Figures
Abstract
Background
Trends in food availability and metabolic risk factors in Brazil suggest a shift toward unhealthy dietary patterns and increased cardiometabolic disease risk, yet little is known about the impact of dietary and metabolic risk factors on cardiometabolic mortality in Brazil.
Methods
Based on data from Global Burden of Disease (GBD) Study, we used comparative risk assessment to estimate the burden of 11 dietary and 4 metabolic risk factors on mortality due to cardiovascular diseases and diabetes in Brazil in 2010. Information on national diets and metabolic risks were obtained from the Brazilian Household Budget Survey, the Food and Agriculture Organization database, and large observational studies including Brazilian adults. Relative risks for each risk factor were obtained from meta-analyses of randomized trials or prospective cohort studies; and disease-specific mortality from the GBD 2010 database. We quantified uncertainty using probabilistic simulation analyses, incorporating uncertainty in dietary and metabolic data and relative risks by age and sex. Robustness of findings was evaluated by sensitivity to varying feasible optimal levels of each risk factor.
Results
In 2010, high systolic blood pressure (SBP) and suboptimal diet were the largest contributors to cardiometabolic deaths in Brazil, responsible for 214,263 deaths (95% uncertainty interval [UI]: 195,073 to 233,936) and 202,949 deaths (95% UI: 194,322 to 211,747), respectively. Among individual dietary factors, low intakes of fruits and whole grains and high intakes of sodium were the largest contributors to cardiometabolic deaths. For premature cardiometabolic deaths (before age 70 years, representing 40% of cardiometabolic deaths), the leading risk factors were suboptimal diet (104,169 deaths; 95% UI: 99,964 to 108,002), high SBP (98,923 deaths; 95%UI: 92,912 to 104,609) and high body-mass index (BMI) (42,643 deaths; 95%UI: 40,161 to 45,111).
Citation: de Oliveira Otto MC, Afshin A, Micha R, Khatibzadeh S, Fahimi S, Singh G, et al. (2016) The Impact of Dietary and Metabolic Risk Factors on Cardiovascular Diseases and Type 2 Diabetes Mortality in Brazil. PLoS ONE 11(3): e0151503. https://doi.org/10.1371/journal.pone.0151503
Editor: Pratibha V. Nerurkar, College of Tropical Agriculture and Human Resources, University of Hawaii, UNITED STATES
Received: July 17, 2015; Accepted: February 29, 2016; Published: March 18, 2016
Copyright: © 2016 de Oliveira Otto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: Dr. Otto was supported by the National Heart, Lung and Blood Institute of the National Institutes of Health under award number R01HL085710 and by unrestricted educational grants from Bunge LLC and from Swiss Re, which had no role in the study design, data collection and analysis,or data interpretation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Competing interests: Dr. Otto was supported by unrestricted educational grants from Bunge LLC and from Swiss Re. Dr. Mozaffarian reports ad hoc honoraria or consulting from Bunge, Haas Avocado Board, Nutrition Impact, Amarin, Astra Zeneca, Boston Heart Diagnostics, GOED, and Life Sciences Research Organization; and scientific advisory boards, Unilever North America and Elysium Health. Harvard University holds a patent, listing Dr. Mozaffarian as one of three co-inventors, for use of trans-palmitoleic acid to prevent and treat insulin resistance, type 2 diabetes, and related conditions. The other authors report no conflicts. There are no products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
Introduction
Brazil is undergoing rapid economic, demographic and behavioral transitions that are producing major impacts on health. Cardiometabolic diseases including cardiovascular disease (CVD) and type 2 diabetes are the major causes of mortality, leading to nearly 35% of deaths among Brazilian adults [1]. National trends show increasing prevalence of suboptimal levels of well- established cardiometabolic risk factors over last three decades. Trends in national food availability and consumption demonstrate shifts toward unhealthy dietary patterns characterized by higher intakes of processed foods, refined grains, and sugar sweetened beverages; and lower intakes of healthy, minimally processed foods such as fish, fruits, and vegetables [2, 3]. At the same time, the prevalence of adverse metabolic risk factors such as obesity are rising, with a 2–6 fold increase in prevalence of overweight and obesity among adults over the last four decades [4]. Approximately one-third of Brazilian adults have dyslipidemia [5], and about one-third have hypertension [6]. Clearly, the prevalence of suboptimal levels of chronic disease risk factors, particularly those related to poor diet and metabolic health, is substantial, leading to increasing burden on cardiometabolic mortality among Brazilian adults.
To design evidence-informed policies to address this problem, there is a need for clear, consistent data on burdens due to specific risk factors. Yet, the impact of major dietary and metabolic risk factors on cardiometabolic mortality in Brazil according to sex and age has not been clearly established. To address these crucial questions, we used data from the 2010 Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study to evaluate the impact of 11 dietary and 4 metabolic risk factors on cardiometabolic mortality in Brazil in 2010 by sex and age.
Methods
The 2010 GBD Study is an international collaborative effort since 1990 to produce comprehensive and comparable estimates of the burdens of diseases, injuries and risk factors in 187 countries and territories [7, 8]. Details on methods and standardized data collection protocol have been previously described [9, 10]. We conducted a population-level comparative risk assessment analysis within Brazil for 15 dietary and metabolic risk factors to quantify the overall burdens of disease due to each factor by age and sex.
