Global, regional, and national burden of aortic aneurysm attributable to body mass index from 1990 to 2021 and prediction to 2040: a cross-sectional study

Abstract

Background:

High body mass index (BMI) is a recognized risk factor for cardiovascular diseases, but its impact on aortic aneurysms (AA) remains unclear. This study analyzes the global burden of AA attributed to high BMI in 204 countries between 1990 and 2021.

Methods:

Using Global Burden of Disease (GBD) 2021 data, we analyzed AA-related deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs), assessing 1990–2021 trends via estimated annual percentage change (EAPC) and projecting 2022–2040 burden with Bayesian Age-Period-Cohort (BAPC) model. The BMI-AA association was investigated through multivariable logistic regression and subgroup analyses in UK Biobank (UKB) participants, supplemented by Mendelian randomization (MR) analyses using Integrative Epidemiology Unit Open GWAS Project (IEU OpenGWAS project) summary statistics.

Results:

In 2021, AA deaths and DALYs due to high BMI rose to 11 540 and 247 361, respectively, compared to 1990. Despite this increase, ASMR and ASDR declined, with EAPCs of −1.36 and −1.13, respectively. The burden of AA due to high BMI increased in individuals under 45 but decreased significantly in those over 65. Males exhibited higher burden than females, though reductions were greater in males. High sociodemographic index (SDI) regions had elevated burden but declining trends. After adjusting for potential confounding factors, individuals with BMI ≥30 kg/m² exhibited a higher risk of AA (OR: 1.28, 95% CI: 1.13–1.46, P = 0.001) compared to those with BMI ≤23 kg/m², and subgroup analyses revealed significant interactions for gender and drinking. MR was adopted to estimate the causal relationships between BMI and AA, and demonstrated that genetically predicted higher BMI was associated with an increased risk of AA. By 2040, the global burden of AA attributable to high BMI is projected to gradually rise.

Conclusion:

Our study highlights that high BMI-related AA remains a major global health issue, with younger men, older women, and lower SDI countries bearing greater burdens. Both UKB data and MR analyses confirm a robust link between elevated BMI and AA risk. Effective BMI management strategies are essential to reduce the future global burden of AA.

Introduction

Aortic aneurysm (AA) represents a major public health challenge characterized by the pathological dilation of the aorta. If not addressed, this condition can result in severe, life-threatening complications, including aortic dissection or rupture. AA has an incidence rate of 5 to 10 cases per 100 000 person-years in the general population^[1]. As an important cause of cardiovascular mortality globally, AA affects millions of individuals each year and imposes a substantial burden on healthcare systems due to the increasing number of hospitalizations, surgeries, and associated costs. Despite its severity, AA has not received the same level of attention as other cardiovascular diseases like coronary heart disease and stroke, even though it accounts for an estimated 150 000–200 000 deaths annually, a figure comparable to the mortality rate of certain cancers, such as bladder cancer^[2]. The silent progression of AA, often without symptoms, leads to sudden death in many cases, with a survival rate of only around 20% following rupture^[3]. Currently, there is no effective medication to prevent or reverse the progression of AA, highlighting the critical need for a deeper understanding of its epidemiological characteristics and the development of effective preventive and therapeutic strategies.

Body mass index (BMI), calculated from height and weight, is a well-established metric of adiposity and a practical indicator of body fat distribution. Although widely used in epidemiological studies to assess obesity-related health risks, BMI is an indirect measure. Persistently elevated BMI, however, often reflects pathological fat accumulation in non-adaptive compartments such as visceral and perivascular adipose tissue. This ectopic fat deposition promotes hypertension, systemic inflammation, and arterial wall stress–key drivers of cardiovascular disease pathogenesis^[4,5]. Nevertheless, the impact of the rise in BMI on AA burden remains complex and controversial. Some research suggested that high BMI was positively related to an increased risk of AA, while other studies found no significant link. A large nationwide community-based prospective cohort study found a positive correlation between BMI and AA-related mortality in Japanese men^[6]. Two international database studies reported an inverse relationship between temporal trends in BMI and mortality from AA disease^[7,8], while a prospective British study found no such association^[9]. A recent meta-analysis revealed a positive but nonlinear relationship between BMI and the risk of AA occurrence, with postoperative mortality following a “U”-shaped curve^[10].

However, to date, most prior studies have centered on specific populations or limited cohorts, lacking a comprehensive assessment of long-term global trends in high BMI-attributable AA burden. Notably, there remains a gap in understanding how this burden has evolved over time across regions, sexes, age groups, and socio-demographic contexts. Moreover, previous research has rarely combined diverse methodologies to explore both epidemiological patterns and causal links^[11,12]. This inconsistency presents challenges in the prevention, treatment, and prognosis of AA, highlighting the necessity for further research into the impact of BMI on the burden of AA over time. Understanding this relationship is essential, as it could offer valuable insights for the prevention and management of AA, especially given an increasing rate of global obesity^[13].

Considering this scenario, the study’s primary objective was to conduct the first comprehensive, multi-level analysis of AA burden attributable to high BMI, integrating Global Burden of Disease (GBD) data with UK Biobank (UKB) and Mendelian randomization (MR) datasets. Specifically, we aimed to systematically evaluate the global, regional, and national burden of AA related to BMI from 1990 to 2021, and analyze the age, sex, and socioeconomic patterns in BMI-related AA disease burden. To strengthen causal inference, we further integrated large-scale UKB data and MR genetic evidence, providing robust support for a potential causal relationship between high BMI and increased AA risk. Additionally, we projected future trends in AA burden attributable to BMI up to the year 2040. By offering detailed insights into how BMI impacts AA burden across populations and over time, this study bridges a literature gap. In accordance with the strengthening the reporting of cohort, cross-sectional, and case-control studies in surgery (STROCSS) guideline, the results provide valuable information for guiding public health policies and interventions to address the global AA burden.

HIGHLIGHTS

• The global burden of aortic aneurysm attributable to high BMI has increased significantly from 1990 to 2021, particularly among individuals under 45, with notable regional disparities.

• Despite a rise in overall deaths and DALYs, both age-standardized mortality and DALY rates for aortic aneurysm related to high BMI have decreased globally since 1990, with a more significant decrease among males.

• Findings from both the UKB data and MR analyses support a consistent association between elevated BMI and increased risk of AA.

• While the burden in high SDI regions is declining, the global burden is projected to continue rising by 2040.

Methods

Data sources

In this cross-sectional study, the 2021 GBD study offers a comprehensive evaluation of 369 diseases, injuries, and conditions, as well as 88 risk factors, across 204 nations and regions. The GBD estimates are generated through a standardized analytical framework that integrates diverse data sources, including vital registration systems, health surveys, and hospital records^[14]. The analysis grounded in the most recent epidemiological data and refined standardized methodologies, underpinned the investigation into the burden of AA associated with high BMI. GBD data, accessible via the GHDx online platform (https://vizhub.healthdata.org/gbd-results/), provide crucial insights into worldwide health patterns. Through this platform, we selected disease categories, associated risk factors, and demographic variables to align with the research objectives. The methodological framework used for data collection and processing has been thoroughly documented in earlier publications^[15]. From the GBD 2021 dataset, we extracted annual data on AA-related deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) spanning the years 1990–2021.

