Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Region Stockholm | OTHER_GOV |
| Karolinska Institutet | OTHER |
| Göteborg University | OTHER |
Not provided
Not provided
Not provided
Not provided
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.:
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.
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.
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| SCAPIS Stockholm reexamination substudy | In 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Coronary computer tomography (CCTA) | Diagnostic Test | Siemens Naeotom Alpha Photon Counting CT scanner |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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). | Typically within 1 months of enrollment. |
| 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. | 3-8 years |
| 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. | Typically within 1 months of enrollment |
| 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. | Typically within 1 months of enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| 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). |
Not provided
Inclusion Criteria: Subjects already included in the main / general SCAPIS reexamination study in Stockholm.
Exclusion Criteria: Inability to provide consent.
Not provided
Not provided
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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jonas Spaak, MD, PhD, Professor | Contact | +4681235500 | jonas.spaak@ki.se |
| Name | Affiliation | Role |
|---|---|---|
| Rebecka Hultgren | Karolinska Institutet | Principal Investigator |
| Karin Leander | Karolinska Institutet | Principal Investigator |
| Paolo Parini |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cardiovascular Research Lab, Dept Cardiology, Danderyd University Hospital | Recruiting | Stockholm | 18288 | Sweden |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26096600 | Background | 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. |
| Label | URL |
|---|---|
| List to study website including data platform and published studies | View source |
Not provided
After 6 years most data will be available, pending relevant ethical approvals, through the data-sharing platform at scapis.org
Some data is already available, some core data will be released fall 2026, all data will be available 2032
All researchers based in Sweden, or international researchers in collaboration with a researcher based in Sweden, are welcome to apply for data. An approval from the Swedish Ethical Review Board (Etikprövningsmyndigheten) for the research project is obligatory before an application for data from SCAPIS
Not provided
Not provided
Not provided
Not provided
Blood, plasma, saliva.
| Echocardiograhy and aortic ultrasound | Diagnostic Test | Echocardiograhy and aortic ultrasound using General Electric Vivid E95 |
|
| Liver elastography | Diagnostic Test | Liver elastography using Fibroscan ultrasound method. |
|
| Arterial stiffness | Diagnostic Test | Arterial stiffness using Arteriograph, Tensiomed, Hungary and Photoplethysmography and ECG by ADInstrument |
|
| 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). | Typically within 1 months of enrollment |
| 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). | Typically within 1 months of enrollment |
| Prevalence of Abdominal Aortic Aneurysm | Prevalence of abdominal aortic aneurysm ≥ 30 mm as determined by abdominal ultrasound imaging. | Typically within 1 months of enrolment |
| Typically within 1 month of enrollment |
| 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. | Typically within 3 months of enrollment |
| 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. | Typically within 1 month of enrollment |
| 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. | SCAPIS baseline and SCAPIS2 re-examination (8-10 years). |
| Arterial stiffness | Associations between immune mediators, lipid mediators, oral microbiota, and pulse wave velocity by Arteriograph and PPG features of arterial stiffness. | Typically within 3 months of enrollment |
| 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. | Typically within 1 month of enrollment |
| 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 | Typically within 1 month of enrollment |
| 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 | Typically within 3 months of enrollment |
| 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. | Typically within 3 months of enrollment |
| 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. | Typically within 3 months of enrollment |
| Left ventricular mass and hypertension | Prevalence and associations between blood pressure, hypertension diagnosis and left ventricular mass index | Typically within 3 months of enrollment |
| Left ventricular and left atrial strain | Prevalence and associations between left ventricular and left atrial strain and baseline characteristics | Typically within 3 months of enrollment |
| 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. | Typically within 3 months of enrollment |
| 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. | Typically within 3 months of enrollment |
| Prevalence of hepatic fibrosis | Prevalence of increased estimated hepatic stiffness defined as ≥8 kPa by liver elastography (Fibroscan) | Typically within 3 months of enrollment |
| Predictors of hepatic disease | Associations between baseline characteristics immune mediators, lipid mediators, oral microbiota, vascular stiffness, and prevalent hepatic disease | Typically within 3 months of enrollment |
| Prevalence of abdominal aortic subaneurysm | Prevalence of abdominal aortic subaneurysm ≥ 25 mm as determined by abdominal ultrasound | Typically within 3 months of enrollment |
| Predictors and factors associated with infrarenal aortic diameter | Prediction of Infrarenal aortic diameter as measured by ultrasound using all available subject data. | Typically within 3 months of enrollment |
| 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. | Typically, within 12 months of enrolment |
| 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 | Typically, within 12 months of enrolment |
| 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. | Typically, within 12 months of enrolment |
| 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. | Typically, within 12 months of enrolment |
| 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. | Typically within 3 months of enrollment |
| 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. | Typically within 3 months of enrollment |
| 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. | Typically within 2 months of enrollment |
| 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. | Typically within 3 months of enrollment |
| 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 | Typically within 3 months of enrollment |
| 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. | Typically within 3 months of enrolment |
| Karolinska Institutet |
| Principal Investigator |
| Daniel Andersson | Karolinska Institutet | Principal Investigator |
| Matteo Pedrelli | Karolinska Institutet | Principal Investigator |
| Apostolos Bossios | Karolinska Institutet | Principal Investigator |
| Georgios Belibasakis | Karolinska Institutet | Principal Investigator |
| Tomas Jernberg | Karolinska Institutet | Principal Investigator |
| Hannes Hagström | Karolinska Institutet | Principal Investigator |
| Magnus Bäck | Karolinska Institutet | Principal Investigator |
| Bahira Shahim | Karolinska Institutet | Principal Investigator |
| Zhichao Zhou | Karolinska Institutet | Principal Investigator |
| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| D000082862 | Aortic Valve Disease |
| D006333 | Heart Failure |
| D008171 | Lung Diseases |
| D029424 | Pulmonary Disease, Chronic Obstructive |
| D005234 | Fatty Liver |
| D007249 | Inflammation |
| D050171 | Dyslipidemias |
| D001014 | Aortic Aneurysm |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D001161 | Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |
| D006349 | Heart Valve Diseases |
| D012140 | Respiratory Tract Diseases |
| D008173 | Lung Diseases, Obstructive |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D008107 | Liver Diseases |
| D004066 | Digestive System Diseases |
| D052439 | Lipid Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D000783 | Aneurysm |
| D001018 | Aortic Diseases |
Not provided
Not provided
| ID | Term |
|---|---|
| D059289 | Vascular Stiffness |
| ID | Term |
|---|---|
| D002320 | Cardiovascular Physiological Phenomena |
| D002943 | Circulatory and Respiratory Physiological Phenomena |
Not provided
Not provided