Cardiovascular Diseases, Microvascular Complications
Conditions
Keywords
microvascular, non-invasive, microcirculation, spatial frequency domain imaging, cardiac magnetic resonance imaging, laser speckle contrast imaging, laser doppler flowmetry
Brief summary
Microvascular dysfunction, particularly endothelial dysfunction, is increasingly recognized as a key mechanism underlying various cardiovascular diseases (CVD), including heart failure, ischemic heart disease, atherosclerosis, stroke, dementia, and kidney failure. Chronic low-grade inflammation linked to metabolic syndrome may further drive systemic microvascular impairment. Early detection of these subclinical processes using non-invasive assessments could facilitate timely interventions to prevent disease progression. SCAPIS 2 Spectrum is a prospective observational sub-study of the Swedish Cardiopulmonary Bioimage Study (SCAPIS-2), recruiting approximately 900 subjects aged 60-75 years. The study is organized into five arms-obstructive coronary artery disease (O-CAD), angina with nonobstructive coronary arteries (ANOCA), metabolic syndrome with diabetes, left ventricular systolic dysfunction, and left ventricular diastolic dysfunction-each defined by specific inclusion and exclusion criteria. Participants will undergo a comprehensive microvascular assessment using investigational devices (including Perimed Periflux EPOS, PeriCam MultiFlow, and TCI P4) alongside stress cardiac magnetic resonance imaging (stress-CMR) for cardiac-specific evaluation.
Detailed description
Non-invasive microvascular Assessment A comprehensive microvascular evaluation is performed using three investigational devices designed to capture detailed information on dermal perfusion. The Perimed Periflux 6000 EPOS employs diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF) for a single-point multi-modal assessment. The PeriCam MultiFlow performs imaging of dermal perfusion using multi-exposure laser contrast imaging (MELSCI) and measures blood oxygen saturation via multispectral imaging (MSI). Additionally, the TCI P4 utilizes spatial frequency domain imaging technology for quantification of perfusion and chromophore concentrations in the skin. Functional methods, including Post-Occlusive Reactive Hyperemia (PORH), Flow-Motion Analysis, and Thermal Provocation, are applied to assess dynamic microvascular responses. Cardiac Magnetic Resonance Imaging (CMR) Stress#CMR is conducted using adenosine infusion and contrast enhancement to evaluate cardiac-specific microvascular function. This approach includes first-pass perfusion imaging and quantitative myocardial blood flow analyses, which allow for calculation of myocardial perfusion reserve.
Interventions
None listed
Sponsors
HJN Sverige AB/Neko Health
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Participated in the SCAPIS baseline (SCAPIS 1 study, 10 years ago) * Cohort specific inclusion criteria apply, as stated in 'groups' section * Has been invited to SCAPIS 2 core Singed informed consent to SCAPIS 2 core
Exclusion criteria
* Scarring, tattoos, or amputations that preclude device examination * Cohort specific
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation between device-derived skin microcirculation variables and CCTA severity metrics including CAD-RADS (0-5) and plaque burden | Baseline | Assess the correlation between skin microcirculation parameters measured by investigational devices (icluding PORH; 42°C local heat plateau; flow-motion endothelial-band power; iontophoresis acetylcholine response), coronary CT angiography (CCTA) severity metrics: CAD-RADS score, 0-5, zero is equivalent to no plaque and 5 indicates complete occlusionan (fully blocked) and Plaque burden indices (e.g., Segment Involvement Score \[SIS\]) The device-derived microcirculation parameters are: Baseline perfusion, Perfusion after provocation, Peak perfusion, Time to peak, Recovery time, Capillary recruitment and Oxygen saturation (StO₂) The goal is to determine if these microcirculation measurements reflect coronary disease severity. |
| Discriminative performance of device-derived microcirculatory variables for significant coronary stenosis | Baseline | Evaluate the ability of microcirculatory variables measured by investigational devices to discriminate between patients with and without significant coronary stenosis, defined as CAD-RADS ≥ 3 (≥50% luminal narrowing on coronary CT angiography). |
| Proportion of ANOCA participants (CAD-RADS <3 with angina per Rose questionnaire) who meet CMD criteria on stress-cardiac MRI using perfusion assessment | Baseline | Calculate the proportion of participants classified as ANOCA (defined as CAD-RADS \< 3 and angina symptoms per Rose Angina Questionnaire) who fulfill criteria for coronary microvascular dysfunction (CMD) based on stress cardiac MRI perfusion assessment according to site-standard protocols |
| Proportion of INOCA participants (CAD-RADS <3 with ishemia per Rose questionnaire) who meet CMD criteria on stress-cardiac MRI using perfusion assessment. | Baseline and at stress cardiac MRI- visit within 3 months | Calculate the proportion of participants classified as INOCA (CAD-RADS \< 3 and objective ischemia evidence) who fulfill CMD criteria based on stress cardiac MRI perfusion assessment.. |
