Dietary intake at the population level

We did a systematic review of the scientific literature to identify nationally or subnationally representative nutrition surveys providing data on consumption of each dietary factor (appendix). We also searched the Global Health Data Exchange website for nationally or subnationally representative nutrition surveys and household budget surveys. Additionally, for food groups, we used national sales data from Euromonitor and national availability data from United Nations Food and Agriculture Organization food balance sheets. For nutrients, we used data on their national availability from the Global Nutrient Database.²⁰ For sodium, we collected data on 24 h urinary sodium, where available. For trans fat, we used sales data from Euromonitor on hydrogenated vegetable oil. The list of all dietary data sources used in GBD 2017 is publicly available at the Global Health Data Exchange website. For each dietary factor, we computed a data representativeness index as the fraction of countries for which we identified any data on the risk factor exposure (table).

Our dietary data were from multiple sources and were affected by different types of biases. We considered 24 h diet recall as the gold standard method for assessing mean intake at the population level and adjusted dietary data from other sources accordingly (appendix). Some types of dietary data (ie, availability, sales, and household data) were only available for all-age groups and both sexes. To split these data into standard age-specific and sex-specific groups, we first estimated the global age and sex patterns of intake using data from nutrition surveys and then used those patterns to split the availability, sales, and household data.

We used the spatiotemporal Gaussian process regression method to estimate the mean intake of each dietary risk factor by age, sex, country, and year (appendix). To improve our estimates in data-sparse models, we tested a wide range of covariates with plausible relationships with intake and included the covariates with best fit and coefficients in the expected direction (appendix).

Effect size of dietary risks on disease endpoints

For each diet–disease pair, we used data from published meta-analyses of prospective observational studies to estimate the relative risk of mortality and morbidity.²¹ For diet–disease pairs for which evidence was only available on morbidity, we assumed that the estimated relative risks were also applied to mortality (appendix). Considering the relationship of diet and metabolic risk factors and the well established age trend of the relative risks of metabolic risks for cardiovascular disease and type 2 diabetes, we used the age trend of the relative risks of metabolic risk factors²² to estimate the age-specific relative risk of dietary risks for cardiovascular disease and type 2 diabetes (appendix). To estimate the impact of sodium on outcomes, we first estimated the relationship between urinary sodium and change in systolic blood pressure, and then estimated the relationship between change in systolic blood pressure and disease outcomes.¹⁴

Optimal level of intake

We defined the optimal level of intake as the level of risk exposure that minimises the risk from all causes of death. To estimate the optimal intake for each dietary factor, we first calculated the level of intake associated with the lowest risk of mortality from each disease endpoint based on the studies included in the meta-analyses of the dietary relative risks. Then, we calculated the optimal level of intake as the weighted mean of these numbers using the global proportion of deaths from each disease as the weight. To reflect the uncertainty of optimal level of intake, we assumed a uniform uncertainty distribution of 20% above and below the mean.¹³ For sodium, the evidence supporting the selection of the optimal level of intake was uncertain.^{23, 24} Therefore, we included a uniform distribution of different optimal levels of intake in the uncertainty estimation sampling.

Disease-specific deaths and disability-adjusted life-years

Data on disease-specific deaths and disability-adjusted life-years (DALYs) by age, sex, country, and year were obtained from GBD 2017. The GBD approach to estimating cause-specific mortality and DALYs has been described in detail elsewhere.^{25, 26}

Disease burden of dietary risks

We used the GBD comparative risk assessment approach to estimate the population attributable fraction for each diet–disease pair by age, sex, country, and year.^{10, 11, 12, 13} Then, we estimated the number of deaths and DALYs attributable to each dietary risk factor by multiplying the population attributable fraction by the total number of disease-specific deaths and DALYs.

To position countries on the development continuum, we used the Socio-demographic Index (SDI), which is a summary measure calculated on the basis of lag-distributed income per capita, mean educational attainment of individuals aged 15 years or older, and total fertility rate among women younger than 25 years.^{12, 13} To estimate gaps in intake or excess of intake of individual components of diet, we compared the current intake of each dietary factor with the midpoint of its optimal range of intake (table). High intake of a dietary component refers to an intake level higher than the midpoint of the optimal range of intake, and low intake refers to an intake level lower than the midpoint of the optimal range of intake.

