A Detailed Examination of the Worldwide Impact of Type 2 Diabetes Linked to Dietary Risks: Insights from the Global Burden of Disease Study (1990-2021)

A Detailed Examination of the Worldwide Impact of Type 2 Diabetes Linked to Dietary Risks: Insights from the Global Burden of Disease Study (1990-2021) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Detailed Examination of the Worldwide Impact of Type 2 Diabetes Linked to Dietary Risks: Insights from the Global Burden of Disease Study (1990-2021) Hui Wang, Linhan He, Xiaocui Wang, Yingxuan Du, Zhilian Mu, Ying Shi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7168726/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : Type 2 diabetes mellitus (T2DM) is a considerable public health concern on the worldwide. This study analyzes the impact of dietary related type 2 diabetes mellitus (T2DM) on disability-adjusted life years (DALYs) and deaths, utilizing Global Burden of Disease (GBD) data from 1990 to 2021 across 204 countries. Methods : T2DM due to dietary risk were analyzed by the study for 204 different countries/regions from 1991 to 2021. Key indicators included DALYs and death tolls, which were assessed according to age, gender, and socio-demographic index (SDI). The study utilized descriptive and trend analyses, along with ARIMA model for future predictions. Trends were quantified using Age-standardized rates (ASR) and estimated annual percentage change (EAPC) of the variables were used to quantify them. Results : DALYs due to dietary risks increased from 6,450,217 in 1990 to 19,146,810 in 2021, with ASR increasing from 159.96 to 221.34 per 100,000. Deaths increased from 164,060 to 381,416, but age-standardized death rates somewhat dropped. China, India, and the U.S. reported the highest T2DM burdens; 260% increase in DALYs middle SDI regions, while low SDI regions had the highest ASR; the age group of 65-69 showed a significant increase in DALYs, with males surpassing females in 2021. Predictions for 2050 suggest global DALYs will reach 17,865,944 for males and 18,121,264 for females, with deaths estimated at 264,822 for males and 305,383 for females, alongside increasing ASR. Conclusions : The study highlights the considerable influence of dietary risks on the global prevalence of T2DM based on the GBD database. There is an urgent need for improvements in global dietary habits, health education, and food policy regulations to reduce the impact of T2DM on public health. Type 2 Diabetes Mellitus Dietary risk Global burden of disease Public health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Type 2 diabetes mellitus (T2DM), a prevalent chronic metabolic disorder, has been recognized as one of the most significant public health challenges of the 21st century. According to the latest projections from the International Diabetes Federation (IDF), the global number of people with diabetes is expected to reach 780 million by 2045 1,2 . T2DM not only significantly impairs quality of life but also substantially increases the risk of cardiovascular diseases, chronic kidney disease, and certain types of cancers. These bring substantial burdens on both individual health and socioeconomic systems 3-6 . T2DM incidence is the combined effect of genetic predisposition and environmental exposures, such as diet, physical activity, and lifestyle patterns. Dietary factor, which is important and adjustable, play a key role in the onset and progression of T2DM 7 . Dietary patterns have significant changed due to the acceleration of urbanization and globalization, which has also caused high sugar, high fat, and low fiber to gradually replace conventional, healthy dietary patterns. This unhealthy habit not only led to the prevalence of obesity and metabolic syndrome but also directly increase the risk of T2DM 7,8 . A systematic review and meta-analysis included 155 studies and 5,086 participants, revealed that diets containing high fructose-containing sugar supplements and calories may accelerate ectopic fat deposition, thereby contributing to the development of insulin resistance and impaired glycemic regulation 9 . A prospective cohort study among US females and males, the total risk ratios of 1.62 for red meat and 1.51 for processed meat, which was substantially positively correlated with the risk of T2DM 10 . However, a web-based study revealed that intakes of dietary fiber, whole grains, fruits, and vegetables were significantly negatively correlated with the risk of T2DM, emphasizing the important role of a healthy diet in the prevention of T2DM 11 . Although numerous studies have explored the link between diet and T2DM，there is a need for more comprehensive analysis of global trends of T2DM incidence attributable to dietary risks 8 . Traditional epidemiological studies are often limited to specific regions or populations, fall short in comprehensively reflect the global burden and future trends of T2DM attributable to dietary risks worldwide. This study methodically analyzes the effects of dietary risks on the global burden of T2DM, as determined by disability-adjusted life years(DALYs)，deaths, years of life lost(YLLs), and years lived with disability(YLDs)，in 204 countries, based on the global Burden of Disease(GBD) study from 1990 to 2021. The analysis takes into account various factors such as regional differences, the Socio-Demographic Index (SDI), age groups, and gender. Through this study, we can gain a deep understanding of the differences in T2DM caused by dietary risks among people of different regions, ethnicities, ages, and genders on a global scale. Additionally, based on data from 1990 to 2021, the study predicts the impact of dietary risks on T2DM-related DALYs and death cases by 2050. Data from this study are used to support the management of T2DM in the world of TDM prevention and control. Methods 1. We utilized publicly available GBD 2021 resources to extract T2DM burden data for 204 countries/regions from the GBD Results tool (citation: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2022; available from https://vizhub.healthdata.org/gbd-results/). 2. The disease burden of T2DM attributable to dietary risks from 1990 to 2021 was assessed using several indicators: Disability-Adjusted Life Years (DALYs), deaths, Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and their corresponding age-standardized rates (ASRs) with annual statistics. We used descriptive, trend, and predictive analyses, stratifying by age (in 5-year intervals) and gender to ensure the refinement and comparability of the data. We analyzed the estimated values of DALYs, deaths, YLDs, YLLs related to diabetes, and their corresponding 95% uncertainty intervals (UI). DALYs, a composite measure of health loss, were calculated as the sum of YLLs and YLDs, with 1 DALY representing the loss of 1 year of healthy life. For the Socio-Demographic Index (SDI) analysis, we utilized the SDI, based on fertility, education, and per capita income data, to quantify the development level of a country or region. Recognizing the correlation between SDI and disease incidence and mortality, we categorized countries and regions into five SDI classes (low, lower-middle, middle, upper-middle, and high) to examine the relationship between T2DM burden and socioeconomic development. Descriptive Analysis: 2.11 Global Description: Calculate the global incidence and prevalence of T2DM and the distribution of dietary risk factors. 2.12 SDI Regional Description: Analyze the T2DM burden and dietary risk differences in areas with different socioeconomic levels, stratified by SDI (low, lower-middle, middle, upper-middle, and high). 2.13 GBD Regional Description: Describe the regional characteristics of T2DM and dietary risks, divided by GBD regions (such as East Asia, South Asia, North America, etc.). 2.14 Age and Gender Stratification: Analyze the age and gender distribution patterns of T2DM incidence and dietary risks, stratified by each 5-year age group and gender. Trend Analysis: 2.21 Global Trends: Analyze the temporal trends of global T2DM incidence and dietary risk factors from 1990 to 2021. 2.22 SDI Regional Trends: Evaluate the temporal trends of T2DM and dietary risks in areas with different socioeconomic levels, stratified by SDI. 2.23 GBD Regional Trends: Analyze the temporal trends of T2DM and dietary risks in each region, divided by GBD regions. 2.24 Age and Gender Trends: Assess the age and gender trend changes in T2DM incidence and dietary risks, stratified by each 5-year age group and gender. Predictive Analysis: 2.31 Prediction Model: Use the ARIMA model (Autoregressive Integrated Moving Average model) and ES model to predict the trends of T2DM incidence and dietary risk factors from 2022 to 2050. 2.32 Predictive Variables: Include demographic data (such as age, gender), SDI index, dietary risk factors (such as sugar-sweetened beverage consumption, red meat intake, dietary fiber intake, etc.). 2.33 Prediction Results: Provide predicted values of T2DM incidence for the global, SDI regions, GBD regions, and age-gender stratification, and evaluate the impact of dietary risk factors. Statistical analysis Through age-standardized rates (ASR), disability-adjusted life years (DALYs), and estimated annual percentage changes (EAPCs), a quantitative analysis of the time trends in T2DM incidence, DALYs, and mortality rates was conducted. In which, ASR was calculated as referred in the literature 12 . EAPC was estimated using the generalized linear model as the formula is as follow: EAPC =100×(e β )−1), β is the coefficient of the time variable in the regression model, and e is the base of the natural logarithm (approximately equal to 2.718). A positive EAPC indicates an increasing trend, while a negative EAPC indicates a decreasing trend. All statistical analyses were executed using RStudio (RStudio-2024.12.0-467). The "dplyr" package was used for data manipulation and cleaning, the "ggplot2" package was used for data visualization, and the "forecast" package was used for the ARIMA model fitting and prediction. Results 1. Changes in the DALYs of T2DM attribution to Dietary Risks Throughout the world, there has been a rise in the impact of dietary risks on T2DM, as indicated by the disability-adjusted life years (DALYs). 