探讨新诊断的2型糖尿病患者血清Betatrophin浓度与代谢参数的相关性及其预测作用
发布时间：2018-09-03 12:31
【摘要】：背景及目的：糖尿病的患病率逐年上升,成为当今威胁人类健康的重要慢性疾病之一。据IDF统计,2011年全球糖尿病患病总人数达3.66亿,预期至2030年将增加至5.52亿(患病率达7.7%),而这其中又以亚洲地区的增长速度最为突出。最新流行病学调查研究显示,我国成人糖尿病患病率已达11.6%,总患病人数达1.14亿,跃居世界第一位。2型糖尿病占糖尿病患病总数的90%,主要表现为胰岛p细胞功能受损以及胰岛素抵抗。传统的口服降糖药以及胰岛素注射等方式虽然能够有效地改善糖尿病患者高血糖状态,却不能修复或补充受损的胰岛μ细胞,从根本上延缓疾病的进展。修复受损的胰岛μ细胞、增加胰岛p细胞的数量、改善机体胰岛素抵抗状态,成为治疗糖尿病的理想途径。在糖尿病发病初期针对病理生理学发病机制进行防治,可以在有效改善患者血糖谱的同时改善机体胰岛素抵抗状态,延缓疾病的进展及相关并发症的发生。Betatrophin是一种由193个氨基酸组成的内分泌蛋白,高表达于胰岛素受体拮抗剂S961诱导的严重胰岛素抵抗小鼠模型肝脏组织内,并能以剂量依赖的方式促进小鼠胰岛p细胞大量增殖。因其能促进胰岛p细胞增殖并参与机体血脂调节等不同作用,分别被命名为：betatrophin、ANGPTL8、RIFL以及Lipasin等。目前研究结果表明,人体血清betatrophin浓度与糖尿病、肥胖以及血脂谱之间的关系仍然存在诸多分歧。部分临床研究发现糖尿病、胰岛素抵抗以及血脂异常患者循环血清betatrophin浓度发生改变,并随着种族以及人群差异其变化趋势不尽相同；与此同时,一些基础研究却发现betatrophin并不能刺激体外培养的人类胰岛p细胞增殖,不参与人体血糖稳态的调节。作为一种新发现的促代谢因子,betatrophin在新诊断的中国2型糖尿病患者体内的表达情况仍未被阐明,循环血清betatrophin浓度与血糖、血脂、胰岛素抵抗以及各代谢指标的关系也不明确,betatrophin可否作为预测2型糖尿病的一个生化指标,这些问题都亟待解决。有鉴于此,本研究通过探讨了新诊断的2型糖尿病患者与非糖尿病人群循环血清betatrophin浓度及其与各代谢指标的相关关系,旨在寻求betatrophin与2型糖尿病、胰岛素抵抗以及其他代谢参数之间的关系,并探索betatrophin浓度对中国人群2型糖尿病的可能预测作用,以期为临床以及基础研究提供依据。方法：本研究共纳入131例研究对象,其中新诊断2型糖尿病患者73例,男性38例,女性35例,均来自于广东省广州市南方医科大学珠江医院内分泌科2014年8月至2014年12月的门诊及住院病人；另外按照年龄、性别、体质指数(BMI)匹配,入选了58例非糖尿病受试者作为对照,其中男性31例,女性27例,均来源于同时期南方医科大学北(?)医院体检中心。糖尿病组和对照组分别按照BMI各分为正常体重(BMI24kg/m2)、超重(24kg/m2≤BMI28kg/m2)以及肥胖(BMI≥28kg/m2)三个亚组。排除：1)正在接受口服降糖药治疗以及合并大血管病变的2型糖尿病(T2DM)患者；2)近1个月内服用过可能影响血糖和血脂药物的患者；3)其他类型糖尿病患者；4)病毒性肝炎、肝衰竭、肿瘤、严重精神障碍、接受血液透析的慢性肾功能不全、充血性心力衰竭及其他严重疾病的患者。该项目符合赫尔辛基宣言伦理准则标准并获得珠江医院伦理委员会同意,所有患者均签署知情同意书。采用体格检查以及问卷调查表格的形式,由经过统一培训的医务人员收集受试者的一般人口学资料,包括现病史、既往史、家族史以及用药史等。详细询问每位受试者近半年来有无稳定或不稳定型心绞痛病史、心肌梗塞病史、脑卒中或其他脑血管意外病史,以排除不合格的患者；询问患者吸烟史(每日吸烟量、持续时间)、饮酒史(每日饮酒量、持续时间)、高血压病史、血脂异常病史；询问患者既往用药史。由经过统一培训的医务人员对每位受试者行体格检查,记录受试者体重、身高、血压、腰围、臀围以及心率等。采集所有受试者清晨空腹(禁食12小时)肘前静脉血液标本3.0ml,常温静置2小时,离心机分离血清(2000-3000转/分,20min),自然凝固10-20min后,提取血清于EP管内,编号、封口后置于-80℃低温冰箱保存,以待检测指标时使用。检测每位受试者空腹血糖(FPG)、胰岛素(FINs)、C-肽、总胆固醇(TC)、甘油三酯(TG)、低密度脂蛋白胆固醇(LDL-C)、高密度脂蛋白胆固醇(HDL-C)、尿素氮(BUN)、肌酐(Cr)、谷丙转氨酶(ALT)、谷草转氨酶(AST)以及尿酸(UA)水平。采用稳态模型胰岛素抵抗指数(HOMA-IR)、稳态模型胰岛素分泌指数(HOMA-%p)以及定量胰岛素敏感性检测指数(QUICKI)评价β细胞功能。HOMA-IR的计算公式为：胰岛素(mIU/1)*血糖(mmol/1)/22.5(胰岛素和血糖采用的单位下同)；HOMA-%β的计算公式为：20*胰岛素/(血糖-3.5)；QUICKI指数=1/[log(FINs) l+log(FPG)] (FINs单位采用μU/ml, FPG单位采用mg/dl)。血清Betatrophin浓度采用中国武汉伊艾博科技有限公司生产的ELISA试剂盒测定(中国武汉,产品批号No.E11644h),严格按照说明书的指示对每份血样重复测量两次,变异指数大于15%的样本弃除。在450nm波长下采用酶联仪检测各样本分光光度值(OD),每份标本检测两次,取平均值计算最终OD值。以标准物的浓度为横坐标,平均OD值为纵坐标,绘制标准曲线。依据标准曲线计算出每份血样betatrophin的浓度。统计学运用SPSS 20.0软件进行统计分析。K-S检验用于检测资料的正态性与否。正态分布的定量资料采用x±s表示,组间比较采用两独立样本t检验或者ANOVA方差分析；非正态分布的资料采用中位数及四分位间距表示,组间比较采用Mann-Whitney U检验。计数资料以百分数表示,组间比较采用x2检验及趋势分析。采用Pearson相关分析分析betatrophin与各代谢指标的相关性。以P0.05为差异有统计学意义。采用人工受试者曲线分析血清betatrophin浓度对2型糖尿病的预测作用。采用Youden指数评估最佳切点,Youden指数的计算公式为：特异性+敏感性-1结果：第一节两组患者的一般临床资料对比本研究共纳入131例研究对象,2型糖尿病患者73例以及年龄、性别、BMI匹配的非糖尿病对照组58例。两组患者在年龄、性别、BMI、WHR、TG、LDL-C、 ALT.AST水平上均没有显著差异(P0.05)。2型糖尿病组患者血清betatrophin浓度是对照组患者的1.8倍(中位数：747.12pg/ml vs.407.41pg/ml,P0.001).第二节各亚组血清betatrophin浓度的表达情况2.1按照BMI指数将T2DM组、对照组各分为正常体重、超重和肥胖三个亚组：结果显示对照组中超重和肥胖患者血清betatrophin浓度显著高于正常体重者(中位数：肥胖的对照组vs.超重的对照组vs.正常体重的对照组：592.02pg/ml vs.501.57pg/ml vs.155.39 pg/ml, P0.05),而T2DM组患者中仅肥胖患者betatrophin浓度升高(1003.28pg/ml)。肥胖的T2DM组患者血清betarophin浓度是对照组中正常体重者的6.5倍(中位数：肥胖的糖尿病患者vs.正常体重的非糖尿病患者：1003.28 pg/ml vs.155.29 pg/ml, P0.001)。2.2按照腰围水平将T2DM组、对照组各分为腹型肥胖和非腹型肥胖两个亚组：结果显示腹型肥胖组患者血清betatrophin浓度显著高于非腹型肥胖组(中位数：腹型肥胖的糖尿病组vs.非腹型肥胖的糖尿病组vs.腹型肥胖的对照组vs.