Rising global burden of cancer attributable to metabolic risks from 1990 to 2021: Insights from the Global Burden of Disease Study 2021

Rising global burden of cancer attributable to metabolic risks from 1990 to 2021: Insights from the Global Burden of Disease Study 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 Rising global burden of cancer attributable to metabolic risks from 1990 to 2021: Insights from the Global Burden of Disease Study 2021 Jingyao Tu, Xinyi Chen, Yuting Huang, Chunya Li, Ziqi Wang, Hang Liang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7868307/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The substantial rise in cancer burden driven by metabolic factors has become an urgent global health concern. This study aims to provide current estimates and analyze temporal changes in cancers attributed to metabolic risks. Methods Data concerning the global burden of metabolism-attributed cancers (MACs) were sourced from the Global Burden of Disease (GBD) 2021 dataset. We extracted data on estimated cancer-related deaths and disability-adjusted life years (DALYs) linked to metabolic risks from the GBD 2021 database, categorizing them by metabolic factor, sex, age, and socio-demographic index (SDI). Trends in age-standardized DALY rates (ASDR) over time were assessed using estimated annual percentage changes (EAPC). Results In 2021, the global DALYs attributed to MACs were approximately 15.58 million (95% uncertainty interval [UI]: 44.31 to 27.15 million), corresponding to an ASDR of 179.36 (95% UI: 51.23 to 312.41) per 100,000 person-years. Over the past three decades, there was a significant global increase in ASDR for cancers linked to metabolic risks (EAPC: 0.55, 95% confidence interval [CI]: 0.51 to 0.59). Although males had a higher cancer burden than females, females experienced a relatively greater increase in ASDR (EAPC: males 0.79 vs. females 0.36). The proportion of DALYs attributable to MACs rose progressively with advancing age, surpassing 89% in populations older than 50 years. Regionally, the cancer burden related to metabolic risks remained notably higher in high and high-middle SDI regions compared to lower SDI regions during the study period. However, lower SDI regions exhibited a markedly faster growth rate (EAPC across SDI levels from high to low: 0.20, 0.53, 1.44, 2.45, and 1.36, respectively). Central Europe had the highest ASDR due to MACs (357.87, 95% UI: 106.55 to 620.39 per 100,000 person-years), while the fastest increase occurred in Southern Sub-Saharan Africa (EAPC: 2.55, 95% CI: 2.22 to 2.88). Colon and rectum cancer in males and breast cancer in females were the predominant contributors to metabolism-related ASDR. Conclusion The rising global burden of cancers attributable to metabolic factors highlights inadequacies in existing prevention strategies. Immediate and sustained interventions are critically needed at both global and regional levels to manage metabolic risk factors effectively, aiming to reverse the current trends and mitigate the escalating burden of MACs. Metabolism-attributed cancers Disability-adjusted life years Global burden Metabolic factors Socio-demographic index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cancer is a critical challenge in society, public health, and the global economy the 21st century. In 2022, approximately 20 million new cancer cases were reported worldwide, with nearly 10 million cancer-associated deaths [ 1 ]. The vast scale of the disease, along with regional and developmental disparities in cancer incidence, underscores the urgent need to intensify targeted cancer control measures on a global scale [ 2 ]. Various risk factors significantly contribute to the development and overall burden of cancer [ 3 ]. Among these, metabolic risk factors—particularly high fasting plasma glucose (FPG) and high body mass index (BMI)—are becoming increasingly prevalent worldwide [ 4 ]. In 2021, the age-standardized adult prevalence of overweight and obesity was estimated to be 45.1%, affecting about 2.11 billion adults [ 5 ]; the global age-standardized prevalence of diabetes was estimated to be 6.1%, affecting about 529 million people of all ages [ 6 ]. Understanding how metabolic risk factors influence cancer incidence and mortality, as well as tracking their temporal trends, is critical for developing effective strategies in cancer prevention and control [ 7 ]. Previous research has explored the global burden of cancers associated with metabolic factors [ 8 , 9 ]. However, these analyses have primarily relied on data from GLOBOCAN, which predominantly defines cancer burden in terms of morbidity or mortality, ignoring the additional health burden such as living with disability. Besides, existing studies based on the Global Burden of Disease (GBD) database tended to focus on a specific type of cancer burden or a certain metabolic factor, lacking a systematic and comparative assessment of the full spectrum of cancer burden attributable to metabolic factors [ 10 – 14 ]. These limitations significantly hinder the targeted implementation of cancer screening and preventive measures for vulnerable groups. Herein, our study extracted data from the Global Burden of Disease Study 2021 (GBD 2021) and comprehensively evaluate temporal trends and geographic disparities in disability-adjusted life years (DALYs) number and age-standardized DALY rates (ASDRs) for all types of cancer attributable to metabolic risks across 204 countries and territories from 1990 to 2021. Through a detailed examination of the impacts of metabolic risk factors, this study addresses existing research gaps, facilitates precise risk stratification, and informs resource allocation to enhance global cancer prevention and control initiatives. Methods Data Sources The Global Burden of Disease (GBD) database systematically assesses the global and regional burden of various diseases, injuries, and associated risk factors by integrating multiple sources of data, applying sophisticated statistical modeling methods, and rigorously quantifying uncertainty [ 15 , 16 ]. In this study, publicly available data from the GBD 2021 covering 204 countries and territories from 1990 to 2021 were analyzed. Specifically, data on causes of mortality, DALYs, and their corresponding 95% uncertainty intervals (UIs) were accessed through the GBD Results Tool ( https://vizhub.healthdata.org/gbd-results ). The extracted data were stratified and analyzed based on demographic factors (age, sex, year) and geographic regions. The study complies with ethical standards outlined by the Declaration of Helsinki. Given that the GBD database is openly available and contains no personally identifiable data, ethical approval or informed consent was not required. Relevant Definitions In the framework employed by the GBD, risk factors are classified into three main categories: behavioral, metabolic, and environmental/occupational factors [ 17 ]. Metabolic risks specifically include high FPG, high low-density lipoprotein cholesterol (LDL-C), high systolic blood pressure (SBP), high BMI, impaired kidney function (IKF), and low bone mineral density (BMD). DALY represent a composite metric for disease burden, combining both the years of life prematurely lost due to death and the years spent living with disability. According to GBD methodology, the disease burden linked to metabolic risk factors is assessed by estimating potential reductions in disease-specific DALYs if populations were exposed to an ideal theoretical minimum risk level. The GBD database categorizes data from these 204 countries and territories into 21 distinct geographical regions, further classifying them based on the Socio-demographic Index (SDI). The SDI, an indicator of developmental status, is calculated by averaging rankings across three key dimensions: fertility rates among women under 25 years old, education attainment in individuals aged 15 years and older, and lag-adjusted per capita income. The SDI scale ranges from 0 (lowest level of development) to 1 (highest level), enabling the grouping of countries and territories into five developmental quintiles for comparative analysis. Statistical Analysis To quantify the cancer burden attributable to metabolic risk factors, both absolute values and age-standardized rates for DALYs and mortality were examined. Results were reported alongside their respective 95% uncertainty intervals (UIs) to provide comprehensive insight into the estimates. Additionally, the estimated annual percentage change (EAPC), a widely recognized measure for evaluating temporal trends in age-standardized rates, was calculated to assess changes in DALYs linked to metabolic risks over time. The calculation of EAPC assumes a linear correlation between the natural logarithm of age-standardized rates and calendar years, modeled as y = α + βx + ε. Here, y denotes the natural logarithm of the age-standardized rate, and x represents the calendar year. EAPC values were computed using the formula 100×(e β −1), and corresponding 95% confidence intervals (CIs) were estimated using linear regression analysis. A positive EAPC accompanied by a 95% CI entirely above zero indicates an increasing trend, while a negative EAPC with a 95% CI entirely below zero indicates a decreasing trend. Trends with a 95% CI crossing zero were considered statistically nonsignificant, indicating no substantial temporal change [ 18 , 19 ]. Moreover, the association between SDI and age-standardized rates was examined for all 204 countries and territories using Spearman rank correlation analysis, with statistical significance set at a p-value less than 0.05. Global distribution patterns and regional differences in metabolism-attributed cancers (MACs) burden were explored by assessing trends according to sex and five-year intervals. Additionally, geographic maps were created to depict the distribution of DALY counts, age-standardized rates, and percentage variations in MAC burden across all included countries and territories. Results 3.1. Global burden of all causes or cancers attributable to metabolic risks Between 1990 and 2021, the burden of disease, as measured by DALYs attributable to metabolic risk factors, exhibited a consistent decline. Globally, the age-standardized death rate (ASDR) associated with metabolic risks across all causes was estimated at 6,737.88 (95% UI: 5,968.90 to 7,496.49) per 100,000 person-years in 1990. Encouragingly, this figure steadily decreased over time, reaching 5,582.42 (4,839.55 to 6,345.44) per 100,000 person-years by 2021 (Fig. 1 A and Table 1 ). The primary metabolic risk factors included high BMI, elevated FPG, increased LDL, high SBP, KD, and low BMD. Despite the persistent decline in ASDRs linked to high SBP over this period, it continued to represent the leading contributor to DALYs attributable to metabolic risks. Concurrently, ASDRs related to high BMI and elevated FPG exhibited an upward trajectory, positioning these factors as the second and third largest contributors to the DALY burden from metabolic risks (Fig. 1 B). Additionally, Fig. 1 C illustrates the distribution of DALYs attributable to metabolic risks across five SDI regions in 2021. Regarding ASDRs specifically linked to cancers, the impact of metabolic risks has become increasingly evident on a global scale. By 2021, the estimated ASDR of cancers attributable to metabolic risks had reached 179.36 (51.23 to 312.41) per 100,000 person-years (Fig. 1 D). Unlike the overall declining trend in the contribution of metabolic risks to DALYs across all causes, high BMI and elevated FPG played progressively larger roles in the cancer-related disease burden (Fig. 1 E). Considerable heterogeneity was noted in cancer mortality attributable to these metabolic risk factors. In 2021, high BMI contributed most significantly to the ASDR in high-SDI regions, with a stepwise decrease observed from high- to low-SDI regions. A similar pattern was found for high FPG, where its impact peaked in high-SDI regions and gradually diminished in lower-SDI regions (Fig. 1 F and S1). 