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Forecasting the Global Burden of Peripheral Artery Disease from 2021 to 2050: A Population-Based Study
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Liling Deng1, Chenzhen Du1, Lu Liu2, Yanzhong Wang2, Haotian Gu3, David G. Armstrong4, Joseph L. Mills5, Dirk Hochlenert6, Huacong Deng1, Junlin Ran1, Yan Chen1, Xiaoyan Jiang1, Yu Ma1, *, Qiu Chen7, *, Wuquan Deng1, 2, *, on behalf of the “Flow and Toe” Research Team (FORT)
Research. Vol 8 Article ID 0702
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Research. Vol 8 Article ID 0702
• Research Article •
Forecasting the Global Burden of Peripheral Artery Disease from 2021 to 2050: A Population-Based Study
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Liling Deng1, Chenzhen Du1, Lu Liu2, Yanzhong Wang2, Haotian Gu3, David G. Armstrong4, Joseph L. Mills5, Dirk Hochlenert6, Huacong Deng1, Junlin Ran1, Yan Chen1, Xiaoyan Jiang1, Yu Ma1, *, Qiu Chen7, *, Wuquan Deng1, 2, *, on behalf of the “Flow and Toe” Research Team (FORT)
Affiliations
  • 1School of Medicine, Diabetic Foot Medical Research Center, Department of Endocrinology, Chongqing University Central Hospital, Chongqing University, Chongqing 400014, China.
  • 2School of Life Course and Population Health Sciences, King’s College London, London WC2R 2LS, UK.
  • 3British Heart Foundation Centre, King’s College London, London WC2R 2LS, UK.
  • 4Department of Surgery, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA.
  • 5Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA.
  • 6Ambulantes Zentrum für Diabetologie, Endoskopie und Wundheilung, Köln 50733, Germany.
  • 7Diabetic Foot Diagnosis and Treatment Center, Department of Endocrinology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610500, China.
About Author:

† These authors contributed equally to this work.

‡ Members of the “Flow and Toe” Research Team (FORT) are listed in Appendix A.

Published: 2025-07-20 doi: 10.34133/research.0702
Outline
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Vascular disease is the leading cause of death worldwide. Predicting the burden of vascular disease and identifying modifiable key risk factors are critical for developing effective prevention strategies. This study aimed to project the global and regional burden of peripheral artery disease (PAD) from 2021 to 2050, with a specific focus on the impact of modifiable key risk factors and the potential benefits of their management. Compared to the 2021 Global Burden of Disease Study (GBD 2021), the number of PAD cases worldwide is projected to increase by 220% by 2050, reaching a staggering 360 million (95% uncertainty interval, 270 to 450). Age-standardized mortality is expected to double, while disability-adjusted life years (DALYs) are forecasted to rise from 19.7 to 33.1 per 100,000. Among individuals aged ≥65 years, PAD prevalence is projected to surge to 21.7% in women and 14.8% in men. Moreover, over 50% of PAD cases are expected to occur in low- and middle-income countries (LMICs). Metabolic diseases are anticipated to be the primary drivers of the rising PAD burden, with diabetes playing a key role in increasing PAD prevalence and severity. By effectively managing metabolic risk factors, age-standardized prevalence could be reduced by 36%, mortality by 17%, and DALYs by 10%. As metabolic risks, particularly diabetes, continue to rise alongside population aging, the global PAD burden is expected to increase substantially, especially in LMICs. Importantly, proactive metabolic risk management strategies have the potential to markedly alleviate the burden of vascular disease and reduce the growing geographic health disparities.

Liling Deng, Chenzhen Du, Lu Liu, Yanzhong Wang, Haotian Gu, David G. Armstrong, Joseph L. Mills, Dirk Hochlenert, Huacong Deng, Junlin Ran, Yan Chen, Xiaoyan Jiang, Yu Ma, Qiu Chen, Wuquan Deng, on behalf of the “Flow and Toe” Research Team (FORT). Forecasting the Global Burden of Peripheral Artery Disease from 2021 to 2050: A Population-Based Study[J]. Research, 2025 , 8 (7) : 0702 - . DOI: 10.34133/research.0702
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Introduction
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With global aging, industrialization, and urbanization, chronic noncommunicable diseases have risen dramatically, establishing peripheral artery disease (PAD) as a growing public health concern [1–3]. According to the Global Burden of Disease (GBD) 2021 estimates, over 113 million individuals worldwide are affected by PAD, a condition whose global prevalence may exceed that of coronary artery disease (CAD) [4]. PAD is associated with substantial morbidity, including varying levels of limb amputation, particularly among older adults [5], and is frequently accompanied by cardiovascular and cerebrovascular diseases, leading to a substantially increased risk of adverse vascular events and all-cause mortality, with a 5-year mortality rate estimate of at least 30% [1,6–8]. Furthermore, PAD imposes a substantial economic burden, with annual hospitalization costs in the United States alone reaching $6.31 billion [9].
