Human Urine as Test Material in 1 H NMR-Based Metabonomics: Recommendations for Sample Preparation and Storage Michael Lauridsen, † Steen H. Hansen, † Jerzy W. Jaroszewski, ‡ and Claus Cornett* ,† Department of Pharmaceutics and Analytical Chemistry and Department of Medicinal Chemistry, The Danish University of Pharmaceutical Sciences, Universitetsparken 2, DK-2100 Copenhagen, Denmark Metabonomic approaches are believed to have the capa- bility of revolutionizing diagnosis of diseases and assess- ment of patient conditions after medical interventions. In order to ensure comparability of metabonomic 1 H NMR data from different studies, we suggest validated sample preparation guidelines for human urine based on a stability study that evaluates effects of storage time and temperature, freeze-drying, and the presence of preserva- tives. The results indicated that human urine samples should be stored at or below -25 °C, as no changes in the 1 H NMR fingerprints have been observed during storage at this temperature for 26 weeks. Formation of acetate, presumably due to microbial contamination, was occasionally observed in samples stored at 4 °C without addition of a preservative. Addition of a preserving agent is not mandatory provided that the samples are stored at -25 °C. Thus, no differences were observed between 1 H NMR spectra of nonpreserved urines and urines with added sodium azide and stored at -25 °C, whereas the presence of sodium fluoride caused a shift of especially citrate resonances. Freeze-drying of urine and reconstitu- tion in D 2 O at pH 7.4 resulted in the disappearance of the creatinine CH 2 signal at δ 4.06 due to deuteration. A study evaluating the effects of phosphate buffer concentra- tion on signal variability and assessment of the probability of citrate or creatinine resonances crossing bucket border (a boundary between adjacent integrated regions) led to the conclusion that a minimum buffer concentration of 0.3 M is adequate for normal urines used in this study. However, final buffer concentration of 1 M will be required for very concentrated urines. The availability of valid data is a fundamental prerequisite for a successful outcome of any study. In order to integrate metabo- nomic analysis into the physician’s arsenal of diagnostic tools, databases comprising multivariate data from disease models are necessary. Such databases should have a predictive or at least confirmatory capability when confronted with data obtained from a patient. To achieve this goal, major precautions are necessary during creation of the database. Conditions under which the data are collected, in particular, the conditions of sample storage and sample preparation, may have a major impact on the content of a multivariate data set. In this paper, generation of valid 1 H NMR data from human urine samples is discussed. A set of guidelines for sample preparation for metabonomic studies with human urine is proposed for the first time. In the past decade, metabonomic approaches have enriched toxicology and diagnostics. 1-4 Metabolic changes in the rat have been studied most extensively. 5-8 In most cases, the metabonomic investigations are conducted using 1 H NMR spectroscopy, but LC/ MS 9,10 and GC/MS approaches 11 have been pursued. Metabo- nomic techniques capable of predicting the presence of coronary heart disease have been developed. 12,13 Furthermore, some cancer types have been investigated using metabonomic approaches, e.g., breast cancer and ovarian cancer. 14,15 Biomarkers that have emerged from these studies can be very useful in exploration of the biochemical pathways involved in these diseases. In the studies * Corresponding author. E-mail: cc@dfuni.dk. Fax: + 45 3530 6030. † Department of Pharmaceutics and Analytical Chemistry. ‡ Department of Medicinal Chemistry. (1) Lindon, J. C.; Holmes, E.; Nicholson, J. K. Expert Rev. Mol. Diagn. 2004, 4, 189-199. (2) Lindon, J. C.; Holmes, E.; Bollard, M. E.; Stanley, E. G.; Nicholson, J. K. Biomarkers 2004, 9,1-31. (3) Nicholson, J. K.; Wilson, I. D. Prog. Nucl. Magn. Reson. Spectrosc. 1989, 21, 449-501. (4) Robosky, L. C.; Robertson, D. G.; Baker, J. D.; Rane, S.; Reily, M. D. Comb. Chem. High Throughput Screening 2002, 5, 651-662. (5) Gartland, K. P. 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