3.2. The burden of cancers attributable to metabolic factors, by cancer type We further examined the DALY burden linked to metabolic risk factors across various cancers. From 1990 to 2021, colorectal, pancreatic, and breast cancers consistently ranked as the top three malignancies associated with metabolic factors. In 2021, breast, colorectal, and pancreatic cancers exhibited the highest ASDRs attributable to metabolic risks, recorded at 38.23, 37.68, and 27.20 per 100,000 person-years, respectively. Meanwhile, colorectal, pancreatic, and liver cancers showed the highest ASDRs from metabolic factors, reaching 54.27, 40.11, and 23.04 per 100,000 person-years in the same year (Fig. 2 A). Between 1990 and 2021, liver cancer demonstrated the most rapid increase in DALYs attributable to metabolic risks in both sexes, with an estimated annual percentage change (EAPC) of 1.84 (95% CI: 1.78 to 1.91) in females and 2.34 (95% CI: 2.22 to 2.47) in males. The trends observed in DALY numbers closely mirrored those in ASDR (Fig. 2 B). Among females, breast and colorectal cancers each contributed 21% of cancer-related DALYs due to metabolic factors, followed by pancreatic (15%) and uterine cancer (11%), alongside 10 additional cancer types. In males, colorectal cancer accounted for the highest proportion (30%) of DALYs linked to metabolic risks, followed by pancreatic (23%) and liver cancer (13%), along with eight other cancer types (Tables 2 and 3 ). Corresponding death data are shown in Table S2 and S3 3.3. The burden of cancers attributable to metabolic factors, by sex and age Between 1990 and 2021, the ASDR attributable to metabolic factors increased more rapidly in males (EAPC: 0.79, 95% CI: 0.72 to 0.85) than in females (EAPC: 0.36, 95% CI: 0.32 to 0.39) (Fig. 2 C, S2). In 2021, cancer-related DALYs linked to metabolic factors reached 8.34 million (95% UI: 2.26 to 14.34 million) in females, reflecting a 177% increase from 1990. Meanwhile, the corresponding burden in males was 7.25 million (95% UI: 2.17 to 12.64 million), marking a 144% rise over the same period (Fig. 2 D). To investigate age-related trends in DALYs, we stratified individuals into 16 age groups, beginning at age 20 with five-year intervals. As illustrated in Fig. 2 E, ASDR attributable to metabolic risks in females increased progressively with age, plateauing between 70 and 79 years before entering another phase of growth. In males, ASDR steadily rose with age, peaking between 90 and 94 years before subsequently declining. Notably, in individuals aged 75 to 94 years, ASDR was lower in females than in males, whereas in those younger than 74 or older than 95 years, the reverse trend was observed. The global DALY burden for MRNs showed an age-dependent increase in both sexes, peaking at 65 to 69 years before gradually declining. The burden of metabolism-related DALYs was higher in females than males beyond the age of 50, whereas the reverse was true for individuals aged 50 years or younger. Further analysis of cancer-specific DALYs attributable to metabolic factors across age groups revealed that ASDR was substantially higher in individuals aged 70 years and older, followed by those between 50 and 69 years, with the lowest DALY rates observed in individuals aged 15 to 49 years (Fig. 2 F and G, S3 and S4). The middle-aged and elderly population, particularly those aged 50 to 69 years, represented the largest proportion of total DALYs rate due to MRNs (Fig. 2 H and I). 3.4. The burden of cancers attributable to metabolic factors, by SDI The SDI-stratified regional analysis revealed that from 1990 to 2021, both the absolute number and rate of cancer-related DALYs attributable to metabolic factors exhibited a steady upward trend across all five SDI categories in both sexes. Additionally, the DALY burden increased progressively with rising SDI levels (Fig. 3 A and B). In 2021, the number of cancer-related DALYs due to metabolic factors across high to low SDI regions was 5.17, 4.42, 3.97, 1.59, and 0.42 million, respectively, while the corresponding DALY rates per 100,000 population were 257.44, 223.53, 142.40, 105.22, and 76.63 (Figs. 3 C and D). This pattern indicates that regions with higher SDI levels bear a greater DALY burden compared to lower SDI regions. However, over the past three decades, lower SDI regions have experienced a more pronounced increase in DALY rates. The EAPC values for DALY rates were 0.20, 0.53, 1.44, 2.45, and 1.36 across SDI categories, respectively (Fig. 3 E and Table 1 ). This marked increase in regions with lower SDI indicates a narrowing gap in mortality burden between high- and low-SDI regions. In essence, the cancer mortality burden associated with metabolic risks is steadily transitioning from higher to lower SDI areas. 3.5. The burden of cancers attributable to metabolic factors, by region Between 1990 and 2021, most GBD regions experienced an increase in the rate of cancer DALYs attributable to metabolic factors, with the most pronounced rise observed in Southern Sub-Saharan Africa (EAPC: 2.55, 95% CI: 2.22 to 2.88). In contrast, Western Europe exhibited no significant change (EAPC: 0.05, 95% CI: -0.01 to 0.10). Notably, high-income Asia Pacific was the only region to show a declining trend (EAPC: -0.17, 95% CI: -0.27 to -0.08). In 2021, Central Europe had the highest rate of cancer DALYs linked to metabolic factors (357.87, 95% UI: 106.55 to 620.39 per 100,000 person-years), whereas South Asia reported the lowest (69.82, 95% UI: 17.42 to 124.82 per 100,000 person-years) (Tables 1 ; Fig. S1 ). In terms of absolute numbers, East Asia accounted for the largest burden, with 3.40 million metabolism-related cancer DALYs (95% UI: 0.88 to 6.12 million), while Andean Latin America recorded the lowest, at 0.12 million (95% UI: 0.05 to 1.98 million) (Tables 1 , Figure S5). Figure 4 presents the ranking of various cancer types based on their contributions to the age-standardized DALY rates of MACs across different regions in 2021, analyzed collectively for both sexes and separately for males and females. For both sexes combined, colon and rectum cancer was the predominant contributor to age-standardized DALY rates of MACs per 100,000 person-years in most geographic regions. Nevertheless, pancreatic cancer ranked highest in High-income Asia Pacific, breast cancer dominated in Oceania, and liver cancer had the largest impact in Western Sub-Saharan Africa. Among females, colon and rectum cancer or breast cancer typically ranked first in most regions; the only exception was High-income Asia Pacific, where pancreatic cancer was the primary contributor. For males, colon and rectum cancer remained the top contributor in nearly all regions, except for High-income Asia Pacific, where pancreatic cancer had the greatest burden, and Western Sub-Saharan Africa, where liver cancer led. 3.6. The burden of cancers attributable to metabolic factors, by country A significant geographical disparity was observed across countries and territories (Fig. 5 ). In 2021, the highest DALY rate for MRNs was reported in Tonga (492.19, 95% UI: 133.31 to 888.49 per 100,000 person-years), followed by Nauru and American Samoa. In contrast, the lowest burden was recorded in Bangladesh (42.73, 95% UI: 11.21 to 79.58 per 100,000 person-years), along with Burundi and Nepal (Fig. 5 A and B). From 1990 to 2021, the largest increase in DALYs rate occurred in Cabo Verde (2.53 times), and the largest increase in DALYs number occurred in United Arab Emirates (10.16 times) (Fig. 5 C and D). Globally, the majority of countries and territories exhibited a notable upward trend in DALY rates for MRNs, as reflected by EAPC values (Table S1 ). The most pronounced increases were observed in Lesotho (EAPC: 4.77, 95% CI: 4.26 to 5.28), Cabo Verde, and Zimbabwe. Conversely, some countries, including Ethiopia (EAPC: −0.78, 95% CI: −1.03 to − 0.52), the Czech Republic, and Singapore, experienced a modest decline in DALY rates for MRNs (Figure S6). 3.7. The relationship between the DALYs burden of cancers attributable to metabolic factors and SDI We further examined the trends in age-standardized DALY rates across different SDI regions from 1990 to 2021 (Fig. 6 ). Overall, as SDI increases, the age-standardized DALY rate follows an upward trajectory. However, when SDI surpasses approximately 0.75—particularly in regions such as High-income Asia Pacific, High-income North America, Western Europe, and Australasia—the DALY rate exhibits minimal growth or even a decline (Fig. 6 A). Figure 6 B illustrates the relationship between SDI and age-standardized DALY rates across countries in 2021. A similar pattern is observed, where the DALY rate increases with SDI up to around 0.8 but then declines as SDI continues to rise. Notably, based on SDI alone, Tonga and Nauru exhibited substantially higher-than-expected age-standardized DALY rates. As depicted in Fig. 6 C, a significant negative correlation was found between the EAPC of DALY rates for MACs and SDI (R = − 0.45, P < 0.001). A comparable inverse correlation was also identified between EAPC and DALY rates (R = − 0.18, P = 0.012) (Fig. 6 D). Discussion With the latest data from the Global Burden of Disease (GBD) 2021, this research systematically evaluates the relationship between metabolic risks and cancer burden across different cancer types, metabolic risk factors, sex, age groups, regions, and time periods. Our findings indicate that metabolic risks have become a major and steadily increasing contributor to cancer burden worldwide. Among these risks, high BMI and high FPG are identified as the primary metabolic drivers, with distinct patterns across demographic and geographic distributions. The burden of cancers attributable to high FPG is more pronounced in males, whereas cancers linked to high BMI are more prevalent in females. Additionally, individuals aged 50 years and older bear the highest burden of MACs, and the ASDRs for these cancers have risen significantly in middle, low-middle, and low SDI regions over recent decades. From 1990 to 2021, high and high-middle SDI regions have consistently carried the greatest cancer burden attributable to metabolic risk factors; however, lower SDI regions have recently seen a rapid increase in obesity and diabetes prevalence, driven mainly by economic development, urbanization, and lifestyle transitions. Dietary transitions towards higher consumption of processed foods, refined carbohydrates, and saturated fats, combined with declining physical activity, have contributed to these trends [ 5 , 6 ]. This shift is particularly evident in regions such as the Middle East and North Africa, where the economic transformation of Gulf countries has led to an increasing prevalence of obesity and diabetes, paralleling a rising cancer burden [ 20 ]. Similarly, sub-Saharan Africa and South Asia, regions historically characterized by relatively low cancer incidence rates, have recently experienced notable increases in metabolic disorders, highlighting the urgent need for targeted interventions [ 21 , 22 ]. Our findings indicate that the most pronounced increases in ASDRs of cancers attributable to metabolic risks occurred in lower SDI regions, where healthcare systems often lack sufficient resources to effectively address complex non-communicable diseases. Due to disparities in healthcare infrastructure and cancer control programs, this escalating MACs burden could further deepen existing health inequalities. Additionally, we observed significant differences between sexes regarding the impact of metabolic risks on cancer burdens. In higher SDI regions, men generally exhibit a greater MAC burden, likely attributable to higher prevalence rates of metabolic syndrome and associated lifestyle habits such as smoking and alcohol intake. Conversely, in lower SDI regions, women disproportionately experience a higher MACs burden, potentially driven by cultural and socioeconomic factors limiting physical activity, distinct metabolic responses, hormonal influences, and different patterns of fat distribution [ 23 – 25 ]. Excess adiposity among women is notably linked to elevated triglycerides, hyperglycemia, and systemic inflammation, substantially increasing their risk for breast, endometrial, and colorectal cancers [ 26 – 28 ]. Therefore, implementing targeted public health measures that specifically address sex differences in obesity and diabetes prevention within each regional context is critical for reducing these disparities. Biological mechanisms underlying the association between metabolic risk factors and cancer further underscore the urgency of targeted intervention strategies. Elevated BMI can induce carcinogenesis via multiple interconnected pathways, including insulin resistance, chronic inflammation, and hormonal imbalance [ 26 ]. Excessive adipose tissue disrupts metabolic equilibrium, fostering an environment conducive to tumor initiation and progression[ 29 , 30 ]. Additionally, adipose tissue directly influences sex hormone concentrations, heightening the risk for hormone-sensitive cancers [ 31 , 32 ]. Similarly, elevated blood glucose levels significantly contribute to cancer development by promoting cancer cell metabolism, increasing oxidative stress, and sustaining chronic inflammatory states [ 33 , 34 ]. Hyperglycemia frequently coexists with hyperinsulinemia, stimulating tumor cell growth and inhibiting apoptosis [ 35 , 36 ]. Recent evidence also indicates that hyperglycemia may promote cancer progression through epigenetic alterations, modifying gene-expression patterns linked to malignant phenotypes [ 37 ]. Although detailed molecular mechanisms require further elucidation, these metabolic abnormalities highlight the necessity for comprehensive preventive approaches that simultaneously target obesity and hyperglycemia. Considering the considerable cancer burden associated with metabolic risk factors, adopting comprehensive prevention and intervention strategies is imperative. Strengthening primary prevention through dietary modifications, physical activity promotion, and early identification of metabolic risks should become a priority, especially in lower SDI regions facing rapid increases in obesity and diabetes