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审核状态: Project audit state: |
通过审核 Successful |
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注册号: Registration number: |
ChiCTR2400092624 |
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最近更新日期: Date of Last Refreshed on: |
2024-11-20 15:34:30 |
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注册时间: Date of Registration: |
2024-11-20 00:00:00 |
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注册号状态: |
预注册 |
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Registration Status: |
Prospective registration |
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注册题目: |
基于大数据的妊娠糖尿病精准管理队列研究 |
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Public title: |
A cohort study on precision management of gestational diabetes mellitus based on big data |
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注册题目简写: |
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English Acronym: |
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研究课题的正式科学名称: |
基于大数据的妊娠糖尿病精准管理队列研究 |
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Scientific title: |
A cohort study on precision management of gestational diabetes mellitus based on big data |
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研究课题代号(代码): Study subject ID: |
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在二级注册机构或其它机构的注册号: The registration number of the Partner Registry or other register: |
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申请注册联系人: |
陆莹 |
研究负责人: |
刘云 |
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Applicant: |
Ying Lu |
Study leader: |
Yun Liu |
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申请注册联系人电话: Applicant telephone: |
+86 138 5163 7944 |
研究负责人电话: Study leader's telephone: |
+86 25 68306538 |
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申请注册联系人传真 : Applicant Fax: |
研究负责人传真: Study leader's fax: |
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申请注册联系人电子邮件: Applicant E-mail: |
luying_1208@126.com |
研究负责人电子邮件: Study leader's E-mail: |
liuyun@njmu.edu.cn |
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申请单位网址(自愿提供): Applicant website(voluntary supply): |
研究负责人网址(自愿提供): Study leader's website(voluntary supply): |
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申请注册联系人通讯地址: |
江苏省南京市江东北路368号江苏省人民医院(江苏省妇幼保健院) |
研究负责人通讯地址: |
江苏省南京市广州路300号 |
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Applicant address: |
Jiangsu Provincial People's Hospital, No. 368 Jiangdong North Road, Nanjing, Jiangsu Province (Jiangsu Provincial Maternal and Child Health Hospital) |
Study leader's address: |
No. 300, Guangzhou Road, Nanjing City, Jiangsu Province |
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申请注册联系人邮政编码: Applicant postcode: |
研究负责人邮政编码: Study leader's postcode: |
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申请人所在单位: |
江苏省人民医院(南京医科大学第一附属医院、江苏省妇幼保健院) |
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Applicant's institution: |
Jiangsu Provincial People's Hospital (The First Affiliated Hospital of Nanjing Medical University, Jiangsu Provincial Maternal and Child Health Hospital) |
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研究负责人所在单位: |
江苏省人民医院(南京医科大学第一附属医院) |
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Affiliation of the Leader: |
Jiangsu Provincial People's Hospital (The First Affiliated Hospital of Nanjing Medical University) |
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是否获伦理委员会批准: |
是/Yes |
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Approved by ethic committee: |
Yes |
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伦理委员会批件文号: Approved No. of ethic committee: |
2024-SR-500 |
伦理委员会批件附件: Approved file of Ethical Committee: |
查看附件View |
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批准本研究的伦理委员会名称: |
南京医科大学第一附属医院(江苏省人民医院)伦理委员会 |
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Name of the ethic committee: |
Ethics Committee of the First Affiliated Hospital with Nanjing Medical university |
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伦理委员会批准日期: Date of approved by ethic committee: |
2024-07-09 00:00:00 |
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伦理委员会联系人: |
王嘉楠 |
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Contact Name of the ethic committee: |
Wang JiaNan |
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伦理委员会联系地址: |
江苏省南京市广州路300号 |
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Contact Address of the ethic committee: |
No. 300, Guangzhou Road, Nanjing City, Jiangsu Province |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
