The Swedish CArdioPulmonary BioImage Study: objectives and design

G Bergström, G Berglund, A Blomberg, J Brandberg, G Engström, J Engvall, M Eriksson, U de Faire, A Flinck, M G Hansson, B Hedblad, O Hjelmgren, C Janson, T Jernberg, Å Johnsson, L Johansson, L Lind, C-G Löfdahl, O Melander, C J Östgren, A Persson, M Persson, A Sandström, C Schmidt, S Söderberg, J Sundström, K Toren, A Waldenström, H Wedel, J Vikgren, B Fagerberg, A Rosengren, G Bergström, G Berglund, A Blomberg, J Brandberg, G Engström, J Engvall, M Eriksson, U de Faire, A Flinck, M G Hansson, B Hedblad, O Hjelmgren, C Janson, T Jernberg, Å Johnsson, L Johansson, L Lind, C-G Löfdahl, O Melander, C J Östgren, A Persson, M Persson, A Sandström, C Schmidt, S Söderberg, J Sundström, K Toren, A Waldenström, H Wedel, J Vikgren, B Fagerberg, A Rosengren

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diseases are major causes of death worldwide, but currently recommended strategies for diagnosis and prevention may be outdated because of recent changes in risk factor patterns.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.

Keywords: cardiovascular; epidemiology; metabolism; pulmonary; trial design.

© 2015 The Authors. Journal of Internal Medicine published by John Wiley & Sons Ltd on behalf of Association for Publication of The Journal of Internal Medicine.

Figures

Figure 1
Figure 1
Overview 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.

