Composite Risk Scores

  • Ruth E. Brown
  • Jennifer L. KukEmail author


The following chapter will discuss the history and clinical utility of several different composite risk models. Composite risk models are used to combine the various known risk factors and translate them into a more easily interpretable risk value. The Framingham Risk Algorithm is among the oldest and most widely used risk scores for cardiovascular disease, and over the years, new cardiovascular disease risk algorithms, such as the Reynolds Risk Score and the Pooled Cohort Equations, have been developed. However, the applicability of these scores to ethnically and socioeconomically diverse populations has been questioned. As well, several lifetime cardiovascular disease models have been developed, but the clinical utility of assessing lifetime cardiovascular risk is still debated. Furthermore, different health organizations have developed several criteria for the metabolic syndrome, yet the clinical utility of the metabolic syndrome is still debated. In recent years, staging systems for obesity and cardiometabolic health have been developed to guide medical treatment, though due to their novelty, there is limited research on their effectiveness. However, for a given risk score, there are still individual differences in actual risk score, termed residual risk. This means that even if a patient achieves target levels of metabolic risk factors, some may still experience a cardiac event even if their predicted risk is low. This residual cardiovascular risk that is not accounted for by the risk models is true for all algorithms but can be reduced by adopting a healthy lifestyle or improving other important factors not accounted for by the algorithm. Finally, risk assessment is only valuable if the patient understands what that risk means, and therefore optimal risk communication between health professional and patient is vital for improving patient care. This review will describe the development and clinical utility of the Framingham Risk Score, the Reynolds Risk Score, the Pooled Cohort Equations, lifetime risk scores, the metabolic syndrome, the Edmonton Obesity Staging System, and the Cardiometabolic Disease Staging System. Residual cardiovascular risk and patient communication will also be discussed.


Framingham Risk Score Reynolds Risk Score Metabolic syndrome Edmonton Obesity Staging System Cardiometabolic Disease Staging System Cardiometabolic risk Cardiovascular disease Residual cardiovascular risk Coronary heart disease Obesity 



