Analysis of associations between CVD risk factors and socioeconomic factors over timeLiterature - Danaei G, Singh GM, Paciorek CJ et al - Circulation. 2013 Apr 9;127(14):1493-502. doi: 10.1161/CIRCULATIONAHA.113.001470
The global cardiovascular risk transition: associations of four metabolic risk factors with national income, urbanization, and Western diet in 1980 and 2008.
Danaei G, Singh GM, Paciorek CJ et al; Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group.
Circulation. 2013 Apr 9;127(14):1493-502. doi: 10.1161/CIRCULATIONAHA.113.001470.
BackgroundRisk factors for cardiovascular disease (CVD) have been linked to socioeconomic status . It has been postulated that CVD risk factors increase with wealth and urbanisation, due to a Westernised diet and lifestyle [2,3]. On the other hand, higher income and urban infrastructure may improve access to information on a healthy lifestyle and healthcare, thereby reducing CVD risk factors .
It is important to know how socioeconomic factors and CVD risk factors are linked at the population level and how they have been affected by public health and clinical programs and globalisation of medicines and food.
This study investigated population-level associations of major metabolic risk factors with national income. The aim was not to identify causal relationships (and making such inferences would be inappropriate), but to demonstrate how known CVD risk factors are distributed across different countries in relation to social and economic factors, and how this has changed over time. Country-level risk factor estimates for 199 countries between 1980 and 2008 were analyzed in relation to per capita national income or a measure of Western diet and, in the case of body mass index (BMI), the percentage of people living in urban areas.
- In 1980 population mean BMI, systolic blood pressure (SBP) and total serum cholesterol (TC) were positively correlated with national income (correlation coefficients ranging from 0.34 to 0.50). The association between BMI and income flattened at a gross domestic product (GDP) per capita of Int$7000. Fasting plasma glucose (FPG) showed a weaker association with national income in 1980 (correlation coefficient < 0.15).
- In 2008 an inverted U-shape relationship was seen between BMI in women and the natural logarithm of GDP (Ln(GDP)), with a peak in BMI at middle-income levels. The associations between BMI and Ln(GDP) were weaker in 2008 than in 1980.
- Region-specific differences in the relationship between BMI in women and income exist.
- The association between income and SBP changed over time, into a negative association between SBP and Ln(GDP) in 2008 for women (from 2.76 (95%CI: 2.36-3.62) mmHg per Ln(GDP) in 1980 to -1.85 (95%CI: -2.24 to -1.27) in 2008) and towards a slope of virtually zero for men.
- The association between TC and Ln(GDP) did not differ significantly between 1980 and 2008, and TC was still correlated with national income in 2008 (correlation coefficient: 0.60 (0.53-0.70) in women and 0.62 (0.53-0.67) in men).
- The relationships between risk factors and percentage of population in urban areas mostly showed the same patterns as associations with national income. BMI formed an exception with a positive association in both 1980 and 2008.
- Between 1980 and 2008, the association between BMI and Western diet (WD) maintained a linear-then-flat shape, but shifted upward, showing higher BMI at the same value for WD. In women a reversal of the association was seen at the 80th percentile in 1980, which moved to the 60th percentile in 2008.
- Although FPG was only weakly associated with national income and WD, correlation coefficients of the association with BMI changed from 0.22 (0.02-0.34) and 0.25 (0.14-0.43) in men and women in 1980, to 0.54 (0.42-0.4) and 0.52 (0.40-0.59) in 2008.
This study gives insight into the complex and dynamic associations of metabolic risk factors and affluence and diet. In 2008 only TC retained a strong positive association with national income, while in 1980 also SBP and BMI were positively correlated to income and WD. BMI remained associated with urbanisation, suggesting that urbanisation has an effect irrespective of income and diet.
Global epidemiology of metabolic risk factors has implications for CVD prevention. Regulation and health education should be based on the region-specific associations as described in this study, since affluence cannot sufficiently explain global epidemiology of CVD.
The persistent association between BMI and FPG and the rising BMI levels imply that health systems worldwide will face a global epidemic of hyperglycaemia and diabetes mellitus. This, together with high blood pressure in low-income countries may be the most striking feature of the global CV risk transition in the coming decades.
This is the first data set of its kind. However praiseworthy, the analysis suffers from the striking
Editorial comment 
paucity of global data. The choice of modelling of missing data may have influenced the observations due to the large amount of missing data. Also inadequacy of global surveillance data for CVD risk factors is limiting. Global data that relate CVD risk factors to macroeconomics
across countries will improve our understanding of the causes and consequences of CVD and may facilitate agreement on targets, policies, and interventions.
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