The UKB enrolled over 500 000 participants aged 40–69 years across the United Kingdom between 2006 and 2010. Data from the UKB are available to bona fide researchers upon application through the UKB Access Management System (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access)^[16]. We excluded individuals lacking information on BMI or AA, as well as those missing data on potential covariates, including gender, age, smoking, drinking, dyslipidemia, systolic blood pressure (SBP), diastolic blood pressure (DBP), white blood cell count, neutrophil count, lymphocyte count and diabetes. Ultimately, 350 353 participants remained for final analysis (Supplementary Figure 1, available at: http://links.lww.com/JS9/E311).

The MR study was conducted using European populations to examine the causal relationship between BMI and AA. Genome-wide association study summary data for BMI (ukb-b-19953) and AA (ebi-a-GCST90018783) were obtained from the Integrative Epidemiology Unit open GWAS project (IEU OpenGWAS project). BMI was analyzed in 2018 using 461 460 samples genotyped for 9 851 867 single-nucleotide polymorphisms (SNPs). The AA was examined in 2021 with 479 194 samples genotyped for 24 191 825 SNPs. This cross-sectional study has been reported in line with the STROCSS guideline (Supplementary Table 1, available at: http://links.lww.com/JS9/E312)^[17].

Definitions

In the GBD framework, high BMI for adults (≥20 years) is defined as a BMI exceeding the theoretical minimum risk exposure level of 20–23 kg/m², which represents the range associated with the lowest health risk, rather than clinical thresholds for overweight or obesity^[18,19]. For children (ages 2–19), elevated BMI is classified as overweight or obese according to the criteria established by the International Obesity Task Force^[15]. To estimate exposure levels, the GBD 2021 study employed two distinct modeling approaches. For risk determinants displaying varying exposure across sex and age, a Bayesian meta-regression framework (DisMod-MR 2.1) was employed. Conversely, for risk factors with consistent exposure across age ranges, a spatiotemporal Gaussian process regression method was utilized. The population attributable fraction (PAF) for each risk determinant was subsequently computed based on various elements, including age, sex, location, year, risk function, exposure profile, and the theoretical minimum risk exposure level. The total burden of each risk determinant was assessed by multiplying the number of deaths and DALYs by the corresponding PAF. This methodology enabled a comprehensive evaluation of the influence of risk determinants on public health^[20]. AA was identified through diagnostic codes from the International Classification of Diseases (ICD), specifically codes 441–441.9 in ICD-9 and I71–I71.9 in ICD-10.

DALYs provide a comprehensive assessment of the total burden of AA linked to high BMI. DALYs reflect the overall impact of AA attributable to high BMI by incorporating both years of life lost (YLL) due to premature mortality and years lived with disability (YLD). To calculate YLL, the number of deaths associated with AA in each age group was multiplied by the standard life expectancy for that age. In parallel, YLD were determined by assessing the number of individuals affected by AA as a result of high BMI and multiplying this figure by the disability weight assigned to the condition. This weighting reflected the degree of health impairment on a scale from 0 (denoting complete health) to 1 (signifying death). By means of these computations, DALYs offered a holistic assessment of the burden of AA associated with high BMI.

The Sociodemographic Index (SDI) functions as a composite indicator of development that strongly correlates with health outcomes. It is derived as the geometric mean of three key components, each rated on a scale from 0 to 1: fertility rates among women younger than 25, average years of schooling for individuals aged 15 and above, and lag-adjusted income per capita. In the GBD 2021 analysis, SDI scores are displayed on an adjusted scale, where the index is multiplied by 100; thus, a score of 0 denotes the lowest level of development, while a score of 100 indicates the highest. Based on their SDI, the 204 countries and territories included in the study were categorized into five groups: low, low-middle, middle, high-middle, and high quintiles. This classification aided in understanding the relationship between development and health across different regions.

Statistical analyses

The worldwide burden of AA linked to high BMI was evaluated through several metrics, such as the total number of deaths, DALYs, age-standardized mortality rate (ASMR), age-standardized DALY rate (ASDR), and estimated annual percentage change (EAPC). Uncertainty intervals (UIs) for all metrics are calculated based on the mean estimate across 1000 draws, with 95% UIs defined as the 2.5th and 97.5th percentiles of the distribution. To calculate ASRs, the following formula was used to express the rates per 100 000 individuals:

$$ASR=\frac{{\sum }_{i=1}^A{a}_i{w}_i}{{\sum }_{i=1}^A{w}_i}\times 100,000$$ 1

( $a_i$: the age-specific rate in $i$ the age group; w: the number of people in the corresponding $i$ age group among the standard population; A: the number of age groups)

EAPC is a key metric in epidemiology for assessing temporal trends in ASRs of diseases. It was estimated using a linear regression framework, where the coefficient, symbolized as β, was obtained from the natural logarithm of the ASRs. In this model, y signified ln(ASR), while x represented calendar years. The 95% confidence interval (CI) for the regression coefficient β can be calculated using its estimate and standard error (SE). By substituting the upper and lower bounds of β into the EAPC formula, the 95% CI for the EAPC is obtained. The EAPC was presented along with its 95% CI, offering a thorough perspective on the temporal trends.

y = α + βx + ε

EAPC = 100 * (exp(β) − 1)

An upward trend was indicated when the lower limit of the 95% CI exceeded 0, whereas a downward trend was implied when the upper limit was below 0. Conversely, if 0 lay within the 95% CI, it suggested that there was no statistically significant change in the trend.

Continuous variables were presented as mean ± standard deviation (SD), and categorical variables as number (percentage). Baseline characteristics of participants with and without AA were compared using t–test or Manne–Whitney test for continuous variables and chi-square test for categorical variables. Variables showing significant differences in univariate analyses were included in multivariable logistic regression models to estimate the relationship between BMI and AA, with odds ratios (ORs) and 95% CI presented. Subgroup analyses were carried out for specific variables, including gender (female or male), age (<45, 45–65, or >65 years), drinking (never drinker, occasional drinker, or frequent drinker), smoking (never smoker or ever smoker), and dyslipidemia (no or yes) to evaluate potential modifiers of the relationship between BMI and AA.

Two-sample MR was employed to assess the causal link between BMI and AA via inverse-variance weighted (IVW) analysis. For validation, alternative methods including MR-Egger, weighted median, simple mode, and weighted mode were also applied. Genetic variants with P < 5 × 10⁻⁸ served as instrumental variables. A threshold of r² < 0.001 (clumping distance: 10 000 kb) was set to exclude SNPs that were in a state of linkage disequilibrium.

