Trends, Decomposition Analysis, and Future Predictions of the Burden of Ischemic Stroke Attributable to Kidney Dysfunction in China, 1990-2021: Based on the 2021 GBD Database

Trends, Decomposition Analysis, and Future Predictions of the Burden of Ischemic Stroke Attributable to Kidney Dysfunction in China, 1990-2021: Based on the 2021 GBD Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends, Decomposition Analysis, and Future Predictions of the Burden of Ischemic Stroke Attributable to Kidney Dysfunction in China, 1990-2021: Based on the 2021 GBD Database Dachen Tian, Chong Chen, Ruijin Liu, Cong Wang, Youfang Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6233482/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective Kidney dysfunction is a critical risk factor for ischemic stroke, yet longitudinal analyses of its burden in China remain limited. To evaluate the burden of Ischemic Stroke Attributable to Kidney Dysfunction (ISAKD) in China from 1990 to 2021, this study analyzed trends, gender and age differences, and decomposition of drivers and projected future trends up to 2040. Methods Utilizing the Global Burden of Disease 2021 database, deaths and disability-adjusted life years (DALYs) were analyzed. Joinpoint regression identified temporal trends, decomposition analysis quantified age, population, and epidemiological contributions, and Bayesian Age-Period-Cohort modeling projected future burden. Results From 1990 to 2021, deaths rose from 40,555 to 90,532 and DALYs from 947,578 to 1,875,486. The age-standardized DALY rate (ASDAR) dropped from 6.87 to 4.91 per 100,000, with an average annual percent change (AAPC) of -1.083. ASDAR fell from 129.90 to 92.67 per 100,000 (AAPC: -1.087%). Females had larger ASDR (6.41 to 3.97) and ASDAR (124.09 to 77.87) drops than males (ASDR: 7.65 to 6.38; ASDAR: 139.25 to 112.39). Burden peaked at 70–79, with males showing higher mortality (59.54 vs. 41.97 per 100,000) at 75–79. Decomposition revealed that aging (84.62%) and population growth (67.58%) drove mortality, offset by epidemiology (-52.2%). Aging (278.11%) and epidemiology (132.61%) raised DALYs, while population growth (-310.72%) diminished DALY losses. By 2040, ASDR is projected to fall to 6.90 and ASDAR to 144.35 per 100,000. Conclusions: Though absolute burden increased, ASDR and ASDAR fell, with females exhibiting greater declines than males, reflecting gender differences. The 70–79 age group faced the highest burden. Decomposition shows that aging markedly boosts mortality and DALYs, while population growth raises mortality but cuts DALY losses, and epidemiology curbs mortality yet raises DALY losses. Forecasts of ongoing declines highlight the need for age- and sex-specific interventions. ischemic stroke kidney dysfunction China GBD gender age decomposition prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Ischemic stroke (IS)ranks as a primary global cause of death and disability, substantially contributing to the disease burden [1] . Meanwhile, chronic kidney disease (CKD) impacts over 697 million individuals globally, constituting a significant public health concern [2] . The association between kidney Dysfunction (KD) and IS presents an escalating challenge, worsened by aging populations and evolving epidemiological trends, particularly in China [3] . The study indicates that IS resulted in 6.55 million deaths, with disability-adjusted life years (DALYs) having increased from 79.5 million in 1990 to 102.2 million in 2019 [1] . CKD prevalence has risen by 88.8% since 1990, heightening stroke risk through hypertension, diabetes, and oxidative stress [4] . In China, absolute stroke cases have increased despite reduced age-standardized rates, propelled by demographic shifts [5] . A known risk factor for IS is CKD, which can be caused by both conventional and unconventional mechanisms, such as vascular calcification [6] . Nevertheless, significant research gaps persist. Longitudinal analyses monitoring this burden over decades are scarce [7] . Studies dissecting contributions from aging, population growth, and epidemiology are similarly limited [8] . Regional differences, especially in China, where CKD prevalence is 10.8%, remain understudied [9] . Furthermore, gender and age-specific trends are not integrated with future projections [10] . These deficiencies impede effective prevention strategies. The Global Burden of Disease Study (GBD) 2021 database was employed to evaluate the burden of ISAKD, analyzing trends from 1990 to 2021 and forecasting outcomes through 2040. By bridging these gaps, it seeks to guide targeted interventions and enhance clinical and policy responses. Methods Overview This study aims to systematically assess the burden of ISAKD in China from 1990 to 2021 and forecast its trends to 2040. It examines trends in deaths and DALYs, evaluates sex and age disparities, dissects contributing factors, and investigates future burden traits to guide evidence-based public health interventions. Data Source The latest version of the GBD 2021 database was established by the Institute for Health Metrics and Evaluation at the University of Washington. It encompassed 371 diseases and injuries along with 88 risk factors, compiling health data from 204 countries and territories between 1990 and 2021, and offers a detailed breakdown of health metrics by age, gender, region, and time [11] . Data on ISAKD in China from 1990 to 2021 were extracted from the Global Health Data Exchange platform ( https://ghdx.healthdata.org/gbd-2021/sources ), including deaths and DALYs, classified by sex and age group, to ensure a comprehensive and representative analysis. The GBD 2021 is a publicly available database; thus, ethical approval was not required. Joinpoint Analysis Joinpoint software (version 5.3.0) was implemented in this study to calculate the annual percent change (APC), average annual percent change (AAPC), and their corresponding 95% uncertainty Interval (95% UI) for deaths and DALYs of ISAKD in China. The model with the best fit was selected to appraise trends in the disease burden of ISAKD. For the AAPC estimate, a 95% UI below 0 indicates a declining trend, above 0 suggests an increasing trend, and otherwise implies a stable trend. Decomposition Analysis The decomposition method used in this study was developed by Das Gupta and employs mathematical techniques to break down overall changes into individual components, identifying the specific contribution of each part. This approach is commonly applied in decomposition analyses. This study examines the effects of age structure, population growth, and epidemiological changes on the disease burden of ISAKD in China by utilizing this method. Projection Analysis To predict the burden of ISAKD in China from 2022 to 2040, this study utilized the Bayesian Age-Period-Cohort (BAPC) model, drawing on data from the GBD 2021 database. The BAPC method, implemented through the R package BAPC with the Integrated Nested Laplace Approximation for enhanced computational efficiency, integrates age, period, and cohort effects, delivering probabilistic forecasts with 95% UI to quantify uncertainty. Statistical Software The Joinpoint Regression Program (version 5.3.0) and R software (version 4.4.2) were used for data processing and analysis. To verify the robustness and dependability of the findings, 95% UI were included, and statistical significance was defined at P < 0.05. This study adheres to GBD study guidelines and the STROBE statement, ensuring methodological transparency, reproducibility, and standardized reporting to support academic and policy applications [12] . Results Description of the ISAKD burden in China Table 1 illustrates the burden of ISAKD in China from 1990 to 2021 stratified by