American College of Cardiology


American Heart Association


Atherosclerosis risk in communities




Body mass index


Cardiovascular Health Study, Coronary Artery Risk Development in Young Adults


Coronary heart disease


Cardiometabolic disease staging system


Cardiovascular disease


Dietary approaches to stop hypertension


European Group for the Study of Insulin Resistance


Edmonton Obesity Staging System


Glucagon-peptide 1


High-density lipoprotein cholesterol


Human immunodeficiency virus


High-sensitivity C-reactive protein


International Diabetes Federation


Impaired glucose tolerance


Low-density lipoprotein cholesterol


Metabolic syndrome


Myocardial infarction


National Cholesterol Education Program Adult Treatment Program III


Systolic blood pressure


Type-2 diabetes


Total cholesterol


Waist circumference


World Health Organization


  1. 1.
    Blaum CS, Xue QL, Michelon E, Semba RD, Fried LP. The association between obesity and the frailty syndrome in older women: the Women’s Health and Aging Studies. J Am Geriatr Soc. 2005;53(6):927–34.CrossRefPubMedGoogle Scholar
  2. 2.
    D’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.CrossRefPubMedGoogle Scholar
  3. 3.
    Goff DC, 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. J Am Coll Cardiol. 2014;63(25 Pt B):2935–59.CrossRefPubMedGoogle Scholar
  4. 4.
    Fruchart J-C, Sacks FM, Hermans MP, et al. The residual risk reduction initiative: a call to action to reduce residual vascular risk in dyslipidaemic patient. Diab Vasc Dis Res. 2008;5(4):319–35.CrossRefPubMedGoogle Scholar
  5. 5.
    Fruchart J-C, Davignon J, Hermans MP, et al. Residual macrovascular risk in 2013: what have we learned? Cardiovasc Diabetol. 2014;13:26.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Dawber TR, Meadors GF, Moore FE. Epidemiological approaches to heart disease: the Framingham Heart Study. Am J Public Health Nations Health. 1951;41:279–86.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Bitton A, Gaziano T. The Framingham Heart Study’s impact on global risk assessment. Prog Cardiovasc Dis. 2010;53(1):68–78.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Truett J, Cornfield J, Kannel W. A multivariate analysis of the risk of coronary heart disease in Framingham. J Chron Dis. 1967;20:511–24.CrossRefPubMedGoogle Scholar
  9. 9.
    Bitton A, Gaziano T. The Framingham Heart Study’s impact on global risk assessment. Prog Cardiovasc Dis. 2010;53(1):68–78.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kannel B, McGee D, Gordon T. A general cardiovascular risk profile: the Framingham Study. Am J Cardiol. 1976;38:46–51.CrossRefPubMedGoogle Scholar
  11. 11.
    Anderson KM, Wilson PW, Odell PM, Kannel WB. An updated coronary risk profile. A statement for health professionals. Circulation. 1991;83:356–62.CrossRefPubMedGoogle Scholar
  12. 12.
    Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–47.CrossRefPubMedGoogle Scholar
  13. 13.
    Expert Panel on Detection, Evaluation and T of HBC in A. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97.CrossRefGoogle Scholar
  14. 14.
    Brindle P, Beswick A, Fahey T, Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review. Heart. 2006;92(12):1752–9.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Tzoulaki I, Seretis A, Ntzani EE, Ioannidis JPA. Mapping the expanded often inappropriate use of the Framingham Risk Score in the medical literature. J Clin Epidemiol. 2014;67:571–7.CrossRefPubMedGoogle Scholar
  16. 16.
    Empana J. Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study. Eur Hear J. 2003;24:1903–11.CrossRefGoogle Scholar
  17. 17.
    Tillin T, Hughes AD, Whincup P, et al. Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a U.K. tri-ethnic prospective cohort study (SABRE–Southall And Brent REvisited). Heart. 2014;100:60–7.CrossRefPubMedGoogle Scholar
  18. 18.
    Brindle PM, McConnachie A, Upton MN, Hart CL, Davey Smith G, Watt GCM. The accuracy of the Framingham risk-score in different socioeconomic groups: a prospective study. Br J Gen Pract. 2005;55:838–45.PubMedPubMedCentralGoogle Scholar
  19. 19.
    D’Agostino RB, Grundy SM, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores. JAMA. 2001;286(2):180–7.CrossRefPubMedGoogle Scholar
  20. 20.
    Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355:2631–9.CrossRefPubMedGoogle Scholar
  21. 21.
    Sposito AC, Alvarenga BF, Alexandre AS, et al. Most of the patients presenting myocardial infarction would not be eligible for intensive lipid-lowering based on clinical algorithms or plasma C-reactive protein. Atherosclerosis. 2011;214(1):148–50.CrossRefPubMedGoogle Scholar
  22. 22.
    Pen A, Yam Y, Chen L, Dennie C, McPherson R, Chow BJW. Discordance between Framingham Risk Score and atherosclerotic plaque burden. Eur Heart J. 2013;34:1075–82.CrossRefPubMedGoogle Scholar
