Global and Regional Burden of Thyroid Cancer Attributable to high body mass index from 1990 to 2021 and Modelled Projections to 2041

Global and Regional Burden of Thyroid Cancer Attributable to high body mass index from 1990 to 2021 and Modelled Projections to 2041 | 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 Global and Regional Burden of Thyroid Cancer Attributable to high body mass index from 1990 to 2021 and Modelled Projections to 2041 Guizhang Hou, Tianshu Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7148753/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background Thyroid cancer (TC) has emerged as a globally significant malignancy with rising incidence globally. Epidemiological evidence identifies high body mass index as an independent metabolic risk factor demonstrating dose-dependent associations with both TC mortality and disability-adjusted life years (DALYs). This population-level analysis quantifies the global epidemiological burden of TC attributable to high BMI from 1990 to 2021, employing standardized comparative risk assessment. Inform evidence-based interventions for risk stratification and prevention strategies. Methods This study analyzed global TC burden attributable to high BMI using Global Burden of Disease 2021 data. Age-standardized mortality rates (ASMR) and disability-adjusted life year rates (ASDR) were stratified by year, sex, age group, region, and sociodemographic index (SDI). Temporal trends from 1990 to 2021 were quantified through average annual percentage change (AAPC) and estimated annual percentage change (EAPC) metrics. Three analytical approaches were implemented: 1) decomposition analysis to identify key contributors to burden variations, 2) frontier analysis to benchmark national performance, and 3) health inequality assessment across SDI quintiles. Burden projections through 2041 were using Bayesian age-period-cohort modeling. Results ASMR and ASDR for TC attributable to BMI exhibited sustained increases between 1990 and 2021 globally. Concurrently, mortality and DALY counts rose significantly, with projections indicating continuation of these trends over the next two decades without targeted interventions. Elevated burdens in high-SDI regions correlated with heightened obesity prevalence, aging, and population expansion, with observed socioeconomic disparities widening over time. Consistent gender disparity was observed, with females demonstrating higher susceptibility across all age cohorts. Significant disparities emerged in national-level burden management, as select low-SDI regions performed better than certain high-SDI regions. Conclusion The persistent public health impact of thyroid cancer associated with high BMI requires prioritized prevention, particularly in high SDI regions. Demographic variations across gender and age groups require precision-based interventions. global burden thyroid cancer high BMI mortality health inequalities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1 Introduction Thyroid cancer (TC), a cancerous growth arising from thyroid tissue, has become a growing global health concern as its prevalence continues to escalate worldwide. Differentiated thyroid cancers account for nearly 90% of all cases, with papillary thyroid carcinoma (PTC) constituting the predominant histological subtype (approximately 80–85% of DTCs), while follicular carcinoma represents the second most common form [ 1 ]. In 2021 there were 249,538.02 new cases and 44,798.54 deaths caused by thyroid cancer worldwide [ 2 ]. There may be some differences in incidence and mortality among different countries and territories [ 3 ]. The pathogenesis of this malignancy involves multifaceted mechanisms, with multiple genetic and environmental elements contributing to its progression. The latest data showed that around 1·11 billion adult females and 1·00 billion adult males had obesity and overweight. Without intervention, obesity will take a huge toll on human health and sustainable economic development [ 4 ]. After smoking, obesity constitutes the most prevalent preventable and alterable contributor to carcinogenesis in human populations [ 5 ]. Global projections indicate that obesity-associated malignancies are anticipated to exceed 2 million worldwide by 2070, constituting 7% of total cancer incidence [ 6 ]. Emerging evidence suggests that obesity-associated immunometabolic dysregulation, characterized by persistent low-grade inflammation, endocrine axis disruption, adipokine secretion imbalance, and gut microbiota alterations, constitutes a critical pathogenic nexus in oncogenesis and tumor progression. Current research reveals these interconnected mechanisms exhibit distinct tissue tropism and site-specific carcinogenic effects, with particular organ microenvironments demonstrating differential susceptibility to particular obesity-driven oncogenic pathways [ 7 – 8 ]. Recent studies have underscored a significant association between higher body-mass index (BMI) and increased risk of thyroid cancer [ 9 – 10 ]. Therefore, understanding the worldwide impact of thyroid cancer, especially cases linked to high body mass index, is crucial for policymakers to optimize resource distribution and develop focused preventive measures. The Global Burden of Disease (GBD) 2021 Study provides a comprehensive data repository, systematically quantifying disease and injury burdens across 204 countries and territories, including detailed analyses of TC-related pathologies. Existing studies have primarily focused on the comprehensive disease burden of thyroid cancer (TC) [ 11 ], while the specific mortality and disability-adjusted life years (DALYs) attributable to high body mass index (BMI) remain inadequately characterized. With the open of the latest 2021 version of GBD database, our study aims to systematically assess the global, regional, and national burden of TC-related mortality and DALYs, particularly examining the contribution of high BMI as a modifiable risk factor from 1990–2021, as well as projections up to 2041. 2 Methods and Materials 2.1 Data source For this study, we utilized data from the GBD 2021, which provides a systematic evaluation of disease burden attributed to 88 major risk factors - including high BMI - at global, regional, and national levels, with specific quantification of its impact on TC from 1990–2021 [ 12 ]. 2.2 Statistical analysis 2.2.1 Burden description The study evaluated key health indicators such as disability-adjusted life years (DALYs) and mortality, along with their age-standardized rates (ASDR and ASMR), specifically examining thyroid cancer burden attributable to high body mass index [ 12 ]. We utilized the sociodemographic index (SDI)—a composite socioeconomic development metric—to assess thyroid cancer burden attributable to high BMI across varying socioeconomic contexts, stratifying national populations into five SDI quintiles. To analyze temporal trends in these rates from 1990 to 2021, we calculated both percentage change (PC) and estimated annual percentage change (EAPC). The EAPC was derived through a linear regression model that incorporated annual rate variations, providing a quantitative measure of trend progression (increasing/decreasing) over the study period. 2.2.2 Joinpoint Regression Analysis This study utilized Joinpoint regression analysis to assess temporal trends in ASDR and ASMR of thyroid cancer attributable to high body mass index between 1990 and 2021. The optimal fitting models were selected for comparative analysis to systematically evaluate temporal trends in disease burden. Specifically, an upward trend is indicated when the 95% confidence interval (CI) of the AAPC (Average Annual Percentage Change) estimate remains entirely above zero. Conversely, a downward trend is observed when the CI falls completely below zero, while a stable trend is inferred if the CI encompasses the null value of zero. 2.2.3 Decomposition analysis We conducted a decomposition analysis using Das Gupta's method [ 13 ] to quantify the relative contributions of age structure, epidemiological transitions, and population growth to temporal changes in mortality and disability-adjusted life years (DALYs). This analytical approach enabled systematic assessment of key drivers underlying the evolving global and regional (across five SDI) burden of thyroid cancer attributable to high body mass index between 1990 and 2021. This analysis holds significant value as it enables both identification of root drivers behind BMI-related thyroid cancer burden fluctuations and strategic guidance for precision health initiatives. 2.2.4 Cross-country inequalities analysis To evaluate cross-national disparities in the obesity-related thyroid cancer burden among 204 countries and territories from 1990 to 2021, we conducted inequality analyses using the Slope Index of Inequality (SII) and Concentration Index (CI). Both indicators quantify the extent of disease burden disparities across nations with differing sociodemographic profiles, where higher numerical values correspond to increased inequality in distribution [ 14 ]. 2.2.5 Frontier analysis Through frontier analysis, optimal benchmarks for TC burden attributable to high BMI were established by evaluating national performance against top-performing countries. This methodology identifies exemplary nations demonstrating superior outcomes, serving as reference models for policy improvement. For each country/territory, we quantified the "effective difference" - a metric reflecting the disparity between observed TC burden linked to high BMI and the theoretical minimum burden achievable according to their SDI status [ 15 ]. 2.2.6 Predictive analysis To project the age-standardized mortality and disability-adjusted life years (DALYs) of thyroid cancer attributable to elevated body mass index (BMI) during 2022–2041 under a natural progression scenario, we implemented a Bayesian age-period-cohort (BAPC) modeling framework with integrated nested Laplace approximations [ 16 ]. This advanced analytical approach enables robust projection accuracy while accounting for complex demographic transitions through its hierarchical structure that simultaneously models age-specific risks, temporal trends, and birth cohort effects. 