Approximately 70% of PAD cases are attributed to modifiable risk factors such as smoking, obesity, diabetes, hypertension, and hyperlipidemia [2]. Recent GBD analyses highlight the escalating severity of metabolic risk factors, with diabetes alone projected to affect 1.31 billion people globally by 2050 [10,11]. Consequently, the global PAD burden is anticipated to increase substantially in the coming decades. While anti-smoking initiatives have demonstrated early success in reducing PAD risk, the potential impact of targeted interventions addressing major metabolic risk factors remains unquantified, and to date, no existing models integrate dynamic interactions between demographic shifts, socioeconomic determinants, and metabolic risk to project future PAD burden. Predicting the burden of PAD and identifying modifiable key factors are critical for developing effective prevention strategies.
Disease burden forecasting is crucial for guiding the strategic allocation of healthcare resources [12], anticipating the demands of an aging population, and identifying modifiable risk factors to mitigate adverse outcomes such as cardiovascular events, limb amputations, and mortality among PAD patients [7]. This study utilizes the GBD 2021 database to develop a dynamic prediction model, projecting the global burden of PAD [including prevalence, mortality, and disability-adjusted life years (DALYs)] across 204 countries and territories by 2050. Our approach incorporates key metabolic risk factors, demographic transitions, and socioeconomic indices to provide a comprehensive analysis of PAD burden under scenarios of medical intervention and non-intervention. These projections aim to supplement existing evidence by offering actionable insights into PAD prevention and management strategies in a rapidly changing global landscape.
Results
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In comparison, we estimated a substantial percentage increase of 220% in the total number of PAD cases globally between 2021 and 2050 (Table). In 2021, there were an estimated 113 [95% uncertainty interval (UI), 98 to 131] million individuals living with PAD worldwide, which is projected to increase to 189 (144 to 231) million by 2030, 273 (203 to 339) million by 2040, and 363 (270 to 454) million by 2050. We estimated that the age-standardized prevalence rate, after adjusting for population differences, would increase by 164%, from 1,441.0 (1,248.2 to 1,662.8) per 100,000 in 2021 to 3,803.6 (2,825.0 to 4,749.5) per 100,000 in 2050.
In 2021, there were more females with PAD than males with PAD globally, with a female-to-male ratio of 2.03. We anticipate that this pattern will decrease slightly by 2050 due to a higher growth rate of PAD cases among males, resulting in a female-to-male ratio of 1.84 (Table and Fig. 1). By 2050, the projected prevalence rates of PAD among females aged 40 to 59, 60 to 69, 70 to 79, and ≥80 years would be 2.1% (95% UI, 1.8 to 2.4), 8.7% (7.4 to 10.1), 19.0% (15.5 to 22.6), and 35.3% (27.5 to 43.2), respectively. The corresponding prevalence rates for males in these age groups are projected to be 1.0% (0.9 to 1.1), 2.8% (1.7 to 4.0), 19.6% (8.9 to 28.6), and 20.8% (16.4 to 25.4) (Table and Fig. 1).
Between 2021 and 2050, we estimated that the highest percentage change in total PAD cases would occur in sub-Saharan Africa (711% [95% UI, 437 to 909]), while the lowest percentage increases were projected in Central Europe, Eastern Europe, and Central Asia (41% [22 to 55]). Regarding age-standardized prevalence, the highest percentage change was projected in North Africa and the Middle East (855% [675 to 977]), whereas Central Europe, Eastern Europe, and Central Asia would exhibit the lowest rate of change (184% [147 to 212]) (Table). When examining the projected percentage changes in age-standardized PAD prevalence by country or region, substantial heterogeneity was evident. While increases were anticipated across all countries and regions, high-income regions such as Eastern Europe (206% [95% UI, 176 to 295]) were projected to experience the lowest increases, whereas the highest increases were expected in East Asia (1,231% [507 to 1,710]), eastern sub-Saharan Africa (944% [625 to 1,154]), and North Africa and the Middle East (855% [675 to 977]) (Table and Fig. 2).