  23. 23.
    Ferket BS, van Kempen BJH, Hunink MGM, et al. Predictive value of updating Framingham risk scores with novel risk markers in the U.S. general population. PLoS ONE. 2014;9(2):e88312.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Ridker PM, Cook N. Clinical usefulness of very high and very low levels of C-reactive protein across the full range of Framingham Risk Scores. Circulation. 2004;109(16):1955–9.CrossRefPubMedGoogle Scholar
  25. 25.
    Lloyd-Jones D, Nam B-J, D’Agostino R, et al. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA. 2004;291(18):2204–11.CrossRefPubMedGoogle Scholar
  26. 26.
    Anderson TJ, Grégoire J, Hegele R, et al. 2012 update of the Canadian Cardiovascular Society guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol. 2013;29:151–67.CrossRefPubMedGoogle Scholar
  27. 27.
    Stern RH. Problems with modified Framingham Risk Score. Can J Cardiol. 2014;30:248.e3.CrossRefPubMedGoogle Scholar
  28. 28.
    Ridker P, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds risk score. JAMA. 2007;297(6):611–20.CrossRefPubMedGoogle Scholar
  29. 29.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Cook NR, Paynter NP, Eaton CB, et al. Comparison of the Framingham and Reynolds risk scores for global cardiovascular risk prediction in the Multiethnic Women’s Health Initiative. Circulation. 2012;125(14):1748–56.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Tattersall MC, Gangnon RE, Karmali KN, Keevil JG. Women up, men down: the clinical impact of replacing the Framingham Risk Score with the Reynolds Risk Score in the United States population. PLoS ONE. 2012;7(9):e44347.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Amin NP, Martin SS, Blaha MJ, Nasir K, Blumenthal RS, Michos ED. Headed in the right direction but at risk for miscalculation: a critical appraisal of the 2013 ACC/AHA risk assessment guidelines. J Am Coll Cardiol. 2014;63(25 Pt A):2789–94.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Muntner P, Colantonio LD, Cushman M, et al. Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. JAMA. 2014;311(14):1406–15.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. The Lancet. 2013;382(9907):1762–5.CrossRefGoogle Scholar
  35. 35.
    Kavousi M, Leening MJG, Nanchen D, et al. Comparison of application of the ACC/AHA guidelines, Adult Treatment Panel III guidelines, and European Society of Cardiology guidelines for cardiovascular disease prevention in a European cohort. JAMA. 2014;311(14):1416–23.CrossRefPubMedGoogle Scholar
  36. 36.
    Lloyd-Jones D, Larson M, Beiser A, Levy D. Lifetime risk of developing coronary heart disease. The Lancet. 1999;353:89–92.CrossRefGoogle Scholar
  37. 37.
    Hippisley-cox J, Coupland C, Robson J, Brindle P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ. 2010;341:c6624.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Sniderman AD, Furberg CD. Age as a modifiable risk factor for cardiovascular disease. The Lancet. 2008;371:1547–9.CrossRefGoogle Scholar
  39. 39.
    Berry JD, Liu K, Folsom AR, et al. Prevalence and progression of subclinical atherosclerosis in younger adults with low short-term but high lifetime estimated risk for cardiovascular disease: the CARDIA and MESA studies. Circulation. 2009;119(3):382–9.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Lloyd-Jones DM, Leip EP, Larson MG, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation. 2006;113(6):791–8.CrossRefPubMedGoogle Scholar
  41. 41.
    Berry J, Dyer A, Cai X, et al. Lifetime risks of cardiovascular disease. N Engl J Med. 2012;366(4):321–9.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Pencina MJ, D’Agostino RB, Larson MG, Massaro JM, Vasan RS. Predicting the 30-year risk of cardiovascular disease: the framingham heart study. Circulation. 2009;119:3078–84.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Wilkins JT, Ning H, Berry J, Zhao L, Dyer AR, Lloyd-jones DM. Lifetime risk and years lived free of total cardiovascular disease. JAMA. 2014;308(17):1795–801.CrossRefGoogle Scholar
  44. 44.
    Libby P. The forgotten majority: unfinished business in cardiovascular risk reduction. J Am Coll Cardiol. 2005;46(7):1225–8.CrossRefPubMedGoogle Scholar
  45. 45.
    Sampson U, Fazio S, Linton M. Residual cardiovascular risk despite optimal LDL cholesterol reduction with statins: the evidence, etiology, and therapeutic challenges. Curr Atheroscler Rep. 2012;14(1):1–10.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Wing RR, Bolin P, Brancati FL, et al. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013;369(2):145–54.CrossRefPubMedGoogle Scholar
  47. 47.
    Alberti KGMM, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International. Circulation. 2009;120(16):1640–5.CrossRefPubMedGoogle Scholar
