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Articles

ww

w.thelancet.com

/diabetes-endocrinology Vol 3 May 2015

349

Total cholesterol (mmol/L)

Syst

olic

bloo

d pr

essu

re (m

m H

g)

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7

30 31 31 32 33 45 46 47 48 49 63 64 65 66 68 81 82 83 84 85 38 39 40 41 42 46 47 48 49 50 57 58 59 61 62 66 67 69 70 7124 24 25 26 27 36 37 38 39 40 53 54 55 56 58 72 73 74 75 76 30 31 32 33 34 37 38 39 40 41 48 49 50 51 52 56 58 59 60 6119 19 20 20 21 29 30 31 32 33 44 45 46 47 48 62 63 64 66 67 24 25 26 26 27 30 31 32 32 33 39 40 41 42 43 47 48 49 50 5215 15 15 16 16 23 24 25 25 26 36 36 37 38 39 52 53 54 56 57 19 20 20 21 21 24 24 25 26 27 32 32 33 34 35 38 39 40 41 43

15 16 17 18 19 26 27 29 31 32 38 40 42 44 46 59 61 63 66 68 24 25 27 28 30 32 34 36 38 40 40 42 44 47 49 52 54 57 59 6212 12 13 14 15 20 21 23 24 25 30 32 34 35 37 48 50 53 55 57 18 19 21 22 23 25 26 28 30 31 32 34 35 37 39 42 44 46 49 51

9 9 10 11 11 15 16 17 18 20 24 25 26 28 29 39 41 43 45 47 14 15 16 17 18 19 20 22 23 24 25 26 28 29 31 33 35 37 39 417 7 8 8 8 12 13 13 14 15 18 19 20 22 23 31 32 34 36 38 11 11 12 13 14 15 16 17 18 19 19 20 22 23 24 26 27 29 31 33

7 8 9 10 11 14 16 17 19 21 21 23 25 27 30 38 41 44 47 51 12 13 14 16 17 18 19 21 23 25 22 24 27 29 31 32 35 38 41 445 6 7 7 8 11 12 13 14 15 16 17 19 21 23 29 32 35 37 40 9 10 11 12 13 13 15 16 17 19 17 18 20 22 24 25 27 29 32 344 4 5 5 6 8 9 9 10 11 12 13 14 16 17 22 24 26 29 31 7 7 8 9 10 10 11 12 13 14 13 14 15 16 18 19 20 22 24 263 3 4 4 4 6 6 7 8 8 9 10 11 12 13 17 18 20 22 24 5 5 6 6 7 7 8 9 10 11 9 10 11 12 13 14 15 17 18 20

4 4 5 5 6 8 9 10 11 13 12 13 15 17 19 24 26 29 33 36 8 9 10 11 12 12 14 16 18 20 15 17 19 22 24 24 27 30 33 373 3 3 4 4 6 6 7 8 9 8 9 11 12 14 17 20 22 25 27 5 6 7 8 9 9 10 11 13 15 11 12 14 16 18 18 20 22 25 282 2 2 3 3 4 5 5 6 7 6 7 8 9 10 13 14 16 18 20 4 4 5 6 6 6 7 8 9 11 8 9 10 12 13 13 15 17 19 211 2 2 2 2 3 3 4 4 5 4 5 6 6 7 9 10 12 13 15 3 3 4 4 5 5 5 6 7 8 6 7 7 8 9 9 11 12 14 15

2 3 3 4 4 5 6 7 8 10 8 9 10 12 14 17 20 23 26 30 5 5 6 7 8 8 10 11 13 15 10 11 13 15 18 17 20 23 26 302 2 2 3 3 4 4 5 6 7 5 6 7 8 10 12 14 16 19 22 3 4 4 5 6 6 7 8 9 11 7 8 9 11 13 12 14 16 19 221 1 2 2 2 3 3 4 4 5 4 4 5 6 7 9 10 12 14 16 2 3 3 4 4 4 5 6 6 8 5 6 7 8 9 9 10 12 14 161 1 1 1 1 2 2 2 3 3 3 3 4 4 5 6 7 8 10 11 2 2 2 2 3 3 3 4 5 5 3 4 5 5 6 6 7 8 10 11