To incorporate the uncertainty of parameters (exposure, relative risk, optimal level of intake, and mortality) as well as modelling uncertainty, we followed a Monte Carlo approach. We repeated all calculations 1000 times using one draw of each parameter at each iteration. Using these 1000 draws, we calculated the mean and 95% uncertainty interval (UI) for the final estimates.

All statistical analyses were done in Python, version 3.5.

Overall impact of diet on mortality

Globally, in 2017, dietary risks were responsible for 11 million [95% UI 10–12] deaths (22% [95% UI 21–24] of all deaths among adults) and 255 million (234–274) DALYs (15% [14–17] of all DALYs among adults; appendix). Cardiovascular disease was the leading cause of diet-related deaths (10 million [9–10] deaths) and DALYs (207 million [192–222] DALYs), followed by cancers (913 090 [743 345–1 098 432] deaths and 20 million [17–24] DALYs) and type 2 diabetes (338 714 deaths [244 995–447 003] and 24 million [16–33] DALYs). More than 5 million (95% UI 5–5) diet-related deaths (45% [43–46] of total diet-related deaths) and 177 million (163–192) diet-related DALYs (70% [68–71] of total diet-related DALYs) occurred among adults aged younger than 70 years.

Across the 21 GBD regions, in 2017, the highest age-standardised rates of all diet-related deaths and DALYs among adults aged 25 years or older were observed in Oceania (678 [95% UI 616–746] deaths per 100 000 population and 17 804 [16 041–19 907] DALYs per 100 000 population; appendix). The lowest rates of all diet-related deaths among adults (aged 25 years or older) were observed in high-income Asia Pacific (97 [89–106] deaths per 100 000 population) and the lowest rates of all diet-related DALYs were observed in Australasia (2182 [1955–2444] DALYs per 100 000 population). The regions with the highest rates of diet-related cardiovascular disease deaths and DALYs were central Asia (613 [566–658] deaths per 100 000 population) and Oceania (14 755 [13 212–16 512] DALYs per 100 000 population), whereas the lowest rate of cardiovascular disease deaths and DALYs were observed in high-income Asia Pacific (68 [63–75] deaths per 100 000 population and 1443 [1329–1573] DALYs per 100 000 population). Diet-related cancer death and DALY rates were highest in east Asia (41 [34–49] deaths per 100 000 population and 878 [736–1023] DALYs per 100 000 population) and lowest in north Africa and the Middle East (nine [8–11] deaths per 100 000 population and 203 [169–243] DALYs per 100 000 population). Oceania (60 [44–78] deaths per 100 000 population and 2426 [1737–3198] DALYs per 100 000 population) had the highest age-standardised rate of diet-related diabetes deaths and DALYs, and high-income Asia Pacific had the lowest rates (two [2–3] deaths per 100 000 population and 290 [202–395] DALYs per 100 000 population). In 2017, the highest age-standardised proportions of diet-related deaths and DALYs from cardiovascular disease were observed in Oceania (60% [95% UI 56–63] of deaths) and east Asia (64% [60–68] of DALYs), those from cancer in east Asia (15% [13–18] of deaths and 15% [12–17] of DALYs), and those from type 2 diabetes in high-income North America (41% [34–48] of deaths and 50% [42–58] of DALYs; appendix). The lowest age-standardised proportions of deaths and DALYs from these causes were in western Europe (42% [38–45] of deaths and 44% [41–47] of DALYs), western sub-Saharan Africa (5% [4–6] of deaths and 4% [4–5] DALYs), and southeast Asia (29% [20–38] of deaths and 35% [25–46] of DALYs).

