Diagnosis, Prognosis, and Mechanisms in Panvascular Disease

Official title

A Prospective Research on Diagnosis, Prognosis, and Mechanisms of Panvascular Disease

Overview

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

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.

Interventions

• Other No intervention
  No intervention

Primary outcome measures

• Number of participants with Cardiovascular mortality, Nonfatal myocardial Infarction, Ischemic stroke, Arteriosclerosis Obliterans [Time frame: Up to 5 years from baseline]

Secondary outcome measures (3)

• All-cause death [Time frame: Up to 5 years from baseline]
• Rate of receiving vascular revascularization [Time frame: Up to 5 years from baseline]
• Readmission rate for vascular reasons [Time frame: Up to 5 years from baseline]

Eligibility criteria

Inclusion criteria

• Male or female aged 18 to 80 years old;
• 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;
• Voluntary participation in this study and provision of signed informed consent.

Exclusion criteria

• Severe hemodynamic instability;
• Severe cardiac dysfunction (EF < 30%);
• Severe arrhythmias;
• Severe valvular heart disease;
• Cardiomyopathy caused by non-coronary artery diseases;
• Non-atherosclerotic coronary artery disease (e.g., coronary artery dissection, embolism, etc.);
• Severe non-cardiovascular diseases;
• Women who are pregnant or breastfeeding;
• Individuals who refuse to provide signed informed consent.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Cohort

Study locations

China · 1 center

• Harbin Medical University — Harbin

Publications

• 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. PMID 29304189
• 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. PMID 26979502
• 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. PMID 35157535
• 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. PMID 30586334
• 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. PMID 28444162
• 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. PMID 17374814

Identifiers

NCT: NCT07151183 · KY2024-233

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