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代谢组学技术在慢病健康管理中的应用
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Abstract:
慢性非传染性疾病带来沉重的医疗负担，但是因为病因复杂、发病隐匿，很难预防及有效控制。代谢组学技术大规模检测环境、饮食、免疫、感染和遗传等代谢物数据，无疑是解锁更多与慢性疾病密切相关的潜在生物标志物的金钥匙。
Chronic Non-Communicable Diseases (NCDs) pose a heavy medical burden, but they are difficult to prevent and effectively control due to their complex etiology and insidious onset. Metabolomics technology is undoubtedly the golden key to unlocking more potential biomarkers closely related to chronic diseases, such as large-scale detection of metabolite data such as environment, diet, immunity, infection, and genetics.
| [1] | 卫生计生委疾病预防控制局. 中国居民营养与慢性病状况报告(2015年) [M]. 北京: 人民卫生出版社, 2015 |
| [2] | Bauer Bartley, K. and Haney, R. (2010) Shared Medical Appointments: Improving Access, Outcomes, and Satisfaction for Patients with Chronic Cardiac Diseases. Journal of Cardiovascular Nursing, 25, 13-19. https://doi.org/10.1097/jcn.0b013e3181b8e82e |
| [3] | 金星明. 发育行为临床诊断特点[J]. 中国实用儿科杂志, 2012, 27(3): 183-185. |
| [4] | Mukherjee, S. (2005) Atherosclerosis: Cell Biology and Lipoproteins—Atherosclerosis and Harbingers of Cardiovascular Disease Start in Childhood and Adolescence. Current Opinion in Lipidology, 16, 705-707. https://doi.org/10.1097/01.mol.0000194126.92344.da |
| [5] | Sakakura, K., Nakano, M., Otsuka, F., Ladich, E., Kolodgie, F.D. and Virmani, R. (2013) Pathophysiology of Atherosclerosis Plaque Progression. Heart, Lung and Circulation, 22, 399-411. https://doi.org/10.1016/j.hlc.2013.03.001 |
| [6] | Pauling, L., Robinson, A.B., Teranishi, R. and Cary, P. (1971) Quantitative Analysis of Urine Vapor and Breath by Gas-Liquid Partition Chromatography. Proceedings of the National Academy of Sciences of the United States of America, 68, 2374-2376. https://doi.org/10.1073/pnas.68.10.2374 |
| [7] | Müller, J., Bertsch, T., Volke, J., Schmid, A., Klingbeil, R., Metodiev, Y., et al. (2021) Narrative Review of Metabolomics in Cardiovascular Disease. Journal of Thoracic Disease, 13, 2532-2550. https://doi.org/10.21037/jtd-21-22 |
| [8] | Taegtmeyer, H., Young, M.E., Lopaschuk, G.D., Abel, E.D., Brunengraber, H., Darley-Usmar, V., et al. (2016) Assessing Cardiac Metabolism: A Scientific Statement from the American Heart Association. Circulation Research, 118, 1659-1701. https://doi.org/10.1161/res.0000000000000097 |
| [9] | Rolfe, D.F. and Brown, G.C. (1997) Cellular Energy Utilization and Molecular Origin of Standard Metabolic Rate in Mammals. Physiological Reviews, 77, 731-758. https://doi.org/10.1152/physrev.1997.77.3.731 |
| [10] | Kume, S., Araki, S., Ono, N., Shinhara, A., Muramatsu, T., Araki, H., et al. (2014) Predictive Properties of Plasma Amino Acid Profile for Cardiovascular Disease in Patients with Type 2 Diabetes. PLOS ONE, 9, e101219. https://doi.org/10.1371/journal.pone.0101219 |
| [11] | Ussher, J.R., Elmariah, S., Gerszten, R.E. and Dyck, J.R.B. (2016) The Emerging Role of Metabolomics in the Diagnosis and Prognosis of Cardiovascular Disease. Journal of the American College of Cardiology, 68, 2850-2870. https://doi.org/10.1016/j.jacc.2016.09.972 |