6,450,217 (95% UI: 1,256,122-10,613,945) of the DALYs attributable to T2DM were found in 1990. By 2021, this number had significantly increased to 19,146,810 (95% UI: 4,147,239-31,937,618) (Figure 1A, Table 1). When adjusted for age, the DALYs per 100,000 people increased from 159.96 in 1990 to 221.34 in 2021, with an estimated annual percentage change (EAPC) of 1.59(Figure 1C, Table 1). In 1990, the primary contributor to T2DM-related DALYs was YLLs, whereas by 2021, the primary contributor was YLDs (Supplemental Figure 1, Supplemental Table 1 and 2). However, the global distribution of DALYs due to dietary risks associated with T2DM is not uniform. China has the highest DALYs attributed to T2DM due to dietary risks, increasing from 769,703 (95% UI: 99,186-1,395,086) in 1990 to 2,702,413 (95% UI: 337,623-4,927,021) in 2021, with an EAPC of 2.5. For DALYs, whether in 1990 or 2021, the contribution of YLDs was greater than that of YLLs. The United States of America ranked second in 1990, with 663,227 (95% UI: 149,822-1,054,918), but by 2021, the number of DALYs decreased to the third position, with 1,993,463 (95% UI: 537,271-3,239,962); YLLs were predominant in 1990, while YLDs were predominant in 2021. India’s T2DM-related DALYs increased from 626,542 (95% UI: 135,796-10,763,710) in 1990 to 2,393,928 (95% UI: 588,406-4,005,473) in 2021, ranking second globally, with an EAPC of 2.03. In India, YLLs were the main contributors in both 1990 and 2021 (Figure 1, Table 1, Supplemental Table 1 and 2). In terms of age-standardized DALYs rates, Fiji had the highest rates, which was 1,565 (95% UI: 193-2,656) per 100,000 in 1990 and 1,978.5 (95% UI: 322.59-3,413.58) per 100,00 in 2021. The Democratic People's Republic of Korea recorded the lowest figures, with 55.9 (95% UI: 9.4-101.7) per 100,000 in 1990 and 89.72 (95% UI: 16.71–157.68) per 100,000 in 2021 (Figure 1, Table 1). 2. Trends in T2DM-related deaths attributed to dietary risks Globally, the number of deaths due to T2DM caused by dietary risks has shown an increasing trend from 1990 to 2021, with a significant rise from 164,060 (95% UI: 30,935-265,372) in 1990 to 381,416 (95% UI: 74,328-620,914) in 2021. In contrast, the age-standardized death rates (ASDR) showed a negligible decrease, dropping from 4.55 (95% UI: 0.86-7.35) per 100,000 in 1990 to 4.52 (95% UI: 0.88-7.36) per 100,000 in 2021(Figure 2A and 2B, Table 2). The global distribution of deaths from T2DM due to dietary risk is an increasing trend but various. The top three countries rank in death tolls in 1990 and 2021 are the United States, India, and China. In 1990, the United States had the highest death toll, while in 2021, India ranked first. The United States increased from 17,627 (95% UI: 3,785-27,385) in 1990 to 28,346 (95% UI: 7,190-43,601) in 2021, its EAPC was -0.58. China’s deaths increased from 12,614 (95% UI: 1,477-22,318) to 37,437 (95% UI: 4,278-67,627), with an EAPC of 1.63. India grew from 15,576 (95% UI: 3,270-26,787) to 58,566 (95% UI: 13,380-96,641), and an EAPC of 2.01. Tokelau had the lowest number of deaths in both 1990 and 2021, with 0 (95% UI: 0-0) and 0 (95% UI: 0-1), respectively, with an EAPC of 0.46(Figure 2, Table 2). However, in terms of ASDR, Fiji had the highest rates globally in both 1990 and 2021, increasing from 56.55 (6.73-96.87) to 69.84 (95% UI: 10.94-121.19) per 100,000. In 2021, Japan and Singapore recorded the lowest rates, with 0.62 (95% UI: 0.15-0.99) and 0.43 (95% UI: 0.07-0.74) per 100,000 population, respectively(Figure 2, Table 2). 3. Global analysis of T2DM caused by dietary risks across different SDI levels. Epidemiological research shows that T2DM incidence varies with SDI levels 13 . Therefore, we analyzed global trends in T2DM burden attributable to dietary risks across different SDI levels: low, lower-middle, middle, upper-middle, and high. Figure 3 and Table 3 present global burden data for diet-related T2DM stratified by social-demographic index (SDI), including DALYs, number of deaths, YLDs, and YLLs, from 1990 to 2021. 3.1 SDI-Stratified Characteristics of Global DALYs and age-standardized DALYs rates The burden of T2DM due to dietary risks increased across all SDI levels from 1990 to 2021. The middle SDI region had the highest increase, from 618,717 (95% UI: 91,674-1,037,127) to 1,613,831 (95% UI: 283,462-2,685,994), a 260% increase with an EAPC of 1.34. The lower-middle SDI region increased from 1,066,910 (95% UI: 195,040-1,811,700) to 3,809,852 (95% UI: 855,743-6,298,732), a 257% increase, but with a lower EAPC of 0.23. The high SDI region, despite a larger base number, increased from 1,762,948 (95% UI: 387,133-2,838,065) to 4,561,220 (95% UI: 1,135,855-7,539,088), a 159% increase with an EAPC of 0.74 (Figure 3A and 3B, Figure 3E and 3F, Table 3). In terms of DALYs, in 1990, the low SDI region was primarily affected by YLDs, while other SDI regions were affected by YLLs. By 2021, the high and middle SDI regions were mainly impacted by YLLs, with other SDI regions primarily impacted by YLDs (Table 3). For the ASR of DALYs, low SDI region had the highest ASR in both 1990 and 2021, increasing from 260.26 (95% UI: 37.77-436.80) to 292.19 (95% UI: 50.31-492.72). Meanwhile, the high SDI region showed a 52% increase in ASR, from 162.69 (95% UI: 35.89-261.87) to 247.38 (95% UI: 62.69-408.23) (Figure 3A and 3B, Table 3). 3.2 Geographical disparities in deaths figures and age- standardized deaths rates. The middle SDI region had the most significant increase in deaths, rising by 229% from 34,231 (95% UI: 5,801-58,053) to 112,706 (95% UI: 20,139-190,402), with the EAPC of 0.53. Low SDI region recorded the highest ASR, reaching 8.68 (95% UI: 1.33-14.53) in 2021. The high SDI region’s death ASR decreased from 4.34 (95% UI: 0.88-6.82) to 3.16 (95% UI: 0.72-4.94), with an EAPC of -1.39. The lower-middle SDI region’s ASR increased significantly from 5.47 (95% UI: 0.91-9.28) to 7.01 (95% UI: 1.34-11.53), with the highest EAPC of 0.83 (Figure 3C and 3D, Table 3). 4. The impact of age and gender on the global burden of T2DM caused by dietary risks The analysis shows that the effect of age on the global burden of T2DM due to dietary risks (Figure 4), the highest DALYs were in the 60-64 age group in 1990, reaching 930,269 (95% UI: 178,923-1,544,783), with YLLs being the main contributor. By 2021, the highest DALYs were in the 65-69 age group, amounting to 2,603,520 (95% UI: 553,323-4,386,272). Nevertheless, the age group above 95 years old was the lowest in both 1990 and 2021, with 14,270 (95%UI: 3,061-23,201) and 84,178 (95%UI: 19,467-137,929) respectively, which were mainly contributed by YLLs (Figure 4 A, B, E and F). In terms of deaths, the highest number was in the 75-79 age group in 1990, and in the 70-74 age group by 2021. The 25-29 age group had the lowest number of deaths in both 1990 and 2021. There was variation in each age group from 1990 to 2021 of the death tolls, with the number of deaths in the 25-29 age group increasing in some years and then decreasing (Figure 4C, D, G and H; Supplemental Figure 1). We also analyzed the impact of gender on DALYs related to T2DM caused by dietary risks. Regardless of gender, it shows an overall upward trend, with YLLs contributing more in 1990 and YLDs dominating in 2021. Number of DALYs for females was higher than that for males in 1990 (3,315,616 vs. 3,134,601); however, males is dominate in 2021 (9,750,524 vs. 9,396,286). Age-standardized DALYs were higher in males than females. Females had higher death numbers in both 1990 and 2021, while age-standardized deaths for males were higher in males and showed an increasing trend, which from 4.65 (95% UI: 0.89-7.55) to 4.85 (95% UI: 0.92-7.95). In summary, gender differences significantly affect the distribution of the disease burden of diet-related T2DM, with males dominating the incremental burden. (Figure 5, Table 4) The comprehensive analysis of age and gender showed that number of DALYs in the 65-69 age group was the highest, with males higher than females. For death numbers, the 70-74 age group was the highest, with females higher than males. Men were found to have much higher age-standardized DALYs, deaths, YLDs, and YLLs (Figure 6, Table 4, Supplemental Figure 2). 5. Global Burden of T2DM Due to Dietary Risks by GBD Region At the regional level, the burden of diet-related T2DM in all 21 GBD regions increased from 1990 to 2021; however, the global distribution of DALYs due to T2DM caused by dietary risks was varied, with Asia being particularly severe, showing an increase from 2,616,083 (95% UI: 477,634-4,468,832) in 1990 to 9,250,507 (95% UI: 1,785,180-15,808,286) in 2021, a 254% increase, with an EAPC of 2.03. Oceania had the lowest DALYs in both 1990 and 2021, with 24,390 (95% UI: 3,995-41,680) and 73,953 (95% UI: 13,438-124,792), respectively (Figure 7, Table 5, Supplemental Figure 3, Supplemental Table 3 and 4). However, for age-standardized DALYs, Oceania had the highest values in both 1990 (742.10 [95% UI: 117.64-1266.63]) and 2021 (862.05 [95% UI: 154.31-1465.64]), and an EAPC of 0.83. The minimum values were recorded in East Asia, with figures of 86.82 (95% UI: 11.17-156.93) and 134.85 (95% UI: 17.33-247.24), respectively, with an EAPC of 2.44 (Figure 7A and B, Table 5). In terms of the number of deaths, Asia was at the forefront internationally and increased from 57,953 (95% UI: 10,386-97,935) in 1990 to 175,271 (95% UI: 33,147-289,931) in 2021, with an EAPC of 1.32. Oceania continued to have the lowest number of deaths, recording 686 (95% UI 109-1,194) in 1990 and 1,788 (95% UI 287-3,031) in 2021, yields an EAPC of 0.36. For age-standardized deaths, Oceania had the highest rates in both 1990 and 2021, with 25.9 (95% UI: 4.11-44.44) and 26.27 (95% UI: 4.25-44.14) per 100,000, respectively. In 1990, Eastern Europe recorded the lowest rate in at 1.58 (95% UI: 0.33-2.4] per 100,000, while by 2021, the High-income Asia Pacific had the lowest rate at 0.99 (95% UI: 0.2-1.61) per 100,000, accompanied by an EAPC of -1.19 (Figure 7C and D, Table 6). 