In 2017, among the world's 20 most populous countries, Egypt had the highest age-standardised rate of all diet-related deaths (552 [95% UI 490–620] deaths per 100 000 population) and DALYs (11 837 [10 525–13 268] DALYs per 100 000 population) and Japan had the lowest rate of all diet-related deaths (97 [89–106)] deaths per 100 000 population) and DALYs (2300 [2099–2513] DALYs per 100 000 population; figure 2). China had the highest age-standardised rates of diet-related cardiovascular disease deaths (299 [275–324] deaths per 100 000 population) and Egypt had the highest DALY rates (10 811 [9577–12 209] DALYs per 100 000 population). China had highest rates of diet-related cancer deaths and DALYs (42 [34–49] deaths per 100 000 population and 889 [744–1036] DALYs per 100 000 population), and Mexico had the highest rates of diet-related type 2 diabetes deaths and DALYs (35 [28–44] deaths per 100 000 population and 1605 [1231–2034] DALYs per 100 000 population). Japan had the lowest rate of diet-related cardiovascular disease deaths and DALYs (69 [63–75] deaths per 100 000 population and 1507 [1389–1639] DALYs per 100 000 population) and diabetes deaths and DALYs (one [1–1] death per 100 000 population and 234 [161–321] DALYs per 100 000 population). Egypt had the lowest rate of diet-related cancer deaths and DALYs (five [4–6] deaths per 100 000 population and 120 [96–146] DALYs per 100 000 population; appendix). The highest age-standardised proportion of all diet-related deaths (30% [27–33]) and DALYs (23% [21–25]) in adults aged 25 years or older were observed in Egypt, and the lowest proportion of all diet-related deaths (11% [9–12]) and DALYs (7% [6–8]) in the same age group were observed in Nigeria (appendix). The highest proportions of diet-related cardiovascular disease deaths and DALYs in 2017 were observed in Pakistan (60% [95% UI 57–64] of deaths and 66% [62–69] of DALYs), cancer deaths and DALYs in China (16% [13–18] of deaths and 15% [13–17] of DALYs), and type 2 diabetes deaths and DALYs in the USA (41% [34–49] of deaths and 50% [43–58] of DALYs). The lowest proportions of cardiovascular disease deaths and DALYs were seen in Turkey (42% [38–47] of deaths and 44% [40–49] of DALYs), cancer deaths and DALYs in Egypt (4% [3–4] of deaths and 3% [3–4] of DALYs), and type 2 diabetes deaths and DALYs in Bangladesh (25% [17–34] of deaths and 34% [23–45] of DALYs).

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Figure 2

Age-standardised mortality rate per 100 000 population (A) and DALY rate per 100 000 population (B) attributable to diet in 2017

ATG=Antigua and Barbuda. Isl=Islands. FSM=Federated States of Micronesia. LCA=Saint Lucia. TLS=Timor-Leste. TTO=Trinidad and Tobago. VCT=Saint Vincent and the Grenadines.

Impact of individual components of diet on mortality

A small number of dietary risks had a large impact on health outcomes. In 2017, more than half of diet-related deaths and two-thirds of diet-related DALYs were attributable to high intake of sodium (3 million [95% UI 1–5] deaths and 70 million [34–118] DALYs), low intake of whole grains (3 million [2–4] deaths and 82 million [59–109] DALYs), and low intake of fruits (2 million [1–4] deaths and 65 million [41–92] DALYs; figure 3). Low intake of whole grains was the leading dietary risk factor for DALYs among men and women and the leading dietary risk factor for mortality among women. Sodium ranked first for mortality among men followed by whole grains and fruit. Low intake of whole grains was the leading risk for deaths and DALYs among young adults (aged 25–50 years) and sodium ranked first among older adults (≥70 years).

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Figure 3

Number of deaths and DALYs and age-standardised mortality rate and DALY rate (per 100 000 population) attributable to individual dietary risks at the global and SDI level in 2017

DALY=disability-adjusted life-year. SDI=Socio-demographic Index.

In 2017, across the 21 GBD regions, a diet low in whole grains was the most common leading dietary risk factor for deaths (in 16 regions) and DALYs (in 17 regions; figure 4). A diet high in sodium was the leading dietary risk factor for deaths and DALYs in east Asia and high-income Asia Pacific regions (appendix). In southern sub-Saharan Africa, a diet low in fruits and in central Latin America a diet low in nuts and seeds were the dietary risk factors responsible for the greatest proportion of deaths and DALYs in 2017.

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Figure 4

Age-standardised proportions of deaths and DALYs attributable to individual dietary risks at the global and regional level in 2017

DALYs=disability-adjusted life-years.

High intake of sodium was the leading dietary risk for deaths and DALYs in China, Japan, and Thailand. Low intake of whole grains was the leading dietary risk factor for deaths and DALYs in the USA, India, Brazil, Pakistan, Nigeria, Russia, Egypt, Germany, Iran, and Turkey. In Bangladesh, low intake of fruits was the leading dietary risk associated with deaths and DALYs. In Mexico, low intake of nuts and seeds ranked first for diet-related deaths and DALYs. High consumption of red meat, processed meat, trans fat, and sugar-sweetened beverages were towards the bottom in ranking of dietary risks for deaths and DALYs for most high-population countries (appendix).

