| Literature DB >> 26096600 |
G Bergström1,2, G Berglund3, A Blomberg4, J Brandberg5,6, G Engström3, J Engvall7,8,9, M Eriksson10, U de Faire11,12, A Flinck5,6, M G Hansson13, B Hedblad3, O Hjelmgren1,2, C Janson14, T Jernberg12,15, Å Johnsson5,6, L Johansson16, L Lind17, C-G Löfdahl3,18, O Melander3,19, C J Östgren8, A Persson9,20, M Persson3,19, A Sandström21, C Schmidt1, S Söderberg21, J Sundström17,22, K Toren23, A Waldenström24, H Wedel25, J Vikgren5,6, B Fagerberg1, A Rosengren1.
Abstract
Cardiopulmonary diseases are major causes of death worldwide, but currently recommended strategies for diagnosis and prevention may be outdated because of recent changes in risk factor patterns. The Swedish CArdioPulmonarybioImage Study (SCAPIS) combines the use of new imaging technologies, advances in large-scale 'omics' and epidemiological analyses to extensively characterize a Swedish cohort of 30 000 men and women aged between 50 and 64 years. The information obtained will be used to improve risk prediction of cardiopulmonary diseases and optimize the ability to study disease mechanisms. A comprehensive pilot study in 1111 individuals, which was completed in 2012, demonstrated the feasibility and financial and ethical consequences of SCAPIS. Recruitment to the national, multicentre study has recently started.Entities:
Keywords: cardiovascular; epidemiology; metabolism; pulmonary; trial design
Mesh:
Year: 2015 PMID: 26096600 PMCID: PMC4744991 DOI: 10.1111/joim.12384
Source DB: PubMed Journal: J Intern Med ISSN: 0954-6820 Impact factor: 8.989
Principal aims of SCAPIS
| To use advanced imaging methods to examine atherosclerosis in the coronary and carotid arteries together with information obtained by proteomics/metabolomics/genomics technologies to improve risk prediction for CVD |
| To use advanced imaging methods to examine pulmonary tissue in combination with functional measurements and information obtained by proteomics/metabolomics/genomics technologies to improve diagnosis and risk prediction for COPD |
| To use advanced imaging methods to examine fat deposits together with information obtained by proteomics/metabolomics/genomics technologies to improve understanding of the role of obesity and diabetes in CVD and COPD |
| To improve the understanding of the epidemiology of CVD and COPD |
| To improve the understanding of underlying mechanisms of disease in CVD and COPD |
| To evaluate the cost‐effectiveness and ethics of using new imaging methods and proteomics/metabolomics/genomics technologies in prevention of CVD and COPD |
CVD, cardiovascular disease; COPD, chronic obstructive pulmonary disease.
Figure 1Overview of the information collected from the subjects in SCAPIS. MRI, magnetic resonance imaging; CT, computed tomography; CCTA, coronary computed tomography angiography; ECG, electrocardiogram; HbA1c, glycated haemoglobin; hsCRP, high‐sensitivity C‐reactive protein.
Baseline characteristics of participants observed in the SCAPIS pilot trial and predicted in the future total cohort
| SCAPIS pilot | Predicted in total SCAPIS cohort | |
|---|---|---|
|
|
| |
| Previous CVD (MI, stroke) | 27 (2.4) | 750 |
| Atrial fibrillation | 27 (2.4) | 750 |
| Dyslipidaemia (total) | 289 (26) | 7800 |
| Dyslipidaemia (treated) | 103 (9) | 2790 |
| Hypertension (total) | 370 (33) | 10 200 |
| Hypertension (treated) | 249 (22) | 6600 |
| Diabetes | 87 (8) | 2340 |
| Current smoking | 200 (18) | 5400 |
| Obesity (BMI >30 kg m−2) | 244 (22) | 6600 |
CVD, cardiovascular disease; MI, myocardial infarction; BMI, body mass index.
Previous CVD, atrial fibrillation, hypertension and diabetes data are based on self‐reported health (questionnaire or interview) in combination with laboratory results. Obesity data are derived from direct measurements.
Extrapolation of pilot data to obtain predicted number of participants with different conditions in the total SCAPIS cohort.
Comparison of large population‐based imaging studies
| MESA | Dallas heart study | BioImaging | SCAPIS | |
|---|---|---|---|---|
| Start | 2000 | 2002 | 2008 | 2014 |
| Completion | 2002 | 2004 | 2009 | 2018 |
| Age group (years) | 45–84 | 18–65 |
Men >55–80 | 50–64 |
| Sample size | 6814 (53% women) | 3072 (55% women) | 6101 (56% women) | 30 000 (50% women) |
| Exclusion criteria | Known CVD, treated cancer | None | Claims of CVD, cancer, etc. | None |
| Population | Stratified for ethnicity | Probability sampling (postal addresses); stratified for ethnicity | Members of Humana Health Plan; stratified for ethnicity | Random population sample |
| Participation rate (%) | Not applicable | Not applicable | Not applicable |
49 |
| Imaging | ||||
| Atherosclerosis | ||||
| Carotid – ultrasound | 6814 | – | 6104 | 30 000 |
| Carotid – MRI | – | – | 525 | 3000 |
| Coronary artery calcium | 6814 | 2971 | 6104 | 30 000 |
| Coronary plaque | – | – | 380 | 27 000 |
| Pulmonary | ||||
| MDCT | – | – | – | 30 000 |
| Metabolic | ||||
| Liver | 6814 | 2971 | – | 30 000 |
| Epicardial/abdominal/thigh | 6814 | 2971 | – | 30 000 |
CVD, cardiovascular disease; MDCT, multidetector computed tomography; MRI, magnetic resonance imaging.