CN112365979A - 一种处置指数di估算模型的建立方法 - Google Patents
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Abstract
本发明涉及一种处置指数DI估算模型的建立方法,包括:将年轻肥胖人群作为建模人群,在建模人群中利用相关性分析评价Log(DI)与基线各指标间的关系,在建模人群中根据单因素回归分析筛选与DI相关的变量进入多重线性回归分析中,共筛选出空腹血糖FPG、空腹胰岛素FINS水平、丙氨酸氨基转移酶ALT、收缩压SBP四个变量进入方程中,并建立DI的估算方程。该估算模型在建模人群及验证人群中与实测的DI相关程度较高,并且具有较好的区分度和校准度。
Description
技术领域
本发明属于糖尿病预测指标领域,特别涉及一种处置指数DI估算模型的建立方法。
背景技术
2型糖尿病是肥胖的常见并发症。流行病学研究显示,截至2013年,我国糖尿病患病率高达10.9%,患病人数达1.164亿。胰岛素分泌功能障碍及胰岛素敏感性降低是2型糖尿病发生的两个主要因素。我中心最新的前瞻性临床研究发现,中国人群的糖尿病发生归因于:肥胖导致的胰岛素抵抗(24.4%),β细胞功能障碍(12.4%)。因此,胰岛素敏感性及胰岛素分泌功能是衡量胰岛细胞功能及血糖调节能力的重要指标。
Bergman等通过建立微小模型技术,分析多样本静脉葡萄糖耐量试验中的多点血浆葡萄糖和胰岛素浓度,可计算出胰岛素作用所致的葡萄糖清除定义为胰岛素敏感性指数SI,与第一时相胰岛素分泌反应用以表征胰岛素分泌功能AIRg。而定义处置指数DI为胰岛素敏感性指数与胰岛素分泌反应的乘积,用于衡量胰岛细胞功能,即胰岛β细胞代偿胰岛素抵抗的能力。研究证明,通过微小模型或OGTT计算所得的处置指数DI是2型糖尿病发生的良好预测因素。虽然该方法计算所得的处置指数DI的准确度较高,但多样本静脉葡萄糖耐量试验过程较为繁琐、用时较长、成本较高,许多基层医疗机构可能缺乏开展此项检查的条件与能力。因此,利用常用的生化指标建立处置指数DI的估算的模型存在必要性及临床应用价值。
发明内容
本发明所要解决的技术问题是提供一种处置指数DI估算模型的建立方法,以克服现有技术中处置指数DI的试验方法繁琐、用时较长、成本高的缺陷。
本发明提供一种处置指数DI估算模型的建立方法,包括:
(1)将年轻肥胖人群作为建模人群,建模人群中BMI≥30kg/m2的肥胖患者占85-86%,其中BMI30-40kg/m2的肥胖患者占60-70%;
(2)在建模人群中利用相关性分析评价Log(DI)与基线各指标间的关系,其中基线指标包括:人体测量学指标,血液生化指标,糖代谢指标,腹部脂肪指标;
(3)在建模人群中根据单因素回归分析筛选与DI相关的变量进入多重线性回归分析中,共筛选出空腹血糖FPG、空腹胰岛素FINS水平、丙氨酸氨基转移酶ALT、收缩压SBP四个变量进入方程中,并建立DI的估算方程:
Log(DI)=15.283-0.922*FPG-0.485*Log(FINS)-0.386*Log(ALT)-0.015*SBP。
所述步骤(1)中建模人群年龄在14-45岁间,建模人群平均年龄为20-28岁。
所述步骤(1)中BMI=体重/身高2(kg/m2)。
所述步骤(1)中建模人群中9.5-9.8%为糖尿病患者。
所述步骤(2)中人体测量学指标包括:身高,体重,腰围,臀围和静息血压。
所述步骤(2)中血液生化指标包括空腹血糖及胰岛素、糖化血红蛋白、炎症指标(hsCRP)、肝肾功能(ALT、AST、γ-GT、Cr、UA、FFA等)和血脂(TC、TG、HDL、LDL等)。
所述步骤(2)中糖代谢指标是由减少样本数的Bergman微小模型技术结合多样本静脉葡萄糖耐量试验(FSIVGTT)和其他简易糖代谢评估得到。
所述步骤(2)中腹部脂肪指标包括腹部内脏脂肪面积(VAT)、腹部皮下脂肪面积(SAT)、腹部总脂肪面积(TAT)和内脏脂肪面积比例(VAT/TAT)。
