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Coronary Artery DiseaseSCAPIS-ReExNCT07476703

Expanded Studies on the SCAPIS Stockholm Reexamination Cohort

An observational study of Coronary Artery Disease and Arterial Stiffness, Blood Pressure, run by Danderyd Hospital at 1 site in Sweden.

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Under way, not looking for participants. Everyone needed has enrolled. The study is still running.

Official record

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Trial summary

The Swedish CArdioPulmonary bioImage Study (SCAPIS) is a unique, large-scale national research initiative involving 30,000 randomly selected individuals aged 50-64, recruited between 2014 and 2018. The study is a collaborative effort among six university hospitals across Sweden. Read more about this study

Who is it for?
Ages 56 to 74 yearsHealthy volunteers can take part
What is being tested?
Coronary computer tomography (CCTA), Echocardiograhy and aortic ultrasound and Liver elastography
How many people
1,400 participants
Where
1 siteSweden
Study dates
8 Apr 2024to 30 May 2027
Phase
Observational

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 56 to 74 years
  • All sexes
  • Healthy volunteers accepted
  • Adult, Older adult

Study population: The Swedish CArdioPulmonary bioImage Study (SCAPIS) is a unique, large-scale national research initiative involving 30,000 randomly selected individuals aged 50-64, recruited between 2014 and 2018. A follow-up study, SCAPIS 2, is conducted for half of the original participants. In Stockholm, 2,500 individuals will be re-examined at Danderyd University Hospital and Karolinska Institutet.

Inclusion Criteria: Subjects already included in the main / general SCAPIS reexamination study in Stockholm.

Exclusion Criteria: Inability to provide consent.

Trial locations and contacts

1 site in 1 country.

Sweden1 site
  • Cardiovascular Research Lab, Dept Cardiology, Danderyd University Hospital

    Stockholm, 18288

Other trials in Sweden

About this study

Official title: Expanded Studies of Risk Factors for Pulmonary-, Liver-, and Cardiovascular Disease in the SCAPIS 2 Stockholm Reexamination

The Swedish CArdioPulmonary bioImage Study (SCAPIS) is a unique, large-scale national research initiative involving 30,000 randomly selected individuals aged 50-64, recruited between 2014 and 2018. The study is a collaborative effort among six university hospitals across Sweden. A follow-up study, SCAPIS 2, is conducted for half of the original participants. In Stockholm, 2,500 individuals will be re-examined at Danderyd University Hospital and Karolinska Institutet.

SCAPIS 2 includes a core set of examinations involving blood sampling, questionnaires, and imaging. In addition to these, complementary local investigations are conducted to enable more detailed research questions. This protocol describes the additional studies conducted in the Stockholm cohort. All complementary assessments aim to identify risk factors for current and future lung, liver, and cardiovascular diseases.:

Read more

EXTENDED SAMPLING: Saliva and Blood Samples with Blood Cell Isolation. EXTENDED QUESTIONNAIRES: Dyspnea, Sleep, Respiratory Infections, and Dental Health.

EXTENDED IMAGING AND PHYSIOLOGICAL MEASUREMENTS Cardiac Ultrasound and Abdominal Aortic Measurements. Liver Elastography. Vascular Stiffness by cuff-based pulse wave analysis and Photoplethysmography (PPG). Valvular and Vascular Calcification by CT imaging.

Read the detailed description

The Swedish CArdioPulmonary bioImage Study (SCAPIS) is a unique, large-scale national research initiative involving 30,000 randomly selected individuals aged 50-64, recruited between 2014 and 2018. The study is a collaborative effort among six university hospitals across Sweden. A follow-up study, SCAPIS 2, is conducted for half of the original participants. In Stockholm, 2,500 individuals will be re-examined at Danderyd University Hospital and Karolinska Institutet.

SCAPIS 2 includes a core set of examinations involving blood sampling, questionnaires, and imaging. In addition to these, complementary local investigations are conducted to enable more detailed research questions. This protocol describes the additional studies conducted in the Stockholm cohort:

EXTENDED SAMPLING: Saliva and Blood Samples with Blood Cell Isolation Additional blood samples will be collected to isolate blood cells for detailed immune system profiling, red blood cell function nd for lipid profiles and inflammatory markers. Saliva samples will be analyzed for inflammatory markers and microbial composition.