1 1 2 2 2 3 4 4 5 6 4 5 6 7 9 10 12 15 18 21 3 3 4 4 5 5 6 7 9 10 6 7 8 10 12 11 13 16 19 221 1 1 1 2 2 2 3 4 4 3 3 4 5 6 7 8 10 12 15 2 2 3 3 4 3 4 5 6 7 4 5 6 7 8 8 9 11 13 161 1 1 1 1 1 2 2 2 3 2 2 3 3 4 5 6 7 8 10 1 1 2 2 3 2 3 3 4 5 3 3 4 5 6 5 6 8 9 11

<1 <1 1 1 1 1 1 1 2 2 1 2 2 2 3 3 4 5 6 7 1 1 1 1 2 2 2 2 3 3 2 2 3 3 4 4 4 5 6 8

1 1 1 1 2 2 2 3 3 4 2 3 4 5 6 7 8 10 12 15 2 2 3 3 4 4 5 6 7 9 4 5 6 8 10 9 11 13 16 20<1 1 1 1 1 1 1 2 2 3 2 2 2 3 4 4 5 7 8 10 1 1 2 2 3 3 3 4 5 6 3 3 4 5 7 6 7 9 11 14<1 <1 <1 1 1 1 1 1 2 2 1 1 2 2 3 3 4 5 6 7 1 1 1 1 2 2 2 3 3 4 2 2 3 4 4 4 5 6 8 9<1 <1 <1 <1 <1 1 1 1 1 1 1 1 1 1 2 2 2 3 4 5 1 1 1 1 1 1 1 2 2 3 1 2 2 2 3 3 3 4 5 6

1 1 1 1 2 2 2 3 4 5 2 3 4 5 6 7 9 11 14 17 1 1 2 2 3 2 3 4 5 6 2 3 4 5 6 5 7 9 11 14<1 <1 1 1 1 1 1 2 2 3 1 2 2 3 4 4 6 7 9 12 1 1 1 1 2 1 2 2 3 4 2 2 3 3 4 4 5 6 7 10<1 <1 <1 1 1 1 1 1 2 2 1 1 2 2 3 3 4 5 6 8 <1 1 1 1 1 1 1 2 2 3 1 1 2 2 3 2 3 4 5 6<1 <1 <1 <1 <1 <1 1 1 1 1 1 1 1 1 2 2 2 3 4 5 <1 <1 <1 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 3 4

<1 <1 1 1 1 1 1 2 3 3 1 2 2 3 4 5 6 8 10 14 1 1 1 1 2 2 2 3 4 5 2 2 3 4 5 4 6 8 10 13<1 <1 <1 <1 1 1 1 1 2 2 1 1 2 2 3 3 4 5 7 9 <1 1 1 1 1 1 1 2 2 3 1 1 2 3 3 3 4 5 6 8<1 <1 <1 <1 <1 <1 1 1 1 1 1 1 1 1 2 2 2 3 4 6 <1 <1 <1 1 1 1 1 1 2 2 1 1 1 2 2 2 2 3 4 5<1 <1 <1 <1 <1 <1 <1 1 1 1 <1 <1 1 1 1 1 2 2 3 4 <1 <1 <1 <1 1 <1 1 1 1 1 <1 1 1 1 1 1 1 2 3 3

Mexico

Non-smoker Smoker Non-smoker Smoker

Women Men

Non-diabetic Diabetic

Non-smoker Smoker Non-smoker Smoker

Non-diabetic Diabetic

Age (years)

80

75

70

65

60

55

50

45

40

≥15%10–14%5–9%3–4%2%1%<1%

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www.thelancet.com/diabetes-endocrinology Published online March 26, 2015 http://dx.doi.org/10.1016/S2213-8587(15)00081-9 1

Articles

A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveysKaveh Hajifathalian*, Peter Ueda*, Yuan Lu, Mark Woodward, Alireza Ahmadvand†,Carlos A Aguilar-Salinas†, Fereidoun Azizi†, Renata Cifk ova†, Mariachiara Di Cesare†, Louise Eriksen†, Farshad Farzadfar†, Nayu Ikeda†, Davood Khalili†, Young-Ho Khang†, Vera Lanska†, Luz León-Muñoz†, Dianna Magliano†, Kelias P Msyamboza†, Kyungwon Oh†, Fernando Rodríguez-Artalejo†, Rosalba Rojas-Martinez†, Jonathan E Shaw†, Gretchen A Stevens†, Janne Tolstrup†, Bin Zhou†, Joshua A Salomon, Majid Ezzati, Goodarz Danaei

SummaryBackground Treatment of cardiovascular risk factors based on disease risk depends on valid risk prediction equations. We aimed to develop, and apply in example countries, a risk prediction equation for cardiovascular disease (consisting here of coronary heart disease and stroke) that can be recalibrated and updated for application in diff erent countries with routinely available information.