Assessment of Risk Factors
A total of 15 major modifiable dietary and metabolic risk factors (11 dietary and 4 metabolic factors) were selected based on probable or convincing evidence for causal effects on coronary heart disease (CHD), stroke or diabetes [11–15]. Mean and standard deviation of dietary exposures by age and sex were obtained from the Brazilian National Dietary Survey (NDS), the dietary component of the 2008–09 Household Budget Survey (POF), which included approximately 55,000 households nationwide. A subset including 25% of the households included in the POF was randomly selected for the dietary survey [16, 17]. In total 34,003 Brazilian adults and children ≥10y of age participated in the NDS, providing two days of dietary records (non-response rate:19%). These data were combined with food disappearance information from the Food and Agriculture Organization database to provide national dietary estimates. Data sources for dietary and metabolic exposures from nationally representative surveys and/or epidemiological studies are shown in Table 1.
We evaluated national distributions of body-mass index (BMI), total cholesterol, fasting plasma glucose, and systolic blood pressure (SBP) as previously described [11–14]. Information on exposure levels of metabolic risk factors was obtained from 13 observational studies including Brazilian adults conducted between 1990 and 2008 (Table 1). Age-integrating Bayesian hierarchical modeling methods were used to derive estimates of mean levels of each risk factor and their 95% uncertainty interval (UI) separately by age and sex subgroups. For each exposure, the primary model inputs were nationally representative survey data and exposure levels from epidemiologic studies for each sex and age group, data on the numbers of subjects in each stratum; survey-level indicator covariates for sampling representativeness, food disappearance information from the Food and Agriculture Organization, and country, region (21 regions), and super-region (7 groupings of regions) random effects. This model leverages the interrelationshps between all data to inform final estimates, while appropriately allowing strongest input to Brazil-specific data. [15, 24–26]. Since information on the inherent correlation between age and sex groups resulting from the same underlying Bayesian models was not available, we have not generated p-values for comparison of exposure estimates across subgroups. 95% UIs can be used for informal inference on comparisons between groups: i.e., if the mean value of one estimate is outside the 95% UI of another, these could be considered nominally significantly different.
Etiological Effects of Risk Factors on Mortality
The relative risks (RR) of CHD, stroke, and diabetes mortality or incidence for each dietary and metabolic risk factor were obtained from published systematic reviews and meta-analyses of randomized controlled trials or prospective cohort studies [27]. When no recent systematic review or meta-analysis were available, de novo meta-analyses were conducted [28]. For these analyses, we used evidence available through November 2014. Most observational studies included had adjusted for relevant potential confounders such as age, sex, race, socioeconomic status, education, physical activity, smoking and alcohol consumption. Etiological effects of metabolic risk factors were estimated using pooled analyses of large cohort projects [29]. For each risk factor-disease pair, we used consistent age-varying distributions of RRs as previously described, incorporating smaller relative effects with increasing age [29]. RRs for both sexes combined was used for men and women, since results from meta-analyses indicated no material differences in RRs by sex [29].
Optimal Risk Factor Levels
In order to estimate deaths due to suboptimal distributions of risk factors, the optimal distribution was defined as the exposure associated with the lowest possible disease risk that was feasible and observed in some populations, based on current evidence from epidemiologic studies [12, 15, 23, 29] (Table 1). Optimal intake distribution for dietary factors with protective effects (e.g. fruits, vegetables, whole grains) was defined as the intakes to which beneficial effects may plausibly continue based on evidence from current epidemiological studies. For metabolic risk factors and dietary factors with harmful effects, optimal levels were defined as the exposure levels associated with the lowest level of harm seen in observational, and when relevant randomized trials as long as the selected level was also observed at the population level [29]. The standard deviation of the optimal levels was determined using the mean to SD regression co-efficient [8, 15, 23].
Disease-specific Deaths
The number of deaths due to CHD (ICD-10 codes I20–I25), ischemic stroke (I63, I65–I67, I69.3), hemorrhagic stroke (I60-62, I69.0–2), hypertensive heart disease (I11–I13), aortic aneurysm (I71), rheumatic heart disease (I01, I02.0, I05–I09), inflammatory heart disease (I33, I42), other CVDs, and diabetes (E10–E14) for 2010 were obtained from the GBD 2010 mortality database which provides annual reported data on mortality statistics by country, age, sex, and cause of deaths [30].
Estimating Mortality Attributable to Risk Factors
The population-attributable fraction PAF was estimated as:
where x is the exposure level, P(x) is the actual distribution of exposure in the population, P′(x) is the optimal exposure distribution; RR(x) is the relative risk of mortality at exposure level x, and m is the maximum exposure level. We calculated the number disease-specific deaths attributable to a risk factor by multiplying its PAF by total disease-specific mortality.
We estimated the number of deaths attributed to suboptimal diet by computing the combined population attributable fraction for dietary risk factors based on the following equation: , where PAFi is the individual dietary risk factor, assuming that the contribution of each component is multiplicative; i.e. that the individual dietary contributions are independent [8]. We conducted all analyses separately by sex and age groups (25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, and 80+ years).
Due to joint distributions, multicausality, interaction, and because the effects of some risk factors are partly mediated through other risk factors, the number of deaths attributable to different risk factors cannot be summed. For example, the burden due to high SBP is partly mediated by high sodium intake and low intakes of fruits and vegetables; whereas much of the burden due to high BMI is mediated by high SBP, cholesterol, and glucose. Thus, the numbers of deaths attributable to each risk factor should be considered the total numbers of deaths due to this factor, including its upstream determinants and downstream mediators.
Uncertainty and Sensitivity Analyses
We quantified statistical uncertainty using probabilistic simulation analyses. Using the Monte Carlo simulations, we drew 1000 observations (unbiased random samples) from the normal distribution of exposure, optimal intake levels, and mortality, as well as a log-normal distribution of disease-specific RRs. We reported 95% UI for deaths related to a risk factor based on the 2.5th and 97.5th percentile of the resulting distributions of attributable deaths. These UI include all sources of uncertainty from each component of the analysis, including sampling error and uncertainties associated with model parameters [31]. In sensitivity analyses, we evaluated different optimal levels for each metabolic risk factor. All analyses were conducted using R version 2.15.0.
