Global Burden of Early-Onset Cancers Attributable to Metabolic Risk Factors from 1990 to 2021 and Projections to 2040

Article information

Endocrinol Metab. 2026;.enm.2025.2577

Publication date (electronic) : 2026 January 5

doi : https://doi.org/10.3803/EnM.2025.2577

Wenmei Qiao ¹^,^*

, Kaijian Zhou ²^,^*

, Liang He ³^,^*

, Jiahui Guo ¹, Qiqiang Guo ⁴

, Jing Zhang ⁵

, Yongze Li ¹

¹Department of Endocrinology and Metabolism, The Institute of Endocrinology, NHC Key Laboratory of Diagnosis and Treatment of Thyroid Disease, The First Hospital of China Medical University, Shenzhen, China

²Department of Plastic Surgery, The First Hospital of China Medical University, Shenzhen, China

³Department of Thyroid Surgery, The First Hospital of China Medical University, Shenzhen, China

⁴The College of Basic Medical Science, Health Sciences Institute, China Medical University, Shenzhen, China

⁵School of Public Health, Shenzhen University Medical School, Shenzhen, China

Corresponding authors: Yongze Li. Department of Endocrinology and Metabolism, The Institute of Endocrinology, NHC Key Laboratory of Diagnosis and Treatment of Thyroid Disease, The First Hospital of China Medical University, No. 155, Nanjing Bei Street, Shenyang, China, Tel: +86-13840140101, Fax: +86-24-83283294, E-mail: yzli87@cmu.edu.cn

Qiqiang Guo. The College of Basic Medical Science, Health Sciences Institute, China Medical University, No. 155, Nanjing Bei Street, Shenyang, China, Tel: +86-13504025547, Fax: +86-24-31939614, E-mail: qqguo@cmu.edu.cn

Jing Zhang. School of Public Health, Shenzhen University Medical School, No. 3688, Nanhai Road, Shenzhen, China, Tel: +86-18698877717, Fax: +86-755-86970549, E-mail: zhangjing1985zj@163.com

*These authors contributed equally to this work.

Received 2025 July 24; Revised 2025 September 30; Accepted 2025 October 21.

Abstract

Background

Early-onset cancers (diagnosed in individuals under 50 years of age) are increasingly contributing to the global cancer burden. Among the known contributors, specific metabolic risk factors—namely high body mass index (BMI) and high fasting plasma glucose (FPG)—have emerged as significant determinants. This study assessed the global burden of early-onset cancers attributable to these two metabolic risk factors and projected their trends through 2040.

Methods

Using data from the Global Burden of Disease 2021 study, we analyzed mortality, disability-adjusted life years (DALYs), and estimated annual percentage changes for early-onset cancers linked to metabolic risk factors, stratified by age, sex, and sociodemographic index (SDI). Future trends were projected based on these data.

Results

In 2021, the two metabolic risk factors (high FPG and high BMI) together accounted for 34,112 (95% uncertainty interval [UI], 15,037 to 54,888) deaths and 1,691,418 (95% UI, 762,923 to 2,698,657) DALYs from early-onset cancers, corresponding to mortality and DALY rates of 0.9 (95% UI, 0.4 to 1.4) and 42.8 (95% UI, 19.3 to 68.3) per 100,000 population, respectively. Between 1990 and 2021, both mortality and DALY rates rose significantly, with the greatest increases observed in individuals aged 30–34 years and in low-middle SDI regions. High FPG and high BMI were the leading contributors, with colorectal cancer showing the highest burden and liver cancer the most rapid growth. While high BMI predominated in most SDI regions, high FPG was more prominent in low-SDI countries, particularly among females. Projections indicate a continued rise in mortality and DALY rates through 2040.

Conclusion

High BMI and high FPG are major contributors to the global burden of early-onset cancers, with marked sex and socioeconomic disparities necessitating targeted interventions. Urgent strategies are required to mitigate metabolic risks and enhance early cancer detection, especially among vulnerable populations.

Keywords: Early-onset cancers; Metabolic risk; Fasting plasma glucose; Body mass index

GRAPHICAL ABSTRACT

INTRODUCTION

Cancer has become one of the most significant public health challenges of this century, threatening to surpass cardiovascular disease as the leading cause of premature death from noncommunicable diseases worldwide [1]. It is a multifactorial disease that predominantly affects individuals over the age of 50. However, the incidence of several types of cancer among adults younger than 50 years has been rising in many regions across the globe [2]. The term ‘late-onset’ is typically applied to cancers diagnosed at or after the age of 50, whereas ‘early-onset’ refers to cancers occurring in adults under 50 years of age [3]. Previous studies have reported that early-onset cancers often exhibit distinct characteristics, including later stage at diagnosis and greater aggressiveness, leading to a sharper increase in the cancer burden among younger populations [4]. This pattern has been linked to modern dietary and lifestyle factors that contribute to obesity and elevated blood glucose levels [5].