| [12] | Iliou, A., Mikros, E., Karaman, I., Elliott, F., Griffin, J.L., Tzoulaki, I., et al. (2021) Metabolic Phenotyping and Cardiovascular Disease: An Overview of Evidence from Epidemiological Settings. Heart, 107, 1123-1129. https://doi.org/10.1136/heartjnl-2019-315615 |
| [13] | Deng, C., Huang, X., Ding, H., Wang, B., Qiu, Y., Tang, Y., et al. (2015) Effects of the Main Active Components Combinations of Astragalus and Panax Notoginseng on Energy Metabolism in Brain Tissues after Cerebral Ischemia-Reperfusion in Mice. Pharmacognosy Magazine, 11, 732-739. https://doi.org/10.4103/0973-1296.165572 |
| [14] | Chen, H., Shen, Y., Lin, C., Tsai, K., Lu, C., Shen, C., et al. (2012) Metabolomics Study of Buyang Huanwu Tang Decoction in Ischemic Stroke Mice by 1H NMR. Metabolomics, 8, 974-984. https://doi.org/10.1007/s11306-011-0394-0 |
| [15] | 张天舒, 阮志, 刘霞, 等. MCAO大鼠脑缺血再灌注损伤机制的核磁共振代谢组学研究[J]. 中国药科大学学报, 2016, 47(2): 67-77. |
| [16] | Bory, C., Boulieu, R., Chantin, C. and Mathieu, M. (1990) Diagnosis of Alcaptonuria: Rapid Analysis of Homogentisic Acid by HPLC. Clinica Chimica Acta, 189, 7-11. https://doi.org/10.1016/0009-8981(90)90228-k |
| [17] | Ma, R.C.W. (2021) Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies. Cells, 10, Article 2832. |
| [18] | Wang, Z., Klipfell, E., Bennett, B.J., Koeth, R., Levison, B.S., DuGar, B., et al. (2011) Gut Flora Metabolism of Phosphatidylcholine Promotes Cardiovascular Disease. Nature, 472, 57-63. https://doi.org/10.1038/nature09922 |
| [19] | Winther, S.A., Henriksen, P., Vogt, J.K., et al. (2020) Gut Microbiota Profile and Selected Plasma Metabolites in Type 1 Diabetes without and with Stratification by Albuminuria. Diabetologia, 63, 2713-2724. |
| [20] | 朱昭章, 周凡力, 龙凯, 等. 维持性血液透析患者肠道菌群变化与认知功能的相关性研究[J]. 现代医学与健康研究电子杂志, 2024, 8(9): 10-12. |
| [21] | 吴梦玮. 基于代谢组学探讨脑白质病变痰湿体质患者的代谢特征研究[D]: [硕士学位论文]. 北京: 北京中医药大学, 2022. |
| [22] | 刘佳, 潘荟颖, 张海平, 等. 代谢组学在精准健康管理中的应用专家共识[J]. 健康体检与管理, 2023, 4(1): 3-10. |
| [23] | Li, C., Stražar, M., Mohamed, A.M.T., Pacheco, J.A., Walker, R.L., Lebar, T., et al. (2024) Gut Microbiome and Metabolome Profiling in Framingham Heart Study Reveals Cholesterol-Metabolizing Bacteria. Cell, 187, 1834-1852.e19. https://doi.org/10.1016/j.cell.2024.03.014 |
| [24] | Wishart, D.S., Anchi, G., Eponine, O., et al. (2021) HMDB 5.0: The Human Metabolome Database for 2022. Nucleic Acids Research, 50, D622-D631. |
| [25] | 杨军, 刘心昱, 许国旺. 基于质谱数据的计算代谢组学方法学研究进展[J]. 中国科学(化学), 2022, 52(9): 1580-1591. |
| [26] | 黄文洁, 吴绍文, 刘蕊, 等. 基于质谱的代谢组学数据分析技术研究进展[J]. 广东农业科学, 2022, 49(11): 96-109. |
| [27] | Myers, O.D., Sumner, S.J., Li, S., Barnes, S. and Du, X. (2017) Detailed Investigation and Comparison of the XCMS and Mzmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data. Analytical Chemistry, 89, 8689-8695. https://doi.org/10.1021/acs.analchem.7b01069 |
| [28] | Ju, R., Liu, X., Zheng, F., Zhao, X., Lu, X., Zeng, Z., Lin, X. and Xu, G. (2019) Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data. Analytical Chemistry Journal, 92, 588-592. |
| [29] | Samanipour, S., O’Brien, J.W., Reid, M.J. and Thomas, K.V. (2019) Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data. Analytical Chemistry, 91, 10800-10807. https://doi.org/10.1021/acs.analchem.9b02422 |