In this analysis, we applied the Bayesian Age-Period-Cohort (BAPC) model, using integrated nested Laplace approximations, to predict future patterns of AA linked to high BMI. We then computed the model’s Deviance Information Criterion (DIC) and Root Mean Squared Error (RMSE)^[21,22]. Prior studies have shown that the BAPC model offers improved precision and comprehensiveness compared to alternative projection methods^[23,24]. Finally, to assess the robustness of the BAPC predictions, a sensitivity analysis was performed by fitting an Autoregressive Integrated Moving Average (ARIMA) model^[25].

DIC = D + p_D

RMSE = 

$$\sqrt{\frac{1}{n}}\sum _{i=1}^n{(y_i-{\stackrel{⌢}{y}}_i)}^2$$ 2

All statistical analyses for this study were performed using R software, version 4.4.1, developed by the R Foundation for Statistical Computing in Vienna, Austria.

Results

Global burden of AA attributable to high BMI from 1990 to 2021

Regarding global deaths and DALYs of AA attributable to high BMI, we conducted comprehensive analyses from multiple perspectives. Compared with 1990, the global burden of AA attributable to high BMI has increased in absolute numbers by 2021, with deaths rising from 6429 (95% UI: 3450–10 799) to 11 540 (95% UI: 6196–19 522) (Fig. 1A; Table 1), and DALYs increasing from 137 242 (95% UI: 74 817–230 633) to 247 361 (95% UI: 134 536–414 707) (Fig. 1B; Supplementary Table 2, available at: http://links.lww.com/JS9/E313).

Figure 1.

Temporal trends of AA attributable to high BMI in the global population by sex, from 1990 to 2021. (A) The number of deaths and the ASMR from AA attributable to high BMI in the global population by sex, from 1990 to 2021. (B) The number of DALYs and the ASDR from AA attributable to high BMI in the global population by sex, from 1990 to 2021.

Table 1.

Age standardized death and EAPC of AA attributable to high BMI at global levels, grouped by age and sex, 1990–2021

	Death (95% UI)
	Number of people in 1990	ASMR in 1990	Number of people in 2021	ASMR in 2021	EAPC (95% CI)
Global	6429 [3450 to 10 799]	0.18 [0.10 to 0.31]	11 540 [6196 to 19 522]	0.14 [0.07 to 0.23]	−1.36 [−1.53 to −1.20]
Age group
25–29	17 [9 to 30]	0.004 [0.01 to 0.01]	34 [18 to 58]	0.01 [0.01 to 0.01]	1.31 [1.20 to 1.42]
30–34	31 [17 to 53]	0.01 [0.01 to 0.01]	71 [38 to 117]	0.01 [0.01 to 0.02]	1.15 [1.04 to 1.26]
35–39	55 [29 to 95]	0.02 [0.01 to 0.03]	132 [72 to 219]	0.02 [0.01 to 0.04]	1.00 [0.84 to 1.17]
40–44	85 [46 to 145]	0.03 [0.02 to 0.05]	214 [118 to 354]	0.04 [0.02 to 0.07]	0.69 [0.51 to 0.86]
45–49	128 [71 to 219]	0.05 [0.03 to 0.09]	330 [181 to 551]	0.07 [0.04 to 0.12]	0.33 [0.10 to 0.55]
50–54	234 [129 to 403]	0.11 [0.06 to 0.19]	508 [279 to 840]	0.11 [0.06 to 0.19]	−0.13 [−0.29 to 0.03]
55–59	375 [204 to 650]	0.20 [0.11 to 0.35]	775 [429 to 1315]	0.20 [0.11 to 0.33]	−0.36 [−0.53 to −0.20]
60–64	656 [350 to 1107]	0.41 [0.22 to 0.69]	1102 [596 to 1856]	0.34 [0.19 to 0.58]	−0.93 [−1.07 to −0.78]
65–69	959 [513 to 1620]	0.78 [0.41 to 1.31]	1466 [781 to 2439]	0.53 [0.28 to 0.88]	−1.66 [−1.87 to −1.45]
70–74	1034 [551 to 1716]	1.22 [0.65 to 2.03]	1681 [903 to 2811]	0.82 [0.44 to 1.37]	−2.08 [−2.33 to −1.84]
75–79	1236 [652 to 2091]	2.01 [1.06 to 3.40]	1568 [837 to 2633]	1.19 [0.63 to 2.00]	−2.18 [−2.40 to −1.96]
80–84	847 [438 to 1454]	2.39 [1.24 to 4.11]	1428 [719 to 2488]	1.63 [0.82 to 2.84]	−1.85 [−2.04 to −1.66]
85–89	512 [265 to 886]	3.39 [1.75 to 5.86]	1217 [598 to 2043]	2.66 [1.31 to 4.47]	−1.17 [−1.39 to −0.95]
90–94	202 [100 to 345]	4.73 [2.33 to 8.06]	731 [348 to 1264]	4.09 [1.95 to 7.07]	−0.71 [−0.86 to −0.55]
95+	57 [26 to 99]	5.58 [2.59 to 9.72]	283 [126 to 497]	5.19 [2.32 to 9.11]	−0.51 [−0.64 to −0.38]
Sex
Female	2404 [1282 to 4063]	0.12 [0.06 to 0.21]	4746 [2485 to 7922]	0.10 [0.05 to 0.17]	−1.13 [−1.32 to −0.94]
Male	4024 [2153 to 6787]	0.26 [0.14 to 0.44]	6794 [3660 to 11 691]	0.18 [0.1 to 0.31]	−1.62 [−1.76 to −1.48]

However, from 1990 to 2021, the global burden of AA attributable to high BMI has significantly decreased. The ASMR declined from 0.18 (95% UI: 0.10–0.31) per 100 000 to 0.14 (95% UI: 0.07–0.23) per 100 000, with an EAPC of −1.36 (95% CI: −1.53 to −1.20) (Fig. 1A; Table 1). The ASDR decreased from 3.55 (95% UI: 1.93–5.98) per 100 000 to 2.88 (95% UI: 1.56–4.83) per 100 000, with an EAPC of −1.13 (95% CI: −1.28 to −0.98) (Fig. 1B; Supplementary Table 2, available at: http://links.lww.com/JS9/E313). Although the ASMR and ASDR of AA attributable to high BMI showed a declining trend from 1990 to 2021, there was no significant change from 2015 to 2021.

Global burden of AA attributable to high BMI stratified by age

In 2021, the death rate and DALY rate of AA attributable to high BMI increased with age, becoming more pronounced as age increased (Fig. 2A–B). However, from 1990 to 2021, the burden of AA in individuals under 45 years old increased, with the 25–29 age group being the most affected, showing an EAPC of 1.31 (95% CI: 1.20–1.42) for both ASMR and ASDR. In contrast, the burden decreased in those over 45 years old, with the 75–79 age group exhibiting the most significant decline, with an EAPC of −2.18 (95% CI: −2.40 to −1.96) (Fig. 2C–D; Table 1; Supplementary Table 2, available at: http://links.lww.com/JS9/E313).