Results
Dietary consumption
In 2010, the mean consumption of all evaluated healthful foods by Brazilian adults was below optimal levels, while intakes of unhealthful dietary factors were higher than optimal (Table 2). The largest differences between mean intakes and optimal levels were observed for fruits, whole grains, and nuts, with mean national intakes only between 8% to 30% of optimal. Among unhealthy dietary factors, mean trans-fat intake exceeded the optimal level by 200%, while sodium consumption was over 100% greater than the WHO recommendation of 2.0g/day. In contrast, intakes of fish and vegetables/beans were each closer to optimal levels. Compared to middle-aged and older adults (45y+), younger adults showed lower consumption of healthful foods such as fruits, vegetables and fish, and higher consumption of sugar-sweetened beverages, red and processed meats. Similarly, women exhibited generally healthier dietary habits than men (Table 2).
Metabolic risk factors
National mean levels of metabolic risk factors exceeded optimal levels, with largest differences for SBP (mean [95%UI]: 133.5 mmHg [131.6–135.1] in men and 124.1 mmHg [122.4–125.8] in women; optimal level: 115 mmHg) and BMI (mean [95%UI]: 26.2kg/m2 [26.0, 26.5] in men and 26.5kg/m2 [26.2, 26.7] in women; optimal: 23kg/m2;) (Table 2). When comparing age-adjusted estimates, Brazilian men had higher mean SBP levels and lower mean total cholesterol levels than women (Table 2). On the other hand, mean BMI and fasting plasma glucose concentrations were similar in men vs. women. Mean levels of each of these metabolic risk factors increased with age.
Cardiovascular and Metabolic Death
In 2010, 446,084 total cardiometabolic deaths occurred in Brazil, with 161,261 (36.1%) due to ischemic heart disease, 144,295 (32.3%) to stroke (ischemic and hemorrhagic), and 53,353 (12%) to diabetes. About 4 in 10 (38%) of these deaths occurred prematurely, i.e. before the age of 70y. The leading cause of premature cardiometabolic deaths among Brazilians was ischemic heart disease (67%), followed by hemorrhagic stroke (19%) and diabetes (13%). Major causes of cardiometabolic deaths were similar among men and women (Table 3).
The impact on suboptimal risk factors on cardiometabolic mortality
The impact of suboptimal diet on cardiometabolic mortality in Brazil in 2010 was substantial, estimated to account for nearly 45% of cardiometabolic deaths (202,949 deaths; 95% UI, 194,322 to 211,747). Among individual dietary factors, low intake of fruits was the largest contributor to cardiometabolic mortality, leading to 55,051 deaths (95% UI, 50,534 to 59,658). Low intake of whole grains (53,269 deaths; 95% UI, 50,594 to 56,072) and high intake of sodium (41,751 deaths; 95% UI, 26,190 to 56,070) were the second and third leading dietary factors contributing to cardiometabolic death in the country, followed by low intake of nuts (34,923 deaths; 95%UI, 31,532 to 38,446), low intake of vegetables and beans (30,613 deaths; 95%UI, 27,412 to 33,727)] and high intake of processed meats (30,613 deaths; 95%UI, 27,412 to 33,727)] (Table 4). The number of cardiometabolic deaths (per million adults) was consistently higher in men compared to women, particularly for sodium (463 deaths per million adults; 95%UI, 293 to 626 in men; 290 deaths per million adults; 95%UI, 185 to 401 in women) (Table 4, Figs 1 and 2).
When evaluating the impact of metabolic risk factors on cardiometabolic mortality, we found that high SBP was the largest contributor to cardiometabolic mortality in 2010, leading to 214,263 deaths (95%UI, 195,073 to 233,936) in the period. High fasting plasma glucose was the second largest metabolic risk factor contributing to cardiometabolic death (93,693 cardiometabolic deaths; 95%UI 85,562 to 102,756), followed by high BMI (79,914 deaths; 95%UI, 74,065 to 86,444) and high total cholesterol (50,129 deaths; 95%UI, 36,020 to 69,732). Overall, cardiometabolic mortality rates attributable to metabolic risk factors were similar in males and females, except for high SBP, where the number of cardiometabolic deaths (per million adults) in men was substantially higher than those observed in women (2,251 deaths per million adults; 95%UI, 2,023 to 2,490 in men; 1,608 deaths per million adults; 95%UI, 1,379 to 1,862 in women).
The impact of suboptimal risk factors on premature cardiometabolic mortality
Suboptimal diet was the largest contributor to premature cardiometabolic death in Brazil, accounting for 62% of premature deaths (104,169 premature cardiometabolic deaths,95%UI, 99,964 to 108,002) among men and women in 2010 (Table 5, Figs 3 and 4). The second leading risk factor for early death was high SBP, followed by high BMI, which was the fourth leading risk factor for mortality in all age groups. The relative greater impact of high BMI on premature mortality was mostly due to suboptimal BMI among adults between 45 and 69 years of age.
Sensitivity Analysis
In sensitivity analysis, increasing the optimal level of high SBP from 115 to 120 mmHg decreased the number of SBP-attributable cardiometabolic deaths by about 9% (S1 Table). Lowering the optimal levels of BMI (from 23 to 21 kg/m2), fasting plasma glucose (from 5.3 to 4.9 mmol/l), and total cholesterol (from 4.1 to 3.9 mmol/l) increased their respective attributable mortality by 20%-30%.