The age-standardized mortality rate for PAD was expected to increase by 100% (95% UI, 116 to 117) from 0.9 (0.8 to 0.9) per 100,000 in 2021 to 1.8 (1.6 to 2.0) per 100,000 in 2050 (Table and Fig. 2). Age-standardized DALYs were projected to increase by 67% (46 to 78), rising from 19.7 (16.1 to 25.9) per 100,000 in 2021 to 33.1 (28.6 to 37.8) per 100,000 in 2050, with a higher increase among males (Table and Fig. 2). Further analysis of the projected percentage changes in age-standardized PAD mortality by country or region indicated that South Asia (282% [95% UI, 215 to 331]) would experience the highest increase in mortality by 2050. Although the lowest increases were projected in Oceania (100% [71 to 146]) and western Europe (110% [97 to 128]), mortality rates in these regions would be twice those reported in 2021. Projections for age-standardized DALYs indicated that the highest increases were in South Asia (167% [138 to 178]) and Central Latin America (157% [115 to 161]), while the lowest increases were expected in western Europe (68% [64 to 66]) and high-income North America (70% [62 to 72]) (Table and Figs. 2 and 3). At the national level, the highest projected increases by 2050 are in Qatar (1,691% [1,467 to 1,875]) and the United Arab Emirates (1,553% [1,263 to 1,884]), while the lowest projected increases are in Bulgaria (9% [−4 to 16]) and Estonia (16% [−2 to 31]).
Decomposition of the projected percentage changes in PAD cases suggested that metabolic risks would substantially contribute to the overall increase in prevalence, nearly matching the impact of population aging. In most regions, the increase in PAD cases was attributed to GBD risk factors including high fasting plasma glucose, high body mass index, kidney dysfunction, and high systolic blood pressure. In contrast, smoking-related PAD cases were projected to decline by 2050, primarily due to global anti-smoking initiatives. Although metabolic risks contributed similarly to the projected increase in PAD prevalence at the global level, different patterns are projected to be observed at the regional level. High-income North America was expected to experience the largest contribution of metabolic risks to PAD prevalence, while eastern sub-Saharan Africa was expected to see the smallest contribution (Fig. 4).
Based on these findings, further projections indicated that substantial reductions in PAD burden can be achieved by improving major metabolic risk factors. We constructed a counterfactual baseline scenario that assumes strict implementation of risk management measures globally. Specifically, the age-standardized prevalence of PAD could decrease by 36%, from 3,803.6 (95% UI, 2,825.0 to 4,749.5) per 100,000 to 2,416.4 (2,344.9 to 2,487.9) per 100,000. Mortality could be reduced by 17%, from 1.8 (1.6 to 2.0) per 100,000 to 1.5 (1.4 to 1.5) per 100,000, and DALYs could be reduced by 10%, from 33.1 (28.6 to 37.8) per 100,000 to 29.8 (28.5 to 31.1) per 100,000 (Fig. 5).
Discussion
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Our study provides a comprehensive projection of the global PAD burden, including prevalence, mortality, and DALYs, across 204 countries and regions up to 2050. These projections address gaps in previous studies by providing a detailed assessment of the potential PAD burden resulting from increasing global metabolic risks. While the effectiveness of global anti-smoking campaigns has contributed to a reduction in age-standardized PAD rates, these gains are counterbalanced and may be surmounted by population aging and the increasing prevalence of metabolic diseases. If current trends continue without effective interventions, the global PAD burden is projected to double by 2050.
We estimate that the total number of PAD cases will increase by 220%, potentially affecting 360 million (95% UI, 270 to 450 million) individuals by 2050. While the overall number of cases in females is expected to exceed that in males, the growth rate in males is projected to outpace that in females [2,13]. This finding is consistent with previous reports and suggests that the higher growth rate in males is likely due to higher smoking rates, while the higher total case number in females reflects their longer life expectancy. Biological factors may also contribute to gender differences in PAD prevalence. First, the differences in sex hormones may affect arterial compliance. Second, increased immune molecules, immune cells, and platelets in women with PAD may contribute to the higher female prevalence and altered pathogenesis of the disease. In addition, women have higher resistance to antiplatelet agents [14–16]. The elderly population remains the most vulnerable group, with PAD prevalence among those aged 60 years and above projected to rise from 8.0% in 2021 to 15.2% in 2050. Additionally, as 2/3 of the global population 60 years of age and above will reside in LMICs by 2050 [17], it is expected that over half of PAD patients will live in these regions. The increase in DALYs highlights the substantial healthcare resource utilization and disability risk faced by PAD patients in these regions.
While population aging is a key driver of PAD incidence, modifiable metabolic risk factors and unhealthy behaviors contribute to 70% of PAD cases globally [2]. Public health interventions targeting these risk factors have already been prioritized by health organizations, yet the implementation and effectiveness of these measures remains suboptimal [18]. Among the metabolic risks, diabetes stands out as the most significant contributor to PAD, accounting for 37.7% of age-standardized DALYs.