Figure 2.

AA attributable to high BMI by age group in the global population. (A) The ASMR of AA attributable to high BMI by age group in 2021. (B) The ASDR of AA attributable to high BMI by age group in 2021. (C) The EAPC of ASMR from AA attributable to high BMI by age group, from 1990 to 2021. (D) The EAPC of ASDR from AA attributable to high BMI by age group, from 1990 to 2021.

When categorized into <45, 45–65, and >65 age groups, from 1990 to 2021, the burden in individuals under 45 increased, while in those aged 45–65 slightly decreased. The burden in individuals over 65 showed a significant decline (Fig. 3A–F).

Figure 3.

Temporal trends of AA attributable to high BMI in the global population by age group, from 1990 to 2021. (A) The number of deaths and the ASMR from AA attributable to high BMI in the global population by sex, from 1990 to 2021, age: <45. (B) The number of deaths and the ASMR from AA attributable to high BMI in the global population by sex, from 1990 to 2021, age: 45–65. (C) The number of deaths and the ASMR from AA attributable to high BMI in the global population by sex, from 1990 to 2021, age: >65. (D) The number of DALYs and the ASDR from AA attributable to high BMI in the global population by sex, from 1990 to 2021, age: <45. (E) The number of DALYs and the ASDR from AA attributable to high BMI in the global population by sex, from 1990 to 2021, age: 45–65. (F) The number of DALYs and the ASDR from AA attributable to high BMI in the global population by sex, from 1990 to 2021, age: >65.

Global burden of AA attributable to high BMI stratified by sex

In 2021, the global burden of AA attributable to high BMI was higher in men than in women. The ASMR for men was 0.18 (95% UI: 0.10–0.31) per 100 000, and the ASDR was 3.91 (95% UI: 2.14–6.62) per 100 000. For women, the ASMR was 0.10 (95% UI: 0.05–0.17) per 100 000, and the ASDR was 1.93 (95% UI: 1.03–3.20) per 100 000. In absolute numbers, men had higher death and DALY rates than women (Fig. 4A–D; Table 1; Supplementary Table 2, available at: http://links.lww.com/JS9/E313).

Figure 4.

AA attributable to high BMI in the global population by age group and sex in 2021. (A) The ASMR of AA attributable to high BMI in the global population by age group and sex in 2021. (B) The number of deaths from AA attributable to high BMI in the global population by age group and sex in 2021. (C) The ASDR of AA attributable to high BMI in the global population by age group and sex in 2021. (D) The number of DALYs from AA attributable to high BMI in the global population by age group and sex in 2021.

From 1990 to 2021, the burden of AA attributable to high BMI decreased for both men and women, but the decline was more pronounced in men. The EAPC for ASMR in men was −1.62 (95% CI: −1.76 to −1.48) and for ASDR it was −1.30 (95% CI: −1.44 to −1.17). For women, the EAPC for ASMR was −1.13 (95% CI: −1.32 to −0.94) and for ASDR it was −0.98 (95% CI: −1.16 to −0.80) (Table 1; Supplementary Table 2, available at: http://links.lww.com/JS9/E313). Although the trends were similar for both sexes (with an increase in ASMR and ASDR for those under 45 and a decrease for those over 45), the rise in ASMR and ASDR for women under 45 was smaller compared to men, and the decline in men over 45 was more significant (Fig. 5A–F).

Figure 5.

The EAPC of AA attributable to high BMI in the global population by age group and sex from 1990 to 2021. (A) The EAPC of ASMR from AA attributable to high BMI in the global population by age group and sex from 1990 to 2021. (B) The EAPC of ASMR from AA attributable to high BMI in the global population in male by age group from 1990 to 2021. (C) The EAPC of ASMR from AA attributable to high BMI in the global population in female by age group from 1990 to 2021. (D) The EAPC of ASDR from AA attributable to high BMI in the global population by age group and sex from 1990 to 2021. (E) The EAPC of ASDR from AA attributable to high BMI in the global population in male by age group from 1990 to 2021. (F) The EAPC of ASDR from AA attributable to high BMI in the global population in female by age group from 1990 to 2021.

Global burden of AA attributable to high BMI by SDI super regions

In 2021, the global burden of AA attributable to high BMI was highest in countries with a high SDI, with an ASMR of 0.22 (95% UI: 0.12–0.37) per 100 000 and an ASDR of 4.56 (95% UI: 2.48–7.64) per 100 000. The burden was lowest in low SDI countries, with an ASMR of 0.06 (95% UI: 0.03–0.11) and an ASDR of 1.26 (95% UI: 0.60–2.44). Overall, the burden increased progressively from low SDI regions to high SDI regions (Table 2; Supplementary Table 3, available at: http://links.lww.com/JS9/E314).

Table 2.

Age standardized death and EAPC of AA attributable to high BMI at regional levels, 1990–2021