非腹型肥胖的对照组：1014.89pg/ml vs.551.17pg/ml vs.658.30 pg/ml vs.321.31pg/ml, P0.05)。腹型肥胖的糖尿病患者血清betarophin浓度是非腹型肥胖对照组患者的3.2倍(中位数：腹型肥胖的糖尿病组vs.非腹型肥胖的对照组：1014.89pg/ml vs.321.31pg/ml, P0.001)。第三节血清betatrophin浓度与各代谢指标的相关性3.1血清betatrophin浓度与血糖水平的相关性：对两组患者血清betatrophin浓度与各代谢指标行双变量相关分析,结果提示对照组患者血清betatrophin浓度与FPG水平正相关(P0.05)；而在T2DM组中,betatrophin浓度与FPG没有相关性(P0.05)。3.2血清betatrophin浓度与血脂水平的相关性：双变量相关分析显示,血清betatrophin浓度与TG、TC以及LDL-C水平无显著相关性(P0.05),与HDL-C水平呈负相关(P0.05),按照BMI水平分为6个亚组后,血清betatrophin浓度均与HDL-C水平呈负相关(P0.05)。3.3血清betatrophin浓度与其他各代谢指标的相关性：双变量相关分析显示,对照组患者血清betatrophin浓度与WHR、FINs、C肽、HOMA-IR以及UA水平正相关,与QUICKI负相关(P0.05)；而在T2DM组中,betatrophin浓度仅与年龄呈现出相关性(P0.05)。第四节血清betatrophin浓度对2型糖尿病的预测作用采用ROC曲线评估血清betatrophin浓度对T2DM的预测作用。结果显示预测T2DM的血清betatrophin浓度最佳切点是501.23pg/ml,曲线下面积为0.82(95%CI,0.748-0.885,P0.001),敏感性为83.56%,特异性为72.41%。结论：我们的横断面调查显示,相较于年龄、性别以及BMI匹配的对照组,2型糖尿病和肥胖患者体内血清betatrophin浓度显著上升。相比对照组,新诊断的2型糖尿病患者血清betatrophin浓度上升1.8倍,而肥胖的2型糖尿病患者其浓度是正常体重的对照组的6.5倍,腹型肥胖的2型糖尿病患者血清betarophin浓度是非腹型肥胖对照组患者的3.2倍。与此同时我们的研究首次发现健康人群以及2型糖尿病患者体内HDL-C水平与betatrophin呈负相关。通过ROC曲线分析,我们进一步发现betatrophin或可作为预测2型糖尿病的一个生化指标,其最佳切点为501.23pg/ml。鉴于目前已有的关于betatrophin在人体及动物实验的研究结果,结合本研究结果,认为中国新诊断的2型糖尿病患者血清betatrophin浓度升高；同时在新诊断的2型糖尿病以及健康人群中血清betatrophin与HDL-C水平呈负相关；另外,在中国人群中betatrophin或可作为预测2型糖尿病的一个生化指标。
[Abstract]:BACKGROUND & OBJECTIVE: The prevalence of diabetes mellitus is increasing year by year, and it has become one of the most important chronic diseases threatening human health. According to IDF statistics, the total number of diabetes mellitus cases worldwide in 2011 will reach 366 million, and it is expected to increase to 552 million by 2030 (the prevalence rate is 7.7%). Among them, the latest epidemic is in Asia. Investigation shows that the prevalence of adult diabetes mellitus in China has reached 11.6%, and the total number of diabetic patients has reached 114 million, ranking the first in the world. Type 2 diabetes accounts for 90% of the total number of diabetes mellitus, mainly manifested by impaired pancreatic P-cell function and insulin resistance. Diabetes mellitus patients with hyperglycemia can not repair or replenish damaged islet mu cells, fundamentally delaying the progress of the disease. Repairing damaged islet mu cells, increasing the number of islet P cells, improving insulin resistance, become an ideal way to treat diabetes mellitus. Betatrophin, an endocrine protein consisting of 193 amino acids, is highly expressed in the liver of a mouse model of severe insulin resistance induced by insulin receptor antagonist S961. In vivo and in a dose-dependent manner, it can promote the proliferation of mouse pancreatic islet P cells. Because it can promote the proliferation of pancreatic islet P cells and participate in the regulation of blood lipids and other different roles, they were named as: betatrophin, ANGPTL8, RIFL and Lipasin. Some clinical studies have found that the circulating serum betatrophin levels in patients with diabetes, insulin resistance, and dyslipidemia vary with race and population; at the same time, some basic studies have found that betatrophin does not stimulate in vitro culture. As a newly discovered pro-metabolic factor, betatrophin expression in newly diagnosed Chinese type 2 diabetes mellitus has not been elucidated. The relationship between betatrophin concentration in circulating serum and blood glucose, blood lipids, insulin resistance