The link between diabetes and PAD is well established, with diabetic patients being 2 to 3 times more likely to develop PAD, and nearly half of diabetic foot ulcer patients suffering from the condition [19–22]. Other major risk factors, such as obesity, hypertension, and hyperlipidemia, also substantially contribute to PAD burden. Despite the high prevalence of these conditions, disparities in diagnosis, treatment, and control remain widespread due to unequal healthcare resource distribution [23–25]. Furthermore, studies have shown that important interactions and mediating effects exist among the main risk factors. For example, diabetes and obesity may synergistically exacerbate the risk of developing PAD by promoting systemic inflammatory responses and endocrine imbalances. In addition, smoking, as an independent behavioral risk factor, may exert long-term effects by promoting atherosclerosis and, together with metabolic abnormalities, synergistically deteriorate vascular function [2]. By applying the population attributable fraction (PAF) model, this study has, to some extent, reflected the cumulative effects of these interacting risk factors. However, we acknowledge that further mediation effect analyses are needed to more precisely quantify the pathways and proportions of the influence among these factors, thereby providing theoretical support for targeted prevention strategies.
Chronic kidney disease (CKD), a nontraditional metabolic risk factor, also plays a crucial role in PAD development, with a 29.0% contribution to PAD-related age-standardized DALYs. CKD patients with PAD are at increased risk for limb amputations, cardiovascular events, mortality, and all-cause mortality [26]. Our study highlights that those unhealthy behaviors, such as smoking, physical inactivity, and high sodium intake, account for over 30% of PAD-related age-standardized mortality. Smoking remains the primary behavioral risk factor. Despite progress in smoking cessation campaigns, the long-term impact of smoking on PAD risk persists, with former smokers taking decades to reach the same risk level as nonsmokers [27]. For example, a recent community-based cohort study indicates that former smokers may take up to 30 years to reach the same PAD risk level as nonsmokers [28]. Additionally, the burden associated with physical inactivity and high sodium intake is expected to rise.
Our projections suggest that the combined effects of aging and increasing metabolic risks will partially offset the benefits of smoking cessation and medical interventions targeting metabolic conditions. Under a scenario where medical interventions effectively control metabolic risk factors, we estimated a 36% reduction in age-standardized PAD prevalence, a 17% reduction in mortality, and a 10% reduction in DALYs by 2050. These finding highlights that more than 30% of PAD cases could be preventable through effective risk factor management.
The limited awareness of PAD among healthcare professionals and the public, particularly in socioeconomically disadvantaged regions, contributes to delayed diagnosis and underdiagnosis [29]. Only 10% to 30% of PAD patients present with classic symptoms of intermittent claudication, which complicates early detection and timely treatment [30]. The resting ankle brachial index (ABI), a simple, noninvasive physiological test, remains as the primary method for initial diagnosis of PAD. In addition, other diagnostic testing for PAD is performed to supplement the ABI and includes exercise ABI testing, segmental pressures, leg pressures and Doppler waveforms, toe branchial index, and perfusion imaging [14]. Despite the evidence from high-quality clinical trials, PAD remains under-recognized compared to CAD, leading to suboptimal adherence to guideline-recommended therapies. Global treatment rates for PAD remain low, especially in low-sociodemographic index (SDI) regions, with substantial gaps in the prescription of antiplatelet therapy, statins, and proprotein convertase subtilisin/kexin type 9 inhibitors [31,32]. Furthermore, supervised exercise therapy, a first-line treatment, has demonstrated efficacy comparable to that of endovascular interventions. However, its implementation is hampered by inadequate healthcare infrastructure, particularly in resource-limited settings [33].
Our estimates can inform the planning of resources and healthcare services needed to address the growing PAD burden. Currently, substantial gaps exist globally in PAD awareness, diagnosis, and treatment. Diagnostic and treatment rates are lower in low- and middle-income regions than in high-income countries, where PAD management resources are more readily available. Efforts to address these disparities must include increasing public and healthcare provider awareness, improving access to diagnostic services, and ensuring equitable distribution of PAD management resources [31]. Government health agencies must take a leading role in strengthening PAD-related policies and expanding treatment options, especially in regions with limited healthcare infrastructure. By supporting comprehensive public health strategies and emphasizing the importance of managing metabolic risk factors, we can reduce the future burden of PAD and improve health outcomes globally.
This study has several limitations. Our analysis relies on data from the GBD 2021 study, which integrates estimates of PAD prevalence and mortality from diverse sources. Variations in methodology and data availability across regions may lead to discrepancies between GBD estimates and national or subnational data [10]. However, our focus was on projecting future trends rather than comparing absolute values across datasets. In addition, because the forecasting model is based primarily on GBD historical data and established assumptions, there are inevitably uncertainties introduced by technological advances, healthcare policy adjustments, and socioeconomic shifts. With the further development of the prediction model in the future, this uncertainty may be reduced. For example, dynamic adjustment mechanism is introduced to capture the changes of external environmental factors in a more timely manner and improve the sensitivity and accuracy of the prediction model. The projection of PAD prevalence using PAFs for GBD risk factors is based on assumed causal relationships. If noncausal factors influence the estimates, the projections may over- or underestimate true prevalence. Additionally, the estimation of PAD prevalence within the GBD framework is constrained by limited and heterogeneous data, as population-based studies often lack standardized diagnostic methods. Finally, while this study offers valuable insights into the future PAD burden, it highlights the need for more robust epidemiological research. Standardized diagnostic methods and exploration of novel therapies, such as GLP-1 receptor agonists (GLP1RAs), are essential. Retrospective studies suggest that GLP1RA may reduce the risk of major limb events in diabetic patients [34]. Recent evidence from randomized trials on PAD indicated that semaglutide increased walking distance in patients with symptomatic PAD and type 2 diabetes [35]. Future cardiovascular research should prioritize PAD as a key endpoint to enhance burden estimates and guide prevention strategies.