prevalence [ 38 – 40 ]. Enhancing access to obesity and diabetes management programs and integrating metabolic disease prevention into national cancer control plans will be crucial to effectively reducing the future incidence of metabolic risk-related cancers globally [ 41 ]. Additionally, targeted policy measures—such as urban planning to promote active lifestyles, regulations on unhealthy food consumption, and public health campaigns—can help address the broader determinants of metabolic health [ 42 ]. As digestive system cancers remain the predominant contributors to metabolic risk-attributable cancer mortality, and sex-based and regional disparities further complicate the burden, future research should focus on elucidating mechanistic pathways and developing effective, targeted interventions. Without decisive action at the individual, healthcare, and policy levels, the growing burden of metabolic risk-related cancers will continue to strain global health systems, underscoring the urgency of coordinated public health efforts. Conclusion The substantial and continuing increase in the global cancer burden associated with metabolic risk factors highlights critical gaps in current prevention and control strategies. Despite historically higher burdens in high and high-middle SDI regions, lower SDI regions have experienced more rapid growth in cancer burden linked to obesity and diabetes, driven primarily by socioeconomic and lifestyle transitions. Given the marked regional disparities and sex-specific differences observed, tailored public health interventions addressing metabolic risks are urgently needed. To effectively address this upward trend, comprehensive, region-specific, and sustainable global policies must be implemented globally, prioritizing the management of metabolic risk factors to reduce the growing burden of MACs. Abbreviations MACs Metabolism-attributed cancers DALYs Disability-adjusted life-years ASDR EAPCs:Estimated annual percentage change GBD Global burden of disease SDI Social-demographic index UI Uncertainty interval CI Confidence interval FPG fasting plasma glucose BMI high body mass index (BMI). Declarations Acknowledgements We highly appreciate the works by the Global Burden of Disease Study 2021 collaborators. Authors contributions TJY and LH designed the study; TJY and HYT collected data and verified the accuracy of the data; TJY and LCY prepared manuscript; TJY, WZQ and CYX analyzed and interpreted data. All authors read and approved the final manuscript. Funding This work by the National Natural Science Foundation of China (Grant No. 82404196). Availability of data and materials All analyzed data are retrieved from the GBD 2021 results available online at the Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd-results/). Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. 2 Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. References Bray F, Laversanne M, Sung HYA, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-Cancer J Clin. 2024;74:229–63. Bizuayehu HM, Ahmed KY, Kibret GD, Dadi AF, Belachew SA, Bagade T et al. Global Disparities of Cancer and Its Projected Burden in 2050. Jama Netw Open. 2024; 7. Wu ZH, Xia FN, Lin R. Global burden of cancer and associated risk factors in 204 countries and territories, 1980–2021: a systematic analysis for the GBD 2021. J Hematol Oncol. 2024; 17. Sung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, et al. Global patterns in excess body weight and the associated cancer burden. Ca-Cancer J Clin. 2019;69:88–112. Collaborators GBDAB. Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet. 2025;405:813–38. Collaborators GBDD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402:203–34. Hu JJ, Dong HL, Dong YM, Zhou RX, Teixeira W, He XX et al. Cancer burden attributable to risk factors, 1990–2019: A comparative risk assessment. Iscience. 2024; 27. Arnold M, Pandeya N, Byrnes G, Renehan AG, Stevens GA, Ezzati M, et al. Global burden of cancer attributable to high body-mass index in 2012: a population-based study. Lancet Oncol. 2015;16:36–46. Pearson-Stuttard J, Zhou B, Kontis V, Bentham J, Gunter MJ, Ezzati M. Worldwide burden of cancer attributable to diabetes and high body-mass index: a comparative risk assessment. Lancet Diabetes Endo. 2018;6:E6–15. Cui B, Chen A, Xu CC. Global, regional, and national esophageal cancer deaths and DALYs attributable to diet low in vegetables and fruits, 1990–2019: analysis for the global burden of disease study. Front Nutr. 2025; 11. Han SY, Zhao SY, Zhong R, Li PZ, Pang YW, He S et al. Global burden, trends, and disparities in kidney cancer attributable to smoking from 1990 to 2021. Front Public Health. 2025; 12. Cubelos-Fernández N, Dávila-Batista V, Fernández-Villa T, Castaño-Vinyals G, Perez-Gomez B, Amiano P, et al. Burden of postmenopausal breast cancer attributable to excess body weight: comparative study of body mass index and CUN-BAE in MCC-Spain study. J Epidemiol Commun H. 2025;79:64–71. Zheng Z, Xu SJ, Zhu JC, Yang Q, Ye H, Li M et al. Disease burden of cancers attributable to high fasting plasma glucose from 1990 to 2021 and projections until 2031 in China. Cancer Epidemiol. 2025; 94. Rumgay H, Nethan S, Shah RC, Vignat J, Ayo-Yusuf O, Chaturvedi P, et al. Global burden of oral cancer in 2022 attributable to smokeless tobacco and areca nut consumption: a population attributable fraction analysis. Lancet Oncol. 2024;25:1413–23. Collaborators GBDCoD. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2100–32. Diseases GBD, Injuries C. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2133–61. Collaborators GBDRF. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2162–203. Yi M, Li A, Zhou L, Chu Q, Song Y, Wu K. The global burden and attributable risk factor analysis of acute myeloid leukemia in 195 countries and territories from 1990 to 2017: estimates based on the global burden of disease study 2017. J Hematol Oncol. 2020;13:72. Li T, Zhang H, Lian M, He Q, Lv M, Zhai L, et al. Global status and attributable risk factors of breast, cervical, ovarian, and uterine cancers from 1990 to 2021. J Hematol Oncol. 2025;18:5. Tondro Anamag F, Noori M, Nejadghaderi SA, Sullman MJM, Grieger JA, Kolahi AA, et al. Burden of cancers attributable to high fasting plasma glucose in the Middle East and North Africa region, 1990–2019. Cancer Med. 2023;12:10031–44. Bray F, Parkin DM, African Cancer Registry N. Cancer in sub-Saharan Africa in 2020: a review of current estimates of the national burden, data gaps, and future needs. Lancet Oncol. 2022;23:719–28. Jain U, Rahim F, Jain B, Komanduri A, Arkalgud A, Sabet CJ, et al. Cancer burden across the South Asian Association for Regional Cooperation in 2022. BMJ Oncol. 2024;3:e000466. Singh RB, Pella D, Mechirova V, Kartikey K, Demeester F, Tomar RS, et al. Prevalence of obesity, physical inactivity and undernutrition, a triple burden of diseases during transition in a developing economy. The Five City Study Group. Acta Cardiol. 2007;62:119–27. Knopp RH, Paramsothy P, Retzlaff BM, Fish B, Walden C, Dowdy A, et al. Gender differences in lipoprotein metabolism and dietary response: basis in hormonal differences and implications for cardiovascular disease. Curr Atheroscler Rep. 2005;7:472–9. Group NCDRFC-AW. Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies. Int J Epidemiol. 2017;46:1421–32. Sung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, et al. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin. 2019;69:88–112. Pasqual E, O'Brien K, Rinaldi S, Sandler DP, Kitahara CM. Obesity, obesity-related metabolic conditions, and risk of thyroid cancer in women: results from a prospective cohort study (Sister Study). Lancet Reg Health Am. 2023;23:100537. Rask-Andersen M, Ivansson E, Hoglund J, Ek WE, Karlsson T, Johansson A. Adiposity and sex-specific cancer risk. Cancer Cell. 2023;41:1186–e974. Hopkins BD, Goncalves MD, Cantley LC. Obesity and Cancer Mechanisms: Cancer Metabolism. J Clin Oncol. 2016;34:4277–83. Mitchelson KAJ, O'Connell F, O'Sullivan J, Roche HM, Obesity. Dietary Fats, and Gastrointestinal Cancer Risk-Potential Mechanisms Relating to Lipid Metabolism and Inflammation. Metabolites. 2024; 14. Coradini D, Oriana S. Impact of sex hormones dysregulation and adiposity on the outcome of postmenopausal breast cancer patients. Clin Obes. 2021;11:e12423. Dubey P, Reddy SY, Singh V, Shi T, Coltharp M, Clegg D, et al. Association of Exposure to Phthalate Metabolites With Sex Hormones, Obesity, and Metabolic Syndrome in US Women. JAMA Netw Open. 2022;5:e2233088. Pati S, Irfan W, Jameel A, Ahmed S, Shahid RK. Obesity and Cancer: A Current Overview of Epidemiology, Pathogenesis, Outcomes, and Management. Cancers (Basel). 2023; 15. Gasmi A, Noor S, Menzel A, Dosa A, Pivina L, Bjorklund G. Obesity and Insulin Resistance: Associations with Chronic Inflammation, Genetic and Epigenetic Factors. Curr Med Chem. 2021;28:800–26. Supabphol S, Seubwai W, Wongkham S, Saengboonmee C. High glucose: an emerging association between diabetes mellitus and cancer progression. J Mol Med. 2021;99:1175–93. Zhu B, Qu S. The Relationship Between Diabetes Mellitus and Cancers and Its Underlying Mechanisms. Front Endocrinol. 2022; 13. Lee C, Kim M, Park C, Jo W, Seo JK, Kim S, et al. Epigenetic regulation of Neuregulin 1 promotes breast cancer progression associated to hyperglycemia. Nat Commun. 2023;14:439. Ding YF, Deng AX, Qi TF, Yu H, Wu LP, Zhang HB. Burden of type 2 diabetes due to high body mass index in different SDI regions and projections of future trends: insights from the Global Burden of Disease 2021 study. Diabetol Metab Syndr. 2025; 17. Yang XY, Sun JY, Zhang WJ. Global trends in burden of type 2 diabetes attributable to physical inactivity across 204 countries and territories, 1990–2019. Front Endocrinol. 2024; 15. Zhou XD, Chen QF, Yang WH, Zuluaga M, Targher G, Byrne CD et al. vol 76, 102848,. Burden of disease attributable to high body mass index: an analysis of data from the Global Burden of Disease Study 2021 (2024). Eclinicalmedicine. 2024; 76. Kim DS, Scherer PE. Obesity, Diabetes, and Increased Cancer Progression. Diabetes Metab J. 2021;45:799–812. Wang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol. 2021;9:446–61. Tables Table 1 is available in the Supplementary Files section. Table 2 Summary of age-standardized DALYs rate of cancers attributable to high fasting plasma glucose in 1990 and 2021; stratified by sex and cancer type 1990 Female ASD R , per 100,000 (95% UI) 20 21 Female ASD R , per 100,000 (95% UI) Female EAPC (95% CI) 1990 Male ASD R , per 100,000 (95% UI) 20 21 Male ASD R , per 100,000 (95% UI) Male EA PC (95% CI) Overall 56.27(4.65,109.20) 71.20(3.93,140.42) 0.75(0.70,0.81) 76.50(9.61,145.77) 93.44(12.98,176.81) 0.76(0.67,0.85) By cancer type Bladder 1.45(-0.19,3.29) 1.58(-0.21,3.59) 0.22(0.13,0.32) 5.76(-0.76,13.06) 6.60(-0.85,15.03) 0.47(0.37,0.58) Breast 13.80(-4.02,32.45) 18.05(-5.31,42.71) 0.80(0.76,0.84) NA Colon and rectum 15.49(7.76,23.35) 15.59(7.97,23.67) -0.06(-0.12,0.01) 22.12(11.05,33.38) 25.76(13.05,39.44) 0.55(0.46,0.63) Liver 2.07(0.22,4.29) 3.21(0.35,6.49) 1.57(1.44,1.69) 2.51(0.26,5.12) 4.18(0.47,8.25) 1.73(1.58,1.88) Pancreatic 18.31(2.09,35.89) 25.35(2.90,48.26) 1.12(1.07,1.17) 27.47(3.15,53.86) 38.69(4.43,73.29) 1.29(1.21,1.36) Tracheal, bronchus, and lung 5.16(-1.12,11.66) 7.41(-1.48,16.77) 1.17(1.02,1.32) 18.64(-3.78,41.19) 18.21(-3.63,41.86) 0.05(-0.09,0.19) Abbreviations: ASDR – age-standardized disability-adjusted life years rate; EAPC – estimated annual percentage change; CI – confidence interval; UI – uncertainty interval Table 3 Summary of age-standardized DALYs rate of cancers attributable to high body mass index in 1990 and 2021; stratified by sex and cancer type 1990 Female ASD R , per 100,000 (95% UI) 20 21 Female ASD R , per 100,000 (95% UI) Female EAPC (95% CI) 1990 Male ASD R , per 100,000 (95% UI) 20 21 Male ASD R , per 100,000 (95% UI) Male EA PC (95% CI) Overall 105.26(43.35,171.59) 114.61(46.08,185.86) 0.17(0.13,0.21) 66.66(31.39,106.64) 88.18(39.36,143.40) 0.87(0.82,0.92) By cancer type Breast 20.55(-0.86,41.70) 21.83(-1.01,42.51) 0.11(0.07,0.16) Colon and rectum 24.73(10.59,39.99) 23.96(10.36,37.75) -0.26(-0.31,-0.20) 26.42(10.97,42.88) 31.09(13.36,49.47) 0.48(0.45,0.51) Gallbladder and biliary tract 7.12(4.83,9.72) 5.84(3.94,8.18) -0.81(-0.91,-0.71) 4.11(2.86,5.61) 4.51(2.94,6.48) 0.24(0.20,0.28) Kidney 5.74(2.27,9.33) 5.81(2.34,9.20) -0.06(-0.14,0.03) 10.10(3.92,16.46) 12.52(5.09,20.38) 0.67(0.57,0.78) Leukemia 8.37(6.13,10.93) 7.60(5.51,9.88) -0.39(-0.44,-0.34) 10.29(7.69,13.40) 10.02(7.41,13.14) -0.11(-0.15,-0.06) Liver 5.26(2.05,8.53) 9.52(3.91,15.91) 1.97(1.93,2.01) 8.75(3.66,14.34) 19.05(7.83,33.00) 2.51(2.39,2.63) Multiple myeloma 1.72(-0.62,4.39) 2.17(-0.91,5.37) 0.55(0.45,0.65) 1.92(-0.64,4.92) 2.66(-1.06,6.89) 0.99(0.90,1.08) Non-Hodgkin lymphoma 2.89(0.98,4.92) 3.38(1.12,5.83) 0.14(0.01,0.27) 4.04(1.38,6.76) 4.67(1.53,8.01) 0.29(0.19,0.38) Ovarian 8.72(1.78,16.41) 10.56(2.50,18.57) 0.51(0.45,0.57) Pancreatic 1.03(-0.78,3.85) 2.75(-0.24,7.11) 3.16(3.09,3.22) -0.02(-1.86,3.07) 2.28(-0.92,7.24) 10.76(8.58,12.99) Thyroid 1.87(1.42,2.41) 1.96(1.44,2.55) 0.10(0.06,0.13) 1.06(0.79,1.36) 1.38(1.04,1.79) 0.93(0.87,0.99) Uterine 17.26(12.25,23.16) 19.23(13.80,25.38) 0.27(0.18,0.37) Abbreviations: ASDR – age-standardized disability-adjusted life years rate; EAPC – estimated annual percentage change; CI – confidence interval; UI – uncertainty interval Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Table11.