and metabolic parameters is not clear. Whether etatrophin can be used as a biochemical marker for predicting type 2 diabetes mellitus is an urgent problem. In view of this, this study explored the relationship between betatrophin concentration in circulating serum and metabolic markers in newly diagnosed type 2 diabetes mellitus and non-diabetic people, in order to seek betatrophin and type 2 diabetes mellitus, insulin. Methods: A total of 131 subjects were included in this study, including 73 newly diagnosed type 2 diabetes mellitus patients, 38 males and 35 females, all from Guangdong Province. In addition, 58 non-diabetic subjects (31 males and 27 females) were selected as controls according to age, sex and body mass index (BMI) matching. They all came from the physical examination center of the North (?) Hospital of Southern Medical University at the same time. Urinary disease group and control group were divided into normal body weight (BMI 24kg/m2), overweight (24kg/m2 < BMI 28kg/m2) and obesity (BMI < 28kg/m2). Exclusion: 1) Type 2 diabetes mellitus (T2DM) patients who were receiving oral hypoglycemic therapy and complicated with macrovascular disease were taking drugs that may affect blood glucose and blood lipids within 1 month; Patients with viral hepatitis, liver failure, tumors, severe mental disorders, chronic renal insufficiency undergoing hemodialysis, congestive heart failure, and other serious diseases. The project met the ethical standards of the Helsinki Declaration and was approved by the Pearl River Hospital Ethics Committee. Physical examination and questionnaires were used to collect general demographic data, including current, past, family and medication history. Each subject was asked whether he had a history of stable or unstable angina and myocardial infarction in the past six months. History of seizure, stroke, or other cerebrovascular accident to exclude unqualified patients; history of smoking (daily smoking, duration), drinking (daily drinking, duration), hypertension, and history of dyslipidemia; history of past medication; body movements of each subject by uniformly trained medical staff The body weight, height, blood pressure, waist circumference, hip circumference and heart rate were recorded. Blood samples of anterior cubital vein were collected from all subjects on an empty stomach (fasting for 12 hours) in the morning. Serum was separated by centrifuge (2000-3000 rpm, 20 min). After natural coagulation for 10-20 min, the serum was extracted into EP tube and numbered. The fasting blood glucose (FPG), insulin (FINs), C-peptide, total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), urea nitrogen (BUN), creatinine (Cr), alanine aminotransferase (ALT), glutamic oxaloacetyltransferase (AST) and uric acid (UA) levels were measured in each subject. The homeostasis model insulin resistance index (HOMA-IR), homeostasis model insulin secretion index (HOMA-% p) and quantitative insulin sensitivity index (QUICKI) were used to evaluate the function of beta cells. 