EXTENDED QUESTIONNAIRES: Dyspnea, Sleep, Respiratory Infections, and Dental Health Participants will complete detailed questionnaires covering recent food, drink, and medication intake; oral hygiene; breathing and lung function; sleep quality and daytime somnolence; and history of respiratory infections, including COVID-19.

EXTENDED IMAGING AND PHYSIOLOGICAL MEASUREMENTS: These investigations will be conducted during a separate visit, coordinated with the main study, and take approximately one hour.

  1. Cardiac Ultrasound and Abdominal Aortic Measurements: cardiac structure and function, as well as the diameter of major arteries (e.g., the thoracic and abdominal aorta).
  2. Liver Elastography: An ultrasound-based technique used to assess liver stiffness and fat content.
  3. Vascular Stiffness: Blood pressure cuff-based pulse wave analysis with automated stiffness calculation. Photoplethysmography (PPG) using skin-mounted sensors (commonly used for oxygen saturation monitoring, in cuffless blood pressure monitors and wearables). Single-lead ECG via adhesive electrodes.
  4. Valvular and Vascular Calcification: In the main SCAPIS study, CT imaging of the heart and large vessels will be performed. In SCAPIS 2, images will be re-analyzed using algorithms that quantify calcium deposits in heart valves.

RESEARCH QUESTIONS

Blood and blood cell analyses will be conducted to assess immune activation, lipids and lipid mediators, and immune cell phenotypes. These analyses aim to distinguish chronic from self-limiting cardiovascular inflammation and to detect prevalent, incident, and subclinical cardiovascular disease (CVD) for improved risk assessment.

Saliva biomarkers, with a focus on inflammation and oral microbiota, will be studied to explore links between oral health, respiratory health, systemic inflammation, and comorbidities.

Survey data on sleep, breathing, respiratory infections, and oral health will be used to investigate associations with liver, lung, and cardiovascular diseases, including their risk factors and prognosis.

The prevalence of liver steatosis and fibrosis in the general population wil be examined, along with their associations with CVD and heart failure. It will also be assess whether SCAPIS data can predict liver stiffness.

Risk factors for cardiac dysfunction and valvular disease, as well as progression to heart failure will be analyzed. CT-based calcium scoring in the aorta and valves will be studied for associations to valve disease, heart failure, intervention timing, and progression of aortic dilation between SCAPIS 1 and 2.

Aortic aneurysm prevalence and risk factors will be evaluated, including associations with SCAPIS variables and incident cardiovascular disease. Infrarenal aortic diameter is also used as a subclinical marker of aortic aneurysm.

New techniques using photoplethysmography (PPG) for arterial stiffness and pulse characteristics will be studied in relation to coronary artery disease, cardiac structure and function, valve disease, liver stiffness, biomarkers, and blood pressure.

Finally, SCAPIS and SCAPIS 2 data will be integrated including substudy results for comprehensive risk assessment of clinical and subclinical CVD and to guide prevention strategies.

Being tested
Coronary computer tomography (CCTA), Echocardiograhy and aortic ultrasound, Liver elastography, Arterial stiffness
Keywords
coronary artery disease, arterial stiffness, MASLD, COPD, inflammation, dyslipidemia, aortic aneurysm, oral microbiota
Medical subject headings
Coronary Artery Disease, Aortic Valve Disease, Heart Failure, Lung Diseases, Pulmonary Disease, Chronic Obstructive, Fatty Liver, Inflammation, Dyslipidemias

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Coronary computer tomography (CCTA)Diagnostic test

    Siemens Naeotom Alpha Photon Counting CT scanner

  • Echocardiograhy and aortic ultrasoundDiagnostic test

    Echocardiograhy and aortic ultrasound using General Electric Vivid E95

  • Liver elastographyDiagnostic test

    Liver elastography using Fibroscan ultrasound method.