Discussion
We have used up-to-date data, consistent and comparable methodology to estimate the impact of 11 dietary and 4 metabolic risk factors on cardiometabolic mortality in Brazil in 2010. Suboptimal diet and high SBP were the largest contributors to cardiometabolic death in Brazil. Among individual dietary factors, low intakes of fruits and whole grains and high intakes of sodium were the major risk factors for cardiometabolic death.
Our dietary consumption estimates are consistent with recent studies reporting lower household availability and mean intakes of fruits, rice, beans, nuts, and fish, and relatively high consumption of processed meat, refined sugar, sugar sweetened beverages, and frozen meals in Brazil [2, 32, 33]. Previous studies reported a more than doubling in consumption of highly processed foods such as sausage, frozen meals and soft drinks in the past 3 decades, while energy from traditional foods such as rice and beans decreased between 10–20% in the same period [3]. Overall, our findings show the gap between current and optimal consumption was highest among younger adults (<45y). If these poor habits continue as these younger Brazilians age, the burden of cardiometabolic deaths due to suboptimal diet is likely to increase dramatically over time.
Although reductions in smoking and improved health care access have contributed to a nearly 30% reduction in CVD mortality between 1996 and 2007 in Brazil [5], our results indicate that the population burdens of major metabolic risk factors remain high, especially as it relates to premature mortality. About 60% of premature cardiometabolic deaths were attributable to high SBP, while 23% were due to high fasting plasma glucose, which suggests the need for appropriate measures to reduce the prevalence of these important metabolic risk factors in the country. Recent reports from the Longitudinal Study of Adult Health, a cohort including ~15,000 Brazilian civil servants aged 35–74 years, found that about 50% of participants with diabetes had not been previously diagnosed or received treatment [34]; nearly 1 of 5 participants with measured hypertension had not been previously diagnosed; and about 47% of participants with measured hypertension had not received treatment for this condition [35]. In our investigation, the large burdens of BP highlight the relevance of excess sodium consumption among Brazilian adults and the potential population benefits of sodium reduction programs. Better BP screening and control with medications, and lifestyle treatment of those with prevalent diabetes and pre-diabetes, are also strongly indicated.
The overall adverse metabolic profile may be explained in part by the high prevalence of overweight and obesity, which was the third leading factor for premature cardiometabolic mortality in the country. Currently nearly 50% of Brazilian adults are overweight and 15% are obese [16, 36]. Importantly, obesity rates in the past four decades have been greater among individuals in lower income groups [37]. Excess adiposity increases blood pressure, dyslipidemia, as well as risk of diabetes, IHD, and stroke [38]. It has been well established that substantial reduction in cardiometabolic risk through adherence to a healthy lifestyle including healthy diet and regular exercise. Our findings emphasize the need to improve diet through increasing consumption of healthy foods including fruits, whole grains nuts and beans, and lowering intakes of unhealthy food components such sodium and processed meats, which will help reduce BMI and blood pressure levels in Brazil.
Brazil has had success at implementing large-scale measures that have improved public health. For example, national immunization programs for yellow fever, small pox and poliomyelitis have nearly eradicated these conditions [39, 40]. In their place, cardiometabolic diseases including CHD, stroke, and diabetes are now the leading causes of mortality and the greatest public health challenges. Launched by the Brazilian Ministry of Health in 2011, the Strategic Action Plan to Tackle non-Communicable Chronic Diseases set 12 national goals for 2022, including a 2% yearly reduction in premature mortality due to non-Communicable Chronic Diseases, as well as improvements to national levels of three dietary targets (fruits, leafy vegetables, sodium) and one metabolic risk factor (BMI), in addition to increasing leisure-time physical activity [41]. Our data suggest that substantial reduction in premature mortality could be achieved by expanding the national goals to include other dietary factors such as whole grains, nuts, beans, and fish, as well as lowering processed meats and trans-fats. Our findings also emphasize the need for additional concerted efforts to improve risk factors among young adults and males, especially.
Our study has several strengths. We used consistent and comparable methods to estimate the impact of key dietary and metabolic risk factors on cardiometabolic mortality in Brazil by age and sex, informing national priorities for prevention strategies. We used up-to-date evidence on effect sizes of diet-disease associations based on meta-analyses. We evaluated sensitivity of our findings to alternative optimal levels of dietary and metabolic risk factors. We accounted for potential age- and sex-differences in the current levels of risk factors, the relative magnitude of their effects cardiometabolic mortality, as well as cause-specific mortality.
Potential limitations should be considered. Although we used RRs for associations of dietary and metabolic risk factors from meta-analyses of observational studies adjusting for relevant confounders, residual confounding may be present. To evaluate the magnitude of potential bias on measures of associations, particularly due to residual confounding by dietary factors, a sensitivity analysis was previously performed comparing GBD estimates to RRs from randomized controlled feeding trials evaluating effects of overall dietary patterns on CVD risk factors (DASH, DASH-sodium, OmniHeart) [27]. There was no substantial differences between effect size estimates used in this analysis compared to those of feeding trials, which indicates that our RRs were not substantially affected by correlation of dietary factors [27]. We used the joint population attributable fraction to estimate the number of cardiometabolic deaths attributable to the combined effect of dietary factors and assumed the intakes of individual dietary factors were independent within each age-sex stratum. This could overestimate the cardiometabolic burden of suboptimal diet. On the other hand, the cardiometabolic burden due to suboptimal diet might be underestimated due to measurement error and over-adjustment in the source studies for potential mediators (e.g., blood pressure, BMI, blood lipids). Finally, we focused on cardiometabolic mortality, and dietary and metabolic risk factors could increase the risk of other diseases, such as cancer. Thus, our estimates are a conservative estimate of the overall burden of disease due to these risk factors in Brazil.
