Abstract
Background
Thyroid cancer is the most common endocrine tumor, impacting patients’ quality of life and contributing to a significant societal burden. This study aims to estimate the global burden of thyroid cancer attributable to High Body Mass Index(HBMI) over the past 30 years.
Methods
The mortality rates, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) attributable to HBMI for thyroid cancer were extracted. A generalized linear model with a Gaussian distribution was used to calculate the estimated annual percentage changes (EAPCs) in ASRs, quantifying the temporal trends in the global burden of thyroid cancer due to HBMI. The strength and direction of the association between the Social Developmeant Index (SDI) and DALY rates were measured using Spearman’s rank correlation. The Bayesian age-period-cohort (BAPC) model was used to predict deaths patterns of thyroid cancer from 2020 to 2035.
Results
Globally, the DALYs for thyroid cancer increased from 1.49 in 1990 to 1.68 in 2021, with an EAPC of 0.377 (95% CI: 0.342–0.411). 127 countries or regions showed an upward trend. This trend was particularly pronounced in low SDI, middle SDI, and low-middle SDI regions, while a declining trend was observed in high SDI and high-middle SDI regions.The global ASR of death(ASDR) and DALYs were all higher in females than in males.From 2020 to 2035, the global burden of thyroid cancer, measured in DALYs and ASDR, are both projected to exhibit a gradual upward trend.
Conclusions
High Body Mass Index is associated with thyroid cancer. Comprehensive control of body weight may help mitigate or even prevent the development of thyroid cancer, providing valuable data for future prevention and control efforts.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13044-025-00260-9.
Keywords: Thyroid cancer, High body mass index, Risk factors, Global disease burden, Prediction
Introduction
In recent years, thyroid cancer has emerged as the most prevalent endocrine malignancy worldwide, with a steadily increasing incidence [1]. The latest data reveal that global new cases of thyroid cancer are projected to reach 821,000 in 2022 [2]. China reported 466,000 new cases of thyroid cancer in 2022, accounting for more than half of the global incidence that year [3].
Metabolic risk factors, including diabetes, reduced high-density lipoprotein cholesterol, elevated triglycerides, High Body Mass Index(HBMI), and hypertension, have been extensively studied [4].While these factors are well-established contributors to cardiovascular diseases, increasing evidence suggests they also play a critical role in the development of several cancers, such as breast, colorectal, pancreatic, and prostate cancers [5–7].However, whether these metabolic factors influence the development and clinical characteristics of thyroid cancer remains unclear [8, 9].Over the past four decades, the global prevalence of HBMI has increased significantly [10].There have been reports suggesting that HBMI may influence the onset and prognosis of thyroid cancer; however, this remains inconclusive and warrants further investigation [11].
Hence, a systematic analysis is necessary. The Global Burden of Diseases, Injuries, and Risk Factors Study Data(GBD) is an excellent database to facilitate this analysis.Using the latest data from the 2021 GBD study, we assessed the global burden of thyroid cancer attributable to HBMI, analyzing temporal trends as well as age, geographic, and socioeconomic distributions. Our findings aim to provide valuable insights to inform global public health strategies for thyroid cancer prevention and control.
Methods
Data sources
The Global Burden of Disease (GBD) study—an annual, iterative exercise led by the Institute for Health Metrics and Evaluation (IHME) and a worldwide consortium—provides a comprehensive, standardized, and continuously updated account of population health. It quantifies health loss from > 350 diseases and injuries plus > 80 risk factors at global, national, and sub-national levels over time by expressing both fatal and non-fatal outcomes in a single metric: one disability-adjusted life-year (DALY) equals one lost year of healthy life and comprises years of life lost (YLL) and years lived with disability (YLD) [12]. Drawing on thousands of data sources (vital registration, surveys, registries, literature, administrative records), the study cleans, corrects, and standardises raw inputs for under-reporting, miscoding, and incompleteness, then applies rigorous Bayesian and other statistical tools—CODEm for cause-specific mortality, DisMod-MR for non-fatal disease parameters, and ST-GPR for spatiotemporal smoothing—to generate internally consistent time series for locations and years with missing data [13]. Comparative risk assessment (CRA) attributes burden to specific exposures (e.g., smoking, hypertension, air pollution), while uncertainty is propagated through every step to yield 95% uncertainty intervals. Outputs include mortality, incidence, prevalence, YLLs, YLDs, DALYs, and risk-attributable burden, stratified by age, sex, year, and geography. These estimates guide governments, NGOs, and funders in setting priorities, tracking intervention impact, revealing inequities, identifying research gaps, and fostering a common language for cross-disease and cross-country comparisons.
Risk factor and definition of HBMI
BMI stands for Body Mass Index, which is an indicator used to measure the degree of a person’s body fatness or thinness and their health status. Its calculation method is to divide the body weight (in kilograms) by the square of the height (in meters), and the formula is BMI = Weight(kg)/Height(m)2.From GBD official website, the normal range internationally is between 18.5 and 25.When the BMI exceeds 25, we define it as an HBMI [14].
Statistical method
Age-standardized rates (ASR) were obtained from the GBD 2021 study. DALYs represent the burden of disease or disability, encompassing both short-term and long-term health losses. DALYs are calculated by weighting the YLDs and the severity of disability or disease. To estimate the annual percentage change (EAPC) in age-standardized mortality rates (ASMR) or DALY rates, a Gaussian distribution generalized linear model (GLM) was used [15]. This model quantifies the temporal trends of thyroid cancer global burden attributed to metabolic risk factors. When both the EAPC estimation and its lower bound of its 95% confidence interval (CI) are greater than 0, the disease burden is considered to be increasing. Conversely, when the EAPC estimation and the upper bound of its 95% CI are both < 0, the disease burden is deemed to be declining.Spearman rank correlation was employed to assess the relationship between ASR and the Social Development Index (SDI). The SDI is a composite measure of a country’s level of social and demographic development, which is categorized into high SDI, medium-high SDI, medium SDI, medium-low SDI, and low SDI regions(Supplementary Table 1).Data analysis and graphical illustrations were performed using R software (version 4.0.2). BAPC model is used and biostatistics to analyze the relationship between deaths rate and time in epidemiology. It uses sample information and prior information to obtain unique parameter estimates, and the results obtained are reliable [16].The R packages used in our study include “factoextra,” “tidyverse,” “dplyr,” “ggmap,” and “stats.” A p-value of < 0.05 was considered statistically significant.