	Death (95% UI)
	Number of people in 1990	ASMR in 1990	Number of people in 2021	ASMR in 2021	EAPC (95% CI)
SDI
Low SDI	65 [30 to 126]	0.03 [0.01 to 0.06]	254 [121 to 493]	0.06 [0.03 to 0.11]	1.53 [1.33 to 1.72]
Low-middle SDI	150 [76 to 261]	0.03 [0.01 to 0.05]	876 [466 to 1477]	0.07 [0.03 to 0.11]	2.85 [2.78 to 2.91]
Middle SDI	425 [227 to 710]	0.05 [0.02 to 0.08]	2053 [1114 to 3458]	0.08 [0.04 to 0.14]	1.49 [1.31 to 1.67]
High-middle SDI	1453 [797 to 2498]	0.15 [0.08 to 0.26]	3423 [1814 to 5799]	0.17 [0.09 to 0.29]	0.00 [−0.17 to 0.18]
High SDI	4326 [2290 to 7269]	0.38 [0.20 to 0.64]	4917 [2569 to 8239]	0.22 [0.12 to 0.37]	−2.40 [−2.61 to −2.18]
Region
East Asia	80 [40 to 141]	0.01 [0.00 to 0.02]	597 [303 to 1068]	0.03 [0.01 to 0.05]	3.65 [3.54 to 3.77]
Oceania	3 [1 to 5]	0.12 [0.06 to 0.22]	9 [5 to 16]	0.14 [0.07 to 0.24]	0.37 [0.29 to 0.46]
Southeast Asia	47 [24 to 78]	0.02 [0.01 to 0.04]	304 [160 to 499]	0.05 [0.03 to 0.09]	2.88 [2.82 to 2.93]
High-income Asia Pacific	211 [114 to 353]	0.11 [0.06 to 0.18]	1070 [528 to 1733]	0.19 [0.10 to 0.31]	1.80 [1.70 to 1.89]
Central Asia	36 [19 to 63]	0.08 [0.04 to 0.14]	147 [79 to 258]	0.20 [0.11 to 0.34]	3.05 [2.84 to 3.27]
Central Europe	433 [229 to 738]	0.30 [0.16 to 0.50]	734 [385 to 1272]	0.32 [0.17 to 0.56]	−0.05 [−0.27 to 0.18]
Eastern Europe	596 [321 to 1003]	0.22 [0.12 to 0.36]	1532 [803 to 2582]	0.44 [0.23 to 0.73]	1.94 [1.61 to 2.27]
Western Europe	2456 [1315 to 4107]	0.40 [0.22 to 0.68]	2343 [1214 to 4090]	0.23 [0.12 to 0.39]	−2.53 [−2.78 to −2.28]
Australasia	146 [78 to 256]	0.61 [0.33 to 1.07]	143 [73 to 248]	0.25 [0.13 to 0.43]	−3.53 [−3.70 to −3.35]
Southern Latin America	148 [79 to 258]	0.33 [0.17 to 0.57]	252 [129 to 436]	0.28 [0.15 to 0.49]	−0.50 [−0.76 to −0.25]
High-income North America	1644 [854 to 2848]	0.45 [0.23 to 0.77]	1449 [748 to 2463]	0.22 [0.11 to 0.37]	−3.12 [−3.45 to −2.79]
Andean Latin America	9 [4 to 15]	0.04 [0.02 to 0.08]	42 [21 to 75]	0.07 [0.04 to 0.13]	1.88 [1.70 to 2.06]
Caribbean	42 [23 to 68]	0.17 [0.09 to 0.27]	98 [51 to 165]	0.18 [0.09 to 0.30]	−0.09 [−0.25 to 0.08]
Central Latin America	79 [43 to 135]	0.10 [0.05 to 0.17]	334 [173 to 564]	0.14 [0.07 to 0.23]	0.31 [0.04 to 0.58]
South Asia	64 [27 to 125]	0.01 [0.01 to 0.02]	546 [271 to 953]	0.04 [0.02 to 0.07]	3.94 [3.84 to 4.03]
Tropical Latin America	197 [107 to 330]	0.22 [0.12 to 0.37]	970 [504 to 1699]	0.38 [0.20 to 0.67]	1.49 [1.19 to 1.78]
Central Sub-Saharan Africa	15 [7 to 29]	0.08 [0.04 to 0.15]	57 [26 to 111]	0.12 [0.06 to 0.23]	1.17 [0.88 to 1.46]
North Africa and Middle East	79 [36 to 145]	0.05 [0.02 to 0.09]	418 [217 to 743]	0.10 [0.05 to 0.17]	2.40 [2.30 to 2.51]
Southern Sub-Saharan Africa	54 [28 to 91]	0.22 [0.11 to 0.37]	131 [69 to 223]	0.25 [0.13 to 0.44]	−0.22 [−0.57 to 0.12]
Eastern Sub-Saharan Africa	26 [12 to 51]	0.04 [0.02 to 0.07]	113 [51 to 224]	0.07 [0.03 to 0.14]	1.78 [1.61 to 1.95]
Western Sub-Saharan Africa	66 [26 to 131]	0.09 [0.03 to 0.17]	250 [101 to 517]	0.15 [0.06 to 0.31]	1.63 [1.51 to 1.76]

Over time, although the burden in high SDI countries showed a declining trend, with an EAPC for ASMR of −2.40 (95% CI: −2.61 to −2.18) and for ASDR of −2.21 (95% CI: −2.42 to −2.01), this trend gradually stabilized. Notably, in high SDI countries, the number of deaths and DALYs exhibited a trend of initially increasing, then decreasing, and subsequently rising again. However, in low, low-middle, and middle SDI countries, the burden increased steadily each year, with the most significant increase occurring in low-middle SDI countries, where the EAPC for ASMR was 2.85 (95% CI: 2.78–2.91) and for ASDR it was 2.87 (95% CI: 2.81–2.94). The burden in high-middle SDI countries remained stable, with an EAPC for ASMR of 0 (95% CI: −0.17–0.18) and for ASDR of 0.02 (95% CI: −0.15–0.20) (Table 2; Supplementary Table 3, available at: http://links.lww.com/JS9/E314; Supplementary Figure 2, available at: http://links.lww.com/JS9/E311).

Global burden of AA attributable to high BMI by geographic super regions

In 2021, among 21 super regions, the regions with the lowest burden were East Asia, South Asia, Southeast Asia, Andean Latin America, and Eastern Sub-Saharan Africa (ASMR ≤0.07, ASDR ≤1.69). By contrast, the regions with the highest burden were Eastern Europe, Tropical Latin America, Central Europe, Southern Latin America, and Southern Sub-Saharan Africa (ASMR ≥0.25, ASDR ≥5.49) (Fig. 6A–B; Table 2; Supplementary Table 3, available at: http://links.lww.com/JS9/E314). From 1990 to 2021, there was a positive correlation between SDI and ASMR (R = 0.62, P < 0.001) and ASDR (R = 0.6, P < 0.001) of AA attributable to high BMI across 21 super regions (Fig. 6C–D).

Figure 6.

Trends of AA attributable to high BMI from 1990 to 2021 by geographic super regions. (A) Ranking of ASMR from AA attributable to high BMI in 21 super regions from 1990 to 2021. (B) Ranking of ASDR from AA attributable to high BMI in 21 super regions from 1990 to 2021. (C) The relationship between SDI and ASMR of AA attributable to high BMI by super regions from 1990 to 2021. (D) The relationship between SDI and ASDR of AA attributable to high BMI by super regions from 1990 to 2021.

From 1990 to 2021, the burden decreased in regions such as Western Europe, Australasia, Southern Latin America, and high-income North America. In other regions, the burden either increased or remained stable. The regions with the most significant increases were East Asia, Southeast Asia, Central Asia, and South Asia. The burden remained stable in regions such as Central Europe, the Caribbean, Central Latin America, and Southern Sub-Saharan Africa (Table 2; Supplementary Table 3, available at: http://links.lww.com/JS9/E314).

Burden of AA attributable to high BMI by country

The ASMR across global countries ranged from 0.01 (Timor-Leste) to 1.13 (Montenegro) (Fig. 7A). The five countries with the most significant ASMR reductions were: United Kingdom (EAPC: −4.10), Canada (EAPC: −3.80), Australia (EAPC: −3.61), New Zealand (EAPC: −3.18), and the United States (EAPC: −3.06). The five countries with the most significant ASMR increases were: Oman (EAPC: 8.10), Georgia (EAPC: 7.49), Yemen (EAPC: 6.84), Uzbekistan (EAPC: 6.82), and Sudan (EAPC: 6.47) (Fig. 7B). The ASDR across global countries ranged from 0.29 (Timor-Leste) to 24.35 (Montenegro) (Fig. 7C). The ASDR trends were similar to those of the ASMR (Fig. 7D).

