{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:34:15Z","timestamp":1791506055282,"version":"4.3.4"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>High intake of processed meat is a modifiable risk factor for type 2 diabetes mellitus (T2DM), yet its global disease burden across different socio-demographic contexts remains insufficiently characterized.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed T2DM mortality and disability-adjusted life years (DALYs) attributable to high processed meat intake across 204 countries from 1990 to 2021 using Global Burden of Disease 2021 data. We calculated age-standardized mortality rates (ASMR), age-standardized DALY rates (ASDR), and estimated annual percentage changes (EAPC) to assess temporal trends. Advanced interrupted time series (ITS) analysis was employed to detect structural breaks during the COVID-19 pandemic. Multivariate regression models examined the nonlinear relationship between the socio-demographic index (SDI) and the burden trend.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Processed meat consumption accounted for 20.3% (95% UI: 18.3-23.5) of global T2DM burden in 2021. Attributable deaths increased 118% from 52.7 to 114.9 thousand, while DALYs tripled from 2.0 to 6.1 million over three decades. Quadratic regression identified a critical SDI range (0.47-0.55) where burden growth peaks. Interrupted time series revealed pandemic-induced trend reversals: mortality declined (EAPC: +0.23% to -0.50%) while disability burden continued rising. Intervention modeling showed 10%, 25%, and 50% consumption reductions could prevent 2,330, 5,800, and 11,600 deaths annually, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The global burden of T2DM attributable to processed meat consumption has increased substantially over three decades, with critical growth periods in middle-SDI countries. The COVID-19 pandemic disrupted established trends, revealing complex interactions between socioeconomic factors and dietary risk patterns. Targeted interventions reducing processed meat consumption could prevent thousands of deaths annually, particularly in countries within the critical SDI transition zone.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1635831","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:13:29Z","timestamp":1759205609000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A systematic analysis of the global disease burden of type 2 diabetes mellitus attributable to high intake of processed meat in 204 countries (1990-2021)"],"prefix":"10.3389","volume":"16","author":[{"given":"Wangchu","family":"Ze","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanxia","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baolu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045","volume":"183","author":"Sun","year":"2022","journal-title":"Diabetes Res Clin Pract"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.2337\/dci19-0017","article-title":"Understanding the economic costs of diabetes and prediabetes and what we may learn about reducing the health and economic burden of these conditions","volume":"42","author":"O\u2019Connell","year":"2019","journal-title":"Diabetes Care"},{"key":"B3","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1186\/s12913-018-3772-8","article-title":"Cost-of-illness of type 2 diabetes mellitus in low and lower-middle income countries: a systematic review","volume":"18","author":"Afroz","year":"2018","journal-title":"BMC Health Serv Res"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(23)00914-5","article-title":"Interventions to address global inequity in diabetes: international progress","volume":"402","author":"Walker","year":"2023","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e3558","DOI":"10.1002\/dmrr.3558","article-title":"Insulin resistance and beta-cell dysfunction in newly diagnosed type 2 diabetes: Expression, aggregation and predominance. 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