectrometry (UPLC-ESI-ToF-MS). This workflow complements the toolbox useful for the exploration and monitoring neurodegenerative mechanisms associated with a dysregulation in lipid metabolism.

Differences in the metabolite composition and mechanical properties of extracellular vesicles secreted by hepatic cellular models.

Liver constitutes the major metabolic factory in the organism and is involved in the synthesis, secretion and clearance of many blood-circulating molecules. Previously, we have characterised the protein and RNA cargo of extracellular vesicles (EVs) secreted by two hepatic cellular models, a mouse hepatocyte progenitor cell line (MLP29) and primary rat hepatocytes (RHs). Here, we report the metabolome profile of these vesicles by performing a targeted UHPLC-MS metabolomics analysis of these two cellular models and their respective secreted EVs. Visual inspection of the data through principal component analysis allows clear separation of the metabolic profile of cells and EVs, and also of both cellular models. Correlation matrix supported that lipid composition of EVs is mainly determined by parent cell composition. EVs derived from MLP29 and RHs showed a negative correlation in their percentage composition of ceramides, glycerophospholipids, sphingomyelins and triglycerides. Metabolites enriched in EVs were also different depending on the cellular model. EVs secreted by MLP29 were enriched in different species of sphingomyelins and ceramides underrepresented in EVs secreted by RHs. Remarkably, triglycerides constitute an important percentage of the composition of EVs derived from RHs. We further investigate if the differences in lipid composition were also accompanied by differences in mechanical behaviour, by using atomic force microscopy complemented with nanoindentation-based methodology. To compare the stiffness and brittleness of EVs derived from MLP29 cell line and RH primary cells, FZ curves were performed in the centre of single vesicles and the differences found in their force-vs.-indentation curves suggest that RHs EVs are softer (less stiff) and less resistance to mechanical failure than MLP29 EVs. Therefore, we can conclude that EVs from different origin carry a characteristic lipid composition related to their parental cell composition, and exhibit different mechanical properties. Abbreviations: For the identification of the different species of lipids, the following abbreviations has been employed: Cer, ceramide; ChoE, Cholesteryl Ester; CMH, monohexosylceramide; DAG, diglycerid; LPC, lysophosphatidylcholin; LPI, lysophosphatidyinositol; PC, phosphocoline; PE, phoethanolamine; PI, phosphoinositol; SM, sphingomyelin; TAG, triglycerid.

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).

Role of Aramchol in steatohepatitis and fibrosis in mice.

Nonalcoholic steatohepatitis (NASH) is the advanced form of nonalcoholic fatty liver disease (NAFLD) which sets the stage for further liver damage. The mechanism for the progression of NASH involves multiple parallel hits including oxidative stress, mitochondrial dysfunction, inflammation and others. Manipulation of any of these pathways may be an approach to prevent NASH development and progression. Aramchol (arachidyl-amido cholanoic acid) is presently in a phase IIb NASH study. The aim of this study was to investigate Aramchol's mechanism of action and its effect on fibrosis using the methionine- and choline-deficient (MCD) diet model of NASH. We collected liver and serum from mice fed a MCD diet containing 0.1% methionine (0.1MCD) for four weeks, which developed steatohepatitis and fibrosis, as well as mice receiving a control diet; the metabolomes and proteomes were determined. 0.1MCD fed mice were given Aramchol (5mg/kg/day for the last 2 weeks); liver samples were analyzed histologically. Aramchol administration reduced features of steatohepatitis and fibrosis in 0.1MCD fed mice. Aramchol downregulated stearoyl-CoA desaturase 1 (SCD1), a key enzyme involved in triglyceride biosynthesis whose loss enhances fatty acid ß-oxidation. Aramchol increased the flux through the transsulfuration pathway, leading to a rise in glutathione (GSH) and GSH/GSSG ratio, the main cellular antioxidant that maintains intracellular redox status. Comparison of serum metabolomic pattern between 0.1MCD fed mice and NAFLD patients showed a substantial overlap. CONCLUSIONS: Aramchol treatment improved steatohepatitis and fibrosis by 1) decreasing SCD1, and 2) increasing the flux through the transsulfuration pathway maintaining cellular redox homeostasis. We also demonstrated that the 0.1MCD model resembles the metabolic phenotype observed in about 50% of NAFLD patients, which supports the potential use of Aramchol in NASH treatment.

Chemical pollution in inland shallow lakes in the Mediterranean region (NW Spain): PAHs, insecticides and herbicides in water and sediments.

The possible effect of land uses and human-related geographic patterns (presence of roads and urban settlements) on chemical pollution was evaluated in the waters and sediments of fifty-three Mediterranean shallow lakes. The presence of fifty-nine pollutants (belonging to PAHs, insecticides and herbicides groups) was analysed in these lakes by GC-MS. The studied lakes had similar pollutant concentrations to other lakes worldwide. The distribution of the compounds between water and sediment compartments was strongly influenced by log K(ow) values (an average of 3.61 for compounds found in water and of 4.69 for compounds found in sediments). A multivariate analysis suggested that the concentration of PAHs in water could be related to agricultural activities and not related to local road traffic. When assessing nutrient levels in the lakes, it was observed that eutrophicated lakes [>300 µg L(-1) total phosphorus (TP)] appeared in areas affected by urban or industrial use (at least 2% urban use in a 1-km radius around the lake), whilst lakes with lower TP concentrations were placed in forest areas (60% of forest use in a 1-km radius); in addition, the aqueous concentrations of Σ(PAH) were lower in lakes with higher TP concentrations (>150 µg L(-1) TP), which could be related to the adsorption capacity of PAHs onto suspended matter which is present in mesotrophic and eutrophic lakes, thus being removed from the aqueous phase.

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.

Serum UPLC-MS/MS metabolic profiling in an experimental model for acute-liver injury reveals potential biomarkers for hepatotoxicity.

A key interest in clinical diagnosis and pharmaceutical industry is to have a repertoire of noninvasive biomarkers to-individually or in combination-be able to infer or predict the degree of liver injury caused by pathological conditions or drugs. Metabolomics-a comprehensive study of global metabolites-has become a highly sensitive and powerful tool for biomarker discovery thanks to recent technological advances. An ultra-performance liquid chromatography/time-of-flight tandem mass spectrometry (UPLC/TOF MS/MS)-based metabolomics approach was employed to investigate sera from galactosamine-treated rats to find potential biomarkers for acute liver injury. Hepatic damage was quantified by determining serum transaminase activity and in situ liver histological lesions. Principal component analysis in combination with coefficient of correlation analysis was used for biomarker selection and identification. According to the data, serum levels of several metabolites including glucose, amino acids, and membrane lipids were significantly modified, some of them showing a high correlation with the degree of liver damage determined by histological examination of the livers. In conclusion, this study supports that UPLC-MS/MS based serum metabolomics in experimental animal models could be a powerful approach to search for biomarkers for drug- or disease-induced liver injury.