Metabolic risk factors, including high fasting plasma glucose (FPG) and high body mass index (BMI), are important modifiable determinants that substantially contribute to morbidity and mortality in early-onset cancers [6]. Adolescence and young adulthood represent critical developmental periods that present both unique challenges and opportunities for improving metabolic health [2]. High FPG and high BMI are associated with metabolic abnormalities, insulin resistance, and chronic inflammation, all of which may promote tumor initiation, progression, and metastasis [7]. Understanding the epidemiological patterns of these metabolic risk factors is crucial for elucidating the metabolic underpinnings of early-onset cancers. Such insights can guide the development of early screening, prevention, and intervention strategies, as well as the optimization of personalized treatment approaches to enhance therapeutic efficacy [8].

Previous research on early-onset cancers has primarily focused on overall cancer burden across all age groups and risk factors [5,9] or has briefly discussed associations between certain risk factors and specific early-onset cancers [10,11]. However, the global burden of early-onset cancers attributable specifically to metabolic risk factors remains insufficiently characterized. To address this gap, the present study utilized the latest Global Burden of Disease (GBD) 2021 data, stratified by age subgroup, sex, and sociodemographic index (SDI), to examine global trends in mortality and disability-adjusted life years (DALYs) for early-onset cancers attributable to high BMI and high FPG. Additionally, we projected future trends through 2040.

METHODS

Data source, data collection, and definitions

We obtained metadata on the global burden of early-onset cancers from the Global Health Data Exchange query tool (https://vizhub.healthdata.org/gbd-results/). Data were extracted from the GBD 2021 study for four key metrics attributable to metabolic risk factors: the number of deaths, the number of DALYs, the age-specific mortality rate, and the age-specific DALY rate. All metrics were obtained in accordance with the definitions provided in the GBD Data & Tools Guide, Appendix 3 [12].

Early-onset cancers were defined as those diagnosed before the age of 50 years. The nine cancer types (listed in Supplemental Table S1) were selected according to two a priori criteria: (1) sufficient data density within the GBD 2021 database for the early-onset population (15 to 49 years) and (2) the presence of well-characterized etiologic links between metabolic risk factors and cancer incidence, as specified in the GBD 2021 comparative risk assessment framework. Cancer cases were mapped to the International Classification of Diseases, 10th Revision codes provided in Supplemental Table S1.

The SDI is a composite indicator of development that integrates per capita income, mean years of education, and fertility rates. Countries and territories were categorized annually into time-varying SDI quintiles—low, low-middle, middle, high-middle, and high—according to the GBD 2021 classification. High FPG and high BMI were selected a priori as the principal metabolic exposures. Although the GBD 2021 comparative risk assessment framework quantifies six metabolic risk factors (high FPG, high BMI, high low-density lipoprotein cholesterol, high systolic blood pressure, kidney dysfunction, and low bone mineral density) [12], complete age-specific estimates for most of these risk factors were unavailable or insufficiently stable to support trend modeling for the early-onset population (15 to 49 years). In contrast, high FPG and high BMI have fully disaggregated, age-specific risk–outcome pairs for all incident cancers within this age group, providing sufficient statistical power for robust longitudinal analyses. Therefore, this investigation focused specifically on high FPG and high BMI.

This study does not contain personal or medical information about identifiable living individuals, and animal subjects were not involved. The Institutional Review Board of the First Hospital of China Medical University determined that the study did not need approval because it used publicly available data.

Statistical analysis

All estimates presented in this study—including numbers, rates, corresponding 95% uncertainty intervals (UIs), and estimated annual percentage changes (EAPCs) with 95% confidence intervals (CIs)—were derived directly from the GBD 2021 study. The UIs and CIs correspond to the 2.5th and 97.5th percentiles of the ordered draw-level estimates. Consistent with GBD analytical conventions, the lower bound of the 95% UI for attributable burden was truncated at zero when necessary, as negative attributable fractions are not biologically meaningful. All estimates and UIs reported here follow this convention [12]. Mortality rates, DALY rates, and EAPCs were used to assess the epidemiological trends for early-onset cancers. The EAPC formulas were as follows:

y=α+βx+ɛEAPC=100×(exp(β)-1)

Future trends were projected using a Bayesian age-period-cohort (BAPC) model implemented via the BAPC R package. Mortality and DALY counts were modeled using a negative binomial distribution with a log-link function, with the logarithm of population size included as an offset. Smoothing priors were applied to age, period, and cohort effects to generate stable estimates. Model performance was validated through internal retrospective testing [13].

We used age-specific rates rather than age-standardized rates for all analyses. This approach allows for the comparison of evolving trends within and between 5-year age cohorts (15 to 49 years), which are central to this study. To avoid confusion, the terms ‘mortality rate’ and ‘DALY rate’ in both text and figures always refer to age-specific metrics unless otherwise indicated. The EAPC was estimated through log-linear regression, which assumes a constant exponential trend over time. Although this provides a concise summary measure, it may not capture non-linear changes or abrupt inflections due to interventions, policy reforms, or diagnostic improvements. Projections to 2040 were generated using the BAPC model. It is important to emphasize that these projections represent statistical extrapolations of historical trends and do not account for future innovations or disruptions in prevention, screening, treatment, or public health policy. Accordingly, they should be interpreted as illustrative scenarios assuming continuation of current trajectories rather than as deterministic forecasts.