20 * Insulin / (blood glucose - 3.5); QUICKI index = 1 /[log (FINs) L + log (FPG)] (FINs unit using mu U / ml, FPG unit using mg / dl). Serum Betatrophin concentration was determined by ELISA kit produced by Wuhan Yiebo Technology Co., Ltd. (Wuhan, China, product batch No. E11644h), and repeated strictly according to the instructions of the manual for each blood sample. Samples with a variation index greater than 15% were measured twice and discarded. At 450 nm wavelength, the spectrophotometric value (OD) of each sample was detected by ELISA, and the final OD value was calculated by averaging the OD value of each sample twice. Statistical analysis was carried out by SPSS 20.0 software. K-S test was used to test the normality of data. Quantitative data of normal distribution were expressed by X + s. Intergroup comparisons were performed by two independent samples t-test or ANOVA analysis of variance. Data of non-normal distribution were expressed by median and quartile spacing. Intergroup comparisons were performed by Ma. Nn-Whitney U test. Counting data were expressed as percentage. Comparison between groups was performed by x2 test and trend analysis. Pearson correlation analysis was used to analyze the correlation between betatrophin and metabolic indices. The difference was statistically significant (P 0.05). The best cut-off point of en index was evaluated by Youden index. The formula of Youden index was: specificity + sensitivity - 1 Results: Section 1 Comparison of general clinical data between the two groups. This study included 131 subjects, 73 patients with type 2 diabetes mellitus and 58 non-diabetic controls matched by age, sex, and BMI. There was no significant difference in the levels of DL-C and ALT-AST (P 0.05). The serum betatrophin levels in type 2 diabetes mellitus patients were 1.8 times higher than those in control group (median: 747.12 pg/ml vs. 407.41 pg/ml, P 0.001). The expression of betatrophin serum in each subgroup in Section 2.1 was divided into normal weight, overweight and fat groups according to BMI index. The results showed that the serum betatrophin levels in overweight and obese patients in the control group were significantly higher than those in the normal weight group (median: obese vs. overweight vs. normal weight vs. 592.02 pg/ml vs. 501.57 pg/ml vs. 155.39 pg/ml, P 0.05), whereas in the T2DM group, only obese patients had betatrophin concentrations. The serum betarophin level in obese T2DM group was 6.5 times higher than that in normal weight control group (median: obese diabetes vs. normal weight non-diabetic: 1003.28 pg / ml vs. 155.29 pg / ml, P 0.001). 2.2 According to waist circumference level, T2DM group was divided into abdominal obesity and non-abdominal obesity groups. Two subgroups: the results showed that the serum betatrophin concentration in abdominal obesity group was significantly higher than that in non-abdominal obesity group (median: abdominal obesity group vs. non-abdominal obesity group vs. abdominal obesity control group vs. non-abdominal obesity control group vs. 1014.89pg/ml vs. 551.17pg/ml vs. 658.30 pg/ml vs. 321.31pg/ml, P 0.05). (Median: abdominal obesity vs. abdominal obesity vs. non-abdominal obesity vs. 321.31pg/ml, P 0.001). Section 3. Correlation between serum betatrophin concentration and metabolic parameters Correlation between serum betatrophin level and serum FPG level: Bivariate correlation analysis between betatrophin concentration and metabolic parameters in two groups showed that there was a positive correlation between betatrophin concentration and serum FPG level in control group (P 0.05), while there was no correlation between betatrophin concentration and FPG level in T2DM group (P 0.05). Correlation: Bivariate correlation analysis showed that serum betatrophin levels were not significantly correlated with TG, TC and LDL-C levels (P 0.05), but negatively correlated with HDL-C levels (P 0.05). After divided into six subgroups according to BMI levels, serum betatrophin levels were negatively correlated with HDL-C levels (P 0.05). 