CN117316445A - 一种用于预测妊娠期糖尿病风险的评估模型及其应用 - Google Patents
一种用于预测妊娠期糖尿病风险的评估模型及其应用 Download PDFInfo
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Abstract
本发明公开了一种用于预测妊娠期糖尿病风险的评估模型及其应用，属于生物医学技术领域，本发明公开了一种用于预测妊娠期糖尿病患病风险的产品，所述产品包括检测生物标志物表达水平的试剂，所述生物标志物包括空腹血糖(FPG)、总胆固醇(TC)、脂蛋白(Lipoprotein)和G蛋白偶联受体120(GPR120)。本发明构建的模型为logit(Y)＝2.504*FPG+1.528*TC+0.019*Lipoprotein+0.544*GPR120‑30.625，该模型相较于传统模型显示出在妊娠早期对妊娠期糖尿病(GDM)发病更好的预测价值，有助于临床中对高危GDM人群进行早期识别。
Description
技术领域
本发明涉及生物医学技术领域，特别是涉及一种用于预测妊娠期糖尿病风险的评估模型及其应用。
背景技术
妊娠期糖尿病(GDM)是一种常见的妊娠期疾病，是世界范围内日益严重的公共卫生问题。GDM可能对新生儿和母亲造成短期和长期危害。近年来，随着生活水平的提高、饮食和生活方式的改变，GDM的患病率有所增加，这加重了国民的健康和经济负担。同时GDM还可以促进2型糖尿病(T2DM)的发生，患有GDM的产妇发生2型糖尿病和心血管疾病的可能性更高。
既往已报道多种危险因素影响GDM的发病，如年龄、生活方式、孕前体重指数(BMI)、环境和社会心理因素、脂质代谢紊乱、胎盘激素、空腹血糖(FPG)水平、甲状腺功能等。然而，这些危险因素单因素分析对诊断的准确性较低，传统临床变量显示的曲线下面积(AUC)值为<0.8，而大多数模型显示预测概率与观察风险(即校准)之间的一致性较差。现有的GDM预测模型并没有显示出相当大的或较高的预测能力。因此，建立妊娠早期GDM的标准预测模型是十分有必要的。
同时一些研究人员发现异常血糖水平、GDM和血脂代谢紊乱之间具有相关性。长链脂肪酸的特异性受体中的G蛋白偶联受体120(GPR120)参与脂肪组织中的能量代谢和脂肪形成，并参与多种疾病的发生和进展。有脂质组学研究发现在GDM患者中GPR120的表达水平与总脂质量之间成正相关关系。据报道，GPR120的激活对代谢综合征有潜在的治疗作用，并提高全身胰岛素敏感性。同时，Da等人指出，GPR120激动剂治疗高脂饮食喂养的肥胖小鼠可降低肝脂肪变性，降低高胰岛素血症，增强糖耐量。
既往研究表明，GDM的早期发现对其预防和治疗具有重要意义。但是目前关于GPR120表达水平与GDM之间的风险关系的研究较少。为此，本发明通过检测妊娠早期(12周)GDM患者的GPR120水平，通过回顾性多因素分析，建立妊娠早期的GDM预测模型。
发明内容
本发明的目的是提供一种用于预测妊娠期糖尿病风险的评估模型及其应用，以解决上述现有技术存在的问题，本发明建立的模型相较于传统模型显示出对GDM发病更好的预测价值，有助于临床中对高危GDM人群进行早期识别，为妊娠期糖尿病患者的早期防治提供重要临床预警。
为实现上述目的，本发明提供了如下方案：
本发明提供一种用于预测妊娠期糖尿病患病风险的产品，所述产品包括检测生物标志物表达水平的试剂，所述生物标志物包括空腹血糖、总胆固醇、脂蛋白和GPR120。
本发明还提供检测生物标志物的试剂在制备用于预测妊娠期糖尿病患病风险的产品中的应用，所述生物标志物包括空腹血糖、总胆固醇、脂蛋白和GPR120。
本发明还提供一种用于预测妊娠期糖尿病风险的评估模型，所述评估模型以空腹血糖、总胆固醇、脂蛋白和GPR120表达水平为输入变量，用于预测妊娠期糖尿病患病风险。
进一步地，所述评估模型使用如下公式计算妊娠期糖尿病患病风险的评分：
logit(Y)＝2.504*空腹血糖表达水平+1.528*总胆固醇表达水平+0.019*脂蛋白表达水平+0.544*GPR120表达水平-30.625。
进一步地，若Y值≥0.671，预测妊娠期糖尿病患病风险高；若Y值<0.671，预测妊娠期糖尿病患病风险低。
本发明还提供一种所述的评估模型在设计预测妊娠期糖尿病患病风险的系统或装置中的应用。
