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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: |
ChiCTR2000036927 |
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最近更新日期: Date of Last Refreshed on: |
2020-09-27 22:47:52 |
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注册时间: Date of Registration: |
2020-08-25 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: |
多平台代谢组学新技术与2型糖尿病精准预测研究 |
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Public title: |
Multi-platform Metabolomics Technology and Presicion Prediction of Type 2 Diabetes |
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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: |
多平台代谢组学新技术与2型糖尿病精准预测研究 |
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Scientific title: |
Multi-platform Metabolomics Technology and Presicion Prediction of Type 2 Diabetes |
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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: |
王霜原 |
研究负责人: |
毕宇芳 |
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Applicant: |
Shuanngyuan Wang |
Study leader: |
Yufang Bi |
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申请注册联系人电话: Applicant telephone: |
+86 13061868581 |
研究负责人电话: Study leader's telephone: |
+86 13917189212 |
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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: |
wsy12060@rjh.com.cn |
研究负责人电子邮件: Study leader's E-mail: |
byf10784@rjh.com.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: |
上海市黄浦区瑞金二路197号 |
研究负责人通讯地址: |
上海市黄浦区瑞金二路197号 |
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Applicant address: |
197 Second Ruijin Road, Huangpu District, Shanghai, China |
Study leader's address: |
197 Second Ruijin Road, Huangpu District, Shanghai, China |
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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: |
上海交通大学医学院附属瑞金医院 |
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Applicant's institution: |
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine |
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研究负责人所在单位: |
上海交通大学医学院附属瑞金医院 |
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Affiliation of the Leader: |
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine |
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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: |
(2020)临伦审(218)号Y |
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: |
上海交通大学医学院附属瑞金医院伦理委员会 |
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Name of the ethic committee: |
Ruijin Hospital Ethics Committee, Shanghai JiaoTong University School of Medicine |
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伦理委员会批准日期: Date of approved by ethic committee: |
2020-08-20 00:00:00 |
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伦理委员会联系人: |
王译锋 |
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Contact Name of the ethic committee: |
Yifeng Wang |
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伦理委员会联系地址: |
上海市黄浦区瑞金二路197号 |
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Contact Address of the ethic committee: |
197 Second Ruijin Road, Huangpu District, Shanghai, China |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
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研究实施负责(组长)单位: |
上海交通大学医学院附属瑞金医院 |
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Primary sponsor: |
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China |
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研究实施负责(组长)单位地址: |
上海市黄浦区瑞金二路197号 |
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Primary sponsor's address: |
197 Second Ruijin Road, Huangpu District, Shanghai, China |
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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: |
申请中 |
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Source(s) of funding: |
Under application |
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Target disease: |
type 2 diabetes |
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Target disease code: |
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研究类型: |
观察性研究 |
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Study type: |
Observational 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: |
连续入组 |
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Study design: |
Sequential |
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研究目的: |
本研究将利用色谱-质谱代谢组学检测技术高灵敏度、高特异性和高分离度的优势,建立糖尿病特异性的多平台代谢组学检测新技术,建立2型糖尿病研究前瞻性人群队列,深入探讨糖尿病发生发展相关的代谢表型特征,发现并验证对2型糖尿病具预测价值的小分子标志物。进一步结合前期已发现的传统危险因素、环境暴露组、遗传易感、肠道宏基因组等多组学数据,通过大数据及人工智能算法,构建新预警指标体系及评分模型,以缓解我国糖尿病迅猛发展的趋势,节省我国有限的医疗资源。 |
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Objectives of Study: |
We will take advantage of the high sensitivity, high specificity, and high resolution of the chromatography-mass spectrometry metabolomics detection technology, and establish a new diabetes-specific multi-platform metabolomics detection technology, and rely on the established large prospective population cohort. In-depth discussion of the metabolic phenotypic characteristics related to the occurrence and development of T2DM, discovery and verification of small molecular markers with predictive value for T2DM. Combining the data of traditional risk factors, environmental exposure groups, genetic susceptibility, intestinal metagenomics and other omics data that have been discovered in the early stage, through big data and artificial intelligence algorithms, construct a new T2DM early warning indicator system and scoring model to alleviate the rapid development of diabetes in China The development trend saves China's limited medical resources. |
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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: |
研究使用巢式病例对照方式,选取2020年随访中确定的200例新诊断糖尿病患者纳入病例组,对照组将按1:1的比例进行匹配,匹配因素包括年龄、性别、民族和空腹血糖。 |
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Inclusion criteria |
The study uses a nested case-control method to select 200 newly diagnosed diabetic patients identified during follow-up into the case group. The control group will be matched at a ratio of 1:1. The matching factors include age, gender, ethnicity and fasting blood glucose. |
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排除标准: |
1)癌症史; |
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Exclusion criteria: |
1) history of cancer; |
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研究实施时间: Study execute time: |
从 From 2020-10-01 00:00:00至 To 2022-09-30 00:00:00 |
征募观察对象时间: Recruiting time: |
从From 2020-10-01 00:00:00 至 To 2020-12-31 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: |
男女均可 |
Gender: |
Both |
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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): |
不适用 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
N/A |
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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: |
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Blinding: |
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是否共享原始数据: IPD sharing |
Yes |
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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): |
网络平台(ResMan: http://www.medresman.org.cn),日期:2023年2月 |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
web-based public database (ResMan: http://www.medresman.org.cn), Date: Feb, 2023 |
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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: |
本项目基于即将开展的社区人群10年随访,采集数据为研究对象基线时留取血清中的代谢物。在分析2型糖尿病潜在代谢标志物时,分别使用气相色谱-质谱联用(GC-MS)、液相色谱-质谱联用(LC-MS)和脂质组学等多平台检测技术进行病例和对照组基线血清代谢物检测。 |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
In the analysis of potential metabolic markers of type 2 diabetes, the serum collected from the study subjects at baseline was used, and gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and lipids were used respectively. Multi-platform detection technology such as omics conducts baseline serum metabolite detection in cases and controls. |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
有/Yes |