sex. In 1990, all-age deaths totaled 40,555 (19,451 males, 21,104 females), rising to 90,532 (49,267 males, 41,266 females) by 2021. Similarly, DALYs increased from 947,578 (464,498 males, 483,080 females) to 1,875,486 (1,028,206 males, 847,279 females). Despite these increases, age-standardized rates declined significantly. Deaths rates dropped from 6.866 to 4.914 per 100,000 overall (AAPC: -1.083, 95% UI: -1.428 to -0.737), with males decreasing from 7.649 to 6.384 (AAPC: -0.583, 95% UI: -0.987 to -0.177) and females from 6.411 to 3.965 (AAPC: -1.514, 95% UI: -1.842 to -1.184). DALY rates also fell from 129.901 to 92.675 overall (AAPC: -1.087, 95% UI: -1.4541 to -0.7186), with males from 139.246 to 112.39 (AAPC: -1.087, 95% UI: -1.4541 to -0.7186) and females from 124.087 to 77.867 (AAPC: -1.5208, 95% UI: -1.7641 to -1.2768). These statistically significant declines (95% UIs exclude 0) suggest a reduced per-capita burden, particularly among females (Table 1 ). Table 1 All-age cases and age-standardized rates of death and DALYs for ischemic stroke attributable to kidney dysfunction in China, 1990 and 2021, with AAPC Sex Measure 1990 2021 1990–2021 AAPC All-ages cases Age-standardlzed rates per 100,000 people All-ages cases Age-standardlzed rates per 100,000 people n (95% UI) rate(95% UI) n (95% UI) rate (95% UI) rate (95% UI) Males Deaths 19451 (12936–27317) 7.649 (4.589–11.221) 49267 (31627–71842) 6.384 (3.902–9.243) -0.583 (-0.987-0.177) DALYs 464498 (319394–638738) 139.246 (91.666-198.336) 1028206 (691352–1459096) 112.39 (74.351-158.045) -1.087 (-1.4541-0.7186) Females Deaths 21104 (13816–29368) 6.411 (4.02–9.088) 41266 (25171–60326) 3.965 (2.402–5.846) -1.514 (-1.842-1.184) DALYs 483080 (334394–654108) 124.087 (84.689–170.8) 847279 (550368–1186709) 77.867 (50.457-109.446) -1.5208 (-1.7641-1.2768) Both Deaths 40555 (26558–56078) 6.866 (4.229–9.758) 90532 (56439–129118) 4.914 (3.012–7.029) -1.083 (-1.428-0.737) DALYs 947578 (667953–1287342) 129.901 (87.314-178.836) 1875486 (1299150–2587792) 92.675 (62.157-128.848) -1.087 (-1.4541-0.7186) Abbreviations Used in Table 1 • DALYs: Disability-Adjusted Life Years • AAPC: Average Annual Percent Change • UI: Uncertainty Interval Joinpoint analysis of the ISAKD burden in China Figure 1 illustrates the trends in ASDR (Figure A) and ASDAR (Figure B) for ISAKD in China from 1990 to 2021, analyzed via Joinpoint regression for both sexes combined, female and males. For both sexes, the ASDR decreased from 6.87 per 100,000 in 1990 to 5.82 in 1998, rose to a peak of 7.26 in 2004, and then declined significantly to 4.91 by 2021 (*P* < 0.05). The ASDAR followed a similar pattern, dropping from 129.90 per 100,000 in 1990 to 107.39 in 1998, peaking at 128.74 in 2004, and decreasing to 92.67 by 2021 (*P* < 0.05). Joinpoint analysis identified significant trend shifts for ASDR in 1994, 1999, 2004, and 2007 (APC: -2.80% to + 4.62%) and for ASDAR in 1998, 2001, 2004, 2007, and 2014 (APC: -2.33% to + 5.14%). Females showed a steeper decline in ASDR (6.41 to 3.97 per 100,000) and ASDAR (124.09 to 77.87 per 100,000) compared to males (ASDR: 7.65 to 6.38; ASDAR: 139.25 to 112.39), with significant changes (*P* < 0.05) observed (Fig. 1 ). Burden of ISAKD in China by Age and Sex Figure 2 illustrates the burden of ISAKD in China in 2021 stratified by age and sex. Figure 2 A shows that the number of deaths increases with age, peaking in the 75–79 age group for males at 9,289.88 (95% UI: 5,706.54–13,965.69) and females at 7,351.20 (95% UI: 4,507.59–11,086.41). Crude death rates also rise with age, with males consistently higher than females ( 59.54 vs. 41.97 per 100,000 in the 75–79 age group). Figure 2 B indicates that DALYs peak in the 70–74 age group for males at 208,213.29 (95% UI: 135,334.19–293,747.44) and females at 167,164.00 (95% UI: 111,633.70–238,090.18), with crude DALY rates higher in males ( 805.27 vs. 609.20 per 100,000 in the 70–74 age group, *P < 0.05*). These data reveal a greater burden in males and older age groups (Fig. 2 ). Figure 3 illustrates the burden of ISAKD in China from 1990 to 2021, categorized by sex and year. In Fig. 3 A, the number of deaths increased from 19,451 (95% UI: 12,936–27,317) in males and 21,104 (95% UI: 13,816–29,368) in females in 1990 to 49,267 (95% UI: 31,627–71,842) and 41,266 (95% UI: 25,171–60,326) in 2021, respectively. Conversely, age-standardized death rates declined from 7.65 (95% UI: 4.59–11.22) per 100,000 in males and 6.41 (95% UI: 4.02–9.09) in females in 1990 to 6.38 (95% UI: 3.90–9.24) and 3.97 (95% UI: 2.40–5.85) in 2021. Figure 3 B shows DALYs rising from 464,498 (95% UI: 319,394–638,738) in males and 483,080 (95% UI: 334,394–654,108) in females in 1990 to 1,028,206 (95% UI: 691,352–1,459,096) and 847,279 (95% UI: 550,368–1,186,709) in 2021. Age-standardized DALY rates decreased from 139.25 (95% UI: 91.67–198.34) per 100,000 in males and 124.09 (95% UI: 84.69–170.80) in females in 1990 to 112.39 (95% UI: 74.35–158.04) and 77.87 (95% UI: 50.46–109.45) in 2021. Males exhibited consistently higher rates than females (Fig. 3 ). Decomposition Analysis Figure 4 illustrates the decomposition analysis of changes in deaths (Fig. 4 A) and DALYs (Fig. 4 B) for ISAKD in China from 1990 to 2021, stratified by sex. In Fig. 4 A, deaths increased by 49,977.43 cases overall (both sexes), with males contributing 29,815.7 cases and females 20,161.73 cases. The age effect accounted for 84.62% (both sexes), 75.81% (males), and 99.39% (females) of this increase, while the population effect contributed 67.58% (both sexes), 55.53% (males), and 85.47% (females). The epidemiological change effect reduced deaths by -52.2% (both sexes), -31.34% (males), and − 84.86% (females). In Fig. 4 B, DALYs decreased by 744,028.19 overall (both sexes), with males showing an increase of 566,139.83 and females a decrease of 644,945.21. The age effect contributed 278.11% (both sexes), -167.08% (males), and 170.29% (females), while the population effect was − 310.72% (both sexes), 188.57% (males), and − 184.43% (females), and the epidemiological change effect was 132.61% (both sexes), 78.51% (males), and 114.14% (females) (Fig. 4 ). Prediction Analysis Figure 5 presents the observed and predicted age-standardized rates (ASR) for deaths (Fig. 5 A) and DALYs (Fig. 5 B) due to ISAKD in China from 1990 to 2040, stratified by sex (Both, Males, Females). In Fig. 5 A, the ASR for deaths in both sexes declined from 12.45 (95% UI: 12.30–12.60) per 100,000 in 1990 to 8.90 (95% UI: 8.84–8.96) in 2021, with a predicted decrease to 6.90 (95% UI: -6.18 to 19.97) by 2040. Males showed a higher ASR, dropping from 13.82 (95% UI: 13.56–14.08) to 11.54 (95% UI: 11.43–11.66) in 2021, projected to 10.06 (95% UI: -10.09 to 30.20), while females decreased from 11.64 (95% UI: 11.46–11.81) to 7.19 (95% UI: 7.12–7.26), projected to 4.87 (95% UI: -4.65 to 14.38). In Fig. 5 B, the ASR for DALYs in both sexes fell from 235.24 (95% UI: 234.65–235.83) to 167.84 (95% UI: 167.59–168.09) in 2021, predicting 144.35 (95% UI: -88.67 to 377.36) by 2040. Males declined from 252.01 (95% UI: 250.98–253.05) to 203.50 (95% UI: 203.06–203.94), projected to 185.09 (95% UI: -136.11 to 506.30), and females from 224.69 (95% UI: 223.97–225.42) to 141.02 (95% UI: 140.72–141.33), projected to 111.78 (95% UI: -59.37 to 282.92). The data reflect a consistent downward trend across all groups, with males exhibiting higher rates than females (Fig. 5 ). Discussion Main Methods This study utilized the GBD 2021 database to assess the burden of ISAKD in China from 1990 to 2021, analyzing trends, sex and age differences, contributing factors, and projections to 2040 [13] . The primary methods included Joinpoint regression for trend analysis, decomposition analysis to evaluate