| [30] | 梁丹丹, 李忆涛, 郑晓皎, 等. 代谢组学全功能软件研究进展[J]. 上海交通大学学报(医学版), 2018, 38(7): 805-810. |
| [31] | Neumann, S. and Böcker, S. (2010) Computational Mass Spectrometry for Metabolomics: Identification of Metabolites and Small Molecules. Analytical and Bioanalytical Chemistry, 398, 2779-2788. https://doi.org/10.1007/s00216-010-4142-5 |
| [32] | da Silva, R.R., Dorrestein, P.C. and Quinn, R.A. (2015) Illuminating the Dark Matter in Metabolomics. Proceedings of the National Academy of Sciences, 112, 12549-12550. https://doi.org/10.1073/pnas.1516878112 |
| [33] | Risch, N., Spiker, D., Lotspeich, L., Nouri, N., Hinds, D., Hallmayer, J., Kalaydjieva, L., McCague, P., Dimiceli, S., Pitts, T., et al. (1999) A Genomic Screen of Autism: Evidence for a Multilocus Etiology. The American Journal of Human Genetics, 65, 493-507. |
| [34] | Weiner, D.J., Wigdor, E.M., Ripke, S., et al. (2017) Polygenic Transmission Disequilibrium Confirms That Common and Rare Variation Act Additively to Create Risk for Autism Spectrum Disorders. Nature Genetics, 49, 978-985. |
| [35] | Fisher, R.A. (1919) XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.. Transactions of the Royal Society of Edinburgh, 52, 399-433. https://doi.org/10.1017/s0080456800012163 |
| [36] | Furlong, L.I. (2013) Human Diseases through the Lens of Network Biology. Trends in Genetics, 29, 150-159. https://doi.org/10.1016/j.tig.2012.11.004 |
| [37] | Chakravarti, A. and Turner, T.N. (2016) Revealing Rate‐Limiting Steps in Complex Disease Biology: The Crucial Importance of Studying Rare, Extreme‐phenotype Families. BioEssays, 38, 578-586. https://doi.org/10.1002/bies.201500203 |
| [38] | Jostins, L., Ripke, S., Weersma, R.K., et al. (2012) Host-Microbe Interactions Have Shaped the Genetic Architecture of Inflammatory Bowel Disease. Nature, 491, 119-124. |
| [39] | Claussnitzer, M., Dankel, S.N., Kim, K., Quon, G., Meuleman, W., Haugen, C., et al. (2015) FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. New England Journal of Medicine, 373, 895-907. https://doi.org/10.1056/nejmoa1502214 |
| [40] | Locke, A.E., Kahali, B., Berndt, S.I., et al. (2015) Genetic Studies of Body Mass Index Yield New Insights for Obesity Biology. Nature, 518, 197-206. |
| [41] | Slatkin, M. (2008) Exchangeable Models of Complex Inherited Diseases. Genetics, 179, 2253-2261. https://doi.org/10.1534/genetics.107.077719 |
| [42] | Garrod, A. (1902) The Incidence of Alkaptonuria : A Study in Chemical Individuality. The Lancet, 160, 1616-1620. https://doi.org/10.1016/s0140-6736(01)41972-6 |
| [43] | Adolph, E.F. (1961) Early Concepts of Physiological Regulations. Physiological Reviews, 41, 737-770. https://doi.org/10.1152/physrev.1961.41.4.737 |
| [44] | Billman, G.E. (2020) Homeostasis: The Underappreciated and Far Too Often Ignored Central Organizing Principle of Physiology. Frontiers in Physiology, 11, Article 200. https://doi.org/10.3389/fphys.2020.00200 |
| [45] | World Health Organization (2017) Preventing Noncommunicable Diseases (NCDs) by Reducing Environmental Risk Factors. |