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor invited by journal 18 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 15 Oct, 2025 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-7868307","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545675511,"identity":"67f7164f-c019-43a9-94a2-8f9f03540b0a","order_by":0,"name":"Jingyao Tu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jingyao","middleName":"","lastName":"Tu","suffix":""},{"id":545675512,"identity":"482a7a81-c876-4f9e-9be5-bb77d1cd2afd","order_by":1,"name":"Xinyi Chen","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Chen","suffix":""},{"id":545675513,"identity":"d70cd468-3ce3-45a7-ba75-11f1b7948689","order_by":2,"name":"Yuting Huang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Huang","suffix":""},{"id":545675514,"identity":"2e9d250c-7f17-4207-aef4-5d12607edb5f","order_by":3,"name":"Chunya Li","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chunya","middleName":"","lastName":"Li","suffix":""},{"id":545675515,"identity":"8a5a92d2-5888-4ce7-b654-69da46d92623","order_by":4,"name":"Ziqi Wang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Wang","suffix":""},{"id":545675516,"identity":"6102ba61-cb88-4089-a4e9-a7257f2a6e7a","order_by":5,"name":"Hang Liang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Liang","suffix":""},{"id":545675517,"identity":"b2dda5b3-472c-4601-8719-76082ec3b1cb","order_by":6,"name":"Xianglin Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYNACAyBmZ2xg+NgA4UsQp4WZsYFxJlALD3FaQIAZiHiJ0WJwI/nZY54Cuzx5Z+a2x7Y7DtvbMzAfvM3DYJeHW0uaueEMg+Riw8OM7ca5Zw4n9jCwJVvzMCQX49aSYCbxwYA5cWMzY5t0btvhBB4GHjNpHoYDiQ04taR/k0gwqIdosWw7bM/DwP+NgJYckC2HE+czA7Uwth1m7GHgYcOrRfLMmzLJGQbHEzcAtUj2tqUn9hxmM7acY5CMUwvf8fRt0jx/qhPnt7c/k/jZZm3P3t788MabCjucWhQOwFwIY4AiCBK5OIB8AzpjFIyCUTAKRgE6AAAmRVI6Y8HumgAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Xianglin","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-10-15 12:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7868307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7868307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96250274,"identity":"b846a487-40fe-4f9e-8c3b-d6158a296992","added_by":"auto","created_at":"2025-11-19 07:37:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2853474,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/5c13d539c67eccf25de5667a.docx"},{"id":96250452,"identity":"8fdacd89-4da2-49c4-b8b8-64da30041e5d","added_by":"auto","created_at":"2025-11-19 07:38:24","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9066,"visible":true,"origin":"","legend":"","description":"","filename":"cb0be589681c48cd96cd7c02c9777395.json","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/e60704df2a83889679eca0bf.json"},{"id":96249056,"identity":"56ff997e-f72a-41b7-bc70-95cfdc432b6b","added_by":"auto","created_at":"2025-11-19 07:30:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1538768,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/4958b969d0c9e09293e26c68.docx"},{"id":96250334,"identity":"d787a259-b1ac-479e-844f-804f48c9159d","added_by":"auto","created_at":"2025-11-19 07:38:09","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154250,"visible":true,"origin":"","legend":"","description":"","filename":"cb0be589681c48cd96cd7c02c97773951enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/14bce9f3e1a0d7612b6edcf2.xml"},{"id":96250329,"identity":"a1a635c2-c367-4dc0-88e6-28e418d86481","added_by":"auto","created_at":"2025-11-19 07:38:08","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":459188,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/0b389588c75ed05097a01974.jpeg"},{"id":96112198,"identity":"099529d5-9e77-4a06-8f4f-5bf63a458cb9","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":433494,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/24536d35f57db6b16b3fadf3.jpeg"},{"id":96250398,"identity":"e8088e92-fbf6-4bb8-85ff-d79cdf97b623","added_by":"auto","created_at":"2025-11-19 07:38:15","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":288210,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/52f6b9c33047f2a3fc0d2467.jpeg"},{"id":96248726,"identity":"fd2938fa-76f0-4d50-aff7-6f4e0fe30c19","added_by":"auto","created_at":"2025-11-19 07:29:04","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":774690,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/5435f96d29a194c0fa3d431e.jpeg"},{"id":96112209,"identity":"a5ec5746-2c05-46fc-afab-2c4b024eacc9","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":257112,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/0a2f35a16b605b0df4f3ee14.jpeg"},{"id":96112210,"identity":"ec3b2899-ea92-4864-a356-ab045d1d640b","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":406422,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/421e2305626379f30b697ff7.jpeg"},{"id":96250393,"identity":"6760d0df-a917-4f0e-95e9-8ef076e584b2","added_by":"auto","created_at":"2025-11-19 07:38:15","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79973,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/a4733e173cde5211e10ea509.png"},{"id":96112201,"identity":"3dd40cb3-ec7f-46d5-a988-35cb4a745e26","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82398,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/1413c447eac955cd554ceb6b.png"},{"id":96112196,"identity":"6f743e93-ed1a-450b-8bac-15f11ac7b299","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50443,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/cab4cf6c614730e37161f40d.png"},{"id":96247258,"identity":"98f269e4-c688-4e97-8920-21452ea817a9","added_by":"auto","created_at":"2025-11-19 07:27:18","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107516,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/91e2172f64469e5d9ca25df1.png"},{"id":96112207,"identity":"b3d0ac02-069d-491b-be3b-ae3cf83bfadd","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74621,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/f154e35c1ae1545ed21d5c76.png"},{"id":96112206,"identity":"14aad6be-233e-4205-a448-bb0a33f9ff24","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78215,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/a6abe20637936554235d6bb4.png"},{"id":96112208,"identity":"2a4e946e-1f2d-4b36-9f32-5fa8f6c6083f","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152888,"visible":true,"origin":"","legend":"","description":"","filename":"cb0be589681c48cd96cd7c02c97773951structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/6bcc506f7604c40f288b5d7a.xml"},{"id":96112202,"identity":"55efb554-8fe5-468b-8867-4631d0710641","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160739,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/bcbfd30f53ac8f0a0d7c5917.html"},{"id":96112186,"identity":"b979ac29-10ef-4949-98c4-c4f8d9a17fa5","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1934436,"visible":true,"origin":"","legend":"\u003cp\u003eThe DALYs burden of all causes or cancers attributable to metabolic factors in both sexes. (A) Temporal trend of ASDRs of all causes attributable to metabolic factors from 1990 to 2021. (B) Temporal trend of ASDRs of all causes attributable to different metabolic factors from 1990 to 2021. (C) Proportion of DALYs of all causes attributable to different metabolic factors in 2021. (D) Temporal trend of ASDRs of cancers attributable to metabolic factors from 1990 to 2021. (E) Temporal trend of ASDRs of cancers attributable to different metabolic factors from 1990 to 2021. (F) Proportion of DALYs of cancers attributable to different metabolic factors in 2021.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/9ce50b11153603dd856cabd8.png"},{"id":96112189,"identity":"96b322a8-f768-4c92-96c7-e70628e2052f","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2012715,"visible":true,"origin":"","legend":"\u003cp\u003eThe DALYs burden of cancers attributable to metabolic factors by cancer type, sex, and age. (A) Temporal trend of ASDRs from 1990 to 2021, by cancer type. (B) Temporal trend of DALYs number from 1990 to 2021, by cancer type. (C) Temporal trend of ASDRs from 1990 to 2021, by sex. (D) Temporal trend of DALYs number from 1990 to 2021, by sex. (E) Age-gender trends of DALYs burden in 2021. (F) Age trend of ASDRs in 2021, by cancer type. (G) Temporal trend of ASDRs from 1990 to 2021, by age. (H) Age- trend of DALYs number in 2021, by cancer type. (I) Temporal trend of DALYs number from 1990 to 2021, by age.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/480e0e3681f69424bd49f714.png"},{"id":96248772,"identity":"bfdf0368-d513-4a06-944e-073dd2f27892","added_by":"auto","created_at":"2025-11-19 07:29:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1361694,"visible":true,"origin":"","legend":"\u003cp\u003eThe DALYs burden of cancers attributable to metabolic factors by SDI (A) Temporal trend of ASDRs from 1990 to 2021. (B) Temporal trend of DALYs number from 1990 to 2021. (C) DALYs number in 2021. (D) ASDRs in 2021. (E) EPAC of age-standardized DALYs rates in 2021 from 1990 to 2021.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/8dd3649fab00f7e5f2775e1b.png"},{"id":96250584,"identity":"66ddf89d-b58a-4639-9d10-65a031e2e236","added_by":"auto","created_at":"2025-11-19 07:38:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1389613,"visible":true,"origin":"","legend":"\u003cp\u003eRanked contribution of different cancer type to the DALYs burden of cancers attributable to metabolic factors by 21 GBD regions in 2021, for both sexes combined, females, and males.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/09555ec28eb1370ea0fc4106.png"},{"id":96248826,"identity":"e2dfd7ee-faea-434f-9eea-cc8904b8d1d5","added_by":"auto","created_at":"2025-11-19 07:29:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1878225,"visible":true,"origin":"","legend":"\u003cp\u003eThe DALYs burden of cancers attributed to metabolic factors by 204 countries and territories: (A) DALYs numbers in 2021; (B) ASDRs in 2021; (C) Percentage changes in DALYs numbers from 1990 to 2021; (D) Percentage changes in ASDRs from 1990 to 2021.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/b9b4f79ad8fe22dff2547d32.png"},{"id":96112191,"identity":"ca4dd872-c732-454f-9e53-c7c05e90d217","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1822621,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between SDI and the DALYs burden of cancers attributable to metabolic factors. (A) ASDRs across 21 GBD regions from 1990 to 2021. (B) ASDRs across 204 countries and territories in 2021. (C) EAPC by SDI across 204 countries and territories in 2021. (D) EAPC by ASDR across 204 countries and territories in 2021.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/cdd519043a0be2c7b2a0ba0d.png"},{"id":96362875,"identity":"8a31192e-6860-4398-ab9c-5b50555d3b36","added_by":"auto","created_at":"2025-11-20 10:02:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10800492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/f64d8cd3-4f3b-4752-92f3-fb8f0d30859d.pdf"},{"id":96112193,"identity":"ff918eb7-2fe9-4b08-8f6b-4fa6644618fd","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1538768,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/26e98985d66a9988c92a156f.docx"},{"id":96112188,"identity":"0324f3ea-4a05-4c13-8b4b-222054ead273","added_by":"auto","created_at":"2025-11-17 17:30:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26418,"visible":true,"origin":"","legend":"","description":"","filename":"Table11.docx","url":"https://assets-eu.researchsquare.com/files/rs-7868307/v1/5c0572a65deac1172bd3bfe9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rising global burden of cancer attributable to metabolic risks from 1990 to 2021: Insights from the Global Burden of Disease Study 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is a critical challenge in society, public health, and the global economy the 21st century. In 2022, approximately 20\u0026nbsp;million new cancer cases were reported worldwide, with nearly 10\u0026nbsp;million cancer-associated deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The vast scale of the disease, along with regional and developmental disparities in cancer incidence, underscores the urgent need to intensify targeted cancer control measures on a global scale [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Various risk factors significantly contribute to the development and overall burden of cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among these, metabolic risk factors\u0026mdash;particularly high fasting plasma glucose (FPG) and high body mass index (BMI)\u0026mdash;are becoming increasingly prevalent worldwide [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2021, the age-standardized adult prevalence of overweight and obesity was estimated to be 45.1%, affecting about 2.11\u0026nbsp;billion adults [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; the global age-standardized prevalence of diabetes was estimated to be 6.1%, affecting about 529\u0026nbsp;million people of all ages [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Understanding how metabolic risk factors influence cancer incidence and mortality, as well as tracking their temporal trends, is critical for developing effective strategies in cancer prevention and control [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious research has explored the global burden of cancers associated with metabolic factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, these analyses have primarily relied on data from GLOBOCAN, which predominantly defines cancer burden in terms of morbidity or mortality, ignoring the additional health burden such as living with disability. Besides, existing studies based on the Global Burden of Disease (GBD) database tended to focus on a specific type of cancer burden or a certain metabolic factor, lacking a systematic and comparative assessment of the full spectrum of cancer burden attributable to metabolic factors [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These limitations significantly hinder the targeted implementation of cancer screening and preventive measures for vulnerable groups.