Results
Global burden and regional comparison of thyroid cancer burden attributable to HBMI
Globally, the DALYs for thyroid cancer increased from 1.49 in 1990 to 1.68 in 2021, with an EAPC of 0.377 (95% CI: 0.342–0.411), indicating an upward trend in disease burden. Similarly, the Age-Standardized Death Rate (ASDR) for thyroid cancer remained at 0.06 per 100,000 population in both 1990 and 2021, with an EAPC of 0.2 (95% CI: 0.165–0.235), also reflecting an increasing trend(Table 1). Among 204 countries or regions, 127 countries showed an upward trend(Table 2; Fig. 1). South Asia exhibited the fastest increase in DALYs, at 2.354 (95% CI: 2.317–2.391), while developed regions such as Central Europe (-2.032, 95% CI: -2.327–-1.737) and Western Europe (-1.029, 95% CI: -1.168–-0.89) experienced significant declines. Similarly, ASDR in South Asia showed the most rapid growth, at 2.326 (95% CI: 2.285–2.368), whereas Central Europe (-1.968, 95% CI: -2.245–-1.692) and Western Europe (-1.232, 95% CI: -1.299–-1.166) demonstrated notable reductions(Table 1).Cabo Verde had the highest growth in thyroid cancer DALYs at 4.992 (95% CI: 4.322–5.662), while Poland showed the largest decrease at -3.172 (95% CI: -3.757–-2.588). In terms of ASDR, Iran exhibited the fastest growth at 4.718 (95% CI: 4.152–5.284), whereas Poland recorded the steepest decline at -2.939 (95% CI: -3.48–-2.399) (Table 2; Fig. 1).
Table 1.
The age-standardised rate of thyroid cancer deaths, and dalys in 1990 and 2021 at the global and regional level
| Region | DALYs No.(95%UI) | 1990–2021 | Deaths No.(95%UI) | 1990–2021 | ||
|---|---|---|---|---|---|---|
| 1990 | 2021 | EAPC No.(95% CI) |
1990 | 2021 | EAPC No.(95% CI) |
|
| Global | 1.49 (1.12–1.9 ) | 1.68 (1.26–2.14 ) | 0.377 (0.342–0.411 ) | 0.06 (0.04–0.07 ) | 0.06 (0.05–0.08 ) | 0.2 (0.165–0.235 ) |
| East Asia | 0.92 (0.63–1.27 ) | 1.16 (0.8–1.58 ) | 0.774 (0.664–0.884 ) | 0.04 (0.02–0.05 ) | 0.04 (0.03–0.06 ) | 0.621 (0.525–0.717 ) |
| Southeast Asia | 1.33 (0.98–1.78 ) | 2.06 (1.48–2.74 ) | 1.381 (1.28–1.481 ) | 0.05 (0.03–0.06 ) | 0.07 (0.05–0.1 ) | 1.42 (1.323–1.517 ) |
| Oceania | 1.74 (1.18–2.52 ) | 1.91 (1.21–2.72 ) | 0.277 (0.228–0.325 ) | 0.07 (0.05–0.1 ) | 0.07 (0.05–0.1 ) | 0.245 (0.218–0.272 ) |
| Central Asia | 2.12 (1.58–2.69 ) | 1.82 (1.38–2.35 ) | -0.714 (-1.323–0.104 ) | 0.07 (0.05–0.09 ) | 0.07 (0.05–0.09 ) | -0.368 (-0.974-0.239 ) |
| Central Europe | 3.26 (2.45–4.05 ) | 1.85 (1.4–2.33 ) | -2.032 (-2.327–1.737 ) | 0.12 (0.09–0.15 ) | 0.07 (0.05–0.09 ) | -1.968 (-2.245–1.692 ) |
| Eastern Europe | 1.87 (1.42–2.34 ) | 2.1 (1.6–2.66 ) | 0.332 (-0.074-0.738 ) | 0.07 (0.05–0.09 ) | 0.08 (0.06–0.1 ) | 0.355 (-0.008-0.718 ) |
| High-income Asia Pacific | 1.38 (0.97–1.83 ) | 1.2 (0.84–1.63 ) | -0.353 (-0.598–0.108 ) | 0.06 (0.04–0.08 ) | 0.05 (0.03–0.07 ) | -0.441 (-0.608–0.274 ) |
| Australasia | 1.46 (1.11–1.84 ) | 1.77 (1.26–2.32 ) | 1.303 (0.99–1.617 ) | 0.05 (0.04–0.07 ) | 0.06 (0.04–0.08 ) | 0.912 (0.635–1.188 ) |
| Southern Latin America | 2.9 (2.13–3.71 ) | 2.4 (1.77–3.09 ) | -0.488 (-0.756–0.219 ) | 0.11 (0.08–0.14 ) | 0.09 (0.07–0.12 ) | -0.498 (-0.742–0.255 ) |
| High-income North America | 1.62 (1.24–2.04 ) | 2.07 (1.59–2.58 ) | 0.784 (0.651–0.917 ) | 0.06 (0.04–0.07 ) | 0.07 (0.05–0.09 ) | 0.71 (0.597–0.824 ) |
| Western Europe | 2.23 (1.72–2.83 ) | 1.55 (1.15-2 ) | -1.029 (-1.168–0.89 ) | 0.09 (0.06–0.11 ) | 0.06 (0.04–0.07 ) | -1.232 (-1.299–1.166 ) |
| Caribbean | 1.64 (1.25–2.09 ) | 2.27 (1.72–2.94 ) | 1.24 (1.005–1.474 ) | 0.06 (0.05–0.08 ) | 0.08 (0.06–0.1 ) | 1.193 (0.949–1.437 ) |
| Andean Latin America | 2.9 (2.11–3.83 ) | 4.26 (3.03–5.89 ) | 1.206 (1.096–1.317 ) | 0.11 (0.08–0.15 ) | 0.16 (0.12–0.23 ) | 1.293 (1.177–1.408 ) |
| Central Latin America | 2.63 (1.98–3.3 ) | 3.39 (2.57–4.4 ) | 0.675 (0.469–0.881 ) | 0.1 (0.08–0.13 ) | 0.13 (0.1–0.17 ) | 0.582 (0.395–0.768 ) |