3 Results 3.1 Global Burden of high BMI and TC 3.1.1 Global Burden of TC Attributable to high BMI and Trends in ASDR and ASMR The global DALYs count for TC attributable to high BMI has seen a significantly increase from 61,814.62(95%UI: 46,570.98-79,115.54) in 1990 to 144,954.89(95%UI: 109,229.87–184,747.27) in 2021,reflecting an approximate 1.77-fold increase(Table 1 ).The mortality cases caused by TC attributable to high BMI also shows an substantial increase from 2,198.23(95%UI: 1,641.52-2817.86) in 1990 to 5,254.93(95%UI: 3,913.53-6,652.52),indicating an approximate 2.39-fold increase (Table 2 ).The ASDR exhibited a modest rise, with an AAPC of 0.38(95%CI:0.36–0.40). But the ASMR didn’t have a significantly change, with an AAPC of 0.22(95% CI: 0.20–0.25). Table 1 DALYs for TC attributed to high BMI: age-standardized rates with 95% Uncertainty Intervals and annual percentage change (AAPC) with 95% Confidence Intervals, 1990–2021.Abbreviations: 95% CI, 95% confidential intervals; AAPC, average annual percent change; ASDR, age-standardized disability-adjusted life years rate; DALYs, disability-adjusted life years; BMI, body-mass index; SDI, sociodemographic index; TC, thyroid cancer; UI, uncertainty interval. 1990 2021 1990–2021 DALYs ASDR per 100 000 DALYs ASDR per 100 000 AAPC No.X10³[95%UI] No.[95%UI] No.X10³[95%UI] No.[95%UI] %[95%CI] Global 61.81[46.57–79.12] 1.49[1.12–1.90] 144.95[109.23-184.75] 1.68[1.26–2.14] 0.38[0.36–0.40] Female 40.94[31.06–52.91] 1.87[1.42–2.21] 88.12[64.99-114.47] 1.96[1.44–2.55] 0.14[0.12–0.17] Male 20.88[15.68–26.68] 1.06[0.79–1.36] 56.83[42.68–73.44] 1.38[1.04–1.79] 0.87[0.84–0.90] High SDI 20.54[15.67–25.94] 1.91[1.46–2.41] 32.56[24.82–41.10] 1.75[1.32–2.20] -0.31[-0.36to-0.26] High-middle SDI 18.74[13.89–23.64] 1.81[1.36–2.32] 31.26[23.24–40.27] 1.63[1.21–2.10] -0.36[-0.40to-0.32] Middle SDI 13.30[9.89–17.22] 0.77[0.57-1.00] 47.45[35.19–60.88] 1.72[1.27–2.20] 1.28[1.27–1.31] Low-middle SDI 6.41[4.65–8.63] 0.55[0.40–0.74] 24.62[18.60-32.27] 1.54[1.16–2.02] 1.76[1.74–1.78] Low SDI 2.98[2.07–4.18] 1.06[0.74–1.48] 8.91[6.19–12.57] 1.30[0.96–1.91] 0.87[0.86–0.88] Table 2 Mortality for TC attributed to high BMI: age-standardized rates with 95% Uncertainty Intervals and annual percentage change (AAPC) with 95% Confidence Intervals, 1990–2021.Abbreviations: 95% CI, 95% confidential intervals; AAPC, average annual percent change; ASMR, age-standardized mortality rate; BMI, body-mass index; SDI, sociodemographic index; TC, thyroid cancer; UI, uncertainty interval. 1990 2021 1990–2021 Mortality ASMR per 100 000 Mortality ASMR per 100 000 AAPC No.X10³[95%UI] No.[95%UI] No.X10³[95%UI] No.[95%UI] %[95%CI] Global 2.20[1.64–2.82] 0.06[0.04–0.07] 5.25[3.91–6.65] 0.06[0.05–0.08] 0.22[0.20–0.25] Female 1.49[1.12–1.92] 0.07[0.05–0.09] 3.23[2.33–4.16] 0.07[0.05–0.09] -0.05[-0.08to-0.02] Male 0.70[0.52–0.90] 0.04[0.03–0.05] 2.03[1.51–2.60] 0.05[0.04–0.07] 0.79[0.74–0.83] High SDI 0.81[0.61–1.02] 0.07[0.06–0.09] 1.37[0.98–1.73] 0.06[0.05–0.08] -0.49[-0.54to-0.44] High-middle SDI 0.67[0.50–0.86] 0.07[0.05–0.09] 1.19[0.88–1.51] 0.06[0.04–0.08] -0.43[-0.46to-0.39] Middle SDI 0.43[0.32–0.57] 0.04[0.03–0.06] 1.65[1.20–2.12] 0.06[0.05–0.08] 1.17[1.15–1.18] Low-middle SDI 0.20[0.14–0.26] 0.03[0.02–0.04] 0.78[0.58–1.01] 0.05[0.04–0.07] 1.69[1.67–1.71] Low SDI 0.09[0.60–1.20] 0.04[0.02–0.05] 0.26[0.18–0.36] 0.05[0.03–0.07] 0.99[0.97-1.00] 3.1.2 Joinpoint analysis of the TC burden attributable to high BMI in Global and five SDI regions. The ASDR and ASMR of TC attributable to high BMI in global exhibited an upward trend generally, with AAPC of 0.38 (95% CI: 0.36–0.40) and 0.22(95% CI: 0.20–0.25). Although both of the ASDR and ASMR in Global exhibited a decreasing trend from 1995–1998, with APC of -0.58 (95% CI: -0.78to-0.13) and − 0.62(95% CI: -0.82to-0.19) (Fig. 1 A, Fig. 2 A). From 1990–2021, in Middle SDI, Low-middle SDI and Low SDI, both of the ASDR and ASMR showed an increasing trend (Fig. 1 D-F, Fig. 2 D-F), while in High SDI and High-middle SDI exhibited a downward trend (Fig. 1 B, C, Fig. 2 B, C). 3.1.3 National burden For DALYs, China (23684,95%UI: 16,059 − 32,507), India (15921,95%UI: 11,546 − 20,676), and United States of America (11,323,95%UI: 8,633 − 14,103) had the highest number in 2021. For TC-related Deaths cases attributable to high BMI, China (871,95%UI: 588-1,177), India (506,95%UI: 366–661), and United States of America (428,95%UI: 321–536) were also the peak in 2021. From 1990–2021, the largest increase in ASMR was United Arab Emirates, with EAPC of 0.52(95%CI: 0.40–0.63), while Poland had the largest decrease in both ASMR and ASDR of TC attributable to high BMI, with EAPCs of -0.31(95%CI: -0.38 to-0.25) and − 2.38(95%CI: -2.82 to-1.93), respectively. Meanwhile, the largest increase in ASDR was Zimbabwe, with EAPC of 2.75(95%CI: 2.25–3.26), the followings countries were Ecuador, Iran and Lesotho, with EAPCs of 2.30(95%CI: 1.93–2.70), 2.27(95%CI: 2.0-2.54) and 2.18(95%CI: 1.88–2.48), respectively (Fig. 3 ). 3.1.4 Decomposition Analysis Our study employed decomposition analysis to delineate the multifaceted contributions of aging, population growth, and epidemiological change to both disability-adjusted life years (DALYs) and mortality patterns across five SDI quintiles and gender strata from 1990 to 2021. The decomposition revealed that the DALYs of TC attributed to high BMI was predominantly driven by population growth (55.26%), aging (24.89%) and epidemiological change (19.84%). The mortality of TC attributable to high BMI was increased by population growth (49.85%) of the observed increase, followed by aging (30.58%) and epidemiological change (19.57%). In five SDI regions, population growth was the most obvious reason of not only DALYs in High SDI (78.28%), High-middle SDI (74.66%) and Low SDI (70.62%) but also mortality in High SDI (60.11%), High-middle SDI (81.76%) and Low SDI (68.65%). Middle SDI and Low-middle SDI had the most significant contribution of epidemiological change in DALYs, with (39.38%), and (44.22%) respectively. Meanwhile, epidemiological change was also the highest contribution for mortality in middle SDI (51.33%) and low-middle SDI (54.23%) (Fig. 4 ). 3.1.5 TC burden attributable to high BMI by age and sex For female, the age-specific rates of DALYs and deaths for TC attributable to high BMI both exhibited an upward trend, expect DALYs in 75–89 age group. In male, except for the 90 + age group in both DALYs and deaths, and 75–84 age group in DALYs, the rates of DALYs and deaths also showed an increasing trend in 2021 globally. In 2021 the number of DALYs peaked in 65–69 age group in female while the male peaked in 55–59 age group globally. The number of deaths for TC attributable to high BMI in male peaked in 75–79 age group in 2021 while the female peaked in 70–74 age group. It’s worth noting that in 25–44 age group, the number of DALYs in females exhibited 1.3–1.5 times higher than males, maybe caused by some mechanisms such as Estrogen-Adipose Crosstalk or Pregnancy-Related Thyroiditis. Generally speaking, in 2021, whatever the number of DALYs or deaths, the female were all higher than the male in all age groups in global (Fig. 5 ).For both men and women, the number and the rate of both DALYs and numbers reached the highest levels in Central Europe, Eastern Europe and Central Asia, while High SDI and High-income followed closely behind them (Fig. 6 ). 3.1.6 TCs burden attributable to high BMI was associated with SDI The ASMR and ASDR for TC attributable to high BMI exhibited a decreased trend when SDI got to 0.35 approximately. The peaks occurred when SDI was about 0.65, and then began to show a downward trend again. Meanwhile, Andean Latin America, Central Latin America, Eastern Sub-Saharan Africa and Oceania, all four of them demonstrated ASMR and ASDR above their excepted level of development from 1990 to 2021(Fig. 7 ). In 1990, EAPC was positively correlated with ASDR (R = 0.1134, P < 0.001). When in 2021, the EAPC of TC caused by high BMI was significantly positively correlated with both ASDR (R = 0.1284, P < 0.001) and ASMR (R = 0.2056, P < 0.001) (Fig. 8 ). Among 204 countries, Fiji had the highest ASDR and ASMR in 2021. On the contrary, Tajikistan had the lowest ASDR and ASMR. The association between ASDR and ASMR with SDI had peaks when SDI reached around 0.63 and then started to decline in 2021 (Fig. 9 ). 3.1.7 Cross-country inequality analysis of TCs burden attributable to high BMI From 1990–2021, the value of the Slope Index of Inequality (SII) of DALYs increased, from 2.14 (95% CI, 1.82–2.45) in 1990 to 2.62 (95% CI, 3.21–3.03) in 2021, suggesting a widening gap in health burdens between the highest and lowest SDI regions. Differently, the absolute value of the Concentration Index (CI) declined from − 0.26 (95% CI, -0.45 to -0.07) in 1990 to -0.12 (95% CI, -0.31 to -0.07) in 2021, reflecting reduced relative inequality between low and high SDI regions. Despite this, population distribution data (such as regions exceeding 1200 million population) showed a disproportionate concentration of TC attributable to high BMI in high SDI regions, potentially linked to lifestyle factors or healthcare resource allocation. Furthermore, the deaths rate of TC caused by high BMI exhibited similar trends with DALYs, the SII increased from 0.09 (95% CI, 0.07-1.00) in 1990 to 0.12 (95% CI, 0.10–0.13) in 2021, and the absolute value of the CI decreased from − 0.30 (95% CI, -0.48 to -0.12) in 1990 to -0.17 (95% CI, -0.35 -0.01) in 2021 (Fig. 10 ). 3.1.8 Frontier analysis of TCs burden attributable to high BMI The frontier analysis was conducted to assess potential improvement opportunities in the burden of TC attributable to high BMI in relation to SDI levels from 1990 to 2021. We selected top 10 countries or territories which had the greatest actual potential for improvement, with an effective difference (ef_df) ranging from 3.86 to 6.05 through the analysis. These 10 countries or territories were Fiji (6.05), Ecuador (5.10), Zimbabwe (4.89), United Arab Emirates (4.86), American Samoa (4.85), Saudi Arabia (4.21), Samoa (4.15), Nauru (4.05), Saint Vincent and the Grenadines (4.04), Georgia (3.86). Five countries with a low SDI that were on the frontier involved Niger, Burkina Faso, Chad, Sierra Leone and Benin. Furthermore, countries or territories with a high SDI demonstrating substantial advancement potential relative to their developmental benchmarks were San Marino, United States of America, Monaco, Lithuania and Iceland. Above these 10 countries or territories had the narrowest ASDR deviations from the frontier line in 2021 (Fig. 11 ). 