\u003c/p\u003e\u003cp\u003eHerein, our study extracted data from the Global Burden of Disease Study 2021 (GBD 2021) and comprehensively evaluate temporal trends and geographic disparities in disability-adjusted life years (DALYs) number and age-standardized DALY rates (ASDRs) for all types of cancer attributable to metabolic risks across 204 countries and territories from 1990 to 2021. Through a detailed examination of the impacts of metabolic risk factors, this study addresses existing research gaps, facilitates precise risk stratification, and informs resource allocation to enhance global cancer prevention and control initiatives.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData Sources\u003c/p\u003e\u003cp\u003eThe Global Burden of Disease (GBD) database systematically assesses the global and regional burden of various diseases, injuries, and associated risk factors by integrating multiple sources of data, applying sophisticated statistical modeling methods, and rigorously quantifying uncertainty [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, publicly available data from the GBD 2021 covering 204 countries and territories from 1990 to 2021 were analyzed. Specifically, data on causes of mortality, DALYs, and their corresponding 95% uncertainty intervals (UIs) were accessed through the GBD Results Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The extracted data were stratified and analyzed based on demographic factors (age, sex, year) and geographic regions. The study complies with ethical standards outlined by the Declaration of Helsinki. Given that the GBD database is openly available and contains no personally identifiable data, ethical approval or informed consent was not required.\u003c/p\u003e\u003cp\u003eRelevant Definitions\u003c/p\u003e\u003cp\u003eIn the framework employed by the GBD, risk factors are classified into three main categories: behavioral, metabolic, and environmental/occupational factors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Metabolic risks specifically include high FPG, high low-density lipoprotein cholesterol (LDL-C), high systolic blood pressure (SBP), high BMI, impaired kidney function (IKF), and low bone mineral density (BMD). DALY represent a composite metric for disease burden, combining both the years of life prematurely lost due to death and the years spent living with disability. According to GBD methodology, the disease burden linked to metabolic risk factors is assessed by estimating potential reductions in disease-specific DALYs if populations were exposed to an ideal theoretical minimum risk level. The GBD database categorizes data from these 204 countries and territories into 21 distinct geographical regions, further classifying them based on the Socio-demographic Index (SDI). The SDI, an indicator of developmental status, is calculated by averaging rankings across three key dimensions: fertility rates among women under 25 years old, education attainment in individuals aged 15 years and older, and lag-adjusted per capita income. The SDI scale ranges from 0 (lowest level of development) to 1 (highest level), enabling the grouping of countries and territories into five developmental quintiles for comparative analysis.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTo quantify the cancer burden attributable to metabolic risk factors, both absolute values and age-standardized rates for DALYs and mortality were examined. Results were reported alongside their respective 95% uncertainty intervals (UIs) to provide comprehensive insight into the estimates. Additionally, the estimated annual percentage change (EAPC), a widely recognized measure for evaluating temporal trends in age-standardized rates, was calculated to assess changes in DALYs linked to metabolic risks over time. The calculation of EAPC assumes a linear correlation between the natural logarithm of age-standardized rates and calendar years, modeled as y\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;βx\u0026thinsp;+\u0026thinsp;ε. Here, y denotes the natural logarithm of the age-standardized rate, and x represents the calendar year. EAPC values were computed using the formula 100\u0026times;(e\u003csup\u003eβ\u003c/sup\u003e\u0026minus;1), and corresponding 95% confidence intervals (CIs) were estimated using linear regression analysis. A positive EAPC accompanied by a 95% CI entirely above zero indicates an increasing trend, while a negative EAPC with a 95% CI entirely below zero indicates a decreasing trend. Trends with a 95% CI crossing zero were considered statistically nonsignificant, indicating no substantial temporal change [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, the association between SDI and age-standardized rates was examined for all 204 countries and territories using Spearman rank correlation analysis, with statistical significance set at a p-value less than 0.05. Global distribution patterns and regional differences in metabolism-attributed cancers (MACs) burden were explored by assessing trends according to sex and five-year intervals. Additionally, geographic maps were created to depict the distribution of DALY counts, age-standardized rates, and percentage variations in MAC burden across all included countries and territories.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Global burden of all causes or cancers attributable to metabolic risks\u003c/h2\u003e\n \u003cp\u003eBetween 1990 and 2021, the burden of disease, as measured by DALYs attributable to metabolic risk factors, exhibited a consistent decline. Globally, the age-standardized death rate (ASDR) associated with metabolic risks across all causes was estimated at 6,737.88 (95% UI: 5,968.90 to 7,496.49) per 100,000 person-years in 1990. Encouragingly, this figure steadily decreased over time, reaching 5,582.42 (4,839.55 to 6,345.44) per 100,000 person-years by 2021 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The primary metabolic risk factors included high BMI, elevated FPG, increased LDL, high SBP, KD, and low BMD. Despite the persistent decline in ASDRs linked to high SBP over this period, it continued to represent the leading contributor to DALYs attributable to metabolic risks. Concurrently, ASDRs related to high BMI and elevated FPG exhibited an upward trajectory, positioning these factors as the second and third largest contributors to the DALY burden from metabolic risks (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Additionally, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC illustrates the distribution of DALYs attributable to metabolic risks across five SDI regions in 2021.\u003c/p\u003e\n \u003cp\u003eRegarding ASDRs specifically linked to cancers, the impact of metabolic risks has become increasingly evident on a global scale. By 2021, the estimated ASDR of cancers attributable to metabolic risks had reached 179.36 (51.23 to 312.41) per 100,000 person-years (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Unlike the overall declining trend in the contribution of metabolic risks to DALYs across all causes, high BMI and elevated FPG played progressively larger roles in the cancer-related disease burden (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Considerable heterogeneity was noted in cancer mortality attributable to these metabolic risk factors. In 2021, high BMI contributed most significantly to the ASDR in high-SDI regions, with a stepwise decrease observed from high- to low-SDI regions. A similar pattern was found for high FPG, where its impact peaked in high-SDI regions and gradually diminished in lower-SDI regions (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF and S1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. The burden of cancers attributable to metabolic factors, by cancer type\u003c/h2\u003e\n \u003cp\u003eWe further examined the DALY burden linked to metabolic risk factors across various cancers. From 1990 to 2021, colorectal, pancreatic, and breast cancers consistently ranked as the top three malignancies associated with metabolic factors. In 2021, breast, colorectal, and pancreatic cancers exhibited the highest ASDRs attributable to metabolic risks, recorded at 38.23, 37.68, and 27.20 per 100,000 person-years, respectively. Meanwhile, colorectal, pancreatic, and liver cancers showed the highest ASDRs from metabolic factors, reaching 54.27, 40.11, and 23.04 per 100,000 person-years in the same year (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eBetween 1990 and 2021, liver cancer demonstrated the most rapid increase in DALYs attributable to metabolic risks in both sexes, with an estimated annual percentage change (EAPC) of 1.84 (95% CI: 1.78 to 1.91) in females and 2.34 (95% CI: 2.22 to 2.47) in males. The trends observed in DALY numbers closely mirrored those in ASDR (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Among females, breast and colorectal cancers each contributed 21% of cancer-related DALYs due to metabolic factors, followed by pancreatic (15%) and uterine cancer (11%), alongside 10 additional cancer types. In males, colorectal cancer accounted for the highest proportion (30%) of DALYs linked to metabolic risks, followed by pancreatic (23%) and liver cancer (13%), along with eight other cancer types (Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Corresponding death data are shown in Table S2 and S3\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. The burden of cancers attributable to metabolic factors, by sex and age\u003c/h2\u003e\n \u003cp\u003eBetween 1990 and 2021, the ASDR attributable to metabolic factors increased more rapidly in males (EAPC: 0.79, 95% CI: 0.72 to 0.85) than in females (EAPC: 0.36, 95% CI: 0.32 to 0.39) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC, S2). In 2021, cancer-related DALYs linked to metabolic factors reached 8.34 million (95% UI: 2.26 to 14.34 million) in females, reflecting a 177% increase from 1990. Meanwhile, the corresponding burden in males was 7.25 million (95% UI: 2.17 to 12.64 million), marking a 144% rise over the same period (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). To investigate age-related trends in DALYs, we stratified individuals into 16 age groups, beginning at age 20 with five-year intervals. As illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE, ASDR attributable to metabolic risks in females increased progressively with age, plateauing between 70 and 79 years before entering another phase of growth. In males, ASDR steadily rose with age, peaking between 90 and 94 years before subsequently declining. Notably, in individuals aged 75 to 94 years, ASDR was lower in females than in males, whereas in those younger than 74 or older than 95 years, the reverse trend was observed. The global DALY burden for MRNs showed an age-dependent increase in both sexes, peaking at 65 to 69 years before gradually declining. The burden of metabolism-related DALYs was higher in females than males beyond the age of 50, whereas the reverse was true for individuals aged 50 years or younger. Further analysis of cancer-specific DALYs attributable to metabolic factors across age groups revealed that ASDR was substantially higher in individuals aged 70 years and older, followed by those between 50 and 69 years, with the lowest DALY rates observed in individuals aged 15 to 49 years (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF and G, S3 and S4). The middle-aged and elderly population, particularly those aged 50 to 69 years, represented the largest proportion of total DALYs rate due to MRNs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH and I).