检验医学在亚健康诊疗中的应用展望
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摘要
随着现代生活节奏的加快和生活压力的增大,越来越多的人处于一种介于健康与疾病之间的状态,现代医学称之为"亚健康"。亚健康状态如果持续时间过长且未被及时干预最终会发展成为疾病,因此识别亚健康状态并尽早干预对于预防疾病、保持机体健康状态具有重要意义。亚健康的检测方法有多种,利用医学检验技术通过实验室指标评估机体的健康状态是较为客观的评价方法。目前针对亚健康状态的实验室检测参数及参考值范围尚无明确标准,未来有望通过大数据分析结合机器学习实现对亚健康状态的科学评估。
Increasingly more people are in a state between health and disease due to different life styles, which modern medicine calls "suboptimal health". If the suboptimal health state lasts too long and not interfered in time, it will eventually progress to disease. Early identification of suboptimal health status and early intervention are important for preventing diseases and restoring a healthy state. There are many ways for detecting suboptimal health. A more objective method to diagnose suboptimal health status is by experimental indicators of laboratory medicine. However, there are difficulties in establishing the parameters and reference value ranges in laboratory detection of suboptimal health, and laboratory medicine needs to combine big data analysis and machine learning to make a scientific evaluation of suboptimal health status.
Increasingly more people are in a state between health and disease due to different life styles, which modern medicine calls "suboptimal health". If the suboptimal health state lasts too long and not interfered in time, it will eventually progress to disease. Early identification of suboptimal health status and early intervention are important for preventing diseases and restoring a healthy state. There are many ways for detecting suboptimal health. A more objective method to diagnose suboptimal health status is by experimental indicators of laboratory medicine. However, there are difficulties in establishing the parameters and reference value ranges in laboratory detection of suboptimal health, and laboratory medicine needs to combine big data analysis and machine learning to make a scientific evaluation of suboptimal health status.
引文
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[19]LI F,CHEN Q X,PENG B,CHEN Y,YAO T,WANGG.Microalbuminuria in patients with acute ischemic stroke[J].Neurol Res,2019,41:498-503.
[20]SOUWEINE J S,CORBEL A,RIGOTHIER C,ROQUEC D,HADJADJ S,CRISTOL J P,et al.Interest of albuminuria in nephrology,diabetology and as a marker of cardiovascular risk[J].Ann Biol Clin(Paris),2019,77:26-35.
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[28]ROSS E G,SHAH N H,DALMAN R L,NEAD K T,COOKE J P,LEEPER N J.The use of machine learning for the identification of peripheral artery disease and future mortality risk[J/OL].J Vasc Surg,2016,64:1515-1522.e3.doi:10.1016/j.jvs.2016.04.026.
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[30]CAPRIOTTI E,ALTMAN R B.A new disease-specific machine learning approach for the prediction of cancercausing missense variants[J].Genomics,2011,98:310-317.
[31]SU R,LI Y,ZINK D,LOO L H.Supervised prediction of drug-induced nephrotoxicity based on interleukin-6and-8 expression levels[J/OL].BMC Bioinformatics,2014,15 Suppl 16:S16.doi:10.1186/1471-2105-15-S16-S16.
[32]DROZDOV I,KIDD M,NADLER B,CAMP R L,MANE S M,HAUSO O,et al.Predicting neuroendocrine tumor(carcinoid)neoplasia using gene expression profiling and supervised machine learning[J].Cancer,2009,115:1638-1650.
[33]AI L,TIAN H,CHEN Z,CHEN H,XU J,FANG J Y.Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer[J].Oncotarget,2017,8:9546-9556.
[34]考书健,虞丽娟.基于云和雾计算的亚健康信息管理系统构建方案[J].电子科技,2018,31:79-84.
[35]任思冲,周海琴,彭萍.大数据挖掘促进精准医学发展[J].国际检验医学杂志,2015,36:3499-3501.
[36]ARBEL Y,WEITZMAN D,RAZ R,STEINVIL A,ZELTSER D,BERLINER S,et al.Red blood cell distribution width and the risk of cardiovascular morbidity and all-cause mortality.A population-based study[J].Thromb Haemost,2014,111:300-307.