Conclusion
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Despite its limitations, this study is the first to offer comprehensive projections of the global burden of PAD, providing detailed forecasts for prevalence, mortality, and DALYs. Unlike previous studies that focused primarily on regional estimates, we utilized national-level data from the 2021 GBD dataset to project PAD prevalence by 2050 across countries, world regions, and SDI. By integrating projections of key metabolic risk factors and modeling their impact on PAD prevalence from 1990 to 2021, our analysis provides a more nuanced understanding of the future burden of PAD. Additionally, we employed decomposition analysis to quantify the relative contributions of population growth, aging, and changes in risk factors to the projected trends.
The granularity of our national-level estimates offers policymakers valuable insights into the anticipated rise in PAD cases and the underlying drivers of these increases within specific geographic and socioeconomic contexts. This information is critical for public health planning, particularly in ensuring adequate resource allocation to meet the growing demands of PAD patients and their caregivers. As population growth and aging emerge as major contributors to the increasing burden, urgent research is needed to develop disease-modifying therapies, cost-effective prevention strategies, and interventions targeting modifiable risk factors.
Our study provides a global perspective on PAD projections up to 2050, spanning 204 countries and territories and examining the interplay between metabolic risk factors and disease burden. We highlight the dynamic shifts in PAD prevalence across geographic regions, income groups, age categories, and genders while also exploring the potential impact of effective interventions targeting metabolic risk factors. These findings underscore the potential to substantially curb the rise in PAD prevalence through proactive public health measures, despite the challenges posed by an aging population.
By presenting a robust framework for predicting the future PAD burden, this study provides actionable insights for policymakers, healthcare planners, and researchers. Expanding healthcare service capacity, improving access to cost-effective treatments, and maximizing adherence to evidence-based strategies for PAD management should be prioritized to address the growing global burden. Furthermore, the identification of novel risk factors and the development of innovative interventions remain critical to mitigating the impact of PAD in the decades to come.
Methods
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Overview
Using GBD 2021 estimates as input data, we projected the PAD burden across 204 countries and regions from 2022 to 2050. Our forecasting framework builds on the methodology established by Foreman et al. [36] and incorporates refinements to predict independent health drivers and their associations with specific health outcomes. The prevalence prediction framework integrates key drivers such as sociodemographic characteristics by location, SDI values, gender-age-specific risk factor exposures (e.g., smoking and metabolic risks), and age-specific population proportions. Prevalence and mortality attributable and non-attributable to GBD risk factors were modeled, with additional residual drift from other causes incorporated into the estimates [10].
Data sources
The GBD 2021 study systematically reviewed globally representative data sources for PAD prevalence, mortality, and DALYs. Data visualization tools and the GBD protocol are publicly accessible, with historical population data obtained from the World Health Organization. Future population forecasts were derived using the method proposed by Vollset et al. [17] All concepts, analytical structures, and detailed methodologies related to GBD risk factors have been previously described and documented in prior GBD publications [2,10].
Risk factor forecasts
We evaluated the influence of 6 primary risk factors for PAD [2], as identified in previous study: smoking, high fasting plasma glucose, high body mass index, kidney dysfunction, high sodium intake, and lead exposure. Additionally, we considered other factors including low physical activity, alcohol consumption, dietary patterns, and air pollution. Multivariate regression was applied to address potential correlations among risk factors, ultimately grouping them into 2 broad categories: metabolic risks and behavioral risks. Summary exposure value (SEV) forecasts were estimated using historical trends and weighted averages.
The prevalence forecasts: Attributable to independent risk factors
We forecasted the combined effect of GBD risk factors by calculating the proportion of PAD prevalence attributable to each risk factor. Using both historical and predicted SEV values (1990–2021) along with their associated relative risk estimates, we estimated the PAF for each risk factor based on GBD 2021 data. Subsequently, we estimated the joint PAF for 2 risk categories and computed location-, age group-, and gender-specific scalars for each prediction year.
Scalar=11−PAF
Detailed methodologies for PAF estimation, risk factor mediation, and scalar computation are available in prior GBD projection studies [10,17].
