运动员与体力劳动者代谢组学判别模型的建立
收稿日期: 2015-09-09
修回日期: 2016-07-18
网络出版日期: 2016-09-05
基金资助
航天医学基础与应用国家重点实验室基金资助项目（SMFA11A03），国家自然科学基金资助项目（31101251、81202612）
Urinary Metabonome Differentiates Athletes and Labor Workers
Received date: 2015-09-09
Revised date: 2016-07-18
Online published: 2016-09-05
不同职业的人群健康状态不同，需要不同的健康管理方法，根据各类人群的体质特征建立健康状态的评估方法有助于开展个性化的健康指导. 招募运动员（Athlete，n = 31）和体力劳动者（Labour，n = 42）共73人，分别收集两组志愿者的晨尿. 运用一维核磁共振（1D NMR）技术检测尿液中的代谢产物. 建立主成分（PCA）及正交偏最小二乘判别分析（OPLS-DA）模型筛选2类人群间的差异代谢标志物. 通过可接收操作特征曲线（ROC）评价代谢标志物的假阳性特征，t-test检验代谢标志物的显著性. 利用代谢标志物建立两类人群的偏最小二乘判别分析（PLS-DA）预测模型. 模型的有效性通过内部交叉、置换检验和外部预测检验确认. 结果显示2类人群之间差异的代谢物有24个，通过其中20个代谢标志物建立的预测模型最优（曲线下面积AUC = 0.998）. 内部交叉验证的误判率（FDR）分别为3.2%和0. 内部置换检验的p = 3.34×10-5. 外部预测检验误判率为0. 这为不同职业人群健康预测模型的建立提供了思路.
陈朴 , 于燕波 , 黄贱英 , 李红毅 , 董海胜 , 陈斌 . 运动员与体力劳动者代谢组学判别模型的建立[J]. 波谱学杂志, 2016 , 33(3) : 395 -405 . DOI: 10.11938/cjmr20160304
Under the concept of personal-based health care, different health management strategies are needed for different populations. To achieve this goal, the first step is to characterize the health-related differences among different populations. To this end, we recruited a total of 31 athletes and 42 labor workers to exam population-level differences in their urinary metabonome. First morning urine was collected and stored at -80℃ until use. 1H NMR spectra of the urine samples were collected on a 600 MHz spectrometer. The data collected were then used to build supervised and unsupervised pattern recognition models (PCA model and OPLS-DA model) to differentiate the two populations. Metabolites contributing significantly to the population difference in urinary metabonome were identified by VIP plot, among which false positives were discovered by receiver operating characteristic curve (ROC) and t-test. Predictive PLS-DA model was built, and validated by internal cross-validation, permutation tests and external prediction. The results showed that a PLS-DA model built upon 20 discriminating metabolites had the best predictive accuracy (AUC = 0.998), and the most significant level (p = 3.34×10-5). In addition, all samples from the external prediction set were classified correctly, suggesting that the PLS-DA model built upon 20 discriminating metabolites had high sensitivity and specificity.
[1] Loewenstein R J. An office mental status examination for complex chronic dissociative symptoms and multiple personality disorder[J]. Psychiatric Clinics of North America, 1991, 14: 567-604.
[2] Pronk N P, Katz A S, Lowry M, et al. Reducing occupational sitting time and improving worker health: The Take- a-Stand Project, 2011[J]. Prev Chronic Dis, 2012, 9: 110 323.
[3] Bauer U E, Briss P A, Goodman R A, et al. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA[J]. The Lancet, 2014, 384(9 937): 45-52.
[4] Bartley M, Plewis I. Accumulated labour market disadvantage and limiting long-term illness: Data from the 1971-1991 Office for National Statistics' Longitudinal Study[J]. Int J Epidemiol, 2002, 31(2): 336-341.
[5] Merry L L, Manuel M, Aldrin V G, et al. Transformative impact of proteomics on cardiovascular health and disease a scientific statement from the american heart association[J]. Circulation, 2015, 132(9): 852-872.
[6] Sek W K, In-Hee L, Ignaty L, et al. Summarizing polygenic risks for complex diseases in a clinical whole-genome report[J]. Genet Med, 2014, 17(7): 536-544
[7] Feng W, Themistocles D, Leslie C, et al. Genome-wide gene expression differences in Crohn's disease and ulcerative colitis from endoscopic pinch biopsies: insights into distinctive pathogenesis[J]. Inflamm Bowel Dis, 2007, 13(7): 807-821.
[8] Uta B, Sebastian B, Lars P, et al. Proteomic analysis of the inflamed intestinal mucosa reveals distinctive immune response profiles in Crohn's disease and ulcerative colitis[J]. J Immunol, 2007, 179(1): 295-304.
[9] Timothy M D E, Rachel C. Bioinformatic methods in NMR-based metabolic profiling[J]. Prog Nucl Magn Reson Spectrosc, 2009, 55(4): 361-374.
[10] Chen Bo(陈波), Kang Hai-ning(康海宁), Han Chao(韩超), et al. Applications of NMR spectroscopy and pattern recognition in food analysis(NMR指纹图谱与模式识别方法在食物分析中的应用)[J]. Chinese J Magn Reson(波谱学杂志), 2006, 23(3): 397-407.
[11] Nicholson J K, Holmes E, Kinross J M, et al. Metabolic phenotyping in clinical and surgical environments[J]. Nature, 2012, 491(7 424): 384-392.
[12] Beckonert O, Keun H C, Ebbels T M, et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts[J]. Nat Protoc, 2007, 2(11): 2 692-2 703.
[13] Liu Yue(刘悦), Gao Yun-ling(高运苓), Cheng Ji(程吉), et al. A processing method for spectrum alignment and peak extraction for nmr spectra(一种核磁共振波谱谱峰对齐及谱峰提取的方法)[J]. Chinese J Magn Reson(波谱学杂志), 2015, 32(2): 382-392.
[14] Wishart D S, Tzur D, Knox C, et al. HMDB: The human metabolome database[J]. Nucleic Acids Res, 2007, 35(S1): 521-526.
[15] Zhao Xiu-ju(赵秀举), Wang Yu-lan(王玉兰). Applications of NMR-based metabonomics approaches in the assessment of drug toxicity(代谢组学数据分析与药物毒理研究)[J]. Chinese J Magn Reson(波谱学杂志), 2011, 28(1): 2-17.
[16] Kumazoe M, Fujimura Y, Hidaka S, et al. Metabolic profiling-based data-mining for an effective chemical combination to induce apoptosis of cancer cells[J]. Sci Rep, 2015, 5: 9474.
[17] Eriksson L, Johansson E, Kettaneh-Wold N, et al. Multi-and Megavariate Data Analysis: Principles and Applications[M]. 3rd ed. Umea: Umetrics Academy, 2001.
[18] Chan E C Y, Pasikanti K K, Nicholson J K. Global urinary metabolic profiling procedures using gas chromatography- mass spectrometry[J]. Nat Protoc, 2011, 6(10): 1 483-1 499.
[19] Xu Guang-tong(徐广通), Yuan Hong-fu(袁洪福), Lu Wan-zheng(陆婉珍). Study of quantitative calibration model suitability in near-infrared spectroscopy analysis(近红外光谱定量校正模型适用性研究)[J]. Spectrosc Spect Anal(光谱学与光谱分析), 2001, 21(4): 459-463.
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