Metabolomics and Biomarker Discovery

A longitudinal study of the blood and urine metabolome of Vipera berus envenomated dogs

Envenomation of dogs by the common European adder (Vipera berus) is associated with high morbidity. The cytotoxic venom of Vipera berus contains enzymes with the potential to cause acute kidney injury, among other insults, however robust biomarkers for such effects are lacking. A prospective observational follow-up study of naturally envenomated dogs and controls was conducted to fill knowledge gaps regarding canine Vipera berus envenomation, attempt to identify novel biomarkers of envenomation and related kidney injury, and elucidate potential long-term effects. Blood and urine samples were analyzed with a global metabolomics approach using liquid chromatography-mass spectrometry, uncovering numerous features significantly different between cases and controls. After data processing and feature annotation, eight features in blood and 24 features in urine were investigated in order to elucidate their biological relevance. Several of these are associated with AKI, while some may also originate from disturbed fatty acid β-oxidation and soft tissue damage. A metabolite found in both blood and a venom reference sample may represent identification of a venom component in case dogs. Our findings suggest that envenomated dogs treated according to current best practice are unlikely to suffer permanent injury.

Identification of Novel and Early Biomarkers for Cisplatin-induced Nephrotoxicity and the Nephroprotective Role of Cimetidine using a Pharmacometabolomic-based Approach Coupled with In Vitro Toxicodynamic Modeling and Simulation

Cisplatin is widely used for the treatment of various types of cancer. However, cisplatin-induced nephrotoxicity (CIN) is frequently observed in patients receiving cisplatin therapy which poses a challenge in its clinical utility. Currently used clinical biomarkers for CIN are not adequate for early detection of nephrotoxicity, hence there is a need to identify potential early biomarkers in predicting CIN. In the current study, a combination of in vitro toxicodynamic (TD) modeling and untargeted global metabolomics approach was used to identify novel potential metabolite biomarkers for early detection of CIN. In addition, we investigated the protective role of cimetidine (CIM), an inhibitor of the organic cation transporter 2 (OCT2), in suppressing CIN. We first characterized the time-course of nephrotoxic effects of cisplatin (CIS) and the protective effects of CIM in a human pseudo-immortalized renal proximal tubule epithelial cell line (RPTEC), SA7K cell line. Secondly, we used a mathematical cell-level, in vitro TD modeling approach to quantitatively characterize the time-course effects of CIS and CIM as single agents and combination in SA7K cells. Based on the experimental and modeling results, we selected relevant concentrations of CIS and CIM for our metabolomics study. With the help of PCA (Principal Component Analysis) and PLS-DA (Projection to Latent Structure - Discriminate Analysis) analyses, we confirmed global metabolome changes for different groups (CIS, CIM, CIS+CIM vs control) in SA7K cells. Based on the criterion of a p-value ≤ 0.05 and a fold change ≥ 2 or ≤ 0.5, we identified 20 top metabolites that were significantly changed during the early phase i.e. within first 12 h of CIS treatment. Finally, pathway analysis was conducted that revealed the key metabolic pathways that were most impacted in CIN.

Optimization of Mass Spectrometric Parameters in Data Dependent Acquisition for Untargeted Metabolomics on the Basis of Putative Assignments

Optimization of mass spectrometric parameters for a data dependent acquisition (DDA) experiment is essential to increase the MS/MS coverage and hence increase metabolite identifications in untargeted metabolomics. We explored the influence of mass spectrometric parameters including mass resolution, radio frequency (RF) level, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT), dynamic exclusion, and automatic gain control (AGC) target value on metabolite annotations on an Exploris 480-Orbitrap mass spectrometer. Optimal annotation results were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1 × 10⁴ at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF level at 70%. Furthermore, combining an AGC target value of 5 × 10⁶ and MIT of 100 ms for MS and an AGC target value of 1 × 10⁵ and an MIT of 50 ms for MS/MS scans provided an improved number of annotated metabolites. A 10 s exclusion duration and a two stepped collision energy were optimal for higher spectral quality. These findings confirm that MS parameters do influence metabolomics results, and propose strategies for increasing metabolite coverage in untargeted metabolomics. A limitation of this work is that our parameters were only optimized for one RPLC method on single matrix and may be different for other protocols. Additionally, no metabolites were identified at level 1 confidence. The results presented here are based on metabolite annotations and need to be validated with authentic standards.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. “Classical” tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