3.1.9 Prediction analysis of TCs burden attributable to high BMI We predicted the global burden of TC attributable to high BMI from 2022 to 2041 by the use of BAPC model. The ASDR and ASMR in both sexes exhibited an increasing trend in the next 20 years. The ASDR and ASMR of females are 3.60 per 100,000 (95%CI: 2.79–4.41) and 0.12 per 100,000 (95%CI: 0.08–0.16) in 2041 respectively. Furthermore, the ASDR and ASMR of males are 2.50 per 100,000 (95%CI: 1.72–4.28) and 0.09 per 100,000 (95%CI: 0.06–0.12) in 2041 respectively. Notably, the ASDR and ASMR of females are always higher than males in the next 20 years (Fig. 12 ). 4 Discussion Our comprehensive analysis of the global burden of thyroid cancer (TC) attributable to high body mass index (BMI) from 1990 to 2021 underscores the escalating impact of obesity on TC-related morbidity and mortality. The global disability-adjusted life years (DALYs) and mortality attributable to high BMI increased by 1.77-fold and 2.39-fold, respectively, over this period, aligning with prior studies that highlight obesity as a critical modifiable risk factor for TC through mechanisms such as chronic inflammation, adipokine dysregulation, and hormonal imbalances [ 17 ], [ 9 – 10 ]. Utilizing the latest GBD 2021 dataset, we systematically evaluate current TC burdens attributed to high BMI across global, regional, and national populations, while projecting disease trajectories over the next two decades under current intervention scenarios. The findings offer evidence-based insights to guide policymakers in optimizing healthcare resource distribution and developing precision prevention programs for weight-related TC risks. The analysis demonstrated ASDR and ASMR for thyroid cancer attributable to high BMI exhibited a sustained upward trend between 1990 and 2021, with AAPC of 0.38 and 0.22 in global, respectively. Epidemiological shifts failed to counterbalance the substantial impact of population expansion and aging on rising thyroid cancer burdens attributable to high BMI. This pattern manifested most distinctly in high SDI territories, which typically experience extended life expectancies and accelerated population aging trajectories [ 18 ]. In regions with low SDI, inadequate healthcare infrastructure and constrained health service accessibility frequently lead to delayed detection of early-stage TC, resulting in higher AAPCs for both ASMR and ASDR. The rising prevalence of obesity and high BMI in recent decades has become an indisputable public health concern [ 4 ]. As a systemic metabolic disorder, obesity significantly correlates with multiple comorbidities and increased cancer risks across various organ systems. This global epidemic stems primarily from modern lifestyle transformations characterized by excessive consumption of calorie-dense foods combined with reduced physical activity and prolonged sedentary behaviors [ 19 ]. Particularly in developing regions, improved living standards have paradoxically exacerbated these risk factors through nutritional transitions toward energy-rich diets and decreased occupational energy expenditure. Given the dual challenges of global population growth and accelerated aging demographics, urgent implementation of obesity control measures becomes critical. Policy interventions should prioritize two key strategies: facilitating dietary modifications toward nutritionally balanced patterns and creating environments conducive to regular physical activity. These coordinated efforts could effectively counteract the obesity trajectory while addressing its associated health and economic burdens. The SDI exerted a significant influence on the geographic distribution of high BMI-related TC burdens. Geographic areas with elevated SDI levels generally demonstrated higher ASMR and ASDR. From 1990 to 2021, marked disparities in mortality and DALY metrics emerged between high-SDI and low-SDI regions. This disparity stems principally from high-SDI nations' enhanced capacity for medical detection, including sophisticated diagnostic infrastructure, comprehensive disease surveillance systems, and robust epidemiological data collection frameworks. Previous studies have demonstrated a significant association between the prevalence of elevated BMI and national socioeconomic levels. With rapid economic development in high SDI countries, substantial lifestyle modifications have emerged in recent years. These changes are characterized by increased consumption of fast food, greater reliance on mechanized transportation modes, and prolonged use of electronic devices - all contributing to reduced physical activity and sustained energy surplus. High-income countries are increasingly characterized by a concurrent rise in obesity rates, an ageing population, and persistently low fertility levels, collectively posing substantial challenges to public health systems and long-term socioeconomic stability. Specifically, the growing preference for ultra-processed foods, sedentary commuting patterns, and screen-dominated leisure activities collectively create an obesogenic environment that perpetuates weight gain [ 4 , 20 ]. Therefore, nations should develop tailored strategies based on their socioeconomic conditions. High-SDI countries should prioritize obesity prevention through implementing public health campaigns and promoting nutritional education alongside physical activity initiatives. Conversely, low-SDI nations require focused investments in healthcare infrastructure, particularly enhancing medical insurance coverage and establishing systematic programs for early disease detection and diagnosis. Age and gender-based stratification also showed differential results. The age-standardized mortality rate (ASMR) of TC associated with high BMI demonstrated an age-dependent increase, while females consistently exhibited higher case numbers and age-standardized rates than males throughout all age groups. Epidemiological evidence consistently shows women exhibit greater susceptibility to obesity development [ 21 ]. Consequently, prioritizing strategic allocation of healthcare resources becomes crucial to address the elevated TC risk among elderly women with obesity. This requires establishing systematic pathways for timely access to evidence-based interventions while concurrently developing targeted obesity management strategies. Such coordinated efforts could effectively mitigate BMI-related disease progression and reduce preventable TC morbidity in this vulnerable demographic. The frontier analysis highlighted significant disparities in national capacities to address TC burdens linked to high BMI levels. Lower-SDI countries exemplified by Niger, Burkina Faso, Chad, Sierra Leone, and Benin demonstrated notable success in mitigating BMI-associated GBTC impacts, serving as exemplary models for optimizing health outcomes under resource-constrained conditions. In contrast, several high-SDI nations—particularly San Marino, the United States, Monaco, Lithuania, and Iceland—underperformed in managing BMI-related TC burdens. This discrepancy may stem from geographical influences and dietary patterns. The findings emphasize the critical imperative for underperforming high-SDI countries to strategically reformulate health policies and enhance their implementation frameworks to better address this public health challenge. This study has several notable limitations that warrant consideration. Firstly, although the Global Burden of Disease (GBD) 2021 dataset provides extensive coverage, its epidemiological data for certain low-income nations show significant gaps. This incomplete representation might result in biased estimates of thyroid cancer burden associated with high BMI within these geographical areas. Secondly, the analysis treated high BMI as a unified exposure category without stratifying by severity levels (e.g., overweight vs. obesity). This methodological constraint highlights the need for subsequent investigations to clarify potential dose-response relationships between BMI gradations and thyroid cancer risk. Thirdly, the absence of histopathological subtype differentiation in GBD 2021 records prevents comparative analysis of high BMI's differential effects across thyroid cancer variants. This data limitation obscures potential variations in both epidemiological patterns and BMI-related risk profiles among distinct carcinoma subtypes. 5 Conclusion In summary, the global burden of TC attributable to high BMI is rising inexorably, with pronounced disparities across 204 countries, SDI regions and genders. Addressing this challenge demands a dual focus on obesity prevention and equitable healthcare access, informed by localized epidemiological data and cross-sectoral collaboration. Declarations Ethics approval and consent to participat e Not applicable. Consent for publication Not applicable. Data availability No datasets were generated or analysed during the current study. Competing inte rests The authors declare no competing interests. Funding This article received no funding. Authors’ contributions Guizhang Hou wrote the main manuscript text and prepared figures, Guizhang Hou and Tianshu Gao reviewed the manuscript, Tianshu Gao provided ideas and revision suggestions for the article. References Schlumberger M, Leboulleux S. Current practice in patients with differentiated thyroid cancer. Nat Rev Endocrinol. 2021;17(3):176–88. 10.1038/s41574-020-00448-z . Wu Z, Xia F, 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(1):119. Published 2024 Nov 29. 10.1186/s13045-024-01640-8 Liu D, Zhou L, Li C, et al. Endocrine cancer trends 1990–2021: global disparities and health inequalities. Endocr Relat Cancer. 2024;31(11):e230363. 10.1530/ERC-23-0363 . Published 2024 Oct 4. 2021 GBD, Adult. BMI Collaborators. 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Published 2024 Sep 30. 10.1038/s41419-024-07084-9 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. 10.1016/j.lana.2023.100537 . 