| [46] | Mathers, C.D. and Loncar, D. (2006) Projections of Global Mortality and Burden of Disease from 2002 to 2030: Data Sources, Methods and Results. PLOS Medicine, 3, e442. https://doi.org/10.1371/journal.pmed.0030442 |
| [47] | Balashova, E.E., Maslov, D.L. and Lokhov, P.G. (2018) A Metabolomics Approach to Pharmacotherapy Personalization. Journal of Personalized Medicine, 8, Article 28. https://doi.org/10.3390/jpm8030028 |
| [48] | Leyhe, T. and Müssig, K. (2014) Cognitive and Affective Dysfunctions in Autoimmune Thyroiditis. Brain, Behavior, and Immunity, 41, 261-266. https://doi.org/10.1016/j.bbi.2014.03.008 |
| [49] | Jordan, B., Uer, O., Buchholz, T., et al. (2021) Physical Fatigability and Muscle Pain in Patients with Hashimoto Thyroiditis. Journal of Neurology, 268, 2441-2449. |
| [50] | Noffsinger, E.B. (1999) Will Drop-In Group Medical Appointments (DIGMAs) Work in Practice? The Permanente Journal, 3, 58-67. |
| [51] | Menon, K., Mousa, A., de Courten, M.P., Soldatos, G., Egger, G. and de Courten, B. (2017) Shared Medical Appointments May Be Effective for Improving Clinical and Behavioral Outcomes in Type 2 Diabetes: A Narrative Review. Frontiers in Endocrinology, 8, Article 263. https://doi.org/10.3389/fendo.2017.00263 |
| [52] | Hartzler, M.L., Williams, J., Schoen, J., et al. (2013) Impact of Collaborative Shared Medical Appointments on Diabetes Outcomes in a Family Medicine Clinic. Diabetes Care & Education, 44, 361-372. |
| [53] | Noya, C., Alkon, A., Castillo, E., Kuo, A.C. and Gatewood, E. (2020) Shared Medical Appointments: An Academic-Community Partnership to Improve Care among Adults with Type 2 Diabetes in California Central Valley Region. The Diabetes Educator, 46, 197-205. https://doi.org/10.1177/0145721720906792 |
| [54] | Egger, G., Stevens, J., Volker, N. and Egger, S. (2019) Programmed Shared Medical Appointments for Weight Management in Primary Care: An Exploratory Study in Translational Research. Australian Journal of General Practice, 48, 681-688. https://doi.org/10.31128/ajgp-05-19-4940 |
| [55] | Shibuya, K., Ji, X., Pfoh, E.R., Milinovich, A., Weng, W., Bauman, J., et al. (2020) Association between Shared Medical Appointments and Weight Loss Outcomes and Anti‐Obesity Medication Use in Patients with Obesity. Obesity Science & Practice, 6, 247-254. https://doi.org/10.1002/osp4.406 |
| [56] | Law, T., Jones, S. and Vardaman, S. (2019) Implementation of a Shared Medical Appointment as a Holistic Approach to CHF Management. Holistic Nursing Practice, 33, 354-359. https://doi.org/10.1097/hnp.0000000000000353 |
| [57] | Lin, A., Cavendish, J., Boren, D., Ofstad, T. and Seidensticker, D. (2008) A Pilot Study: Reports of Benefits from a 6-Month, Multidisciplinary, Shared Medical Appointment Approach for Heart Failure Patients. Military Medicine, 173, 1210-1213. https://doi.org/10.7205/milmed.173.12.1210 |
| [58] | Parikh, M. (2019) Characteristics and Components of Medical Group Visits for Chronic Health Conditions: A Systematic Scoping Review. The Journal of Alternative and Complementary Medicine, 25, 683-698. |