| North Africa and Middle East | 1.48 (1.07–2.18 ) | 2.2 (1.64–2.87 ) | 1.534 (1.402–1.665 ) | 0.05 (0.04–0.08 ) | 0.08 (0.06–0.1 ) | 1.407 (1.263–1.551 ) |
| South Asia | 0.7 (0.49–0.97 ) | 1.39 (1-1.81 ) | 2.354 (2.317–2.391 ) | 0.02 (0.02–0.03 ) | 0.05 (0.03–0.06 ) | 2.326 (2.285–2.368 ) |
| Tropical Latin America | 1.86 (1.39–2.34 ) | 1.9 (1.42–2.41 ) | -0.134 (-0.221–0.047 ) | 0.07 (0.05–0.09 ) | 0.07 (0.05–0.09 ) | -0.055 (-0.143-0.033 ) |
| Central Sub-Saharan Africa | 0.66 (0.42–1.02 ) | 0.89 (0.52–1.44 ) | 0.938 (0.797–1.078 ) | 0.03 (0.02–0.04 ) | 0.03 (0.02–0.06 ) | 1.007 (0.87–1.143 ) |
| Southern Sub-Saharan Africa | 1.46 (1.04–1.96 ) | 2.32 (1.71-3 ) | 1.764 (1.507–2.021 ) | 0.05 (0.04–0.07 ) | 0.09 (0.06–0.11 ) | 1.742 (1.487–1.997 ) |
| Western Sub-Saharan Africa | 0.27 (0.19–0.37 ) | 0.3 (0.21–0.41 ) | 0.137 (0.056–0.217 ) | 0.01 (0.01–0.01 ) | 0.01 (0.01–0.01 ) | 0.154 (0.076–0.232 ) |
| Eastern Sub-Saharan Africa | 1.92 (1.3–2.69 ) | 2.27 (1.52–3.42 ) | 0.351 (0.212–0.49 ) | 0.06 (0.04–0.09 ) | 0.08 (0.05–0.12 ) | 0.583 (0.459–0.708 ) |
| Low-middle SDI | 0.9 (0.65–1.21 ) | 1.54 (1.16–2.02 ) | 1.842 (1.808–1.876 ) | 0.03 (0.02–0.04 ) | 0.05 (0.04–0.07 ) | 1.795 (1.759–1.832 ) |
| Low SDI | 1.06 (0.74–1.48 ) | 1.38 (0.96–1.91 ) | 0.763 (0.672–0.855 ) | 0.04 (0.02–0.05 ) | 0.05 (0.03–0.07 ) | 0.92 (0.826–1.014 ) |
| High-middle SDI | 1.81 (1.36–2.32 ) | 1.63 (1.21–2.1 ) | -0.402 (-0.472–0.331 ) | 0.07 (0.05–0.09 ) | 0.06 (0.04–0.08 ) | -0.479 (-0.545–0.414 ) |
| High SDI | 1.91 (1.46–2.41 ) | 1.75 (1.32–2.2 ) | -0.202 (-0.28–0.123 ) | 0.07 (0.06–0.09 ) | 0.06 (0.05–0.08 ) | -0.469 (-0.517–0.421 ) |
| Middle SDI | 1.15 (0.86–1.5 ) | 1.71 (1.27–2.2 ) | 1.285 (1.22–1.349 ) | 0.04 (0.03–0.06 ) | 0.06 (0.05–0.08 ) | 1.178 (1.12–1.236 ) |
Table 2.
The age-standardised rate of thyroid cancer deaths, and dalys at the country level
| country | re_EAPC | re_EAPC(Deaths) |
|---|---|---|
| Afghanistan | 2.266 (2.143–2.39 ) | 2.392 (2.261–2.524 ) |
| Albania | -0.845 (-1.067–0.622 ) | -0.898 (-1.141–0.655 ) |
| Algeria | 1.918 (1.763–2.073 ) | 1.892 (1.704–2.081 ) |
| American Samoa | 1.173 (0.89–1.456 ) | 0.975 (0.719–1.232 ) |
| Andorra | -0.68 (-0.843–0.518 ) | -0.826 (-0.99–0.662 ) |
| Angola | 1.196 (1.088–1.304 ) | 1.254 (1.154–1.353 ) |
| Antigua and Barbuda | 1.557 (1.082–2.032 ) | 1.6 (1.125–2.075 ) |
| Argentina | -0.421 (-0.722–0.12 ) | -0.435 (-0.712–0.157 ) |
| Armenia | 2.028 (1.63–2.426 ) | 2.095 (1.706–2.483 ) |
| Australia | 1.605 (1.29–1.92 ) | 1.165 (0.9–1.43 ) |
| Austria | -1.277 (-1.356–1.198 ) | -1.532 (-1.656–1.408 ) |
| Azerbaijan | -0.143 (-0.246–0.041 ) | 0.03 (-0.068-0.129 ) |
| Bahamas | 1.419 (1.08–1.758 ) | 1.447 (1.108–1.786 ) |
| Bahrain | -0.041 (-0.199-0.116 ) | 0.014 (-0.228-0.257 ) |
| Bangladesh | 1.966 (1.844–2.088 ) | 1.719 (1.621–1.817 ) |
| Barbados | 1.075 (0.873–1.278 ) | 1.169 (0.969–1.368 ) |
| Belarus | 0.488 (-0.066-1.043 ) | 0.734 (0.253–1.216 ) |
| Belgium | -0.672 (-0.885–0.46 ) | -0.859 (-1.076–0.642 ) |
| Belize | 2.06 (1.841–2.279 ) | 1.87 (1.646–2.095 ) |
| Benin | -1.306 (-1.488–1.124 ) | -1.306 (-1.483–1.129 ) |
| Bermuda | -0.071 (-0.539-0.397 ) | -0.254 (-0.726-0.219 ) |
| Bhutan | 0.634 (0.571–0.698 ) | 0.759 (0.699–0.818 ) |
| Bolivia (Plurinational State of) | 0.525 (0.484–0.566 ) | 0.719 (0.679–0.759 ) |
| Bosnia and Herzegovina | -1.308 (-1.536–1.08 ) | -1.25 (-1.515–0.986 ) |
| Botswana | 1.068 (0.724–1.411 ) | 1.139 (0.819–1.458 ) |
| Brazil | -0.175 (-0.265–0.084 ) | -0.091 (-0.184-0.001 ) |
| Brunei Darussalam | 0.633 (0.482–0.783 ) | 0.633 (0.462–0.804 ) |
| Bulgaria | -0.528 (-0.807–0.248 ) | -0.634 (-0.896–0.372 ) |
| Burkina Faso | -1.169 (-1.365–0.973 ) | -1.245 (-1.442–1.048 ) |
| Burundi | -0.787 (-1.008–0.567 ) | -0.636 (-0.836–0.435 ) |
| Cabo Verde | 4.992 (4.322–5.662 ) | 4.592 (4.089–5.095 ) |