Mass spectrometry-based quantitation combined with time-dependent metabolomics to discover metabolic features in human neurogenesis using neural constructs generated from neural progenitor cells

Direct studies focusing on the human brain are difficult to plan and conduct due to ethical and practical reasons. The advent of human pluripotent stem cell (hPSC)-derived neurons has revolutionized the research of the human brain and central nervous system, but relevant analytical techniques have been much less explored. Herein, we have designed a novel bioanalytical strategy to discover the characteristics of human neurogenesis using liquid chromatography-mass spectrometry-based quantitation and time-dependent metabolomics in combination with hPSC-derived neural constructs. To examine the growth of neurons in vitro, a quantitative method for the simultaneous measurement of N-acetylaspartic acid (NAA) and N-acetylglutamic acid (NAG) in a culture medium was established. The analysis of endogenous NAA and NAG concentrations over 28 days of neural cell culture not only illustrated the growth and maturation process of neural progenitors, but also confirmed the successful achievement of human neural constructs. Depending on the quantitative results, day 0, 10, 18, and 28 samples representing different growth phases were selected for further investigation of the global metabolic changes in developing human neurons. A versatile non-targeted, time-dependent metabolomics study identified 17 significantly changed metabolites and revealed the altered metabolic pathways including amino acid metabolism (tryptophan, phenylalanine, aspartate and beta-alanine metabolisms), pantothenate and coenzyme A biosynthesis, fatty acid metabolism, and purine and pyrimidine metabolism. The new metabolite profiles and overall metabolic pathways advance our understanding of human neurodevelopment. Additionally, the bioanalytical approach proposed in this study opens an interesting window for the capture and evaluation of the complex metabolic states of human neural cells, which would potentially be utilized in other in vitro models relevant to pathophysiology and treatment of neurological disorders, benefiting biomarker discovery and metabolic mechanism interpretation.

C9orf16 represents the aberrant genetic programs and drives the progression of PDAC

Background

Pancreatic ductal adenocarcinoma (PDAC), constituting 90% of pancreatic cancers, is the fourth leading cause of cancer-related deaths in the world. Lack of early detection of PDAC contributes to its poor prognosis as patients are often diagnosed at an advanced stage of disease. This is mostly due to the lack of promising diagnostic and therapeutic targets and corresponding drugs.

Methods and results

Here, by bioinformatic analysis of single cell RNA-sequencing data on normal pancreas tissues, primary and metastatic PDAC tumors, we identified a promising PDAC biomarker, C9orf16. The expression of C9orf16, rarely detectable in normal epithelial cells, was upregulated in primary PDAC cancer cells and was further elevated in metastatic PDAC cancer cells. Gain or loss of function of C9orf16 demonstrated its critical functions in regulating the cell proliferation, invasion and chemotherapy resistance of cancer cells. Pathway analysis and functional studies identified MYC signaling pathways as the most activated pathways in regulating C9orf16 expression and in mediating the development and progression of PDAC.

Conclusions

These data suggested a crucial gene regulation system, MYC-C9orf16, which is actively involved in PDAC development and progression, and targeting this system should be a novel diagnostic and therapeutic target for PDAC.