6. Prediction of Global Trends of T2DM Due to Dietary Risks from 2030 to 2050 Based on the analysis of the above results, we also used the ARIMA and ES model to predict the impact of dietary risks on T2DM burden from 2030 to 2050. This model predicts an increase trend worldwide of T2DM due to dietary risk. By 2050, the global burden of diet-related T2DM of males are expected to have 17,865,944 DALYs (95UI: 14,123,935-21,607,953) and females 18,121,264 (95%UI: 14,253,245-21,989,282), with YLDs as the main contributor. For death tolls, males will reach to be 264,822 (95% UI: 115,656-413,989) and females 305,383 (95% UI: 124,195-486,570) in 2050, with age-standardized DALYs and YLDs also show an upward trend (Figure 8, Supplemental Figure 4). Discussion Based on the GBD database, this study systematically analyzed the changes in the global burden of T2DM due to dietary risks from 1990 to 2021, and predicted future trends. According to the global trend, DALYs and number of deaths due to T2DM caused by dietary risks showed an upward trend from 1990 to 2021, shows that the significant impact of dietary factors on the prevalence of T2DM worldwide. In the past decades, unhealthy dietary patterns have become increasingly common globally, with excessive intake of foods high in sugar and fat, as well as low fiber foods, which is associated with an increasing risk of T2DM 11,14,15 . Our analyses revealed the significant regional heterogeneity in the global burden of T2DM caused by dietary risks, highlighting the health challenges associated with the interaction of lifestyle changes, economic development, and region-specific factors. The dramatic increase in the burden of T2DM in Asia, especially in China (number of DALYs increased was almost 251% between 1990 and 2021), is a typical example of development patterns have changed in the context of globalization. The rapid economic growth, the unhealthy dietary pattern (processed foods are rich in high calories, high fat and high sugar, as well as refined carbohydrates) has gradually spread, coupled with the sedentary lifestyle driven the prevalence of the obesity and metabolic syndrome and leading to the growth of T2DM burden. This sharp rise not only poses a serious threat to individual health but also poses a severe challenge to the health system, potentially causes long-term economic and social burdens 16,17 . In contrast, the high ASR observed in small island developing countries in Oceania, like the Marshall Islands, indicates a distinct health issue. Even with a small population base, their significantly elevated ASR highlights that health risks in smaller populations cannot be ignored. This is often related to changes in traditional dietary patterns due to global food trade, where locally fiber-rich and nutritionally balanced traditional foods are being replaced by high-sugar, high-fat, low-nutrient-density imported processed foods. Additionally, the rising obesity rates accompanying modernization have further contributed to the high incidence of T2DM 18,19 . The obvious distinctions between these places highlight the fact that T2DM is a complicated interaction of several factors, including economic development level, cultural shifts, food system changes, and geographic environment; rather than being a single pattern, the prevalence of T2DM is an outcome of the complicated interaction. This also suggests that public health strategies must be customized to local circumstances, addressing the “nutrition transition” issues in developing countries while also emphasizing the protection of traditional diets and the guidance of modern healthy diets for specific vulnerable groups such as those in small island developing countries 20 . The global impact of different SDI dietary risks on T2DM shows significant differences. The middle SDI regions show the highest increase, while the growth is relatively low in the high SDI regions. This indicates that there is no non-linear relationship between economic development level and the effect of dietary risks on T2DM burden. Meanwhile, although the low SDI regions have a lower baseline burden, the ASR holds the highest and has as not significantly improved. There are multiple reasons: 1) Traditional diets in these regions often contain high amounts of sugar, fat, and salt while lacking sufficient fruits, vegetables, and whole grains 21,22 . For example, refined carbohydrates and high sugar beverages are widely used in the diet of South Asia, while sub-Saharan Africa faces the dual challenges of malnutrition and unhealthy dietary 23,24 . 2) Lifestyle: The rapid urbanization process and westernization of lifestyles have resulted in decreased physical activity, further exacerbating the risk of T2DM 25,26 . 3) Socioeconomic factors: Residents in low- and middle-income regions may face more health risks, such as lack of healthy food choices, insufficient medical resources, and relatively low health awareness 27,28 . Middle SDI regions show the highest increase in DALYs, deaths, and YLLs, show that the health risks caused by dietary imbalances during their rapid urbanization. Although the high SDI regions have a relative low increase, the overall burden is still high due to their large population base. Therefore, dietary factors play a key role in the prevalence of T2DM, and their impact varies significantly by region and economic level. This suggests that public health policies should pay attention to improve dietary habits and lifestyles in low and middle-income regions. The impact of age and gender factors on the burden of T2DM caused by dietary risk analyzed in this study illustrates the complexity of this global health issue. In terms of age, people over 60, especially in the age groups of 65-69 and 70-74, showed a significant increase in DALYs, YLLs, and death toll, which is highly consistent with physiological patterns. As individuals age, their physical functions naturally decline, metabolism slows down, pancreatic beta cell function may gradually diminish, along with reducing insulin sensitivity 29 . In addition, years of poor dietary habits and lifestyle make the elderly a high-risk group for T2DM and its serious complications such as cardiovascular disease, kidney disease, retinopathy, etc. Their body’s compensation and resistance to diabetes and its long-term consequences are weakened, leading to the concentrated outbreak of disease burden at this stage 30,31 . Moreover, gender differences exhibit dynamic changes. In 1990, females had a higher number of DALYs than that in males, which may reflect that females generally lived longer, accumulated longer periods of illness, or the influence of specific socio-cultural factors (such as metabolic changes during pregnancy and postpartum, or certain traditional dietary patterns having a greater impact on women) at that time 32,33 . However, by 2021, males not only surpassed females in DALYs, but their age-standardized DALYs and YLLs were also higher than females, indicating that males bear a larger burden of T2DM. This may be related to the increased exposure of males to unhealthy lifestyles such as smoking, excessive alcohol consumption, high-fat and high calorie diets, and lack of exercise in recent years, as well as factors such as occupational stress and social role expectations 34 . In addition, male’s participation in health awareness, disease screening, and early intervention may be relatively low, leading to faster disease progression and earlier occurrence of complications, and thus increasing the burden of death and disability 35 . This phenomenon suggests that the gender differences and age stratification should be pay attention when formulating T2DM prevention and control strategies, and more targeted interventions should be taken for different populations. From 1990 to 2021, all 21 GBD regions had an increase in the burden of T2DM related to dietary risks, but the global distribution was highly uneven. Asia experienced significant increases in DALYs and death numbers, significantly outpacing other regions due to its large population and lifestyle changes linked to economic growth. However, in terms of age-standardized indicators, the situation was completely different. Oceania, despite having the lowest total numbers of DALYs and deaths, consistently ranked at the top in terms of age-standardized DALYs and deaths, indicating that its aging population, high risk exposure, or characteristics of the healthcare system may contribute to a relatively heavier burden. East Asian region shows a contradictory phenomenon: its age standardized DALYs and mortality rates are the lowest, but DALYs increase rapidly, which may be related to the region's large population base, accelerated aging process, and improved medical record completeness. Similarly, the high-income Asia Pacific region has the lowest increase in deaths, even showing negative growth, indicating that it may benefit from better medical interventions and disease management. This also suggests that standardized comparisons based on age structure should be made in consideration in global prevention. Furthermore, precise interventions should base on different regional characteristics, such as population structure, economic level, medical resources, etc., to effectively address the global threat of dietary risk to T2DM 36 . Based on the ARIMA model, predictions of the burden of T2DM caused by dietary risk between 2030 and 2050 will