Non-invasive Microvascular Assessment in Individuals at High Risk of Cardiovascular Disease From the SCAPIS2 Study

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.

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).

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

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..

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

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)

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

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.

Determine whether investigationnal device-derived variables correlate with E/é > 15, indicative of increased left ventricular filling pressures.

Assess general correlation between investigationnal device-derived variables and ProBNP as a biomarker of cardiac stress.

Assess general correlation between investigationnal device-derived variables s and ProBNP as a biomarker of cardiac stress.

Evaluate whether specific cut-off values of investigational device-derived microcirculation variables are associated with confirmed dysfunction/heart failure

Calculate the correlation between the respective variables of the investigational devices and the incidence of HbA1c being > 65 mmol/mol.

Calculate the correlation between the respective variables of the investigational devices and the incidence of nephropathy

Correlation between microcirculatory variables from investigational devices and biomarker panels (thrombomodulin, circulating endothelial cells, VE-cadherin, syndecan-1, hyaluronan, hsCRP, IL-6, GlycA).

Asses the association between myogenic response from device-derived variables and cardiovascular disease incidence; includes incidence of myogenic vs endothelial dysfunction.

Correlation between systemic inflammation biomarkers (e.g., hsCRP, IL-6, GlycA) and device-derived microcirculatory measures; exploratory detection of early inflammatory alterations.

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.

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.