| Cambodia | 1.26 (1.115–1.406 ) | 1.409 (1.257–1.561 ) |
| Cameroon | -1.035 (-1.199–0.871 ) | -1.086 (-1.256–0.916 ) |
| Canada | -0.398 (-0.585–0.21 ) | -0.393 (-0.549–0.237 ) |
| Central African Republic | 0.401 (0.338–0.463 ) | 0.487 (0.43–0.544 ) |
| Chad | -0.594 (-0.773–0.415 ) | -0.71 (-0.899–0.521 ) |
| Chile | -0.895 (-1.165–0.625 ) | -0.901 (-1.134–0.667 ) |
| China | 0.763 (0.648–0.878 ) | 0.612 (0.511–0.713 ) |
| Colombia | -0.029 (-0.416-0.357 ) | -0.093 (-0.455-0.269 ) |
| Comoros | 1.218 (1.034–1.403 ) | 1.401 (1.278–1.524 ) |
| Congo | 0.427 (0.289–0.565 ) | 0.463 (0.339–0.586 ) |
| Cook Islands | -1.242 (-1.621–0.864 ) | -1.42 (-1.813–1.026 ) |
| Costa Rica | -0.097 (-0.384-0.191 ) | -0.022 (-0.262-0.218 ) |
| Croatia | -1.898 (-2.206–1.59 ) | -1.963 (-2.281–1.644 ) |
| Cuba | 1.921 (1.54–2.302 ) | 2.033 (1.645–2.421 ) |
| Cyprus | -1.675 (-1.847–1.502 ) | -2.15 (-2.27–2.03 ) |
| Czechia | -1.926 (-2.072–1.781 ) | -2.137 (-2.261–2.012 ) |
| C么te d’Ivoire | 0.656 (0.559–0.753 ) | 0.557 (0.46–0.654 ) |
| Democratic People’s Republic of Korea | 0.946 (0.893–0.998 ) | 0.965 (0.891–1.038 ) |
| Democratic Republic of the Congo | 1.04 (0.85–1.23 ) | 1.15 (0.973–1.326 ) |
| Denmark | -0.781 (-1.166–0.397 ) | -0.629 (-0.977–0.281 ) |
| Djibouti | 1.378 (1.318–1.437 ) | 1.396 (1.355–1.436 ) |
| Dominica | 0.833 (0.717–0.949 ) | 0.739 (0.64–0.839 ) |
| Dominican Republic | 1.431 (1.302–1.561 ) | 1.293 (1.155–1.432 ) |
| Ecuador | 2.956 (2.475–3.436 ) | 3.152 (2.667–3.636 ) |
| Egypt | 1.805 (1.642–1.967 ) | 1.933 (1.737–2.129 ) |
| El Salvador | 0.57 (0.369–0.772 ) | 0.487 (0.291–0.683 ) |
| Equatorial Guinea | 1.445 (1.248–1.643 ) | 1.562 (1.375–1.75 ) |
| Eritrea | 1.563 (1.492–1.633 ) | 1.692 (1.626–1.758 ) |
| Estonia | -0.247 (-0.671-0.177 ) | -0.011 (-0.385-0.362 ) |
| Eswatini | 1.705 (1.256–2.155 ) | 1.614 (1.23–1.997 ) |
| Ethiopia | -1.143 (-1.367–0.919 ) | -0.872 (-1.083–0.661 ) |
| Fiji | 0.352 (0.23–0.475 ) | 0.462 (0.33–0.595 ) |
| Finland | -1.148 (-1.587–0.708 ) | -1.366 (-1.738–0.995 ) |
| France | -0.705 (-1.299–0.111 ) | -1.345 (-1.768–0.922 ) |
| Gabon | 0.22 (0.098–0.342 ) | 0.304 (0.192–0.416 ) |
| Gambia | 1.077 (0.854–1.301 ) | 1.011 (0.823-1.2 ) |
| Georgia | 2.697 (1.533–3.86 ) | 2.77 (1.603–3.937 ) |
| Germany | -1.504 (-1.727–1.28 ) | -1.852 (-2.062–1.642 ) |
| Ghana | 1.213 (1.068–1.359 ) | 1.418 (1.301–1.534 ) |
| Greece | -0.155 (-0.237–0.072 ) | -0.496 (-0.591–0.402 ) |
| Greenland | -0.203 (-0.686-0.281 ) | -0.15 (-0.611-0.31 ) |
| Grenada | 0.977 (0.499–1.456 ) | 1.207 (0.756–1.658 ) |
| Guam | 2.408 (1.676–3.14 ) | 1.322 (0.633–2.01 ) |
| Guatemala | -0.085 (-0.481-0.312 ) | -0.307 (-0.673-0.059 ) |
| Guinea | 0.647 (0.609–0.685 ) | 0.624 (0.578–0.67 ) |
| Guinea-Bissau | -0.84 (-0.98–0.7 ) | -0.852 (-1.008–0.695 ) |
| Guyana | 1.634 (1.449–1.819 ) | 1.508 (1.324–1.691 ) |
| Haiti | 0.748 (0.661–0.835 ) | 0.754 (0.669–0.839 ) |
| Honduras | 1.73 (1.554–1.907 ) | 1.912 (1.714–2.109 ) |
| Hungary | -2.315 (-2.596–2.035 ) | -2.251 (-2.525–1.977 ) |
| Iceland | -1.118 (-1.634–0.602 ) | -1.147 (-1.689–0.604 ) |
| India | 2.677 (2.609–2.745 ) | 2.81 (2.733–2.888 ) |
| Indonesia | 1.605 (1.443–1.767 ) | 1.693 (1.539–1.846 ) |
| Iran (Islamic Republic of) | 4.887 (4.338–5.436 ) | 4.718 (4.152–5.284 ) |
| Iraq | 1.275 (1.094–1.456 ) | 1.305 (1.11–1.499 ) |
| Ireland | -0.837 (-1.16–0.514 ) | -1.09 (-1.425–0.755 ) |
| Israel | -1.39 (-1.754–1.027 ) | -1.51 (-1.853–1.168 ) |
| Italy | -1.505 (-1.628–1.383 ) | -1.487 (-1.569–1.405 ) |
| Jamaica | 1.891 (1.524–2.258 ) | 1.769 (1.411–2.128 ) |
| Japan | -0.585 (-0.695–0.476 ) | -0.564 (-0.653–0.474 ) |
| Jordan | 0.075 (-0.222-0.372 ) | 0.093 (-0.195-0.38 ) |
| Kazakhstan | -2.158 (-2.752–1.563 ) | -1.808 (-2.389–1.228 ) |
| Kenya | 2.795 (2.574–3.016 ) | 2.833 (2.608–3.059 ) |
| Kiribati | 0.299 (0.116–0.482 ) | 0.438 (0.271–0.605 ) |