3.3 Serum betatrophin levels were negatively correlated with other metabolic parameters. Sex: Bivariate correlation analysis showed that the serum betatrophin concentration in the control group was positively correlated with WHR, FINs, C peptide, HOMA-IR and UA levels, and negatively correlated with QUICKI (P 0.05); in the T2DM group, betatrophin concentration was only correlated with age (P 0.05). The best cut-off point for predicting serum betatrophin concentration was 501.23 pg/ml, the area under the curve was 0.82 (95% CI, 0.748-0.885, P 0.001), the sensitivity was 83.56%, and the specificity was 72.41%. Conclusion: Our cross-sectional study showed that the betatrophin concentration was more sensitive than that of age, sex and BMI matched pairs. Serum betatrophin levels in newly diagnosed type 2 diabetes mellitus were 1.8 times higher than those in the control group, while those in obese type 2 diabetes mellitus were 6.5 times higher than those in normal weight, and those in abdominal obese type 2 diabetes mellitus were not. At the same time, our study found for the first time that there was a negative correlation between HDL-C levels and betatrophin in healthy people and type 2 diabetes mellitus. Through ROC curve analysis, we further found that betatrophin may be a biochemical marker for predicting type 2 diabetes mellitus. The best cut-off point was 501.23 pg/ml. Based on the results of the existing studies on betatrophin in human and animal, it is concluded that the serum betatrophin levels in newly diagnosed type 2 diabetes mellitus patients in China are elevated, and the serum betatrophin levels in newly diagnosed type 2 diabetes mellitus patients and healthy people are negatively correlated with HDL-C levels. Etatrophin can be used as a biochemical marker for predicting type 2 diabetes mellitus.
【学位授予单位】：南方医科大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R587.1
本文编号：2219988
[Abstract]:BACKGROUND & OBJECTIVE: The prevalence of diabetes mellitus is increasing year by year, and it has become one of the most important chronic diseases threatening human health. According to IDF statistics, the total number of diabetes mellitus cases worldwide in 2011 will reach 366 million, and it is expected to increase to 552 million by 2030 (the prevalence rate is 7.7%). Among them, the latest epidemic is in Asia. Investigation shows that the prevalence of adult diabetes mellitus in China has reached 11.6%, and the total number of diabetic patients has reached 114 million, ranking the first in the world. Type 2 diabetes accounts for 90% of the total number of diabetes mellitus, mainly manifested by impaired pancreatic P-cell function and insulin resistance. Diabetes mellitus patients with hyperglycemia can not repair or replenish damaged islet mu cells, fundamentally delaying the progress of the disease. Repairing damaged islet mu cells, increasing the number of islet P cells, improving insulin resistance, become an ideal way to treat diabetes