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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: |
ChiCTR2600118897 |
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最近更新日期: Date of Last Refreshed on: |
2026-02-12 11:19:11 |
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注册时间: Date of Registration: |
2026-02-12 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: |
血尿代谢标志物对骨质疏松性骨折的预测效能研究 |
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Public title: |
Study on the predictive efficacy of blood urine metabolic markers for osteoporotic fractures |
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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: |
血尿代谢标志物对骨质疏松性骨折的预测效能研究 |
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Scientific title: |
Study on the predictive efficacy of blood urine metabolic markers for osteoporotic fractures |
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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: |
宋利格 |
研究负责人: |
宋利格 |
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Applicant: |
Lige Song |
Study leader: |
Lige Song |
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申请注册联系人电话: Applicant telephone: |
+86 21 6569 0520 |
研究负责人电话: Study leader's telephone: |
+86 21 6569 0520 |
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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: |
6songlige@tongji.edu.cn |
研究负责人电子邮件: Study leader's E-mail: |
6songlige@tongji.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: |
上海市杨浦区腾越路450号 |
研究负责人通讯地址: |
上海市杨浦区腾越路450号 |
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Applicant address: |
450 Tengyue Road, yangpu district ,Shanghai, China. |
Study leader's address: |
450 Tengyue Road, yangpu 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: |
同济大学附属杨浦医院 |
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Applicant's institution: |
Yangpu Hospital, School of Medicine, Tongji University |
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研究负责人所在单位: |
上海市杨浦区中心医院 |
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Affiliation of the Leader: |
Shanghai Yangpu District Central Hospital |
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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: |
LL-2025-IIT-010 |
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: |
上海市杨浦区中心医院伦理委员会 |
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Name of the ethic committee: |
Medical Ethics Committee of Yangpu District Central Hospital, Shanghai |
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伦理委员会批准日期: Date of approved by ethic committee: |
2025-12-24 00:00:00 |
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伦理委员会联系人: |
王佩菊 |
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Contact Name of the ethic committee: |
wangpeiju |
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伦理委员会联系地址: |
上海市杨浦区腾越路450号 |
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Contact Address of the ethic committee: |
450 Tengyue Road, yangpu district ,Shanghai, China. |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
+86 21 6569 0520 |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
yzxlunli@126.com |
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研究实施负责(组长)单位: |
上海市杨浦区中心医院 |
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Primary sponsor: |
Shanghai Yangpu District Central Hospital |
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研究实施负责(组长)单位地址: |
上海市杨浦区腾越路450号 |
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Primary sponsor's address: |
450 Tengyue Road, yangpu 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: |
自选课题(自筹) |
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Source(s) of funding: |
Self-selected topic (self-funded) |
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Target disease: |
Osteoportic fracture |
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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: |
病例对照研究 |
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Study design: |
Case-Control study |
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研究目的: |
主要目的:识别并验证骨质疏松性骨折患者与健康对照者之间血液和尿液代谢标志物的差异谱,筛选出具有显著鉴别能力的候选代谢标志物组合。 次要目的:通过通路富集分析,阐释这些差异性代谢物所涉及的潜在生物学通路,为理解骨质疏松性骨折的代谢机制提供线索。 |
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Objectives of Study: |
Main objective: To identify and validate the differential metabolic profiles of blood and urine in patients with osteoporotic fractures compared to healthy controls, and to screen out candidate metabolic marker combinations with significant discriminatory ability.Secondary objective: Through pathway enrichment analysis, to elucidate the potential biological pathways involved in these differential metabolites, and to provide clues for understanding the metabolic mechanism of osteoporotic fractures. |
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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.经影像学(X线或CT)确诊为3个月内的骨质疏松性骨折患者。 |
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Inclusion criteria |
1.Patients diagnosed with osteoporotic fractures within 3 months through imaging methods (X-ray or CT); |
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排除标准: |
1.由重大创伤、恶性肿瘤骨转移、或其他明确非骨质疏松性原因(如Paget‘s病)导致的病理性骨折; |
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Exclusion criteria: |
1.Pathological fractures caused by severe trauma, metastasis of malignant tumors to the bones, or other clearly non-osteoporosis-related reasons (such as Paget's disease); |
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研究实施时间: Study execute time: |
从 From 2026-03-01 00:00:00至 To 2028-03-01 00:00:00 |
征募观察对象时间: Recruiting time: |
从From 2026-03-01 00:00:00 至 To 2026-09-01 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): |
无 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