| Kuwait | 1.005 (0.098–1.912 ) | 1.13 (0.239–2.021 ) |
| Kyrgyzstan | -0.222 (-1.038-0.594 ) | -0.142 (-0.989-0.706 ) |
| Lao People’s Democratic Republic | 0.722 (0.691–0.754 ) | 0.793 (0.767–0.818 ) |
| Latvia | 0.889 (0.483–1.294 ) | 1.245 (0.847–1.642 ) |
| Lebanon | 0.371 (0.226–0.516 ) | 0.382 (0.221–0.544 ) |
| Lesotho | 3.431 (2.945–3.916 ) | 3.231 (2.778–3.683 ) |
| Liberia | -0.649 (-0.82–0.478 ) | -0.827 (-0.985–0.669 ) |
| Libya | 2.058 (1.88–2.236 ) | 1.928 (1.752–2.104 ) |
| Lithuania | 0.568 (0.072–1.063 ) | 0.849 (0.394–1.304 ) |
| Luxembourg | -1.683 (-1.812–1.554 ) | -1.63 (-1.732–1.529 ) |
| Madagascar | 1.123 (0.947–1.298 ) | 1.202 (1.036–1.369 ) |
| Malawi | 1.519 (1.332–1.706 ) | 1.628 (1.445–1.811 ) |
| Malaysia | 0.65 (0.54–0.759 ) | 0.64 (0.487–0.794 ) |
| Maldives | -1.292 (-1.416–1.167 ) | -1.193 (-1.325–1.061 ) |
| Mali | -0.069 (-0.203-0.066 ) | -0.111 (-0.232-0.009 ) |
| Malta | -0.991 (-1.368–0.614 ) | -1.339 (-1.718–0.96 ) |
| Marshall Islands | 0.985 (0.808–1.162 ) | 0.807 (0.652–0.962 ) |
| Mauritania | -1.331 (-1.5–1.161 ) | -1.472 (-1.651–1.292 ) |
| Mauritius | -0.027 (-0.879-0.824 ) | -0.099 (-0.971-0.773 ) |
| Mexico | 1.008 (0.848–1.169 ) | 0.847 (0.695–0.999 ) |
| Micronesia (Federated States of) | 0.676 (0.612–0.741 ) | 0.666 (0.607–0.725 ) |
| Monaco | 0.27 (0.227–0.313 ) | 0.116 (0.089–0.142 ) |
| Mongolia | -0.641 (-1.074–0.208 ) | -0.598 (-1.019–0.177 ) |
| Montenegro | 0.564 (0.429–0.698 ) | 0.739 (0.585–0.894 ) |
| Morocco | 1.277 (1.206–1.349 ) | 1.284 (1.204–1.363 ) |
| Mozambique | 2.274 (2.081–2.466 ) | 2.275 (2.078–2.472 ) |
| Myanmar | 0.062 (-0.088-0.213 ) | 0.252 (0.119–0.385 ) |
| Namibia | 2.054 (1.869–2.238 ) | 2.171 (2.012–2.331 ) |
| Nauru | 1.186 (1-1.373 ) | 1.285 (1.106–1.464 ) |
| Nepal | 2.407 (2.139–2.674 ) | 2.349 (2.098-2.6 ) |
| Netherlands | -0.019 (-0.17-0.131 ) | -0.167 (-0.293–0.04 ) |
| New Zealand | -0.045 (-0.428-0.337 ) | -0.198 (-0.588-0.193 ) |
| Nicaragua | 1.284 (1.124–1.445 ) | 1.253 (1.072–1.433 ) |
| Niger | -1.432 (-1.624–1.24 ) | -1.467 (-1.65–1.283 ) |
| Nigeria | 1.619 (1.503–1.736 ) | 1.538 (1.431–1.646 ) |
| Niue | 0.839 (0.746–0.933 ) | 0.789 (0.707–0.871 ) |
| North Macedonia | -1.013 (-1.137–0.889 ) | -0.812 (-0.947–0.677 ) |
| Northern Mariana Islands | 1.801 (1.22–2.382 ) | 1.801 (1.246–2.355 ) |
| Norway | -0.38 (-0.685–0.074 ) | -0.35 (-0.607–0.094 ) |
| Oman | 2.566 (2.287–2.846 ) | 2.586 (2.309–2.863 ) |
| Pakistan | 1.672 (1.447–1.897 ) | 1.674 (1.455–1.893 ) |
| Palau | 0.425 (0.364–0.485 ) | 0.518 (0.453–0.583 ) |
| Palestine | 0.185 (0.045–0.325 ) | 0.256 (0.072–0.44 ) |
| Panama | 0.801 (0.543–1.059 ) | 1.052 (0.897–1.208 ) |
| Papua New Guinea | 0.578 (0.507–0.648 ) | 0.59 (0.518–0.662 ) |
| Paraguay | 1.113 (1.041–1.186 ) | 1.155 (1.067–1.243 ) |
| Peru | 0.537 (0.336–0.738 ) | 0.514 (0.321–0.708 ) |
| Philippines | 1.589 (1.519–1.658 ) | 1.684 (1.612–1.757 ) |
| Poland | -3.172 (-3.757–2.588 ) | -2.939 (-3.48–2.399 ) |
| Portugal | -1.18 (-1.377–0.983 ) | -1.478 (-1.635–1.321 ) |
| Puerto Rico | 0.072 (-0.226-0.37 ) | -0.267 (-0.563-0.029 ) |
| Qatar | 0.363 (0.048–0.677 ) | 0.037 (-0.352-0.425 ) |
| Republic of Korea | 0.033 (-0.653-0.719 ) | -0.273 (-0.871-0.326 ) |
| Republic of Moldova | 0.255 (-0.008-0.518 ) | 0.084 (-0.143-0.311 ) |
| Romania | -1.3 (-1.621–0.978 ) | -1.199 (-1.497–0.901 ) |
| Russian Federation | 0.225 (-0.216-0.667 ) | 0.252 (-0.142-0.646 ) |
| Rwanda | -1.003 (-1.32–0.685 ) | -0.654 (-0.935–0.374 ) |
| Saint Kitts and Nevis | 0.421 (0.011–0.832 ) | 0.743 (0.379–1.107 ) |
| Saint Lucia | -0.039 (-0.617-0.539 ) | -0.359 (-0.956-0.237 ) |
| Saint Vincent and the Grenadines | 1.386 (0.903–1.87 ) | 1.208 (0.699–1.717 ) |
| Samoa | 0.442 (0.391–0.493 ) | 0.257 (0.209–0.306 ) |
| San Marino | -0.656 (-0.982–0.331 ) | -0.926 (-1.252–0.6 ) |