有益效果
本发明利用简单常用的基线生化指标对处置指数DI进行估算,将空腹血糖、空腹胰岛素、丙氨酸氨基转移酶及收缩压水平这四个指标纳入DI估算模型,该模型主要针对年轻肥胖群体,该模型在建模人群及验证人群中与实测的DI相关程度较高,并且具有较好的区分度和校准度。
附图说明
图1为本发明实施例1中估算Log(DI)与实际Log(DI)在建模组(A)及验证组(B)的相关性。
图2为本发明实施例1中估算Log(DI)区分胰岛细胞功能优劣的ROC曲线。
具体实施方式
下面结合具体实施例,进一步阐述本发明。应理解,这些实施例仅用于说明本发明而不用于限制本发明的范围。此外应理解,在阅读了本发明讲授的内容之后,本领域技术人员可以对本发明作各种改动或修改,这些等价形式同样落于本申请所附权利要求书所限定的范围。
实施例1
1.研究对象
选取2008年5月到2019年12月间在上海瑞金医院肥胖专病门诊就诊或内分泌代谢病科住院的超重及肥胖患者及正常体重健康志愿者共1047人。以系统抽样法随机选取75%入组患者为模型建立组(共785人),剩余25%患者为模型验证组(共262人)。建模组和验证组两组的平均年龄为24.0和24.5岁,BMI为35.5±7.1和35.1±6.9kg/m2。本研究人群中BMI≥30kg/m2的肥胖患者占85.3%,其中BMI30-40kg/m2的肥胖患者占63.4%,以建立肥胖患者适用的估算模型为主要目的且BMI指数分布较为集中。本研究人群中9.6%为糖尿病患者。
(1)入组标准
a.中国国籍的汉族人群;
b.年龄在14至45岁间。
(2)排除标准
a.缺乏静脉糖耐量试验结果或其他重要生化检查结果的患者;
b.因其它继发因素引起的肥胖(如与肥胖相关的遗传性综合征、库欣综合征、药物所致的肥胖等)患者;
c.心、肝、肾及全身器官功能严重障碍的患者(NYHA心功能分级≥III级;ALT和/或AST升高至正常上限3倍以上;GFR<30ml/min);
d.近1月内使用减重或其他干预检查结果的药物的患者;
e.药物滥用、酒精成瘾或有精神障碍病史者。
2.研究方法
(1)人体测量学指标测定
身高和体重测量,腰围、臀围测量,静息血压测量,其中身高以厘米(cm)为单位,精确到0.1cm,体重以公斤(kg)为单位,精确到0.1kg。腰围和臀围测量2次取平均值,以厘米(cm)为单位,精确到0.1cm。
(2)血液生化指标检测
测量前患者隔夜禁食10小时以上,次日清晨留取空腹静脉血标本送检,送检项目包括空腹血糖及胰岛素、糖化血红蛋白、炎症指标(hsCRP)、肝肾功能(ALT、AST、γ-GT、Cr、UA、FFA等);血脂(TC、TG、HDL、LDL等)。
(3)糖代谢评估
减少样本数的Bergman微小模型技术结合多样本静脉葡萄糖耐量试验(FSIVGTT)和其他简易糖代谢评估指标(OMA-IR、HOMA-β等)。
减少样本数的Bergman微小模型技术结合多样本静脉葡萄糖耐量试验(FSIVGTT):患者检测前3天保持稳定的饮食习惯,每日摄入碳水化合物总量保持在250g以上。试验前隔夜禁食至少10小时,次日清晨7-8点于双侧肘静脉留置静脉套管针,并静卧15-30分钟以上,分别通过采样用静脉留置针于0、2、4、8、19、22、30、40、50、70、90和180分钟时留置血样,检测血糖及胰岛素水平。并在第0分钟后的2分钟内于静脉血采样的对侧静脉快速推注50%葡萄糖(以300mg/kg计算);在第20分钟后的1分钟内于静脉血采样的对侧静脉缓慢推注胰岛素(诺和灵R)(以0.03U/kg计算),推注结束后用3ml生理盐水冲管。将各点血糖与胰岛素测定数值输入Bergman MINIMOD计算机软件包中,计算出胰岛素敏感性指数(SI)用来评价胰岛素敏感性;急性胰岛素分泌反应(AIRg)用来评价胰岛β细胞分泌功能;以AIRg和SI的乘积定义为处置指数(DI)。