mellitus. Betatrophin, an endocrine protein consisting of 193 amino acids, is highly expressed in the liver of a mouse model of severe insulin resistance induced by insulin receptor antagonist S961. In vivo and in a dose-dependent manner, it can promote the proliferation of mouse pancreatic islet P cells. Because it can promote the proliferation of pancreatic islet P cells and participate in the regulation of blood lipids and other different roles, they were named as: betatrophin, ANGPTL8, RIFL and Lipasin. Some clinical studies have found that the circulating serum betatrophin levels in patients with diabetes, insulin resistance, and dyslipidemia vary with race and population; at the same time, some basic studies have found that betatrophin does not stimulate in vitro culture. As a newly discovered pro-metabolic factor, betatrophin expression in newly diagnosed Chinese type 2 diabetes mellitus has not been elucidated. The relationship between betatrophin concentration in circulating serum and blood glucose, blood lipids, insulin resistance and metabolic parameters is not clear. Whether etatrophin can be used as a biochemical marker for predicting type 2 diabetes mellitus is an urgent problem. In view of this, this study explored the relationship between betatrophin concentration in circulating serum and metabolic markers in newly diagnosed type 2 diabetes mellitus and non-diabetic people, in order to seek betatrophin and type 2 diabetes mellitus, insulin. Methods: A total of 131 subjects were included in this study, including 73 newly diagnosed type 2 diabetes mellitus patients, 38 males and 35 females, all from Guangdong Province. In addition, 58 non-diabetic subjects (31 males and 27 females) were selected as controls according to age, sex and body mass index (BMI) matching. They all came from the physical examination center of the North (?) Hospital of Southern Medical University at the same time. Urinary disease group and control group were divided into normal body weight (BMI 24kg/m2), overweight (24kg/m2 < BMI 28kg/m2) and obesity (BMI < 28kg/m2). Exclusion: 1) Type 2 diabetes mellitus (T2DM) patients who were receiving oral hypoglycemic therapy and complicated with macrovascular disease were taking drugs that may affect blood glucose and blood lipids within 1 month; Patients with viral hepatitis, liver failure, tumors, severe mental disorders, chronic renal insufficiency undergoing hemodialysis, congestive heart failure, and other serious diseases. The project met the ethical standards of the Helsinki Declaration and was approved by the Pearl River Hospital Ethics Committee. Physical examination and questionnaires were used to collect general demographic data, including current, past, family and medication history. Each subject was asked whether he had a history of stable or unstable angina and myocardial infarction in the past six months. History of seizure, stroke, or other cerebrovascular accident to exclude unqualified patients; history of smoking (daily smoking, duration), drinking (daily drinking, duration), hypertension, and history of dyslipidemia; history of past medication; body movements of each subject by uniformly trained medical staff The body weight, height, blood pressure, waist circumference, hip circumference and heart rate were recorded. Blood samples of anterior cubital vein were collected from all subjects