Incident type 2 diabetes attributable to suboptimal diet in 184 countries

Global Dietary Database

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Abstract

The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.8–14.4 million) incident T2D cases, representing 70.3% (68.8–71.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.0–27.1%)), excess refined rice and wheat intake (24.6% (22.3–27.2%)) and excess processed meat intake (20.3% (18.3–23.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.4–87.7%)) and Latin America and the Caribbean (81.8% (80.1–83.4%)); and lowest proportional burdens were in South Asia (55.4% (52.1–60.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally.

Original languageEnglish
Pages (from-to)982-995
Number of pages14
JournalNature Medicine
Volume29
Issue number4
DOIs
Publication statusPublished - Apr 2023

Bibliographical note

Funding Information:
This research was supported by the Bill and Melina Gates Foundation (grant OPP1176682 to D. Mozaffarian). We acknowledge the Tufts University High Performance Computing Cluster ( https://it.tufts.edu/high-performance-computing ), which was used for the research reported in this paper. This material is based upon work supported by the National Science Foundation under grant number 2018149. The computational resource is under active development by Research Technology, Tufts Technology Services. The funding agency did not contribute to the design or conduct of the study; collection, management, analysis or interpretation of the data; preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication.

Funding Information:
This research was supported by the Bill and Melina Gates Foundation (grant OPP1176682 to D. Mozaffarian). We acknowledge the Tufts University High Performance Computing Cluster (https://it.tufts.edu/high-performance-computing), which was used for the research reported in this paper. This material is based upon work supported by the National Science Foundation under grant number 2018149. The computational resource is under active development by Research Technology, Tufts Technology Services. The funding agency did not contribute to the design or conduct of the study; collection, management, analysis or interpretation of the data; preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication.

Funding Information:
M. OHearn reports research funding from the Gates Foundation, as well as the National Institutes of Health and Vail Innovative Global Research and employment with Food Systems for the Future, outside of the submitted work. L. Lara-Castor reports research funding from the Gate Foundation, as well as the Consejo Nacional de Ciencia y Tecnologia (CONACyT), Friedman School of Nutrition Science and Policy and the American Heart Association, outside of the submitted work. V. Miller reports research funding from the Canadian Institutes of Health Research and the American Heart Association, outside of the submitted work. F. Cudhea, J. Zhang, and P. Shi report research funding form the Gates Foundation, as well as the National Institutes of Health, outside of the submitted work. J. Reedy reports research funding from the Gates Foundation, as well as the National Institutes of Health, Nestlé, Rockefeller Foundation, and Kaiser Permanent Fund at East Bay Community Foundation, outside of the submitted work. J. Wong reports research funding from the National Institutes of Health and membership in the US Preventative Services Task Force (unpaid) and the National Academies of Sciences, Engineering and Medicine Committee on Evaluating the Process to Develop the Dietary Guidelines for Americans, 2020–2025 (unpaid), outside the submitted work. C. Economos reports research funding from the United States Department of Agriculture, the National Institutes of Health, the JPB Foundation and Newman’s Own Foundation. She also reports her position as vice chair to the National Academies of Science Roundtable on Obesity Solutions (unpaid) and her prior advisory board position at Care/Of Scientific. None of the above relate to this paper. R. Micha reports research funding from the Gates Foundation, as well as the National Institutes of Health, Nestlé and Danone, outside the submitted work. She also reports consulting fees as IEG chair of the Global Nutrition Report, outside the submitted work. D. Mozaffarian reports funding from the National Institutes of Health, the Gates Foundation, the Rockefeller Foundation, Vail Innovative Global Research and the Kaiser Permanente Fund at East Bay Community Foundation; personal fees from Acasti Pharma, Barilla, Danone and Motif FoodWorks; is on the scientific advisory board for Beren Therapeutics, Brightseed, Calibrate, DiscernDx, Elysium Health, Filtricine, HumanCo, January, Perfect Day, Tiny Organics and (ended) Day Two and Season Health; has stock ownership in Calibrate and HumanCo; and receives chapter royalties from UpToDate.

Funding Information:
Custom code was developed using R (version 4.0.0) with two-tailed α = 0.05, for cleaning, merging and formatting of all data inputs; calculation of age-adjusted relative risks; comparative risk assessment modeling, including PAF calculations for each dietary factor separately and joint PAF calculations for all dietary factors; summary aggregation of stratum-level PAF estimates at the global, regional and national levels; and data visualization. Given their computational size and complexity, all comparative risk assessment modeling codes were run on the Tufts University High Performance Computing Cluster ( https://it.tufts.edu/high-performance-computing ), supported by the National Science Foundation (grant 2018149, https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018149&HistoricalAwards=false ) under active development by Research Technology ( https://it.tufts.edu/researchtechnology.tufts.edu ), Tufts Technology Services. The statistical code used for this analysis is not publicly available. The GDD can make the statistical code available to researchers upon request. Eligibility criteria for such requests include: utilization for nonprofit purposes only, for appropriate scientific use based on a robust research plan and by investigators from an academic institution. GDD will nominate co-authors to be included on any papers generated using GDD-generated statistical code. If you are interested in requesting access to the statistical code, please submit the following documents: (1) proposed research plan (please download and complete the proposed research plan form https://www.globaldietarydatabase.org/sites/default/files/manual_upload/research-proposal-template.pdf ), (2) data-sharing agreement (please download this form https://www.globaldietarydatabase.org/sites/default/files/manual_upload/tufts-gdd-data-sharing-agreement.docx , complete the highlighted fields and have someone who is authorized to enter your institution into a binding legal agreement with outside institutions sign the document. Note that this agreement does not apply when protected health information or personally identifiable information are shared), (3) email items (1) and (2) to [email protected]. Please use the subject line ‘GDD Code Access Request’. Once all documents have been received, the GDD team will be in contact with you regarding subsequent steps.

Publisher Copyright:
© 2023, The Author(s).

Other keywords

  • Adult
  • Male
  • Humans
  • Female
  • Diabetes Mellitus, Type 2/epidemiology
  • Diet/adverse effects
  • Risk Assessment
  • Income
  • Body Weight
  • Risk Factors
  • Global Health

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