+86 25 68306360 |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
1096493017@qq.com |
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研究实施负责(组长)单位: |
江苏省人民医院(南京医科大学第一附属医院) |
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Primary sponsor: |
Jiangsu Province Hospital (The First Affiliated Hospital with Nanjing Medical University) |
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研究实施负责(组长)单位地址: |
江苏省南京市广州路300号 |
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Primary sponsor's address: |
No. 300, Guangzhou Road, Nanjing City, Jiangsu Province |
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试验主办单位(项目批准或申办者): Secondary sponsor: |
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经费或物资来源: |
2023年度江苏省科技计划专项资金(重点研发计划社会发展);江苏省人民医院2024年度医院科学研究项目支撑计划 |
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Source(s) of funding: |
2023 Jiangsu Provincial Science and Technology Plan Special Fund;2024 Annual Scientific Research Project Support Program of Jiangsu Provincial People Hospital |
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Target disease: |
Gestational diabetes mellitus |
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Target disease code: |
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研究类型: |
干预性研究 |
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Study type: |
Interventional study |
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研究所处阶段: |
其它 | ||||||||||||||||||||||
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Study phase: |
N/A |
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研究设计: |
随机平行对照 |
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Study design: |
Parallel |
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研究目的: |
1、按照IADPSG诊断标准,在产科就诊的孕妇中筛检出GDM孕妇,同时按照年龄、BMI等因素进行匹配,建立GDM孕妇及其子代的前瞻性出生队列,建立基于大数据平台的GDM随访和管理模块。 2、观察GDM患者在产前不同胎龄周期和产后随访周期,每个周期14天内CGM衍生的不同血糖指标(即TIR、TAR、TBR、AUC、夜间MBG、白天MBG、每日MBG、MAGE和CV)的变化、及其与任何选定的主要不良妊娠结局的相关性。 3、确定各类糖脂代谢物、肠道菌群、脑结构/脑皮质厚度、小分子环境暴露、基因多态性以及血糖管理模式对于GDM远期T2DM、CVD进展和新生儿结局的影响,评估糖尿病微血管/大血管并发症发生风险。 4、筛选GDM发生及干预效果的遗传和环境独立危险因素,建立GDM诊断预测模型。 5、通过个体化的健康饮食、体重管理和运动干预对GDM孕妇进行多学科精准管理,评估各干预效果和影响因素,制定针对个体的精准管理方案。 |
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Objectives of Study: |
1.According to the IADPSG diagnostic criteria, screen GDM-positive pregnant women among obstetrics outpatients, while matching them based on factors such as age and BMI. Establish a prospective birth cohort of GDM mothers and their offspring, and develop a Big Data-based follow-up and management module for GDM. 2.Observe changes in different glucose metrics derived from CGM (i.e., TIR, TAR, TBR, AUC, nightly MBG, daily MBG, overall daily MBG, MAGE, and CV) during different gestational age periods before birth and during postnatal follow-up periods, each spanning 14 days. Investigate their correlations with selected major adverse pregnancy outcomes. 3. Determine the impacts of various glycolipid metabolites, gut microbiota, brain structure/cortical thickness, small environmental exposures, gene polymorphisms, and glucose management patterns on the long-term progression of T2DM and CVD in GDM patients and on neonatal outcomes. Assess the risks of microvascular and macrovascular diabetic complications. 4. Screen for genetic and environmental independent risk factors associated with GDM occurrence and intervention effectiveness, and establish a diagnostic prediction model for GDM. 5. Implement individualized health interventions involving dietary adjustments, weight management, and exercise for GDM-positive pregnant women. Evaluate the effects and influencing factors of each intervention, and develop precise management plans tailored to individual patients. |
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药物成份或治疗方案详述: |
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Description for medicine or protocol of treatment in detail: |
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纳入标准: |
1.年龄18-40岁;
2.孕前18.5kg/m^2 |
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Inclusion criteria |
1.Age between 18 and 40 years; 2.Pre-pregnancy BMI between 18.5 kg/m^2 and 28 kg/m^2; 3.Ability to read, understand, and sign the informed consent form; 4.The pregnant woman receives prenatal care and delivers at the study hospital. |
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排除标准: |
1.双胎或多胎妊娠; |
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Exclusion criteria: |