本发明还提供一种预测妊娠期糖尿病患病风险的系统或装置，所述系统或装置利用所述的评估模型计算风险评分。
本发明还提供一种所述的评估模型或所述的系统或装置在筛选防治妊娠期糖尿病药物中的应用。
本发明公开了以下技术效果：
本发明根据统计的临床和实验室检测资料，共纳入26个变量，对26个因素进行LASSO回归分析，获得与妊娠期糖尿病预后显著相关的5个因素(空腹血糖、总胆固醇、脂蛋白、BMI2和GPR120)。之后对5个因素进行Logistics回归分析，构建了由空腹血糖、总胆固醇、脂蛋白和GPR120检测指标为独立危险因素，构建模型为logit(Y)＝2.504*FPG+1.528*TC+0.019*Lipoprotein+0.544*GPR120-30.625。使用受试者工作特性曲线下面积(AUC)和校准图来评估该模型的区分度和校准度，用决策曲线分析(DCA)评估该模型相较于其他传统变量预测模型的临床效益和效用。最终发现，该模型相较于传统模型在妊娠早期对GDM发病更好的预测价值，本发明构建的模型有助于临床中对高危GDM人群进行早期识别，为妊娠期糖尿病患者的早期防治提供重要临床预警。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为患者GDM患病风险预测模型结构化图；其中A：示意图；B：诺谟图(Nomogram)；
图2为使用评估模型预测GDM患病风险的ROC曲线图；A：建模组；B：验证组。
具体实施方式
现详细说明本发明的多种示例性实施方式，该详细说明不应认为是对本发明的限制，而应理解为是对本发明的某些方面、特性和实施方案的更详细的描述。
应理解本发明中所述的术语仅仅是为描述特别的实施方式，并非用于限制本发明。另外，对于本发明中的数值范围，应理解为还具体公开了该范围的上限和下限之间的每个中间值。在任何陈述值或陈述范围内的中间值，以及任何其他陈述值或在所述范围内的中间值之间的每个较小的范围也包括在本发明内。这些较小范围的上限和下限可独立地包括或排除在范围内。
除非另有说明，否则本文使用的所有技术和科学术语具有本发明所述领域的常规技术人员通常理解的相同含义。虽然本发明仅描述了优选的方法和材料，但是在本发明的实施或测试中也可以使用与本文所述相似或等同的任何方法和材料。本说明书中提到的所有文献通过引用并入，用以公开和描述与所述文献相关的方法和/或材料。在与任何并入的文献冲突时，以本说明书的内容为准。
在不背离本发明的范围或精神的情况下，可对本发明说明书的具体实施方式做多种改进和变化，这对本领域技术人员而言是显而易见的。由本发明的说明书得到的其他实施方式对技术人员而言是显而易见得的。本发明说明书和实施例仅是示例性的。
关于本文中所使用的“包含”、“包括”、“具有”、“含有”等等，均为开放性的用语，即意指包含但不限于。
实施例1
1数据来源及预处理
回顾性分析了2020年1月至2022年1月在无锡妇幼保健医院招募的1735名妊娠前三个月的孕妇。采集了孕妇妊娠12周的血液样本。根据75g口服葡萄糖耐量试验的结果，将妊娠24-28周的妇女分为GDM组和对照组。最后根据纳入标准：1)孕妇年龄在21-45岁；2)在我院完成常规孕检及实验室检查的孕妇；3)GDM组和对照组匹配年龄，按照1:1比例纳入。排除标准：1)已诊断为1型或2型糖尿病患者；2)既往有肿瘤病史患者。最终250名孕妇纳入实验(125名GDM患者，125名对照)，按照7:3比例随机分成建模组和验证组。此后，回顾性收集了她们在妊娠第14-16周的实验室和临床资料。通过QPCR方法检测孕妇血液GPR120水平，具体为：
将新鲜的含抗凝血剂的静脉血样本(2mL)在2500×g下离心10分钟，并移除上清层血清。然后，用移液管将红细胞裂解缓冲液(10mL)轻轻加入到血细胞中，混合，轻摇5分钟后2500×g离心5分钟。用磷酸缓冲盐溶液(3mL)冲洗两次。采用TRIzol试剂提取总RNA含量。设计GPR120引物，以下引物序列：GPR120：正向引物5’-TGGAGC CCCATCATCATCAC-3’，反向引物5’-TGCACA GTGACATGT GTT GTA GAG-3’；使用定量SYBR绿色PCR试剂盒(QIAGEN，上海，China)和iCycler iQ(Bio-Rad)PCR仪器进行定量聚合酶链反应(PCR)。
2模型建立