Relationship between diet and SDI

Overall, in 2017, the highest age-standardised rates of all diet-related deaths and DALYs were observed in low-middle SDI countries (344 [95% UI 319–369] deaths per 100 000 population and 7797 [7265–8386] DALYs per 100 000 population) and high-middle SDI countries (347 [324–369] deaths per 100 000 population and 6998 [6534–7454] DALYs per 100 000 population; appendix). The lowest burden of exposure to dietary risk was observed in high SDI countries (139 [129–148] deaths per 100 000 population and 3032 [2802–3265] DALYs per 100 000 population). Low-middle SDI had the highest age-standardised rates of diet-related deaths and DALYs for cardiovascular disease (311 [288–335] deaths per 100 000 population and 6685 [6228–7161] DALYs per 100 000 population) and diabetes (14 [10–18] deaths per 100 000 population and 681 [477–914] DALYs per 100 000 population). High-middle SDI had the highest age-standardised rates of diet-related mortality for cancer (29 [24–34] deaths per 100 000 population and 630 [529–731] DALYs per 100 000 population). The lowest age-standardised rate of diet-related deaths and DALYs for cardiovascular disease (113 [104–122] deaths per 100 000 population and 2156 [2005–2306] DALYs per 100 000 population) and diabetes (five [4–6] deaths per 100 000 population and 444 [324–587] DALYs per 100 000 population) was observed in high SDI countries and lowest mortality rate for cancer was observed in low SDI countries (15 [12–17] deaths per 100 000 population and 324 [268–376] DALYs per 100 000 population). The highest proportions of diet-related deaths and DALYs for all causes were observed in high-middle SDI countries (29% [95% UI 27–31] of deaths and 19% [17–21] of DALYs), the lowest proportion of diet-related deaths was observed in low SDI countries (16% [15–17] of deaths), and the lowest proportion of DALYs was observed in high SDI countries (10% [9–11] of DALYs; appendix). Dietary risks were responsible for 55% [51–59] of cardiovascular disease deaths and 60% [56–63] of DALYs in middle SDI countries, and 46% [42–49] of cardiovascular disease deaths and 49% [46–52] of cardiovascular disease DALYs in high SDI countries. Middle SDI countries had the highest proportion of cancer deaths (12% [10–14]) and DALYs (11% [9–13]) and high SDI countries had the lowest proportion of attributable cancer deaths (8% [7–9]) and DALYs (7% [6–9]). The highest burden of diabetes attributable to diet was observed in high SDI countries (35% [28–43] of deaths and 46% [38–55) of DALYs) and lowest attributable burden was observed in the low SDI countries (31% [22–39] of deaths and 39% [29–50] of DALYs).

High-middle and middle SDI countries were at the greatest risk of deaths and DALYs from high consumption of sodium, whereas high and low-middle SDI countries had the greatest risk caused by a diet low in whole grains (figure 3). In low SDI countries, low intake of fruit was the leading dietary risk for deaths and low intake of whole grains was the leading dietary risk for DALYs. Countries at all levels of SDI other than low SDI had the same four leading dietary risks: high sodium, low whole grains, low fruit, and low nuts and seeds. The four leading dietary risks for low SDI countries were a diet in low whole grains, low in fruit, low in nuts and seeds, and low in vegetables.