A Metabolomic Analysis of Cirrhotic Ascites

Ascites is a common complication of decompensated liver cirrhosis, and yet relatively little is known about its biochemical composition. We conducted two metabolomic investigations, comparing the profile of ascites from 33 cirrhotic patients and postoperative peritoneal drainage fluid from 33 surgical patients (Experiment 1). The profile of paired ascites and plasma was also compared in 17 cirrhotic patients (Experiment 2). Gas chromatography−mass spectrometry-based metabolomics identified 29 metabolites that significantly characterized ascites fluid, whether postoperative drainage fluid or plasma were used as controls. Ten elevated amino acids (glutamine, proline, histidine, tyrosine, glycine, valine, threonine, methionine, lysine, phenylalanine) and seven diminished lipids (laurate, myristate, palmitate, oleate, vaccenate, stearate, cholesterol) largely comprised the cirrhotic ascites metabolomic phenotype that differed significantly (adjusted p < 0.002 to 0.03) from peritoneal drainage fluid or plasma. The pattern of upregulated amino acids in cirrhotic ascites did not indicate albumin proteolysis by peritoneal bacteria. Bidirectional clustering showed that the more severe the cirrhosis, the lower the lipid concentration in ascitic fluid. The metabolomic compartment of ascites in patients with decompensated cirrhosis is characterized by increased amino acids and decreased lipids. These novel findings have potential relevance for diagnostic purposes.

Predictive value of liver cirrhosis using metabolite biomarkers of bile acid in the blood: A protocol for systematic review and meta-analysis

BACKGROUND

Previous studies have indicated that the changes of bile acids are associated with liver cirrhosis. The objective of our study is to perform a systematic review to explore the relationship between bile acids and the pathologic process of cirrhosis, and to find minimally invasive, accurate and reliable potential biomarkers for predicting cirrhosis.

METHODS

EMBASE, the Cochrane Library, PubMed, Web of Science, WanFang Data and Chinese National Knowledge Infrastructure (CNKI) will be searched, using the search strategy of liver cirrhosis, bile acids and metabolomic. The screening process will be conducted strictly based on inclusion and exclusion criteria. Clinical studies based on human including randomized controlled trial, cohort study and case control study will be included without restriction of time. Cochrane collaboration's tool for assessing risk of bias and Newcastle-Ottawa Scale (NOS) will be applied to assess the risk of bias to randomized controlled trial and observational study, respectively. The bile acids and their concentrate which are different between liver cirrhosis and control group will be the mainly outcome. A qualitative analysis will be performed to profile the trajectory change of bile acids, then the meta-analysis will be done for quantitative analysis.

RESULTS

The bile acids profile of liver cirrhosis that has potential predictive value for cirrhosis will be identified.

CONCLUSION

The conclusion of this systematic review will finding potential biomarkers for predicting cirrhosis.

ETHICS AND DISSEMINATION

This systematic review is based on published researches, so there is no ethical approval required. We intend to disseminate our findings in a peer-reviewed journal.

Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnant women. The early detection of GDM provides an opportunity for the effective treatment of hyperglycemia in pregnancy, thus decreasing the risk of adverse perinatal outcomes for mothers and newborns. Metabolomics, an emerging technique, offers a novel point of view in understanding the onset and development of diseases and has been repeatedly used in various gestational periods in recent studies of GDM. Moreover, metabolomics provides varied opportunities in the different diagnoses of GDM from prediabetes or predisposition to diabetes, the diagnosis of GDM at a gestational age several weeks earlier than that used in the traditional method, and the assessment of prognosis considering the physiologic subtypes of GDM and clinical indexes. Longitudinal metabolomics truly facilitates the dynamic monitoring of metabolic alterations over the course of pregnancy. Herein, we review recent advancements in metabolomics and summarize evidence from studies on the application of metabolomics in GDM, highlighting the aspects of the diagnosis and differential diagnoses of GDM in an early stage. We also discuss future study directions concerning the physiologic subtypes, prognosis, and limitations of metabolomics.

Amanida: an R package for meta-analysis of metabolomics non-integral data

SUMMARY

The combination, analysis and evaluation of different studies which try to answer or solve the same scientific question, also known as a meta-analysis, plays a crucial role in answering relevant clinical relevant questions. Unfortunately, metabolomics studies rarely disclose all the statistical information needed to perform a meta-analysis. Here, we present a meta-analysis approach using only the most reported statistical parameters in this field: P-value and fold-change. The P-values are combined via Fisher's method and fold-changes by averaging, both weighted by the study size (n). The amanida package includes several visualization options: a volcano plot for quantitative results, a vote plot for total regulation behaviours (up/down regulations) for each compound, and a explore plot of the vote-counting results with the number of times a compound is found upregulated or downregulated. In this way, it is very easy to detect discrepancies between studies at a first glance.