23:100537. Published 2023 Jun 14. Kwon H, Han KD, Park CY. Weight change is significantly associated with risk of thyroid cancer: A nationwide population-based cohort study. Sci Rep. 2019;9(1):1546. 10.1038/s41598-018-38203-0 . Published 2019 Feb 7. Hu S, Wu X, Jiang H. Trends and projections of the global burden of thyroid cancer from 1990 to 2030. J Glob Health. 2024;14:04084. 10.7189/jogh.14.04084 . Published 2024 May 17. GBD 2021 Risk Factors Collaborators. 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 [published correction appears in Lancet. 2024;404(10449):244. doi: 10.1016/S0140-6736(24)01458-2.]. Lancet. 2024;403(10440):2162–2203. 10.1016/S0140-6736(24)00933-4 Chevan A, Sutherland M. Revisiting Das Gupta: refinement and extension of standardization and decomposition. Demography. 2009;46(3):429–49. https://doi.org/10.1353/dem.0.0060 . Hosseinpoor AR, Bergen N, Schlotheuber A. Promoting health equity: WHO health inequality monitoring at global and national levels. Global health action. 2015;8:29034. https://doi.org/10.3402/gha.v8.29034 . Hu Z, Wang X, Zhang X, Sun W, Mao J. An analysis of the global burden of gallbladder and biliary tract cancer attributable to high BMI in 204 countries and territories: 1990–2021. Front Nutr. 2024;11:1521770. https://doi.org/10.3389/fnut.2024.1521770 . Riebler A, Held L. Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations. Biom J. 2017;59(3):531–49. https://doi.org/10.1002/bimj.201500263 . Matrone A, Ferrari F, Santini F, Elisei R. Obesity as a risk factor for thyroid cancer. Curr Opin Endocrinol Diabetes Obes. 2020;27(5):358–63. https://doi.org/10.1097/MED.0000000000000556 . Guo S, Chen C, Wang Y, Cao Y, Leng Z, Zheng X. Differential Impact of Fertility on Health-Adjusted Life Expectancy of Older Adults Across Countries of Various Levels of Socio-Demographic Index - Worldwide, 1995–2019. China CDC Wkly. 2024;6(39):991–5. https://doi.org/10.46234/ccdcw2024.207 . Kim MS, Shim I, Fahed AC, Do R, Park WY, Natarajan P, Khera AV, Won HH. Association of genetic risk, lifestyle, and their interaction with obesity and obesity-related morbidities. Cell Metabol. 2024;36(7):1494–e15033. https://doi.org/10.1016/j.cmet.2024.06.004 . Islam ANMS, Sultana H, Nazmul Hassan Refat M, Farhana Z, Abdulbasah Kamil A, Rahman M, M. The global burden of overweight-obesity and its association with economic status, benefiting from STEPs survey of WHO member states: A meta-analysis. Prev Med Rep. 2024;46:102882. https://doi.org/10.1016/j.pmedr.2024.102882 . Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metab Clin Exp. 2019;92:6–10. https://doi.org/10.1016/j.metabol.2018.09.005 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviews received at journal 06 Sep, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor invited by journal 23 Jul, 2025 Editor assigned by journal 21 Jul, 2025 Submission checks completed at journal 21 Jul, 2025 First submitted to journal 17 Jul, 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. 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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-7148753","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502356722,"identity":"02062779-8e68-4151-af70-49875bf52900","order_by":0,"name":"Guizhang Hou","email":"","orcid":"","institution":"Liaoning University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guizhang","middleName":"","lastName":"Hou","suffix":""},{"id":502356723,"identity":"2bce3176-c6cb-48e2-bf77-784be005710e","order_by":1,"name":"Tianshu Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYLCCBCA2YGA+/uODgY0dMRoYGyBa2BIkZxSkJROnhQGshUdBmufDIQgPH5B3P3z8wYOaO/bmEjkMxjYGB5gZ2A8f3YBPi+GZtMSGhGPPEnfOyD2QnGNwh4+BJy3tBl4tDTmGDQlshxMMbuQlHM4xeMbMIMFjhl9L/xugln+H7Q1u5Bg2WxgcZmwgpEVeAmhLYtthxg03coyZGYjRYiDxLHFGYt/hxA1nnqUx9hikJbMR8ot8f/KBjz++AR12PPkYw48/Nnb87IeP4bflALoIGz7lYFsaCKkYBaNgFIyCUQAAdddRswDEb/AAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Liaoning University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Tianshu","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-07-17 11:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7148753/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7148753/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89838491,"identity":"f2ca86cc-1538-4182-a486-af487e2b4d5e","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":419628,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal trends in ASDR for TC attributable to high BMI and across five SDI regions from 1990 to 2021. AAPC, average annual percent change; APC, annual percent change; ASDR, age-­standardized disability-­adjusted life years rate; BMI, body-mass index; SDI, sociodemographic index; TC, thyroid cancer.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/5423f949fb977366bf614f71.jpeg"},{"id":89840604,"identity":"0e4f3624-db23-4fcd-97e0-687ee8718d87","added_by":"auto","created_at":"2025-08-25 15:18:06","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":449565,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal trends in ASMR for TC attributable to high BMI and across five SDI regions from 1990 to 2021. AAPC, average annual percent change; APC, annual percent change; ASDR, age-standardized disability-­adjusted life years rate; BMI, body-mass index; SDI, sociodemographic index; TC, thyroid cancer.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/19d45c33dad1ce8d37852bc8.jpeg"},{"id":89839821,"identity":"d2f4cebb-4380-4b34-9329-880b5170a2a3","added_by":"auto","created_at":"2025-08-25 15:10:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":387063,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized DALYs rate (ASDR) (A) and Age-standardized mortality rate (ASMR) (B) of TC attributable to high BMI in 204 countries and territories from 1990-2021. The estimated annual percentage change (EAPC) of ASDR (C) and ASMR (D) of TC attributable to high BMI from 1990 to 2021. DALYs, disability-adjusted life years; TC, thyroid cancer.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/008cbd825186284945c413e6.jpeg"},{"id":89838498,"identity":"8d6fab4b-52ca-4895-9f9c-c539fcf4ca52","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":180115,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition analysis of DALYs and mortality due to TC attributed to high BMI from 1990 to 2021 across five SDI regions. DALYs, disability-adjusted life years; TC, thyroid cancer; SDI, socio-demographic index.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/4885e1a037fc1caf4af2b372.jpeg"},{"id":89840606,"identity":"a04db1b4-69ad-4c03-a56e-8cbf88bab81c","added_by":"auto","created_at":"2025-08-25 15:18:06","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":241204,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific numbers and rates of DALYs and deaths of TC attributable to high BMI by age and sex in 2021. The bars show the number of TC deaths and DALYs attributable to high BMI. The line shows the age-specific death rates and DALYs rates for females and males attributable to high BMI at the global level. The shaded areas show the 95% UI for the rates. DALYs, disability-adjusted life years; TC, thyroid cancer; UI, uncertainty interval.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/2d56a7d737852285cd762288.jpeg"},{"id":89838502,"identity":"1ddf0b22-8fd1-483d-9a69-79b9d9262d5b","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":183664,"visible":true,"origin":"","legend":"\u003cp\u003eThe number and the rate of DALYs and Deaths attributable to high BMI in Central Europe, Eastern Europe, Central Asia, Southeast Asia, East Asia, Oceania, Sub-Saharan Africa, High-income and five SDI regions.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/25fad6865c9fcbbe4cd3a4ac.jpeg"},{"id":89838506,"identity":"b824eee8-6379-4727-96dc-951aa5f9a5ff","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":191636,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized DALY rate and ASMR of TC due to high BMI in 21 GBD regions by the SDI from 1990–2021. ASDR, age-standardized DALY rate; ASMR, age-standardized mortality rate; GBD, global burden of disease; SDI, sociodemographic index, TC, thyroid cancer.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/ff40cfc3b24227aef57021ea.jpeg"},{"id":89838511,"identity":"30a0fabf-64a9-41a8-80ff-757f2aac7542","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":135484,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between EAPCs and ASR of GBTC in 1990 and 2021. EAPCs, estimated annual percentage changes; ASR, age-standardized rate.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/56529bce2ae6de7eda1ce974.jpeg"},{"id":89838504,"identity":"0be4d939-0ce8-4bf3-bee5-662d9af71d2f","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":793157,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between TC attributable to high BMI ASDR (A) and ASMR (B) with SDI at 204 countries and regions in 2021.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/0315ffaa8a33cbae64e1b0fc.jpeg"},{"id":89838507,"identity":"9e5c9679-e776-41f8-894e-ad4b36e95a0f","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":174743,"visible":true,"origin":"","legend":"\u003cp\u003eInequality analysis of DALYs and deaths in TC caused by high BMI from 1990 and 2021 around the world. DALYs, disability-adjusted life years; TC, thyroid cancer; BMI, body mass index.\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/1c0048b5f37c56268ca9ef5c.jpeg"},{"id":89838509,"identity":"c067e4ac-79e1-475e-9d3f-314f72f3b730","added_by":"auto","created_at":"2025-08-25 15:02:06","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":173265,"visible":true,"origin":"","legend":"\u003cp\u003eFrontier analysis based on SDI and ASDR of TC attributable to high BMI from 1990 to 2021, and their correlation in 2021. SDI, socio-demographic index; ASDR, age-standardized DALY rate; TC, thyroid cancer.\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/6bcda93f217a8fbb38f7fe0b.jpeg"},{"id":89838521,"identity":"5c3d7972-9015-4f25-b323-a9afd3da0d9b","added_by":"auto","created_at":"2025-08-25 15:02:07","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":87665,"visible":true,"origin":"","legend":"\u003cp\u003eProjects the age-standardized DALYs rates (per 100,000 population) and age-standardized mortality rates (per 100,000 population) of TC attributable to high BMI in males and females from 2022 to 2041.