continue to increase, suggesting that global public health policies need to pay more attention to the role of dietary factors in the prevention of T2DM. Moreover, policy should consider multi-level intervention measures, such as strengthening health education, raising public awareness of healthy eating, advocating for balanced diets, and reducing the intake of high sugar, high-fat, and low fiber foods. And policies should encourage the food industry to produce healthier foods, such as reducing the sugar and fat content in processed foods. At the same time, it is important to increase funding for research on the prevention and treatment of T2DM, thus developing more effective prevention and treatment methods to cope with the increasing burden of T2DM. Study Limitations Although this study utilized comprehensive GBD data, there are still some limitations. First, the assessment of dietary risks is mainly based on food intake data, which may have reporting biases and inaccuracies. Second, this study does not fully consider the influence of other potential factors such as gene environment interactions, physical activity, air pollution, and psychosocial factors, etc., which may interact with dietary risks and affect the development of T2DM. Future studies could consider these factors to more comprehensively reveal T2DM pathogenesis and global burden of T2DM, providing a basis for developing more precise and effective prevention and control strategies. Future Directions Future studies should further explore the causal relationship between dietary risks and T2DM, especially at the individual and population levels. In addition, more studies are needed to evaluate the effectiveness of different dietary interventions in different regions and populations, particularly in resource limited areas. In addition, considering the interaction between dietary risk and other lifestyle factors such as physical activity, smoking, and alcohol consumption, future research should adopt a comprehensive approach to evaluate the combined impact of these factors on the burden of T2DM. Finally, with the changes in global population structure and the acceleration of urbanization, future studies should focus on dietary risks and T2DM burden in specific populations, such as the elderly, children, adolescents, in order to develop more precise public health strategies. Conclusions This study reveals the significant impact of dietary risk on the global burden of T2DM by the analysis with the GBD database. Without effective interventions, the burden of T2DM is projected to rise in the next few decades, posing a huge challenge to public health systems. Therefore, it is crucial to implement effective dietary health strategies to alleviate the future burden of T2DM. Declarations Funding This work was funded by the Young PhD Talents Cultivation Project (No. 2024YQB027 to H.W.), the Science Innovation Program Led by Academicians in Chongqing (No. cstc2017jcyj-yszxX0003 to H.W.), and the outstanding young talents training of Third Military Medical University (school administration [2016], no. 609 to H.W.). Author contributions H.W., LH.H. and XC.W.: data acquisition, analysis, interpretation and statistical analysis; H.W., Q.T., and HT. Z.: data analysis, interpretation, and manuscript drafting; YX.D., ZL.M.,: data acquisition, interpretation; Y.S., Y.W. and Q.Q.: data analysis and critical revision of the manuscript; H.W.: study's concept and design, data analysis and interpretation, manuscript drafting, critical revision for important intellectual content, securing study funding, and supervising the study. All authors have reviewed and approved the manuscript for publication. 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Supplementary Files Table1.docx Table2.docx Table3.docx Table4.docx Table5.docx Table6.docx SupplementalData.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7168726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490871387,"identity":"1adbb517-238a-4ea7-ace1-2623f8326664","order_by":0,"name":"Hui 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1","display":"","copyAsset":false,"role":"figure","size":528013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003ca href=\"https://view.paperpass.com/report/3tsp68613740c77d4/htmls/sentence_detail.html\" target=\"right\"\u003e\u003cstrong\u003eDALYs of T2DM attribution to Dietary Risks\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003efrom 204 countries.\u003c/strong\u003e(A) Number of DALYS in T2DM caused by Dietary risk. (B) Age-standardized rates of DALYs in 2021. (C) EAPC Map of age-standardized DALYs rates of T2DM. (D) Changes in the Number of DALYs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/12b103fd43e3de815384cb74.png"},{"id":87765813,"identity":"a4ff4687-c199-42ac-b439-990367d2f0b3","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":520166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003ca href=\"https://view.paperpass.com/report/3tsp68613740c77d4/htmls/sentence_detail.html\" target=\"right\"\u003e\u003cstrong\u003eDeaths of T2DM attribution to Dietary Risks\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003efrom 204 countries.\u003c/strong\u003e(A) Number of Deaths in T2DM caused by Dietary risk. (B) Age-standardized rates of Deaths in 2021. (C) EAPC Map of age-standardized Deaths rates of T2DM. (D) Changes in the Number of Deaths.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/f6d6199ce4c07ab12bc06c27.png"},{"id":87765812,"identity":"3a769b36-c7bc-4c12-9f1a-32eda442ec20","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254986,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal analysis of T2DM caused by dietary risks across different SDI levels from 1990 to 2021. \u003c/strong\u003e(A) Number of DALYs in T2DM caused by Dietary risk in different SDI levels area. (B) Age-standardized rates of DALYs in different SDI levels area. (C) Number of Deaths in T2DM caused by Dietary risk in different SDI levels area. (B) Age-standardized rates of Deaths in different SDI levels area.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/222ed6b69e7c6dbb4feb7b70.png"},{"id":87766833,"identity":"b90e5e1a-c026-4f5b-b369-adb260b8f391","added_by":"auto","created_at":"2025-07-28 18:20:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":513135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe impact of age on the global burden of T2DM caused by dietary risks. \u003c/strong\u003e(A-B) Number and Age-standardized rates of DALYs in T2DM caused by Dietary risk in different age group in 2021. (C-D) Number and Age- standardized rates of Deaths in T2DM caused by Dietary risk in different age group in 2021. (E-F) Number and Age- standardized rates of DALYs in T2DM caused by Dietary risk in different age group from 1990-2021. (G-H) Number and Age- standardized rates of Deaths in T2DM caused by Dietary risk in different age group from 1990-2021.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/a04de31a677a276141283d6f.png"},{"id":87765817,"identity":"b31d5052-4ba0-4fa3-80b9-1d4332cedf43","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":165773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effect of gender on the global burden of T2DM caused by dietary risks. \u003c/strong\u003e(A-B) Number and Age-standardized rates of DALYs in T2DM caused by Dietary risk in different gender group from 1990-2021. (C-D) Number and Age- standardized rates of Deaths in T2DM caused by Dietary risk in different gender group from 1990-2021.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/1970dcf3e70249021a53957f.png"},{"id":87765821,"identity":"ec2c2502-26d3-40d3-acf9-ca443f127757","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":274717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effect of gender and age on the global burden of T2DM caused by dietary risks. \u003c/strong\u003e(A-B) Number and Age-standardized rates of DALYs in T2DM caused by Dietary risk in 2021. (C-D) Number and Age- standardized rates of Deaths in T2DM caused by Dietary risk in 2021.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/b0bb279255155d68cb14ed92.png"},{"id":87766627,"identity":"0f3f18b5-91cf-4779-893a-a778638c1843","added_by":"auto","created_at":"2025-07-28 18:12:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":693894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal Burden of T2DM Due to Dietary Risks by GBD Region. \u003c/strong\u003e(A-B) Number and Age-standardized rates of DALYs in T2DM caused by Dietary risk in different region in 2021. (C-D) Number and Age- standardized rates of Deaths in T2DM caused by Dietary risk in different region in 2021.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/9c7815b1696429f05b4a1c50.png"},{"id":87765825,"identity":"7d5fa4f0-6308-4c24-9c39-2c12c35249ed","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":190416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of Global Trends of T2DM Due to Dietary Risks from 2030 to 2050. \u003c/strong\u003e(A-B) Prediction of number of DALYs and Deaths in T2DM caused by Dietary risk by ARIMA model in different gender group from 2030 to 2050. (C-D) Prediction of number of DALYs and Deaths in T2DM caused by Dietary risk by ES model in different gender group from 2030 to 2050.