Achieving the ability to identify individuals who are susceptible to drug-induced liver injury (DILI) would represent a major advance in personalized medicine. Clayton et al. demonstrated that the pattern of endogenous metabolites in urine could predict susceptibility to acetaminophen-induced liver injury in rats. We designed a clinical study to test this approach in healthy adults who received 4 g of acetaminophen per day for 7 days. Urine metabolite profiles obtained before the start of treatment were not sufficient to distinguish which of the subjects would develop mild liver injury, as indicated by a rise in alanine aminotransferase (ALT) to a level more than twice the baseline value (responders). However, profiles obtained shortly after the start of treatment, but prior to ALT elevation, could distinguish responders from nonresponders. Statistical analyses revealed that predictive metabolites included those derived from the toxic metabolite N-acetyl paraquinone imine (NAPQI), but that the inclusion of endogenous metabolites was required for significant prediction. This "early-intervention pharmaco-metabonomics" approach should now be tested in clinical trials of other potentially hepatotoxic drugs.

Metabolomic evaluation of urine and liver was conducted to assess the biochemical changes that occur as a result of alcohol-induced liver injury. Male C57BL/6J mice were fed an isocaloric control-or alcohol-containing liquid diet with 35% of calories from corn oil, 18% protein and 47% carbohydrate/alcohol for up to 36 days ad libitum. Alcohol treatment was initiated at 7 g/kg/day and gradually reached a final dose of 21 g/kg/day. Urine samples were collected at 22, 30 and 36 days and in additional treatment groups, liver and serum samples were harvested at 28 days. Steatohepatitis was induced in the alcohol-fed group since a 5-fold increase in serum alanine aminotransferase activity, a 6-fold increase in liver injury score (necrosis, inflammation and steatosis) and an increase in lipid peroxidation in liver were observed. Liver and urine samples were analyzed by nuclear magnetic resonance spectroscopy and electrospray infusion/Fourier transform ion cyclotron resonance-mass spectrometry. In livers of alcohol-treated mice the following changes were noted. Hypoxia and glycolysis were activated as evidenced by elevated levels of alanine and lactate. Tyrosine, which is required for L-DOPA and dopamine as well as thyroid hormones, was elevated possibly reflecting alterations of basal metabolism by alcohol. A 4-fold increase in the prostacyclin inhibitor 7,10,13,16-docosatetraenoic acid, a molecule important for regulation of platelet formation and blood clotting, may explain why chronic drinking causes serious bleeding problems. Metabolomic analysis of the urine revealed that alcohol treatment leads to decreased excretion of taurine, a metabolite of glutathione, and an increase in lactate, n-acetylglutamine and n-acetylglycine. Changes in the latter two metabolites suggest an inhibition of the kidney enzyme aminoacylase I and may be useful as markers for alcohol consumption.

For both the urinary and serum metabolome, a single day of dietary standardization appears to provide all of the normalization that is achievable within the strict controls implemented in a clinical research setting. After 24 h, the subjects remain in their metabolic space; the remaining intra- and intersubject variations appear to be influenced by variables such as genetics, age, and lifestyle.

Cancer cachexia remains a challenging clinical problem with complex pathophysiology and unreliable diagnostic tools. A blood test to detect this metabolic derangement would aid in early treatment of these patients. A 1 H NMR-based metabolomics approach was used to determine the unique metabolic fingerprint of cachexia and to search for biomarkers in serum samples taken from an established murine model of cancer cachexia. Male CD2F1 mice received a subcutaneous flank injection of C26 adenocarcinoma cells to induce experimental cancer-related cachexia. Two molecular markers of muscle atrophy, upregulation of the E3 ubiquitin ligase Muscle Ring Finger 1 (MuRF1) and aberrant glycosylation of b-dystroglycan (b-DG), were used to confirm muscle wasting in the tumorbearing mice. Serum samples were collected for metabolomic analysis during the development of the cachexia: at baseline, when the tumor was palpable, and when the mice demonstrated cachexia. The unsupervised statistical analysis demonstrated a distinct metabolic profile with the onset of cachexia. The critical metabolic changes associated with cachexia included increased levels of very low density lipoprotein (VLDL) and low density lipoprotein (LDL), with decreased serum glucose levels. Regression analysis demonstrated a very high correlation of the presence of aberrant glycosylation of b-DG with the unique metabolic profile of cachexia. This study demonstrates for the first time that metabolomics has potential as a diagnostic tool in cancer cachexia, and in further elucidating simultaneous metabolic pathway alterations due to this syndrome. In addition, variations in VLDL and LDL deserve more investigation as surrogate serum biomarkers for cancer cachexia.

We compared the performance of gas chromatography time-of-flight mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) for metabolite biomarker discovery. Metabolite extracts from 109 human serum samples were analyzed on both platforms with a pooled serum sample analyzed after every 9 biological samples for the purpose of quality control (QC). The experimental data derived from the pooled QC samples showed that the GC×GC-MS platform detected about three times as many peaks as the GC-MS platform at a signal-to-noise ratio SNR ≥ 50, and three times the number of metabolites were identified by mass spectrum matching with a spectral similarity score Rsim ≥ 600. Twenty-three metabolites had statistically significant abundance changes between the patient samples and the control samples in the GC-MS data set while 34 metabolites in the GC×GC-MS data set showed statistically significant differences. Among these two groups of metabolite biomarkers, nine metabolites were detected in both the GC-MS and GC×GC-MS data sets with the same direction and similar magnitude of abundance changes between the control and patient sample groups. Manual verification indicated that the difference in the number of the biomarkers discovered using these two platforms was mainly due to the limited resolution of chromatographic peaks by the GC-MS platform, which can result in severe peak overlap making subsequent spectrum deconvolution for metabolite identification and quantification difficult.