Figure 7.

AA attributable to high BMI by countries in 2021. (A) The ASMR of AA attributable to high BMI by countries in 2021. (B) The ASDR of AA attributable to high BMI by countries in 2021. (C) The EAPC of ASMR from AA attributable to high BMI by countries in 2021. (D) The EAPC of ASDR from AA attributable to high BMI by countries in 2021.

In 2021, there was a positive correlation between a country’s SDI and ASMR (R = 0.46, P < 0.001) and ASDR (R = 0.43, P < 0.001) associated with high BMI (Fig. 8A–B). However, from 1990 to 2021, there was a negative correlation between a country’s SDI and the annual trend in the burden of AA attributable to high BMI (Fig. 8C–D).

Figure 8.

The relationship between SDI and AA attributable to high BMI by countries. (A) The relationship between SDI and ASMR of AA attributable to high BMI by countries in 2021. (B) The relationship between SDI and ASDR of AA attributable to high BMI by countries in 2021. (C) The relationship between SDI and EAPC of ASMR from AA attributable to high BMI by countries from 1990 to 2021. (D) The relationship between SDI and EAPC of ASDR from AA attributable to high BMI by countries from 1990 to 2021.

The impact of high BMI and other risk factors on AA

Based on GBD 2021 data, there are five Level 2 risk factors for AA: high BMI, tobacco use, dietary risks, high SBP, and environmental risks. These factors were analyzed in three age strata: <45, 45–65, and >65 years (Fig. 9A). From 1990 to 2021, only the impact of high BMI on AA increased across all three age groups: PAF of deaths rose from 5.1% to 7.3% in the <45 group, from 7.7% to 9.2% in the 45–65 group, and from 7.6% to 7.8% in the >65 group. Moreover, in the <45 group, PAF of deaths increased by 2.2 percentage points, representing a greater rise than that observed in the other two age categories. In 2021, AA attributable to high BMI was greatest in the 45–65 group compared with the younger and older groups, representing a distinct characteristic compared to other risk factors. The contribution of risk factors to DALYs for AA was similar to their contribution to deaths for AA (Supplementary Figure 3, available at: http://links.lww.com/JS9/E311).

Figure 9.

The impact of high BMI and other risk factors on AA. (A) Level 2 risk factors contributing to AA deaths in 1990 and 2021 by different age groups. (B) Subgroup analyses of the association between BMI and AA. Each subgroup analysis was adjusted for gender, age, BMI, smoking, drinking, cholesterol, DBP, SBP, white blood cell count, neutrophil count, and diabetes. (C) Forest plot of Mendelian randomization analysis.

In a cohort of 350 353 participants (mean age 56.95 years, 45.9% male), 3,708 (1.06%) individuals were diagnosed with AA. Participants were stratified by BMI (≤23, 23–30, and ≥30 kg/m²), and significant differences in gender, age, BMI, smoking, drinking, dyslipidemia, SBP, DBP, white blood cell count, neutrophil count, and diabetes were observed between AA and non-AA groups (Table 3). All of these factors were associated with AA risk in univariate logistic regression (Table 4). In multivariable models using the ≤23 kg/m² group as reference, the 23–30 kg/m² group lost significance after full adjustment, whereas the ≥30 kg/m² group consistently showed elevated AA risk (Model 5: OR: 1.28, 95% CI: 1.13–1.46, P = 0.001) (Table 5). Subgroup analyses revealed significant interactions for gender (P for interaction = 0.023) and drinking (P for interaction = 0.007): among males and among non-drinkers and frequent drinkers, both the 23–30 kg/m² and ≥30 kg/m² groups had significantly increased AA risk, whereas no significant associations were observed in females or occasional drinkers (Fig. 9B).

Table 3.

The baseline characteristics of the study population with and without AA

	Total (n = 350 353)	AA
Variables	Overall	No (n = 346 645)	Yes (n = 3708)	P
Gender, n (%)				<0.001
Female	189 510 (54.1)	188 747 (54.4)	763 (20.6)
Male	160 843 (45.9)	157 898 (45.6)	2945 (79.4)
Age (years, M ± SD)	56.95 (±8.04)	56.89 (±8.04)	62.35 (±5.93)	<0.001
BMI, n (%)				<0.001
≤23 kg/m2	50 105 (14.3)	49 777 (14.4)	328 (8.8)
23-30 kg/m2	208 212 (59.4)	206 029 (59.4)	2183 (58.9)
≥30 kg/m2	92 036 (26.3)	90 839 (26.2)	1197 (32.3)
Smoking, n (%)				<0.001
Never smoker	138 158 (39.4)	137 338 (39.6)	820 (22.1)
Ever smoker	212 195 (60.6)	209 307 (60.4)	2888 (77.9)
Drinking, n (%)				<0.001
Never drinker	30 372 (8.7)	30 039 (8.7)	333 (9.0)
Occasional drinker	82 781 (23.6)	82 017 (23.7)	764 (20.6)
Frequent drinker	237 200 (67.7)	234 589 (67.7)	2611 (70.4)
Dyslipidemia, n (%)				<0.001
No	174 587 (49.8)	172 990 (49.9)	1597 (43.1)
Yes	175 766 (50.2)	173 655 (50.1)	2111 (56.9)
SBP (mmHg, M ± SD)	143.93 (±19.99)	143.90 (±20.00)	147.45 (±19.52)	<0.001
DBP (mmHg, M ± SD)	84.77 (±10.49)	84.76 (±10.48)	86.13 (±11.47)	<0.001
White blood cell (109 cells/L, M ± SD)	6.95 (±2.20)	6.94 (±2.20)	7.44 (±2.18)	<0.001
Neutrophil (109 cells/L, M ± SD)	4.28 (±1.44)	4.28 (±1.44)	4.68 (±1.62)	<0.001
Lymphocyte (109 cells/L, M ± SD)	1.98 (±1.25)	1.98 (±1.25)	1.99 (±1.08)	0.819
Diabetes, n (%)				<0.001
No	328 326 (93.7)	324 930 (93.7)	3396 (91.6)
Yes	22 027 (6.3)	21 715 (6.3)	312 (8.4)

BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure.

Table 4.

Univariate analysis of variables with AA

Variable	OR (95% CI)	P
Gender
Female	Ref
Male	4.61 (4.26–5.00)	<0.001
Age (years)	1.12 (1.11–1.12)	<0.001
BMI (kg/m2)
≤23	Ref
23–30	1.61 (1.43–1.81)	<0.001
≥30	2.00 (1.77–2.26)	<0.001
Smoking
Never smoker	Ref
Ever smoker	2.31 (2.14–2.50)	<0.001
Drinking
Never drinker	Ref
Occasional drinker	0.84 (0.74–0.96)	0.008
Frequent drinker	1.00 (0.90–1.13)	0.945
Dyslipidemia
No	Ref
Yes	1.32 (1.23–1.41)	<0.001
SBP (mmHg)	1.01 (1.01–1.01)	<0.001
DBP (mmHg)	1.01 (1.01–1.02)	<0.001
White blood cell (109 cells/L)	1.04 (1.03–1.05)	<0.001
Neutrophil (109 cells/L)	1.16 (1.14–1.19)	<0.001
Lymphocyte (109 cells/L)	1.00 (0.97–1.02)	0.819
Diabetes
No	Ref
Yes	1.37 (1.22–1.54)	<0.001

BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure.

