糖调节受损患者代谢组学结果与中医症候相关性的初步研究

【作者】 黄允瑜；

【导师】 赵进喜；

【作者基本信息】 北京中医药大学 ， 中医内科学， 2011， 博士

【摘要】 目的：探索初发糖尿病患者代谢组学结果与临床症状的相关性。方法：首先,通过对社区1539人开展健康体检,根据结果选择既往无糖尿病病史,空腹静脉血糖在6.1.6.9mmol/L范围的患者共143人。通知其初步结果,同时告知OGTT试验注意事项,随后进行口服75g无水葡萄糖的OGTT试验,留取空腹和2小时血样,进行相关化验指标检测,同时留取身体测量数据,进行体检前一日饮食回顾性调查。根据OGTT结果,结合年龄、性别因素进行匹配,选择正常组、糖尿病前期组和糖尿病组各35例的空腹和2小时血清样本,采用超高效液相色谱-质谱分析技术进行代谢组学检测。所用仪器为美国Waters公司ACQUITY-超高效液相色谱系统和Waters公司Micromassmi-cro Q-Tof串级四级杆-时间飞行质谱。检测结果分别应用PCA、PLS-DA和OPLS-DA的方法进行分析。根据代谢组学结果,结合患者临床症状进行logistic分析,寻找两者之间的相关性,再检测两组之间的代谢产物差异。基线资料采用多组的方差分析。结果：1.3组之间性别差异无统计学意义,年龄、BMI、HbAlc、FPG、OGTT2小时血糖水平都存在具有统计学意义的差异。2.代谢组学检测结果：①组间比较,应用PCA方法分析未能有效的对3组人群z之间的空腹及OGTT2小时血样进行区分。应用PLS-DA和OPLS-DA的方法分析可以区分糖尿病组和正常组空腹血样,但不能区分OGTT2小时血样,也不能区分糖尿病前期组和糖尿病组空腹及OGTT2小时的血样,不能区分糖尿病前期组和正常组空腹及OGTT2小时的血样。②组内空腹和OGTT2小时血样的比较,3组空腹和OGTT2小时的血样检测结果应用PCA分析发现变化方向基本一致。③单组血样分析结果,应用PCA分析发现糖尿病组空腹血样的代谢组学检测结果可以区分为两个亚组,代谢物鉴定发现两亚组之间血中的LPC、色氨酸和苯丙氨酸水平差异有统计学意义。其他单组采用PCA、PLS-DA和OPLS-DA的方法均未能获得有效的区分。3.对糖尿病组两个亚组之间的中医症状应用logistic分析后发现腰腿怕冷和夜尿频的症状组合在两组之间的差异具有统计学意义。对比后发现,糖尿病组肾阳虚亚组的色氨酸和苯丙氨酸水平下调,LPC水平上调。结论：1.依据OGTT结果可以将人群区分为3个组,但是采用代谢组学检测的方法只能区分正常组和糖尿病组的空腹血样,不能将糖尿病前期组的空腹及OGTT2小时血样从正常组和糖尿病组的血样中区分出来。这种现象提示我们,也许处于不同的疾病诊断状态的人群,由于疾病程度、其他状态较为接近,其代谢状态也可以表现为相似的状态。2.正常组及糖尿病前期组的代谢水平较为接近,尚未能发现有效的区分,单组分析也未能有效的区分为不同亚组。但是初发糖尿病患者的代谢水平具有差异,应用PCA分析可以有效的将糖尿病组的空腹血样区分为2个亚组,并且和临床症状有一定的相关性,分析发现为肾阳虚亚组和非肾阳虚亚组。3.糖尿病组空腹血样的2个亚组,初步检测分析表明,两组血样的苯丙氨酸、色氨酸和LPC3个代谢产物水平差异具有统计学意义,肾阳虚亚组的苯丙氨酸和色氨酸水平下调,LPC水平上调,由苯丙氨酸和色氨酸的代谢途径可知,苯丙氨酸是人体内合成儿茶酚胺类物质及甲状腺物质的底物,色氨酸是人体内合成褪黑激素的底物,因此苯丙氨酸和色氨酸水平的下调和肾阳虚证之间的联系具有生物学意义。说明2型糖尿病肾阳虚证具有一定的代谢基础。更多还原