Methods We used data from eight prospective cohort studies to estimate coeffi cients of the risk equation with proportional hazard regressions. The risk prediction equation included smoking, blood pressure, diabetes, and total cholesterol, and allowed the eff ects of sex and age on cardiovascular disease to vary between cohorts or countries. We developed risk equations for fatal cardiovascular disease and for fatal plus non-fatal cardiovascular disease. We validated the risk equations internally and also using data from three cohorts that were not used to create the equations. We then used the risk prediction equation and data from recent (2006 or later) national health surveys to estimate the proportion of the population at diff erent levels of cardiovascular disease risk in 11 countries from diff erent world regions (China, Czech Republic, Denmark, England, Iran, Japan, Malawi, Mexico, South Korea, Spain, and USA).

Findings The risk score discriminated well in internal and external validations, with C statistics generally 70% or more. At any age and risk factor level, the estimated 10 year fatal cardiovascular disease risk varied substantially between countries. The prevalence of people at high risk of fatal cardiovascular disease was lowest in South Korea, Spain, and Denmark, where only 5–10% of men and women had more than a 10% risk, and 62–77% of men and 79–82% of women had less than a 3% risk. Conversely, the proportion of people at high risk of fatal cardiovascular disease was largest in China and Mexico. In China, 33% of men and 28% of women had a 10-year risk of fatal cardiovascular disease of 10% or more, whereas in Mexico, the prevalence of this high risk was 16% for men and 11% for women. The prevalence of less than a 3% risk was 37% for men and 42% for women in China, and 55% for men and 69% for women in Mexico.

Interpretation We developed a cardiovascular disease risk equation that can be recalibrated for application in diff erent countries with routinely available information. The estimated percentage of people at high risk of fatal cardiovascular disease was higher in low-income and middle-income countries than in high-income countries.

Funding US National Institutes of Health, UK Medical Research Council, Wellcome Trust.

IntroductionThe fact that treatment for cardiometabolic risk factors such as blood pressure and cholesterol should be based on disease risk, instead of the levels of individual risk factors, is now widely accepted.1–3 Risk-based treatment is included in clinical guidelines in many countries,4,5 although debate continues on the appropriate threshold for treatment. Risk-based multidrug treatment and counselling has also been assessed as a cost-eff ective intervention for reduction of the burden of non-communicable diseases worldwide.6 As part of the global response to non-communicable diseases, countries have agreed to a target of 50% coverage of multidrug treatment and counselling for people aged 40 years and older who are at high risk of cardiovascular disease, including coronary heart disease and stroke.7

Risk-based treatment depends on prediction of cardiovascular disease risk for each individual, which is most accurately done with risk prediction equations (often via risk charts or web-based risk calculators).3,8–10 To measure progress towards the global non-communicable disease treatment target, information about the number of people in each country who are at high risk of cardiovascular disease will be needed, which will require an appropriate risk prediction equation and nationally representative data for risk factors. A risk prediction equation (or risk score) estimates a person’s risk of cardiovascular disease during a specifi c period (eg, 10 years) based on their levels of risk factors and the average cardiovascular disease risk in the population. The risk score has a set of coeffi cients, usually hazard

Lancet Diabetes Endocrinol 2015

Published OnlineMarch 26, 2015http://dx.doi.org/10.1016/S2213-8587(15)00081-9

See Online/Commenthttp://dx.doi.org/10.1016/S2213-8587(15)00002-9

*Joint fi rst authors

†These authors contributed equally and are listed alphabetically

Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA (K Hajifathalian MD, P Ueda PhD, Y Lu MSc, J A Saloman PhD, G Danaei ScD); Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, USA (K Hajifathalian); The George Institute for Global Health, Nuffi eld Department of Population Health, University of Oxford, Oxford, UK (M Woodward PhD); The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia (M Woodward); Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA (M Woodward); MRC-PHE Centre for Environment and Health (A Ahmadvand MD, M Di Cesare PhD, B Zhou MSc, M Ezzati FMedSci), and Department of Epidemiology and Biostatistics, School of Public Health (A Ahmadvand, M Di Cesare, B Zhou, M Ezzati), Imperial College London, London, UK; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran (A Ahmadvand, F Farzadfar MD); Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición, Salvador Zubirán,