| Sao Tome and Principe | 1.641 (1.508–1.774 ) | 1.552 (1.435–1.669 ) |
| Saudi Arabia | 2.736 (2.493–2.98 ) | 2.294 (2.03–2.559 ) |
| Senegal | -0.878 (-1.03–0.725 ) | -0.882 (-1.035–0.73 ) |
| Serbia | -0.364 (-0.538–0.19 ) | -0.605 (-0.775–0.435 ) |
| Seychelles | 0.226 (-0.067-0.519 ) | 0.277 (-0.018-0.573 ) |
| Sierra Leone | -0.382 (-0.529–0.234 ) | -0.644 (-0.813–0.475 ) |
| Singapore | -0.702 (-0.971–0.433 ) | -0.444 (-0.686–0.201 ) |
| Slovakia | -1.327 (-1.441–1.213 ) | -1.319 (-1.431–1.207 ) |
| Slovenia | -1.994 (-2.243–1.744 ) | -1.875 (-2.099–1.652 ) |
| Solomon Islands | 1.315 (1.238–1.392 ) | 1.248 (1.184–1.312 ) |
| Somalia | 1.04 (0.993–1.088 ) | 1.142 (1.097–1.186 ) |
| South Africa | 1.204 (1.037–1.371 ) | 1.355 (1.14–1.57 ) |
| South Sudan | 1.386 (1.11–1.662 ) | 1.354 (1.113–1.594 ) |
| Spain | -1.011 (-1.16–0.863 ) | -0.986 (-1.081–0.89 ) |
| Sri Lanka | -0.386 (-0.613–0.159 ) | -0.417 (-0.63–0.203 ) |
| Sudan | 2.11 (1.926–2.294 ) | 2.104 (1.915–2.294 ) |
| Suriname | 0.625 (0.443–0.807 ) | 0.619 (0.437–0.801 ) |
| Sweden | -0.512 (-0.771–0.253 ) | -0.591 (-0.821–0.36 ) |
| Switzerland | -1.647 (-2.13–1.163 ) | -1.507 (-1.926–1.089 ) |
| Syrian Arab Republic | 2.659 (2.558–2.761 ) | 2.655 (2.566–2.744 ) |
| Taiwan (Province of China) | 1.009 (0.777–1.24 ) | 0.649 (0.463–0.836 ) |
| Tajikistan | -1.216 (-1.355–1.076 ) | -1.229 (-1.418–1.039 ) |
| Thailand | 0.487 (0.219–0.754 ) | 0.324 (0.095–0.553 ) |
| Timor-Leste | 1.321 (1.12–1.523 ) | 1.312 (1.136–1.489 ) |
| Togo | -0.466 (-0.585–0.347 ) | -0.501 (-0.631–0.371 ) |
| Tokelau | 0.663 (0.565–0.761 ) | 0.583 (0.481–0.685 ) |
| Tonga | 0.98 (0.806–1.155 ) | 1.096 (0.926–1.265 ) |
| Trinidad and Tobago | 0.38 (0.121–0.638 ) | 0.156 (-0.108-0.421 ) |
| Tunisia | 1.395 (1.334–1.455 ) | 1.218 (1.148–1.288 ) |
| Turkey | -0.14 (-0.304-0.024 ) | -0.128 (-0.335-0.078 ) |
| Turkmenistan | -0.888 (-1.856-0.081 ) | -0.969 (-1.947-0.009 ) |
| Tuvalu | 0.823 (0.746-0.9 ) | 0.769 (0.697–0.842 ) |
| Uganda | 2.565 (2.372–2.757 ) | 2.569 (2.408–2.73 ) |
| Ukraine | 0.541 (0.178–0.904 ) | 0.445 (0.119–0.771 ) |
| United Arab Emirates | 2.145 (1.663–2.628 ) | 3.021 (2.407–3.634 ) |
| United Kingdom | -0.041 (-0.181-0.098 ) | -0.195 (-0.325–0.066 ) |
| United Republic of Tanzania | 1.244 (1.143–1.346 ) | 1.324 (1.229–1.42 ) |
| United States of America | 0.899 (0.759–1.038 ) | 0.826 (0.707–0.945 ) |
| United States Virgin Islands | -0.57 (-0.707–0.432 ) | -1.136 (-1.303–0.969 ) |
| Uruguay | 0.348 (0.259–0.437 ) | 0.232 (0.148–0.316 ) |
| Uzbekistan | 2.837 (2.219–3.454 ) | 2.959 (2.358–3.561 ) |
| Vanuatu | 1.204 (1.127–1.282 ) | 1.141 (1.071–1.21 ) |
| Venezuela (Bolivarian Republic of) | 0.947 (0.789–1.106 ) | 0.929 (0.781–1.078 ) |
| Viet Nam | 3.621 (3.341–3.902 ) | 3.378 (3.138–3.619 ) |
| Yemen | 2.74 (2.54–2.939 ) | 2.717 (2.513–2.921 ) |
| Zambia | 2.558 (2.233–2.882 ) | 2.595 (2.302–2.888 ) |
| Zimbabwe | 3.659 (2.997–4.32 ) | 3.327 (2.78–3.873 ) |
Fig. 1.
Age-standardised DALYs (a) and Deaths (b) rate of thyroid cancer per 100,000 population of both sexes in 2021
DALYs, disability-adjusted life years;
Different SDI regional comparison of thyroid cancer burden attributable to HBMI
As illustrated in Fig. 2, the global burden of thyroid cancer attributable to HBMI, measured in both DALYs and ASDR, showed an increasing trend from 1990 to 2021,which was also shown in Table 1.This increasing trend was particularly pronounced in regions with low SDI, middle SDI, and low-middle SDI. However, in regions with high SDI and high-middle SDI, a declining trend was observed. Despite this, the burden in high-SDI regions remained significantly higher than in low-SDI regions(Fig. 2).EAPCs exhibited a declining trend over time in high-SDI and high-middle-SDI regions, becoming particularly pronounced after 2010. In contrast, an increasing trend was observed in low-SDI, low-middle-SDI, and middle-SDI regions, with the most notable rise seen in low-middle-SDI regions, at 1.842 (95% CI: 1.808–1.876) (Table 1).