1.Twin or multiple pregnancies; 2.Pre-existing diabetes mellitus (PGDM), including gestational diabetes diagnosed during pregnancy (fasting blood glucose >=7.0 mmol/L or OGTT 2-hour blood glucose >= 11.1 mmol/L); 3.Poorly controlled pre-existing chronic hypertension (blood pressure >= 140/90 mmHg); 4.Coexisting severe liver or kidney disease (alanine aminotransferase greater than 2.5 times the upper limit of normal, creatinine greater than 132 mmol/L); 5.Current or recent use of medications affecting glucose metabolism, including steroids, hydroxyprogesterone caproate, antiretroviral drugs, etc.; 6.Mental disorders and severe psychological conditions (measured using scales: PHQ-9 for depression and GAD-7 for anxiety); 7.Coexisting severe infections; 8.Inability to adequately understand the requirements of the project, poor compliance, refusal to use continuous glucose monitoring (CGM) or to perform self-monitored blood glucose (SMBG) tests as required. |
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研究实施时间: Study execute time: |
从 From 2024-06-01 00:00:00至 To 2029-12-31 00:00:00 |
征募观察对象时间: Recruiting time: |
从From 2024-11-25 00:00:00 至 To 2027-10-31 00:00:00 |
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干预措施: Interventions: |
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研究实施地点: Countries of recruitment and research settings: |
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测量指标: Outcomes: |
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采集人体标本:
Collecting sample(s)
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征募研究对象情况: Recruiting status: |
尚未开始 Not yet recruiting |
年龄范围: Participant age: |
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性别: |
女性 |
Gender: |
Female |
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随机方法(请说明由何人用什么方法产生随机序列): |
由研究者使用R产生随机数列 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
The researchers generated a random number sequence using R |
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是否公开试验完成后的统计结果: Calculated Results after the Study Completed public access: |
不公开/Private |
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盲法: |
开放标签 |
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Blinding: |
Open-label study |
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是否共享原始数据: IPD sharing |
No |
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共享原始数据的方式(说明:请填入公开原始数据日期和方式,如采用网络平台,需填该网络平台名称和网址): |
无 |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
N/A |
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数据采集和管理(说明:数据采集和管理由两部分组成,一为病例记录表(Case Record Form, CRF),二为电子采集和管理系统(Electronic Data Capture, EDC),如ResMan即为一种基于互联网的EDC: |
(1)围孕期随访 所有研究对象在指定孕周范围内(孕23-24+6周、孕25-26+6周、孕27-28+6周、孕29-30+6周、孕31-32+6周、孕33-34+6周、孕35-36+6周、孕37-38+6周、孕39-40+6周、产后、产后6周)接受同一名产科医师11次检查。 (2)远期随访 所有研究对象及其子代在产后6个月、9个月、12个月分别接受一次随访,在产后1年后每年接受一次随访,研究对象随访内容包括体格检查(身高、体重、腹围、臀围、心率、血压)、血生化、OGTT、糖化血红蛋白、甲状腺功能、尿常规等,研究对象所生子代的随访内容包括体格检查(身高、体重、头围、心率、血压)、空腹血糖、心理行为发育等。 (3)大数据系统随访信息补充 依托江苏省妇幼健康信息系统中孕妇建卡、产前检查、产前筛查/诊断、产时、产后随访、孕产妇抑郁症筛查、0-6岁儿童保健、新生儿疾病筛查、三网监测等模块信息对随访内容进行补充,通过孕产妇和儿童保健号、身份证信息进行精准匹配。 |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
(1) Perinatal Follow-Up All study participants will undergo 11 examinations by the same obstetrician at specified gestational weeks (23-24+6 weeks, 25-26+6 weeks, 27-28+6 weeks, 29-30+6 weeks, 31-32+6 weeks, 33-34+6 weeks, 35-36+6 weeks, 37-38+6 weeks, 39-40+6 weeks, postpartum, and 6 weeks postpartum). (2) Longitudinal Follow-Up All study participants and their offspring will be followed up at 6, 9, and 12 months postpartum, and annually thereafter. The follow-up content for the participants included physical examinations (height, weight, waist circumference, hip circumference, heart rate, blood pressure), biochemical tests, oral glucose tolerance test (OGTT), glycosylated hemoglobin, thyroid function, and urinalysis. The follow-up content for their offspring included physical examinations (height, weight, head circumference, heart rate, blood pressure), fasting blood glucose, and psychobehavioral development. (3) Big Data System Follow-Up Information Supplement Relying on the Jiangsu Maternal and Child Health Information System, which includes modules such as prenatal registration, antenatal care, prenatal screening/diagnosis, labor, postpartum follow-up, maternal depression screening, 0-6 years child health care, newborn disease screening, and three-network monitoring, the follow-up content was supplemented. Precise matching will be performed using maternal and child health care numbers and ID card information. |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
有/Yes |