116 155 193 232 271 116 155 193 232 271 116 155 193 232 271 116 155 193 232 271 116 155 193 232 271 116 155 193 232 271 116 155 193 232 271

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Lancet Diabetes Endocrinol. 2015 May;3(5):339-55.

Articles

ww

w.thelancet.com

/diabetes-endocrinology Vol 3 May 2015

349

Total cholesterol (mmol/L)

Syst

olic

bloo

d pr

essu

re (m

m H

g)

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

180160140120

3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7

30 31 31 32 33 45 46 47 48 49 63 64 65 66 68 81 82 83 84 85 38 39 40 41 42 46 47 48 49 50 57 58 59 61 62 66 67 69 70 7124 24 25 26 27 36 37 38 39 40 53 54 55 56 58 72 73 74 75 76 30 31 32 33 34 37 38 39 40 41 48 49 50 51 52 56 58 59 60 6119 19 20 20 21 29 30 31 32 33 44 45 46 47 48 62 63 64 66 67 24 25 26 26 27 30 31 32 32 33 39 40 41 42 43 47 48 49 50 5215 15 15 16 16 23 24 25 25 26 36 36 37 38 39 52 53 54 56 57 19 20 20 21 21 24 24 25 26 27 32 32 33 34 35 38 39 40 41 43

15 16 17 18 19 26 27 29 31 32 38 40 42 44 46 59 61 63 66 68 24 25 27 28 30 32 34 36 38 40 40 42 44 47 49 52 54 57 59 6212 12 13 14 15 20 21 23 24 25 30 32 34 35 37 48 50 53 55 57 18 19 21 22 23 25 26 28 30 31 32 34 35 37 39 42 44 46 49 51

9 9 10 11 11 15 16 17 18 20 24 25 26 28 29 39 41 43 45 47 14 15 16 17 18 19 20 22 23 24 25 26 28 29 31 33 35 37 39 417 7 8 8 8 12 13 13 14 15 18 19 20 22 23 31 32 34 36 38 11 11 12 13 14 15 16 17 18 19 19 20 22 23 24 26 27 29 31 33

7 8 9 10 11 14 16 17 19 21 21 23 25 27 30 38 41 44 47 51 12 13 14 16 17 18 19 21 23 25 22 24 27 29 31 32 35 38 41 445 6 7 7 8 11 12 13 14 15 16 17 19 21 23 29 32 35 37 40 9 10 11 12 13 13 15 16 17 19 17 18 20 22 24 25 27 29 32 344 4 5 5 6 8 9 9 10 11 12 13 14 16 17 22 24 26 29 31 7 7 8 9 10 10 11 12 13 14 13 14 15 16 18 19 20 22 24 263 3 4 4 4 6 6 7 8 8 9 10 11 12 13 17 18 20 22 24 5 5 6 6 7 7 8 9 10 11 9 10 11 12 13 14 15 17 18 20

4 4 5 5 6 8 9 10 11 13 12 13 15 17 19 24 26 29 33 36 8 9 10 11 12 12 14 16 18 20 15 17 19 22 24 24 27 30 33 373 3 3 4 4 6 6 7 8 9 8 9 11 12 14 17 20 22 25 27 5 6 7 8 9 9 10 11 13 15 11 12 14 16 18 18 20 22 25 282 2 2 3 3 4 5 5 6 7 6 7 8 9 10 13 14 16 18 20 4 4 5 6 6 6 7 8 9 11 8 9 10 12 13 13 15 17 19 211 2 2 2 2 3 3 4 4 5 4 5 6 6 7 9 10 12 13 15 3 3 4 4 5 5 5 6 7 8 6 7 7 8 9 9 11 12 14 15

