Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study

Salim Yusuf, Philip Joseph, Sumathy Rangarajan, Shofiqul Islam, Andrew Mente, Perry Hystad, Michael Brauer, Vellappillil Raman Kutty, Rajeev Gupta, Andreas Wielgosz, Khalid F AlHabib, Antonio Dans, Patricio Lopez-Jaramillo, Alvaro Avezum, Fernando Lanas, Aytekin Oguz, Iolanthe M Kruger, Rafael Diaz, Khalid Yusoff, Prem Mony, Jephat Chifamba, Karen Yeates, Roya Kelishadi, Afzalhussein Yusufali, Rasha Khatib, Omar Rahman, Katarzyna Zatonska, Romaina Iqbal, Li Wei, Hu Bo, Annika Rosengren, Manmeet Kaur, Viswanathan Mohan, Scott A Lear, Koon K Teo, Darryl Leong, Martin O'Donnell, Martin McKee, Gilles Dagenais, Salim Yusuf, Philip Joseph, Sumathy Rangarajan, Shofiqul Islam, Andrew Mente, Perry Hystad, Michael Brauer, Vellappillil Raman Kutty, Rajeev Gupta, Andreas Wielgosz, Khalid F AlHabib, Antonio Dans, Patricio Lopez-Jaramillo, Alvaro Avezum, Fernando Lanas, Aytekin Oguz, Iolanthe M Kruger, Rafael Diaz, Khalid Yusoff, Prem Mony, Jephat Chifamba, Karen Yeates, Roya Kelishadi, Afzalhussein Yusufali, Rasha Khatib, Omar Rahman, Katarzyna Zatonska, Romaina Iqbal, Li Wei, Hu Bo, Annika Rosengren, Manmeet Kaur, Viswanathan Mohan, Scott A Lear, Koon K Teo, Darryl Leong, Martin O'Donnell, Martin McKee, Gilles Dagenais

Abstract

Background: Global estimates of the effect of common modifiable risk factors on cardiovascular disease and mortality are largely based on data from separate studies, using different methodologies. The Prospective Urban Rural Epidemiology (PURE) study overcomes these limitations by using similar methods to prospectively measure the effect of modifiable risk factors on cardiovascular disease and mortality across 21 countries (spanning five continents) grouped by different economic levels.

Methods: In this multinational, prospective cohort study, we examined associations for 14 potentially modifiable risk factors with mortality and cardiovascular disease in 155 722 participants without a prior history of cardiovascular disease from 21 high-income, middle-income, or low-income countries (HICs, MICs, or LICs). The primary outcomes for this paper were composites of cardiovascular disease events (defined as cardiovascular death, myocardial infarction, stroke, and heart failure) and mortality. We describe the prevalence, hazard ratios (HRs), and population-attributable fractions (PAFs) for cardiovascular disease and mortality associated with a cluster of behavioural factors (ie, tobacco use, alcohol, diet, physical activity, and sodium intake), metabolic factors (ie, lipids, blood pressure, diabetes, obesity), socioeconomic and psychosocial factors (ie, education, symptoms of depression), grip strength, and household and ambient pollution. Associations between risk factors and the outcomes were established using multivariable Cox frailty models and using PAFs for the entire cohort, and also by countries grouped by income level. Associations are presented as HRs and PAFs with 95% CIs.

Findings: Between Jan 6, 2005, and Dec 4, 2016, 155 722 participants were enrolled and followed up for measurement of risk factors. 17 249 (11·1%) participants were from HICs, 102 680 (65·9%) were from MICs, and 35 793 (23·0%) from LICs. Approximately 70% of cardiovascular disease cases and deaths in the overall study population were attributed to modifiable risk factors. Metabolic factors were the predominant risk factors for cardiovascular disease (41·2% of the PAF), with hypertension being the largest (22·3% of the PAF). As a cluster, behavioural risk factors contributed most to deaths (26·3% of the PAF), although the single largest risk factor was a low education level (12·5% of the PAF). Ambient air pollution was associated with 13·9% of the PAF for cardiovascular disease, although different statistical methods were used for this analysis. In MICs and LICs, household air pollution, poor diet, low education, and low grip strength had stronger effects on cardiovascular disease or mortality than in HICs.

Interpretation: Most cardiovascular disease cases and deaths can be attributed to a small number of common, modifiable risk factors. While some factors have extensive global effects (eg, hypertension and education), others (eg, household air pollution and poor diet) vary by a country's economic level. Health policies should focus on risk factors that have the greatest effects on averting cardiovascular disease and death globally, with additional emphasis on risk factors of greatest importance in specific groups of countries.

Funding: Full funding sources are listed at the end of the paper (see Acknowledgments).

Copyright © 2020 Elsevier Ltd. All rights reserved.

Figures

1a and b:. Variations in the associations…
1a and b:. Variations in the associations between 12 modifiable risk factors and a) cardiovascular disease and b) death in high-, middle-, and low-income countries.
HDL = high density lipoprotein, HIC = high income countries, HR = hazard ratio, LIC = low income countries, MIC = middle income countries.
Figure 2:. Risk of myocardial infarction and…
Figure 2:. Risk of myocardial infarction and stroke associated with 12 modifiable risk factors.
HDL = high density lipoprotein, HR = hazard ratio, MI = myocardial infarction.
Figure 3:. Risk of CV death and…
Figure 3:. Risk of CV death and Non-CV death associated with 12 individual or household level modifiable risk factors.
CV = cardiovascular, HDL = high density lipoprotein, HR = hazard ratio, MI = myocardial infarction.
Figure 4:. Population attributable fractions for CVD…
Figure 4:. Population attributable fractions for CVD and mortality associated with 12 individual or clusters of modifiable risk factors.
Data are derived from PAF estimates summarized in table 4. Estimates for individual risk factors were truncated at a lower limit of 0, as this is the lowest threshold to demarcate a relationship with increased risk. HDL = high density lipoprotein, HIC = high income countries, LIC = low income countries, MIC = middle income countries, PAF = population attributable fraction
Figure 5:. Population attributable fractions for 12…
Figure 5:. Population attributable fractions for 12 individual and population level risk factors with CVD, MI and Stroke.
Estimates for individual risk factors were truncated at a lower limit of 0, as this is the lowest threshold to demarcate a relationship with increased risk. Depress = symptoms of depression, HDL = high density lipoprotein, MI = myocardial infarction, PAF = population attributable fraction.
Figure 6:. Population attributable fractions for individual…
Figure 6:. Population attributable fractions for individual risk factors and all-cause mortality, CV deaths and non-CV death.
** Not included as PARs and 95% confidence intervals were negative, but potentially related to reverse causality. Estimates for individual risk factors were truncated at a lower limit of 0, as this is the lowest threshold to demarcate a relationship with increased risk. CV = cardiovascular, Depress = symptoms of depression, HDL = high density lipoprotein, PAF = population attributable fraction.

Source: PubMed

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