在建模组中，包含了87名GDM患者和93名对照患者，对于统计的临床和实验室检测资料，共纳入年龄、舒张压、收缩压、孕前BMI1、孕12周BMI2、产次、总胆红素、直接胆红素、总蛋白、白蛋白、球蛋白、丙氨酸氨基转移酶、天冬氨酸氨基转移酶、肌酸激酶、尿酸、肌酐、微球蛋白、空腹血糖、总胆固醇、甘油三脂、高密度脂蛋白、低密度脂蛋白、载脂蛋白A1、载脂蛋白B、脂蛋白和GPR120浓度26个变量。两组数据的比较满足正态分布的资料采用非配对t检验，不满足正态分布的资料采用Mann-WhitneyU检验，分类数据的比较采用卡方检验。采用LASSO回归分析来确定最优预测因素，该方法有效地解决了变量之间的多重共线性问题。对26个因素进行LASSO回归分析，当λ取一倍标准误时，得到与妊娠期糖尿病预后显著相关的5个因素(空腹血糖、总胆固醇、脂蛋白、BMI2和GPR120)，P值＜0.05。将5个变量纳入二分类Logistics回归分析，构建了由空腹血糖、总胆固醇、脂蛋白和GPR120检测指标为独立危险因素的评估模型(如表1所示)。
表1二分类logistics回归分析结果
构建模型为：
logit(Y)＝2.504*FPG+1.528*TC+0.019*Lipoprotein+0.544*GPR120-30.625；
其中，logit(Y)＝ln[Y/(1-Y)]。
图1为患者GDM患病风险预测模型结构化图，可以根据孕妇12周血液中空腹血糖(FPG)、总胆固醇(TC)、脂蛋白(Lipoprotein)、GPR120的水平可预测发生妊娠期糖尿病的风险。
当评估模型的Y值大于等于0.671时，将其判定为GDM患病高风险人群，当评估模型的Y值小于0.671时，将其判定为GDM患病低风险人群。
3临床样本验证
在验证组中，包含38名GDM患者和32名对照组，通过构建评估模型的ROC曲线(图2中B)，计算曲线下面积，即AUC面积，本实施例建立的模型在建模组和验证组中均能有效在早期发病前判断妊娠期糖尿病患者(见表2)。
表2该模型在建模组和验证组早期预测妊娠期糖尿病的准确性
| AUC(95％CI) | 灵敏度 | 特异度 | |
| 建模组 | 0.996 | 0.989 | 0.977 |
| 验证组 | 0.992 | 0.969 | 0.947 |
具体计算结果示例如下：
样本1(已确诊妊娠期糖尿病的发病前样本)，患者12周时血液FPG：4.44mmol/L，TC：7.31mmol/L，lipoprotein：378.5mg/L，GPR120：4.234mmol/L。
logit(Y)＝2.504*FPG+1.528*TC+0.019*Lipoprotein+0.544*GPR120-30.625＝1.157。
经计算，Y值＝0.768，大于模型阈值0.671，预测患者有较高的GDM患病风险，与后续确诊患病结果一致，再次证实本评估模型能够准确、有效预测孕妇早期发病前妊娠期糖尿病患病风险。
在建模组和验证组中，进一步比较了构建的评估模型与传统模型(空腹血糖)的预测准确性，通过比较两组间ROC曲线(图2)，得到曲线下面积建模组(0.996:0.935)和验证组(0.992:0.875)，可以发现，评估模型明显优于传统空腹血糖预测患病风险的准确性。
以上所述的实施例仅是对本发明的优选方式进行描述，并非对本发明的范围进行限定，在不脱离本发明设计精神的前提下，本领域普通技术人员对本发明的技术方案做出的各种变形和改进，均应落入本发明权利要求书确定的保护范围内。
Claims (8)
1.一种用于预测妊娠期糖尿病患病风险的产品，其特征在于，所述产品包括检测生物标志物表达水平的试剂，所述生物标志物包括空腹血糖、总胆固醇、脂蛋白和GPR120。
2.检测生物标志物的试剂在制备用于预测妊娠期糖尿病患病风险的产品中的应用，其特征在于，所述生物标志物包括空腹血糖、总胆固醇、脂蛋白和GPR120。
3.一种用于预测妊娠期糖尿病风险的评估模型，其特征在于，所述评估模型以空腹血糖、总胆固醇、脂蛋白和GPR120表达水平为输入变量，用于预测妊娠期糖尿病患病风险。
4.根据权利要求3所述的评估模型，其特征在于，所述评估模型使用如下公式计算妊娠期糖尿病患病风险的评分：
logit(Y)＝2.504*空腹血糖表达水平+1.528*总胆固醇表达水平+0.019*脂蛋白表达水平+0.544*GPR120表达水平-30.625；其中，logit(Y)＝ln[Y/(1-Y)]。