AVAILABILITY AND IMPLEMENTATION

Amanida code and documentation are at CRAN and https://github.com/mariallr/amanida.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolic Rewiring and the Characterization of Oncometabolites

Simple Summary

Oncometabolites are produced by cancer cells and assist the cancer to proliferate and progress. Oncometabolites occur as a result of mutated enzymes in the tumor tissue or due to hypoxia. These processes result in either the abnormal buildup of a normal metabolite or the accumulation of an unusual metabolite. Definition of the metabolic changes that occur due to these processes has been accomplished using metabolomics, which mainly uses mass spectrometry platforms to define the content of small metabolites that occur in cells, tissues, organs and organisms. The four classical oncometabolites are fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate, which operate by inhibiting 2-oxoglutarate-dependent enzyme reactions that principally regulate gene expression and response to hypoxia. Metabolomics has also revealed several putative oncometabolites that include lactate, kynurenine, methylglyoxal, sarcosine, glycine, hypotaurine and (2R,3S)-dihydroxybutanoate. Metabolomics will continue to be critical for understanding the metabolic rewiring involving oncometabolite production that underpins many cancer phenotypes.

Abstract

The study of low-molecular-weight metabolites that exist in cells and organisms is known as metabolomics and is often conducted using mass spectrometry laboratory platforms. Definition of oncometabolites in the context of the metabolic phenotype of cancer cells has been accomplished through metabolomics. Oncometabolites result from mutations in cancer cell genes or from hypoxia-driven enzyme promiscuity. As a result, normal metabolites accumulate in cancer cells to unusually high concentrations or, alternatively, unusual metabolites are produced. The typical oncometabolites fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate inhibit 2-oxoglutarate-dependent dioxygenases, such as histone demethylases and HIF prolyl-4-hydroxylases, together with DNA cytosine demethylases. As a result of the cancer cell acquiring this new metabolic phenotype, major changes in gene transcription occur and the modification of the epigenetic landscape of the cell promotes proliferation and progression of cancers. Stabilization of HIF1α through inhibition of HIF prolyl-4-hydroxylases by oncometabolites such as fumarate and succinate leads to a pseudohypoxic state that promotes inflammation, angiogenesis and metastasis. Metabolomics has additionally been employed to define the metabolic phenotype of cancer cells and patient biofluids in the search for cancer biomarkers. These efforts have led to the uncovering of the putative oncometabolites sarcosine, glycine, lactate, kynurenine, methylglyoxal, hypotaurine and (2R,3S)-dihydroxybutanoate, for which further research is required.

Adenomyosis is associated with specific proton nuclear magnetic resonance (1H-NMR) serum metabolic profiles

OBJECTIVE

To determine whether the adenomyosis phenotype affects the proton nuclear magnetic resonance (¹H-NMR)-based serum metabolic profile of patients.

DESIGN

Cohort study.

SETTING

University hospital-based research center.

PATIENTS

Seventy-seven patients who underwent laparoscopy for a benign gynecologic condition.

INTERVENTIONS

Pelvic magnetic resonance imaging and collection of a venous peripheral blood sample were performed during the preoperative workup. The women were allocated to the adenomyosis group (n = 32), or the control group (n = 45). The adenomyosis group was further subdivided into two groups: diffuse adenomyosis of the inner myometrium (n = 14) and focal adenomyosis of the outer myometrium (n = 18). Other adenomyosis phenotypes were excluded.

MAIN OUTCOME MEASURES

Metabolomic profiling based on ¹H-NMR spectroscopy in combination with statistical approaches.