References

    1. Wilhelmsen L, Welin L, Svardsudd K et al Secular changes in cardiovascular risk factors and attack rate of myocardial infarction among men aged 50 in Gothenburg, Sweden. Accurate prediction using risk models. J Intern Med 2008; 263: 636–43.
    1. Nichols M, Townsend N, Scarborough P, Rayner M. European cardiovascular disease statistics 4th edition 2012: EuroHeart II. Eur Heart J 2013; 34: 3007.
    1. Bjorck L, Rosengren A, Bennett K, Lappas G, Capewell S. Modelling the decreasing coronary heart disease mortality in Sweden between 1986 and 2002. Eur Heart J 2009; 30: 1046–56.
    1. Eriksson M, Holmgren L, Janlert U et al Large improvements in major cardiovascular risk factors in the population of northern Sweden: the MONICA study 1986‐2009. J Intern Med 2011; 269: 219–31.
    1. van Lammeren GW, den Ruijter HM, Vrijenhoek JE et al Time‐dependent changes in atherosclerotic plaque composition in patients undergoing carotid surgery. Circulation 2014; 129: 2269–76.
    1. Socialstyrelsen . Causes of Death 2011. Stockholm: Socialstyrelsen, 2012.
    1. WHO . Global atlas on cardiovascular disease prevention and control, 2011.
    1. WHO . Global status report on noncommunicable diseases 2010, 2011.
    1. Braunwald E. Acute myocardial infarction–the value of being prepared. N Engl J Med 1996; 334: 51–2.
    1. Deo R, Albert CM. Epidemiology and genetics of sudden cardiac death. Circulation 2012; 125: 620–37.
    1. Dudas K, Lappas G, Stewart S, Rosengren A. Trends in out‐of‐hospital deaths due to coronary heart disease in Sweden (1991 to 2006). Circulation 2011; 123: 46–52.
    1. Ogden CL, Yanovski SZ, Carroll MD, Flegal KM. The epidemiology of obesity. Gastroenterology 2007; 132: 2087–102.
    1. Despres JP, Lemieux I, Bergeron J et al Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol 2008; 28: 1039–49.
    1. Lilja M, Eliasson M, Eriksson M, Soderberg S. A rightward shift of the distribution of fasting and post‐load glucose in northern Sweden between 1990 and 2009 and its predictors. Data from the Northern Sweden MONICA study. Diabet Med 2013; 30: 1054–62.
    1. Rosengren A. Declining cardiovascular mortality and increasing obesity: a paradox. Can Med Assoc J 2009; 181: 127–8.
    1. Capewell S, Buchan I. Why have sustained increases in obesity and type 2 diabetes not offset declines in cardiovascular mortality over recent decades in Western countries? Nutr Metab Cardiovasc Dis 2012; 22: 307–11.
    1. Dulloo AG, Montani JP. Body composition, inflammation and thermogenesis in pathways to obesity and the metabolic syndrome: an overview. Obes Rev 2012; 13(Suppl 2): 1–5.
    1. Lozano R, Naghavi M, Foreman K et al Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380: 2095–128.
    1. Carolan BJ, Sutherland ER. Clinical phenotypes of chronic obstructive pulmonary disease and asthma: recent advances. J Allergy Clin Immunol 2013; 131: 627–34; quiz 35.
    1. Salvi SS, Barnes PJ. Chronic obstructive pulmonary disease in non‐smokers. Lancet 2009; 374: 733–43.
    1. Lamprecht B, Schirnhofer L, Kaiser B, Buist S, Studnicka M. Non‐reversible airway obstruction in never smokers: results from the Austrian BOLD study. Respir Med 2008; 102: 1833–8.
    1. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance–United States, 1999‐2011. Chest 2013; 144: 284–305.
    1. Lopez‐Campos JL, Ruiz‐Ramos M, Soriano JB. Mortality trends in chronic obstructive pulmonary disease in Europe, 1994‐2010: a joinpoint regression analysis. Lancet Respir Med 2014; 2: 54–62.
    1. Jansson SA, Backman H, Stenling A, Lindberg A, Ronmark E, Lundback B. Health economic costs of COPD in Sweden by disease severity–has it changed during a ten years period? Respir Med 2013; 107: 1931–8.
    1. Engstrom G, Hedblad B, Janzon L. Reduced lung function predicts increased fatality in future cardiac events. A population‐based study. J Intern Med 2006; 260: 560–7.
    1. Finkelstein J, Cha E, Scharf SM. Chronic obstructive pulmonary disease as an independent risk factor for cardiovascular morbidity. Int J Chron Obstruct Pulmon Dis 2009; 4: 337–49.