Prevalence forecasts: Not attributable to independent risk factors
The GBD risk-deleted prevalence was estimated by dividing total PAD prevalence by the risk factor scalar. To refine our prediction model, we evaluated associations between risk factors and PAD prevalence (logit-transformed), employing gender-stratified models adjusted for 5-year age groups and global regions. Covariates associated with PAD prevalence estimates for both males and females, with effect directions consistent with those reported by the Lancet Commission Report, were incorporated into the model [37]. Additionally, the SDI was included as a covariate, with SDI projections derived by calculating the weighted average of historical changes.
Gender-stratified linear regression models were used to predict logit-transformed, risk-deleted PAD prevalence, incorporating 5-year age groups, global regions, and SDI as covariates. To account for uncertainty in input estimates, we performed 2,000 model iterations, drawing from the distributions of each input variable. Residual trends unexplained by the covariates were modeled using a random walk (autoregressive integrated moving average, ARIMA[0,1,0]), with predicted residuals added to the final prevalence estimates [10]. We utilized a grid search over possible combinations of autoregressive (p), differencing (d), and moving average (q) orders, guided by information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). This process ensured that the chosen model minimizes the information loss while maintaining parsimony.
Logitprevalancesex=β0+β1SDI+∑i=212βiAgei+∑i=13204βiCountryi+α
Comprehensive prevalence forecasts
The total PAD prevalence was projected as the product of risk-deleted prevalence and forecasted risk factor scalar. To ensure consistency between historical and future means and UIs, we applied an intercept shift to the predicted logit-transformed prevalence. Total PAD cases were calculated by multiplying prevalence projections by population forecasts. Using Das Gupta decomposition methods, we quantified the contributions of population growth, aging, and nondemographic factors to changes in PAD cases from 2021 to 2050 [37,38].
Death and DALY forecasts
The forecasting framework for PAD-related mortality and DALYs mirrored that used for prevalence. This included (a) baseline mortality modeled using population characteristics, SDI, regions, and gender; (b) gender- and age-specific projections of risk factor exposure by location; and (c) a random walk model with attenuated drift to account for residual mortality trends.
Improved behavioral and metabolic risks
This analysis refers to the hypothetical scenario of the GBD Foresight Visualization tool. Exposure to all dietary risk factors as well as high low-density lipoprotein cholesterol, body mass index, fasting plasma glucose, and systolic blood pressure are eliminated by 2050. (https://vizhub.healthdata.org/gbd-foresight/).
Analytical tools
All analyses were conducted using Python (version 3.12.7) and R (version 4.4.2).
Funding
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  • Noncommunicable Chronic Diseases-National Science and Technology Major Project of China(2023ZD0509400&402)
  • National Natural Science Foundation of China(82370903)
  • Integrated Traditional Chinese and Western Medicine Chronic Disease Management Project 2024(CXZH2024065)
  • National Institute of Diabetes and Digestive and Kidney Diseases(1R01124789-01A1)
  • National Science Foundation Center to Stream Healthcare in Place(CNS #2052578)
References
Less
1.
Polonsky TS, McDermott MM. Lower extremity peripheral artery disease without chronic limb-threatening ischemia: A review. JAMA. 2021;325(21):2188–2198.
2.
GBD 2019 Peripheral Artery Disease Collaborators. Global burden of peripheral artery disease and its risk factors, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Glob Health. 2023;11(10):e1553–e1565.
3.
Dai Y, Teng D, Zhang C, Wang H, Lai Y, Ding S, Han Y, Dou L, Yang S, Ma Y, et al.Priorities in tackling noncommunicable diseases among the population aged 60 years and older in China, 1990-2021: A population-based study. Ageing Res Rev. 2024;102:Article 102574.
4.
Bauersachs R, Zeymer U, Brière JB, Marre C, Bowrin K, Huelsebeck M. Burden of coronary artery disease and peripheral artery disease: A literature review. Cardiovasc Ther. 2019;2019:8295054.
5.
Jones WS, Patel MR, Dai D, Subherwal S, Stafford J, Calhoun S, Peterson ED. Temporal trends and geographic variation of lower-extremity amputation in patients with peripheral artery disease: Results from U.S. Medicare 2000-2008. J Am Coll Cardiol. 2012;60(21):2230–2236.
6.
Armstrong DG, Swerdlow MA, Armstrong AA, Conte MS, Padula WV, Bus SA. Five year mortality and direct costs of care for people with diabetic foot complications are comparable to cancer. J Foot Ankle Res. 2020;13(1):16.
7.
Allison MA, Armstrong DG, Goodney PP, Hamburg NM, Kirksey L, Lancaster KJ, Mena-Hurtado CI, Misra S, Treat-Jacobson DJ, White Solaru KT, et al.Health disparities in peripheral artery disease: A scientific statement from the American Heart Association. Circulation. 2023;148(3):286–296.
8.