  • Arterial stiffnessDiagnostic test

    Arterial stiffness using Arteriograph, Tensiomed, Hungary and Photoplethysmography and ECG by ADInstrument

Study groups in NCT07476703
Study groupRoleWhat they receive
SCAPIS Stockholm reexamination substudyIn SCAPIS (2014 -2018) 30.000 randomly selected adults between the ages of 50 - 64 were included. SCAPIS Reexamination is the re-assessment study of half of the original study cohort. In Stockholm (Danderyd University Hospital site) that corresponds to 2500 adults that are now between 6-10 years older than in the first SCAPIS study. The present study is a prospective, observational substudy of a convenient sample from the SCAPIS reexamination in Stockholm.
Receives: Diagnostic Test: Coronary computer tomography (CCTA); Diagnostic Test: Echocardiograhy and aortic ultrasound; Diagnostic Test: Liver elastography; Diagnostic Test: Arterial stiffness

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Diagnostic Performance of Machine Learning Analysis of Finger Photoplethysmography for Detection of Coronary Artery Disease (CAD-RADS ≥2)

    Area under the receiver operating characteristic curve (AUC-ROC) for prediction of CAD-RADS ≥2 using machine learning analysis of finger photoplethysmography. CAD-RADS 2.0 classification is based on coronary computed tomography angiography (CCTA), where stenosis severity is graded 0-5, atherosclerotic burden is quantified using segment involvement score (SIS) and calcification using Agatston Units (CAC-score).

    Measured: Typically within 1 months of enrollment.

  2. Long-Term Incident Major Adverse Renal and Cardiovascular Events (MARCE)

    Incidence of major adverse renal and cardiovascular events during follow-up. Associations will be evaluated with baseline immune mediators, lipid mediators, oral microbiota composition, vascular stiffness, steatohepatitis, and fibrosis measures.

    Measured: 3-8 years

  3. Immune Activation Biomarkers and Prevalent Atherosclerotic Cardiovascular Disease

    Correlation between circulating biomarkers of immune activation and the degree of calcification and/or atherosclerosis at examined cardiovascular sites. Atherosclerosis and calcification are assessed by CCTA and/or ultrasound imaging and/or prior diagnosis of atherosclerotic cardiovascular disease. Immune profiling distinguishes resolving and non-resolving inflammatory phenotypes.

    Measured: Typically within 1 months of enrollment

  4. Lipid Mediator Profiles and Prevalent Atherosclerotic Cardiovascular Disease

    Correlation between circulating lipid mediator profiles and degree of calcification and/or atherosclerosis at examined cardiovascular sites. Lipid profiling characterizes inflammatory phenotypes and evaluates associations with prior diagnosis of atherosclerotic cardiovascular disease, calcification, and/or imaging-defined atherosclerosis assessed by CCTA and/or ultrasound.

    Measured: Typically within 1 months of enrollment

  5. Correlation Between Oral Microbiota Composition and Prevalent Coronary Artery Disease

    Correlation between oral viral, fungal, and bacterial microbiota composition and degree of coronary artery disease as assessed by CCTA (CAD-RADS, SIS and CAC-score).

    Measured: Typically within 1 months of enrollment

  6. Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)

    Prevalence of increased estimated hepatic lipid content defined as controlled attenuation parameter (CAP) >285 dB/m measured by transient elastography (FibroScan).

    Measured: Typically within 1 months of enrollment

  7. Prevalence of Abdominal Aortic Aneurysm

    Prevalence of abdominal aortic aneurysm ≥ 30 mm as determined by abdominal ultrasound imaging.

    Measured: Typically within 1 months of enrolment

Secondary outcomes (28)
  1. Multimodal artificial intelligence (AI) model for prediction of coronary artery disease (CAD)

    Machine learning methods will be applied to identify the most informative predictors of CAD. Multimodal data, including ECG-derived features, lipid mediator measurements, inflammatory biomarkers, ultrasound variables, and oral microbiota profiles, will be used to develop and evaluate a prediction model for CAD, defined as CAD-RADS ≥2. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination and calibration. Clinical decision-curve analysis will be used to assess the clinical utility of the model by estimating net benefit across relevant decision thresholds and comparing model-guided decisions with current clinical practice (the pooled cohort equation).

    Measured: Typically within 1 month of enrollment

  2. Photoplethysmograpy (PPG) to predict prevalent coronary artery disease

    Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 and SIS and CAC-score when adding PPG features to age and sex.

    Measured: Typically within 3 months of enrollment

  3. Incremental Predictive Value of Photoplethysmography Beyond Traditional Cardiovascular Risk Models

    Improvement in AUC-ROC for predicting CAD-RADS category, segment involvement score (SIS), and coronary artery calcium (CAC) score when adding machine learning-derived PPG features to traditional cardiovascular risk factors including the Pooled Cohort Equation.