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/242b5b598f516ee0b3d37abb.png"},{"id":88834865,"identity":"d69cab18-46cd-46b3-8622-1531e7c892e1","added_by":"auto","created_at":"2025-08-12 00:47:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3603033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/b70fb3cd-38aa-4739-9a1d-9b217cff3ad7.pdf"},{"id":87766625,"identity":"9db46782-6eea-4900-a5b0-08b926a4a9ca","added_by":"auto","created_at":"2025-07-28 18:12:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31388,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/63eaaad994ee84ebd53471c5.docx"},{"id":87765811,"identity":"553c150c-19d7-4974-8891-aa72ca1c70d2","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30116,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/52802ad9126e3a819801cd10.docx"},{"id":87766628,"identity":"ccb89936-662e-43f1-8d82-8f1be42ba607","added_by":"auto","created_at":"2025-07-28 18:12:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32739,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/7cc465fe8716ab9d6c66b184.docx"},{"id":87765819,"identity":"007d67e2-2ec3-420f-81c8-dca5f3d49f00","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30964,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/fecbae813b87d847270f66e5.docx"},{"id":87766630,"identity":"9f5a42bf-eab4-447c-95fb-43bfdbf7b20a","added_by":"auto","created_at":"2025-07-28 18:12:38","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":31339,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/b0328f99fd7e8b31f9ea2553.docx"},{"id":87765822,"identity":"3f3fa493-a545-4ee9-a58b-da20039f2c33","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":31251,"visible":true,"origin":"","legend":"","description":"","filename":"Table6.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/ab529f5b42abda5324b9b356.docx"},{"id":87765826,"identity":"4fd9356b-bfed-490b-bb00-727bb9c656cf","added_by":"auto","created_at":"2025-07-28 18:04:38","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2791310,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalData.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168726/v1/ea10cb2f7e6d02304a283b64.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Detailed Examination of the Worldwide Impact of Type 2 Diabetes Linked to Dietary Risks: Insights from the Global Burden of Disease Study (1990-2021)","fulltext":[{"header":"Background","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM), a prevalent chronic metabolic disorder, has been recognized as one of the most significant public health challenges of the 21st century. According to the latest projections from the International Diabetes Federation (IDF), the global number of people with diabetes is expected to reach 780 million by 2045\u003csup\u003e1,2\u003c/sup\u003e. T2DM not only significantly impairs quality of life but also substantially increases the risk of cardiovascular diseases, chronic kidney disease, and certain types of cancers. These bring substantial burdens on both individual health and socioeconomic systems\u003csup\u003e3-6\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM incidence is the combined effect of genetic predisposition and environmental exposures, such as diet, physical activity, and lifestyle patterns. Dietary factor, which is important and adjustable, play a key role in the onset and progression of T2DM\u003csup\u003e7\u003c/sup\u003e. Dietary patterns have significant changed due to the acceleration of urbanization and globalization, which has also caused high sugar, high fat, and low fiber to gradually replace conventional, healthy dietary patterns. This unhealthy habit not only led to the prevalence of obesity and metabolic syndrome but also directly increase the risk of T2DM\u003csup\u003e7,8\u003c/sup\u003e. A systematic review and meta-analysis included 155 studies and 5,086 participants, revealed that diets containing high fructose-containing sugar supplements and calories may accelerate ectopic fat deposition, thereby contributing to the development of insulin resistance and impaired glycemic regulation\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA prospective cohort study among US females and males, the total risk ratios of 1.62 for red meat and 1.51 for processed meat, which was substantially positively correlated with the risk of T2DM\u003csup\u003e10\u003c/sup\u003e. However, a web-based study revealed that intakes of dietary fiber, whole grains, fruits, and vegetables were significantly negatively correlated with the risk of T2DM, emphasizing the important role of a healthy diet in the prevention of T2DM\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough numerous studies have explored the link between diet and T2DM，there is a need for more comprehensive analysis of global trends of T2DM incidence attributable to dietary risks\u003csup\u003e8\u003c/sup\u003e.\u0026nbsp;Traditional epidemiological studies are often limited to specific regions or populations, fall short in comprehensively reflect the global burden and future trends of T2DM attributable to dietary risks worldwide.\u003c/p\u003e\n\u003cp\u003eThis study methodically analyzes the effects of dietary risks on the global burden of T2DM, as determined by disability-adjusted life years(DALYs)，deaths, years of life lost(YLLs), and years lived with disability(YLDs)，in 204 countries, based on the global Burden of Disease(GBD) study from 1990 to 2021. The analysis takes into account various factors such as regional differences, the Socio-Demographic Index (SDI), age groups, and gender. Through this study, we can gain a deep understanding of the differences in T2DM caused by dietary risks among people of different regions, ethnicities, ages, and genders on a global scale. Additionally, based on data from 1990 to 2021, the study predicts the impact of dietary risks on T2DM-related DALYs and death cases by 2050. Data from this study are used to support the management of T2DM in the world of TDM prevention and control.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e1. We utilized publicly available GBD 2021 resources to extract T2DM burden data for 204 countries/regions from the GBD Results tool (citation: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2022; available from https://vizhub.healthdata.org/gbd-results/).\u003c/p\u003e\n\u003cp\u003e2. The disease burden of T2DM attributable to dietary risks from 1990 to 2021 was assessed using several indicators: Disability-Adjusted Life Years (DALYs), deaths, Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and their corresponding age-standardized rates (ASRs) with annual statistics.\u003c/p\u003e\n\u003cp\u003eWe used descriptive, trend, and predictive analyses, stratifying by age (in 5-year intervals) and gender to ensure the refinement and comparability of the data. We analyzed the estimated values of DALYs, deaths, YLDs, YLLs related to diabetes, and their corresponding 95% uncertainty intervals (UI). DALYs, a composite measure of health loss, were calculated as the sum of YLLs and YLDs, with 1 DALY representing the loss of 1 year of healthy life.\u003c/p\u003e\n\u003cp\u003eFor the Socio-Demographic Index (SDI) analysis, we utilized the SDI, based on fertility, education, and per capita income data, to quantify the development level of a country or region. Recognizing the correlation between SDI and disease incidence and mortality, we categorized countries and regions into five SDI classes (low, lower-middle, middle, upper-middle, and high) to examine the relationship between T2DM burden and socioeconomic development.\u003c/p\u003e\n\u003cp\u003eDescriptive Analysis: 2.11 Global Description: Calculate the global incidence and prevalence of T2DM and the distribution of dietary risk factors. 2.12 SDI Regional Description: Analyze the T2DM burden and dietary risk differences in areas with different socioeconomic levels, stratified by SDI (low, lower-middle, middle, upper-middle, and high). 2.13 GBD Regional Description: Describe the regional characteristics of T2DM and dietary risks, divided by GBD regions (such as East Asia, South Asia, North America, etc.). 2.14 Age and Gender Stratification: Analyze the age and gender distribution patterns of T2DM incidence and dietary risks, stratified by each 5-year age group and gender.\u003c/p\u003e\n\u003cp\u003eTrend Analysis: 2.21 Global Trends: Analyze the temporal trends of global T2DM incidence and dietary risk factors from 1990 to 2021. 2.22 SDI Regional Trends: Evaluate the temporal trends of T2DM and dietary risks in areas with different socioeconomic levels, stratified by SDI. 2.23 GBD Regional Trends: Analyze the temporal trends of T2DM and dietary risks in each region, divided by GBD regions. 2.24 Age and Gender Trends: Assess the age and gender trend changes in T2DM incidence and dietary risks, stratified by each 5-year age group and gender.\u003c/p\u003e\n\u003cp\u003ePredictive Analysis: 2.31 Prediction Model: Use the ARIMA model (Autoregressive Integrated Moving Average model) and ES model to predict the trends of T2DM incidence and dietary risk factors from 2022 to 2050. 2.32 Predictive Variables: Include demographic data (such as age, gender), SDI index, dietary risk factors (such as sugar-sweetened beverage consumption, red meat intake, dietary fiber intake, etc.). 2.33 Prediction Results: Provide predicted values of T2DM incidence for the global, SDI regions, GBD regions, and age-gender stratification, and evaluate the impact of dietary risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough age-standardized rates (ASR), disability-adjusted life years (DALYs), and estimated annual percentage changes (EAPCs), a quantitative analysis of the time trends in T2DM incidence, DALYs, and mortality rates was conducted. In which, ASR was calculated as referred in the literature\u003csup\u003e12\u003c/sup\u003e. EAPC was estimated using the generalized linear model as the formula is as follow: \u0026nbsp;\u003cem\u003eEAPC\u003c/em\u003e=100\u0026times;(e\u003cem\u003e\u0026beta;\u003c/em\u003e)\u0026minus;1),\u0026nbsp;\u0026beta;\u0026nbsp;is the coefficient of the time variable in the regression model, and e is the base of the natural logarithm (approximately equal to 2.718). A positive EAPC indicates an increasing trend, while a negative EAPC indicates a decreasing trend.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were executed using RStudio (RStudio-2024.12.0-467). The \u0026quot;dplyr\u0026quot; package was used for data manipulation and cleaning, the \u0026quot;ggplot2\u0026quot; package was used for data visualization, and the \u0026quot;forecast\u0026quot; package was used for the ARIMA model fitting and prediction.