Experimental and analytical variation in human urine in 1H NMR spectroscopy-based metabolic phenotyping studies
Experimental and analytical variation in human urine in 1H NMR spectroscopy-based metabolic phenotyping studies
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DOI：
10.1021/ac070212f
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发表时间：
2007-07-15
影响因子：
7.4
通讯作者：
Nicholson, Jeremy K.
中科院分区：
文献类型：
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作者：
Maher, Anthony D.;Zirah, Severine F. M.;Nicholson, Jeremy K.
H-1 NMR 光谱可能为尿液和血浆等生物体液的高通量代谢筛选提供可靠的方法，但样品处理和制备需要仔细优化，以确保光谱准确报告生物状态或疾病状态。我们研究了储存温度和时间对两名参与者的人类尿液 H-1 NMR 谱图的影响，这些尿液在四天不同的时间内每天收集 3 次。使用现代化学计量方法对这些进行分析。研究发现，分析和制备变异（在 -40 摄氏度和室温之间测试）和储存时间（至 24 小时）对样品分类的影响远小于生物变异。使用统计全相关光谱和判别函数方法来识别由于制备和生物学而高度可变的特定代谢物。即使对于同一天和禁食至少 6 小时后收集的尿液，也观察到代谢物谱存在显着的个体差异。还研究了在不同温度下长期储存的影响，结果表明，如果冷冻至少 3 个月，尿液是稳定的，并且在室温下长期储存（1-3 个月）会产生由细菌活性解释的代谢特征。食物和药物的预采样（例如前一天）摄入也会强烈影响尿液代谢特征，这表明集体详细的参与者历史元数据对于解释代谢表型和避免错误的生物标志物发现非常重要。
H-1 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 H-1 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.