Rantner B, Kollerits B, Pohlhammer J, Stadler M, Lamina C, Peric S, Klein-Weigel P, Mühlthaler H, Fraedrich G, Kronenberg F. The fate of patients with intermittent claudication in the 21st century revisited—Results from the CAVASIC Study. Sci Rep. 2017;8:45833.
9.
Kohn CG, Alberts MJ, Peacock WF, Bunz TJ, Coleman CI. Cost and inpatient burden of peripheral artery disease: Findings from the National Inpatient Sample. Atherosclerosis. 2019;286:142–146.
10.
GBD 2021 Forecasting Collaborators. Burden of disease scenarios for 204 countries and territories, 2022-2050: A forecasting analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2204–2256.
11.
Yang K, Yang X, Jin C, Ding S, Liu T, Ma B, Sun H, Zhang J, Li Y. Global burden of type 1 diabetes in adults aged 65 years and older, 1990-2019: Population based study. BMJ. 2024;385:Article e078432.
12.
Huo Z, Booth J, Monks T, Knight P, Watson L, Peters M, Pagel C, Ramnarayan P, Li K. Dynamic mortality prediction in critically ill children during interhospital transports to PICUs using explainable AI. NPJ Digit Med. 2025;8(1):108.
13.
Song P, Rudan D, Zhu Y, Fowkes FJI, Rahimi K, Fowkes FGR, Rudan I. Global, regional, and national prevalence and risk factors for peripheral artery disease in 2015: An updated systematic review and analysis. Lancet Glob Health. 2019;7(8):e1020–e1030.
14.
Gornik HL, Aronow HD, Goodney PP, Arya S, Brewster LP, Byrd L, Chandra V, Drachman DE, Eaves JM, Ehrman JK, et al.2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS guideline for the management of lower extremity peripheral artery disease: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149(24):e1313–e1410.
15.
Kavurma MM, Boccanfuso L, Cutmore C, Passam F, Patel S, Hennessy A, Loa J, Figtree GA, Golledge J, Robinson DA, et al.A hidden problem: Peripheral artery disease in women. Eur Heart J Qual Care Clin Outcomes. 2023;9(4):342–350.
16.
Cartland SP, Stanley CP, Bursill C, Passam F, Figtree GA, Patel S, Loa J, Golledge J, Robinson DA, Aitken SJ, et al.Sex, endothelial cell functions, and peripheral artery disease. Int J Mol Sci. 2023;24(24):17439.
17.
Vollset SE, Goren E, Yuan CW, Cao J, Smith AE, Hsiao T, Bisignano C, Azhar GS, Castro E, Chalek J, et al.Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: A forecasting analysis for the Global Burden of Disease Study. Lancet. 2020;396(10258):1285–1306.
18.
Adou C, Magne J, Gazere N, Aouida M, Chastaingt L, Aboyans V. Global epidemiology of lower extremity artery disease in the 21st century (2000-21): A systematic review and meta-analysis. Eur J Prev Cardiol. 2024;31(7):803–811.
19.
Hinchliffe RJ, Forsythe RO, Apelqvist J, Boyko EJ, Fitridge R, Hong JP, Katsanos K, Mills JL, Nikol S, Reekers J, et al.Guidelines on diagnosis, prognosis, and management of peripheral artery disease in patients with foot ulcers and diabetes (IWGDF 2019 update). Diabetes Metab Res Rev. 2020;36(Suppl 1):e3276.
20.
Gallagher KA, Mills JL, Armstrong DG, Conte MS, Kirsner RS, Minc SD, Plutzky J, Southerland KW, Tomic-Canic M, American Heart Association Council on Peripheral Vascular Disease; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; and Council on Lifestyle and Cardiometabolic Health. Current status and principles for the treatment and prevention of diabetic foot ulcers in the cardiovascular patient population: A scientific statement from the American Heart Association. Circulation. 2024;149(4):e232–e253.
21.
Zhang Y, Lazzarini PA, McPhail SM, van Netten JJ, Armstrong DG, Pacella RE. Global disability burdens of diabetes-related lower-extremity complications in 1990 and 2016. Diabetes Care. 2020;43(5):964–974.
22.
Rui S, Dai L, Zhang X, He M, Xu F, Wu W, Armstrong DG, You Y, Xiao X, Ma Y, et al.Exosomal miRNA-26b-5p from PRP suppresses NETs by targeting MMP-8 to promote diabetic wound healing. J Control Release. 2024;372:221–233.
23.
NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: A pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957–980.
24.
GBD 2021 US Obesity Forecasting Collaborators. National-level and state-level prevalence of overweight and obesity among children, adolescents, and adults in the USA, 1990-2021, and forecasts up to 2050. Lancet. 2024;404(10469):2278–2298.
25.