Fig. 2.
Temporal trend in the DALYs (a) and Deaths (b) rate of thyroid cancer attributable to HBMI for different SDI regions, 1990–2021
DALYs, disability-adjusted life years; HBMI, high body mass index
Comparison of thyroid cancer burden attributable to HBMI across age groups and sex
From 1990 to 2021, the global ASR of death and DALYs were all higher in females than in males, showing a significant sex gap starting from 20 to 24 years to ≥ 95 years. They increased markedly across all age groups in males, and a consistently upward growth trend was shown in the age group of 25–34 years. The ASDR and DALYs showed an increasing trend among women aged 20–49 and those over 85 years old(Figs. 3 and 4).
Fig. 3.
The global disease burden of DALYs rate of thyroid cancer with different sex attributable to HBMI from 1990 to 2021
DALYs, disability-adjusted life years; HBMI, high body mass index
Fig. 4.
The global disease burden of Deaths rate of thyroid cancer with different sex attributable to HBMI from 1990 to 2021
HBMI, high body mass index
Global trends in projected thyroid cancer burden from 2020 to 2035
The study included individuals aged 20 years and older, dividing them into 14 five-year age groups. From 2020 to 2035, the global burden of thyroid cancer, measured in DALYs and ASDR, are both projected to exhibit a gradual upward trend, rising from lower to higher levels(Figs. 5 and 6) ,especially for those under the age of 59 (Figs. 7 and 8).
Fig. 5.
The temporal trends of age-standardised DALYs rate of thyroid cancer between 1990 and 2035
DALYs, disability-adjusted life years
Fig. 6.
The temporal trends of age-standardised Deaths rate of thyroid cancer between 1990 and 2035
Fig. 7.
The temporal trends of age-standardised DALYs-age-rate of thyroid cancer between 1990 and 2035
DALYs, disability-adjusted life years
Fig. 8.
The temporal trends of age-standardised Deaths-age-rate of thyroid cancer between 1990 and 2035
Discussion
The GBD study, led by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, serves as a comprehensive database for assessing and analyzing the health impacts of diseases, injuries, and risk factors at both global and regional levels [17].In this study, we leveraged the GBD database to analyze the global burden and temporal trends of thyroid cancer attributable to HBMI from 1990 to 2021, disaggregated by year, age group, region, and economic level.
The SDI is a composite measure used to assess the development status of a country or region, derived from data on per capita income, average educational attainment, and fertility rates. It ranges from 0 to 1, with higher values indicating greater levels of societal development [18]. Our findings reveal a distinct regional disparity in the burden of thyroid cancer attributable to metabolic risk factors, with higher SDI regions experiencing a significantly greater burden compared to lower SDI regions. Specifically, the burden has increased sharply in low, middle, and low-middle SDI regions, while showing a declining trend in high and high-middle SDI regions.These findings underscore substantial differences in thyroid cancer-related DALYs and ASDR across regions with varying levels of socioeconomic development. The observed decline in thyroid cancer mortality in high SDI regions is promising and likely reflects progress in early detection and timely treatment, underscoring the effectiveness of advanced healthcare systems and cancer control strategies in more developed areas. Conversely, the persistently rising mortality rates in low SDI regions emphasize the urgent need to improve access to high-quality healthcare and implement robust cancer control programs in these underserved areas [19].