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. The burden of cancers attributable to metabolic factors, by SDI\u003c/h2\u003e\n \u003cp\u003eThe SDI-stratified regional analysis revealed that from 1990 to 2021, both the absolute number and rate of cancer-related DALYs attributable to metabolic factors exhibited a steady upward trend across all five SDI categories in both sexes. Additionally, the DALY burden increased progressively with rising SDI levels (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). In 2021, the number of cancer-related DALYs due to metabolic factors across high to low SDI regions was 5.17, 4.42, 3.97, 1.59, and 0.42 million, respectively, while the corresponding DALY rates per 100,000 population were 257.44, 223.53, 142.40, 105.22, and 76.63 (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC and D). This pattern indicates that regions with higher SDI levels bear a greater DALY burden compared to lower SDI regions. However, over the past three decades, lower SDI regions have experienced a more pronounced increase in DALY rates. The EAPC values for DALY rates were 0.20, 0.53, 1.44, 2.45, and 1.36 across SDI categories, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This marked increase in regions with lower SDI indicates a narrowing gap in mortality burden between high- and low-SDI regions. In essence, the cancer mortality burden associated with metabolic risks is steadily transitioning from higher to lower SDI areas.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. The burden of cancers attributable to metabolic factors, by region\u003c/h2\u003e\n \u003cp\u003eBetween 1990 and 2021, most GBD regions experienced an increase in the rate of cancer DALYs attributable to metabolic factors, with the most pronounced rise observed in Southern Sub-Saharan Africa (EAPC: 2.55, 95% CI: 2.22 to 2.88). In contrast, Western Europe exhibited no significant change (EAPC: 0.05, 95% CI: -0.01 to 0.10). Notably, high-income Asia Pacific was the only region to show a declining trend (EAPC: -0.17, 95% CI: -0.27 to -0.08). In 2021, Central Europe had the highest rate of cancer DALYs linked to metabolic factors (357.87, 95% UI: 106.55 to 620.39 per 100,000 person-years), whereas South Asia reported the lowest (69.82, 95% UI: 17.42 to 124.82 per 100,000 person-years) (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). In terms of absolute numbers, East Asia accounted for the largest burden, with 3.40 million metabolism-related cancer DALYs (95% UI: 0.88 to 6.12 million), while Andean Latin America recorded the lowest, at 0.12 million (95% UI: 0.05 to 1.98 million) (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure S5).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the ranking of various cancer types based on their contributions to the age-standardized DALY rates of MACs across different regions in 2021, analyzed collectively for both sexes and separately for males and females. For both sexes combined, colon and rectum cancer was the predominant contributor to age-standardized DALY rates of MACs per 100,000 person-years in most geographic regions. Nevertheless, pancreatic cancer ranked highest in High-income Asia Pacific, breast cancer dominated in Oceania, and liver cancer had the largest impact in Western Sub-Saharan Africa. Among females, colon and rectum cancer or breast cancer typically ranked first in most regions; the only exception was High-income Asia Pacific, where pancreatic cancer was the primary contributor. For males, colon and rectum cancer remained the top contributor in nearly all regions, except for High-income Asia Pacific, where pancreatic cancer had the greatest burden, and Western Sub-Saharan Africa, where liver cancer led.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. The burden of cancers attributable to metabolic factors, by country\u003c/h2\u003e\n \u003cp\u003eA significant geographical disparity was observed across countries and territories (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In 2021, the highest DALY rate for MRNs was reported in Tonga (492.19, 95% UI: 133.31 to 888.49 per 100,000 person-years), followed by Nauru and American Samoa. In contrast, the lowest burden was recorded in Bangladesh (42.73, 95% UI: 11.21 to 79.58 per 100,000 person-years), along with Burundi and Nepal (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). From 1990 to 2021, the largest increase in DALYs rate occurred in Cabo Verde (2.53 times), and the largest increase in DALYs number occurred in United Arab Emirates (10.16 times) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC and D). Globally, the majority of countries and territories exhibited a notable upward trend in DALY rates for MRNs, as reflected by EAPC values (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The most pronounced increases were observed in Lesotho (EAPC: 4.77, 95% CI: 4.26 to 5.28), Cabo Verde, and Zimbabwe. Conversely, some countries, including Ethiopia (EAPC: \u0026minus;0.78, 95% CI: \u0026minus;1.03 to \u0026minus;\u0026thinsp;0.52), the Czech Republic, and Singapore, experienced a modest decline in DALY rates for MRNs (Figure S6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. The relationship between the DALYs burden of cancers attributable to metabolic factors and SDI\u003c/h2\u003e\n \u003cp\u003eWe further examined the trends in age-standardized DALY rates across different SDI regions from 1990 to 2021 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Overall, as SDI increases, the age-standardized DALY rate follows an upward trajectory. However, when SDI surpasses approximately 0.75\u0026mdash;particularly in regions such as High-income Asia Pacific, High-income North America, Western Europe, and Australasia\u0026mdash;the DALY rate exhibits minimal growth or even a decline (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB illustrates the relationship between SDI and age-standardized DALY rates across countries in 2021. A similar pattern is observed, where the DALY rate increases with SDI up to around 0.8 but then declines as SDI continues to rise. Notably, based on SDI alone, Tonga and Nauru exhibited substantially higher-than-expected age-standardized DALY rates. As depicted in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC, a significant negative correlation was found between the EAPC of DALY rates for MACs and SDI (R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.45, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A comparable inverse correlation was also identified between EAPC and DALY rates (R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.18, P\u0026thinsp;=\u0026thinsp;0.012) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the latest data from the Global Burden of Disease (GBD) 2021, this research systematically evaluates the relationship between metabolic risks and cancer burden across different cancer types, metabolic risk factors, sex, age groups, regions, and time periods. Our findings indicate that metabolic risks have become a major and steadily increasing contributor to cancer burden worldwide. Among these risks, high BMI and high FPG are identified as the primary metabolic drivers, with distinct patterns across demographic and geographic distributions. The burden of cancers attributable to high FPG is more pronounced in males, whereas cancers linked to high BMI are more prevalent in females. Additionally, individuals aged 50 years and older bear the highest burden of MACs, and the ASDRs for these cancers have risen significantly in middle, low-middle, and low SDI regions over recent decades.\u003c/p\u003e\u003cp\u003eFrom 1990 to 2021, high and high-middle SDI regions have consistently carried the greatest cancer burden attributable to metabolic risk factors; however, lower SDI regions have recently seen a rapid increase in obesity and diabetes prevalence, driven mainly by economic development, urbanization, and lifestyle transitions. Dietary transitions towards higher consumption of processed foods, refined carbohydrates, and saturated fats, combined with declining physical activity, have contributed to these trends [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This shift is particularly evident in regions such as the Middle East and North Africa, where the economic transformation of Gulf countries has led to an increasing prevalence of obesity and diabetes, paralleling a rising cancer burden [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, sub-Saharan Africa and South Asia, regions historically characterized by relatively low cancer incidence rates, have recently experienced notable increases in metabolic disorders, highlighting the urgent need for targeted interventions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our findings indicate that the most pronounced increases in ASDRs of cancers attributable to metabolic risks occurred in lower SDI regions, where healthcare systems often lack sufficient resources to effectively address complex non-communicable diseases. Due to disparities in healthcare infrastructure and cancer control programs, this escalating MACs burden could further deepen existing health inequalities.\u003c/p\u003e\u003cp\u003eAdditionally, we observed significant differences between sexes regarding the impact of metabolic risks on cancer burdens. In higher SDI regions, men generally exhibit a greater MAC burden, likely attributable to higher prevalence rates of metabolic syndrome and associated lifestyle habits such as smoking and alcohol intake. Conversely, in lower SDI regions, women disproportionately experience a higher MACs burden, potentially driven by cultural and socioeconomic factors limiting physical activity, distinct metabolic responses, hormonal influences, and different patterns of fat distribution [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Excess adiposity among women is notably linked to elevated triglycerides, hyperglycemia, and systemic inflammation, substantially increasing their risk for breast, endometrial, and colorectal cancers [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, implementing targeted public health measures that specifically address sex differences in obesity and diabetes prevention within each regional context is critical for reducing these disparities.\u003c/p\u003e\u003cp\u003eBiological mechanisms underlying the association between metabolic risk factors and cancer further underscore the urgency of targeted intervention strategies. Elevated BMI can induce carcinogenesis via multiple interconnected pathways, including insulin resistance, chronic inflammation, and hormonal imbalance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Excessive adipose tissue disrupts metabolic equilibrium, fostering an environment conducive to tumor initiation and progression[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, adipose tissue directly influences sex hormone concentrations, heightening the risk for hormone-sensitive cancers [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, elevated blood glucose levels significantly contribute to cancer development by promoting cancer cell metabolism, increasing oxidative stress, and sustaining chronic inflammatory states [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Hyperglycemia frequently coexists with hyperinsulinemia, stimulating tumor cell growth and inhibiting apoptosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Recent evidence also indicates that hyperglycemia may promote cancer progression through epigenetic alterations, modifying gene-expression patterns linked to malignant phenotypes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although detailed molecular mechanisms require further elucidation, these metabolic abnormalities highlight the necessity for comprehensive preventive approaches that simultaneously target obesity and hyperglycemia.