In conclusion, our estimates of the impact of suboptimal dietary and metabolic risk factors on cardiometabolic mortality in Brazil in 2010 indicate that the major risk factors were suboptimal diet, particularly low fruits and whole grains and high sodium, high SBP, high fasting plasma glucose and high BMI. Our findings inform priorities for policy measures to reduce CVD and diabetes mortality in Brazilian adults.
Supporting Information
S1 Table. Cardiometabolic deaths (95% UI) attributable to suboptimal risk factors in Brazil in 2010, estimated using lower reference levels.
https://doi.org/10.1371/journal.pone.0151503.s001
(DOCX)
Acknowledgments
Dr. Otto was supported by the National Heart, Lung and Blood Institute of the National Institutes of Health under award number R01HL085710 and by unrestricted educational grants from Bunge LLC and from Swiss Re. Dr. Mozaffarian was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (R21 HL109924). Dr. Mozaffarian reports ad hoc honoraria or consulting from Bunge, Haas Avocado Board, Nutrition Impact, Amarin, Astra Zeneca, Boston Heart Diagnostics, GOED, and Life Sciences Research Organization; and scientific advisory boards, Unilever North America and Elysium Health. This work was undertaken as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. The results in this paper are prepared independently of the final estimates of the Global Burden of Diseases, Injuries, and Risk Factors study. We thank the Russia Longitudinal Monitoring Survey Phase 2, funded by the USAID and NIH (R01-HD38700), Higher School of Economics and Pension Fund of Russia, and the University of North Carolina Population Center (5 R24 HD050924) (Source: "Russia Longitudinal Monitoring survey, RLMS-HSE”), conducted by HSE and ZAO “Demoscope” together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS (RLMS-HSE sites: http://www.cpc.unc.edu/projects/rlms, http://www.hse.ru/org/hse/rlms) for sharing data with us. We thank Barbara Bowman, MS, PhD, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA; Patricia Constante Jamie, PhD, School of Public Health, University of São Paulo, Sao Paolo, Brazil; Karen Lock, PhD, London School of Hygiene and Tropical Medicine, London, UK; and Joceline Pomerleau, PhD, London School of Hygiene and Tropical Medicine, London, UK for advising and guidance on initial search strategy. We thank Louise Dekker, MSc, Jenna Golan, MPH, Shadi Kalantarian, MD, MPH, Liesbeth Smit, PhD, and Georgina Waweru Harvard School of Public Health, Boston, MA, USA for assistance with data collection.
The Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE) authorship group includes: Core Group: Dariush Mozaffarian, MD, DrPh, Tufts University Friedman School of Nutrition Science and Health Policy, Boston, MA, USA; Majid Ezzati, PhD, Imperial College London, London, UK; Saman Fahimi, MD, MPH, University of Cambridge, Cambridge, UK; Shahab Khatibzadeh, MD, MPH, Harvard School of Public Health, Boston, MA, USA; Renata Micha, PhD, Tufts University Friedman School of Nutrition Science and Health Policy, Boston, MA, USA; John Powles, MB, BS, MA, FFPH, University of Cambridge, Cambridge, UK; Peilin Shi, PhD, Tufts University Friedman School of Nutrition Science and Health Policy, Boston, MA, USA. Other Members: Ibrahim Elmadfa, PhD, Institute of Nutritional Sciences, University of Vienna, Vienna, Austria; Mayuree Rao, BA, The Warren Alpert Medical School of Brown University, Providence, RI, USA; Pattra Wirojratana, Harvard School of Public Health, Boston, MA, USA; Gitanjali Singh, PhD, Tufts University Friedman School of Nutrition Science and Health Policy, Boston, MA USA. Dietary Exposure Imputation: Stephen S. Lim, PhD, Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, USA; Kathryn G. Andrews, MPH, African Leaders Malaria Alliance, Dar es Salaam, Tanzania; Rebecca E Engell, BA, Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, USA. Dietary Exposures—Corresponding Members (alphabetical order): Pamela A. Abbott, PhD, University of Aberdeen, United Kingdom; Morteza Abdollahi, MD, MPH, National Nutrition and Food Technology Research Institute Iran; Enrique Abeyá Gilardon, MD, MPH, Ministerio de Salud, Argentina; Habibul Ahsan, MD, University of Chicago, USA; Mohannad Abed Alfattah Al Nsour, MD, Eastern Mediterranean Public Health Network (EMPHNET), Jordan; Suad N. Al-Hooti, MSc, Kuwait Institute for Scientific Research, Kuwait; Carukshi Arambepola, MD, Faculty of Medicine, University of Colombo, Sri Lanka; Hubert Barennes, PhD, Institut Francophone pour la Médecine Tropicale, Lao PDR; Simon Barquera, PhD, Instituto Nacional de Salud Publica (INSP), Mexico; Ana Baylin, MD, DrPH, University of Michigan, US; Wulf Becker, PhD, National Food Agency, Sweden; Peter Bjerregaard, MD, DrMedSci, National Institute of Public Health, University of Southern Denmark, Denmark; Lesley T. Bourne, PhD, Environment and Health Research Unit, Medical Research Council, South Africa; Neville Calleja, MD, MFPH, Department of Health Information & Research, Malta; Mario V. Capanzana, PhD, Food and Nutrition Research Institute, Philippines; Katia Castetbon, PhD, Institut de Veille Sanitaire, France; Hsing-Yi Chang, DrPH, National Health Research Institutes, Taiwan; Yu Chen, PhD, New York University School of Medicine, United States; Melanie J. Cowan, MPH, WHO, Switzerland; Stefaan De Henauw, MD, PhD, Ghent University, Department of Public Health, Belgium; Eric L. Ding, ScD, Harvard Medical School and Harvard School of Public Health, USA; Charmaine A. Duante, MSc, Food and Nutrition Research Institute,Department of Science and