\u003c/p\u003e","description":"","filename":"image12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/2b5ecf3fa1af9b42ab6c0338.jpeg"},{"id":89842076,"identity":"cae732c7-189e-41f5-8833-1e0d6e5f097b","added_by":"auto","created_at":"2025-08-25 15:34:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4439802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7148753/v1/d07339ad-eb99-4a80-b86a-b10b6ef75e54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global and Regional Burden of Thyroid Cancer Attributable to high body mass index from 1990 to 2021 and Modelled Projections to 2041","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThyroid cancer (TC), a cancerous growth arising from thyroid tissue, has become a growing global health concern as its prevalence continues to escalate worldwide. Differentiated thyroid cancers account for nearly 90% of all cases, with papillary thyroid carcinoma (PTC) constituting the predominant histological subtype (approximately 80\u0026ndash;85% of DTCs), while follicular carcinoma represents the second most common form [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2021 there were 249,538.02 new cases and 44,798.54 deaths caused by thyroid cancer worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There may be some differences in incidence and mortality among different countries and territories [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The pathogenesis of this malignancy involves multifaceted mechanisms, with multiple genetic and environmental elements contributing to its progression.\u003c/p\u003e\u003cp\u003eThe latest data showed that around 1\u0026middot;11\u0026nbsp;billion adult females and 1\u0026middot;00\u0026nbsp;billion adult males had obesity and overweight. Without intervention, obesity will take a huge toll on human health and sustainable economic development [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. After smoking, obesity constitutes the most prevalent preventable and alterable contributor to carcinogenesis in human populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Global projections indicate that obesity-associated malignancies are anticipated to exceed 2\u0026nbsp;million worldwide by 2070, constituting 7% of total cancer incidence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Emerging evidence suggests that obesity-associated immunometabolic dysregulation, characterized by persistent low-grade inflammation, endocrine axis disruption, adipokine secretion imbalance, and gut microbiota alterations, constitutes a critical pathogenic nexus in oncogenesis and tumor progression. Current research reveals these interconnected mechanisms exhibit distinct tissue tropism and site-specific carcinogenic effects, with particular organ microenvironments demonstrating differential susceptibility to particular obesity-driven oncogenic pathways [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent studies have underscored a significant association between higher body-mass index (BMI) and increased risk of thyroid cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, understanding the worldwide impact of thyroid cancer, especially cases linked to high body mass index, is crucial for policymakers to optimize resource distribution and develop focused preventive measures.\u003c/p\u003e\u003cp\u003eThe Global Burden of Disease (GBD) 2021 Study provides a comprehensive data repository, systematically quantifying disease and injury burdens across 204 countries and territories, including detailed analyses of TC-related pathologies. Existing studies have primarily focused on the comprehensive disease burden of thyroid cancer (TC) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while the specific mortality and disability-adjusted life years (DALYs) attributable to high body mass index (BMI) remain inadequately characterized. With the open of the latest 2021 version of GBD database, our study aims to systematically assess the global, regional, and national burden of TC-related mortality and DALYs, particularly examining the contribution of high BMI as a modifiable risk factor from 1990\u0026ndash;2021, as well as projections up to 2041.\u003c/p\u003e"},{"header":"2 Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data source\u003c/h2\u003e\u003cp\u003eFor this study, we utilized data from the GBD 2021, which provides a systematic evaluation of disease burden attributed to 88 major risk factors - including high BMI - at global, regional, and national levels, with specific quantification of its impact on TC from 1990\u0026ndash;2021 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Burden description\u003c/h2\u003e\u003cp\u003eThe study evaluated key health indicators such as disability-adjusted life years (DALYs) and mortality, along with their age-standardized rates (ASDR and ASMR), specifically examining thyroid cancer burden attributable to high body mass index [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We utilized the sociodemographic index (SDI)\u0026mdash;a composite socioeconomic development metric\u0026mdash;to assess thyroid cancer burden attributable to high BMI across varying socioeconomic contexts, stratifying national populations into five SDI quintiles. To analyze temporal trends in these rates from 1990 to 2021, we calculated both percentage change (PC) and estimated annual percentage change (EAPC). The EAPC was derived through a linear regression model that incorporated annual rate variations, providing a quantitative measure of trend progression (increasing/decreasing) over the study period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Joinpoint Regression Analysis\u003c/h2\u003e\u003cp\u003eThis study utilized Joinpoint regression analysis to assess temporal trends in ASDR and ASMR of thyroid cancer attributable to high body mass index between 1990 and 2021. The optimal fitting models were selected for comparative analysis to systematically evaluate temporal trends in disease burden. Specifically, an upward trend is indicated when the 95% confidence interval (CI) of the AAPC (Average Annual Percentage Change) estimate remains entirely above zero. Conversely, a downward trend is observed when the CI falls completely below zero, while a stable trend is inferred if the CI encompasses the null value of zero.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Decomposition analysis\u003c/h2\u003e\u003cp\u003eWe conducted a decomposition analysis using Das Gupta's method [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] to quantify the relative contributions of age structure, epidemiological transitions, and population growth to temporal changes in mortality and disability-adjusted life years (DALYs). This analytical approach enabled systematic assessment of key drivers underlying the evolving global and regional (across five SDI) burden of thyroid cancer attributable to high body mass index between 1990 and 2021. This analysis holds significant value as it enables both identification of root drivers behind BMI-related thyroid cancer burden fluctuations and strategic guidance for precision health initiatives.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Cross-country inequalities analysis\u003c/h2\u003e\u003cp\u003eTo evaluate cross-national disparities in the obesity-related thyroid cancer burden among 204 countries and territories from 1990 to 2021, we conducted inequality analyses using the Slope Index of Inequality (SII) and Concentration Index (CI). Both indicators quantify the extent of disease burden disparities across nations with differing sociodemographic profiles, where higher numerical values correspond to increased inequality in distribution [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5 Frontier analysis\u003c/h2\u003e\u003cp\u003eThrough frontier analysis, optimal benchmarks for TC burden attributable to high BMI were established by evaluating national performance against top-performing countries. This methodology identifies exemplary nations demonstrating superior outcomes, serving as reference models for policy improvement. For each country/territory, we quantified the \"effective difference\" - a metric reflecting the disparity between observed TC burden linked to high BMI and the theoretical minimum burden achievable according to their SDI status [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.2.6 Predictive analysis\u003c/h2\u003e\u003cp\u003eTo project the age-standardized mortality and disability-adjusted life years (DALYs) of thyroid cancer attributable to elevated body mass index (BMI) during 2022\u0026ndash;2041 under a natural progression scenario, we implemented a Bayesian age-period-cohort (BAPC) modeling framework with integrated nested Laplace approximations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This advanced analytical approach enables robust projection accuracy while accounting for complex demographic transitions through its hierarchical structure that simultaneously models age-specific risks, temporal trends, and birth cohort effects.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Global Burden of high BMI and TC\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Global Burden of TC Attributable to high BMI and Trends in ASDR and ASMR\u003c/h2\u003e\u003cp\u003eThe global DALYs count for TC attributable to high BMI has seen a significantly increase from 61,814.62(95%UI: 46,570.98-79,115.54) in 1990 to 144,954.89(95%UI: 109,229.87\u0026ndash;184,747.27) in 2021,reflecting an approximate 1.77-fold increase(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).The mortality cases caused by TC attributable to high BMI also shows an substantial increase from 2,198.23(95%UI: 1,641.52-2817.86) in 1990 to 5,254.93(95%UI: 3,913.53-6,652.52),indicating an approximate 2.39-fold increase (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).The ASDR exhibited a modest rise, with an AAPC of 0.38(95%CI:0.36\u0026ndash;0.40). But the ASMR didn\u0026rsquo;t have a significantly change, with an AAPC of 0.22(95% CI: 0.20\u0026ndash;0.25).