All statistical analyses were performed using R software version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria). A two-tailed P<0.05 was considered statistically significant. It should also be noted that GBD-derived risk-attributable burden estimates reflect statistical associations based on population-attributable fractions and do not imply direct causation. Further methodological details are provided in the Supplemental Methods.

RESULTS

Global patterns

Both the numbers and rates of deaths and DALYs attributable to the two assessed metabolic risk factors (high FPG and high BMI) increased over the past 30 years. In 2021, an estimated 34,112 deaths (95% UI, 15,037 to 54,888) and 1,691,418 DALYs (95% UI, 762,923 to 2,698,657) were attributable to these two risks worldwide. The global mortality rate was 0.9 per 100,000 population (95% UI, 0.4 to 1.4), and the DALY rate was 42.8 per 100,000 population (95% UI, 19.3 to 68.3) for these metabolic factors. From 1990 to 2021, both mortality and DALY rates attributable to the combined metabolic risks rose significantly, with EAPCs of 1.35% (95% CI, 1.26 to 1.44) and 1.31% (95% CI, 1.23 to 1.40), respectively (Table 1).

Table 1

The Deaths, DALYs, Mortality Rate, DALY Rate and Corresponding EAPC of Early-Onset Cancers Attributable to Metabolic Risks from 1990 to 2021, Stratified by Sex, Age, and SDI

In 2021, high FPG and high BMI were the principal metabolic risk factors contributing to mortality (0.3 [95% UI, 0.0 to 0.6] and 0.6 [95% UI, 0.4 to 0.8] per 100,000, respectively) and DALY (13.7 [95% UI, 0.9 to 26.6] and 29.4 [95% UI, 18.1 to 41.9] per 100,000, respectively) rates for early-onset cancers. Over the past three decades, mortality and DALY rates attributable to high FPG (EAPCs: 1.26% [95% CI, 1.18 to 1.35] and 1.25% [95% CI, 1.17 to 1.33], respectively) and high BMI (EAPCs: 1.42% [95% CI, 1.33 to 1.52] and 1.37% [95% CI, 1.29 to 1.45], respectively) showed consistent increases (Supplemental Tables S2–S5).

Among early-onset cancers in 2021, colon and rectal cancers exhibited the highest mortality and DALY rates attributable to high FPG and high BMI (0.3 per 100,000 [95% UI, 0.1 to 0.4] and 12.3 per 100,000 [95% UI, 5.6 to 18.7], respectively). Throughout the study period, all mortality and DALY rates attributable to these metabolic risks increased, with liver cancer showing the most pronounced growth (EAPCs: 2.67% [95% CI, 2.47 to 2.87] and 2.61% [95% CI, 2.42 to2.80], respectively) (Tables 2, 3 and Supplemental Tables S6, S7).

Table 2

The deaths, Mortality Rate and Corresponding EAPC of Specific Early-Onset Cancers Attributable to Metabolic Risks from 1990 to 2021

Table 3

The DALYs, DALY Rate, and Corresponding EAPC of Specific Early-Onset Cancers Attributable to Metabolic Risks from 1990 to 2021

Global patterns by sex

In 2021, males exhibited higher mortality and DALY rates attributable to high FPG and high BMI than females (mortality: 1.0 [95% UI, 0.4 to 1.6] vs. 0.8 [95% UI, 0.3 to 1.2] per 100,000; DALY: 48.2 [95% UI, 21.0 to 77.9] vs. 37.4 [95% UI, 17.5 to 57.2] per 100,000). The increase in mortality among males was approximately 1.15 times higher than that among females (EAPCs: 1.40% [95% CI, 1.31 to 1.50] vs. 1.28% [95% CI, 1.19 to 1.38]), whereas the rise in DALY rates was 1.08 times greater in males (EAPCs: 1.36% [95% CI, 1.28 to 1.44] vs. 1.26% [95% CI, 1.17 to 1.34]) (Table 1).

The burden of early-onset cancers attributable to high BMI was greater than that due to high FPG in both sexes, with corresponding EAPCs reflecting faster growth for high BMI. However, for early-onset cancers attributable to high FPG, the number of deaths among females surpassed that among males by 2021—a reversal of the 1990 pattern—driven by a more rapid increase in female mortality (EAPCs: 1.38% [95% CI, 1.31 to 1.46] vs. 1.14% [95% CI, 1.02 to 1.26]) (Supplemental Table S8). A similar sex-specific trend was observed for DALY rates (Supplemental Tables S2–S5).

