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 language | English |
|---|---|
| Pages (from-to) | 982-995 |
| Number of pages | 14 |
| Journal | Nature Medicine |
| Volume | 29 |
| Issue number | 4 |
| Early online date | 17 Apr 2023 |
| DOIs | |
| Publication status | Published - Apr 2023 |
Bibliographical note
Funding Information:This research was supported by the Bill and Melinda 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.
Data Availability Statement
All data used in this analysis are publicly available from the following sources: (1) individual dietary intake estimate distribution data (GDD, Download 2018 Final Estimates: https://www.globaldietarydatabase.org/data-download); (2) stratum-specific global mean BMI, converted to overweight- and underweight-prevalence distribution data (NCD-RisC, Data Downloads: https://ncdrisc.org/data-downloads.html); (3) T2D burden-incidence-estimate distribution data (Global Health Data Exchange, Global Burden of Disease Study 2019 Results Tool: https://vizhub.healthdata.org/gbd-results/); (4) linear, BMI-stratified effects of dietary factors on weight gain or weight loss: ref. 60; (5) direct, proportional, age-adjusted effects of BMI on T2D: ref. 23; (6) direct, proportional, age-adjusted effects of diet on T2D: whole grains, ref. 62; all remaining dietary factors, ref. 61; (7) optimal intake levels for dietary factors: ref. 12; (8) population demographic data: UN Population Division (age, sex, urbanicity), ref. 65; (9) SDI data from Global Health Data Exchange: Global Burden of Disease Study 2019 SDI 1950–2019: https://ghdx.healthdata.org/record/ihme-data/gbd-2019-socio-demographic-index-sdi-1950-2019; (10) FAO Food Balance Sheet data for the energy availability of ‘wheat and products’ and ‘rice and products’ (kcal per capita per d): United Nations FAO: Food Availability Data: http://www.fao.org/faostat/en/#home; (11) global glycemic load estimates for wheat and rice products: ref. 73; (12) caloric content per 100 g for wheat and rice products: US Department of Agriculture Agricultural Research Service Food and Nutrient Database for Dietary Studies 2017–2018: https://www.ars.usda.gov/nea/bnrc/fsrg.Code availability
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 info@globaldietarydatabase.org. 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.
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