  48. 48.
    Reaven G. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37:1595–607.CrossRefPubMedGoogle Scholar
  49. 49.
    Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15:5395–53.Google Scholar
  50. 50.
    Després J-P. Is visceral obesity the cause of the metabolic syndrome? Ann Med. 2006;38(1):52–63.CrossRefPubMedGoogle Scholar
  51. 51.
    Organization WH. Definition, diagnosis, and classification of diabetes mellitus and its complications: report of a WHO consultation. Part 1: diagnosis and classification of diabetes mellitus. 1999.Google Scholar
  52. 52.
    Balkau B, Charles M. Comment on the provisional report from the WHO. Diabet Med. 1999;16:442–3.CrossRefPubMedGoogle Scholar
  53. 53.
    Zimmet P, Alberti G, Shaw J. A new IDF worldwide definition of the metabolic syndrome: the rationale and the results. Diabetes Voice. 2005;50(3):31–3.Google Scholar
  54. 54.
    Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–52.CrossRefPubMedGoogle Scholar
  55. 55.
    Cheung BMY, Ong KL, Man YB, Wong LYF, Lau C-P, Lam KSL. Prevalence of the metabolic syndrome in the United States National Health and Nutrition Examination Survey 1999–2002 according to different defining criteria. J Clin Hypertens. 2006;8:562–70.CrossRefGoogle Scholar
  56. 56.
    Lakka H-M, Laaksonen DE, Lakka T, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;288(21):2709–16.CrossRefPubMedGoogle Scholar
  57. 57.
    McNeill AM, Rosamond WD, Girman CJ, et al. The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care. 2005;28(2):385–90.CrossRefPubMedGoogle Scholar
  58. 58.
    Marcovecchio ML, Chiarelli F. Metabolic syndrome in youth: chimera or useful concept? Curr Diab Rep. 2013;13:56–62.CrossRefPubMedGoogle Scholar
  59. 59.
    Sabin M, Magnussen CG, Juonala M, Cowley M, Shield JPH. The role of pharmacotherapy in the prevention and treatment of paediatric metabolic syndrome–Implications for long-term health: part of a series on Pediatric Pharmacology, guest edited by Gianvincenzo Zuccotti, Emilio Clementi, and Massimo Molteni. Pharm Res. 2012;65:397–401.CrossRefGoogle Scholar
  60. 60.
    Shahar E. Metabolic syndrome? A critical look from the viewpoints of causal diagrams and statistics. J Cardiovasc Med. 2010;11(10):772–9.CrossRefGoogle Scholar
  61. 61.
    Carr DB, Utzschneider KM, Hull RL, et al. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes. 2004;53:2087–94.CrossRefPubMedGoogle Scholar
  62. 62.
    Jennings CL, Lambert EV, Collins M, Levitt NS, Goedecke JH. The atypical presentation of the metabolic syndrome components in black African women: the relationship with insulin resistance and the influence of regional adipose tissue distribution. Metabolism. 2009;58:149–57.CrossRefPubMedGoogle Scholar
  63. 63.
    Kuk J, Ardern C. Age and sex differences in the clustering association with mortality risk. Diabetes Care. 2010;33(11):2457–61.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Wildman RP, Muntner P, Reynolds K, Mcginn AP. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering. Arch Intern Med. 2008;168(15):1617–24.CrossRefPubMedGoogle Scholar
  65. 65.
    Kahn R, Buse J, Ferrannini E, Stern M. The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care. 2005;28:2289–304.CrossRefPubMedGoogle Scholar
  66. 66.
    Guize L, Thomas F, Pannier B, Bean K, Jego B, Benetos A. All-cause mortality associated with specific combinations of the metabolic syndrome according to recent definitions. Diabetes Care. 2007;30:2381–7.CrossRefPubMedGoogle Scholar
  67. 67.
    Liao Y, Kwon S, Shaughnessy S, et al. Critical evaluation of adult treatment panel III criteria in identifying insulin resistance with dyslipidemia. Diabetes Care. 2004;27:978–83.CrossRefPubMedGoogle Scholar
  68. 68.
    Ford E. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome. Diabetes Care. 2005;28(7):1769–78.CrossRefPubMedGoogle Scholar
  69. 69.
    Sattar N. Why metabolic syndrome criteria have not made prime time: a view from the clinic. Int J Obes. 2008;32:S30–S4.CrossRefGoogle Scholar
  70. 70.
    Benetos A, Thomas F, Pannier B, Bean K, Jégo B, Guize L. All-cause and cardiovascular mortality using the different definitions of metabolic syndrome. Am J Cardiol. 2008;102:188–91.CrossRefPubMedGoogle Scholar
  71. 71.
    Wannamethee SG, Shaper a G, Lennon L, Morris RW. Metabolic syndrome vs Framingham Risk Score for prediction of coronary heart disease, stroke, and type 2 diabetes mellitus. Arch Intern Med. 2005;165:2644–50.CrossRefPubMedGoogle Scholar
  72. 72.
    Stern M, Williams K, Gonzalez-Villalpando C, Hunt K, Haffner S. Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease? Diabetes Care. 2004;27(11):2676–81.CrossRefPubMedGoogle Scholar
  73. 73.
    Ford ES. Rarer than a blue moon: the use of a diagnostic code for the metabolic syndrome in the U.S. Diabetes Care. 2005;28(7):1808–9.CrossRefPubMedGoogle Scholar
