Since 1990, GBD studies have systematically quantified health and health loss across age, sex, location, and year. In GBD 2023, PAD was defined as an ankle-brachial index (ABI) ≤ 0.90, with corresponding ICD-10 codes (I70.2-I70.8, I73-I73.9) [16]. We retrieved data on the disability-adjusted life years (DALYs) of PAD attributable to all metabolic risks and their corresponding uncertainty intervals (UIs) in China and G20 countries from 1990 to 2023. The G20 countries included China, Canada, Argentina, Türkiye, Australia, Germany, Brazil, France, India, Indonesia, Mexico, Italy, Japan, Republic of Korea, Russian Federation, Saudi Arabia, South Africa, United Kingdom, United States of America, and European Union. All data for this study were obtained from GBD 2023 via the Global Health Data Exchange results tool (https://vizhub.healthdata.org/gbd-results/). The GBD study used anonymized data, and the University of Washington Institutional Review Board granted a waiver of informed consent [17]. This study adheres to the accurate and transparent health estimates reporting (GATHER) guidelines [18].

DALYs, age-standardized DALY rates (ASDRs), population-attributable fractions (PAFs), and average annual percentage changes (AAPCs) were used as burden metrics. DALYs are a comprehensive metric for assessing the overall burden of disease and injury. It combines health losses resulting from both fatal and nonfatal outcomes to measure the health status of a population over a specific period, and has been widely adopted by global health institutions, such as the WHO [7]. The age-standardized rate (ASR) represents a weighted average of age-specific rates, eliminating the confounding effect of differences in age distribution between populations. It is calculated via a specific formula based on the GBD standard population structure (Supplementary Material 1.1). The standard population structure used in this study was downloaded directly from the Institute for Health Metrics and Evaluation (IHME) resources, which have been previously validated and widely applied [19–21]. AAPC is a summary measure of the trend over a prespecified fixed interval. It is calculated via a specific formula and remains valid even if the trend changes during this period (Supplementary Material 1.2). If the 95% confidence interval (CI) for AAPC is entirely above zero, it indicates an overall increasing trend; if it is entirely below zero, it indicates an overall declining trend; if the 95% CI contains zero, the trend is considered stable.

GBD 2023 provides a comprehensive estimate of exposure levels, relative health risks, and attributable burdens for 88 risk factors [7]. In GBD 2023, all risk factors were classified into a risk factor hierarchy with four levels. Metabolic risk factors were classified as level 1 and then disaggregated at level 2 into 6 risk factors, including high fasting plasma glucose, high systolic blood pressure, high body-mass index, kidney dysfunction, high low-density lipoprotein cholesterol, and low bone mineral density (definitions in Supplementary Material 1.3). All analyses were based on the GBD comparative risk assessment framework and employed sophisticated analytical methods, including disease model meta-regression, spatiotemporal Gaussian process regression, and the burden of proof method [7]. These methods generate estimates of the risk factor-attributable burden and clarify the contribution of modifiable risk factors. The population-attributable fraction (PAF) was used to quantify the contribution of metabolic risks, which is the proportional change in health risk that would occur if exposure to a risk factor were reduced to the theoretical minimum risk exposure level. Notably, PAFs for different risk factors are not mutually exclusive, individuals may be exposed to multiple risk factors simultaneously, so the sum of PAFs can exceed 100%. All methods are detailed in the GBD 2023 Collaborator’s report [7].

Decomposition analysis was conducted to quantify the contributions of population growth, aging, and epidemiological changes to the observed changes in metabolic risk-attributable DALYs. Epidemiological changes reflect the combined effects of all factors other than age structure and population size, including changes in preventive measures, medical technologies, and risk factor exposure. The decomposition results include both the absolute and relative contributions of each factor. The detailed specifications of the decomposition methodology are described in Supplementary Material 1.4 and have been widely established in previous studies [22–24].

We applied the ARIMA model to project future burden trends by 2035. As a widely applied time series analysis method, this model effectively captures trends and cyclical patterns within time series data and projects future changes based on existing data [25–27]. The ARIMA model combines autoregression (AR), integration (I), and moving average (MA) components. We first applied the differencing method to stabilize the time series data and used the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test to confirm stationarity. Then, a Q-Q plot was utilized to assess whether the residuals followed a normal distribution. Next, we compared the goodness-of-fit of different models using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), selecting the model with the smallest criterion value. Finally, the Ljung-Box test was used to verify the robustness of the residuals. When the residuals of the ARIMA model exhibit randomness (white noise), the model is considered the best linear predictor for short-term time series forecasting. Separate ARIMA models were independently fitted for males, females, and both-sex aggregated dataset. The both-sex projected DALYs were directly generated from its own ARIMA model, rather than derived by summing sex-specific forecasting outputs. The detailed analysis method is presented in Supplementary Material 1.5.

In this study, we stratified the metabolic risk-attributable PAD burden by age and sex to evaluate disparities. All the statistical analyses and data visualizations were performed via R (version 4.4.2) and JD_GBDR (V2.7.5, Jingding Medical Technology Co., Ltd), and P < 0.05 was considered statistically significant.