the effects of aging, population growth, and epidemiological shifts, and BAPC modeling for future estimates [14, 15] . The research explored the temporal evolution of the burden, demographic disparities, and driving factors. Findings revealed a significant rise in all-age deaths and DALYs over the period, though age-standardized rates declined, with females experiencing greater reductions than males. The burden peaked in the 70–79 age group, with males showing higher crude rates. Decomposition analysis indicated that aging substantially increased deaths and DALYs, population growth elevated deaths but reduced DALY losses, and epidemiological changes lowered deaths while increasing DALY losses [16] . Projections suggest a continued decline in age-standardized rates by 2040. These findings suggest a dual pattern: although the absolute burden of ISAKD has increased due to demographic changes, the burden has declined, reflecting advancements in public health [17] . The greater decline in females points to sex-specific improvements in risk factor management or healthcare access [8] . The high burden in the 70–79 age group highlights aging as a key determinant in China, emphasizing the need for improved geriatric care [18] . The projected continued decline in ASDR and ASDAR to 2040 suggests the potential sustainability of current interventions, although absolute numbers may remain high due to population dynamics. KD increases the risk of IS through traditional pathways, such as hypertension and diabetes, which are common in CKD patients [19, 20] , and non-traditional mechanisms, including uremia and oxidative stress [21] . The greater decline in ASR among females may be associated with the protective effects of estrogen or lifestyle factors such as lower smoking rates [22] . The highest burden in the 70–79 age group corresponds to the elevated prevalence of CKD and comorbidities in older adults, increasing stroke susceptibility [18] . Decomposition analysis indicates that, despite pressures from aging and population growth, epidemiological measures such as enhanced hypertension control have reduced the burden [16] . Our findings are consistent with Kelly & Rothwell (2020), who associated KD with an increased stroke risk [19] . Likewise, GBD 2021 Diseases and Injuries Collaborators reported global declines in stroke ASR, aligning with our observed trends [13] . However, the marked improvement in females in our study contrasts with Li et al., who observed minimal sex disparities globally [23] . This difference may arise from China-specific factors, such as enhanced healthcare access for females following the 1990s reforms [24] . The use of the GBD 2021 database provides comprehensive, standardized data spanning 31 years, improving comparability [13] . Advanced methods, such as Joinpoint regression and decomposition analysis, offer robust insights into trends and drivers, outperforming simpler descriptive approaches [25] . Projections to 2040 provide a forward-looking perspective, which is uncommon in similar studies. GBD data are based on modeled estimates, which may introduce bias due to underlying assumptions [26] . The lack of regional or individual-level data restricts granularity, potentially obscuring urban-rural disparities [26] . Causal mechanisms remain unexamined due to the observational design, and unadjusted confounders may influence results [26] . Future research should include primary data from diverse regions in China to address spatial heterogeneity. Longitudinal cohort studies could clarify causal pathways between KD and stroke, accounting for confounders. Incorporating biomarkers could improve burden estimates, increasing precision. Conclusion The burden of ISAKD in China has shown a dual trend: while absolute deaths and DALYs have risen due to aging, age-standardized rates have declined, reflecting improvements in epidemiological factors and healthcare. Females experienced greater reductions in burden compared to males, and the highest burden was observed among older adults, particularly males. Aging and population growth were the primary drivers of increased deaths, partially offset by epidemiological improvements. Projections indicate a continued decline in the burden by 2040. Targeted interventions are needed for high-risk groups, particularly older adults and males, to address aging-related challenges and ensure equitable healthcare access. Future research should focus on causal pathways and regional disparities to refine prevention strategies. Declarations Author Contribution Significant contributions were made to this study by each author. Dachen Tian and Chong Chen, as co-first authors, jointly directed the concept, methodology, and data analysis. Ruijin Liu provided assistance in the design of the study and contributed to data acquisition and analysis. Cong Wang, Youfang Wang, Yingying Shen, Yue Chen, and Juqiang Hu supported the investigation and assisted with data curation. Debin Liu, as the corresponding author, offered guidance, designed the study framework, and reviewed the manuscript. All authors participated in drafting or editing the article and approving the final version. Dachen Tian, Chong Chen, and Ruijin Liu were responsible for accessing and verifying the underlying data. Data sharing statement The GBD 2021 data resources can be accessed online via the Global Health Data Exchange (GHDx) query tool at http://ghdx.healthdata.org/gbd-results-tool. Declaration of interests None. Acknowledgments We express our gratitude to the Institute for Health Metrics and Evaluation (IHME) for permitting the use of the Global Burden of Disease data in this study. Founding The research funder was not involved in any step in the process of this manuscript. Ethical Approval Since the GBD 2021 database is publicly available and does not involve individual-level privacy, ethical review was not required. Author Contribution D.T. and C.C., as co-first authors, jointly developed the study concept, designed the methodology, and performed the data analysis. R.L. assisted in designing the study and contributed to data acquisition and analysis. C.W., Y.W., Y.S., Y.C., and J.H. supported the investigation and assisted with data curation. D.L., as the corresponding author, provided guidance, designed the study framework, and reviewed the manuscript. D.T., C.C., and R.L. were responsible for accessing and verifying the underlying data. All authors (D.T., C.C., R.L., C.W., Y.W., Y.S., Y.C., J.H., and D.L.) participated in drafting or editing the manuscript and approved the final version. References Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795-820. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-33. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Mar, 2026 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviewers invited by journal 20 Apr, 2025 Editor invited by journal 21 Mar, 2025 Editor assigned by journal 19 Mar, 2025 Submission checks completed at journal 19 Mar, 2025 First submitted to journal 15 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6233482","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433144586,"identity":"1de1dcce-8194-4015-a4d0-59d8c0c20ced","order_by":0,"name":"Dachen Tian","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dachen","middleName":"","lastName":"Tian","suffix":""},{"id":433144587,"identity":"bb893454-3c29-40e6-b6e5-e808670f90cd","order_by":1,"name":"Chong Chen","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Chen","suffix":""},{"id":433144588,"identity":"96711814-9f8e-4b0f-93e4-bba2e8380229","order_by":2,"name":"Ruijin Liu","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruijin","middleName":"","lastName":"Liu","suffix":""},{"id":433144589,"identity":"da394ca5-5b53-41db-9877-f9ffdabbce16","order_by":3,"name":"Cong Wang","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Wang","suffix":""},{"id":433144590,"identity":"589f835e-8d69-488c-9972-a69365596ae7","order_by":4,"name":"Youfang Wang","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Youfang","middleName":"","lastName":"Wang","suffix":""},{"id":433144591,"identity":"e92d51b9-2af0-465f-bd76-04e7c7321184","order_by":5,"name":"Yingying Shen","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Shen","suffix":""},{"id":433144592,"identity":"f51ed053-9a96-47ca-a72d-0b239774a896","order_by":6,"name":"Yue Chen","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Chen","suffix":""},{"id":433144593,"identity":"3a3348f1-4896-460f-b3de-43208f593a77","order_by":7,"name":"Juqiang Hu","email":"","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juqiang","middleName":"","lastName":"Hu","suffix":""},{"id":433144594,"identity":"1168308a-23d1-4d4e-8c6c-108205d29153","order_by":8,"name":"Debin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDCCAzwMBxgMJHjs2xsbH34gXkuBhZwBz+FmYwlitTAwfKgwNpBIbxPgIUYH3/Heg4cLDCQSt0s+bGOQYLCT020goEXyzLmEwzOAWnbOTmx7UMCQbGx2gIAWgxs5Bod5gFoabie2G0gwHEjcRlDL/TdQLTcPtknwEKXlBg9Yi7HBDUYitUieyUsAaZGT7EkEBrIBEX7hO3728GeeP3U8/OzHHz78UGEnR1ALujtJUz4KRsEoGAWjAAcAAJF7RtVwpNoXAAAAAElFTkSuQmCC","orcid":"","institution":"Linyi People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Debin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-15 14:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6233482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6233482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79179715,"identity":"84f58048-53c2-48ee-965b-e29a2d7117e6","added_by":"auto","created_at":"2025-03-25 10:17:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99944,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Age-Standardized Death and DALY Rates for Ischemic Stroke Attributable to Kidney Dysfunction in China, 1990-2021: Joinpoint Regression Analysis\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6233482/v1/84c24499a8eae93b7df2fb31.png"},{"id":79178841,"identity":"fed55e31-6036-4952-b0d6-fcdd7530fb06","added_by":"auto","created_at":"2025-03-25 10:09:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63774,"visible":true,"origin":"","legend":"\u003cp\u003epresents the number of deaths and DALYs, along with their crude rates, for ischemic stroke attributable to kidney dysfunction in China in 2021, stratified by age and sex. Figure 2A displays death numbers and crude death rates; Figure 2B shows DALY numbers and crude DALY rates. Data are sourced from the 2021 Global Burden of Disease Study, with values presented as point estimates and 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6233482/v1/fd5c90bb57bd19aaad8a1b24.png"},{"id":79178852,"identity":"96cc5a7b-a540-4028-aa03-0b480b4148b7","added_by":"auto","created_at":"2025-03-25 10:09:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76839,"visible":true,"origin":"","legend":"\u003cp\u003epresents the number of deaths and disability-adjusted life years (DALYs), along with their age-standardized rates, for ischemic stroke attributable to kidney dysfunction in China from 1990 to 2021, stratified by year and sex. Figure 3A displays the number of deaths and age-standardized death rates per 100,000 population; Figure 3B shows DALY numbers and age-standardized DALY rates per 100,000 population. Data are sourced from the Global Burden of Disease Study, with values reported as point estimates and 95% confidence intervals.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6233482/v1/3b27c25d404879cb464910e0.png"},{"id":79178843,"identity":"979c23ee-15fd-45ec-82f1-1f003d7567f9","added_by":"auto","created_at":"2025-03-25 10:09:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38181,"visible":true,"origin":"","legend":"\u003cp\u003epresents the decomposition of changes in deaths (A) and DALYs (B) for ischemic stroke attributable to kidney dysfunction in China between 1990 and 2021, stratified by sex. The analysis quantifies the overall difference and contributions of age effect, population effect, and epidemiological change effect, expressed as percentages. Data are sourced from the Global Burden of Disease Study 2021.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6233482/v1/bd85af596ee877def1141eb6.png"},{"id":79178849,"identity":"77bd0ba3-9dac-4c46-8881-fe30a8a0a2f2","added_by":"auto","created_at":"2025-03-25 10:09:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66675,"visible":true,"origin":"","legend":"\u003cp\u003edisplays the observed and predicted age-standardized rates for deaths (A) and DALYs (B) due to ischemic stroke attributable to kidney dysfunction in China from 1990 to 2040, stratified by sex (Both, Males, Females). Solid lines represent observed values up to 2021, and dashed lines indicate predicted values from 2022 to 2040. Data are sourced from the Global Burden of Disease Study.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6233482/v1/f46df3eebf95318b15871b10.png"},{"id":79181276,"identity":"0eed22b2-dca8-4b78-994c-9c847259d05a","added_by":"auto","created_at":"2025-03-25 10:33:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1122217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6233482/v1/25ccbfb6-df0c-464e-aa41-f358c321310c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends, Decomposition Analysis, and Future Predictions of the Burden of Ischemic Stroke Attributable to Kidney Dysfunction in China, 1990-2021: Based on the 2021 GBD Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke (IS)ranks as a primary global cause of death and disability, substantially contributing to the disease burden\u003csup\u003e[1]\u003c/sup\u003e. Meanwhile, chronic kidney disease (CKD) impacts over 697\u0026nbsp;million individuals globally, constituting a significant public health concern \u003csup\u003e[2]\u003c/sup\u003e. The association between kidney Dysfunction (KD) and IS presents an escalating challenge, worsened by aging populations and evolving epidemiological trends, particularly in China \u003csup\u003e[3]\u003c/sup\u003e. The study indicates that IS resulted in 6.55\u0026nbsp;million deaths, with disability-adjusted life years (DALYs) having increased from 79.5\u0026nbsp;million in 1990 to 102.2\u0026nbsp;million in 2019\u003csup\u003e[1]\u003c/sup\u003e. CKD prevalence has risen by 88.8% since 1990, heightening stroke risk through hypertension, diabetes, and oxidative stress \u003csup\u003e[4]\u003c/sup\u003e. In China, absolute stroke cases have increased despite reduced age-standardized rates, propelled by demographic shifts \u003csup\u003e[5]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA known risk factor for IS is CKD, which can be caused by both conventional and unconventional mechanisms, such as vascular calcification\u003csup\u003e[6]\u003c/sup\u003e. Nevertheless, significant research gaps persist. Longitudinal analyses monitoring this burden over decades are scarce\u003csup\u003e[7]\u003c/sup\u003e. Studies dissecting contributions from aging, population growth, and epidemiology are similarly limited\u003csup\u003e[8]\u003c/sup\u003e. Regional differences, especially in China, where CKD prevalence is 10.8%, remain understudied\u003csup\u003e[9]\u003c/sup\u003e. Furthermore, gender and age-specific trends are not integrated with future projections \u003csup\u003e[10]\u003c/sup\u003e. These deficiencies impede effective prevention strategies.