None |
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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: |
无 |
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Blinding: |
None |
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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): |
不共享 |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
do not share |
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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. 源数据类型与收集 本研究将收集以下多维度源数据,所有数据收集均遵循标准操作程序,并确保其清晰、准确、可溯源。病例报告表:本研究设计有专用的电子版病例报告表,用于系统收集以下核心信息: 人口学资料:受试者编号、姓名缩写、性别、出生日期、身高、体重。临床病史:骨质疏松性骨折的诊断信息(骨折部位、日期、影像学报告编号)、既往骨折史、骨质疏松症及相关合并症(如糖尿病、风湿性疾病)的病史。生活方式与风险因素:吸烟史、饮酒史、体力活动水平、女性绝经年龄。用药史:当前及近期(过去6个月)所有用药记录,特别是影响骨代谢的药物(如糖皮质激素、抗骨质疏松药等)。医疗病历:作为CRF信息的佐证,将经过去标识化处理后,摘录相关的门诊/住院病历、手术记录、影像学报告(X线、CT、DXA骨密度报告)中的关键客观数据。调查问卷:采用标准化问卷收集生活方式等主观信息,问卷将由经过培训的研究人员辅助受试者完成,以确保填写质量。实验室结果:常规实验室指标:血常规、肝肾功能、钙磷代谢等。骨转换标志物:如β-CTX、PINP的检测报告。代谢组学原始与衍生数据:原始数据:质谱仪输出的原始谱图文件。过程数据:样品序列信息、质控样本报告、数据预处理(峰提取、对齐、归一化)后的数据矩阵。结果数据:鉴定出的代谢物列表、相对浓度/峰面积、差异分析结果(p值、VIP值、FC值)。样本管理信息:样本采集日期、时间、处理流程记录、分装位置、存储位置及唯一标识码,确保从受试者到每一份冻存管的完整溯源链。 2. 数据库建立、安全与质量控制 所有临床及样本信息将建立专门的电子研究数据库进行管理。数据库平台:使用 Microsoft Excel 工作簿建立结构化数据库。不同类别的数据(如人口学、临床、问卷、实验室)将存放于同一工作簿的不同工作表内,并通过唯一的受试者识别码进行关联。数据安全:访问控制:数据库文件将存储在课题组专用的、有权限控制的加密计算机或安全服务器中。文件本身将设置高强度密码保护,仅限主要研究者和指定的数据管理员访问。隐私保护:数据库中将使用唯一研究编号替代受试者姓名等直接标识符。包含个人标识信息的文件(如知情同意书)将与此研究数据库物理或电子分离保存。逻辑校对与质量控制:预设逻辑校验规则:在Excel中,利用数据验证功能设定逻辑校对程序,例如:设定数值范围(如年龄>18岁,BMI在合理区间)、性别与疾病编码一致性检查、必填字段强制完成等。双人录入与核对:所有由纸质源数据转录至电子数据库的过程,均采用“双人独立录入、第三方核对”模式,对不一致处追溯原始记录进行修正,确保数据转录的准确性。定期核查:数据管理员将每月对数据库进行随机抽查和逻辑一致性检查,生成数据质量报告。 |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
1. Source Data Type and CollectionThis study will collect the following multi-dimensional source data. All data collection follows standard operating procedures and ensures clarity, accuracy, and traceability. Case Report Form: This study has a dedicated electronic case report form for systematic collection of the following core information:Demographic data: Subject identification number, initials of name, gender, date of birth, height, weight. Clinical history: Diagnostic information for osteoporotic fractures (fracture site, date, imaging report number), history of previous fractures, history of osteoporosis and related comorbidities (such as diabetes, rheumatic diseases), lifestyle and risk factors (such as smoking history, alcohol consumption history, physical activity level, age of menopause for females). Medication history: Current and recent (past 6 months) medication records, especially drugs affecting bone metabolism (such as glucocorticoids, anti-osteoporosis drugs, etc.). Medical records: As supporting evidence for CRF information, relevant outpatient/ medical records, surgical records, and imaging reports (X-ray, CT, DXA bone density reports) will be extracted and key objective data will be recorded. Questionnaire: Standardized questionnaires are used to collect subjective information such as lifestyle. The questionnaire will be completed by trained researchers to ensure the quality of filling. Laboratory results: Routine laboratory indicators: blood routine, liver and kidney function, calcium and phosphorus metabolism, etc. Bone turnover markers: such as β-CTX, PINP test reports. Metabolomics raw and derived data: Raw data: original spectrum files output by mass spectrometers. Process data: sample sequence information, quality control sample reports, data preprocessing (peak extraction, alignment, normalization) data matrix. Result data: list of identified metabolites, relative concentration/peak area, differential analysis results (p-value, VIP value, FC value). Sample management information: sample collection date, time, processing flow record, packaging location, storage location, and unique identification code, ensuring a complete traceability chain from the subject to each frozen tube.2. Database Establishment, Security and Quality ControlAll clinical and sample information will be managed in a dedicated electronic research database. Database platform: A structured database will be established using Microsoft Excel workbooks. Different types of data (such as demographics, clinical, questionnaire, laboratory) will be stored in different worksheets of the same workbook and linked through the unique subject identification code. Data security: Access control: Database files will be stored in the subject group's dedicated, permission-controlled encrypted computer or secure server. The files themselves will be protected by a high-strength password, and only the main researcher and designated data administrators will have access. Privacy protection: The database will use a unique research number instead of direct identifiers such as the subject's name. Files containing personal identification information (such as informed consent forms) will be physically or electronically separated and stored for this research database. Logical verification and quality control: Pre-set logical verification rules: In Excel, use the data validation function to set logical verification programs, such as: setting numerical ranges (such as age > 18 years, BMI within a reasonable range), consistency check of gender and disease codes, mandatory completion of required fields, etc. Double-entry and verification: All processes of transcribing paper source data to the electronic database will adopt the "two independent entries by two people, third-party verification" mode. For inconsistencies, trace back to the original records for correction to ensure the accuracy of data transcription. Regular verification: The data administrator will conduct random spot checks and logical consistency checks of the database monthly, generating a data quality report. |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
有/Yes |