其他简易糖代谢评估方法:葡萄糖负荷后血糖曲线下面积(Area Under theCurve of Glucose,AUCG),以时间为横坐标,血糖值为纵坐标,计算以OGTT中各点血糖值连线与坐标轴围成的多边梯形的面积,反映血糖升高的程度,单位mmoll-1min。胰岛素抵抗相关指标:1)葡萄糖负荷后胰岛素曲线下面积(Area Under the Curve of Insulin,AUCI),以时间为横坐标,胰岛素值为纵坐标,计算以OGTT中各点胰岛素值连线与坐标轴围成的多边梯形的面积,反映胰岛素抵抗的程度,单位μUml-1min;2)Matsuda指数(Matsuda index)=10000/(FPG×FINS×meanPG×meanINS)0.5;3)自我平衡模型分析法(Homeostasis modelassessment,HOMA)中的HOMA-IR=FPG(mmol/L)×FINS(mIU/L)/22.5。胰岛β细胞功能相关指标:1)自我平衡模型分析法HOMA-β=20×FINS(mIU/L)/[FPG(mmol/L)-3.5]×100%;2)早期胰岛素分泌指数(ΔI30/ΔG30)=(0.5hIns-FINS)/(0.5hPG-FPG)。
(4)腹部脂肪定量测定
测量腹部内脏脂肪面积(VAT)、腹部皮下脂肪面积(SAT)及腹部总脂肪面积(TAT),内脏脂肪面积比例(VAT/TAT):患者行腹部CT平扫经软件识别计算各部位脂肪面积。CT扫描条件(Light speed QXi;GE Healthcare):管电压120kV,固定管电流,层厚10mm,旋转速度11.5毫米/转(HQ模式,螺距1:3),10mm增量重建。扫描时患者取仰卧位,于吸气时经脐水平第4-5腰椎之间扫描腹部横断面。取CT图像在Fat Scan(N2 System)软件中,设定脂肪组织CT值范围为0-50Hu,分别测量腹部内脏脂肪面积(VAT)、腹部皮下脂肪面积(SAT)及腹部总脂肪面积(TAT)。
(5)建模组及验证组一般临床资料比较
将入组人群按照系统抽样法随机抽取75%作为模型建立组,共785人,余下25%为验证组共262人。如表1所示,两组患者性别、年龄匹配,建模组与验证组的BMI为35.5±7.1kg/m2和35.1±6.9kg/m2,无明显差异(P=0.392)。两组的人体测量学指标、脂肪分布指标,如腰围、腰臀比、内脏脂肪面积及内脏脂肪比例均无明显差异。血压、肝酶等代谢指标也呈匹配状态。衡量糖代谢状态的FPG、FINS、HbA1c、HOMA-IR和HOMA-β组间同样无明显差异。微小模型分析的静脉葡萄糖耐量试验结果显示,两组胰岛素敏感性SI、急性胰岛素分泌能力AIRg与胰岛细胞功能指标处置指数DI均无明显差异。其中,建模组的中位DI为674.6,验证组的中位DI为629.8(P=0.757)。
表1建模组与验证组一般资料比较
正态分布指标以均数±标准差表示,非正态分布指标以中位数(四分位间距)表示。
数据采用SPSS 23.0统计学软件包分析。各项指标均进行正态分布检验,非正态分布的变量取对数转换后进行分析。所有正态分布的变量用均数±标准差(means±SD)描述,非正态分布的变量用中位数(四分位间距)描述,分类变量用百分率(%)表示。数值变量两组间比较采用独立样本t检验。
(6)DI与各指标相关性分析
在模型建立人群中利用相关性分析评价Log(DI)与基线各指标间的关系。如表2所示,处置指数DI与患者年龄、身高、BMI、人体测量学指标、脂肪分布、血压、血糖、血脂、肝酶等代谢指标均存在显著相关性。其中,处置指数DI与空腹血糖的相关性最高,相关性系数R=-0.427(P<0.001)。与空腹胰岛素水平的相关性为R=-0.248,P<0.001。在衡量身体成分的指标中,与BMI的相关性最高达R=-0.304(P<0.001)。在肝酶、血脂、炎症指标等生化指标中与ALT的相关性最高,R=-0.268,P<0.001。
表2 Log(DI)与基线各指标相关性
R:相关性系数
(7)DI多重线性回归分析及方程建立