Global patterns by age

Between 1990 and 2021, both mortality and DALY rates for early-onset cancers attributable to high FPG and high BMI increased across all age groups. The burden of these metabolic risks rose steadily with age, peaking among individuals aged 30–34 years, where mortality and DALY burdens were roughly three times higher than those in the 20–24-year-old group (EAPCs for mortality: 0.90% [95% CI, 0.80 to 0.99] vs. 0.34% [95% CI, 0.31 to 0.37]; EAPCs for DALY: 0.93% [95% CI, 0.84 to 1.03] vs. 0.36% [95% CI, 0.33 to 0.39]) (Table 1, Supplemental Fig. S1). Age-specific mortality and DALY trends from 1990 to 2021 are presented in Supplemental Figs. S1, S2.

Specifically, a high BMI was the primary factor contributing to early-onset cancers across various age groups. For a high BMI, the mortality growth trend was the highest in the 30–34- and 44–49-year-old age groups and was approximately three times greater than that in the 20–24-year-old age group. With respect to high FPG, the mortality growth trend was most pronounced in the 30–34-year-old age group and was approximately twice that in the 40–45-year-old age group. The pattern for DALYs mirrored this trend (Supplemental Tables S2–S5).

Global patterns by the SDI

Between 1990 and 2021, the number of deaths and DALYs was greatest in middle-SDI regions, the mortality rate in high-middle SDI regions was five times (mortality rate: 1.4 [95% UI, 0.6 to 2.3] vs. 0.3 [95% UI, 0.1 to 0.4] per 100,000) that of low-SDI regions, and the DALY rate was five times (DALYs rate: 70.5 [95% UI, 30.4 to 113.3] vs.14.3 [95% UI, 7.4 to 21.8] per 100,000) higher. The EAPC decreased with an increasing SDI, with the highest increases (2.47% [95% CI, 2.42 to 2.51] for the mortality rate and 2.42% [95% CI, 2.37 to 2.46] for the DALY rate) occurring in the low-middle-SDI regions, which were nearly three times greater than those in the high-SDI regions (Table 1).

High BMI remained the predominant risk factor contributing to the burden of early-onset cancers across all regions. However, when examining temporal growth trends, in low-middle-SDI regions, the increase in mortality attributable to high FPG (EAPC=2.60% [95% CI, 2.54 to 2.65]) exceeded that attributable to high BMI (EAPC=2.39% [95% CI, 2.23 to 2.41]). A similar reversal was observed for DALY rate trends (high FPG: EAPC=2.60% [95% CI, 2.54 to 2.65]; high BMI: EAPC=2.32% [95% CI, 2.23 to 2.41]).

For high BMI, the most pronounced increases in mortality and DALY rates were observed in middle-SDI countries. In contrast, for high FPG, mortality and DALY rates rose most rapidly in low- to middle-SDI regions (Supplemental Tables S2–S5).

Projection of mortality and DALY rates to 2040

Fig. 1 illustrates global trends in age-specific mortality and DALY rates for early-onset cancers attributable to high BMI and high FPG from 1990 to 2021, along with projections through 2040. Mortality attributable to both high BMI and high FPG is projected to continue rising steadily. Similarly, DALY rates for both metabolic risks show an upward trajectory. By 2040, the DALY rates for early-onset cancers attributable to high BMI and high FPG are projected to reach 46.0 and 28.8 per 100,000 population, respectively.

Fig. 1

Global trends in mortality for early-onset cancers attributable to (A) high body mass index, (B) high fasting plasma glucose, and disability-adjusted life years (DALYs) rates for early-onset cancers attributable to (C) high body mass index, (D) high fasting plasma glucose from 1990 to 2021, with projections to 2040.

DISCUSSION

This study provides a comprehensive analysis of the global burden of early-onset cancers attributable to two metabolic risk factors (high FPG and high BMI) among adults younger than 50 years. Over the past three decades, the burden of early-onset cancers linked to these metabolic risks has increased not only in terms of deaths and DALYs but also in mortality and DALY rates. The differential patterns by sex, age, and socioeconomic level highlight the critical importance of integrating strategies for glycemic control and weight management into targeted preventive interventions for at-risk populations [5,14]. It is imperative to emphasize that these GBD estimates, derived from population-attributable fractions, reflect statistical associations and do not establish direct causation. While the biological plausibility is strong, the observed burdens signify the potential reduction in cancer cases that might be achieved if exposure to these metabolic risks was minimized.

Over recent decades, despite improved understanding of the mechanisms linking obesity and metabolic dysfunction to cancer, high BMI has continued to rise globally. Currently, approximately 40% of adults and 16% of children are classified as obese or overweight, representing a steady increase over the past 40 years [15]. The growing prevalence of childhood obesity, coupled with the tendency for obese children to remain obese as adults, suggests that more individuals will experience prolonged exposure to obesity-related cancer-promoting conditions. Cumulative evidence indicates that longer exposure duration is associated with higher cancer incidence [16]. Older age groups exhibit higher mortality from early-onset cancers associated with elevated BMI, and with global aging and increasing life expectancy, absolute numbers of deaths and DALYs related to BMI-linked cancers have risen substantially. This trend correlates with increased sedentary behavior, disruptions in healthcare, and elevated stress levels [17].