on an empty stomach (fasting for 12 hours) in the morning. Serum was separated by centrifuge (2000-3000 rpm, 20 min). After natural coagulation for 10-20 min, the serum was extracted into EP tube and numbered. The fasting blood glucose (FPG), insulin (FINs), C-peptide, total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), urea nitrogen (BUN), creatinine (Cr), alanine aminotransferase (ALT), glutamic oxaloacetyltransferase (AST) and uric acid (UA) levels were measured in each subject. The homeostasis model insulin resistance index (HOMA-IR), homeostasis model insulin secretion index (HOMA-% p) and quantitative insulin sensitivity index (QUICKI) were used to evaluate the function of beta cells. 20 * Insulin / (blood glucose - 3.5); QUICKI index = 1 /[log (FINs) L + log (FPG)] (FINs unit using mu U / ml, FPG unit using mg / dl). Serum Betatrophin concentration was determined by ELISA kit produced by Wuhan Yiebo Technology Co., Ltd. (Wuhan, China, product batch No. E11644h), and repeated strictly according to the instructions of the manual for each blood sample. Samples with a variation index greater than 15% were measured twice and discarded. At 450 nm wavelength, the spectrophotometric value (OD) of each sample was detected by ELISA, and the final OD value was calculated by averaging the OD value of each sample twice. Statistical analysis was carried out by SPSS 20.0 software. K-S test was used to test the normality of data. Quantitative data of normal distribution were expressed by X + s. Intergroup comparisons were performed by two independent samples t-test or ANOVA analysis of variance. Data of non-normal distribution were expressed by median and quartile spacing. Intergroup comparisons were performed by Ma. Nn-Whitney U test. Counting data were expressed as percentage. Comparison between groups was performed by x2 test and trend analysis. Pearson correlation analysis was used to analyze the correlation between betatrophin and metabolic indices. The difference was statistically significant (P 0.05). The best cut-off point of en index was evaluated by Youden index. The formula of Youden index was: specificity + sensitivity - 1 Results: Section 1 Comparison of general clinical data between the two groups. This study included 131 subjects, 73 patients with type 2 diabetes mellitus and 58 non-diabetic controls matched by age, sex, and BMI. There was no significant difference in the levels of DL-C and ALT-AST (P 0.05). The serum betatrophin levels in type 2 diabetes mellitus patients were 1.8 times higher than those in control group (median: 747.12 pg/ml vs. 407.41 pg/ml, P 0.001). The expression of betatrophin serum in each subgroup in Section 2.1 was divided into normal weight, overweight and fat groups according to BMI index. The results showed that the serum betatrophin levels in overweight and obese patients in the control group were significantly higher than those in the normal weight group (median: obese vs. overweight vs. normal weight vs. 592.02 pg/ml vs. 501.57 pg/ml vs. 155.39 pg/ml, P 0.05), whereas in the T2DM group, only obese patients had betatrophin concentrations. The serum betarophin level in obese T2DM group was 6.5 times higher than that in normal weight control group (median: obese diabetes vs. normal weight non-diabetic: 1003.28 pg / ml vs. 155.29 pg / ml, P 0.001). 