    Measured: Typically within 1 month of enrollment

  4. Lipid Mediator Profiles and Coronary Artery Disease Severity

    Association between circulating lipid mediator profiles including plasma remnant cholesterol and coronary artery disease severity assessed by CCTA using CAD-RADS, SIS and CAC-score.

    Measured: SCAPIS baseline and SCAPIS2 re-examination (8-10 years).

  5. Arterial stiffness

    Associations between immune mediators, lipid mediators, oral microbiota, and pulse wave velocity by Arteriograph and PPG features of arterial stiffness.

    Measured: Typically within 3 months of enrollment

  6. Prevalence of valvular disease and aortic calcification

    Prevalende of mild, -moderate and severe aortic and mitral stenosis and insufficiency as determined by cardiac echo in relation to valvular and aortic calcification.

    Measured: Typically within 1 month of enrollment

  7. Machine learning to predict aortic and mitral valve disease

    Machine learning to predict and to identify top predictors for aortic stenosis, insufficiency and mitral stenosis of insufficiency

    Measured: Typically within 1 month of enrollment

  8. Agreement between CCTA and cardiac doppler ultrasound

    Agreement between valvular characterisation on CCTA and degree of aortic and mitral stenosis and insufficiency by cardiac ultrasond

    Measured: Typically within 3 months of enrollment

  9. Photoplethysmography (PPG) and aortic valve disease

    Finger PPG features will be will be used to develop and evaluate a machine learning prediction model for aortic valvular disease (stenosis or insufficiency) detected on cardiac ultraound. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination (Receiver Operating Characteristic) and calibration.

    Measured: Typically within 3 months of enrollment

  10. Prevalence and predictors of thoracic aortic dilatation

    The prevalence of thoracic aortic dilatation (>39 mm) will be assessed in the study population using coronary CT angiography (CCTA). Available multimodal data, including clinical characteristics, ECG-derived features, physiological measurements, laboratory biomarkers, and imaging variables, will be used to identify independent predictors of thoracic aortic dilatation and to develop prediction models. Model development will include cross-validation to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination (Receiver Operating Characteristics) and calibration.

    Measured: Typically within 3 months of enrollment

  11. Left ventricular mass and hypertension

    Prevalence and associations between blood pressure, hypertension diagnosis and left ventricular mass index

    Measured: Typically within 3 months of enrollment

  12. Left ventricular and left atrial strain

    Prevalence and associations between left ventricular and left atrial strain and baseline characteristics

    Measured: Typically within 3 months of enrollment

  13. Correlation between arterial stiffness and left ventricular hypertrophy

    Carotid femoral pulse wave velocity (PWV) in relation to left ventricular mass index (LVMI), as assessed by echocardiography.

    Measured: Typically within 3 months of enrollment

  14. Photoplethysmography and cardiac function

    Photoplethysmography (PPG)-derived features will be used to develop and evaluate prediction models for cardiac function, including left ventricular ejection fraction (LVEF) and degree of diastolic dysfunction (graded by echocardiography). Machine learning methods will be applied to identify the most informative predictors. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of (Receiver Operation Caracteristics) and calibration.

    Measured: Typically within 3 months of enrollment

  15. Prevalence of hepatic fibrosis

    Prevalence of increased estimated hepatic stiffness defined as ≥8 kPa by liver elastography (Fibroscan)

    Measured: Typically within 3 months of enrollment

  16. Predictors of hepatic disease

    Associations between baseline characteristics immune mediators, lipid mediators, oral microbiota, vascular stiffness, and prevalent hepatic disease

    Measured: Typically within 3 months of enrollment

  17. Prevalence of abdominal aortic subaneurysm

    Prevalence of abdominal aortic subaneurysm ≥ 25 mm as determined by abdominal ultrasound

    Measured: Typically within 3 months of enrollment

  18. Predictors and factors associated with infrarenal aortic diameter

    Prediction of Infrarenal aortic diameter as measured by ultrasound using all available subject data.

    Measured: Typically within 3 months of enrollment

  19. Association of oral dysbiosis and proteome deregulation with respiratory health, lung imaging, lung function and respiratory symptoms.

    Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and lung health, as presented by symptoms captured by the questionnaires, lung function and lung imaging.

    Measured: Typically, within 12 months of enrolment

  20. Prevalence of oral dysbiosis and proteome deregulation, and their associations with airway diseases such as asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, and lung parenchymal diseases.

    Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and prevalence and presentation of airway and lung parenchyma diseases

    Measured: Typically, within 12 months of enrolment

  21. Association of oral dysbiosis and proteome deregulation with respiratory health and systemic inflammation

    Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and prevalence of airway and lung parenchyma diseases and the presence of systemic inflammation, as measured by blood inflammatory proteins and urine mediators.

    Measured: Typically, within 12 months of enrolment

  22. Association of oral dysbiosis and proteome deregulation with respiratory health and comorbidities

    Association between changes in indexes in microbiome dysbiosis, increase in selected pathogen bacterial counts and relative abundances, and increase in specific inflammatory protein concentrations and prevalence of airway diseases and comorbidities, such as cardiovascular, inflammatory and immune-mediated systemic diseases.

    Measured: Typically, within 12 months of enrolment

  23. Targeted plasma inflammatory markers to predict prevalent coronary artery disease

    Targeted plasma inflammatory protein panel quantified using Olink Target 48 Cytokine Panels (PEA technology) together with lipid profiling to predict CADRADS, SIS and CAC-score.

    Measured: Typically within 3 months of enrollment

  24. Gene expression profiling in peripheral blood mononuclear cells (PBMC) and remnant cholesterol

    Gene expression profiling in peripheral blood mononuclear cells (PBMC) to characterize immune/inflammatory signatures associated with remnant cholesterol levels.

    Measured: Typically within 3 months of enrollment

  25. Additional value of Plasma Apolipoproteins to Predict Prevalent Coronary Artery Disease

    Additional valie of plasma levels of ApoB, ApoAI, ApoE, and ApoCIII quantified by commercially available ELISA kits to predict prevalent CAD as CADRADS, SIS- and CAC-score.

    Measured: Typically within 2 months of enrollment

  26. Prevalence of lipid composition

    Prevalence of lipoprotein lipid composition (total cholesterol, free cholesterol, cholesteryl esters, triglycerides, phospholipids) and surface/core ratio metrics analyzed by size-exclusion chromatography.

    Measured: Typically within 3 months of enrollment

  27. Erythrocyte Dysfunction and Coronary Artery Disease

    Association between measures of erythrocyte function and coronary artery disease severity assessed by CCTA using CAD-RADS, SIS and CAC-score

    Measured: Typically within 3 months of enrollment

  28. Machine learning of photoplethysmography (PPG) to predict mitral valve disease

    Finger PPG features will be will be used to develop and evaluate a machine learning prediction model for mitral valv disease (stenosis or insufficiency) detected on cardiac ultraound. Cross-validation will be used to reduce the risk of overfitting. Model performance will be evaluated using measures of discrimination (Receiver Operating Characteristic) and calibration.

    Measured: Typically within 3 months of enrolment

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted17 Mar 2026
  2. Study start8 Apr 2024
  3. Primary completion30 Mar 2026
  4. Study completion30 May 2027 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
1,400 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07476703
Sponsor's protocol ID
2024-01556-01
Acronym
SCAPIS-ReEx
Status last verified
Oct 2026
First submitted
2 Mar 2026
Last updated
21 Oct 2026
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Yes: After 6 years most data will be available, pending relevant ethical approvals, through the data-sharing platform at scapis.org

Publications

  • Bergstrom G, Berglund G, Blomberg A, Brandberg J, Engstrom G, Engvall J, Eriksson M, de Faire U, Flinck A, Hansson MG, Hedblad B, Hjelmgren O, Janson C, Jernberg T, Johnsson A, Johansson L, Lind L, Lofdahl CG, Melander O, Ostgren CJ, Persson A, Persson M, Sandstrom A, Schmidt C, Soderberg S, Sundstrom J, Toren K, Waldenstrom A, Wedel H, Vikgren J, Fagerberg B, Rosengren A. The Swedish CArdioPulmonary BioImage Study: objectives and design. J Intern Med. 2015 Dec;278(6):645-59. doi: 10.1111/joim.12384. Epub 2015 Jun 19.

    PubMed 26096600

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