| Proportion of ANOCA participants who have INOCA | Baseline and at stress cardiac MRI- visit within 3 months | Among participants identified with ANOCA (non-obstructive coronary artery disease on CT angiography), the proportion who demonstrate myocardial ischemia (INOCA) will be calculated. Ischemia will be assessed using cardiac MRI stress perfusion imaging, which is considered the gold standard for myocardial microcirculatory dysfunction |
| Assess whether peripheral microvascular function reflects myocardial perfusion abnormalities in CMD and mild CAD | Baseline and at stress cardiac MRI- visit within 3 months | This outcome examines whether skin microcirculation parameters measured by investigational devices correlate with quantitative/ semiquantitative myocardial perfusion metrics obtained from stress cardiac-MRI in participants with: Coronary Microvascular Dysfunction (CMD) Non-obstructive coronary disease (CAD-RADS \<3) |
| Correlation between investigational device variables and presence of Coronary Microvascular Dysfunction (CMD). | Baseline and at stress cardiac MRI- visit within 3 months | Evaluate whether distinct cut-off values in device-derived microvascular parameters correlate with significant CMD. CMD will be defined by cardiac MRI stress perfusion imaging. The following variables will be assessed using investigational devices |
| Correlation of skin microcirculation variables with echocardiographic marker for diastolic function | Baseline | Assess the general correlation between investigational device variables and E/é ratio (echocardiographic marker) to assess diastolic function. A high E/é ratio suggests elevated left atrial pressure and impaired relaxation (diastolic dysfunction). A low E/é ratio indicates normal filling pressures. |
| Association of skin microcirculation variables with elevated echocardiographic marker for diastolic dysfunction. | Baseline | Determine whether investigationnal device-derived variables correlate with E/é \> 15, indicative of increased left ventricular filling pressures. |
| Correlation of skin microcirculation variables with biomarker for cardiac stress | Baseline | Assess general correlation between investigationnal device-derived variables and ProBNP as a biomarker of cardiac stress. |
| Association of skin microcirculation variables with elevated biomarker for measuring cardiac stress | Baseline | Assess general correlation between investigationnal device-derived variables s and ProBNP as a biomarker of cardiac stress. |
| Identification of device variable thresholds predicting diastolic dysfunction/heart failure | Baseline and at stress cardiac MRI- visit within 3 months | Evaluate whether specific cut-off values of investigational device-derived microcirculation variables are associated with confirmed dysfunction/heart failure |
| Correlation of skin microcirculation variables with high levels of a biomarker for average blood glucose | Baseline | Calculate the correlation between the respective variables of the investigational devices and the incidence of HbA1c being \> 65 mmol/mol. |
| Correlation of device-derived microcirculation variables with nephropathy | Baseline | Calculate the correlation between the respective variables of the investigational devices and the incidence of nephropathy |
| Correlation betweed device-derived microcirculatory variables with endothelial/glycocalyx/inflammation biomarkers | Baseline | Correlation between microcirculatory variables from investigational devices and biomarker panels (thrombomodulin, circulating endothelial cells, VE-cadherin, syndecan-1, hyaluronan, hsCRP, IL-6, GlycA). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlation of Myogenic Response with Cardiovascular Disease | Baseline | Asses the association between myogenic response from device-derived variables and cardiovascular disease incidence; includes incidence of myogenic vs endothelial dysfunction. |
| Association of Vascular Inflammation with Microcirculatory Variables | Baseline | Correlation between systemic inflammation biomarkers (e.g., hsCRP, IL-6, GlycA) and device-derived microcirculatory measures; exploratory detection of early inflammatory alterations. |
| Mechanisms of endothelial damage and Cardiovascular Disease devlopment | Baseline | Analysis of biomarker panels (endothelial/glycocalyx damage: thrombomodulin, VE-cadherin, syndecan-1, hyaluronan) to identify progression patterns and explore the correlation with device-derived microcirculatory variables. |
| Development of multi-modal Cardiovascular Disease risk score | Baseline | Creation and validation of a composite risk score integrating microcirculatory variables derived from investigationa devices and biomarker data; evaluation of predictive performance for Cardiovascular Disease endpoints. |
Countries
Sweden
Contacts
CONTACTMattias Windå, Chief Science and Innovation Officer
Outcome results
None listed