Table 5.

Multivariable odds ratio of BMI for AA

Models	BMI
	≤23 kg/m2	23–30 kg/m2		≥30 kg/m2
		OR (95% CI)	P	OR (95% CI)	P
Model 1	Ref	1.61 (1.43–1.81)	<0.001	2.00 (1.77–2.26)	<0.001
Model 2	Ref	1.05 (0.94–1.19)	0.398	1.39 (1.23–1.58)	<0.001
Model 3	Ref	1.05 (0.93–1.18)	0.437	1.34 (1.19–1.52)	<0.001
Model 4	Ref	1.03 (0.91–1.16)	0.651	1.30 (1.15–1.48)	<0.001
Model 5	Ref	1.03 (0.92–1.17)	0.599	1.28 (1.13–1.46)	0.001

Model 1: No adjustment.

Model 2: Adjustment for gender and age.

Model 3: Adjustment for Model 2 and smoking and drinking.

Model 4: Adjustment for Model 3 and dyslipidemia, SBP, DBP, and diabetes.

Model 5: Adjustment for Model 4 and white blood cell and neutrophil.

MR analysis via the IVW method demonstrated a causal relationship between genetically predicted high BMI and AA risk (OR: 1.25, 95% CI: 1.08–1.45, P = 0.003) (Fig. 9C). Detailed MR results are provided in the Supplementary Material (Supplementary Table 4, available at: http://links.lww.com/JS9/E315; Supplementary Figure, available at: 4A–D http://links.lww.com/JS9/E311).

Future projections of AA burden attributable to high BMI

Projections indicate that by 2040, the global burden of AA attributable to high BMI will continue to increase, with ASMR and ASDR showing upward trends (Fig. 10A–B). Both males and females are expected to gradually increase (Supplementary Figure 5A–D, available at: http://links.lww.com/JS9/E311).

Figure 10.

Future forecasts of AA attributable to high BMI in the global population. (A) Future forecasts of ASMR from AA attributable to high BMI in the global population. (B) Future forecasts of ASDR from AA attributable to high BMI in the global population.

In the age groups <45 years and 45–65 years, the ASMR and ASDR of AA attributable to high BMI are expected to rise by 2040, while for the age group >65 years, the ASMR and ASDR of AA attributable to high BMI are expected to remain stable (Supplementary Figure 6A–F, available at: http://links.lww.com/JS9/E311).

In low, low-middle, and middle SDI regions, the ASMR and ASDR attributable to high BMI are expected to increase by 2040. In high-middle SDI regions, the ASMR is expected to decline, while in high SDI regions, the ASMR is projected to first decrease and then stabilize. The ASDR in high-middle SDI regions is expected to remain stable, while it is expected to decline in high SDI regions (Supplementary Figure 7A–J, available at: http://links.lww.com/JS9/E311). The model validation data for BAPC, including DIC and RMSE, are presented in the Supplementary Material (Supplementary Table 5, available at: http://links.lww.com/JS9/E316). The trend predicted by ARIMA was also consistent with that of the BAPC (Supplementary Figure 8, available at: http://links.lww.com/JS9/E311).

Discussion

To our knowledge, this is the first study to systematically assess the global burden, temporal trends, and future projections of AA attributable to high BMI. Our findings highlighted that high BMI remains a key contributor to the global AA burden, particularly affecting younger men, older women, and lower SDI countries. This situation underscored the need for targeted interventions to mitigate its impact. By integrating UKB and MR analyses, we further reinforced the association between BMI and AA risk. These results provide a foundation for informing public health policies, optimizing resource allocation, and guiding future research directions.

The present study demonstrated a significant rise in the absolute number of AA attributable to BMI from 1990 to 2021. However, despite this increase in absolute terms, the mortality rate and DALY per 100 000 populations have decreased when adjusted for age and time. The seemingly paradoxical trend may be explained by advances in imaging that allow earlier detection of asymptomatic AA, improvements in surgical techniques and perioperative care, and greater public awareness of obesity-related risks that promote better prevention and timely intervention. It aligned with trends observed in other cardiovascular diseases, where better treatment outcomes coexisted with increasing burden due to aging populations and global lifestyle changes^[26,27]. These findings reveal temporal and regional patterns of BMI-related AA burden and emphasize the need for global monitoring of obesity-related vascular outcomes. Although alternative measures like waist-to-hip ratio (WHR) and weight-adjusted waist index (WWI) may better capture central obesity and cardiovascular risk, BMI remains the most practical and widely used adiposity metric in large-scale studies like GBD due to its simplicity and availability^[28,29]. Moreover, WHR and WWI lack standardized thresholds and are less frequently accessible in population datasets^[30]. Our study provides essential epidemiological evidence linking BMI to AA burden, supporting future research with more refined adiposity measures.

Furthermore, a notable age-related difference was observed in this study. Among individuals under 45, the burden of AA attributable to high BMI has risen markedly, likely due to the growing prevalence of obesity in younger populations and the earlier onset of cardiovascular risk factors. Emerging evidence suggests that genetic predisposition, family history, and increased exposure to environmental and lifestyle factors such as pollution and smoking may also contribute to early-onset AA^[31]. Early-life adiposity may accelerate the accumulation of vascular and metabolic risks, such as dyslipidemia and hypertension, which combined with excess body weight, contribute to the development of AA^[32]. On the other hand, for those aged 65 and above, the global burden of AA has declined. This could be due to enhanced awareness, expanded screening efforts, and better medical and surgical approaches for aortic disease in older adults. Additionally, cohort effects such as healthier lifestyles and improved chronic disease management in recent decades may also play a role^[33]. This age-related disparity underscores the need for age-tailored prevention and intervention, particularly by focusing on modifiable risk factors in younger groups while ensuring optimal long-term care for the elderly^[34].