| [59] | Romanelli, R.J., Dolginsky, M., Byakina, Y., Bronstein, D. and Wilson, S. (2017) A Shared Medical Appointment on the Benefits and Risks of Opioids Is Associated with Improved Patient Confidence in Managing Chronic Pain. Journal of Patient Experience, 4, 144-151. https://doi.org/10.1177/2374373517706837 |
| [60] | Federman, D.G., Poulin, L.M., Ruser, C.B. and Kravetz, J.D. (2018) Implementation of Shared Medical Appointments to Offer Battlefield Acupuncture Efficiently to Veterans with Pain. Acupuncture in Medicine, 36, 124-126. https://doi.org/10.1136/acupmed-2016-011315 |
| [61] | Borren, N.Z., Woude, C.J.V.D. and Ananthakrishnan, A.N. (2019) Fatigue in IBD: Epidemiology, Pathophysiology and Management. Nature Reviews Gastroenterology & Hepatology, 16, 247-259. |
| [62] | Nicolson, G.L. (2014) Mitochondrial Dysfunction and Chronic Disease: Treatment with Natural Supplements. Integrative Medicine, 13, 35-43. |
| [63] | Davis, J.M., Alderson, N.L. and Welsh, R.S. (2000) Serotonin and Central Nervous System Fatigue: Nutritional Considerations. The American Journal of Clinical Nutrition, 72, 573S-578S. https://doi.org/10.1093/ajcn/72.2.573s |
| [64] | Martin, F., Su, M., Xie, G., Guiraud, S.P., Kussmann, M., Godin, J., et al. (2017) Urinary Metabolic Insights into Host-Gut Microbial Interactions in Healthy and IBD Children. World Journal of Gastroenterology, 23, 3643-3654. https://doi.org/10.3748/wjg.v23.i20.3643 |
| [65] | Margină, D., Ungurianu, A., Purdel, C., Nițulescu, G.M., Tsoukalas, D., Sarandi, E., et al. (2020) Analysis of the Intricate Effects of Polyunsaturated Fatty Acids and Polyphenols on Inflammatory Pathways in Health and Disease. Food and Chemical Toxicology, 143, Article ID: 111558. https://doi.org/10.1016/j.fct.2020.111558 |
| [66] | Clish, C.B. (2015) Metabolomics: An Emerging but Powerful Tool for Precision Medicine. Molecular Case Studies, 1, a000588. https://doi.org/10.1101/mcs.a000588 |
| [67] | Tsoukalas, D., Sarandi, E. and Georgaki, S. (2021) The Snapshot of Metabolic Health in Evaluating Micronutrient Status, the Risk of Infection and Clinical Outcome of COVID-19. Clinical Nutrition ESPEN, 44, 173-187. https://doi.org/10.1016/j.clnesp.2021.06.011 |
| [68] | Ventres, W. (2010) Running Group Visits in Your Practice. JAMA, 303, 1204-1205. https://doi.org/10.1001/jama.2010.329 |
| [69] | Bailey, S.C., Brega, A.G., Crutchfield, T.M., Elasy, T., Herr, H., Kaphingst, K., et al. (2014) Update on Health Literacy and Diabetes. The Diabetes Educator, 40, 581-604. https://doi.org/10.1177/0145721714540220 |
| [70] | Smith, S.G., O'Conor, R., Curtis, L.M., Waite, K., Deary, I.J., Paasche-Orlow, M., et al. (2015) Low Health Literacy Predicts Decline in Physical Function among Older Adults: Findings from the LitCog Cohort Study. Journal of Epidemiology and Community Health, 69, 474-480. https://doi.org/10.1136/jech-2014-204915 |
| [71] | Wadsworth, K.H., Archibald, T.G., Payne, A.E., Cleary, A.K., Haney, B.L. and Hoverman, A.S. (2019) Shared Medical Appointments and Patient-Centered Experience: A Mixed-Methods Systematic Review. BMC Family Practice, 20, Article No. 97. https://doi.org/10.1186/s12875-019-0972-1 |