【Abstract】 Objective:To explore the correlations between the clinical manifestations and metabonomics profiles in primary type 2 diabetes mellitus (T2DM) patients.Methods:In the 1539 persons who accepted the health examination in Hang-xing Community,143 cases with Impaired fasting glucose (fasting venous blood glucose ranged from 6.1 to 6.9 mmol/L) were detected and recruited in this study. After then an OGTT was performed for every patient, blood serum sample was collected at 2 time spots:fasting and 2 hours after 75g glucose being administrated orally. T2DM relative routine laboratory parameters were tested, and body measured data and diet survey for proxima luce were collected at the same time with OGTT. According to the OGTT, the patients were divided into 3 groups with diagnosis of health, pre-diabetic state, and diabetes mellitus, respectively.35 cases in each group were included and matched each other by age and gender. For the metabolomics investigations blood serum samples were collected after an overnight fasting and 2 hours after glucose load under standardized conditions and immediately stored in aliquots at-80 C. The UPLC system was coupled to a q TOF-MS (Micromass, Manchester, UK) equipped with an electrospray source operat-ing in either positive or negative ion mode, produced by Waters Corporation in USA. The pre-processed UPLC-qTOF-MS data were exported into Soft Indepen-dent Modelling of Class Analogy (SIMCA)-P (version 11.0, Umetrics AB, Umea, Sweden) for analysis and visualization by multivariate statistical methods. After Pareto scaling and OSC-filtering, principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal signal correction PLS-DA (OPLS-DA) were applied. The predictive ability of the model was assess-ed by internal validation using 7-fold cross-validation and response permutation testing. The correlation between clinical manifestations and matabonomics profil-es was analyzed with logistic regression approach, performed on SAS9.1.3 soft-ware (No.195557). Statistical significance was set at P\0.05. For the detection of metabolite ion masses with major influence on the group membership the S-plot was used according to. The metabolite heat map was generated using MultiExperi-ment View V4.1 (www.tm4.org). Results:The baseline data showed no significant difference in gender among the 3 groups, but significantly different in age, BMI, HbA1c, fasting plasma glucose, OGTT 2 hours plasma glucose.The metabonomics analysis revealed that:PCA can’t distinguish the 3 groups clearly by the fasting and OGTT 2 hours blood serum samples. The healthy and diabetic individuals could be distinguished by PLS-DA and OPLS-DA with fasting blood samples but negatively discerned with OGTT 2 hours blood serum samples; the pre-diabetic state cases can’t be distinguished from neither health cases nor DM cases. PCA approach indicated there existed similar shifting directions from fasting to OGTT 2 hours state in all 3 groups. Two subgroups were identified by PCA in T2DM groups, further analysis proved that between the 2 subgroups, the principle distinctive metabolites were LPC, tryptophan and phenylalanine. No positive discovery was obtained in healthy and T2DM groups analysis by PCA, PLS-DA and OPLS-DA.Logistic regression analysis manifested that there were significant difference in the occurrence of TCM symptoms combined with "fearing of cold in low back and legs" and "frequent nocturia". These two symptoms were regarded as the symbol of Kidney Yang deficiency pattern according to TCM theory. In the sub-group with kidney Yang deficiency pattern diagnosis, tryptophan and pheny-lalanine were down regulated, and LPC was up regulated.Conclusions:1) OGTT can tell the difference among the 3 groups, yet metabonomics analysis approach can’t distinguish the pre-diabetic patients from healthy and T2DM patients, neither by fasting blood serum samples nor OGTT 2 hours samples. This result posed us a hypothesis that although diagnosed with different diseases period, the patients might have similar metabolic state due to similar disease states and environment.2) The metabolic states were similar in the cases of healthy and pre-diabetic groups, non-effective approaches were explored to find differences between these 2 groups. However, primary T2DM led to different metabolic state, which could be detected by metabonomics analysis, and PCA could distinguish T2DM individuals into 2 subgroups by the fasting blood samples. The two subgroups could be identified as kidney Yang deficiency group and non-kidney Yang deficiency group according to TCM theory.3) Between the 2 subgroups of T2DM cases, there were 3 metabolites showed different distribution: LPC, phenylalanine and tryptophan. In kidney Yang deficiency group, LPC was up regulated and phenylalanine and tryptophan were down regulated. Phenylalanine acts as the substrate in synthesis of catecholamines and thyroid hormones, trypto-phan is substrate in synthesis of melatonin, which supported the results that there were correlation between the down-regulation of phenylalanine and tryptophan with kidney Yang deficiency pattern in biomedical way. In all, there should exist the metabolic change in the development of kidney Yang deficiency pattern in T2DM patients.更多还原