[37]GOLINO H F,AMARAL L S,DUARTE S F,GOMESC M,SOARES TDE J,DOS REIS L A,et al.Predicting increased blood pressure using machine learning[J/OL].J Obes,2014,2014:637635.doi:10.1155/2014/637635.
[2]WU S,XUAN Z,LI F,XIAO W,FU X,JIANG P,et al.Work-recreation balance,health-promoting lifestyles and suboptimal health status in southern China:a crosssectional study[J/OL].Int J Environ Res Public Health,2016,13(3).pii:E339.doi:10.3390/ijerph13030339.
[3]HOU H,FENG X,LI Y,MENG Z,GUO D,WANG F,et al.Suboptimal health status and psychological symptoms among Chinese college students:a perspective of predictive,preventive and personalised health[J].EPMAJ,2018,9:367-377.
[4]李晓静,徐国和.中国亚健康研究及干预对策的探讨[J].中国慢性病预防与控制,2011,19:427-429.
[5]周雅芳,何剪太,张阳德,刘蔚东,孙维佳,曾强,等.基于数学模型的中国亚健康特征评价方法与危险因素分析[J].中国组织工程研究与临床康复,2010,14:5609-5613.
[6]黄敏,马健,李金晏.210例亚健康人群血液生化指标检测的临床分析[J].中国医药指南,2012,10:494-495.
[7]陈亮,聂滨.122例亚健康人群血液生化指标检测的临床分析[J].重庆医学,2016,45:2111-2113.
[8]朱嵘.《亚健康中医临床指南》解读[J].中国中医药现代远程教育,2009,7:79-80.
[9]刘津.亚健康的检测及评估方法[J].中国健康心理学杂志,2010,18:235-237.
[10]BI J L,CHEN J,SUN X M,NIE X L,LIU Y Y,LUO R,et al.The development and evaluation of a sub-health self-rating scale for university students in China[J/OL].BMC Public Health,2019,19:330.doi:10.1186/s12889-019-6650-3.
[11]许军,薛允莲,刘贵浩,冯叶芳,许梦瑶,谢娟,等.中国城镇居民亚健康评定量表的常模构建[J].南方医科大学学报,2019,39:271-278.
[12]薛银萍,李莲,程琦.亚健康状态机体免疫功能改变与逍遥丸干预的效果[J].世界中医药,2017,12:2939-2941,2945.
[13]李伟,商庆新.亚健康在检验医学方面的进展[J].辽宁中医药大学学报,2012,14:91-93.
[14]FITZGERALD S P,MCCONNELL R I,HUXLEY A.Simultaneous analysis of circulating human cytokines using a high-sensitivity cytokine biochip array[J].J Proteome Res,2008,7:450-455.
[15]HORACEK J M,KUPSA T,VASATOVA M,JEBAVY L,ZAK P.Evaluation of serum levels of multiple cytokines and adhesion molecules in patients with newly diagnosed acute lymphoblastic leukemia using biochip array technology[J].Exp Oncol,2013,35:229-230.
[16]JIANG M,ZHANG S,YANG Z,LIN H,ZHU J,LIU L,et al.Self-recognition of an inducible host lncRNA by RIG-Ifeedback restricts innate immune response[J/OL].Cell,2018,173:906-919.e13.doi:10.1016/j.cell.2018.03.064.
[17]赵益业,林燕钊,赵彩琼,蔡宇.亚健康状态的神经内分泌机制及中医养生学防治[J].江苏中医药,2008,40:9-11.
[18]闫宇翔,杨兴华,杨姗姗,董晶,刘佑琴,王嵬.亚健康状态与心血管疾病危险因素关系[J].中国公共卫生,2010,26:1362-1364.
[19]LI F,CHEN Q X,PENG B,CHEN Y,YAO T,WANGG.Microalbuminuria in patients with acute ischemic stroke[J].Neurol Res,2019,41:498-503.