The observed historical shift from a male-predominant to a female-predominant burden of high-FPG-related cancers is particularly concerning and suggests a rapidly evolving, sex-specific metabolic risk landscape. Within the GBD framework, which incorporates sex-specific relative risks where available [12], this pattern more likely reflects differential changes in exposure prevalence—specifically, a faster-growing epidemic of prediabetes and high FPG among young women—rather than inherent biological differences in risk per unit of exposure [18]. Potential contributors to this demographic shift may include changing socioeconomic circumstances, behavioral and reproductive factors, physical activity disparities, and unequal access to preventive healthcare that disproportionately affect young women [19]. Nonetheless, given the overlapping UIs for sex-specific rates, these interpretations should be made cautiously and underscore the need for future research into the evolving determinants of sex-specific metabolic risk exposure. Supporting this interpretation, a systematic review and meta-analysis found that cancer incidence among patients with diabetes was 70% higher than among those without diabetes [20]. Similarly, a study of the global burden of early-onset cancers attributed to high FPG reported that crude death rates increased with age [21]. Higher mortality and DALY rates in older age groups may be explained by the greater prevalence of diabetes and its comorbidities, which elevate both cancer incidence and mortality risk [22].

Marked disparities in metabolic cancer burden across SDI quintiles underscore a widening global inequity. Although the SDI encompasses more than income alone, it is closely correlated with national wealth and health infrastructure. Thus, the mechanisms observed across SDI categories may partially reflect underlying economic gradients. High-SDI regions currently bear the highest absolute burden, driven largely by historical exposure and aging populations [23]. However, the most rapid increases are now emerging in low- and middle-SDI regions, signaling a growing public health crisis. This acceleration is likely driven by the ‘double burden’ of malnutrition—characterized by the coexistence of undernutrition and overconsumption of ultra-processed foods and sugar-sweetened beverages—coupled with rapid urbanization and limited access to preventive healthcare [24]. Addressing these disparities requires a tiered, region-specific approach: prioritizing primary prevention through fiscal and regulatory measures in low-SDI settings; integrating metabolic screening and strengthening referral systems in middle-SDI countries; and refining early detection strategies and investing in mechanistic research in high-SDI regions to identify and mitigate the unique drivers of early-onset cancers [25].

These findings collectively demonstrate that metabolic dysfunction is a pivotal and modifiable driver of early-onset malignancies, with particularly strong etiologic associations observed for colorectal and liver cancers. These links may be mediated through mechanisms such as gut microbiome dysbiosis [26] and metabolic dysfunction-associated steatotic liver disease-related hepatocarcinogenesis [27]. The results also identify young adults with obesity or hyperglycemia as a distinct, high-risk clinical population warranting earlier risk assessment and preventive intervention.

Ultimately, preventing early-onset cancers requires a paradigm shift toward a life-course approach to metabolic health, beginning in adolescence and extending through young adulthood. Health systems, particularly those in resource-limited regions, must adapt to this emerging challenge. Public health strategies should prioritize metabolic risk mitigation as a central pillar of cancer prevention. Key evidence-based recommendations include: (1) implementing multisectoral policies to curb obesity and hyperglycemia in younger populations through fiscal tools (e.g., sugar taxes), regulatory restrictions on unhealthy food marketing, and incentives for healthier food systems; (2) integrating metabolic risk screening into primary care for early detection and intervention, with a focus on individuals exhibiting elevated BMI or FPG; and (3) addressing socioeconomic inequities by improving health literacy, infrastructure, and access to preventive care in low-SDI regions, while supporting research into sex-specific mechanisms and interventions [28,29]. A comprehensive life-course approach that combines education, environmental modification, and equitable healthcare access is essential to curb the escalating burden of early-onset cancers.

