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Updated: Jul 14, 2026

Assessing Hepatic Metabolic Changes During Progressive Colonization of Germ-free Mouse by 1H NMR Spectroscopy
Published on: December 15, 2011
Experimental and analytical variation in human urine in 1H NMR spectroscopy-based metabolic phenotyping studies
Anthony D Maher1, Séverine F M Zirah, Elaine Holmes
1Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, Imperial College London, South Kensington SW7 2AZ, United Kingdom.
Abstract:
1H NMR spectroscopy potentially provides a robust approach for high-throughput metabolic screening of biofluids such as urine and plasma, but sample handling and preparation need careful optimization to ensure that spectra accurately report biological status or disease state. We have investigated the effects of storage temperature and time on the 1H NMR spectral profiles of human urine from two participants, collected three times a day on four different days. These were analyzed using modern chemometric methods. Analytical and preparation variation (tested between -40 degrees C and room temperature) and time of storage (to 24 h) were found to be much less influential than biological variation in sample classification. Statistical total correlation spectroscopy and discriminant function methods were used to identify the specific metabolites that were hypervariable due to preparation and biology. Significant intraindividual variation in metabolite profiles were observed even for urine collected on the same day and after at least 6 h fasting. The effect of long-term storage at different temperatures was also investigated, showing urine is stable if frozen for at least 3 months and that storage at room temperature for long periods (1-3 months) results in a metabolic profile explained by bacterial activity. Presampling (e.g., previous day) intake of food and medicine can also strongly influence the urinary metabolic profiles indicating that collective detailed participant historical meta data are important for interpretation of metabolic phenotypes and for avoiding false biomarker discovery.
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