Technology, Philippines; Pablo Duran, PhD, Dirección Nacional de Maternidad e Infancia, Ministerio de Salud de la Nación, Argentina; Ibrahim Elmadfa, Professor, Institute of Nutritional Sciences, University of Vienna, Austria; Heléne Enghardt Barbieri, National Food Agency, Uppsala, Sweden; Farshad Farzadfar, MD, ScD, Tehran University of Medical Sciences, Iran; Dulitha N. Fernando, PhD, Faculty of Medicine, University of Colombo, Sri Lanka; Aida Filipovic Hadziomeragic, MD, MSc, Institute of Public Health of Federation of Bosnia and Herzegovina, Bosnia and Herzegovina; Regina M. Fisberg, PhD, Faculty of Public Health,University of São Paulo, Brazil; Simon Forsyth, BA (UQ), School of Population Health, University of Queensland, Australia; Didier Garriguet, MSc, Statistics Canada, Canada; Jean-Michel Gaspoz, MD, MPH, Geneva University Hospitals and Faculty of Medicine of Geneva,Switzerland; Dorothy Gauci, Post Graduate Diploma (Epidemiology), Department of Health Information and Research, Malta; Brahmam N.V. Ginnela, MB, BS, DPH, National Institute of Nutrition, Indian Council of Medical Research, India; Idris Guessous, MD, Geneva University Hospitals, Switzerland; Martin C. Gulliford, FFPH, King's College London, UK; Wilbur Hadden; Christian Haerpfer, PhD, University of Aberdeen, United Kingdom; Daniel J. Hoffman, PhD, Rutgers, the State University of New Jersey, USA; Anahita Houshiar-Rad MSc, National Nutrition and Food Technology Research Institute Shahid Beheshti University of Medical Sciences, Iran; Inge Huybrechts, PhD, Ghent University, Department of Public Health, Belgium; Nahla C. Hwalla, PhD, American University of Beirut, Lebanon; Hajah Masni Ibrahim, SM, Ministry of Health, Brunei; Manami Inoue, MD, PhD, Epidemiology and Prevention Division, Research Center for Cancer Prevention and Screening, National Cancer Center, Japan; Maria D. Jackson, PhD, University of the West Indies, Jamaica; Lars Johansson, PhD, Norwegian Directorate of Health, Norway; Lital Keinan- Boker, MD, PhD, Ministry of Health, Israel; Cho-il Kim, PhD, Korea Health Industry Development Institute, Republic of Korea; Eda Koksal, PhD, Gazi University, Turkey; Hae- Jeung Lee; Yanping Li, PhD, Harvard School of Public Health, USA; Nur Indrawaty Lipoeto, PhD, Andalas University, Indonesia; Guansheng Ma, PhD, National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention, China; Guadalupe L. Mangialavori, MS RD, Ministerio de Salud de la Nación (National Health Ministry), Argentina; Yasuhiro Matsumura, PhD, Bunkyo University, Japan; Stephen T. McGarvey, PhD, Brown University, USA; Mei Fen Chan; Gert B.M. Mensink, PhD, Robert Koch Institute, Germany; Rafael A. Monge-Rojas, PhD, Costa Rican Institute for Research and Education and Nutrition and Health (INCIENSA), Costa Rica; Abdulrahman O. Musaiger, PhD, Arab Center for Nutrition, Bahrain; Nagalla Balakrishna, PhD, National Institute of Nutrition, Hyderabad, India; Androniki Naska, PhD, Dept. of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Greece; Marga C. Ocke, PhD, National Institute for Public Health and the Environment, Netherlands; Maciej Oltarzewski, MSc, National Food and Nutrition Institute, Poland; Philippos Orfanos, MSc, Dept. of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Greece; Marja- Leena Ovaskainen, PhD, National Institute for Health andWelfare, Finland; Wen-Harn Pan, PhD, Division of Preventive Medicine and Health Services Research, Institute of Population Health Sciences, National Health Research Institutes, Taiwan; Demosthenes B. Panagiotakos, PhD, Harokopio University, Greece; Gulden A. Pekcan, PhD, Hacettepe University Department of Nutrition and Dietetics, Turkey; Stefka Petrova, MD, PhD, National Center of Public Health and Analyses, Bulgaria; Noppawan Piaseu, PhD, Mahidol University, Thailand; Christos Pitsavos, MD, Athens University Medical School, Greece; Luz Gladys Posada, RD, MS, Universidad de Antioquia, Colombia; Leanne M. Riley, MSc, WHO, Switzerland; Luz Maria Sánchez-Romero, MD MSc, National Institute of Public Health, Mexico; Rusidah BT. Selamat, MSc, Nutrition Division, Ministry of Health, Malaysia; Sangita Sharma; Abla Mehio Sibai, PhD, American University of Beirut Faculty of Health Sciences, Lebanon; Rosely Sichieri, MD,PhD, State University of Rio de Janeiro, Brazil; Chansimaly Simmala, MD, Institut of Tropical Medecin, Laos; Laufey Steingrimsdottir, PhD, Professor, Iceland; Gillian Swan; Elżbieta Halina. Sygnowska, MSc PhD, National Institute of Cardiology, Poland; Lucjan Szponar, MD;PhD, National Food and Nutrition Institute, Poland; Heli Tapanainen, MSc, National Institute for Health and Welfare, Finland; Robert Templeton; Anastasia Thanopoulou, MD, PhD, Diabetes Center, 2nd Department of Internal Medicine, National University of Athens, Hippokration General Hospital, Greece; Holmfridur Thorgeirsdóttir, MSc, Directorate of Health, Iceland; Inga Thorsdottir; Antonia Trichopoulou, MD, Hellenic Health Foundation, Greece; Shoichiro Tsugane, MD, PhD, National Cancer Center, Japan; Aida Turrini, MD, National Research Institute on Food and Nutrition, Italy; Sirje Vaask, PhD, Tallinn University of Technology, Estonia; Coline van Oosterhout, National Institute for Public Health and the Environment, Netherlands; J Lennert Veerman, PhD, The University of Queensland, Australia; Nowak Verena; Anna Waskiewicz, MSc, PhD., Institute of Cardiology, Department of Cardiovascular Diseases Epidemiology, Prevention and Health Promotion, Poland; Sahar Zaghloul, PhD., National Nutrition Institute, Egypt; Gábor Zajkás, MD, National Institute of Food and Nutrition Sciences, Hungary.