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eChanges in the DALYs of T2DM attribution to Dietary Risks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the world, there has been a rise in the impact of dietary risks on T2DM, as indicated by the disability-adjusted life years (DALYs). 6,450,217 (95% UI: 1,256,122-10,613,945) of the DALYs attributable to T2DM were found in 1990. By 2021, this number had significantly increased to 19,146,810 (95% UI: 4,147,239-31,937,618) (Figure 1A, Table 1). When adjusted for age, the DALYs per 100,000 people increased from 159.96 in 1990 to 221.34 in 2021, with an estimated annual percentage change (EAPC) of 1.59(Figure 1C, Table 1). In 1990, the primary contributor to T2DM-related DALYs was YLLs, whereas by 2021, the primary contributor was YLDs (Supplemental Figure 1, Supplemental Table 1 and 2).\u003c/p\u003e\n\u003cp\u003eHowever, the global distribution of DALYs due to dietary risks associated with T2DM is not uniform. China has the highest DALYs attributed to T2DM due to dietary risks, increasing from 769,703 (95% UI: 99,186-1,395,086) in 1990 to 2,702,413 (95% UI: 337,623-4,927,021) in 2021, with an EAPC of 2.5. For DALYs, whether in 1990 or 2021, the contribution of YLDs was greater than that of YLLs. The United States of America ranked second in 1990, with 663,227 (95% UI: 149,822-1,054,918), but by 2021, the number of DALYs decreased to the third position, with 1,993,463 (95% UI: 537,271-3,239,962); YLLs were predominant in 1990, while YLDs were predominant in 2021. India\u0026rsquo;s T2DM-related DALYs increased from 626,542 (95% UI: 135,796-10,763,710) in 1990 to 2,393,928 (95% UI: 588,406-4,005,473) in 2021, ranking second globally, with an EAPC of 2.03. In India, YLLs were the main contributors in both 1990 and 2021 (Figure 1, Table 1, Supplemental Table 1 and 2).\u003c/p\u003e\n\u003cp\u003eIn terms of age-standardized DALYs rates, Fiji had the highest rates, which was 1,565 (95% UI: 193-2,656) per 100,000 in 1990 and 1,978.5 (95% UI: 322.59-3,413.58) per 100,00 in 2021. The Democratic People\u0026apos;s Republic of Korea recorded the lowest figures, with 55.9 (95% UI: 9.4-101.7) per 100,000 in 1990 and 89.72 (95% UI: 16.71\u0026ndash;157.68) per 100,000 in 2021 (Figure 1, Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Trends in T2DM-related deaths attributed to dietary risks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobally, the number of deaths due to T2DM caused by dietary risks has shown an increasing trend from 1990 to 2021, with a significant rise from 164,060 (95% UI: 30,935-265,372) in 1990 to 381,416 (95% UI: 74,328-620,914) in 2021. In contrast, the age-standardized death rates (ASDR) showed a negligible decrease, dropping from 4.55 (95% UI: 0.86-7.35) per 100,000 in 1990 to 4.52 (95% UI: 0.88-7.36) per 100,000 in 2021(Figure 2A and 2B, Table 2).\u003c/p\u003e\n\u003cp\u003eThe global distribution of deaths from T2DM due to dietary risk is an increasing trend but various. The top three countries rank in death tolls in 1990 and 2021 are the United States, India, and China. In 1990, the United States had the highest death toll, while in 2021, India ranked first. The United States increased from 17,627 (95% UI: 3,785-27,385) in 1990 to 28,346 (95% UI: 7,190-43,601) in 2021, its EAPC was -0.58. China\u0026rsquo;s deaths increased from 12,614 (95% UI: 1,477-22,318) to 37,437 (95% UI: 4,278-67,627), with an EAPC of 1.63. India grew from 15,576 (95% UI: 3,270-26,787) to 58,566 (95% UI: 13,380-96,641), and an EAPC of 2.01. Tokelau had the lowest number of deaths in both 1990 and 2021, with 0 (95% UI: 0-0) and 0 (95% UI: 0-1), respectively, with an EAPC of 0.46(Figure 2, Table 2).\u003c/p\u003e\n\u003cp\u003eHowever, in terms of ASDR, Fiji had the highest rates globally in both 1990 and 2021, increasing from 56.55 (6.73-96.87) to 69.84 (95% UI: 10.94-121.19) per 100,000. In 2021, Japan and Singapore recorded the lowest rates, with 0.62 (95% UI: 0.15-0.99) and 0.43 (95% UI: 0.07-0.74) per 100,000 population, respectively(Figure 2, Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Global analysis of T2DM caused by dietary risks across different SDI levels.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEpidemiological research shows that T2DM incidence varies with SDI levels\u003csup\u003e13\u003c/sup\u003e. Therefore, we analyzed global trends in T2DM burden attributable to dietary risks across different SDI levels: low, lower-middle, middle, upper-middle, and high. Figure 3 and Table 3 present global burden data for diet-related T2DM stratified by social-demographic index (SDI), including DALYs, number of deaths, YLDs, and YLLs, from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 SDI-Stratified Characteristics of Global DALYs and age-standardized DALYs rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe burden of T2DM due to dietary risks increased across all SDI levels from 1990 to 2021. The middle SDI region had the highest increase, from 618,717 (95% UI: 91,674-1,037,127) to 1,613,831 (95% UI: 283,462-2,685,994), a 260% increase with an EAPC of 1.34. The lower-middle SDI region increased from 1,066,910 (95% UI: 195,040-1,811,700) to 3,809,852 (95% UI: 855,743-6,298,732), a 257% increase, but with a lower EAPC of 0.23. The high SDI region, despite a larger base number, increased from 1,762,948 (95% UI: 387,133-2,838,065) to 4,561,220 (95% UI: 1,135,855-7,539,088), a 159% increase with an EAPC of 0.74\u0026nbsp;(Figure 3A and 3B, Figure 3E and 3F, Table 3). In terms of DALYs, in 1990, the low SDI region was primarily affected by YLDs, while other SDI regions were affected by YLLs. By 2021, the high and middle SDI regions were mainly impacted by YLLs, with other SDI regions primarily impacted by YLDs (Table 3).\u003c/p\u003e\n\u003cp\u003eFor the ASR of DALYs,\u0026nbsp;low SDI region had the highest ASR in both 1990 and 2021, increasing from 260.26 (95% UI: 37.77-436.80) to 292.19 (95% UI: 50.31-492.72). Meanwhile, the high SDI region showed a 52% increase in ASR, from 162.69 (95% UI: 35.89-261.87) to 247.38 (95% UI: 62.69-408.23) (Figure 3A and 3B, Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Geographical disparities in deaths figures and age- standardized deaths rates.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe middle SDI region had the most significant increase in deaths, rising by 229% from 34,231 (95% UI: 5,801-58,053) to 112,706 (95% UI: 20,139-190,402), with the EAPC of 0.53. Low SDI region recorded the highest ASR, reaching 8.68 (95% UI: 1.33-14.53) in 2021. The high SDI region\u0026rsquo;s death ASR decreased from 4.34 (95% UI: 0.88-6.82) to 3.16 (95% UI: 0.72-4.94), with an EAPC of -1.39. The lower-middle SDI region\u0026rsquo;s ASR increased significantly from 5.47 (95% UI: 0.91-9.28) to 7.01 (95% UI: 1.34-11.53), with the highest EAPC of 0.83\u0026nbsp;(Figure 3C and 3D, Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. The impact of age and gender on the global burden of T2DM caused by dietary risks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis shows that the effect of age on the global burden of T2DM due to dietary risks (Figure 4), the highest DALYs were in the 60-64 age group in 1990, reaching 930,269 (95% UI: 178,923-1,544,783), with YLLs being the main contributor. By 2021, the highest DALYs were in the 65-69 age group, amounting to 2,603,520 (95% UI: 553,323-4,386,272). Nevertheless, the age group above 95 years old was the lowest in both 1990 and 2021, with 14,270 (95%UI: 3,061-23,201) and 84,178 (95%UI: 19,467-137,929) respectively, which were mainly contributed by YLLs (Figure 4 A, B, E and F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of deaths, the highest number was in the 75-79 age group in 1990, and in the 70-74 age group by 2021. The 25-29 age group had the lowest number of deaths in both 1990 and 2021. There was variation in each age group from 1990 to 2021 of the death tolls, with the number of deaths in the 25-29 age group increasing in some years and then decreasing (Figure 4C, D, G and H; Supplemental Figure 1).\u003c/p\u003e\n\u003cp\u003eWe also analyzed the impact of gender on DALYs related to T2DM caused by dietary risks. Regardless of gender, it shows an overall upward trend, with YLLs contributing more in 1990 and YLDs dominating in 2021. Number of DALYs for females was higher than that for males in 1990 (3,315,616 vs. 3,134,601); however, males is dominate in 2021 (9,750,524 vs. 9,396,286). Age-standardized DALYs were higher in males than females. Females had higher death numbers in both 1990 and 2021, while age-standardized deaths for males were higher in males and showed an increasing trend, which from 4.65 (95% UI: 0.89-7.55) to 4.85 (95% UI: 0.92-7.95). In summary, gender differences significantly affect the distribution of the disease burden of diet-related T2DM, with males dominating the incremental burden. (Figure 5, Table 4)\u003c/p\u003e\n\u003cp\u003eThe comprehensive analysis of age and gender showed that number of DALYs in the 65-69 age group was the highest, with males higher than females. For death numbers, the 70-74 age group was the highest, with females higher than males. Men were found to have much higher age-standardized DALYs, deaths, YLDs, and YLLs (Figure 6, Table 4, Supplemental Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Global Burden of T2DM Due to Dietary Risks by GBD Region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the regional level, the burden of diet-related T2DM in all 21 GBD regions increased from 1990 to 2021; however, the global distribution of DALYs due to T2DM caused by dietary risks was varied, with Asia being particularly severe, showing an increase from 2,616,083 (95% UI: 477,634-4,468,832) in 1990 to 9,250,507 (95% UI: 1,785,180-15,808,286) in 2021, a 254% increase, with an EAPC of 2.03. Oceania had the lowest DALYs in both 1990 and 2021, with 24,390 (95% UI: 3,995-41,680) and 73,953 (95% UI: 13,438-124,792), respectively (Figure 7, Table 5, Supplemental Figure 3, Supplemental Table 3 and 4).