5.根据权利要求4所述的评估模型，其特征在于，若Y值≥0.671，预测妊娠期糖尿病患病风险高；若所述Y值<0.671，预测妊娠期糖尿病患病风险低。
6.一种如权利要求3-5任一项所述的评估模型在设计预测妊娠期糖尿病患病风险的系统或装置中的应用。
7.一种预测妊娠期糖尿病患病风险的系统或装置，其特征在于，所述系统或装置利用权利要求3-5任一项所述的评估模型计算风险评分。
8.一种如权利要求3-5任一项所述的评估模型或权利要求7所述的系统或装置在筛选防治妊娠期糖尿病药物中的应用。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120309030A1 (en) * | 2009-10-29 | 2012-12-06 | Tethys Bioscience, Inc. | Method for determining risk of diabetes |
| CN113484511A (zh) * | 2021-06-30 | 2021-10-08 | 华中科技大学 | 妊娠期糖尿病的孕早期血液脂质生物标志物的筛选及应用 |
| CN114236123A (zh) * | 2021-12-13 | 2022-03-25 | 深圳华大基因股份有限公司 | 标志物在预测妊娠期糖尿病风险中的应用 |
| WO2022131544A1 (ko) * | 2020-12-16 | 2022-06-23 | 가톨릭대학교 산학협력단 | 고위험 임신성 당뇨병 예측모델 설계 방법 및 장치 |
-
2023
- 2023-09-22 CN CN202311237544.5A patent/CN117316445A/zh active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120309030A1 (en) * | 2009-10-29 | 2012-12-06 | Tethys Bioscience, Inc. | Method for determining risk of diabetes |
| WO2022131544A1 (ko) * | 2020-12-16 | 2022-06-23 | 가톨릭대학교 산학협력단 | 고위험 임신성 당뇨병 예측모델 설계 방법 및 장치 |
| CN113484511A (zh) * | 2021-06-30 | 2021-10-08 | 华中科技大学 | 妊娠期糖尿病的孕早期血液脂质生物标志物的筛选及应用 |
| CN114236123A (zh) * | 2021-12-13 | 2022-03-25 | 深圳华大基因股份有限公司 | 标志物在预测妊娠期糖尿病风险中的应用 |
Non-Patent Citations (5)
| Title |
|---|
| 何晴雯: "游离脂肪酸受体GPR120在妊娠期糖尿病中的作用及其临床意义", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》, 15 December 2019 (2019-12-15), pages 19 - 42 * |
| 怀莹莹等: "妊娠期糖尿病预测模型的构建及效能分析", 《山东医药》, 15 March 2023 (2023-03-15), pages 51 - 54 * |
| 方红霞;魏金彩;李红杰;赵红丽;常辉;: "妊娠糖尿病预测模型的建立与评价", 中国妇幼卫生杂志, no. 03, 20 May 2020 (2020-05-20) * |
| 谭育松;: "长春地区妊娠期糖尿病风险预测模型的临床研究", 中国妇幼保健, no. 16, 15 August 2020 (2020-08-15) * |
| 费晓萍;祝亚平;沈建芳;刘丽;王琴;李晨波;李文娟;: "孕早期(8-14周)空腹血浆葡萄糖、体质指数、糖化血红蛋白以及血脂对妊娠期糖尿病预测的临床意义", 中国实验诊断学, no. 11, 25 November 2018 (2018-11-25) * |
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