Diagnosis, Prognosis, and Mechanisms in Panvascular Disease

NCT ID: NCT07151183

Last Updated: 2025-09-03

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-12-01

Study Completion Date

2030-12-31

Brief Summary

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This study aims to establish a standardized cohort for panvascular diseases, encompassing biological materials such as DNA samples, along with extensive patient medical records and follow-up information. The design of this database will enable it to serve as a comprehensive resource for future medical research.

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.

Detailed Description

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Panvascular diseases are a group of complex disorders affecting the entire vascular system, with their development influenced by multiple risk factors, including genetic susceptibility, metabolic abnormalities (such as hypertension, hyperlipidemia, and diabetes), inflammatory responses, oxidative stress, and unhealthy lifestyle habits (such as smoking and physical inactivity). In-depth research into the mechanisms of these factors can help uncover the core drivers of the disease, providing a theoretical foundation for early prevention and intervention. Additionally, elucidating specific molecular pathways (such as inflammatory signaling, lipid metabolism, and endothelial dysfunction-related pathways) and molecular regulatory mechanisms (such as non-coding RNAs and epigenetic modifications) can offer new targets for precise diagnosis and treatment. By integrating multi-omics data and high-throughput technologies, the molecular networks of panvascular diseases can be systematically clarified, advancing the development of personalized medicine. This will significantly improve patient outcomes, reduce disease burden, and hold substantial scientific value and clinical application prospects.

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

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Panvascular Diseases Cardiovascular Diseases

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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No intervention

No intervention

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

1. Male or female aged 18 to 80 years old;
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

1. Severe hemodynamic instability;
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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Jinwei Tian

OTHER

Sponsor Role lead

Responsible Party

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Jinwei Tian

Doctor

Responsibility Role SPONSOR_INVESTIGATOR

Locations

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Harbin Medical University

Harbin, Heilongjiang, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Tian J Jinwei Tian, MD, PhD

Role: CONTACT

+86-0451-86605180

Wang Y Yan Wang, MD, PhD

Role: CONTACT

+86-13936462066

Facility Contacts

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Wang Y Yan Wang, MD, PhD

Role: primary

+86-13936462066

References

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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.

Reference Type BACKGROUND
PMID: 29304189 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 26979502 (View on PubMed)

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.

Reference Type RESULT
PMID: 35157535 (View on PubMed)

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.

Reference Type RESULT
PMID: 30586334 (View on PubMed)

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.

Reference Type RESULT
PMID: 28444162 (View on PubMed)

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.

Reference Type RESULT
PMID: 17374814 (View on PubMed)

Other Identifiers

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KY2024-233

Identifier Type: -

Identifier Source: org_study_id

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