\u003c/p\u003e\n\u003cp\u003eHowever, for age-standardized DALYs, Oceania had the highest values in both 1990 (742.10 [95% UI: 117.64-1266.63]) and 2021 (862.05 [95% UI: 154.31-1465.64]), and an EAPC of 0.83. The minimum values were recorded in East Asia, with figures of 86.82 (95% UI: 11.17-156.93) and 134.85 (95% UI: 17.33-247.24), respectively, with an EAPC of 2.44 (Figure 7A and B, Table 5).\u003c/p\u003e\n\u003cp\u003eIn terms of the number of deaths, Asia was at the forefront internationally and increased from 57,953 (95% UI: 10,386-97,935) in 1990 to 175,271 (95% UI: 33,147-289,931) in 2021, with an EAPC of 1.32. Oceania continued to have the lowest number of deaths, recording 686 (95% UI 109-1,194) in 1990 and 1,788 (95% UI 287-3,031) in 2021, yields an EAPC of 0.36. For age-standardized deaths, Oceania had the highest rates in both 1990 and 2021, with 25.9 (95% UI: 4.11-44.44) and 26.27 (95% UI: 4.25-44.14) per 100,000, respectively. In 1990, Eastern Europe recorded the lowest rate in at 1.58 (95% UI: 0.33-2.4] per 100,000, while by 2021, the High-income Asia Pacific had the lowest rate at 0.99 (95% UI: 0.2-1.61) per 100,000, accompanied by an EAPC of -1.19 (Figure 7C and D, Table 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Prediction of Global Trends of T2DM Due to Dietary Risks from 2030 to 2050\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the analysis of the above results, we also used the ARIMA and ES model to predict the impact of dietary risks on T2DM burden from 2030 to 2050. This model predicts an increase trend worldwide of T2DM due to dietary risk. By 2050, the global burden of diet-related T2DM of males are expected to have 17,865,944 DALYs (95UI: 14,123,935-21,607,953) and females 18,121,264 (95%UI: 14,253,245-21,989,282), with YLDs as the main contributor. For death tolls, males will reach to be 264,822 (95% UI: 115,656-413,989) and females 305,383 (95% UI: 124,195-486,570) in 2050, with age-standardized DALYs and YLDs also show an upward trend (Figure 8, Supplemental Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on the GBD database, this study systematically analyzed the changes in the global burden of T2DM due to dietary risks from 1990 to 2021, and predicted future trends. According to the global trend, DALYs and number of deaths due to T2DM caused by dietary risks showed an upward trend from 1990 to 2021, shows that the significant impact of dietary factors on the prevalence of T2DM worldwide. In the past decades, unhealthy dietary patterns have become increasingly common globally, with excessive intake of foods high in sugar and fat, as well as low fiber foods, which is associated with an increasing risk of T2DM\u003csup\u003e11,14,15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur analyses revealed the significant regional heterogeneity in the global burden of T2DM caused by dietary risks, highlighting the health challenges associated with the interaction of lifestyle changes, economic development, and region-specific factors. The dramatic increase in the burden of T2DM in Asia, especially in China (number of DALYs increased was almost 251% between 1990 and 2021), is a typical example of development patterns have changed in the context of globalization. The rapid economic growth, the unhealthy dietary pattern (processed foods are rich in high calories, high fat and high sugar, as well as refined carbohydrates) has gradually spread, coupled with the sedentary lifestyle driven the prevalence of the obesity and metabolic syndrome and leading to the growth of T2DM burden. This sharp rise not only poses a serious threat to individual health but also poses a severe challenge to the health system, potentially causes long-term economic and social burdens\u003csup\u003e16,17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, the high ASR observed in small island developing countries in Oceania, like the Marshall Islands, indicates a distinct health issue. Even with a small population base, their significantly elevated ASR highlights that health risks in smaller populations cannot be ignored. This is often related to changes in traditional dietary patterns due to global food trade, where locally fiber-rich and nutritionally balanced traditional foods are being replaced by high-sugar, high-fat, low-nutrient-density imported processed foods. Additionally, the rising obesity rates accompanying modernization have further contributed to the high incidence of T2DM\u003csup\u003e18,19\u003c/sup\u003e. The obvious distinctions between these places highlight the fact that T2DM is a complicated interaction of several factors, including economic development level, cultural shifts, food system changes, and geographic environment; rather than being a single pattern, the prevalence of T2DM is an outcome of the complicated interaction. This also suggests that public health strategies must be customized to local circumstances, addressing the “nutrition transition” issues in developing countries while also emphasizing the protection of traditional diets and the guidance of modern healthy diets for specific vulnerable groups such as those in small island developing countries\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe global impact of different SDI dietary risks on T2DM shows significant differences. The middle SDI regions show the highest increase, while the growth is relatively low in the high SDI regions. This indicates that there is no non-linear relationship between economic development level and the effect of dietary risks on T2DM burden. Meanwhile, although the low SDI regions have a lower baseline burden, the ASR holds the highest and has as not significantly improved. There are multiple reasons: 1) Traditional diets in these regions often contain high amounts of sugar, fat, and salt while lacking sufficient fruits, vegetables, and whole grains\u003csup\u003e21,22\u003c/sup\u003e. For example, refined carbohydrates and high sugar beverages are widely used in the diet of South Asia, while sub-Saharan Africa faces the dual challenges of malnutrition and unhealthy dietary\u003csup\u003e23,24\u003c/sup\u003e. 2) Lifestyle: The rapid urbanization process and westernization of lifestyles have resulted in decreased physical activity, further exacerbating the risk of T2DM\u003csup\u003e25,26\u003c/sup\u003e. 3) Socioeconomic factors: Residents in low- and middle-income regions may face more health risks, such as lack of healthy food choices, insufficient medical resources, and relatively low health awareness\u003csup\u003e27,28\u003c/sup\u003e. Middle SDI regions show the highest increase in DALYs, deaths, and YLLs, show that the health risks caused by dietary imbalances during their rapid urbanization. Although the high SDI regions have a relative low increase, the overall burden is still high due to their large population base. Therefore, dietary factors play a key role in the prevalence of T2DM, and their impact varies significantly by region and economic level. This suggests that public health policies should pay attention to improve dietary habits and lifestyles in low and middle-income regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impact of age and gender factors on the burden of T2DM caused by dietary risk analyzed in this study illustrates the complexity of this global health issue. In terms of age, people over 60, especially in the age groups of 65-69 and 70-74, showed a significant increase in DALYs, YLLs, and death toll, which is highly consistent with physiological patterns. As individuals age, their physical functions naturally decline, metabolism slows down, pancreatic beta cell function may gradually diminish, along with reducing insulin sensitivity\u003csup\u003e29\u003c/sup\u003e. In addition, years of poor dietary habits and lifestyle make the elderly a high-risk group for T2DM and its serious complications such as cardiovascular disease, kidney disease, retinopathy, etc. Their body’s compensation and resistance to diabetes and its long-term consequences are weakened, leading to the concentrated outbreak of disease burden at this stage\u003csup\u003e30,31\u003c/sup\u003e. Moreover, gender differences exhibit dynamic changes. In 1990, females had a higher number of DALYs than that in males, which may reflect that females generally lived longer, accumulated longer periods of illness, or the influence of specific socio-cultural factors (such as metabolic changes during pregnancy and postpartum, or certain traditional dietary patterns having a greater impact on women) at that time\u003csup\u003e32,33\u003c/sup\u003e. However, by 2021, males not only surpassed females in DALYs, but their age-standardized DALYs and YLLs were also higher than females, indicating that males bear a larger burden of T2DM. This may be related to the increased exposure of males to unhealthy lifestyles such as smoking, excessive alcohol consumption, high-fat and high calorie diets, and lack of exercise in recent years, as well as factors such as occupational stress and social role expectations\u003csup\u003e34\u003c/sup\u003e. In addition, male’s participation in health awareness, disease screening, and early intervention may be relatively low, leading to faster disease progression and earlier occurrence of complications, and thus increasing the burden of death and disability\u003csup\u003e35\u003c/sup\u003e. This phenomenon suggests that the gender differences and age stratification should be pay attention when formulating T2DM prevention and control strategies, and more targeted interventions should be taken for different populations.