Author Contributions
Conceived and designed the experiments: MO AA DM GD ME GS RM SF. Analyzed the data: MO. Wrote the paper: MO. Revised and provided comments to the manuscript: AA RM SK SF GS RS CM ML ME DM.
References
- 1.
Ministerio da Saude Brasil. Informacoes de Saude 2013 [June 2015]. Available: http://www.datasus.gov.br.
- 2. Levy RB, Claro RM, Mondini L, Sichieri R, Monteiro CA. Regional and socioeconomic distribution of household food availability in Brazil, in 2008–2009. Rev Saude Publica. 2012;46(1):6–15. Epub 2011/12/21. pmid:22183512.
- 3. Martins AP, Levy RB, Claro RM, Moubarac JC, Monteiro CA. Increased contribution of ultra-processed food products in the Brazilian diet (1987–2009). Rev Saude Publica. 2013;47(4):656–65. pmid:24346675.
- 4. Conde WL, Monteiro CA. Nutrition transition and double burden of undernutrition and excess of weight in Brazil. Am J Clin Nutr. 2014;100(6):1617S–22S. pmid:25411303.
- 5. Schmidt MI, Duncan BB, Azevedo e Silva G, Menezes AM, Monteiro CA, Barreto SM, et al. Chronic non-communicable diseases in Brazil: burden and current challenges. Lancet. 2011;377(9781):1949–61. Epub 2011/05/13. pmid:21561658.
- 6. Picon RV, Fuchs FD, Moreira LB, Riegel G, Fuchs SC. Trends in prevalence of hypertension in Brazil: a systematic review with meta-analysis. PLOS one. 2012;7(10):e48255. Epub 2012/11/03. pmid:23118964; PubMed Central PMCID: PMC3485225.
- 7. GBD Study. The global burden of diseases, injuries, and risk factors. Operations manual. 2008. Available: http://www.globalburden.org/GBD_Study_Operations_Manual_Jan_20_2009.pdf.
- 8. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2224–60. pmid:23245609; PubMed Central PMCID: PMC4156511.
- 9. Micha R, Kalantarian S, Wirojratana P, Byers T, Danaei G, Elmadfa I, et al. Estimating the global and regional burden of suboptimal nutrition on chronic disease: methods and inputs to the analysis. Eur J Clin Nutr. 66(1):119–29. Epub 2011/09/15. ejcn2011147 [pii] pmid:21915137.
- 10. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2013;380(9859):2224–60. Epub 2012/12/19. pmid:23245609.
- 11. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet. 2011;378(9785):31–40. pmid:21705069.
- 12. Danaei G, Finucane MM, Lin JK, Singh GM, Paciorek CJ, Cowan MJ, et al. National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5.4 million participants. Lancet. 2011;377(9765):568–77. pmid:21295844.
- 13. Farzadfar F, Finucane MM, Danaei G, Pelizzari PM, Cowan MJ, Paciorek CJ, et al. National, regional, and global trends in serum total cholesterol since 1980: systematic analysis of health examination surveys and epidemiological studies with 321 country-years and 3.0 million participants. Lancet. 2011;377(9765):578–86. pmid:21295847.
- 14. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet. 2011;377(9765):557–67. pmid:21295846.
- 15. Micha R, Kalantarian S, Wirojratana P, Byers T, Danaei G, Elmadfa I, et al. Estimating the global and regional burden of suboptimal nutrition on chronic disease: methods and inputs to the analysis. Eur J Clin Nutr. 2012;66(1):119–29. pmid:21915137.
- 16.
Instituto Brasileiro de Geografia e Estatistica (IBGE). Pesquisa de Orcamentos Familiares 2008–2009—Analise do consumo alimentar pessoal. Rio de Janeiro: IBGE, 2011.
- 17. Souza Ade M, Pereira RA, Yokoo EM, Levy RB, Sichieri R. Most consumed foods in Brazil: National Dietary Survey 2008–2009. Rev Saude Publica. 2013;47 Suppl 1:190s–9s. Epub 2013/07/11. pmid:23703263.
- 18.
Lock K, Pomerleau J, Causer L, McKee M. Low fruit and vegetable consumption. In: Ezzati M, Lopez AD, Rodgers A, Murray CJL, editors. Comparative quantification of health risks: Global and regional burden of disease attributable to selected major risk factors. Geneva: WHO; 2004. p. 597–728.
- 19. Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Willett WC. Trans fatty acids and cardiovascular disease. N Engl J Med. 2006;354(15):1601–13. Epub 2006/04/14. 354/15/1601 [pii] pmid:16611951.