\u003c/p\u003e \u003cp\u003eThe Global Burden of Disease Study (GBD) 2021 database was employed to evaluate the burden of ISAKD, analyzing trends from 1990 to 2021 and forecasting outcomes through 2040. By bridging these gaps, it seeks to guide targeted interventions and enhance clinical and policy responses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eOverview\u003c/p\u003e \u003cp\u003eThis study aims to systematically assess the burden of ISAKD in China from 1990 to 2021 and forecast its trends to 2040. It examines trends in deaths and DALYs, evaluates sex and age disparities, dissects contributing factors, and investigates future burden traits to guide evidence-based public health interventions.\u003c/p\u003e \u003cp\u003eData Source\u003c/p\u003e \u003cp\u003eThe latest version of the GBD 2021 database was established by the Institute for Health Metrics and Evaluation at the University of Washington. It encompassed 371 diseases and injuries along with 88 risk factors, compiling health data from 204 countries and territories between 1990 and 2021, and offers a detailed breakdown of health metrics by age, gender, region, and time\u003csup\u003e[11]\u003c/sup\u003e. Data on ISAKD in China from 1990 to 2021 were extracted from the Global Health Data Exchange platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/gbd-2021/sources\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/gbd-2021/sources\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including deaths and DALYs, classified by sex and age group, to ensure a comprehensive and representative analysis. The GBD 2021 is a publicly available database; thus, ethical approval was not required.\u003c/p\u003e \u003cp\u003eJoinpoint Analysis\u003c/p\u003e \u003cp\u003eJoinpoint software (version 5.3.0) was implemented in this study to calculate the annual percent change (APC), average annual percent change (AAPC), and their corresponding 95% uncertainty Interval (95% UI) for deaths and DALYs of ISAKD in China. The model with the best fit was selected to appraise trends in the disease burden of ISAKD. For the AAPC estimate, a 95% UI below 0 indicates a declining trend, above 0 suggests an increasing trend, and otherwise implies a stable trend.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition Analysis\u003c/h2\u003e \u003cp\u003eThe decomposition method used in this study was developed by Das Gupta and employs mathematical techniques to break down overall changes into individual components, identifying the specific contribution of each part. This approach is commonly applied in decomposition analyses. This study examines the effects of age structure, population growth, and epidemiological changes on the disease burden of ISAKD in China by utilizing this method.\u003c/p\u003e \u003cp\u003eProjection Analysis\u003c/p\u003e \u003cp\u003eTo predict the burden of ISAKD in China from 2022 to 2040, this study utilized the Bayesian Age-Period-Cohort (BAPC) model, drawing on data from the GBD 2021 database. The BAPC method, implemented through the R package BAPC with the Integrated Nested Laplace Approximation for enhanced computational efficiency, integrates age, period, and cohort effects, delivering probabilistic forecasts with 95% UI to quantify uncertainty.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Software\u003c/h3\u003e\n\u003cp\u003eThe Joinpoint Regression Program (version 5.3.0) and R software (version 4.4.2) were used for data processing and analysis. To verify the robustness and dependability of the findings, 95% UI were included, and statistical significance was defined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This study adheres to GBD study guidelines and the STROBE statement, ensuring methodological transparency, reproducibility, and standardized reporting to support academic and policy applications\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescription of the ISAKD burden in China\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the burden of ISAKD in China from 1990 to 2021 stratified by sex. In 1990, all-age deaths totaled 40,555 (19,451 males, 21,104 females), rising to 90,532 (49,267 males, 41,266 females) by 2021. Similarly, DALYs increased from 947,578 (464,498 males, 483,080 females) to 1,875,486 (1,028,206 males, 847,279 females). Despite these increases, age-standardized rates declined significantly. Deaths rates dropped from 6.866 to 4.914 per 100,000 overall (AAPC: -1.083, 95% UI: -1.428 to -0.737), with males decreasing from 7.649 to 6.384 (AAPC: -0.583, 95% UI: -0.987 to -0.177) and females from 6.411 to 3.965 (AAPC: -1.514, 95% UI: -1.842 to -1.184). DALY rates also fell from 129.901 to 92.675 overall (AAPC: -1.087, 95% UI: -1.4541 to -0.7186), with males from 139.246 to 112.39 (AAPC: -1.087, 95% UI: -1.4541 to -0.7186) and females from 124.087 to 77.867 (AAPC: -1.5208, 95% UI: -1.7641 to -1.2768). These statistically significant declines (95% UIs exclude 0) suggest a reduced per-capita burden, particularly among females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAll-age cases and age-standardized rates of death and DALYs for ischemic stroke attributable to kidney dysfunction in China, 1990 and 2021, with AAPC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e \u003cp\u003eAAPC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll-ages cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge-standardlzed rates per 100,000 people\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll-ages cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAge-standardlzed rates per 100,000 people\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erate(95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003erate (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003erate (95% UI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19451 (12936\u0026ndash;27317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.649 (4.589\u0026ndash;11.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49267 (31627\u0026ndash;71842)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.384 (3.902\u0026ndash;9.243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.583 (-0.987-0.177)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e464498 (319394\u0026ndash;638738)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139.246 (91.666-198.336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1028206 (691352\u0026ndash;1459096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e112.39 (74.351-158.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-1.087 (-1.4541-0.7186)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21104 (13816\u0026ndash;29368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.411 (4.02\u0026ndash;9.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41266 (25171\u0026ndash;60326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.965 (2.402\u0026ndash;5.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-1.514 (-1.842-1.184)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e483080 (334394\u0026ndash;654108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.087 (84.689\u0026ndash;170.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e847279 (550368\u0026ndash;1186709)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.867 (50.457-109.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-1.5208 (-1.7641-1.2768)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40555 (26558\u0026ndash;56078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.866 (4.229\u0026ndash;9.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90532 (56439\u0026ndash;129118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.914 (3.012\u0026ndash;7.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-1.083 (-1.428-0.737)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e947578 (667953\u0026ndash;1287342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129.901 (87.314-178.