2 3 3 4 4 5 6 7 8 10 8 9 10 12 14 17 20 23 26 30 5 5 6 7 8 8 10 11 13 15 10 11 13 15 18 17 20 23 26 302 2 2 3 3 4 4 5 6 7 5 6 7 8 10 12 14 16 19 22 3 4 4 5 6 6 7 8 9 11 7 8 9 11 13 12 14 16 19 221 1 2 2 2 3 3 4 4 5 4 4 5 6 7 9 10 12 14 16 2 3 3 4 4 4 5 6 6 8 5 6 7 8 9 9 10 12 14 161 1 1 1 1 2 2 2 3 3 3 3 4 4 5 6 7 8 10 11 2 2 2 2 3 3 3 4 5 5 3 4 5 5 6 6 7 8 10 11

1 1 2 2 2 3 4 4 5 6 4 5 6 7 9 10 12 15 18 21 3 3 4 4 5 5 6 7 9 10 6 7 8 10 12 11 13 16 19 221 1 1 1 2 2 2 3 4 4 3 3 4 5 6 7 8 10 12 15 2 2 3 3 4 3 4 5 6 7 4 5 6 7 8 8 9 11 13 161 1 1 1 1 1 2 2 2 3 2 2 3 3 4 5 6 7 8 10 1 1 2 2 3 2 3 3 4 5 3 3 4 5 6 5 6 8 9 11

<1 <1 1 1 1 1 1 1 2 2 1 2 2 2 3 3 4 5 6 7 1 1 1 1 2 2 2 2 3 3 2 2 3 3 4 4 4 5 6 8

1 1 1 1 2 2 2 3 3 4 2 3 4 5 6 7 8 10 12 15 2 2 3 3 4 4 5 6 7 9 4 5 6 8 10 9 11 13 16 20<1 1 1 1 1 1 1 2 2 3 2 2 2 3 4 4 5 7 8 10 1 1 2 2 3 3 3 4 5 6 3 3 4 5 7 6 7 9 11 14<1 <1 <1 1 1 1 1 1 2 2 1 1 2 2 3 3 4 5 6 7 1 1 1 1 2 2 2 3 3 4 2 2 3 4 4 4 5 6 8 9<1 <1 <1 <1 <1 1 1 1 1 1 1 1 1 1 2 2 2 3 4 5 1 1 1 1 1 1 1 2 2 3 1 2 2 2 3 3 3 4 5 6

1 1 1 1 2 2 2 3 4 5 2 3 4 5 6 7 9 11 14 17 1 1 2 2 3 2 3 4 5 6 2 3 4 5 6 5 7 9 11 14<1 <1 1 1 1 1 1 2 2 3 1 2 2 3 4 4 6 7 9 12 1 1 1 1 2 1 2 2 3 4 2 2 3 3 4 4 5 6 7 10<1 <1 <1 1 1 1 1 1 2 2 1 1 2 2 3 3 4 5 6 8 <1 1 1 1 1 1 1 2 2 3 1 1 2 2 3 2 3 4 5 6<1 <1 <1 <1 <1 <1 1 1 1 1 1 1 1 1 2 2 2 3 4 5 <1 <1 <1 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 3 4

<1 <1 1 1 1 1 1 2 3 3 1 2 2 3 4 5 6 8 10 14 1 1 1 1 2 2 2 3 4 5 2 2 3 4 5 4 6 8 10 13<1 <1 <1 <1 1 1 1 1 2 2 1 1 2 2 3 3 4 5 7 9 <1 1 1 1 1 1 1 2 2 3 1 1 2 3 3 3 4 5 6 8<1 <1 <1 <1 <1 <1 1 1 1 1 1 1 1 1 2 2 2 3 4 6 <1 <1 <1 1 1 1 1 1 2 2 1 1 1 2 2 2 2 3 4 5<1 <1 <1 <1 <1 <1 <1 1 1 1 <1 <1 1 1 1 1 2 2 3 4 <1 <1 <1 <1 1 <1 1 1 1 1 <1 1 1 1 1 1 1 2 3 3

Mexico

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Non-diabetic Diabetic

Non-smoker Smoker Non-smoker Smoker

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Age (years)

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75

70

65

60

55

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45

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≥15%10–14%5–9%3–4%2%1%<1%

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México Mujeres Hombres

Sin diabetes Con diabetes Sin diabetes Con diabetes

Edad (años)

Colesterol total (mg/dl)

No Fumador Fumador No Fumador Fumador No Fumador Fumador No Fumador Fumador

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