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDALYs for TC attributed to high BMI: age-standardized rates with 95% Uncertainty Intervals and annual percentage change (AAPC) with 95% Confidence Intervals, 1990\u0026ndash;2021.Abbreviations: 95% CI, 95% confidential intervals; AAPC, average annual percent change; ASDR, age-standardized disability-adjusted life years rate; DALYs, disability-adjusted life years; BMI, body-mass index; SDI, sociodemographic index; TC, thyroid cancer; UI, uncertainty interval.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASDR per 100 000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASDR per 100 000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eAAPC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo.X10\u0026sup3;[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo.[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo.X10\u0026sup3;[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo.[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e%[95%CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.81[46.57\u0026ndash;79.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.49[1.12\u0026ndash;1.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144.95[109.23-184.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.68[1.26\u0026ndash;2.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38[0.36\u0026ndash;0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.94[31.06\u0026ndash;52.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.87[1.42\u0026ndash;2.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.12[64.99-114.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.96[1.44\u0026ndash;2.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14[0.12\u0026ndash;0.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.88[15.68\u0026ndash;26.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06[0.79\u0026ndash;1.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.83[42.68\u0026ndash;73.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.38[1.04\u0026ndash;1.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87[0.84\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.54[15.67\u0026ndash;25.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.91[1.46\u0026ndash;2.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.56[24.82\u0026ndash;41.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.75[1.32\u0026ndash;2.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.31[-0.36to-0.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.74[13.89\u0026ndash;23.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.81[1.36\u0026ndash;2.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.26[23.24\u0026ndash;40.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.63[1.21\u0026ndash;2.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.36[-0.40to-0.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.30[9.89\u0026ndash;17.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77[0.57-1.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.45[35.19\u0026ndash;60.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.72[1.27\u0026ndash;2.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.28[1.27\u0026ndash;1.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.41[4.65\u0026ndash;8.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55[0.40\u0026ndash;0.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.62[18.60-32.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.54[1.16\u0026ndash;2.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.76[1.74\u0026ndash;1.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.98[2.07\u0026ndash;4.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06[0.74\u0026ndash;1.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.91[6.19\u0026ndash;12.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.30[0.96\u0026ndash;1.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87[0.86\u0026ndash;0.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMortality for TC attributed to high BMI: age-standardized rates with 95% Uncertainty Intervals and annual percentage change (AAPC) with 95% Confidence Intervals, 1990\u0026ndash;2021.Abbreviations: 95% CI, 95% confidential intervals; AAPC, average annual percent change; ASMR, age-standardized mortality rate; BMI, body-mass index; SDI, sociodemographic index; TC, thyroid cancer; UI, uncertainty interval.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASMR per 100 000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASMR per 100 000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eAAPC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo.X10\u0026sup3;[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo.[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo.X10\u0026sup3;[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo.[95%UI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e%[95%CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.20[1.64\u0026ndash;2.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06[0.04\u0026ndash;0.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.25[3.91\u0026ndash;6.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06[0.05\u0026ndash;0.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22[0.20\u0026ndash;0.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.49[1.12\u0026ndash;1.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07[0.05\u0026ndash;0.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.23[2.33\u0026ndash;4.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07[0.05\u0026ndash;0.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.05[-0.08to-0.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70[0.52\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04[0.03\u0026ndash;0.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.03[1.51\u0026ndash;2.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05[0.04\u0026ndash;0.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79[0.74\u0026ndash;0.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81[0.61\u0026ndash;1.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07[0.06\u0026ndash;0.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.37[0.98\u0026ndash;1.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06[0.05\u0026ndash;0.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.49[-0.54to-0.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67[0.50\u0026ndash;0.86]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07[0.05\u0026ndash;0.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.19[0.88\u0026ndash;1.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06[0.04\u0026ndash;0.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.43[-0.46to-0.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43[0.32\u0026ndash;0.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04[0.03\u0026ndash;0.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.65[1.20\u0026ndash;2.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06[0.05\u0026ndash;0.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.17[1.15\u0026ndash;1.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.20[0.14\u0026ndash;0.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03[0.02\u0026ndash;0.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.78[0.58\u0026ndash;1.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05[0.04\u0026ndash;0.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69[1.67\u0026ndash;1.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.09[0.60\u0026ndash;1.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04[0.02\u0026ndash;0.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26[0.18\u0026ndash;0.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05[0.03\u0026ndash;0.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99[0.97-1.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.1.2 Joinpoint analysis of the TC burden attributable to high BMI in Global and five SDI regions.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ASDR and ASMR of TC attributable to high BMI in global exhibited an upward trend generally, with AAPC of 0.38 (95% CI: 0.36\u0026ndash;0.40) and 0.22(95% CI: 0.20\u0026ndash;0.25). Although both of the ASDR and ASMR in Global exhibited a decreasing trend from 1995\u0026ndash;1998, with APC of -0.58 (95% CI: -0.78to-0.13) and \u0026minus;\u0026thinsp;0.62(95% CI: -0.82to-0.19) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom 1990\u0026ndash;2021, in Middle SDI, Low-middle SDI and Low SDI, both of the ASDR and ASMR showed an increasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-F, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F), while in High SDI and High-middle SDI exhibited a downward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 National burden\u003c/h2\u003e\u003cp\u003eFor DALYs, China (23684,95%UI: 16,059\u0026thinsp;\u0026minus;\u0026thinsp;32,507), India (15921,95%UI: 11,546\u0026thinsp;\u0026minus;\u0026thinsp;20,676), and United States of America (11,323,95%UI: 8,633\u0026thinsp;\u0026minus;\u0026thinsp;14,103) had the highest number in 2021. For TC-related Deaths cases attributable to high BMI, China (871,95%UI: 588-1,177), India (506,95%UI: 366\u0026ndash;661), and United States of America (428,95%UI: 321\u0026ndash;536) were also the peak in 2021. From 1990\u0026ndash;2021, the largest increase in ASMR was United Arab Emirates, with EAPC of 0.52(95%CI: 0.40\u0026ndash;0.63), while Poland had the largest decrease in both ASMR and ASDR of TC attributable to high BMI, with EAPCs of -0.31(95%CI: -0.38 to-0.25) and \u0026minus;\u0026thinsp;2.38(95%CI: -2.82 to-1.93), respectively. Meanwhile, the largest increase in ASDR was Zimbabwe, with EAPC of 2.75(95%CI: 2.25\u0026ndash;3.26), the followings countries were Ecuador, Iran and Lesotho, with EAPCs of 2.30(95%CI: 1.93\u0026ndash;2.70), 2.27(95%CI: 2.0-2.54) and 2.18(95%CI: 1.88\u0026ndash;2.48), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Decomposition Analysis\u003c/h2\u003e\u003cp\u003eOur study employed decomposition analysis to delineate the multifaceted contributions of aging, population growth, and epidemiological change to both disability-adjusted life years (DALYs) and mortality patterns across five SDI quintiles and gender strata from 1990 to 2021. The decomposition revealed that the DALYs of TC attributed to high BMI was predominantly driven by population growth (55.26%), aging (24.89%) and epidemiological change (19.84%). The mortality of TC attributable to high BMI was increased