Ashkan Afshin, Patrick John Sur, Kairsten A Fay, Leslie Cornaby, Giannina Ferrara, Joseph S Salama, Erin C Mullany, Kalkidan Hassen Abate, Cristiana Abbafati, Zegeye Abebe, Mohsen Afarideh, Anju Aggarwal, Sutapa Agrawal, Tomi Akinyemiju, Fares Alahdab, Umar Bacha, Victoria F Bachman, Hamid Badali, Alaa Badawi, Isabela M Bensenor, Eduardo Bernabe, Stan H Biryukov, Sibhatu Kassa K Biadgilign, Leah E Cahill, Juan J Carrero, Kelly M Cercy, Lalit Dandona, Rakhi Dandona, Anh Kim Dang, Meaza Girma Degefa, Maysaa El Sayed Zaki, Alireza Esteghamati, Sadaf Esteghamati, Jessica Fanzo, Carla Sofia E Sá Farinha, Maryam S Farvid, Farshad Farzadfar, Valery L Feigin, Joao C Fernandes, Luisa Sorio Flor, Nataliya A Foigt, Mohammad H Forouzanfar, Morsaleh Ganji, Johanna M Geleijnse, Richard F Gillum, Alessandra C Goulart, Giuseppe Grosso, Idris Guessous, Samer Hamidi, Graeme J Hankey, Sivadasanpillai Harikrishnan, Hamid Yimam Hassen, Simon I Hay, Chi Linh Hoang, Masako Horino, Farhad Islami, Maria D Jackson, Spencer L James, Lars Johansson, Jost B Jonas, Amir Kasaeian, Yousef Saleh Khader, Ibrahim A Khalil, Young-Ho Khang, Ruth W Kimokoti, Yoshihiro Kokubo, G Anil Kumar, Tea Lallukka, Alan D Lopez, Stefan Lorkowski, Paulo A Lotufo, Rafael Lozano, Reza Malekzadeh, Winfried März, Toni Meier, Yohannes A Melaku, Walter Mendoza, Gert B M Mensink, Renata Micha, Ted R Miller, Mojde Mirarefin, Viswanathan Mohan, Ali H Mokdad, Dariush Mozaffarian, Gabriele Nagel, Mohsen Naghavi, Cuong Tat Nguyen, Molly R Nixon, Kanyin L Ong, David M Pereira, Hossein Poustchi, Mostafa Qorbani, Rajesh Kumar Rai, Christian Razo-García, Colin D Rehm, Juan A Rivera, Sonia Rodríguez-Ramírez, Gholamreza Roshandel, Gregory A Roth, Juan Sanabria, Tania G Sánchez-Pimienta, Benn Sartorius, Josef Schmidhuber, Aletta Elisabeth Schutte, Sadaf G Sepanlou, Min-Jeong Shin, Reed J D Sorensen, Marco Springmann, Lucjan Szponar, Andrew L Thorne-Lyman, Amanda G Thrift, Mathilde Touvier, Bach Xuan Tran, Stefanos Tyrovolas, Kingsley Nnanna Ukwaja, Irfan Ullah, Olalekan A Uthman, Masoud Vaezghasemi, Tommi Juhani Vasankari, Stein Emil Vollset, Theo Vos, Giang Thu Vu, Linh Gia Vu, Elisabete Weiderpass, Andrea Werdecker, Tissa Wijeratne, Walter C Willett, Jason H Wu, Gelin Xu, Naohiro Yonemoto, Chuanhua Yu, and Christopher J L Murray.