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1875486 (1299150\u0026ndash;2587792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.675 (62.157-128.848)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-1.087 (-1.4541-0.7186)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations Used in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026bull; DALYs: Disability-Adjusted Life Years\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026bull; AAPC: Average Annual Percent Change\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026bull; UI: Uncertainty Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eJoinpoint analysis of the ISAKD burden in China\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the trends in ASDR (Figure A) and ASDAR (Figure B) for ISAKD in China from 1990 to 2021, analyzed via Joinpoint regression for both sexes combined, female and males. For both sexes, the ASDR decreased from 6.87 per 100,000 in 1990 to 5.82 in 1998, rose to a peak of 7.26 in 2004, and then declined significantly to 4.91 by 2021 (*P* \u0026lt; 0.05). The ASDAR followed a similar pattern, dropping from 129.90 per 100,000 in 1990 to 107.39 in 1998, peaking at 128.74 in 2004, and decreasing to 92.67 by 2021 (*P* \u0026lt; 0.05). Joinpoint analysis identified significant trend shifts for ASDR in 1994, 1999, 2004, and 2007 (APC: -2.80% to +\u0026thinsp;4.62%) and for ASDAR in 1998, 2001, 2004, 2007, and 2014 (APC: -2.33% to +\u0026thinsp;5.14%). Females showed a steeper decline in ASDR (6.41 to 3.97 per 100,000) and ASDAR (124.09 to 77.87 per 100,000) compared to males (ASDR: 7.65 to 6.38; ASDAR: 139.25 to 112.39), with significant changes (*P* \u0026lt; 0.05) observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBurden of ISAKD in China by Age and Sex\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the burden of ISAKD in China in 2021 stratified by age and sex. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows that the number of deaths increases with age, peaking in the 75\u0026ndash;79 age group for males at 9,289.88 (95% UI: 5,706.54\u0026ndash;13,965.69) and females at 7,351.20 (95% UI: 4,507.59\u0026ndash;11,086.41). Crude death rates also rise with age, with males consistently higher than females ( 59.54 vs. 41.97 per 100,000 in the 75\u0026ndash;79 age group). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB indicates that DALYs peak in the 70\u0026ndash;74 age group for males at 208,213.29 (95% UI: 135,334.19\u0026ndash;293,747.44) and females at 167,164.00 (95% UI: 111,633.70\u0026ndash;238,090.18), with crude DALY rates higher in males ( 805.27 vs. 609.20 per 100,000 in the 70\u0026ndash;74 age group, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05*). These data reveal a greater burden in males and older age groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the burden of ISAKD in China from 1990 to 2021, categorized by sex and year. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the number of deaths increased from 19,451 (95% UI: 12,936\u0026ndash;27,317) in males and 21,104 (95% UI: 13,816\u0026ndash;29,368) in females in 1990 to 49,267 (95% UI: 31,627\u0026ndash;71,842) and 41,266 (95% UI: 25,171\u0026ndash;60,326) in 2021, respectively. Conversely, age-standardized death rates declined from 7.65 (95% UI: 4.59\u0026ndash;11.22) per 100,000 in males and 6.41 (95% UI: 4.02\u0026ndash;9.09) in females in 1990 to 6.38 (95% UI: 3.90\u0026ndash;9.24) and 3.97 (95% UI: 2.40\u0026ndash;5.85) in 2021. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB shows DALYs rising from 464,498 (95% UI: 319,394\u0026ndash;638,738) in males and 483,080 (95% UI: 334,394\u0026ndash;654,108) in females in 1990 to 1,028,206 (95% UI: 691,352\u0026ndash;1,459,096) and 847,279 (95% UI: 550,368\u0026ndash;1,186,709) in 2021. Age-standardized DALY rates decreased from 139.25 (95% UI: 91.67\u0026ndash;198.34) per 100,000 in males and 124.09 (95% UI: 84.69\u0026ndash;170.80) in females in 1990 to 112.39 (95% UI: 74.35\u0026ndash;158.04) and 77.87 (95% UI: 50.46\u0026ndash;109.45) in 2021. Males exhibited consistently higher rates than females (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDecomposition Analysis\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the decomposition analysis of changes in deaths (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and DALYs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) for ISAKD in China from 1990 to 2021, stratified by sex. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, deaths increased by 49,977.43 cases overall (both sexes), with males contributing 29,815.7 cases and females 20,161.73 cases. The age effect accounted for 84.62% (both sexes), 75.81% (males), and 99.39% (females) of this increase, while the population effect contributed 67.58% (both sexes), 55.53% (males), and 85.47% (females). The epidemiological change effect reduced deaths by -52.2% (both sexes), -31.34% (males), and \u0026minus;\u0026thinsp;84.86% (females). In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, DALYs decreased by 744,028.19 overall (both sexes), with males showing an increase of 566,139.83 and females a decrease of 644,945.21. The age effect contributed 278.11% (both sexes), -167.08% (males), and 170.29% (females), while the population effect was \u0026minus;\u0026thinsp;310.72% (both sexes), 188.57% (males), and \u0026minus;\u0026thinsp;184.43% (females), and the epidemiological change effect was 132.61% (both sexes), 78.51% (males), and 114.14% (females) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrediction Analysis\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the observed and predicted age-standardized rates (ASR) for deaths (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and DALYs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) due to ISAKD in China from 1990 to 2040, stratified by sex (Both, Males, Females). In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the ASR for deaths in both sexes declined from 12.45 (95% UI: 12.30\u0026ndash;12.60) per 100,000 in 1990 to 8.90 (95% UI: 8.84\u0026ndash;8.96) in 2021, with a predicted decrease to 6.90 (95% UI: -6.18 to 19.97) by 2040. Males showed a higher ASR, dropping from 13.82 (95% UI: 13.56\u0026ndash;14.08) to 11.54 (95% UI: 11.43\u0026ndash;11.66) in 2021, projected to 10.06 (95% UI: -10.09 to 30.20), while females decreased from 11.64 (95% UI: 11.46\u0026ndash;11.81) to 7.19 (95% UI: 7.12\u0026ndash;7.26), projected to 4.87 (95% UI: -4.65 to 14.38). In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, the ASR for DALYs in both sexes fell from 235.24 (95% UI: 234.65\u0026ndash;235.83) to 167.84 (95% UI: 167.59\u0026ndash;168.09) in 2021, predicting 144.35 (95% UI: -88.67 to 377.36) by 2040. Males declined from 252.01 (95% UI: 250.98\u0026ndash;253.05) to 203.50 (95% UI: 203.06\u0026ndash;203.94), projected to 185.09 (95% UI: -136.11 to 506.30), and females from 224.69 (95% UI: 223.97\u0026ndash;225.42) to 141.02 (95% UI: 140.72\u0026ndash;141.33), projected to 111.78 (95% UI: -59.37 to 282.92). The data reflect a consistent downward trend across all groups, with males exhibiting higher rates than females (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMain Methods\u003c/p\u003e \u003cp\u003eThis study utilized the GBD 2021 database to assess the burden of ISAKD in China from 1990 to 2021, analyzing trends, sex and age differences, contributing factors, and projections to 2040\u003csup\u003e[13]\u003c/sup\u003e. The primary methods included Joinpoint regression for trend analysis, decomposition analysis to evaluate the effects of aging, population growth, and epidemiological shifts, and BAPC modeling for future estimates\u003csup\u003e[14, 15]\u003c/sup\u003e. The research explored the temporal evolution of the burden, demographic disparities, and driving factors. Findings revealed a significant rise in all-age deaths and DALYs over the period, though age-standardized rates declined, with females experiencing greater reductions than males. The burden peaked in the 70\u0026ndash;79 age group, with males showing higher crude rates. Decomposition analysis indicated that aging substantially increased deaths and DALYs, population growth elevated deaths but reduced DALY losses, and epidemiological changes lowered deaths while increasing DALY losses\u003csup\u003e[16]\u003c/sup\u003e. Projections suggest a continued decline in age-standardized rates by 2040.