    1. Newman AB, Nieto FJ, Guidry U et al Relation of sleep‐disordered breathing to cardiovascular disease risk factors: the Sleep Heart Health Study. Am J Epidemiol 2001; 154: 50–9.
    1. Leone N, Courbon D, Thomas F et al Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. Am J Respir Crit Care Med 2009; 179: 509–16.
    1. Gorter PM, van Lindert AS, de Vos AM et al Quantification of epicardial and peri‐coronary fat using cardiac computed tomography; reproducibility and relation with obesity and metabolic syndrome in patients suspected of coronary artery disease. Atherosclerosis 2008; 197: 896–903.
    1. Hadamitzky M, Achenbach S, Al‐Mallah M et al Optimized prognostic score for coronary computed tomographic angiography: results from the CONFIRM registry (COronary CT Angiography EvaluatioN For Clinical Outcomes: an InteRnational Multicenter Registry). J Am Coll Cardiol 2013; 62: 468–76.
    1. Motoyama S, Sarai M, Harigaya H et al Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 2009; 54: 49–57.
    1. Prahl U, Holdfeldt P, Bergstrom G, Fagerberg B, Hulthe J, Gustavsson T. Percentage white: a new feature for ultrasound classification of plaque echogenicity in carotid artery atherosclerosis. Ultrasound Med Biol 2010; 36: 218–26.
    1. van den Bouwhuijsen QJ, Vernooij MW, Hofman A, Krestin GP, van der Lugt A, Witteman JC. Determinants of magnetic resonance imaging detected carotid plaque components: the Rotterdam Study. Eur Heart J 2012; 33: 221–9.
    1. Coxson HO, Dirksen A, Edwards LD et al The presence and progression of emphysema in COPD as determined by CT scanning and biomarker expression: a prospective analysis from the ECLIPSE study. Lancet Respir Med 2013; 1: 129–36.
    1. Christensen SE, Moller E, Bonn SE et al Two new meal‐ and web‐based interactive food frequency questionnaires: validation of energy and macronutrient intake. J Med Internet Res 2013; 15: e109.
    1. WHO . Definition and Diagnosis of Diabetes Mellitus and Intermediate hyperglycaemia, 2006.
    1. WHO . Waist circumference and waist–hip ratio: report of a WHO expert consultation, 2008.
    1. Aboyans V, Criqui MH, Abraham P et al Measurement and interpretation of the ankle‐brachial index: a scientific statement from the American Heart Association. Circulation 2012; 126: 2890–909.
    1. Buist AS, McBurnie MA, Vollmer WM et al International variation in the prevalence of COPD (the BOLD Study): a population‐based prevalence study. Lancet 2007; 370: 741–50.
    1. Macintyre N, Crapo RO, Viegi G et al Standardisation of the single‐breath determination of carbon monoxide uptake in the lung. Eur Respir J 2005; 26: 720–35.
    1. Ostling G, Persson M, Hedblad B, Goncalves I. Comparison of grey scale median (GSM) measurement in ultrasound images of human carotid plaques using two different softwares. Clin Physiol Funct Imaging 2013; 33: 431–5.
    1. Millon A, Mathevet JL, Boussel L et al High‐resolution magnetic resonance imaging of carotid atherosclerosis identifies vulnerable carotid plaques. J Vasc Surg 2013; 57: 1046–51 e2.
    1. McCollough CH, Ulzheimer S, Halliburton SS, Shanneik K, White RD, Kalender WA. Coronary artery calcium: a multi‐institutional, multimanufacturer international standard for quantification at cardiac CT. Radiology 2007; 243: 527–38.
    1. Otsuka K, Fukuda S, Tanaka A et al Prognosis of vulnerable plaque on computed tomographic coronary angiography with normal myocardial perfusion image. Eur Heart J Cardiovasc Imaging 2014; 15: 332–40.
    1. Ovrehus KA, Marwan M, Botker HE, Achenbach S, Norgaard BL. Reproducibility of coronary plaque detection and characterization using low radiation dose coronary computed tomographic angiography in patients with intermediate likelihood of coronary artery disease (ReSCAN study). Int J Cardiovasc Imaging 2012; 28: 889–99.
    1. Joe E, Kim SH, Lee KB et al Feasibility and accuracy of dual‐source dual‐energy CT for noninvasive determination of hepatic iron accumulation. Radiology 2012; 262: 126–35.
    1. Hatano S. Experience from a multicentre stroke register: a preliminary report. Bull World Health Organ 1976; 54: 541–53.
    1. Ludvigsson JF, Andersson E, Ekbom A et al External review and validation of the Swedish national inpatient register. BMC Public Health 2011; 11: 450.