  74. 74.
    Zemel M. Dietary pattern and hypertension: the DASH study. Nutr Rev. 1997;55(8):303–5.CrossRefPubMedGoogle Scholar
  75. 75.
    Bhattacharyya OK, Estey E, Cheng AYY. Update on the Canadian Diabetes Association 2008 clinical practice guidelines. Can Fam Physician. 2009;55:39–43.PubMedPubMedCentralGoogle Scholar
  76. 76.
    Ye H, Charpin-El Hamri G, Zwicky K, Christen M, Folcher M, Fussenegger M. Pharmaceutically controlled designer circuit for the treatment of the metabolic syndrome. Proc Natl Acad Sci U S A. 2013;110(1):141–6.CrossRefPubMedGoogle Scholar
  77. 77.
    Simmons RK, Alberti KGMM, Gale E a M, et al. The metabolic syndrome: useful concept or clinical tool? Report of a WHO Expert Consultation. Diabetologia. 2010;53(4):600–5.CrossRefPubMedGoogle Scholar
  78. 78.
    Sharma a M, Kushner RF. A proposed clinical staging system for obesity. Int J Obes (Lond). 2009;33(3):289–95.CrossRefGoogle Scholar
  79. 79.
    National Heart, Lung, and Blood Institute Obesity Education Initiative Expert Panel on the Identification, Evaluation and T of O and O in A. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. 1998.Google Scholar
  80. 80.
    Kuk J, Ardern C. Are metabolically normal but obese individuals at lower risk for all-cause mortality? Diabetes Care. 2009;32(12):2297–9.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Wildman RP, Muntner P, Reynolds K, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering. Arch Intern Med. 2008;168(15):1617–24.CrossRefPubMedGoogle Scholar
  82. 82.
    Kantartzis K, Machann J, Schick F, et al. Effects of a lifestyle intervention in metabolically benign and malign obesity. Diabetologia. 2011;54(4):864–8.CrossRefPubMedGoogle Scholar
  83. 83.
    Karelis a D, Messier V, Brochu M, Rabasa-Lhoret R. Metabolically healthy but obese women: effect of an energy-restricted diet. Diabetologia. 2008;51(9):1752–4.CrossRefPubMedGoogle Scholar
  84. 84.
    Kuk J, Ardern C, Church T, et al. Edmonton Obesity Staging System: association with weight history and mortality risk. Appl Physiol Nutr Metab. 2011;36:570–6.CrossRefPubMedGoogle Scholar
  85. 85.
    Padwal R, Pajewski N, Allison D, Sharma A. Using the Edmonton obesity staging system to predict mortality in a population-representative cohort of people with overweight and obesity. CMAJ. 2011;183(14):1059–66.CrossRefGoogle Scholar
  86. 86.
    O’Connor KG, Harman SM, Stevens TE, et al. Interrelationships of spontaneous growth hormone axis activity, body fat, and serum lipids in healthy elderly women and men. Metabolism. 1999;48(11):1424–31.CrossRefPubMedGoogle Scholar
  87. 87.
    Guo F, Moellering DR, Garvey WT. The progression of cardiometabolic disease: validation of a new cardiometabolic disease staging system applicable to obesity. Obesity (Silver Spring). 2014;22:110–8.CrossRefGoogle Scholar
  88. 88.
    Guo F, Moellering DR, Garvey WT. The progression of cardiometabolic disease: validation of a new cardiometabolic disease staging system applicable to obesity. Obesity. 2014;22:110–8.CrossRefPubMedGoogle Scholar
  89. 89.
    Edwards A, Elwyn G, Mulley A. Explaining risks: turning numerical data into meaningful pictures. BMJ. 2002;324:827–30.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Wells S, Kerr A, Eadie S, Wiltshire C, Jackson R. ‘Your Heart Forecast’: a new approach for describing and communicating cardiovascular risk? Heart. 2010;96(9):708–13.CrossRefPubMedGoogle Scholar
  91. 91.
    Gigerenzer G, Edwards A. Simple tools for understanding risks: from innumeracy to insight. BMJ. 2003;327:741–4.CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Goodyear-Smith F, Arrol B, Chan L, Jackson R, Wells S, Kenealy T. Patients prefer pictures to numbers to express cardiovascular benefit. Ann Fam Med. 2008;6:213–7.CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Jackson R. Lifetime risk: does it help to decide who gets statins and when? Curr Opin Lipidol. 2014;25(4):247–53.CrossRefPubMedGoogle Scholar
  94. 94.
    Edwards A, Elwyn G, Mulley A. Explaining risks: turning numerical data into meaningful pictures. BMJ. 2002;324:827–30.CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Gigerenzer G, Edwards A. Simple tools for understanding risks: from innumeracy to insight. BMJ. 2003;327:741–4.CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Bonner C, Jansen J, McKinn S, et al. Communicating cardiovascular disease risk: an interview study of General Practitioners’ use of absolute risk within tailored communication strategies. BMC Fam Pract. 2014;15:106.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Kinesiology and Health ScienceYork UniversityTorontoCanada
  2. 2.School of Kinesiology and Health ScienceYork UniversityTorontoCanada

Personalised recommendations