\u003c/p\u003e\u003cp\u003eConsidering the considerable cancer burden associated with metabolic risk factors, adopting comprehensive prevention and intervention strategies is imperative. Strengthening primary prevention through dietary modifications, physical activity promotion, and early identification of metabolic risks should become a priority, especially in lower SDI regions facing rapid increases in obesity and diabetes prevalence [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Enhancing access to obesity and diabetes management programs and integrating metabolic disease prevention into national cancer control plans will be crucial to effectively reducing the future incidence of metabolic risk-related cancers globally [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, targeted policy measures\u0026mdash;such as urban planning to promote active lifestyles, regulations on unhealthy food consumption, and public health campaigns\u0026mdash;can help address the broader determinants of metabolic health [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. As digestive system cancers remain the predominant contributors to metabolic risk-attributable cancer mortality, and sex-based and regional disparities further complicate the burden, future research should focus on elucidating mechanistic pathways and developing effective, targeted interventions. Without decisive action at the individual, healthcare, and policy levels, the growing burden of metabolic risk-related cancers will continue to strain global health systems, underscoring the urgency of coordinated public health efforts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe substantial and continuing increase in the global cancer burden associated with metabolic risk factors highlights critical gaps in current prevention and control strategies. Despite historically higher burdens in high and high-middle SDI regions, lower SDI regions have experienced more rapid growth in cancer burden linked to obesity and diabetes, driven primarily by socioeconomic and lifestyle transitions. Given the marked regional disparities and sex-specific differences observed, tailored public health interventions addressing metabolic risks are urgently needed. To effectively address this upward trend, comprehensive, region-specific, and sustainable global policies must be implemented globally, prioritizing the management of metabolic risk factors to reduce the growing burden of MACs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMACs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMetabolism-attributed cancers\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDALYs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisability-adjusted life-years\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEAPCs:Estimated annual percentage change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal burden of disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSocial-demographic index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUncertainty interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efasting plasma glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh body mass index (BMI).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe highly appreciate the works by the Global Burden of Disease Study 2021 collaborators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTJY and LH designed the study; TJY and HYT collected data and verified the accuracy of the data; TJY and LCY prepared manuscript; TJY, WZQ and CYX analyzed and interpreted data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work by the National Natural Science Foundation of China (Grant No. 82404196).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyzed data are retrieved from the GBD 2021 results available online at the Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd-results/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eDepartment of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung HYA, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-Cancer J Clin. 2024;74:229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBizuayehu HM, Ahmed KY, Kibret GD, Dadi AF, Belachew SA, Bagade T et al. Global Disparities of Cancer and Its Projected Burden in 2050. Jama Netw Open. 2024; 7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu ZH, Xia FN, Lin R. Global burden of cancer and associated risk factors in 204 countries and territories, 1980\u0026ndash;2021: a systematic analysis for the GBD 2021. J Hematol Oncol. 2024; 17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, et al. Global patterns in excess body weight and the associated cancer burden. Ca-Cancer J Clin. 2019;69:88\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDAB. Global, regional, and national prevalence of adult overweight and obesity, 1990\u0026ndash;2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet. 2025;405:813\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402:203\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu JJ, Dong HL, Dong YM, Zhou RX, Teixeira W, He XX et al. Cancer burden attributable to risk factors, 1990\u0026ndash;2019: A comparative risk assessment. Iscience. 2024; 27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArnold M, Pandeya N, Byrnes G, Renehan AG, Stevens GA, Ezzati M, et al. Global burden of cancer attributable to high body-mass index in 2012: a population-based study. Lancet Oncol. 2015;16:36\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePearson-Stuttard J, Zhou B, Kontis V, Bentham J, Gunter MJ, Ezzati M. Worldwide burden of cancer attributable to diabetes and high body-mass index: a comparative risk assessment. Lancet Diabetes Endo. 2018;6:E6\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCui B, Chen A, Xu CC. Global, regional, and national esophageal cancer deaths and DALYs attributable to diet low in vegetables and fruits, 1990\u0026ndash;2019: analysis for the global burden of disease study. Front Nutr. 2025; 11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan SY, Zhao SY, Zhong R, Li PZ, Pang YW, He S et al. Global burden, trends, and disparities in kidney cancer attributable to smoking from 1990 to 2021. Front Public Health. 2025; 12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCubelos-Fern\u0026aacute;ndez N, D\u0026aacute;vila-Batista V, Fern\u0026aacute;ndez-Villa T, Casta\u0026ntilde;o-Vinyals G, Perez-Gomez B, Amiano P, et al. Burden of postmenopausal breast cancer attributable to excess body weight: comparative study of body mass index and CUN-BAE in MCC-Spain study. J Epidemiol Commun H. 2025;79:64\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng Z, Xu SJ, Zhu JC, Yang Q, Ye H, Li M et al. Disease burden of cancers attributable to high fasting plasma glucose from 1990 to 2021 and projections until 2031 in China. Cancer Epidemiol. 2025; 94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRumgay H, Nethan S, Shah RC, Vignat J, Ayo-Yusuf O, Chaturvedi P, et al. Global burden of oral cancer in 2022 attributable to smokeless tobacco and areca nut consumption: a population attributable fraction analysis. Lancet Oncol. 2024;25:1413\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDCoD. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2100\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiseases GBD, Injuries C. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2133\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDRF. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2162\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYi M, Li A, Zhou L, Chu Q, Song Y, Wu K. The global burden and attributable risk factor analysis of acute myeloid leukemia in 195 countries and territories from 1990 to 2017: estimates based on the global burden of disease study 2017. J Hematol Oncol. 2020;13:72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi T, Zhang H, Lian M, He Q, Lv M, Zhai L, et al. Global status and attributable risk factors of breast, cervical, ovarian, and uterine cancers from 1990 to 2021. J Hematol Oncol. 2025;18:5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTondro Anamag F, Noori M, Nejadghaderi SA, Sullman MJM, Grieger JA, Kolahi AA, et al. Burden of cancers attributable to high fasting plasma glucose in the Middle East and North Africa region, 1990\u0026ndash;2019. Cancer Med. 2023;12:10031\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBray F, Parkin DM, African Cancer Registry N. Cancer in sub-Saharan Africa in 2020: a review of current estimates of the national burden, data gaps, and future needs. Lancet Oncol. 2022;23:719\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJain U, Rahim F, Jain B, Komanduri A, Arkalgud A, Sabet CJ, et al. Cancer burden across the South Asian Association for Regional Cooperation in 2022. BMJ Oncol. 2024;3:e000466.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh RB, Pella D, Mechirova V, Kartikey K, Demeester F, Tomar RS, et al. Prevalence of obesity, physical inactivity and undernutrition, a triple burden of diseases during transition in a developing economy. The Five City Study Group. Acta Cardiol. 2007;62:119\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnopp RH, Paramsothy P, Retzlaff BM, Fish B, Walden C, Dowdy A, et al. Gender differences in lipoprotein metabolism and dietary response: basis in hormonal differences and implications for cardiovascular disease. Curr Atheroscler Rep. 2005;7:472\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGroup NCDRFC-AW. Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies. Int J Epidemiol. 2017;46:1421\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, et al. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin. 2019;69:88\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePasqual E, O'Brien K, Rinaldi S, Sandler DP, Kitahara CM. Obesity, obesity-related metabolic conditions, and risk of thyroid cancer in women: results from a prospective cohort study (Sister Study). Lancet Reg Health Am. 2023;23:100537.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRask-Andersen M, Ivansson E, Hoglund J, Ek WE, Karlsson T, Johansson A. Adiposity and sex-specific cancer risk. Cancer Cell. 2023;41:1186\u0026ndash;e974.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHopkins BD, Goncalves MD, Cantley LC. Obesity and Cancer Mechanisms: Cancer Metabolism. J Clin Oncol. 2016;34:4277\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMitchelson KAJ, O'Connell F, O'Sullivan J, Roche HM, Obesity. Dietary Fats, and Gastrointestinal Cancer Risk-Potential Mechanisms Relating to Lipid Metabolism and Inflammation. Metabolites. 2024; 14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoradini D, Oriana S. Impact of sex hormones dysregulation and adiposity on the outcome of postmenopausal breast cancer patients. Clin Obes. 2021;11:e12423.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDubey P, Reddy SY, Singh V, Shi T, Coltharp M, Clegg D, et al. Association of Exposure to Phthalate Metabolites With Sex Hormones, Obesity, and Metabolic Syndrome in US Women. JAMA Netw Open. 2022;5:e2233088.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePati S, Irfan W, Jameel A, Ahmed S, Shahid RK. Obesity and Cancer: A Current Overview of Epidemiology, Pathogenesis, Outcomes, and Management. Cancers (Basel). 2023; 15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGasmi A, Noor S, Menzel A, Dosa A, Pivina L, Bjorklund G. Obesity and Insulin Resistance: Associations with Chronic Inflammation, Genetic and Epigenetic Factors. Curr Med Chem. 2021;28:800\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSupabphol S, Seubwai W, Wongkham S, Saengboonmee C. High glucose: an emerging association between diabetes mellitus and cancer progression. J Mol Med. 2021;99:1175\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu B, Qu S. The Relationship Between Diabetes Mellitus and Cancers and Its Underlying Mechanisms. Front Endocrinol. 2022; 13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee C, Kim M, Park C, Jo W, Seo JK, Kim S, et al. Epigenetic regulation of Neuregulin 1 promotes breast cancer progression associated to hyperglycemia. Nat Commun. 2023;14:439.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing YF, Deng AX, Qi TF, Yu H, Wu LP, Zhang HB. Burden of type 2 diabetes due to high body mass index in different SDI regions and projections of future trends: insights from the Global Burden of Disease 2021 study. Diabetol Metab Syndr. 2025; 17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang XY, Sun JY, Zhang WJ. Global trends in burden of type 2 diabetes attributable to physical inactivity across 204 countries and territories, 1990\u0026ndash;2019. Front Endocrinol. 2024; 15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou XD, Chen QF, Yang WH, Zuluaga M, Targher G, Byrne CD et al. vol 76, 102848,. Burden of disease attributable to high body mass index: an analysis of data from the Global Burden of Disease Study 2021 (2024). Eclinicalmedicine. 2024; 76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim DS, Scherer PE. Obesity, Diabetes, and Increased Cancer Progression. Diabetes Metab J. 2021;45:799\u0026ndash;812.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol. 2021;9:446\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cb\u003eTable 1 is available in the Supplementary Files section.\u003c/b\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eSummary of age-standardized DALYs rate of cancers attributable to high fasting plasma glucose in 1990 and 2021; stratified by sex and cancer type\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"934\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Female ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e, per 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Female ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;EAPC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Male ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Male ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEA\u003c/strong\u003e\u003cstrong\u003ePC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e56.27(4.65,109.20)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e71.20(3.93,140.42)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.75(0.70,0.81) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e76.50(9.61,145.77)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e93.44(12.98,176.81) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.76(0.67,0.85) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eBy cancer type\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.45(-0.19,3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.58(-0.21,3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.22(0.13,0.32) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e5.76(-0.76,13.