- 20. Mozaffarian D, Micha R, Wallace S. Effects on coronary heart disease of increasing polyunsaturated fat in place of saturated fat: a systematic review and meta-analysis of randomized controlled trials. PLOS Medicine / Public Library of Science. 2010;7(3):e1000252.
- 21. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937–52. Epub 2004/09/15. S0140-6736(04)17018-9 [pii]. pmid:15364185.
- 22.
James WPT, Jackson-Leach R, Mhurchu CN, Kalamara E, Shayeghi M, et al. Overweight and obesity (High body mass index). In: Ezzati M, Lopez AD, Rodgers A, Murray CJL, editors. Comparative quantification of health risks: Global and regional burden of disease attributable to selected major risk factors. Geneva: WHO; 2004. p. 497–596.
- 23. Afshin A, Micha R, Khatibzadeh S, Mozaffarian D. Consumption of nuts and legumes and risk of incident ischemic heart disease, stroke, and diabetes: a systematic review and meta-analysis. Am J Clin Nutr. 2014;100(1):278–88. pmid:24898241; PubMed Central PMCID: PMC4144102.
- 24. Ezzati M, et al. Rethinking the "diseases of affluence" paradigm: global patterns of nutritional risks in relation to economic development. PLOS Med. 2005;2(5):133.
- 25. Stevens G, Dias RH, Thomas KJ, Rivera JA, Carvalho N, Barquera S, et al. Characterizing the epidemiological transition in Mexico: national and subnational burden of diseases, injuries, and risk factors. PLOS Med. 2008;5(6):e125. pmid:18563960.
- 26. Singh GM, Micha R, Khatibzadeh S, Shi P, Lim S, Andrews KG, et al. Global, Regional, and National Consumption of Sugar-Sweetened Beverages, Fruit Juices, and Milk: A Systematic Assessment of Beverage Intake in 187 Countries. PLOS one. 2015;10(8):e0124845. Epub 2015/08/06. pmid:26244332; PubMed Central PMCID: PMCPmc4526649.
- 27. Khatibzadeh S, Micha R, Afshin A, Rao M, Yakoob MY, Mozaffarian D. Abstract P060: Major Dietary Risk Factors for Chronic Diseases: A Systematic Review of the Current Evidence for Causal Effects and Effect Sizes. Circulation. 2012;125(10 Supplement):AP060.
- 28. Afshin A, Micha R, Khatibzadeh S, Mozaffarian D. Abstract MP21: Consumption of Nuts and Beans and Risk of Incident Coronary Heart Disease, Stroke, and Diabetes Mellitus: A Systematic Review and Meta-analysis. Circulation. 2013;127(12 Supplement):AMP21.
- 29. Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLOS one. 2013;8(7):e65174. pmid:23935815; PubMed Central PMCID: PMCPMC3728292.
- 30. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, 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. 2013;380(9859):2095–128. Epub 2012/12/19. pmid:23245604.
- 31. Murray CJ, Atkinson C, Bhalla K, Birbeck G, Burstein R, Chou D, et al. The state of US health, 1990–2010: burden of diseases, injuries, and risk factors. JAMA. 2013;310(6):591–608. pmid:23842577.
- 32. Levy-Costa RB, Sichieri R, Pontes Ndos S, Monteiro CA. [Household food availability in Brazil: distribution and trends (1974–2003)]. Rev Saude Publica. 2005;39(4):530–40. Epub 2005/08/23. /S0034-89102005000400003. pmid:16113900.
- 33. Bezerra IN, Goldman J, Rhodes DG, Hoy MK, Moura Souza A, Chester DN, et al. Difference in adult food group intake by sex and age groups comparing Brazil and United States nationwide surveys. Nutrition journal. 2014;13:74. pmid:25047421; PubMed Central PMCID: PMC4118205.
- 34. Schmidt MI, Hoffmann JF, de Fatima Sander Diniz M, Lotufo PA, Griep RH, Bensenor IM, et al. High prevalence of diabetes and intermediate hyperglycemia—The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Diabetology & metabolic syndrome. 2014;6:123. pmid:25788987; PubMed Central PMCID: PMCPMC4363046.
- 35. Chor D, Pinho Ribeiro AL, Sa Carvalho M, Duncan BB, Andrade Lotufo P, Araujo Nobre A, et al. Prevalence, Awareness, Treatment and Influence of Socioeconomic Variables on Control of High Blood Pressure: Results of the ELSA-Brasil Study. PLOS one. 2015;10(6):e0127382. pmid:26102079; PubMed Central PMCID: PMCPMC4478044.
- 36.
Melo ME. Os Números da Obesidade no Brasil: VIGITEL 2009 e POF 2008–2009. 2010.
- 37. Monteiro CA, Conde WL, Popkin BM. Income-specific trends in obesity in Brazil: 1975–2003. Am J Public Health. 2007;97(10):1808–12. Epub 2007/09/01. pmid:17761560; PubMed Central PMCID: PMC1994168.
- 38.
Hu F. Obesity Epidemiology. New York: Oxford University Press; 2008.
- 39. Levy D, de Almeida LM, Szklo A. The Brazil SimSmoke policy simulation model: the effect of strong tobacco control policies on smoking prevalence and smoking-attributable deaths in a middle income nation. PLOS Med. 2012;9(11):e1001336. pmid:23139643; PubMed Central PMCID: PMC3491001.
- 40. Paim J, Travassos C, Almeida C, Bahia L, Macinko J. The Brazilian health system: history, advances, and challenges. Lancet. 2011;377(9779):1778–97. Epub 2011/05/13. pmid:21561655.
- 41.
Ministry of Health B. Strategic action plan to tackle noncommunicable diseases (NCD) in Brazil 2011–2022. Ministry of Health, 2011.