RESULTS

The serum metabolic profiles of the patients with adenomyosis indicated lower concentrations of 3-hydroxybutyrate, glutamate, and serine compared with controls. Conversely, the concentrations of proline, choline, citrate, 2-hydroxybutyrate, and creatinine were higher in the adenomyosis group. The focal adenomyosis of the outer myometrium and the diffuse adenomyosis phenotypes also each exhibited a specific metabolic profile.

CONCLUSION

Serum metabolic changes were detected in women with features of adenomyosis compared with their disease-free counterparts, and a number of specific metabolic pathways appear to be engaged according to the adenomyosis phenotype. The metabolites with altered levels are particularly involved in immune activation as well as cell proliferation and cell migration. Nevertheless, this study did find evidence of a correlation between metabolite levels and symptoms thought to be related to adenomyosis. Further studies are required to determine the clinical significance of these differences in metabolic profiles.

Leukocyte proteomics coupled with serum metabolomics identifies novel biomarkers and abnormal amino acid metabolism in Kawasaki disease

Kawasaki disease (KD) is a systemic vasculitis that can lead to severe cardiovascular complications, whereas the development and clinical usage of specific biomarkers might help diagnose KD and avoid certain complications. To this end, the molecular profiles of acute KD patients with coronary artery lesions (CAL) were first investigated through leukocyte proteomics and serum metabolomics assays. A total of 269 differentially abundant proteins and 35 differentially abundant metabolites with the top fold-changed levels were identified in acute KD patients compared to those in the healthy controls. Among them, several highly promising candidate marker proteins and metabolites indicative of KD progression were further analysed, such as the increased proteins ALPL, NAMPT, and S100P, as well as the decreased proteins C1QB and apolipoprotein family members. Moreover, metabolites, including succinic acid, dGMP, hyaluronic acid, L-tryptophan, propionylcarnitine, inosine, and phosphorylcholine, were found to be highly accurate at distinguishing between KD patients and healthy controls. Interestingly, the abnormal expression levels of a distinct set of proteins and metabolites in acute KD patients can be restored to normal levels upon intravenous immunoglobulin (IVIG) treatment. Overall, this work has revealed novel biomarkers and abnormal amino-acid metabolism as a prominent feature involved in KD patients with CAL. SIGNIFICANCE: KD is frequently concomitant with the development of life-threatening coronary vasculitis. Here, the profiles of leukocyte proteomics and serum metabolomics in acute KD patients with CALs were first investigated, and several hub molecules identified here could be used as supplemental biomarkers for KD diagnosis. Moreover, the metabolomic abnormalities especially the amino acids are particularly prominent in KD patients. Interestingly, the abnormal expression levels of a distinct set of proteins and metabolites in acute KD patients can be restored to normal levels upon IVIG treatment. Therefore, these findings might help understand the IVIG activities and also the underlying mechanisms of IVIG-resistant patients, thereby providing a new perspective for the exploration of mechanisms related to KD.

Biomarkers of cholestasis

Cholestasis is a major pathological manifestation, often resulting in detrimental liver conditions, which occurs in a variety of indications collectively termed cholestatic liver diseases. The frequent asymptomatic character and complexity of cholestasis, together with the lack of a straightforward biomarker, hampers early detection and treatment of the condition. The 'omics' era, however, has resulted in a plethora of cholestatic indicators, yet a single clinically applicable biomarker for a given cholestatic disease remains missing. The criteria to fulfil as an ideal biomarker as well as the challenging molecular pathways in cholestatic liver diseases advocate for a scenario in which multiple biomarkers, originating from different domains, will be assessed concomitantly. This review gives an overview of classical clinical and novel molecular biomarkers in cholestasis, focusing on their benefits and drawbacks.

Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology

Dynamic changes in circulating and tissue metabolites and lipids occur in response to exercise-induced cellular and whole-body energy demands to maintain metabolic homeostasis. The metabolome and lipidome in a given biological system provides a molecular snapshot of these rapid and complex metabolic perturbations. The application of metabolomics and lipidomics to map the metabolic responses to an acute bout of aerobic/endurance or resistance exercise has dramatically expanded over the past decade thanks to major analytical advancements, with most exercise-related studies to date focused on analyzing human biofluids and tissues. Experimental and analytical considerations, as well as complementary studies using animal model systems, are warranted to help overcome challenges associated with large human interindividual variability and decipher the breadth of molecular mechanisms underlying the metabolic health-promoting effects of exercise. In this review, we provide a guide for exercise researchers regarding analytical techniques and experimental workflows commonly used in metabolomics and lipidomics. Furthermore, we discuss advancements in human and mammalian exercise research utilizing metabolomic and lipidomic approaches in the last decade, as well as highlight key technical considerations and remaining knowledge gaps to continue expanding the molecular landscape of exercise biology.

Advances on diagnostic biomarkers of pancreatic ductal adenocarcinoma: A systems biology perspective

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is usually diagnosed at an advanced stage when curative surgery is no longer an option. Robust diagnostic biomarkers with high sensitivity and specificity for early detection are urgently needed. Systems biology provides a powerful tool for understanding diseases and solving challenging biological problems, allowing biomarkers to be identified and quantified with increasing accuracy, sensitivity, and comprehensiveness. Here, we present a comprehensive overview of efforts to identify biomarkers of PDAC using genomics, transcriptomics, proteomics, metabonomics, and bioinformatics. Systems biology perspective provides a crucial “network” to integrate multi-omics approaches to biomarker identification, shedding additional light on early PDAC detection.

An update on potential biomarkers for diagnosing diabetic foot ulcer at early stage

As one of major chronic complications of diabetes, diabetic foot ulcer (DFU) is the main cause of disability and death. The clinical diagnosis and prognosis of DFU is inadequate. For clinicians, if the risk stratification of DFU can be obtained earlier in diabetic patients, the hospitalization, disability and mortality rate will be reduced. In addition to the inflammatory biomarkers that have been widely concerned and used, e.g., procalcitonin, pentraxin-3, C-reactive protein (CRP), interleukins (ILs), and tumor necrosis factor-α (TNF-α), etc., a more comprehensive prediction of the risk and severity of DFU is needed to reflect new biomarkers for therapeutic intervention effects. Along with the development of systems biology technology, genomics, proteomics, metabolomics and microbiome have been used in the studies on DFU for better understanding of the disease. In this review, new biomarkers that are expected to assist in the accurate diagnosis and risk stratification of DFU will be discussed and summarized in detail.

Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy

In recent years, there has been a plethora of attempts to discover biomarkers that are more reliable than α-fetoprotein for the early prediction and prognosis of hepatocellular carcinoma (HCC). Efforts have involved such fields as genomics, transcriptomics, epigenetics, microRNA, exosomes, proteomics, glycoproteomics, and metabolomics. HCC arises against a background of inflammation, steatosis, and cirrhosis, due mainly to hepatic insults caused by alcohol abuse, hepatitis B and C virus infection, adiposity, and diabetes. Metabolomics offers an opportunity, without recourse to liver biopsy, to discover biomarkers for premalignant liver disease, thereby alerting the potential of impending HCC. We have reviewed metabolomic studies in alcoholic liver disease (ALD), cholestasis, fibrosis, cirrhosis, nonalcoholic fatty liver (NAFL), and nonalcoholic steatohepatitis (NASH). Specificity was our major criterion in proposing clinical evaluation of indole-3-lactic acid, phenyllactic acid, N-lauroylglycine, decatrienoate, N-acetyltaurine for ALD, urinary sulfated bile acids for cholestasis, cervonoyl ethanolamide for fibrosis, 16α-hydroxyestrone for cirrhosis, and the pattern of acyl carnitines for NAFL and NASH. These examples derive from a large body of published metabolomic observations in various liver diseases in adults, adolescents, and children, together with animal models. Many other options have been tabulated. Metabolomic biomarkers for premalignant liver disease may help reduce the incidence of HCC.