1D-¹H-nuclear magnetic resonance metabolomics reveals age-related changes in metabolites associated with experimental venous thrombosis.

OBJECTIVE: Age is a significant risk factor for the development of venous thrombosis (VT), but the mechanism(s) that underlie this risk remain(s) undefined and poorly understood. Aging is known to adversely influence inflammation and affect metabolism. Untargeted metabolomics permits an agnostic assessment of the physiological landscape and lends insight into the mechanistic underpinnings of clinical phenotypes. The objective of this exploratory study was to test the feasibility of a metabolomics approach for identifying potential metabolic mechanisms of age-related VT. METHODS: We subjected whole blood samples collected from young and old nonthrombosed controls and VT mice 2 days after thrombus induction using the electrolytic inferior vena cava, to a methanol:chloroform extraction and assayed the resulting aqueous fractions using 1D-(1)H- nuclear magnetic resonance. Normalized mouse metabolite data were compared across groups using analysis of variance (ANOVA) with Holm-Sidak post-testing. In addition, associations between metabolite concentrations and parameters of thrombosis such as thrombus and vein wall weights, and markers of inflammation, vein wall P- and E-selectin levels, were assessed using linear regression. The relatedness of the found significant metabolites was visually assessed using a bioinformatics tool, Metscape, which generates compound-reaction-enzyme-gene networks to aid in the interpretation of metabolomics data. RESULTS: Old mice with VT had a greater mean vein wall weight compared with young mice with VT (P < .05). Clot weight differences between old and young mice followed the same trend as vein wall weight (0.011 ± 0.04 g vs 0.008 ± 0.003 g; P = not significant). Glutamine (ANOVA, P < .01), proline (ANOVA, P < .01), and phenylalanine (ANOVA, P < .05) levels were increased in old VT mice compared with age-matched controls and young VT mice. Betaine and/or trimethylamine N-oxide levels were increased in aged mice compared with young animals. Vein wall weight was strongly associated with glutamine (P < .05), and phenylalanine (P < .01) concentrations and there was a trend toward an association with proline (P = .09) concentration. Vein wall P-selectin, but not E-selectin levels, were increased in old VT mice and were associated with the three found metabolites of age-related VT. Collectively, with the addition of glutamate, these metabolites form a single compound-reaction-enzyme-gene network that was generated by Metscape. CONCLUSIONS: We used 1D-(1)H-nuclear magnetic resonance-metabolite profiling to identify, for the first time, in an experimental model, three potential metabolites, glutamine, phenylalanine, and proline, associated with age-related VT. These metabolites are metabolically related and their levels are associated with vein wall weight and P-selectin concentrations. In aggregate, these findings provide a "roadmap" of pathways that could be interrogated in future studies, which could include provocation of the glutamine, phenylalanine, and proline pathways in the vein wall. This study introduces metabolomics as a new approach to furthering knowledge about the mechanisms of age-related VT.

Whole Blood Reveals More Metabolic Detail of the Human Metabolome than Serum as Measured by 1H-NMR Spectroscopy: Implications for Sepsis Metabolomics.

Serum is a common sample of convenience for metabolomics studies. Its processing time can be lengthy and may result in the loss of metabolites including those of red blood cells (RBCs). Unlike serum, whole blood (WB) is quickly processed, minimizing the influence of variable hemolysis while including RBC metabolites. To determine differences between serum and WB metabolomes, both sample types, collected from healthy volunteers, were assayed by H-NMR (proton nuclear magnetic resonance) spectroscopy. A total of 34 and 50 aqueous metabolites were quantified from serum and WB, respectively. Free hemoglobin (Hgb) levels in serum were measured, and the correlation between Hgb and metabolite concentrations was determined. Most metabolites detected in serum were at higher concentrations in WB with the exception of acetoacetate and propylene glycol. The 18 unique metabolites of WB included adenosine, AMP, ADP, and ATP, which are associated with RBC metabolism. The use of serum results in the underrepresentation of a number of metabolic pathways including branched-chain amino acid degradation and glycolysis and gluconeogenesis. The range of free Hgb in serum was 0.03 to 0.01 g/dL, and eight metabolites were associated (P ≤ 0.05) with free Hgb. The range of free Hgb in serum samples from 18 sepsis patients was 0.02 to 0.46 g/dL. Whole blood and serum have unique aqueous metabolite profiles, but the use of serum may introduce potential pathway bias. Use of WB for metabolomics may be particularly important for studies in diseases such as sepsis in which RBC metabolism is altered, and mechanical and sepsis-induced hemolysis contributes to variance in the metabolome.

Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock.

RATIONALE: Sepsis therapeutics have a poor history of success in clinical trials, due in part to the heterogeneity of enrolled patients. Pharmacometabolomics could differentiate drug response phenotypes and permit a precision medicine approach to sepsis. OBJECTIVES: To use existing serum samples from the phase 1 clinical trial of l-carnitine treatment for severe sepsis to metabolically phenotype l-carnitine responders and nonresponders. METHODS: Serum samples collected before (T0) and after completion of the infusion (T24, T48) from patients randomized to either l-carnitine (12 g) or placebo for the treatment of vasopressor-dependent septic shock were assayed by untargeted (1)H-nuclear magnetic resonance metabolomics. The normalized, quantified metabolite data sets of l-carnitine- and placebo-treated patients at each time point were compared by analysis of variance with post-hoc testing for multiple comparisons. Pathway analysis was performed to statistically rank metabolic networks. MEASUREMENTS AND MAIN RESULTS: Thirty-eight metabolites were identified in all samples. Concentrations of 3-hydroxybutyrate, acetoacetate, and 3-hydroxyisovalerate were different at T0 and over time in l-carnitine-treated survivors versus nonsurvivors. Pathway analysis of pretreatment metabolites revealed that synthesis and degradation of ketone bodies had the greatest impact in differentiating l-carnitine treatment response. Analysis of all patients based on pretreatment 3-hydroxybutyrate concentration yielded distinct phenotypes. Using the T0 median 3-hydroxybutyrate level (153 µM), patients were categorized as either high or low ketone. l-Carnitine-treated low-ketone patients had greater use of carnitine as evidenced by lower post-treatment l-carnitine levels. The l-carnitine responders also had faster resolution of vasopressor requirement and a trend toward a greater improvement in mortality at 1 year (P = 0.038) compared with patients with higher 3-hydroxybutyrate. CONCLUSIONS: The results of this preliminary study, which were not readily apparent from the parent clinical trial, show a unique metabolite profile of l-carnitine responders and introduce pharmacometabolomics as a viable strategy for informing l-carnitine responsiveness. The approach taken in this study represents a concrete example for the application of precision medicine to sepsis therapeutics that warrants further study.