Department of Health Metrics Sciences (A Afshin MD, Prof S I Hay FMedSci, I A Khalil MD, Prof A H Mokdad PhD, Prof M Naghavi MD, Prof S E Vollset DrPH, Prof T Vos PhD, Prof C J L Murray DPhil), Institute for Health Metrics and Evaluation (A Afshin MD, P J Sur MPH, K A Fay BS, L Cornaby BS, G Ferrara BA, J S Salama MSc, E C Mullany BA, S H Biryukov BS, K M Cercy BS, Prof L Dandona MD, Prof R Dandona PhD, Prof V L Feigin PhD, M H Forouzanfar MD, Prof S I Hay FMedSci, S L James MD, I A Khalil MD, Prof A D Lopez PhD, Prof R Lozano MD, Prof A H Mokdad PhD, Prof M Naghavi MD, M R Nixon PhD, K L Ong PhD, G A Roth MD, R J D Sorensen MPH, Prof S E Vollset DrPH, Prof T Vos PhD, Prof C J L Murray DPhil), Department of Epidemiology (A Aggarwal PhD), School of Medicine (V F Bachman BA), Division of Cardiology (G A Roth), Department of Global Health (R J D Sorensen), University of Washington, Seattle, WA, USA; School of Medicine, University of California Riverside, Riverside, CA, USA (P J Sur); Department of Population and Family Health, Jimma University, Jimma, Ethiopia (K H Abate PhD); Department of Law, Philosophy and Economic Studies, La Sapienza University, Rome, Italy (C Abbafati PhD); Human Nutrition Department (Z Abebe MSc), Institute of Public Health (Y A Melaku MPH), University of Gondar, Gondar, Ethiopia; Endocrinology and Metabolism Research Center (M Afarideh MD, Prof A Esteghamati MD, M Ganji MD), Endocrine Research Center (S Esteghamati MD), Non-communicable Diseases Research Center (F Farzadfar MD), Hematologic Malignancies Research Center (A Kasaeian PhD), Digestive Diseases Research Institute (Prof R Malekzadeh MD, H Poustchi PhD, G Roshandel PhD, S G Sepanlou MD), Hematology-Oncology and Stem Cell Transplantation Research Center (A Kasaeian), Tehran University of Medical Sciences, Tehran, Iran; Vital Strategies, Gurugram, India (S Agrawal PhD); Department of Epidemiology, University of Kentucky, Lexington, KY, USA (T Akinyemiju PhD); Evidence Based Practice Center, Mayo Clinic Foundation for Medical Education and Research, Rochester, MN, USA (F Alahdab MD); School of Health Sciences, University of Management and Technology, Lahore, Pakistan (U Bacha MPhil); Department of Medical Mycology, Mazandaran University of Medical Sciences, Sari, Iran (H Badali PhD); Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto, ON, Canada (A Badawi PhD); Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada (A Badawi); Department of Internal Medicine (I M Bensenor PhD, A C Goulart PhD), Department of Medicine (Prof P A Lotufo DrPH), Center for Clinical and Epidemiological Research (A C Goulart), University of São Paulo, São Paulo, Brazil; Dental Institute, King's College London, London, UK (E Bernabe PhD); Independent Consultant, Addis Ababa, Ethiopia (S K K Biadgilign MPH); Department of Medicine, Dalhousie University, Halifax, NS, Canada (L E Cahill PhD); Department of Nutrition (L E Cahill, M S Farvid PhD, Prof W C Willett MD), T.H. Chan School of Public Health, Harvard University, Boston, MA, USA (A L Thorne-Lyman ScD); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (J J Carrero PhD, Prof E Weiderpass PhD); Public Health Foundation of India, Gurugram, India (S Agrawal, Prof L Dandona, Prof R Dandona, G A Kumar PhD); Institute for Global Health Innovations, Duy Tan University, Hanoi, Vietnam (A K Dang MD, C T Nguyen MPH); Department of Nutrition and Dietetics, Mekelle University, Mekelle, Ethiopia (M G Degefa BSc); Department of Clinical Pathology, Mansoura University, Mansoura, Egypt (Prof M El Sayed Zaki PhD); Berman Institute of Bioethics (Prof J Fanzo PhD), Bloomberg School of Public Health (A L Thorne-Lyman), Johns Hopkins University, Baltimore, MD, USA; National Statistical Office, Lisbon, Portugal (C S E S Farinha MSc); Nutrition and Food Systems Division (Prof J Fanzo), Trade and Markets Division (J Schmidhuber PhD), Food and Agriculture Organization of the United Nations, Rome, Italy; National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand (Prof V L Feigin); Center for Biotechnology and Fine Chemistry, Catholic University of Portugal, Porto, Portugal (J C Fernandes PhD); Sergio Arouca National School of Public Health, Rio De Janeiro, Brazil (L S Flor MPH); Federal University of Espírito Santo, Vitoria, Brazil (L S Flor); Institute of Gerontology, National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine (N A Foigt PhD); Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands (Prof J M Geleijnse PhD); Department of Community and Family Medicine, Division of General Internal Medicine, Howard University, Washington, DC, USA (R F Gillum MD); Registro Tumori Integrato, Vittorio Emanuele University Hospital Polyclinic, Catania, Italy (G Grosso PhD); Unit of Population Epidemiology, Department of Community Medicine, Primary Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland (I Guessous PhD); Department of Ambulatory Care and Community Medicine, University of Lausanne, Lausanne, Switzerland (I Guessous); School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates (Prof S Hamidi DrPH); School of Medicine, University of Western Australia, Perth, WA, Australia (Prof G J Hankey MD); Neurology Department, Sir Charles Gairdner Hospital, Perth, WA, Australia (Prof G J Hankey); Cardiology Department, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India (Prof S Harikrishnan MD); Public Health Department, Mizan-Tepi University, Teppi, Ethiopia (H Y Hassen MPH); Unit of Epidemiology and Social Medicine, University Hospital Antwerp, Wilrijk, Belgium (H Y Hassen); Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam (C L Hoang BSc, G T Vu BA, L G Vu BSc); Nevada Division of Public and Behavioral Health, Carson City, NV, USA (M Horino MPH, M Mirarefin MPH); Surveillance and Health Services Research, American Cancer Society, Atlanta, GA, USA (F Islami PhD); Department of