In 2023, the total DALYs of PAD were 184 222 (95% UI: 96 253 to 329 430) in China and 1 262 488 (95% UI: 975 578 to 1 692 600) in G20 countries. The attributable DALYs were 124 709 (95% UI: 65 939 to 229 079) and 852 838 (95% UI: 638 987 to 1 193 377), respectively.

Among these, metabolic risk-attributable DALYs were 100 298 (95% UI: 51 116 to 184 384) in China and 704 214 (95% UI: 495 812 to 1 001 461) in G20 countries, accounting for 54.5% and 55.8% of the total DALYs, and 80.4% and 82.6% of the attributable DALYs, respectively (Table 1). From 1990 to 2023, metabolic risk-attributable DALYs increased by 210.9% in China (AAPC: 3.55 [95% CI: 3.46 to 3.65]) and 114.6% in G20 countries (AAPC: 2.41 [95% CI: 2.26 to 2.55]). However, during this period, the ASDR decreased by 11.4% (AAPC: −0.35 [95% CI: −0.46 to −0.25]) in China and 23.8% (AAPC: −0.77 [95% CI: −0.89 to −0.65]) in G20 countries (Table 1).

Notably, metabolic risk-attributable DALYs showed an increasing trend across all G20 member countries from 1990 to 2023, with China ranking eighth regarding the growth magnitude. In terms of ASDR, 12 out of 20 G20 members presented declining trends over this period, and China ranked ninth regarding the decline magnitude (Supplementary Table 1).

Notable sex disparities in the metabolic risk-attributable PAD burden were observed (Supplementary Table 2; Figure 1). In China, DALYs increased consistently from 1990 to 2023 for both sexes, whereas the ASDR showed a slight fluctuating decline, with consistently higher DALYs and ASDRs in females than in males. During this period, DALYs for both sexes in G20 countries also increased, but the ASDR decline was more pronounced. Although females consistently had higher DALYs than males, their ASDRs were significantly lower than those of males.

The metabolic risk-attributable PAD burden exhibited significant age disparities (Supplementary Table 3; Figure 2). In China, DALYs peaked in the 70–74 years group and then declined for both sexes in 2023, whereas the ASDR generally continued to increase with advancing age. Females had significantly higher DALYs and ASDRs in the age groups above 55 years. In G20 countries, DALYs peaked in the 70–74 years group for males and the 75–79 years group for females during this period. Males had higher DALYs and ASDRs in younger age groups, but were surpassed by females above the 75 years group for DALYs and the 85 years group for ASDRs.

High fasting plasma glucose, high systolic blood pressure, high body-mass index, and kidney dysfunction were identified as specific metabolic risk factors for PAD burden in GBD 2023. Kidney dysfunction was the leading metabolic risk factor, accounting for 28.3% of the population-attributable burden in China and 31.1% in G20 countries, followed by high fasting plasma glucose (27.4% in China and 27.1% in G20 countries). High body-mass index (11.5% in China and 20.3% in G20 countries) and high systolic blood pressure (12.9% in China and 12.5% in G20 countries) had relatively lower impacts (Table 1).

From 1990 to 2023, the PAFs for kidney dysfunction and high systolic blood pressure remained relatively stable in both China and the G20 countries. However, the PAFs for high fasting plasma glucose and high body-mass index exhibited a marked increase in both China and the G20 countries, with China experiencing a greater increase than the G20 countries (Figure 3). Similar trends were observed for both sexes (Supplementary Figures 1, 2).

Across all age groups, a similar pattern for PAFs was observed in China and the G20 countries (Figure 4). The overall PAFs for metabolic risks increased steadily with advancing age in 2023. Among the specific metabolic risk factors, kidney dysfunction and high fasting plasma glucose showed an overall increase in PAFs with advancing age. High systolic blood pressure remained relatively stable across age groups, whereas high body-mass index showed an overall decline with advancing age. Similar trends were observed for both sexes (Supplementary Figures 3, 4).

This study quantified the contributions of population growth, aging, and epidemiological changes to the changes in metabolic risk-attributable PAD DALYs from 1990 to 2023 (Supplementary Table 4). Overall, DALYs increased by 68 039 in China and 388 560 in the G20 countries. In China, population growth contributed 65.4%, and aging contributed 46.3%, while epidemiological changes showed a negative contribution (−11.7%). In contrast, for the G20 countries, population growth contributed 104.2%, and aging contributed 34.4%, whereas epidemiological changes had a greater negative contribution (−38.6%).

Based on the ARIMA model, the DALYs of PAD attributable to metabolic risks in China are projected to continue increasing, reaching 153 890 (95% CI: 132 271 to 179 042) in China by 2035, representing a growth of 53.4% compared with that in 2023 (Supplementary Table 5; Figure 5). Meanwhile, the ASDR initially increases but then decreases, with an overall increase of 1.6% from 2023 to 2035(Supplementary Table 5; Supplementary Figure 5). In contrast, the DALYs in G20 countries are projected to increase by 32% during the same period, whereas the ASDR is projected to decline by 8.8% (Supplementary Table 5; Supplementary Figure 5). Similar trends are observed for both sexes in China and the G20 countries. Although some subgroups still exhibit residual non-stationarity, the ARIMA model generally fits well (Supplementary Tables 6, 7).