by population growth (49.85%) of the observed increase, followed by aging (30.58%) and epidemiological change (19.57%). In five SDI regions, population growth was the most obvious reason of not only DALYs in High SDI (78.28%), High-middle SDI (74.66%) and Low SDI (70.62%) but also mortality in High SDI (60.11%), High-middle SDI (81.76%) and Low SDI (68.65%). Middle SDI and Low-middle SDI had the most significant contribution of epidemiological change in DALYs, with (39.38%), and (44.22%) respectively. Meanwhile, epidemiological change was also the highest contribution for mortality in middle SDI (51.33%) and low-middle SDI (54.23%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.1.5 TC burden attributable to high BMI by age and sex\u003c/h2\u003e\u003cp\u003eFor female, the age-specific rates of DALYs and deaths for TC attributable to high BMI both exhibited an upward trend, expect DALYs in 75\u0026ndash;89 age group. In male, except for the 90\u0026thinsp;+\u0026thinsp;age group in both DALYs and deaths, and 75\u0026ndash;84 age group in DALYs, the rates of DALYs and deaths also showed an increasing trend in 2021 globally. In 2021 the number of DALYs peaked in 65\u0026ndash;69 age group in female while the male peaked in 55\u0026ndash;59 age group globally. The number of deaths for TC attributable to high BMI in male peaked in 75\u0026ndash;79 age group in 2021 while the female peaked in 70\u0026ndash;74 age group. It\u0026rsquo;s worth noting that in 25\u0026ndash;44 age group, the number of DALYs in females exhibited 1.3\u0026ndash;1.5 times higher than males, maybe caused by some mechanisms such as Estrogen-Adipose Crosstalk or Pregnancy-Related Thyroiditis. Generally speaking, in 2021, whatever the number of DALYs or deaths, the female were all higher than the male in all age groups in global (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).For both men and women, the number and the rate of both DALYs and numbers reached the highest levels in Central Europe, Eastern Europe and Central Asia, while High SDI and High-income followed closely behind them (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.1.6 TCs burden attributable to high BMI was associated with SDI\u003c/h2\u003e\u003cp\u003eThe ASMR and ASDR for TC attributable to high BMI exhibited a decreased trend when SDI got to 0.35 approximately. The peaks occurred when SDI was about 0.65, and then began to show a downward trend again. Meanwhile, Andean Latin America, Central Latin America, Eastern Sub-Saharan Africa and Oceania, all four of them demonstrated ASMR and ASDR above their excepted level of development from 1990 to 2021(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In 1990, EAPC was positively correlated with ASDR (R\u0026thinsp;=\u0026thinsp;0.1134, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When in 2021, the EAPC of TC caused by high BMI was significantly positively correlated with both ASDR (R\u0026thinsp;=\u0026thinsp;0.1284, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ASMR (R\u0026thinsp;=\u0026thinsp;0.2056, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Among 204 countries, Fiji had the highest ASDR and ASMR in 2021. On the contrary, Tajikistan had the lowest ASDR and ASMR. The association between ASDR and ASMR with SDI had peaks when SDI reached around 0.63 and then started to decline in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.1.7 Cross-country inequality analysis of TCs burden attributable to high BMI\u003c/h2\u003e\u003cp\u003eFrom 1990\u0026ndash;2021, the value of the Slope Index of Inequality (SII) of DALYs increased, from 2.14 (95% CI, 1.82\u0026ndash;2.45) in 1990 to 2.62 (95% CI, 3.21\u0026ndash;3.03) in 2021, suggesting a widening gap in health burdens between the highest and lowest SDI regions. Differently, the absolute value of the Concentration Index (CI) declined from \u0026minus;\u0026thinsp;0.26 (95% CI, -0.45 to -0.07) in 1990 to -0.12 (95% CI, -0.31 to -0.07) in 2021, reflecting reduced relative inequality between low and high SDI regions. Despite this, population distribution data (such as regions exceeding 1200\u0026nbsp;million population) showed a disproportionate concentration of TC attributable to high BMI in high SDI regions, potentially linked to lifestyle factors or healthcare resource allocation. Furthermore, the deaths rate of TC caused by high BMI exhibited similar trends with DALYs, the SII increased from 0.09 (95% CI, 0.07-1.00) in 1990 to 0.12 (95% CI, 0.10\u0026ndash;0.13) in 2021, and the absolute value of the CI decreased from \u0026minus;\u0026thinsp;0.30 (95% CI, -0.48 to -0.12) in 1990 to -0.17 (95% CI, -0.35 -0.01) in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.1.8 Frontier analysis of TCs burden attributable to high BMI\u003c/h2\u003e\u003cp\u003eThe frontier analysis was conducted to assess potential improvement opportunities in the burden of TC attributable to high BMI in relation to SDI levels from 1990 to 2021. We selected top 10 countries or territories which had the greatest actual potential for improvement, with an effective difference (ef_df) ranging from 3.86 to 6.05 through the analysis. These 10 countries or territories were Fiji (6.05), Ecuador (5.10), Zimbabwe (4.89), United Arab Emirates (4.86), American Samoa (4.85), Saudi Arabia (4.21), Samoa (4.15), Nauru (4.05), Saint Vincent and the Grenadines (4.04), Georgia (3.86). Five countries with a low SDI that were on the frontier involved Niger, Burkina Faso, Chad, Sierra Leone and Benin. Furthermore, countries or territories with a high SDI demonstrating substantial advancement potential relative to their developmental benchmarks were San Marino, United States of America, Monaco, Lithuania and Iceland. Above these 10 countries or territories had the narrowest ASDR deviations from the frontier line in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.1.9 Prediction analysis of TCs burden attributable to high BMI\u003c/h2\u003e\u003cp\u003eWe predicted the global burden of TC attributable to high BMI from 2022 to 2041 by the use of BAPC model. The ASDR and ASMR in both sexes exhibited an increasing trend in the next 20 years. The ASDR and ASMR of females are 3.60 per 100,000 (95%CI: 2.79\u0026ndash;4.41) and 0.12 per 100,000 (95%CI: 0.08\u0026ndash;0.16) in 2041 respectively. Furthermore, the ASDR and ASMR of males are 2.50 per 100,000 (95%CI: 1.72\u0026ndash;4.28) and 0.09 per 100,000 (95%CI: 0.06\u0026ndash;0.12) in 2041 respectively. Notably, the ASDR and ASMR of females are always higher than males in the next 20 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur comprehensive analysis of the global burden of thyroid cancer (TC) attributable to high body mass index (BMI) from 1990 to 2021 underscores the escalating impact of obesity on TC-related morbidity and mortality. The global disability-adjusted life years (DALYs) and mortality attributable to high BMI increased by 1.77-fold and 2.39-fold, respectively, over this period, aligning with prior studies that highlight obesity as a critical modifiable risk factor for TC through mechanisms such as chronic inflammation, adipokine dysregulation, and hormonal imbalances [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Utilizing the latest GBD 2021 dataset, we systematically evaluate current TC burdens attributed to high BMI across global, regional, and national populations, while projecting disease trajectories over the next two decades under current intervention scenarios. The findings offer evidence-based insights to guide policymakers in optimizing healthcare resource distribution and developing precision prevention programs for weight-related TC risks. The analysis demonstrated ASDR and ASMR for thyroid cancer attributable to high BMI exhibited a sustained upward trend between 1990 and 2021, with AAPC of 0.38 and 0.22 in global, respectively. Epidemiological shifts failed to counterbalance the substantial impact of population expansion and aging on rising thyroid cancer burdens attributable to high BMI. This pattern manifested most distinctly in high SDI territories, which typically experience extended life expectancies and accelerated population aging trajectories [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In regions with low SDI, inadequate healthcare infrastructure and constrained health service accessibility frequently lead to delayed detection of early-stage TC, resulting in higher AAPCs for both ASMR and ASDR.\u003c/p\u003e\u003cp\u003eThe rising prevalence of obesity and high BMI in recent decades has become an indisputable public health concern [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a systemic metabolic disorder, obesity significantly correlates with multiple comorbidities and increased cancer risks across various organ systems. This global epidemic stems primarily from modern lifestyle transformations characterized by excessive consumption of calorie-dense foods combined with reduced physical activity and prolonged sedentary behaviors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Particularly in developing regions, improved living standards have paradoxically exacerbated these risk factors through nutritional transitions toward energy-rich diets and decreased occupational energy expenditure. Given the dual challenges of global population growth and accelerated aging demographics, urgent implementation of obesity control measures becomes critical. Policy interventions should prioritize two key strategies: facilitating dietary modifications toward nutritionally balanced patterns and creating environments conducive to regular physical activity. These coordinated efforts could effectively counteract the obesity trajectory while addressing its associated health and economic burdens.