Community Health and Psychiatry, University of the West Indies, Mona, Jamaica (Prof M D Jackson PhD); Independent Consultant, Eiksmarka, Norway (L Johansson DrPH); Department of Ophthalmology (Prof J B Jonas MD), Medical Clinic V (Prof W März MD), Heidelberg University, Mannheim, Germany; Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, China (Prof J B Jonas); Department of Public Health and Community Medicine, Jordan University of Science and Technology, Ramtha, Jordan (Prof Y S Khader PhD); Institute of Health Policy and Management, Department of Health Policy and Management, Seoul National University, Seoul, South Korea (Prof Y-H Khang MD); Department of Nutrition, Simmons College, Boston, MA, USA (R W Kimokoti MD); Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan (Prof Y Kokubo PhD); Population and Work Ability Program, Finnish Institute of Occupational Health, Helsinki, Finland (T Lallukka PhD); Department of Public Health, University of Helsinki, Helsinki, Finland (T Lallukka); Department of Medicine (Prof T Wijeratne MD), University of Melbourne, Melbourne, VIC, Australia (Prof A D Lopez PhD); Institute of Nutrition, Friedrich Schiller University Jena, Jena, Germany (Prof S Lorkowski PhD); Innovation Office (T Meier PhD), Competence Cluster for Nutrition and Cardiovascular Health (NUTRICARD), Jena, Germany (Prof S Lorkowski); Non-Communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran (Prof R Malekzadeh, S G Sepanlou); Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria (Prof W März); Institute for Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Halle, Germany (T Meier); Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia (Y A Melaku); Peru Country Office, United Nations Population Fund (UNFPA), Lima, Peru (W Mendoza MD); Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany (G B M Mensink PhD); Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA (R Micha PhD, D Mozaffarian MD); Pacific Institute for Research and Evaluation, Calverton, MD, USA (T R Miller PhD); School of Public Health, Curtin University, Perth, WA, Australia (T R Miller); Department of Diabetology, Madras Diabetes Research Foundation, Chennai, India (V Mohan DSc); Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany (Prof G Nagel PhD); REQUIMTE/LAQV, University of Porto, Oporto, Portugal (Prof D M Pereira PhD); Cartagena University, Cartagena, Colombia (Prof D M Pereira); Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran (M Qorbani PhD); Society for Health and Demographic Surveillance, Suri, India (R K Rai MPH); Department of Economics, University of Goettingen, Göttingen, Germany (R K Rai); Center for Population Health Research (C Razo-García PhD), Center for Nutrition and Health Research (Prof J A Rivera PhD, S Rodríguez-Ramírez DSc), National Institute of Public Health, Cuernavaca, Mexico (T G Sánchez-Pimienta MSc); Department of Epidemiology & Population Health, Montefiore Medical Center, Bronx, NY, USA (C D Rehm PhD); Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran (G Roshandel); Department of Surgery, Marshall University, Huntington, WV, USA (Prof J Sanabria MD); Department of Nutrition and Preventive Medicine, Case Western Reserve University, Cleveland, OH, USA (Prof J Sanabria); Department of Public Health Medicine, University of Kwazulu-Natal, Durban, South Africa (Prof B Sartorius PhD); Hypertension in Africa Research Team (HART), North-West University, Potchefstroom, South Africa (Prof A E Schutte PhD); Unit for Hypertension and Cardiovascular Disease, South African Medical Research Council, Cape Town, South Africa (Prof A E Schutte); Korea University, Seoul, South Korea (Prof M-J Shin PhD); Oxford Martin Programme on the Future of Food and Centre on Population Approaches for Non-Communicable Disease Prevention, Nuffield Department of Population Health, University of Oxford, Oxford, UK (M Springmann PhD); National Food and Nutrition Institute, Warsaw, Poland (L Szponar MD); Monash University, Melbourne, VIC, Australia (Prof A G Thrift PhD); Nutritional Epidemiology Research Team, National Institute of Health and Medical Research, Paris, France (M Touvier PhD); Department of Health Economics, Hanoi Medical University, Hanoi, Vietnam (B X Tran PhD); Research and Development Unit, San Juan de Dios Sanitary Park, Sant Boi De Llobregat, Spain (S Tyrovolas PhD); Carlos III Health Institute, Biomedical Research Networking Center for Mental Health Network (CIBERSAM), Madrid, Spain (S Tyrovolas); Department of Internal Medicine, Federal Teaching Hospital, Abakaliki, Nigeria (K N Ukwaja MD); Gomal Center of Biochemistry and Biotechnology, Gomal University, Dera Ismail Khan, Pakistan (I Ullah PhD); TB Culture Laboratory, Mufti Mehmood Memorial Teaching Hospital, Dera Ismail Khan, Pakistan (I Ullah); Division of Health Sciences, University of Warwick, Coventry, UK (O A Uthman PhD); Department of Social Work, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden (M Vaezghasemi PhD); UKK Institute, Tampere, Finland (Prof T J Vasankari MD); Department of Research, Cancer Registry of Norway, Oslo, Norway (Prof E Weiderpass); Demographic Change and Ageing Research Area, Federal Institute for Population Research, Wiesbaden, Germany (A Werdecker PhD); Independent Consultant, Staufenberg, Germany (A Werdecker); Department of Psychology, La Trobe University, Melbourne, VIC, Australia (Prof T Wijeratne); Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA (Prof W C Willett); The George Institute for Global Health, University of New South Wales, Newtown, NSW, Australia (J H Wu PhD); School of Medicine, Nanjing University, Nanjing, China (Prof G Xu MD); Department of Psychopharmacology, National Center of Neurology and Psychiatry, Tokyo, Japan (N Yonemoto MPH); and Global Health Institute, Department of Epidemiology and Biostatistics, Wuhan University, Wuhan, China (Prof C Yu PhD).