[20]SOUWEINE J S,CORBEL A,RIGOTHIER C,ROQUEC D,HADJADJ S,CRISTOL J P,et al.Interest of albuminuria in nephrology,diabetology and as a marker of cardiovascular risk[J].Ann Biol Clin(Paris),2019,77:26-35.
[21]ZHANG L,CHEN X,SU T,LI H,HUANG Q,WU D,et al.Circulating miR-499 are novel and sensitive biomarker of acute myocardial infarction[J].J Thorac Dis,2015,7:303-308.
[22]SILVA D C P D,CARNEIRO F D,ALMEIDA K C,FERNANDES-SANTOS C.Role of miRNAs on the pathophysiology of cardiovascular diseases[J].Arq Bras Cardiol,2018,111:738-746.
[23]HOOD J L,SAN R S,WICKLINE S A.Exosomes released by melanoma cells prepare sentinel lymph nodes for tumor metastasis[J].Cancer Res,2011,71:3792-3801.
[24]WANG Z,CHEN J Q,LIU J L,TIAN L.Exosomes in tumor microenvironment:novel transporters and biomarkers[J/OL].J Transl Med,2016,14:297.doi:10.1186/s12967-016-1056-9.
[25]TAYLOR D D,GERCEL-TAYLOR C.MicroRNAsignatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer[J].Gynecol Oncol,2008,110:13-21.
[26]MELO S A,LUECKE L B,KAHLERT C,FERNANDEZA F,GAMMON S T,KAYE J,et al.Glypican-1 identifies cancer exosomes and detects early pancreatic cancer[J].Nature,2015,523:177-182.
[27]FI NK ELSTEI N J,JEONG I C.Machine lear ning approaches to personalize early prediction of asthma exacerbations[J].Ann N Y Acad Sci,2017,1387:153-165.
[28]ROSS E G,SHAH N H,DALMAN R L,NEAD K T,COOKE J P,LEEPER N J.The use of machine learning for the identification of peripheral artery disease and future mortality risk[J/OL].J Vasc Surg,2016,64:1515-1522.e3.doi:10.1016/j.jvs.2016.04.026.
[29]ZOU Q,QU K,LUO Y,YIN D,JU Y,TANG H.Predicting diabetes mellitus with machine learning techniques[J/OL].Front Genet,2018,9:515.doi:10.3389/fgene.2018.00515.
[30]CAPRIOTTI E,ALTMAN R B.A new disease-specific machine learning approach for the prediction of cancercausing missense variants[J].Genomics,2011,98:310-317.
[31]SU R,LI Y,ZINK D,LOO L H.Supervised prediction of drug-induced nephrotoxicity based on interleukin-6and-8 expression levels[J/OL].BMC Bioinformatics,2014,15 Suppl 16:S16.doi:10.1186/1471-2105-15-S16-S16.
[32]DROZDOV I,KIDD M,NADLER B,CAMP R L,MANE S M,HAUSO O,et al.Predicting neuroendocrine tumor(carcinoid)neoplasia using gene expression profiling and supervised machine learning[J].Cancer,2009,115:1638-1650.
[33]AI L,TIAN H,CHEN Z,CHEN H,XU J,FANG J Y.Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer[J].Oncotarget,2017,8:9546-9556.
[34]考书健,虞丽娟.基于云和雾计算的亚健康信息管理系统构建方案[J].电子科技,2018,31:79-84.
[35]任思冲,周海琴,彭萍.大数据挖掘促进精准医学发展[J].国际检验医学杂志,2015,36:3499-3501.
[36]ARBEL Y,WEITZMAN D,RAZ R,STEINVIL A,ZELTSER D,BERLINER S,et al.Red blood cell distribution width and the risk of cardiovascular morbidity and all-cause mortality.A population-based study[J].Thromb Haemost,2014,111:300-307.
[37]GOLINO H F,AMARAL L S,DUARTE S F,GOMESC M,SOARES TDE J,DOS REIS L A,et al.Predicting increased blood pressure using machine learning[J/OL].J Obes,2014,2014:637635.doi:10.1155/2014/637635.