This study has several limitations inherent to the data sources and methodological framework of the GBD study [5]. First, our findings depend on the accuracy and completeness of the GBD 2021 input data. Although the GBD framework strives for global comprehensiveness, data quality and availability remain uneven across regions, particularly in low-SDI countries. These discrepancies may lead to underestimation or overestimation of the true burden in certain regions and affect the overall generalizability of our estimates. Second, our analysis was necessarily restricted to cancer types with robust data and well-established metabolic risk–outcome relationships within the GBD framework. Consequently, other significant metabolic risk factors prevalent in younger populations—such as hypertension, dyslipidemia, and renal dysfunction—were not included. Likewise, the analysis was limited to nine cancer types and did not encompass other high-burden early-onset cancers (e.g., female breast cancer, gastric cancer), which may also be influenced by metabolic factors. Third, the analytical models used in this study have inherent methodological constraints. The EAPC model, which assumes log-linearity, may smooth over short-term fluctuations or inflection points in temporal trends. More importantly, the BAPC projections are based on historical associations and do not incorporate potential future paradigm shifts in metabolic health management, cancer prevention, or treatment. Thus, the projections represent a ‘status quo’ scenario and likely underestimate the potential impact of coordinated global interventions addressing metabolic risk factors. As with all GBD comparative risk assessments, our results quantify statistical associations rather than direct causation. The population attributable fraction (PAF) methodology assumes that observed associations are causal and that risk exposure precedes disease outcome. While this assumption is supported by extensive epidemiological and mechanistic evidence for both high BMI and high FPG, unmeasured confounding variables could still influence these estimates. This distinction is crucial for policy interpretation, as it underscores that interventions targeting these metabolic risks may—but are not guaranteed to—achieve the estimated reductions in disease burden. Despite these limitations, the GBD study remains the most comprehensive and standardized global effort to quantify health loss. Our analysis contributes important evidence on the substantial and rising burden of early-onset cancers attributable to two key, modifiable metabolic risk factors.

In conclusion, high BMI and high FPG have emerged as the predominant metabolic risk factors contributing to global mortality and DALY rates for early-onset cancers. High BMI was the leading risk factor in most regions, particularly in high- and middle-SDI areas, whereas high FPG was more prominent in low-SDI regions, especially among females. Reducing obesity prevalence and improving glycemic control could substantially mitigate the future cancer burden, particularly among adolescents and young adults. Policymakers and healthcare systems should prioritize interventions that address metabolic risks, enhance early detection, and ensure equitable access to prevention and treatment—especially in low- and middle-SDI regions where the burden of early-onset cancers is accelerating.

Supplementary Material

Supplemental Fig. S1.

Age-specific early-onset cancer burden attributable to metabolic risks. DALY, disability-adjusted life year.

enm-2025-2577-Supplemental-Fig-S1.pdf

Supplemental Fig. S2.

Age-specific trends in metabolic risk-attributable burden (indexed to 1990). DALY, disability-adjusted life year.

enm-2025-2577-Supplemental-Fig-S2.pdf

SUPPLEMENTAL METHODS

enm-2025-2577-Supplemental-Methods.pdf

Supplemental Table S1.

The International Classification of Disease Version 10 Codes of 9 Specific Early-Onset Cancers

enm-2025-2577-Supplemental-Table-S1.pdf

Supplemental Table S2.

The Deaths, Mortality Rate and Corresponding EAPC of Early-Onset Cancers Attributable to High BMI from 1990 to 2021, Stratified by Sex, Age, and SDI

enm-2025-2577-Supplemental-Table-S2.pdf

Supplemental Table S3.

The DALYs, DALY Rate and Corresponding EAPC of Early-Onset Cancers Attributable to High BMI from 1990 to 2021, Stratified by Sex, Age, and SDI

enm-2025-2577-Supplemental-Table-S3.pdf

Supplemental Table S4.

The Deaths, Mortality Rate and Corresponding EAPC of Early-Onset Cancers Attributable to High FPG from 1990 to 2021, Stratified by Sex, Age, and SDI

enm-2025-2577-Supplemental-Table-S4.pdf

Supplemental Table S5.

The DALYs, DALY Rate and Corresponding EAPC of Early-Onset Cancers Attributable to High FPG from 1990 to 2021, Stratified by Sex, Age, and SDI

enm-2025-2577-Supplemental-Table-S5.pdf

Supplemental Table S6.

The Deaths, Mortality Rate, and Corresponding EAPC of Specific Early-Onset Cancers Attributable to High BMI and High FPG from 1990 to 2021

enm-2025-2577-Supplemental-Table-S6.pdf

Supplemental Table S7.

The DALYs, DALY Rate, and Corresponding EAPC of Specific Early-Onset Cancers Attributable to High BMI and High FPG from 1990 to 2021

enm-2025-2577-Supplemental-Table-S7.pdf

Supplemental Table S8.

Sex-Specific Burden of Early-Onset Cancers Attributable to High FPG, 1990 and 2021

enm-2025-2577-Supplemental-Table-S8.pdf

Notes

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

ACKNOWLEDGMENTS

This work is supported by the National Natural Science Foundation of China (Grant No. 82470826 and 82304201) and Joint Funds of the Liaoning Provincial Natural Science Foundation (Grant No. 2023-MSLH-409). The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or the writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

The data used for analyses are publicly available at https://ghdx.healthdata.org/gbd-results-tool.

AUTHOR CONTRIBUTIONS

Conception or design: L.H., Y.L. Acquisition, analysis, or interpretation of data: W.Q., K.Z., L.H., J.G., Q.G., J.Z., Y.L. Drafting the work or revising: W.Q., K.Z., L.H., J.G., Q.G., J.Z., Y.L. Final approval of the manuscript: W.Q., K.Z., L.H., J.G., Q.G., J.Z., Y.L.