\u003c/p\u003e \u003cp\u003eThese findings suggest a dual pattern: although the absolute burden of ISAKD has increased due to demographic changes, the burden has declined, reflecting advancements in public health \u003csup\u003e[17]\u003c/sup\u003e. The greater decline in females points to sex-specific improvements in risk factor management or healthcare access \u003csup\u003e[8]\u003c/sup\u003e. The high burden in the 70\u0026ndash;79 age group highlights aging as a key determinant in China, emphasizing the need for improved geriatric care \u003csup\u003e[18]\u003c/sup\u003e. The projected continued decline in ASDR and ASDAR to 2040 suggests the potential sustainability of current interventions, although absolute numbers may remain high due to population dynamics.\u003c/p\u003e \u003cp\u003eKD increases the risk of IS through traditional pathways, such as hypertension and diabetes, which are common in CKD patients\u003csup\u003e[19, 20]\u003c/sup\u003e, and non-traditional mechanisms, including uremia and oxidative stress\u003csup\u003e[21]\u003c/sup\u003e. The greater decline in ASR among females may be associated with the protective effects of estrogen or lifestyle factors such as lower smoking rates\u003csup\u003e[22]\u003c/sup\u003e. The highest burden in the 70\u0026ndash;79 age group corresponds to the elevated prevalence of CKD and comorbidities in older adults, increasing stroke susceptibility\u003csup\u003e[18]\u003c/sup\u003e. Decomposition analysis indicates that, despite pressures from aging and population growth, epidemiological measures such as enhanced hypertension control have reduced the burden\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings are consistent with Kelly \u0026amp; Rothwell (2020), who associated KD with an increased stroke risk \u003csup\u003e[19]\u003c/sup\u003e. Likewise, GBD 2021 Diseases and Injuries Collaborators reported global declines in stroke ASR, aligning with our observed trends \u003csup\u003e[13]\u003c/sup\u003e. However, the marked improvement in females in our study contrasts with Li et al., who observed minimal sex disparities globally\u003csup\u003e[23]\u003c/sup\u003e. This difference may arise from China-specific factors, such as enhanced healthcare access for females following the 1990s reforms\u003csup\u003e[24]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe use of the GBD 2021 database provides comprehensive, standardized data spanning 31 years, improving comparability\u003csup\u003e[13]\u003c/sup\u003e. Advanced methods, such as Joinpoint regression and decomposition analysis, offer robust insights into trends and drivers, outperforming simpler descriptive approaches\u003csup\u003e[25]\u003c/sup\u003e. Projections to 2040 provide a forward-looking perspective, which is uncommon in similar studies.\u003c/p\u003e \u003cp\u003eGBD data are based on modeled estimates, which may introduce bias due to underlying assumptions \u003csup\u003e[26]\u003c/sup\u003e. The lack of regional or individual-level data restricts granularity, potentially obscuring urban-rural disparities \u003csup\u003e[26]\u003c/sup\u003e. Causal mechanisms remain unexamined due to the observational design, and unadjusted confounders may influence results \u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFuture research should include primary data from diverse regions in China to address spatial heterogeneity. Longitudinal cohort studies could clarify causal pathways between KD and stroke, accounting for confounders. Incorporating biomarkers could improve burden estimates, increasing precision.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe burden of ISAKD in China has shown a dual trend: while absolute deaths and DALYs have risen due to aging, age-standardized rates have declined, reflecting improvements in epidemiological factors and healthcare. Females experienced greater reductions in burden compared to males, and the highest burden was observed among older adults, particularly males. Aging and population growth were the primary drivers of increased deaths, partially offset by epidemiological improvements. Projections indicate a continued decline in the burden by 2040. Targeted interventions are needed for high-risk groups, particularly older adults and males, to address aging-related challenges and ensure equitable healthcare access. Future research should focus on causal pathways and regional disparities to refine prevention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant contributions were made to this study by each author. Dachen Tian and Chong Chen, as co-first authors, jointly directed the concept, methodology, and data analysis. Ruijin Liu provided assistance in the design of the study and contributed to data acquisition and analysis. Cong Wang, Youfang Wang, Yingying Shen, Yue Chen, and Juqiang Hu supported the investigation and assisted with data curation. Debin Liu, as the corresponding author, offered guidance, designed the study framework, and reviewed the manuscript. All authors participated in drafting or editing the article and approving the final version. Dachen Tian, Chong Chen, and Ruijin Liu were responsible for accessing and verifying the underlying data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GBD 2021 data resources can be accessed online via the Global Health Data Exchange (GHDx) query tool at http://ghdx.healthdata.org/gbd-results-tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the Institute for Health Metrics and Evaluation (IHME) for permitting the use of the Global Burden of Disease data in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFounding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research funder was not involved in any step in the process of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the GBD 2021 database is publicly available and does not involve individual-level privacy, ethical review was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.T. and C.C., as co-first authors, jointly developed the study concept, designed the methodology, and performed the data analysis. R.L. assisted in designing the study and contributed to data acquisition and analysis. C.W., Y.W., Y.S., Y.C., and J.H. supported the investigation and assisted with data curation. D.L., as the corresponding author, provided guidance, designed the study framework, and reviewed the manuscript. D.T., C.C., and R.L. were responsible for accessing and verifying the underlying data. All authors (D.T., C.C., R.L., C.W., Y.W., Y.S., Y.C., J.H., and D.L.) participated in drafting or editing the manuscript and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795-820.\u003c/li\u003e\n\u003cli\u003eGlobal, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-33.\u003c/li\u003e\n\u003cli\u003eGlobal burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204-22.\u003c/li\u003e\n\u003cli\u003eKelly DM, Ademi Z, Doehner W, Lip GYH, Mark P, Toyoda K, et al. Chronic Kidney Disease and Cerebrovascular Disease: Consensus and Guidance From a KDIGO Controversies Conference. Stroke. 2021;52(7):e328-e46.