Kitahara’s study found that greater height and excess adiposity throughout adulthood are associated with higher incidence of most major types of thyroid cancer. When reducing the global adult BMI distribution to below 25 kg/m² could prevent approximately 8–14% of thyroid cancer cases worldwide. In this study, the burden of thyroid cancer attributable to HBMI was found to be associated with advancing age, with the highest burden observed in older age groups. The disease burden among females consistently exceeded that of males across all age groups, aligning with prior studies indicating that the incidence of thyroid cancer in women is approximately four times higher than in men [20]. Our age-specific analysis revealed that the global thyroid cancer DALYs and ASDR were highest among individuals aged 85 years and older, generally increasing with age.
Compared to 1990, we observed a decline in ASDR among women aged 50–84 years by 2021, while ASDR increased in all other age groups. Research suggests that estrogen may promote the initiation, proliferation, and metastatic potential of thyroid cancer cells [21–24]. After the age of 50, most women transition into perimenopause, accompanied by a gradual decline in estrogen levels, which may explain the observed reduction in ASDR among women in this age group. These age-specific trends provide valuable insights for developing targeted screening and prevention strategies tailored to different demographic groups.
HBMI induces insulin resistance, elevates levels of thyroid-stimulating hormone, and triggers the expression of various inflammatory and adipogenic factors. It can also lead to hyperglycemia and hyperlipidemia, all of which are closely linked to the development of thyroid cancer [25]. Chronic obesity-induced insulin resistance significantly increases insulin levels in the body, which may stimulate the expression of vascular endothelial growth factor (VEGF) and promote the proliferation of vascular endothelial cells in tumors. Hyperinsulinemia further enhances the expression of insulin-like growth factor 1 (IGF-1). On one hand, IGF-1 promotes cell proliferation and inhibits apoptosis, directly contributing to the initiation and progression of tumors [26]. On the other hand, it activates intracellular protein kinase B, inducing the TSH-stimulated IGF-1 receptor/mTOR signaling pathway, which promotes cell proliferation and suppresses programmed cell death [27]. Chronic inflammation increases the secretion of pro-inflammatory factors such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-8 (IL-8). These factors activate relevant pathways, enhancing the synthesis and secretion of chemokines, which play a critical role in the malignant transformation and progression of thyroid cells [28]. Adipokines, including leptin, adiponectin, and resistin, influence metabolism and are associated with the development and progression of thyroid cancer [29]. Implementation of WHO ‘Best-Buys’ policies—including sugar taxes, front-of-package food labeling, and restrictions on marketing of unhealthy foods to children—could lead to a population-wide BMI reduction of 0.5–1.0 kg/m² over a decade, with an associated 3–6% decrease in thyroid cancer incidence.
This GBD-based study utilizes the latest data to analyze the global burden of thyroid cancer, including mortality rates and DALYs, while assessing the contribution of HBMI and predicting future trends. However, there are several limitations to our study that should be considered when interpreting the results.
First, there is significant variation in data quality across different countries and regions. Many lower-income countries lack robust population-based cancer registries, which may lead to an underestimation of the thyroid cancer burden in these areas.
Second, our projections for the future burden of thyroid cancer are based on current data, and it is important to account for potential confounding factors and changes over time. Regular tracking and updates are necessary.Third, our study relies heavily on the methodological framework of the GBD study, which may introduce potential biases in data collection and modeling methods.Despite these limitations, our study provides valuable insights into the estimated burden and trends of thyroid cancer attributable to metabolic risk factors, laying a foundation for future research and policy development on thyroid cancer prevention and control.
Conclusion
HBMI are associated with thyroid cancer. Comprehensive control of body weight may help mitigate or even prevent the development of thyroid cancer, providing valuable data for future prevention and control efforts.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to thank our families for their constant support and encouragement throughout this research.
Abbreviations
- HBMI
High Body Mass Index
- DALYs
Disability-adjusted Life Years
- ASRs
Age-standardized Rates
- EAPCs
Estimated Annual Percentage Changes
- SDI
Social Development Index
- BAPC
Bayesian Age-period-cohort
- ASDR
Age-standardized Rates of Death
- GBD
Global Burden of Diseases, Injuries, and Risk Factors Study Data
- CODEm
Cause of Death Ensemble Model
- YLLs
Years of Life Lost
- YLDs
Years Lived with Disability
- UI
Uncertainty Intervals
- ASMR
Age-standardized Mortality Rates
- GLM
Generalized Linear Model
- IHME
Institute for Health Metrics and Evaluation
- VEGF
Vascular Endothelial Growth Factor
- IGF-1
Insulin-like Growth Factor 1
- TNF-α
Tumor Necrosis Factor-alpha
- IL-6
Interleukin-6
- IL-8
Interleukin-8
Author contributions
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or critically revising it for important intellectual content; agreed to submit to the current journal; gave final approval for the version to be published; and agreed to be accountable for all aspects of the work.
Funding
This study was supported by Hangzhou biomedicine and health industry development support science and technology project (No. 2022WJCY024; No. 2021WJCY238; No. 2021WJCY047; No. 2021WJCY115); Hangzhou Normal University Dengfeng Project“Clinical Medicine Revitalization Plan” The First People’s Hospital of Jiande Special Project (No. LCYXZXJH001); Interdisciplinary Research Project of Hangzhou Normal University (No. 2024JCXK05).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval
All analyses were based on Global Health Data, thus no ethical approval and patient consent are required.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xinjun Zhang, Hua Fan and Qingwen Yu contributed equally to this work.
Contributor Information
Mingwei Wang, Email: wmw990556@hznu.edu.cn.
Yongran Cheng, Email: 2020000339@hmc.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.