    1. Jernberg T, Attebring MF, Hambraeus K et al The Swedish Web‐system for enhancement and development of evidence‐based care in heart disease evaluated according to recommended therapies (SWEDEHEART). Heart 2010; 96: 1617–21.
    1. Asplund K, Hulter Asberg K, Norrving B et al Riks‐stroke ‐ a Swedish national quality register for stroke care. Cerebrovasc Dis 2003; 15(Suppl 1): 5–7.
    1. Troeng T, Malmstedt J, Bjorck M. External validation of the Swedvasc registry: a first‐time individual cross‐matching with the unique personal identity number. Eur J Vasc Endovasc Surg 2008; 36: 705–12.
    1. Rao QA, Newhouse JH. Risk of nephropathy after intravenous administration of contrast material: a critical literature analysis. Radiology 2006; 239: 392–7.
    1. Goergen SK, Rumbold G, Compton G, Harris C. Systematic review of current guidelines, and their evidence base, on risk of lactic acidosis after administration of contrast medium for patients receiving metformin. Radiology 2010; 254: 261–9.
    1. Becker J, Babb J, Serrano M. Glomerular filtration rate in evaluation of the effect of iodinated contrast media on renal function. AJR Am J Roentgenol 2013; 200: 822–6.
    1. McDonald RJ, McDonald JS, Carter RE et al Intravenous contrast material exposure is not an independent risk factor for dialysis or mortality. Radiology 2014; 273: 714–25.
    1. Viberg J, Hansson MG, Langenskiold S, Segerdahl P. Incidental findings: the time is not yet ripe for a policy for biobanks. Eur J Hum Genet 2013; 22: 437–41.
    1. Horeweg N, van der Aalst CM, Thunnissen E et al Characteristics of lung cancers detected by computer tomography screening in the randomized NELSON trial. Am J Respir Crit Care Med 2013; 187: 848–54.
    1. Harrison M, Rigby D, Vass C, Flynn T, Louviere J, Payne K. Risk as an attribute in discrete choice experiments: a systematic review of the literature. Patient 2014; 7: 151–70.
    1. Conroy RM, Pyorala K, Fitzgerald AP et al Estimation of ten‐year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003; 24: 987–1003.
    1. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C‐reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008; 118: 2243–51, 4p following 51.
    1. D'Agostino RB Sr, Vasan RS, Pencina MJ et al General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008; 117: 743–53.
    1. Goff DC Jr, Lloyd‐Jones DM, Bennett G et al 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014; 129: S49–73.
    1. Hippisley‐Cox J, Coupland C, Vinogradova Y et al Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008; 336: 1475–82.
    1. Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health Nations Health 1951; 41: 279–81.
    1. Boysen G, Nyboe J, Appleyard M et al Stroke incidence and risk factors for stroke in Copenhagen, Denmark. Stroke 1988; 19: 1345–53.
    1. Hofman A, Darwish Murad S, van Duijn CM. The Rotterdam Study: 2014 objectives and design update. Eur J Epidemiol 2013; 28: 889–926.
    1. Harris TB, Launer LJ, Eiriksdottir G et al Age, Gene/Environment Susceptibility‐Reykjavik Study: multidisciplinary applied phenomics. Am J Epidemiol 2007; 165: 1076–87.
    1. Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10‐year follow‐up of the prospective cardiovascular Munster (PROCAM) study. Circulation 2002; 105: 310–5.
    1. Vartiainen E, Laatikainen T, Peltonen M et al Thirty‐five‐year trends in cardiovascular risk factors in Finland. Int J Epidemiol 2010; 39: 504–18.
    1. Danesh J, Saracci R, Berglund G et al EPIC‐Heart: the cardiovascular component of a prospective study of nutritional, lifestyle and biological factors in 520,000 middle‐aged participants from 10 European countries. Eur J Epidemiol 2007; 22: 129–41.
    1. Regan EA, Hokanson JE, Murphy JR et al Genetic epidemiology of COPD (COPDGene) study design. COPD 2010; 7: 32–43.
    1. The ARIC investigators . The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol 1989; 129: 687–702.
    1. Stolk RP, Rosmalen JG, Postma DS et al Universal risk factors for multifactorial diseases: LifeLines: a three‐generation population‐based study. Eur J Epidemiol 2008; 23: 67–74.