在建模人群中根据单因素回归分析筛选与DI相关的变量进入多重线性回归分析中,共筛选出空腹血糖、空腹胰岛素水平、丙氨酸氨基转移酶、收缩压四个变量进入方程中,并建立DI的估算方程:Log(DI)=15.283-0.922*FPG-0.485*Log(FINS)-0.386*Log(ALT)-0.015*SBP。如表3所示,方程中FPG标准化系数β=-0.465,P<0.001,Log(FINS)的β=-0.120,P=0.039,Log(ALT)的β=-0.126,P=0.031,SBP的β=-0.107,P=0.045。
表3 Log(DI)与相关指标多重线性回归分析
Variables | β | p |
FPG | -0.465 | <0.001 |
Log(FINS) | -0.120 | 0.039 |
Log(ALT) | -0.126 | 0.031 |
SBP | -0.107 | 0.045 |
β:标准化系数
估算模型采用逐步法多元线性回归分析建立。正态分布的变量之间的相关性分析采用Pearson相关性分析,非正态分布的变量之间相关性分析采用Spearman相关性分析。
(8)DI估算模型的验证
a.DI估算模型在建模及验证人群中的相关性分析
根据上述DI估算方程以FPG、Log(FINS)、Log(ALT)、SBP指标结果在建模人群及验证人群中分别计算出估算的Log(DI)值,并与实际测得的Log(DI)值进行相关性分析。如图1所示,在建模组估算的Log(DI)与实测的Log(DI)间的相关性系数为R=0.557,P<0.001,在验证人群中二者的相关性系数高达R=0.660,P<0.001。
b.DI估算模型的区分度及校准度分析
在整体人群中通过估算DI区分胰岛细胞功能优劣的ROC曲线下面积评价该估算模型的区分度。将人群的处置指数Log(DI)进行四分位分组,其中DI较低的25%患者,即Log(DI)<5.289,为胰岛细胞功能较差组。根据估算的Log(DI)是否能区分胰岛细胞功能较差患者绘制ROC曲线,如图2所示该ROC曲线下面积为0.786(P<0.001),95%置信区间为0.746-0.826。估算模型曲线下面积大于0.75,提示该估算模型的区分能力较好。并通过Hosmer-Lemeshow拟合优度检验评价估算模型的校准能力。结果显示校准度:Hosmer-Lemeshowχ2=14.610,P=0.067>0.05,提示模型预测值与实际观测值之间差异没有统计学显著性,预测模型有较好的校准能力。
综上所述,本发明通过简单生化指标新建并验证了处置指数DI的估算模型,为简化胰岛细胞功能评估检测方法,优化临床诊断路径提供了新的思路。
Claims (6)
1.一种处置指数DI估算模型的建立方法,包括:
(1)将年轻肥胖人群作为建模人群,建模人群中BMI≥30kg/m2的肥胖患者占85-86%,其中BMI30-40kg/m2的肥胖患者占60-70%;
(2)在建模人群中利用相关性分析评价Log(DI)与基线各指标间的关系,其中基线指标包括:人体测量学指标,血液生化指标,糖代谢指标,腹部脂肪指标;
(3)在建模人群中根据单因素回归分析筛选与DI相关的变量进入多重线性回归分析中,共筛选出空腹血糖FPG、空腹胰岛素FINS水平、丙氨酸氨基转移酶ALT、收缩压SBP四个变量进入方程中,并建立DI的估算方程:
Log(DI)=15.283-0.922*FPG-0.485*Log(FINS)-0.386*Log(ALT)-0.015*SBP。
2.根据权利要求1所述的方法,其特征在于,所述步骤(1)中建模人群年龄在14-45岁间,建模人群平均年龄为20-28岁。
3.根据权利要求1所述的方法,其特征在于,所述步骤(2)中人体测量学指标包括:身高,体重,腰围,臀围和静息血压。
4.根据权利要求1所述的方法,其特征在于,所述步骤(2)中血液生化指标包括空腹血糖及胰岛素、糖化血红蛋白、炎症指标、肝肾功能和血脂。
