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. 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.
Study Type
OBSERVATIONAL
Enrollment
1,400
Siemens Naeotom Alpha Photon Counting CT scanner
Echocardiograhy and aortic ultrasound using General Electric Vivid E95
Liver elastography using Fibroscan ultrasound method.
Arterial stiffness using Arteriograph, Tensiomed, Hungary and Photoplethysmography and ECG by ADInstrument
Cardiovascular Research Lab, Dept Cardiology, Danderyd University Hospital
Stockholm, Sweden
RECRUITINGDiagnostic 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).
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: Typically within 1 months of enrollment
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).
Time frame: 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).
Time frame: Typically within 1 months of enrollment
Prevalence of Abdominal Aortic Aneurysm
Prevalence of abdominal aortic aneurysm ≥ 30 mm as determined by abdominal ultrasound imaging.
Time frame: Typically within 1 months of enrolment
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).
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: Typically within 3 months of enrollment
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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.
Time frame: 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
Time frame: 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
Time frame: 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.
Time frame: 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.
Time frame: Typically within 3 months of enrollment
Left ventricular mass and hypertension
Prevalence and associations between blood pressure, hypertension diagnosis and left ventricular mass index
Time frame: 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
Time frame: 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.
Time frame: 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.
Time frame: Typically within 3 months of enrollment
Prevalence of hepatic fibrosis
Prevalence of increased estimated hepatic stiffness defined as ≥8 kPa by liver elastography (Fibroscan)
Time frame: 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
Time frame: Typically within 3 months of enrollment
Prevalence of abdominal aortic subaneurysm
Prevalence of abdominal aortic subaneurysm ≥ 25 mm as determined by abdominal ultrasound
Time frame: 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.
Time frame: 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.
Time frame: 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
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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
Time frame: 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.
Time frame: Typically within 3 months of enrolment