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 1 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.

* Department of Clinical Pharmacy and the NMR Metabolomics Laboratory, College of Pharmacy,† Center for Computational Medicine and Bioinformatics, and‡ Department of Emergency Medicine, School of Medicine, University of Michigan, Ann Arbor, Michigan; § Department of Emergency Medicine, University of Mississippi Medical Center, Jackson, Mississippi; and∥ Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, Michigan