2.2 According to waist circumference level, T2DM group was divided into abdominal obesity and non-abdominal obesity groups. Two subgroups: the results showed that the serum betatrophin concentration in abdominal obesity group was significantly higher than that in non-abdominal obesity group (median: abdominal obesity group vs. non-abdominal obesity group vs. abdominal obesity control group vs. non-abdominal obesity control group vs. 1014.89pg/ml vs. 551.17pg/ml vs. 658.30 pg/ml vs. 321.31pg/ml, P 0.05). (Median: abdominal obesity vs. abdominal obesity vs. non-abdominal obesity vs. 321.31pg/ml, P 0.001). Section 3. Correlation between serum betatrophin concentration and metabolic parameters Correlation between serum betatrophin level and serum FPG level: Bivariate correlation analysis between betatrophin concentration and metabolic parameters in two groups showed that there was a positive correlation between betatrophin concentration and serum FPG level in control group (P 0.05), while there was no correlation between betatrophin concentration and FPG level in T2DM group (P 0.05). Correlation: Bivariate correlation analysis showed that serum betatrophin levels were not significantly correlated with TG, TC and LDL-C levels (P 0.05), but negatively correlated with HDL-C levels (P 0.05). After divided into six subgroups according to BMI levels, serum betatrophin levels were negatively correlated with HDL-C levels (P 0.05). 3.3 Serum betatrophin levels were negatively correlated with other metabolic parameters. Sex: Bivariate correlation analysis showed that the serum betatrophin concentration in the control group was positively correlated with WHR, FINs, C peptide, HOMA-IR and UA levels, and negatively correlated with QUICKI (P 0.05); in the T2DM group, betatrophin concentration was only correlated with age (P 0.05). The best cut-off point for predicting serum betatrophin concentration was 501.23 pg/ml, the area under the curve was 0.82 (95% CI, 0.748-0.885, P 0.001), the sensitivity was 83.56%, and the specificity was 72.41%. Conclusion: Our cross-sectional study showed that the betatrophin concentration was more sensitive than that of age, sex and BMI matched pairs. Serum betatrophin levels in newly diagnosed type 2 diabetes mellitus were 1.8 times higher than those in the control group, while those in obese type 2 diabetes mellitus were 6.5 times higher than those in normal weight, and those in abdominal obese type 2 diabetes mellitus were not. At the same time, our study found for the first time that there was a negative correlation between HDL-C levels and betatrophin in healthy people and type 2 diabetes mellitus. Through ROC curve analysis, we further found that betatrophin may be a biochemical marker for predicting type 2 diabetes mellitus. The best cut-off point was 501.23 pg/ml. Based on the results of the existing studies on betatrophin in human and animal, it is concluded that the serum betatrophin levels in newly diagnosed type 2 diabetes mellitus patients in China are elevated, and the serum betatrophin levels in newly diagnosed type 2 diabetes mellitus patients and healthy people are negatively correlated with HDL-C levels. Etatrophin can be used as a biochemical marker for predicting type 2 diabetes mellitus.
【学位授予单位】：南方医科大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R587.1
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