AAf prepared the first draft. KAF and KMC constructed the figures and tables. AAf and PJS developed models for dietary risks. CJLM provided overall guidance. JSS managed the project. AAf and ECM finalised the manuscript on the basis of comments from other authors and reviewer feedback. All other authors provided data, reviewed results, or reviewed and contributed to the paper.

JMG reports grants from Unilever. LJ reports personal fees from Mills Scientific Council. SL reports personal fees from Amgen, Berlin-Chemie, Merck Sharp & Dohme (MSD), Novo Nordisk, Sanofi-Aventis, Synlab, Unilever, and Upfield, and non-financial support from Preventicus. SL is also a member of the Scientific Board of the German Nutrition Society and a coauthor of the evidence-based guideline Fat Intake and Prevention of Nutrition-Related Diseases of the German Nutrition Society. WMä reports grants and personal fees from Siemens Diagnostics, Aegerion Pharmaceuticals, Amgen, AstraZeneca, Danone Research, Pfizer, BASF, Numares AG, and Berlin-Chemie; personal fees from Hoffmann LaRoche, MSD, Sanofi, and Synageva; grants from Abbott Diagnostics; and employment with Synlab Holding Deutschland GmbH. WMe is currently a program analyst for Population and Development at the Peru Country Office of the United Nations Population Fund-UNFPA, an institution that does not necessarily endorse this study. RMi reports grants from the US National Institutes of Health, Bill & Melinda Gates Foundation, and Unilever; and personal fees from World Bank and Bunge. DM reports research funding from the US National Institutes of Health and the, Bill & Melinda Gates Foundation; personal fees from GOED, DSM, Nutrition Impact, Pollock Communications, Bunge, Indigo Agriculture, Amarin, Acasti Pharma, and America's Test Kitchen; scientific advisory board roles with Elysium Health (with stock options), Omada Health, and DayTwo; and chapter royalties from UpToDate. In addition, DM is listed as a co-inventor on patents US8889739 and US9987243 issued to Tufts University (Somerville, MA, USA; unlicensed) for use of trans-palmitoleic acid to prevent and treat insulin resistance, type 2 diabetes, and related conditions, as well as reduce metabolic risk factors. CDR reports personal fees from Dairy Management Institute. AES reports personal fees from IEM, Novartis, Servier, and Abbott. AGT reports grants from National Health and Medical Research Council, Australia. All other authors declare no competing interests.