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This study aims to investigate the pathogenesis of Peripheral Artery Disease (PAD) and Carotid Artery Stenosis (CAS) using a comprehensive multi-omics and multi-modal imaging approach. The study will enroll patients diagnosed with PAD or CAS and perform advanced imaging techniques, including NIR-II Imaging, DUS-based V-flow Imaging, and Laser Speckle Imaging, to assess vascular structure and function. Simultaneously, single-cell transcriptomics, metabolomics, lipidomics, and proteomics analyses will be conducted on patient samples to identify key molecular targets and pathways involved in disease progression. Machine learning algorithms will be employed to integrate imaging and multi-omics data, enabling the development of predictive models for more accurate disease diagnosis and stratification. The findings from this study are expected to provide novel insights into the molecular mechanisms underlying PAD and CAS and contribute to the development of personalized therapeutic strategies.
Background and Rationale Peripheral Artery Disease (PAD) and Carotid Artery Stenosis (CAS) are prevalent vascular disorders associated with significant morbidity and mortality. Despite advances in diagnostic and therapeutic approaches, the molecular mechanisms driving these diseases remain poorly understood. This study leverages cutting-edge multi-omics technologies and advanced imaging modalities to unravel the complex pathogenesis of PAD and CAS, with the ultimate goal of identifying novel biomarkers and therapeutic targets.
Study Objectives Primary Objective: To integrate multi-modal imaging data (NIR-II Imaging, DUS-based V-flow Imaging, and Laser Speckle Imaging) with multi-omics data using machine learning algorithms for improved disease prediction and stratification.
Study Design
This is a prospective, observational study involving patients diagnosed with PAD or CAS. The study will include the following components:
Imaging Analysis:
Multi-Omics Analysis:
Imaging and multi-omics data will be integrated using advanced machine learning algorithms to develop predictive models for disease diagnosis, progression, and therapeutic response.
Study Population The study will enroll patients diagnosed with PAD or CAS, along with age- and sex-matched healthy controls. Inclusion and exclusion criteria will be applied to ensure a homogeneous study population.
Expected Outcomes
Ethical Considerations The study protocol has been reviewed and approved by the Institutional Review Board (IRB) to ensure the protection of human subjects. Informed consent will be obtained from all participants prior to their enrollment in the study.
Significance This study represents a pioneering effort to integrate multi-omics and multi-modal imaging data for a comprehensive understanding of PAD and CAS. The findings are expected to advance the field of vascular biology and contribute to the development of precision medicine approaches for these debilitating diseases.
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| Measure | Description | Time Frame |
|---|---|---|
| Time related NIR-II parameters for CAS and PAD patients | In this study, 5-minute NIR-II imaging video of each patient was processed into time-intensity curves to quantify the imaging results. Three time related parameters on time-intensity curves were extracted, including:T start (s), Tmax (s), T 1/2 (s). Note: "s" is used as the unit "second". | Baseline, 6 months |
| Intensity related NIR-II parameters for CAS and PAD patients | In this study, 5-minute NIR-II imaging video of each patient was processed into time-intensity curves to quantify the imaging results. The intensity related parameters on time-intensity curves were extracted as Imax (Fi). Note: "Fi" is used as the unit "fluorescence intensity". | Baseline, 6 months |
| Time-intensity related NIR-II parameters for CAS and PAD patients | In this study, 5-minute NIR-II imaging video of each patient was processed into time-intensity curves to quantify the imaging results. Two time-intensity related parameters on time-intensity curves were extracted, including:Ingress rate (Fi/s), Engress rate (Fi/s). Note: "s" is used as the unit "second" and "Fi" is used as the unit "fluorescence intensity". | Baseline, 6 months |
| Assessment of Wall Shear Stress (WSS) Using V-flow Imaging | V-flow imaging will be used to measure WSS in the carotid and peripheral arteries of patients with PAD and CAS. WSS (Pa), a critical hemodynamic parameter, will be calculated based on blood flow velocity and vessel geometry. This metric will help evaluate endothelial function and vascular remodeling associated with disease progression. Note: "Pa" is used as the unit "Pascal". | Baseline, 6 months |