Ochmann S, von Polenz I, Marcus ME, Theilmann M, Flood D, Agoudavi K, Aryal KK, Bahendeka S, Bicaba B, Bovet P, et al.Diagnostic testing for hypertension, diabetes, and hypercholesterolaemia in low-income and middle-income countries: A cross-sectional study of data for 994 185 individuals from 57 nationally representative surveys. Lancet Glob Health. 2023;11(9):e1363–e1371.
26.
Bourrier M, Ferguson TW, Embil JM, Rigatto C, Komenda P, Tangri N. Peripheral artery disease: Its adverse consequences with and without CKD. Am J Kidney Dis. 2020;75(5):705–712.
27.
Chugh A, Arora M, Jain N, Vidyasagaran A, Readshaw A, Sheikh A, Eckhardt J, Siddiqi K, Chopra M, Mishu MP, et al.The global impact of tobacco control policies on smokeless tobacco use: A systematic review. Lancet Glob Health. 2023;11(6):e953–e968.
28.
Ding N, Sang Y, Chen J, Ballew SH, Kalbaugh CA, Salameh MJ, Blaha MJ, Allison M, Heiss G, Selvin E, et al.Cigarette smoking, smoking cessation, and long-term risk of 3 major atherosclerotic diseases. J Am Coll Cardiol. 2019;74(4):498–507.
29.
Bevan GH, White Solaru KT. Evidence-based medical management of peripheral artery disease. Arterioscler Thromb Vasc Biol. 2020;40(3):541–553.
30.
McDermott MM. Lower extremity manifestations of peripheral artery disease: The pathophysiologic and functional implications of leg ischemia. Circ Res. 2015;116(9):1540–1550.
31.
Bates KJ, Moore MM, Cibotti-Sun M. 2024 lower extremity peripheral artery disease guideline-at-a-glance. J Am Coll Cardiol. 2024;83(24):2605–2609.
32.
Colantonio LD, Hubbard D, Monda KL, Mues KE, Huang L, Dai Y, Jackson EA, Brown TM, Rosenson RS, Woodward M, et al.Atherosclerotic risk and statin use among patients with peripheral artery disease. J Am Coll Cardiol. 2020;76(3):251–264.
33.
Treat-Jacobson D, McDermott MM, Bronas UG, Campia U, Collins TC, Criqui MH, Gardner AW, Hiatt WR, Regensteiner JG, Rich K, et al.Optimal exercise programs for patients with peripheral artery disease: A scientific statement from the American Heart Association. Circulation. 2019;139(4):e10–e33.
34.
Lin DS, Yu AL, Lo HY, Lien CW, Lee JK, Chen WJ. Major adverse cardiovascular and limb events in people with diabetes treated with GLP-1 receptor agonists vs SGLT2 inhibitors. Diabetologia. 2022;65(12):2032–2043.
35.
Bonaca MP, Catarig AM, Houlind K, Ludvik B, Nordanstig J, Ramesh CK, Rasouli N, Sourij H, Videmark A, Verma S, et al.Semaglutide and walking capacity in people with symptomatic peripheral artery disease and type 2 diabetes (STRIDE): A phase 3b, double-blind, randomised, placebo-controlled trial. Lancet. 2025;405:1580–1593.
36.
Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan CW, et al.Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: Reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet. 2018;392(10159):2052–2090.
37.
GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet. Public Health. 2022;7(2):e105–e125.
38.
Gupta PD. A general method of decomposing a difference between two rates into several components. Demography. 1978;15(1):99–112.
Appendix
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Cite this Article
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Article Info
doi: 10.34133/research.0702
  • Receive Date：2025-03-28
  • Online Date：2026-11-02
  • Published：2025-07-20
Article Data
Affiliations
History
  • Received：2025-03-28
  • Revised：2025-04-16
  • Accepted：2025-04-22
Funding
Noncommunicable Chronic Diseases-National Science and Technology Major Project of China(2023ZD0509400&402)
National Natural Science Foundation of China(82370903)
Integrated Traditional Chinese and Western Medicine Chronic Disease Management Project 2024(CXZH2024065)
National Institute of Diabetes and Digestive and Kidney Diseases(1R01124789-01A1)
National Science Foundation Center to Stream Healthcare in Place(CNS #2052578)
Affiliations
    1School of Medicine, Diabetic Foot Medical Research Center, Department of Endocrinology, Chongqing University Central Hospital, Chongqing University, Chongqing 400014, China.
    2School of Life Course and Population Health Sciences, King’s College London, London WC2R 2LS, UK.
    3British Heart Foundation Centre, King’s College London, London WC2R 2LS, UK.
    4Department of Surgery, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA.
    5Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA.
    6Ambulantes Zentrum für Diabetologie, Endoskopie und Wundheilung, Köln 50733, Germany.
    7Diabetic Foot Diagnosis and Treatment Center, Department of Endocrinology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610500, China.

Corresponding:

* Address correspondence to: (Y.M.);
(Q.C.);
(W.D.)
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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