\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, all 21 GBD regions had an increase in the burden of T2DM related to dietary risks, but the global distribution was highly uneven. Asia experienced significant increases in DALYs and death numbers, significantly outpacing other regions due to its large population and lifestyle changes linked to economic growth. However, in terms of age-standardized indicators, the situation was completely different. Oceania, despite having the lowest total numbers of DALYs and deaths, consistently ranked at the top in terms of age-standardized DALYs and deaths, indicating that its aging population, high risk exposure, or characteristics of the healthcare system may contribute to a relatively heavier burden. East Asian region shows a contradictory phenomenon: its age standardized DALYs and mortality rates are the lowest, but DALYs increase rapidly, which may be related to the region's large population base, accelerated aging process, and improved medical record completeness. Similarly, the high-income Asia Pacific region has the lowest increase in deaths, even showing negative growth, indicating that it may benefit from better medical interventions and disease management. This also suggests that standardized comparisons based on age structure should be made in consideration in global prevention. Furthermore, precise interventions should base on different regional characteristics, such as population structure, economic level, medical resources, etc., to effectively address the global threat of dietary risk to T2DM\u003csup\u003e36\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the ARIMA model, predictions of the burden of T2DM caused by dietary risk between 2030 and 2050 will continue to increase, suggesting that global public health policies need to pay more attention to the role of dietary factors in the prevention of T2DM. Moreover, policy should consider multi-level intervention measures, such as strengthening health education, raising public awareness of healthy eating, advocating for balanced diets, and reducing the intake of high sugar, high-fat, and low fiber foods. And policies should encourage the food industry to produce healthier foods, such as reducing the sugar and fat content in processed foods. At the same time, it is important to increase funding for research on the prevention and treatment of T2DM, thus developing more effective prevention and treatment methods to cope with the increasing burden of T2DM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough this study utilized comprehensive GBD data, there are still some limitations. First, the assessment of dietary risks is mainly based on food intake data, which may have reporting biases and inaccuracies. Second, this study does not fully consider the influence of other potential factors such as gene environment interactions, physical activity, air pollution, and psychosocial factors, etc., which may interact with dietary risks and affect the development of T2DM. Future studies could consider these factors to more comprehensively reveal T2DM pathogenesis and global burden of T2DM, providing a basis for developing more precise and effective prevention and control strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture studies should further explore the causal relationship between dietary risks and T2DM, especially at the individual and population levels. In addition, more studies are needed to evaluate the effectiveness of different dietary interventions in different regions and populations, particularly in resource limited areas. In addition, considering the interaction between dietary risk and other lifestyle factors such as physical activity, smoking, and alcohol consumption, future research should adopt a comprehensive approach to evaluate the combined impact of these factors on the burden of T2DM. Finally, with the changes in global population structure and the acceleration of urbanization, future studies should focus on dietary risks and T2DM burden in specific populations, such as the elderly, children, adolescents, in order to develop more precise public health strategies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study reveals the significant impact of dietary risk on the global burden of T2DM by the analysis with the GBD database. Without effective interventions, the burden of T2DM is projected to rise in the next few decades, posing a huge challenge to public health systems. Therefore, it is crucial to implement effective dietary health strategies to alleviate the future burden of T2DM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Young PhD Talents Cultivation Project (No. 2024YQB027 to H.W.), the Science Innovation Program Led by Academicians in Chongqing (No. cstc2017jcyj-yszxX0003 to H.W.), and the outstanding young talents training of Third Military Medical University (school administration [2016], no. 609 to H.W.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.W., LH.H. and XC.W.: data acquisition, analysis, interpretation and statistical analysis; H.W., Q.T., and HT. Z.: data analysis, interpretation, and manuscript drafting; YX.D., ZL.M.,: data acquisition, interpretation; Y.S., Y.W. and Q.Q.: data analysis and critical revision of the manuscript; H.W.: study's concept and design, data analysis and interpretation, manuscript drafting, critical revision for important intellectual content, securing study funding, and supervising the study. All authors have reviewed and approved the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that they possess no conflicting interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi Y, Teng D, Shi X, et al. 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From molecular to physical function: The aging trajectory. \u003cem\u003eCurr Res Physiol\u003c/em\u003e 2025; \u003cstrong\u003e8\u003c/strong\u003e: 100138.\u003c/li\u003e\n\u003cli\u003eColineaux H, Neufcourt L, Delpierre C, Kelly-Irving M, Lepage B. Explaining biological differences between men and women by gendered mechanisms. \u003cem\u003eEmerg Themes Epidemiol\u003c/em\u003e 2023; \u003cstrong\u003e20\u003c/strong\u003e(1): 2.\u003c/li\u003e\n\u003cli\u003eIvan S, Daniela O, Jaroslava BD. Sex differences matter: Males and females are equal but not the same. \u003cem\u003ePhysiol Behav\u003c/em\u003e 2023; \u003cstrong\u003e259\u003c/strong\u003e: 114038.\u003c/li\u003e\n\u003cli\u003eLauretta R, Sansone M, Sansone A, Romanelli F, Appetecchia M. Gender in Endocrine Diseases: Role of Sex Gonadal Hormones. \u003cem\u003eInt J Endocrinol\u003c/em\u003e 2018; \u003cstrong\u003e2018\u003c/strong\u003e: 4847376.\u003c/li\u003e\n\u003cli\u003eKim SH, Lee SY, Kim CW, et al. Impact of Socioeconomic Status on Health Behaviors, Metabolic Control, and Chronic Complications in Type 2 Diabetes Mellitus. \u003cem\u003eDiabetes Metab J\u003c/em\u003e 2018; \u003cstrong\u003e42\u003c/strong\u003e(5): 380-93.\u003c/li\u003e\n\u003cli\u003eGlobal, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Neurol\u003c/em\u003e 2021; \u003cstrong\u003e20\u003c/strong\u003e(10): 795-820.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus, Dietary risk, Global burden of disease, Public health","lastPublishedDoi":"10.21203/rs.3.rs-7168726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7168726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: Type 2 diabetes mellitus (T2DM) is a considerable public health concern on the worldwide. This study analyzes the impact of dietary related type 2 diabetes mellitus (T2DM) on disability-adjusted life years (DALYs) and deaths, utilizing Global Burden of Disease (GBD) data from 1990 to 2021 across 204 countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: T2DM due to dietary risk were analyzed by the study for 204 different countries/regions from 1991 to 2021. Key indicators included DALYs and death tolls, which were assessed according to age, gender, and socio-demographic index (SDI). The study utilized descriptive and trend analyses, along with ARIMA model for future predictions. Trends were quantified using Age-standardized rates (ASR) and estimated annual percentage change (EAPC) of the variables were used to quantify them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: DALYs due to dietary risks increased from 6,450,217 in 1990 to 19,146,810 in 2021, with ASR increasing from 159.96 to 221.34 per 100,000. Deaths increased from 164,060 to 381,416, but age-standardized death rates somewhat dropped. China, India, and the U.S. reported the highest T2DM burdens; 260% increase in DALYs middle SDI regions, while low SDI regions had the highest ASR; the age group of 65-69 showed a significant increase in DALYs, with males surpassing females in 2021. Predictions for 2050 suggest global DALYs will reach 17,865,944 for males and 18,121,264 for females, with deaths estimated at 264,822 for males and 305,383 for females, alongside increasing ASR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The study highlights the considerable influence of dietary risks on the global prevalence of T2DM based on the GBD database. There is an urgent need for improvements in global dietary habits, health education, and food policy regulations to reduce the impact of T2DM on public health.\u003c/p\u003e","manuscriptTitle":"A Detailed Examination of the Worldwide Impact of Type 2 Diabetes Linked to Dietary Risks: Insights from the Global Burden of Disease Study (1990-2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 18:04:33","doi":"10.21203/rs.3.rs-7168726/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94f35742-f815-42c8-a7a1-8ab9fab61c8a","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-12T00:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 18:04:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7168726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7168726","identity":"rs-7168726","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}