\u003c/p\u003e\u003cp\u003eThe SDI exerted a significant influence on the geographic distribution of high BMI-related TC burdens. Geographic areas with elevated SDI levels generally demonstrated higher ASMR and ASDR. From 1990 to 2021, marked disparities in mortality and DALY metrics emerged between high-SDI and low-SDI regions. This disparity stems principally from high-SDI nations' enhanced capacity for medical detection, including sophisticated diagnostic infrastructure, comprehensive disease surveillance systems, and robust epidemiological data collection frameworks. Previous studies have demonstrated a significant association between the prevalence of elevated BMI and national socioeconomic levels. With rapid economic development in high SDI countries, substantial lifestyle modifications have emerged in recent years. These changes are characterized by increased consumption of fast food, greater reliance on mechanized transportation modes, and prolonged use of electronic devices - all contributing to reduced physical activity and sustained energy surplus. High-income countries are increasingly characterized by a concurrent rise in obesity rates, an ageing population, and persistently low fertility levels, collectively posing substantial challenges to public health systems and long-term socioeconomic stability.\u003c/p\u003e\u003cp\u003eSpecifically, the growing preference for ultra-processed foods, sedentary commuting patterns, and screen-dominated leisure activities collectively create an obesogenic environment that perpetuates weight gain [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, nations should develop tailored strategies based on their socioeconomic conditions. High-SDI countries should prioritize obesity prevention through implementing public health campaigns and promoting nutritional education alongside physical activity initiatives. Conversely, low-SDI nations require focused investments in healthcare infrastructure, particularly enhancing medical insurance coverage and establishing systematic programs for early disease detection and diagnosis.\u003c/p\u003e\u003cp\u003eAge and gender-based stratification also showed differential results. The age-standardized mortality rate (ASMR) of TC associated with high BMI demonstrated an age-dependent increase, while females consistently exhibited higher case numbers and age-standardized rates than males throughout all age groups. Epidemiological evidence consistently shows women exhibit greater susceptibility to obesity development [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Consequently, prioritizing strategic allocation of healthcare resources becomes crucial to address the elevated TC risk among elderly women with obesity. This requires establishing systematic pathways for timely access to evidence-based interventions while concurrently developing targeted obesity management strategies. Such coordinated efforts could effectively mitigate BMI-related disease progression and reduce preventable TC morbidity in this vulnerable demographic.\u003c/p\u003e\u003cp\u003eThe frontier analysis highlighted significant disparities in national capacities to address TC burdens linked to high BMI levels. Lower-SDI countries exemplified by Niger, Burkina Faso, Chad, Sierra Leone, and Benin demonstrated notable success in mitigating BMI-associated GBTC impacts, serving as exemplary models for optimizing health outcomes under resource-constrained conditions. In contrast, several high-SDI nations\u0026mdash;particularly San Marino, the United States, Monaco, Lithuania, and Iceland\u0026mdash;underperformed in managing BMI-related TC burdens. This discrepancy may stem from geographical influences and dietary patterns. The findings emphasize the critical imperative for underperforming high-SDI countries to strategically reformulate health policies and enhance their implementation frameworks to better address this public health challenge.\u003c/p\u003e\u003cp\u003eThis study has several notable limitations that warrant consideration. Firstly, although the Global Burden of Disease (GBD) 2021 dataset provides extensive coverage, its epidemiological data for certain low-income nations show significant gaps. This incomplete representation might result in biased estimates of thyroid cancer burden associated with high BMI within these geographical areas. Secondly, the analysis treated high BMI as a unified exposure category without stratifying by severity levels (e.g., overweight vs. obesity). This methodological constraint highlights the need for subsequent investigations to clarify potential dose-response relationships between BMI gradations and thyroid cancer risk. Thirdly, the absence of histopathological subtype differentiation in GBD 2021 records prevents comparative analysis of high BMI's differential effects across thyroid cancer variants. This data limitation obscures potential variations in both epidemiological patterns and BMI-related risk profiles among distinct carcinoma subtypes.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, the global burden of TC attributable to high BMI is rising inexorably, with pronounced disparities across 204 countries, SDI regions and genders. Addressing this challenge demands a dual focus on obesity prevention and equitable healthcare access, informed by localized epidemiological data and cross-sectoral collaboration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participat\u003c/strong\u003e\u003cstrong\u003ee\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\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting inte\u003c/strong\u003e\u003cstrong\u003erests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuizhang Hou wrote the main manuscript text and prepared figures, Guizhang Hou and Tianshu Gao reviewed the manuscript, Tianshu Gao provided ideas and revision suggestions for the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchlumberger M, Leboulleux S. 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The epidemiology of obesity. Metab Clin Exp. 2019;92:6\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.metabol.2018.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2018.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"global burden, thyroid cancer, high BMI, mortality, health inequalities","lastPublishedDoi":"10.21203/rs.3.rs-7148753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7148753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThyroid cancer (TC) has emerged as a globally significant malignancy with rising incidence globally. Epidemiological evidence identifies high body mass index as an independent metabolic risk factor demonstrating dose-dependent associations with both TC mortality and disability-adjusted life years (DALYs). This population-level analysis quantifies the global epidemiological burden of TC attributable to high BMI from 1990 to 2021, employing standardized comparative risk assessment. Inform evidence-based interventions for risk stratification and prevention strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study analyzed global TC burden attributable to high BMI using Global Burden of Disease 2021 data. Age-standardized mortality rates (ASMR) and disability-adjusted life year rates (ASDR) were stratified by year, sex, age group, region, and sociodemographic index (SDI). Temporal trends from 1990 to 2021 were quantified through average annual percentage change (AAPC) and estimated annual percentage change (EAPC) metrics. Three analytical approaches were implemented: 1) decomposition analysis to identify key contributors to burden variations, 2) frontier analysis to benchmark national performance, and 3) health inequality assessment across SDI quintiles. Burden projections through 2041 were using Bayesian age-period-cohort modeling.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eASMR and ASDR for TC attributable to BMI exhibited sustained increases between 1990 and 2021 globally. Concurrently, mortality and DALY counts rose significantly, with projections indicating continuation of these trends over the next two decades without targeted interventions. Elevated burdens in high-SDI regions correlated with heightened obesity prevalence, aging, and population expansion, with observed socioeconomic disparities widening over time. Consistent gender disparity was observed, with females demonstrating higher susceptibility across all age cohorts. Significant disparities emerged in national-level burden management, as select low-SDI regions performed better than certain high-SDI regions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe persistent public health impact of thyroid cancer associated with high BMI requires prioritized prevention, particularly in high SDI regions. Demographic variations across gender and age groups require precision-based interventions.\u003c/p\u003e","manuscriptTitle":"Global and Regional Burden of Thyroid Cancer Attributable to high body mass index from 1990 to 2021 and Modelled Projections to 2041","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 15:02:01","doi":"10.21203/rs.3.rs-7148753/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-29T11:01:45+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"249295358038089533012642233808116908354","date":"2025-11-25T22:27:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T14:45:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224239406439874070479813764737336958311","date":"2025-11-24T14:20:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242897315455701069986036473642245217613","date":"2025-11-23T20:40:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163706265925879334389632040274463991013","date":"2025-10-20T14:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104376366229028885678578439481967306248","date":"2025-09-17T22:16:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-06T08:49:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316865104402399686283369458550048094851","date":"2025-08-14T01:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278958693262422031522182702837853200919","date":"2025-08-14T01:21:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T14:57:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-23T11:15:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-21T07:47:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-21T07:42:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-07-17T11:39:21+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":"59c14ef8-7dfd-46d2-8914-272244b6d858","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T16:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 15:02:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7148753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7148753","identity":"rs-7148753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}