\u003c/li\u003e\n\u003cli\u003eYang G, Wang Y, Zeng Y, Gao GF, Liang X, Zhou M, et al. Rapid health transition in China, 1990-2010: findings from the Global Burden of Disease Study 2010. Lancet. 2013;381(9882):1987-2015.\u003c/li\u003e\n\u003cli\u003eKelly DM, Georgakis MK, Franceschini N, Blacker D, Viswanathan A, Anderson CD. Interplay Between Chronic Kidney Disease, Hypertension, and Stroke: Insights From a Multivariable Mendelian Randomization Analysis. Neurology. 2023;101(20):e1960-e9.\u003c/li\u003e\n\u003cli\u003eWyld M, Webster AC. Chronic Kidney Disease is a Risk Factor for Stroke. Journal of Stroke and Cerebrovascular Diseases. 2021;30(9):105730.\u003c/li\u003e\n\u003cli\u003eMa Q, Li R, Wang L, Yin P, Wang Y, Yan C, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2021;6(12):e897-e906.\u003c/li\u003e\n\u003cli\u003eWang L, Xu X, Zhang M, Hu C, Zhang X, Li C, et al. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med. 2023;183(4):298-310.\u003c/li\u003e\n\u003cli\u003eMasson P, Webster AC, Hong M, Turner R, Lindley RI, Craig JC. Chronic kidney disease and the risk of stroke: a systematic review and meta-analysis. Nephrol Dial Transplant. 2015;30(7):1162-9.\u003c/li\u003e\n\u003cli\u003eMurray CJL. Findings from the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2259-62.\u003c/li\u003e\n\u003cli\u003eVandenbroucke JP, von Elm E, Altman DG, G\u0026oslash;tzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4(10):e297.\u003c/li\u003e\n\u003cli\u003eGlobal incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133-61.\u003c/li\u003e\n\u003cli\u003eKim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335-51.\u003c/li\u003e\n\u003cli\u003eClegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual per cent change in trend analysis. Stat Med. 2009;28(29):3670-82.\u003c/li\u003e\n\u003cli\u003eFeigin VL, Roth GA, Naghavi M, Parmar P, Krishnamurthi R, Chugh S, et al. Global burden of stroke and risk factors in 188 countries, during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol. 2016;15(9):913-24.\u003c/li\u003e\n\u003cli\u003eWu S, Wu B, Liu M, Chen Z, Wang W, Anderson CS, et al. Stroke in China: advances and challenges in epidemiology, prevention, and management. Lancet Neurol. 2019;18(4):394-405.\u003c/li\u003e\n\u003cli\u003eZhang L, Wang F, Wang L, Wang W, Liu B, Liu J, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet. 2012;379(9818):815-22.\u003c/li\u003e\n\u003cli\u003eKelly D, Rothwell PM. Disentangling the multiple links between renal dysfunction and cerebrovascular disease. J Neurol Neurosurg Psychiatry. 2020;91(1):88-97.\u003c/li\u003e\n\u003cli\u003eWakasugi M, Yokoseki A, Wada M, Sanpei K, Momotsu T, Sato K, et al. Stroke incidence and chronic kidney disease: A hospital-based prospective cohort study. Nephrology (Carlton). 2022;27(7):577-87.\u003c/li\u003e\n\u003cli\u003eDaenen K, Andries A, Mekahli D, Van Schepdael A, Jouret F, Bammens B. Oxidative stress in chronic kidney disease. Pediatr Nephrol. 2019;34(6):975-91.\u003c/li\u003e\n\u003cli\u003eAppelros P, Stegmayr B, Ter\u0026eacute;nt A. Sex differences in stroke epidemiology: a systematic review. Stroke. 2009;40(4):1082-90.\u003c/li\u003e\n\u003cli\u003eLi XY, Kong XM, Yang CH, Cheng ZF, Lv JJ, Guo H, et al. Global, regional, and national burden of ischemic stroke, 1990-2021: an analysis of data from the global burden of disease study 2021. EClinicalMedicine. 2024;75:102758.\u003c/li\u003e\n\u003cli\u003eChai P, Zhang Y, Zhou M, Liu S, Kinfu Y. Health system productivity in China: a comparison of pre- and post-2009 healthcare reform. Health Policy Plan. 2020;35(3):257-66.\u003c/li\u003e\n\u003cli\u003eWang S, Dong Z, Wan X. Global, regional, and national burden of inflammatory bowel disease and its associated anemia, 1990 to 2019 and predictions to 2050: An analysis of the global burden of disease study 2019. Autoimmun Rev. 2024;23(3):103498.\u003c/li\u003e\n\u003cli\u003eMurray CJL. The Global Burden of Disease Study at 30 years. Nat Med. 2022;28(10):2019-26.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ischemic stroke, kidney dysfunction, China, GBD, gender, age, decomposition, prediction","lastPublishedDoi":"10.21203/rs.3.rs-6233482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6233482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eKidney dysfunction is a critical risk factor for ischemic stroke, yet longitudinal analyses of its burden in China remain limited. To evaluate the burden of Ischemic Stroke Attributable to Kidney Dysfunction (ISAKD) in China from 1990 to 2021, this study analyzed trends, gender and age differences, and decomposition of drivers and projected future trends up to 2040.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing the Global Burden of Disease 2021 database, deaths and disability-adjusted life years (DALYs) were analyzed. Joinpoint regression identified temporal trends, decomposition analysis quantified age, population, and epidemiological contributions, and Bayesian Age-Period-Cohort modeling projected future burden.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 1990 to 2021, deaths rose from 40,555 to 90,532 and DALYs from 947,578 to 1,875,486. The age-standardized DALY rate (ASDAR) dropped from 6.87 to 4.91 per 100,000, with an average annual percent change (AAPC) of -1.083. ASDAR fell from 129.90 to 92.67 per 100,000 (AAPC: -1.087%). Females had larger ASDR (6.41 to 3.97) and ASDAR (124.09 to 77.87) drops than males (ASDR: 7.65 to 6.38; ASDAR: 139.25 to 112.39). Burden peaked at 70\u0026ndash;79, with males showing higher mortality (59.54 vs. 41.97 per 100,000) at 75\u0026ndash;79. Decomposition revealed that aging (84.62%) and population growth (67.58%) drove mortality, offset by epidemiology (-52.2%). Aging (278.11%) and epidemiology (132.61%) raised DALYs, while population growth (-310.72%) diminished DALY losses. By 2040, ASDR is projected to fall to 6.90 and ASDAR to 144.35 per 100,000.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThough absolute burden increased, ASDR and ASDAR fell, with females exhibiting greater declines than males, reflecting gender differences. The 70\u0026ndash;79 age group faced the highest burden. Decomposition shows that aging markedly boosts mortality and DALYs, while population growth raises mortality but cuts DALY losses, and epidemiology curbs mortality yet raises DALY losses. Forecasts of ongoing declines highlight the need for age- and sex-specific interventions.\u003c/p\u003e","manuscriptTitle":"Trends, Decomposition Analysis, and Future Predictions of the Burden of Ischemic Stroke Attributable to Kidney Dysfunction in China, 1990-2021: Based on the 2021 GBD Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 10:09:21","doi":"10.21203/rs.3.rs-6233482/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-26T08:23:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T22:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162810902804383249874149192287883596605","date":"2025-05-09T14:42:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191832042829744766252752721496290114203","date":"2025-04-20T12:43:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-20T09:55:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-21T06:08:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T10:30:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-19T10:22:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-03-15T14:35:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"148c86c6-49e3-43fe-a354-9b397063275e","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-20T10:08:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 10:09:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6233482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6233482","identity":"rs-6233482","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}