    1. Collins R. Protocol for a Large‐scale Prospective Epidemiological Resource ‐ The UK biobank. . 2014.
    1. Lind L, Elmstahl S, Bergman E et al EpiHealth: a large population‐based cohort study for investigation of gene‐lifestyle interactions in the pathogenesis of common diseases. Eur J Epidemiol 2013; 28: 189–97.
    1. Bild DE, Bluemke DA, Burke GL et al Multi‐ethnic study of atherosclerosis: objectives and design. Am J Epidemiol 2002; 156: 871–81.
    1. Victor RG, Haley RW, Willett DL et al The Dallas Heart Study: a population‐based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. Am J Cardiol 2004; 93: 1473–80.
    1. Falk E, Sillesen H, Muntendam P, Fuster V. The high‐risk plaque initiative: primary prevention of atherothrombotic events in the asymptomatic population. Curr Atheroscler Rep 2011; 13: 359–66.
    1. Folsom AR, Kronmal RA, Detrano RC et al Coronary artery calcification compared with carotid intima‐media thickness in the prediction of cardiovascular disease incidence: the Multi‐Ethnic Study of Atherosclerosis (MESA). Arch Intern Med 2008; 168: 1333–9.
    1. Elias‐Smale SE, Proenca RV, Koller MT et al Coronary calcium score improves classification of coronary heart disease risk in the elderly: the Rotterdam study. J Am Coll Cardiol 2010; 56: 1407–14.
    1. Polonsky TS, McClelland RL, Jorgensen NW et al Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA 2010; 303: 1610–6.
    1. Erbel R, Mohlenkamp S, Moebus S et al Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. J Am Coll Cardiol 2010; 56: 1397–406.
    1. Hendel RC, Patel MR, Kramer CM et al ACCF/ACR/SCCT/SCMR/ASNC/NASCI/SCAI/SIR 2006 appropriateness criteria for cardiac computed tomography and cardiac magnetic resonance imaging: a report of the American College of Cardiology Foundation Quality Strategic Directions Committee Appropriateness Criteria Working Group, American College of Radiology, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, American Society of Nuclear Cardiology, North American Society for Cardiac Imaging, Society for Cardiovascular Angiography and Interventions, and Society of Interventional Radiology. J Am Coll Cardiol 2006; 48: 1475–97.
    1. Perk J, De Backer G, Gohlke H et al European Guidelines on cardiovascular disease prevention in clinical practice (version 2012). The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts). Eur Heart J 2012; 33: 1635–701.
    1. Waugh N, Black C, Walker S, McIntyre L, Cummins E, Hillis G. The effectiveness and cost‐effectiveness of computed tomography screening for coronary artery disease: systematic review. Health Technol Assess 2006; 10: iii–iv, ix‐x, 1‐41.
    1. Ostrom MP, Gopal A, Ahmadi N et al Mortality incidence and the severity of coronary atherosclerosis assessed by computed tomography angiography. J Am Coll Cardiol 2008; 52: 1335–43.
    1. Min JK, Shaw LJ, Devereux RB et al Prognostic value of multidetector coronary computed tomographic angiography for prediction of all‐cause mortality. J Am Coll Cardiol 2007; 50: 1161–70.
    1. Graham MM, Faris PD, Ghali WA et al Validation of three myocardial jeopardy scores in a population‐based cardiac catheterization cohort. Am Heart J 2001; 142: 254–61.
    1. Mark DB, Nelson CL, Califf RM et al Continuing evolution of therapy for coronary artery disease. Initial results from the era of coronary angioplasty. Circulation 1994; 89: 2015–25.
    1. Tanaka A, Shimada K, Yoshida K et al Non‐invasive assessment of plaque rupture by 64‐slice multidetector computed tomography–comparison with intravascular ultrasound. Circ J 2008; 72: 1276–81.
    1. Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ 2005; 330: 1076–9.
    1. Voight BF, Peloso GM, Orho‐Melander M et al Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 2012; 380: 572–80.
    1. Rothman KJ, Gallacher JE, Hatch EE. Why representativeness should be avoided. Int J Epidemiol 2013; 42: 1012–4.
    1. Rosengren A, Eriksson H, Hansson PO et al Obesity and trends in cardiovascular risk factors over 40 years in Swedish men aged 50. J Intern Med 2009; 266: 268–76.

Source: PubMed

3
订阅