Diagnosis, Prognosis, and Mechanisms in Panvascular Disease
NCT ID: NCT07151183
Last Updated: 2025-09-03
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
RECRUITING
1000 participants
OBSERVATIONAL
2024-12-01
2030-12-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Upon data collection, researchers will conduct various statistical analyses to enhance our understanding of the factors and mechanisms contributing to various panvascular diseases, including coronary heart disease, myocardial infarction, stroke, and peripheral vascular disease. These statistical analyses will also aid in identifying more effective treatment strategies for these conditions.
By amassing a large volume of data from a significant number of patients with panvascular diseases, researchers will be able to perform highly precise analyses of the factors influencing the onset, progression, and treatment of these diseases. The results of these precise analyses can then be utilized to optimize clinical practices for the prevention and treatment of panvascular diseases.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Prospective Cohort Study of Panvascular Disease
NCT06295861
Metabolomics Characterization of Biomarkers of ASCVD and Prediction Model
NCT05148182
Prognosis Prediction System of Patients With Cardiovascular and Cerebrovascular Diseases Based on Multi-omics
NCT06001073
Risk Factors of Individuals With Coronary Artery Disease
NCT00260104
Multi-omics Merge for Ensemble Subtyping for Atherosclerotic Cardiovascular Disease
NCT06471803
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
This study aims to establish a standardized cohort for panvascular diseases, encompassing various biological materials such as DNA samples, as well as comprehensive patient medical records and long-term follow-up information. The database will systematically collect multidimensional data, including patient questionnaire data (e.g., lifestyle, family history, dietary habits), imaging examination results (e.g., ultrasound, CT, MRI), DNA and other biochemical indicators extracted from blood samples, as well as non-invasive physiological parameters such as blood pressure and heart rate, and examinations related to arterial health assessment (e.g., pulse wave velocity, ankle-brachial index). By integrating these multi-source data, researchers will be able to conduct in-depth analyses of the genetic, metabolic, and clinical characteristics of panvascular diseases, identify disease-related biomarkers and predictive factors, and thereby provide a valuable resource for investigating the mechanisms of panvascular diseases.
Based on this database, researchers can systematically explore the risk factors of panvascular diseases and their dynamic evolution patterns, uncovering the key driving mechanisms behind disease development. Furthermore, through high-throughput sequencing, multi-omics analysis, and machine learning technologies, researchers can identify potential molecular targets and therapeutic strategies, advancing the field of precision medicine. These research outcomes will not only contribute to the development of novel diagnostic methods and personalized treatment plans but also provide a scientific basis for the early prevention and intervention of panvascular diseases, ultimately improving patient prognosis and reducing disease burden. The establishment of this resource platform will provide critical support for research and clinical practice in panvascular diseases, holding profound scientific significance and practical value.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
PROSPECTIVE
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
No intervention
No intervention
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
2. Diagnosed or suspected panvascular disease, defined as the presence of atherosclerotic lesions in at least two or more vascular regions, such as intracranial arteries, carotid arteries, coronary arteries, aorta, and peripheral arteries;
3. Voluntary participation in this study and provision of signed informed consent.
Exclusion Criteria
2. Severe cardiac dysfunction (EF \< 30%);
3. Severe arrhythmias;
4. Severe valvular heart disease;
5. Cardiomyopathy caused by non-coronary artery diseases;
6. Non-atherosclerotic coronary artery disease (e.g., coronary artery dissection, embolism, etc.);
7. Severe non-cardiovascular diseases;
8. Women who are pregnant or breastfeeding;
9. Individuals who refuse to provide signed informed consent.
18 Years
80 Years
ALL
Yes
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Jinwei Tian
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Jinwei Tian
Doctor
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Harbin Medical University
Harbin, Heilongjiang, China
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
References
Explore related publications, articles, or registry entries linked to this study.
Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform. 2019 Nov 27;20(6):1957-1971. doi: 10.1093/bib/bbx170.
Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016 Jul;17(7):451-9. doi: 10.1038/nrm.2016.25. Epub 2016 Mar 16.
Aitekenov S, Sultangaziyev A, Abdirova P, Yussupova L, Gaipov A, Utegulov Z, Bukasov R. Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers. Crit Rev Anal Chem. 2023;53(7):1561-1590. doi: 10.1080/10408347.2022.2036941. Epub 2022 Feb 14.
Matsuura Y, Kanter JE, Bornfeldt KE. Highlighting Residual Atherosclerotic Cardiovascular Disease Risk. Arterioscler Thromb Vasc Biol. 2019 Jan;39(1):e1-e9. doi: 10.1161/ATVBAHA.118.311999. No abstract available.
Ozcan C, Deleskog A, Schjerning Olsen AM, Nordahl Christensen H, Lock Hansen M, Hilmar Gislason G. Coronary artery disease severity and long-term cardiovascular risk in patients with myocardial infarction: a Danish nationwide register-based cohort study. Eur Heart J Cardiovasc Pharmacother. 2018 Jan 1;4(1):25-35. doi: 10.1093/ehjcvp/pvx009.
Steg PG, Bhatt DL, Wilson PW, D'Agostino R Sr, Ohman EM, Rother J, Liau CS, Hirsch AT, Mas JL, Ikeda Y, Pencina MJ, Goto S; REACH Registry Investigators. One-year cardiovascular event rates in outpatients with atherothrombosis. JAMA. 2007 Mar 21;297(11):1197-206. doi: 10.1001/jama.297.11.1197.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
KY2024-233
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.