5.根据权利要求1所述的方法,其特征在于,所述步骤(2)中糖代谢指标是由减少样本数的Bergman微小模型技术结合多样本静脉葡萄糖耐量试验和其他简易糖代谢评估得到。
6.根据权利要求1所述的方法,其特征在于,所述步骤(2)中腹部脂肪指标包括腹部内脏脂肪面积、腹部皮下脂肪面积、腹部总脂肪面积和内脏脂肪面积比例。
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115547495A (zh) * | 2022-09-02 | 2022-12-30 | 广东药科大学 | 一种综合评价糖脂代谢水平的系统及其应用 |
CN117766151A (zh) * | 2023-12-28 | 2024-03-26 | 中国药科大学 | 基于酮体动力学模型描述人肝生酮能力预测nafld患者病程进展的方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101193592A (zh) * | 2005-05-05 | 2008-06-04 | 香港理工大学 | 用于预测人血糖水平的方法 |
CN103077301A (zh) * | 2012-12-24 | 2013-05-01 | 浙江大学医学院附属邵逸夫医院 | 代谢综合征组分积聚预测亚临床心血管病变的方法 |
CN103793594A (zh) * | 2013-12-30 | 2014-05-14 | 上海交通大学医学院附属瑞金医院 | 一种构建肠道-胰岛调控轴相关功能评估模型的方法与应用 |
CN111261279A (zh) * | 2019-11-06 | 2020-06-09 | 浙江大学 | 一种针对普通人群的颈动脉硬化与颈动脉斑块预测模型的建立方法 |
-
2020
- 2020-11-12 CN CN202011261666.4A patent/CN112365979A/zh active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101193592A (zh) * | 2005-05-05 | 2008-06-04 | 香港理工大学 | 用于预测人血糖水平的方法 |
CN103077301A (zh) * | 2012-12-24 | 2013-05-01 | 浙江大学医学院附属邵逸夫医院 | 代谢综合征组分积聚预测亚临床心血管病变的方法 |
CN103793594A (zh) * | 2013-12-30 | 2014-05-14 | 上海交通大学医学院附属瑞金医院 | 一种构建肠道-胰岛调控轴相关功能评估模型的方法与应用 |
CN111261279A (zh) * | 2019-11-06 | 2020-06-09 | 浙江大学 | 一种针对普通人群的颈动脉硬化与颈动脉斑块预测模型的建立方法 |
Non-Patent Citations (1)
Title |
---|
李艳华: "糖耐量正常2型糖尿病高危人群筛查方法的研究", 《中国优秀博硕士学位论文全文数据库(硕士) 医药卫生科技辑》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115547495A (zh) * | 2022-09-02 | 2022-12-30 | 广东药科大学 | 一种综合评价糖脂代谢水平的系统及其应用 |
CN115547495B (zh) * | 2022-09-02 | 2023-09-12 | 广东药科大学 | 一种综合评价糖脂代谢水平的系统及其应用 |
CN117766151A (zh) * | 2023-12-28 | 2024-03-26 | 中国药科大学 | 基于酮体动力学模型描述人肝生酮能力预测nafld患者病程进展的方法 |
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