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Characteristic	Mortality					DALY
	1990	2021		1990–2021		1990		2021		1990–2021
	No. of deaths (95% UI)	Mortality rate per 100,000 (95% UI)	No. of deaths (95% UI)	Mortality rate per 100,000 (95% UI)	EAPC (95% CI)	No. of DALYs (95% UI)	DALY rate per 100,000 (95% UI)	No. of DALYs (95% UI)	DALY rate per 100,000 (95% UI)	EAPC (95% CI)
	Global	15,033 (7,368–22,761)	0.6 (0.3–0.8)	34,112 (15,037–54,888)	0.9 (0.4–1.4)	1.35 (1.26–1.44)	752,543 (379,499–1,128,568)	27.8 (14–41.6)	1,691,418 (762,923–2,698,657)	42.8 (19.3–68.3)	1.31 (1.23–1.40)
	Sex
Male	8,503 (3,784–13,458)	0.6 (0.3–1.0)	19,482 (8,267–31,733)	1.0 (0.4–1.6)	1.4 (1.31–1.50)	424,763 (195,512–667,154)	30.9 (14.2–48.6)	962,956 (419,870–1,557,414)	48.2 (21.0–77.9)	1.36 (1.28–1.44)
Female	6,531 (3,550–9,814)	0.5 (0.3–0.7)	14,631 (6,600–22,620)	0.8 (0.3–1.2)	1.28 (1.19–1.38)	327,780 (183,167–486,804)	24.5 (13.7–36.4)	728,462 (340,456–1,114,350)	37.4 (17.5–57.2)	1.26 (1.17–1.34)
Age, yr
20–24	614 (440–815)	0.1 (0.1–0.2)	837 (582–1,122)	0.1 (0.1–0.2)	0.34 (0.31–0.37)	42,000 (30,099–55,792)	8.5 (6.1–11.3)	57,551 (40,112–77,173)	9.6 (6.7–12.9)	0.36 (0.33–0.39)
25–29	878 (595–1,233)	0.2 (0.1–0.3)	1,461 (887–2,103)	0.2 (0.2–0.4)	0.75 (0.68–0.82)	55,887 (37,859–78,544)	12.6 (8.6–17.7)	93,653 (56,719–134,574)	15.9 (9.6–22.9)	0.78 (0.71–0.85)
30–34	1,318 (784–1,889)	0.3 (0.2–0.5)	2,830 (1,514–4,190)	0.5 (0.3–0.7)	0.9 (0.8–0.99)	77,306 (46,087–110,811)	20.1 (12.0–28.8)	167,471 (89,677–247,917)	27.7 (14.8–41.0)	0.93 (0.84–1.03)
35–39	2,316 (1,191–3,473)	0.7 (0.3–1.0)	4,790 (2,359–7,471)	0.9 (0.4–1.3)	0.7 (0.63–0.77)	124,533 (64,065–186,165)	35.4 (18.2–52.9)	260,212 (128,184–403,270)	46.4 (22.9–71.9)	0.74 (0.66–0.81)
40–44	3,740 (1,705–5,853)	1.3 (0.6–2.0)	8,318 (3,606–13,407)	1.7 (0.7–2.7)	0.66 (0.6–0.71)	182,628 (83,622–285,397)	63.7 (29.2–99.6)	409,882 (178,920–657,024)	81.9 (35.8–131.3)	0.69 (0.63–0.74)
45–49	6,167 (2,447–9,990)	2.7 (1.1–4.3)	15,877 (5,912–26,502)	3.4 (1.2–5.6)	0.7 (0.62–0.79)	270,189 (108,221–437,675)	116.4 (46.6–188.5)	702,649 (263,716–1,171,949)	148.4 (55.7–247.5)	0.73 (0.65–0.82)
Location
High SDI	4,144 (1,931–6,476)	0.9 (0.4–1.4)	5,945 (2,555–9,463)	1.2 (0.5–1.9)	0.71 (0.53–0.89)	204,474 (98,987–316,020)	44.4 (21.5–68.6)	295,515 (130,948–466,306)	58.8 (26.1–92.8)	0.75 (0.58–0.91)
High-middle SDI	4,668 (2,210–7,169)	0.8 (0.4–1.3)	9,008 (3,798–14,579)	1.4 (0.6–2.3)	1.58 (1.46–1.71)	232,831 (114,223–354,435)	41.3 (20.2–62.8)	443,597 (191,236–713,179)	70.5 (30.4–113.3)	1.54 (1.42–1.65)
Middle-SDI	4,488 (2,221–6,891)	0.5 (0.2–0.8)	12,559 (5,463–19,671)	1.0 (0.4–1.6)	2.35 (2.28–2.43)	227,940 (116,507–347,166)	25 (12.8–38.1)	622,785 (277,162–969,895)	49.6 (22.1–77.3)	2.25 (2.18–2.31)
Low-middle SDI	1,312 (723–1,933)	0.2 (0.1–0.4)	5,027 (2,369–7,728)	0.5 (0.2–0.8)	2.47 (2.42–2.51)	66,146 (38,103–95,974)	12 (6.9–17.4)	250,315 (121,014–381,187)	24.6 (11.9–37.5)	2.42 (2.37–2.46)
Low SDI	401 (224–602)	0.2 (0.1–0.3)	1,540 (770–2,378)	0.3 (0.1–0.4)	1.36 (1.24–1.49)	20,150 (11,527–29,910)	9.1 (5.2–13.5)	77,593 (39,969–118,365)	14.3 (7.4–21.8)	1.37 (1.25–1.48)

Cancer type	1990		2021		1990–2021
	No. of deaths (95% UI)	Mortality rate per 100,000 (95% UI)	No. of deaths (95% UI)	Mortality rate per 100,000 (95% UI)	EAPC (95% CI)
	Total cancers	15,033 (7,368–22,761)	0.555 (0.272–0.84)	34,112 (15,037–54,888)	0.864 (0.381–1.39)	1.35 (1.26–1.44)
Bladder cancer	89 (0–196)	0.003 (0.000–0.007)	166 (0–354)	0.004 (0.000–0.009)	0.65 (0.52–0.78)
Colon and rectum cancer	4,504 (1,924–6,939)	0.166 (0.071–0.256)	9,894 (4,510–14,972)	0.251 (0.114–0.379)	1.26 (1.19–1.32)
Gallbladder and biliary tract cancer	576 (400–774)	0.021 (0.015–0.029)	1,067 (737–1,452)	0.027 (0.019–0.037)	0.67 (0.62–0.72)
Kidney cancer	991 (383–1586)	0.037 (0.014–0.058)	2,127 (849–3,402)	0.054 (0.022–0.086)	1.09 (0.94–1.24)
Liver cancer	1,666 (600–2,706)	0.061 (0.022–0.1)	5,754 (2,205–9,871)	0.146 (0.056–0.25)	2.67 (2.47–2.87)
Ovarian cancer	730 (124–1,388)	0.027 (0.005–0.051)	2,022 (473–3,611)	0.051 (0.012–0.091)	1.88 (1.76–1.99)
Thyroid cancer	269 (201–349)	0.010 (0.007–0.013)	572 (426–740)	0.014 (0.011–0.019)	1.2 (1.15–1.24)
Tracheal, bronchus, and lung cancer	741 (0–1,670)	0.027 (0.000–0.062)	1,199 (0–2,697)	0.030 (0.000–0.068)	0.29 (0.06–0.52)
Uterine cancer	1,053 (707–1,428)	0.039 (0.026–0.053)	2,202 (1,545–2,910)	0.056 (0.039–0.074)	0.99 (0.87–1.11)

Cancer type	1990		2021		1990–2021
	No. of DALYs (95% UI)	DALY rate per 100,000 (95% UI)	No. of DALYs (95% UI)	DALY rate per 100,000 (95% UI)	EAPC (95% CI)
	Total cancers	752,543 (379,499–1,128,568)	27.764 (14.001–41.637)	1,691,418 (762,923–2,698,657)	42.836 (19.321–68.345)	1.31 (1.23–1.4)
Bladder cancer	4,304 (0–9,478)	0.159 (0.000–0.35)	8,171 (0–17,466)	0.207 (0.000–0.442)	0.69 (0.57–0.82)
Colon and rectum cancer	221,383 (94,417–341,125)	8.168 (3.483–12.585)	487,581 (222,285–738,361)	12.348 (5.629–18.699)	1.26 (1.19–1.32)
Gallbladder and biliary tract cancer	27,414 (19,023–36,873)	1.011 (0.702–1.36)	50,624 (34,977–68,881)	1.282 (0.886–1.744)	0.65 (0.60–0.70)
Kidney cancer	49,154 (18,991–78,691)	1.813 (0.701–2.903)	106,609 (42,911–170,931)	2.700 (1.087–4.329)	1.14 (1–1.28)
Liver cancer	80,741 (29,113–131,074)	2.979 (1.074–4.836)	276,729 (106,104–474,656)	7.008 (2.687–12.021)	2.61 (2.42–2.80)
Ovarian cancer	35,645 (5,898–67,875)	1.315 (0.218–2.504)	99,915 (23,387–178,579)	2.530 (0.592–4.523)	1.92 (1.81–2.03)
Thyroid cancer	15,298 (11,501–19,860)	0.564 (0.424–0.733)	34,520 (25,743–44,769)	0.874 (0.652–1.134)	1.41 (1.36–1.46)
Tracheal, bronchus, and lung cancer	34,498 (0–78,048)	1.273 (0.000–2.879)	55,441 (0–124,597)	1.404 (0.000–3.155)	0.25 (0.03–0.47)
Uterine cancer	53,498 (36,107–72,573)	1.974 (1.332–2.678)	114,177 (80,122–150,221)	2.892 (2.029–3.804)	1.06 (0.95–1.17)