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6.60(-0.85,15.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.47(0.37,0.58) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13.80(-4.02,32.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e18.05(-5.31,42.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.80(0.76,0.84) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 370px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eColon and rectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e15.49(7.76,23.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e15.59(7.97,23.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.06(-0.12,0.01) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e22.12(11.05,33.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e25.76(13.05,39.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.55(0.46,0.63) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.07(0.22,4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.21(0.35,6.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.57(1.44,1.69) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.51(0.26,5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.18(0.47,8.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.73(1.58,1.88) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ePancreatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e18.31(2.09,35.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e25.35(2.90,48.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.12(1.07,1.17) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e27.47(3.15,53.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e38.69(4.43,73.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.29(1.21,1.36) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTracheal, bronchus, and lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.16(-1.12,11.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e7.41(-1.48,16.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.17(1.02,1.32) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e18.64(-3.78,41.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e18.21(-3.63,41.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.05(-0.09,0.19) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eASDR \u0026ndash; age-standardized disability-adjusted life years rate; EAPC \u0026ndash; estimated annual percentage change; CI \u0026ndash; confidence interval; UI \u0026ndash; uncertainty interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eSummary of age-standardized DALYs rate of cancers attributable to high body mass index in 1990 and 2021; stratified by sex and cancer type\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"929\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Female ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e, per 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Female ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;EAPC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Male ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Male ASD\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eper 100,000 (95% UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEA\u003c/strong\u003e\u003cstrong\u003ePC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e105.26(43.35,171.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e114.61(46.08,185.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.17(0.13,0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e66.66(31.39,106.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e88.18(39.36,143.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.87(0.82,0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003eBy cancer type\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e20.55(-0.86,41.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e21.83(-1.01,42.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.11(0.07,0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eColon and rectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e24.73(10.59,39.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e23.96(10.36,37.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.26(-0.31,-0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e26.42(10.97,42.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e31.09(13.36,49.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.48(0.45,0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eGallbladder and biliary tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e7.12(4.83,9.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.84(3.94,8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.81(-0.91,-0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.11(2.86,5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.51(2.94,6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.24(0.20,0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eKidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.74(2.27,9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.81(2.34,9.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.06(-0.14,0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e10.10(3.92,16.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e12.52(5.09,20.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.67(0.57,0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8.37(6.13,10.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e7.60(5.51,9.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.39(-0.44,-0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e10.29(7.69,13.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e10.02(7.41,13.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.11(-0.15,-0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.26(2.05,8.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e9.52(3.91,15.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.97(1.93,2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e8.75(3.66,14.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e19.05(7.83,33.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.51(2.39,2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMultiple myeloma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.72(-0.62,4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.17(-0.91,5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.55(0.45,0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.92(-0.64,4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.66(-1.06,6.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.99(0.90,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eNon-Hodgkin lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.89(0.98,4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.38(1.12,5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.14(0.01,0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.04(1.38,6.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.67(1.53,8.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.29(0.19,0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eOvarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8.72(1.78,16.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e10.56(2.50,18.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.51(0.45,0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ePancreatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.03(-0.78,3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.75(-0.24,7.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3.16(3.09,3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-0.02(-1.86,3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.28(-0.92,7.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e10.76(8.58,12.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eThyroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.87(1.42,2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.96(1.44,2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.10(0.06,0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.06(0.79,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.38(1.04,1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.93(0.87,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eUterine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e17.26(12.25,23.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e19.23(13.80,25.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.27(0.18,0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eASDR \u0026ndash; age-standardized disability-adjusted life years rate; EAPC \u0026ndash; estimated annual percentage change; CI \u0026ndash; confidence interval; UI \u0026ndash; uncertainty interval\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolism-attributed cancers, Disability-adjusted life years, Global burden, Metabolic factors, Socio-demographic index","lastPublishedDoi":"10.21203/rs.3.rs-7868307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7868307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe substantial rise in cancer burden driven by metabolic factors has become an urgent global health concern. This study aims to provide current estimates and analyze temporal changes in cancers attributed to metabolic risks.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData concerning the global burden of metabolism-attributed cancers (MACs) were sourced from the Global Burden of Disease (GBD) 2021 dataset. We extracted data on estimated cancer-related deaths and disability-adjusted life years (DALYs) linked to metabolic risks from the GBD 2021 database, categorizing them by metabolic factor, sex, age, and socio-demographic index (SDI). Trends in age-standardized DALY rates (ASDR) over time were assessed using estimated annual percentage changes (EAPC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn 2021, the global DALYs attributed to MACs were approximately 15.58\u0026nbsp;million (95% uncertainty interval [UI]: 44.31 to 27.15\u0026nbsp;million), corresponding to an ASDR of 179.36 (95% UI: 51.23 to 312.41) per 100,000 person-years. Over the past three decades, there was a significant global increase in ASDR for cancers linked to metabolic risks (EAPC: 0.55, 95% confidence interval [CI]: 0.51 to 0.59). Although males had a higher cancer burden than females, females experienced a relatively greater increase in ASDR (EAPC: males 0.79 vs. females 0.36). The proportion of DALYs attributable to MACs rose progressively with advancing age, surpassing 89% in populations older than 50 years. Regionally, the cancer burden related to metabolic risks remained notably higher in high and high-middle SDI regions compared to lower SDI regions during the study period. However, lower SDI regions exhibited a markedly faster growth rate (EAPC across SDI levels from high to low: 0.20, 0.53, 1.44, 2.45, and 1.36, respectively). Central Europe had the highest ASDR due to MACs (357.87, 95% UI: 106.55 to 620.39 per 100,000 person-years), while the fastest increase occurred in Southern Sub-Saharan Africa (EAPC: 2.55, 95% CI: 2.22 to 2.88). Colon and rectum cancer in males and breast cancer in females were the predominant contributors to metabolism-related ASDR.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe rising global burden of cancers attributable to metabolic factors highlights inadequacies in existing prevention strategies. Immediate and sustained interventions are critically needed at both global and regional levels to manage metabolic risk factors effectively, aiming to reverse the current trends and mitigate the escalating burden of MACs.\u003c/p\u003e","manuscriptTitle":"Rising global burden of cancer attributable to metabolic risks from 1990 to 2021: Insights from the Global Burden of Disease Study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 17:30:35","doi":"10.21203/rs.3.rs-7868307/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-09T02:36:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T19:09:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36856922712456577596217360171002469799","date":"2025-11-08T19:07:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58149275820633025598251331581744583030","date":"2025-11-07T10:06:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49275231450868164201603087040154672866","date":"2025-11-06T04:48:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T03:16:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-18T08:55:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T04:41:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T04:40:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-10-15T12:46:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42e5f374-df77-40d9-ad17-5b3fd6faf15d","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T17:30:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 17:30:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7868307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7868307","identity":"rs-7868307","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}