AA is a typical sex-related aortic diseases, and understanding sex disparities is crucial for making more informed surgical decisions. Our findings indicated that men bore a greater burden of AA related to high BMI compared to women, aligning with prior research that suggested a higher risk of developing AA in men^[35,36]. Notably, among individuals under 45, the burden increase was more pronounced in males than in females. However, in those over 45, the decline in AA burden was more significant in men than in women. This discrepancy can be attributed to higher rates of obesity and associated comorbidities such as hypertension and smoking among younger men, which can exacerbate the impact of high BMI on cardiovascular health. Differences in health behaviors between sexes may also contribute to the observed differences in AA burden^[37]. Furthermore, sex hormones are increasingly recognized as key modulators of aortic pathology. Testosterone promotes vascular damage, inflammation, and adverse remodeling, increasing AA risk in men, while estrogen protects vascular health by reducing oxidative stress and inflammation. After menopause, declining estrogen levels lead to increased visceral adiposity and vascular stiffness, partly explaining the slower decline in AA burden among older women^[38]. Recent studies further confirm that hormonal factors differentially regulate aortic wall integrity and vascular homeostasis between sexes^[39]. Therefore, the AA burden in younger men and older women deserves particular attention, along with focused prevention strategies.

The study also revealed considerable national, regional, and economic disparities in the burden of AA attributable to high BMI. From low SDI regions to high SDI regions, the burden of AA increased. However, over time, high SDI countries showed a decreasing trend, while low, low-middle, and middle SDI countries experienced a rising trend. This divergence likely reflects earlier adoption of public health measures in high SDI countries, including stronger healthcare infrastructure, broader access to early screening and diagnostic services, and extensive weight management programs^[40,41]. Population-level interventions, such as taxation on sugary drinks, urban planning initiatives to promote physical activity, and educational campaigns targeting obesity prevention, have further driven sustained improvements in cardiovascular health^[42]. In contrast, many low and middle SDI countries are experiencing a surge in obesity-related AA rates, yet often lack the necessary healthcare resources, policies, and public awareness to support effective interventions. Promoting healthy lifestyles and strengthening healthcare systems tailored to local contexts will be essential to reverse rising obesity-related AA trends in these regions. Overall, these findings underscored the urgent need for national and regional efforts to reduce obesity and improve cardiovascular health, particularly in lower-income areas^[43,44].

To enhance the robustness of our findings, we integrated GBD data with UKB and MR analyses. Multivariable regression within the UKB cohort validated the association between high BMI and increased AA risk after adjusting for traditional cardiovascular risk factors, strengthening its epidemiological and clinical relevance. The MR approach further established a potential causal relationship between genetically predicted high BMI and AA risk. This combined methodology solidifies the connection between BMI and AA burden beyond mere observational associations. Given AA’s complex etiology, additional research is necessary to elucidate underlying mechanisms. Obesity-related adipose dysfunction may drive systemic inflammation, oxidative stress, and vascular remodeling, exacerbated by hypertension, dyslipidemia, and unhealthy lifestyles^[45,46]. These interactions likely contribute to observed age and sex disparities. Clinically, our findings highlight BMI as a simple, accessible tool for identifying individuals at elevated AA risk and assessing preoperative prognosis. In the context of the global obesity epidemic, incorporating BMI into AA screening strategies could enhance early detection and prevention. Nevertheless, alternative adiposity measures require standardized thresholds and broader validation before clinical application^[47].

The projections from our study indicate an increase in the global burden of AA attributable to high BMI by 2040. This trend is particularly significant in low, low-middle, and middle SDI regions. Limited access to preventative care, insufficient healthcare resources, and a lack of awareness regarding the risks associated with high BMI may exacerbate the issue in these regions. Conversely, in high SDI regions, the ASMR is projected to first decrease and then stabilize, while the ASDR is anticipated to decline, reflecting the more advanced healthcare systems and effective preventive measures in these areas^[43]. These findings highlight an urgent need for targeted interventions, particularly in lower-income regions where the rising burden of AA associated with obesity is likely to continue^[44]. Effective public health policies focusing on obesity prevention, improved nutrition, and cardiovascular health could help curb these trends. Additionally, countries with lower SDI should be prioritized for international support in developing healthcare strategies aimed at reducing AA burden through early detection, lifestyle interventions, and management of high BMI.

Several limitations should be recognized in this study. First, the GBD data are based on modeled estimates, which may be affected by reporting bias, data quality, and regional heterogeneity. Second, our study used BMI as the obesity measure, but alternative indicators like waist circumference and body fat percentage were unavailable in the GBD database, which constrained our analysis. Moreover, the BMI threshold in our study, based on the GBD, differs from the WHO’s definition of high BMI, potentially causing interpretation differences; future research should explore the impact of varying cut-off points. Finally, the projections for 2040 relied on current trends and did not consider possible future medical advances or global health policy changes. Prospective cohort studies are needed in the future to further validate the findings.

Conclusion

Our analysis reveals that the burden of AA attributable to high BMI has increased globally in absolute terms, though mortality and DALY rates have declined when adjusted for age and time. Individuals under 45 have a growing burden, while older populations see a decrease. Men face a higher burden than women, and the AA burden in younger men and older women deserves particular attention. Regional differences persist, with lower SDI countries facing upward trends. Projections indicate a continued global increase in high-BMI-attributable AA burden by 2040. UKB cohort data confirms the epidemiological association between high BMI and increased AA risk, and MR analyses provided genetic evidence supporting a causal link. Therefore, addressing these challenges requires sustained global efforts, including obesity prevention, better healthcare access, and targeted interventions for high-risk populations.

Ethical approval and consent to participate

Ethical approval and informed consent were waived for analyses of GBD 2021 data, as these data are publicly available and contain no identifiable individual-level information. Summary-level GWAS data from the IEU OpenGWAS project were also publicly available; the original studies had obtained ethical approval and informed consent from participants. Individual-level data from UK Biobank were accessed under license – UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (REC reference: 21/NW/0157), and all participants provided written informed consent prior to enrolment. All procedures were conducted in accordance with the principles of the Declaration of Helsinki.

Sources of funding

This work was supported by the Natural Science Foundation of Liaoning Province (2024-MSLH-554) and Basic Scientific Research Project of Educational Department of Liaoning Province (JYTMS20230083).

Author contributions

J.J., M.X., and Y.H. finished data collection and data analysis. T.L. and Z.Y. contributed to the statistical analysis and interpretation of data. J.J. and M.X. drafted the manuscript, and T.L. critically revised the manuscript. T.L., C.M., and S.X. conceived the study and designed the protocol. All authors have read and approved the final manuscript.

Conflicts of interest disclosure

The authors declare that the research was conducted in the absence of any commercial of financial relationships that could be construed as a potential conflict of interest.

Research registration unique identifying number (UIN)

Part of this research has been conducted using the UK Biobank Resource under Application 197250. UK Biobank data have approval from the North West Multi-centre Research Ethics Committee (REC reference: 21/NW/0157).

Guarantor

Tan Li.

Provenance and peer review

Not commissioned, externally peer-reviewed.

Data availability statement

GBD 2021 data are retrieved from the GBD 2021 results on the GHDx online platform (https://vizhub.healthdata.org/gbd-results/). Genome-wide association study summary data are obtained from the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/). Individual-level data that support the findings of this study are available under license from UK Biobank (https://www.ukbiobank.ac.uk/), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. All processed datasets generated during this study are included in the article and its supplementary files.