| Assessment of Microvascular Perfusion in the Dorsum of the Foot Using Laser Speckle Imaging in Patients with PAD and CAS |
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Inclusion Criteria:
Exclusion Criteria:
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The study population will consist of patients diagnosed with Peripheral Artery Disease (PAD) or Carotid Artery Stenosis (CAS) who are admitted to the Department of Vascular Surgery at the Second Hospital of Shanxi Medical University. Eligible participants will be males or females aged between 18 and 85 years. Patients must be conscious, fully informed about the study, and willing to provide written informed consent. The study aims to enroll a diverse cohort to ensure representative findings. Exclusion criteria include non-atherosclerotic stenosis, prior interventional or surgical treatments for PAD, significant cardiac, hepatic, or renal dysfunction, acute infections, and other conditions that may confound the study results. Pregnant or breastfeeding women and individuals who have participated in other clinical trials within the past 3 months will also be excluded.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yijie Ning | Contact | 15735010056 | N15735010056@163.com | |
| Liuming Shi | Contact | shiliuming228@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Honglin Dong | Second Hospital of Shanxi Medical University | Principal Investigator |
| Ruijing Zhang | Second Hospital of Shanxi Medical University | Study Director |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39929338 | Background | Ning Y, Hu J, Zhu Y, Tang W, Yan S, Li H, Zhang Z, Lu C, Ren K, Shi P, Yao T, Wang Q, Zhao Y, Gao T, Zhang R, Dong H. NIR-II imaging-based detection of early changes in lower limb perfusion in type 2 diabetes patients without peripheral artery disease. Diabetes Res Clin Pract. 2025 Mar;221:112038. doi: 10.1016/j.diabres.2025.112038. Epub 2025 Feb 8. |
| Label | URL |
|---|---|
| Related Info | View source |
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Starting 1 year after publication
If there are researchers who need to get the shared data, please contact Yijie Ning via email and explain the purpose of using the data. We will send the shared data to your email.
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| ID | Term |
|---|---|
| D058729 | Peripheral Arterial Disease |
| D016893 | Carotid Stenosis |
| ID | Term |
|---|---|
| D050197 | Atherosclerosis |
| D001161 | Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |
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Peripheral blood (20-30 mL) will be collected from patients with PAD and CAS. Density gradient centrifugation will be used to isolate PBMCs for single-cell transcriptomics to analyze cell type-specific gene expression. Serum will be separated for proteomics, lipidomics, and metabolomics analyses to identify disease biomarkers and molecular pathways. PBMCs will be cryopreserved, and serum aliquots will be stored at -80°C. Sample quality will be ensured through cell viability assessment and visual inspection for hemolysis.
Laser speckle imaging (LSI) will be used to evaluate microvascular perfusion in the dorsum of the foot in patients with Peripheral Artery Disease (PAD). This non-invasive imaging technique will quantify blood flow dynamics in the microcirculation by analyzing the speckle contrast generated by laser illumination. The perfusion metrics, including fluorescence intensity (FI), will be derived from LSI to assess microvascular function. These measurements will provide insights into peripheral microvascular perfusion deficits and their correlation with disease severity, helping to identify functional impairments and evaluate therapeutic outcomes.
| Baseline, 6 months |
| Single-cell Transcriptomics for CAS and PAD patients | Gene expression levels will be quantified as transcripts per million (TPM) or reads per kilobase per million (RPKM). | Baseline, 6 months |
| Proteomics for CAS and PAD patients | Protein abundance will be measured in intensity units (AU) or nanograms per milliliter (ng/mL) | Baseline, 6 months |
| Lipidomics for CAS and PAD patients | Lipid species concentrations will be reported in micromoles per liter (µmol/L). | Baseline, 6 months |
| Metabolomics for CAS and PAD patients | Metabolite levels will be quantified in micromoles per liter (µmol/L). | Baseline, 6 months |
| D002318 |
| Cardiovascular Diseases |
| D016491 | Peripheral Vascular Diseases |
| D002340 | Carotid Artery Diseases |
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |