Global Ischemic Stroke Burden Due to High BMI, 1990–2021: An Age-Period-Cohort Study and Future Risk Prediction Using GBD Data

Global Ischemic Stroke Burden Due to High BMI, 1990–2021: An Age-Period-Cohort Study and Future Risk Prediction Using GBD Data | 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 Global Ischemic Stroke Burden Due to High BMI, 1990–2021: An Age-Period-Cohort Study and Future Risk Prediction Using GBD Data Fei Peng, Mingyan Bian, You Li, Shanshan Ma, Bu Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7557435/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : In this study, we used Global Burden of Disease (GBD) data to analyze the worldwide burden of ischemic stroke caused by high body mass index (BMI). The objective was to describe the current distribution of the disease burden and predict future trends to support public health organizations and governments of various countries in formulating targeted healthcare policies. Methods : We analyzed crucial disease burden indicators for ischemic stroke caused by high BMI across five Socio-demographic Index (SDI) regions, 21 GBD regions, and 204 countries. These indicators were compared and visualized by age, sex, and SDI. We performed inequality and frontier analyses of disease burden across different SDI regions and used age-period-cohort (APC) analysis to explore the drivers of the disease burden. The Bayesian APC method was applied to predict future trends. Results : Between 1990 and 2021, the number of ischemic stroke deaths and disability-adjusted life years (DALYs) because of high BMI nearly doubled, increasing by 95.7% and 108.8%, respectively. Conversely, the age-standardized mortality rate (ASMR) and age-standardized disability adjusted life year rate(ASDR) declined, with an estimated annual percentage change of −1.103 and −0.583, respectively. Countries with an SDI of approximately 0.7 experienced the greatest burden. Health inequality analysis revealed that the previously observed higher disease burden in high-income areas decreased by 2021. APC analysis revealed a significant increase in risk for birth cohorts after 1980, particularly in individuals aged 20–39. Projections suggest a rise in global ASMR to 2.252 per 100,000 by 2036, with a more pronounced increase among males. Conclusions : Between 1990 and 2021, deaths and DALYs from ischemic stroke caused by high BMI increased substantially. In terms of economic development, the disease burden has shifted, with ASMR and ASDR rising in low- and middle-income areas. From the perspective of population characteristics, the disease burden among young people has been increasing annually; however, 65-year-old women bear a heavier disease burden than men do. These findings are a crucial warning to public health management departments worldwide, highlighting the need for targeted policies to address the growing impact of high BMI on ischemic stroke. Ischemic stroke High body mass index Global Burden of Disease Age-Period-Cohort Analysis Health Inequality Prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Background Ischemic stroke refers to an acute cerebrovascular event caused by the blockage of cerebral blood flow to the brain by thrombosis or embolism, resulting in local brain tissue ischemia and hypoxic necrosis. It accounts for approximately 70–80% of all instances of stroke and is one of the leading causes of mortality and disability among adults globally( 1 ). With advances in diagnostic technologies, early ischemic stroke diagnosis is more feasible. Current treatments focus on rapidly restoring cerebral blood flow to minimize infarct size, primarily through intravenous thrombolysis and arterial thrombectomy( 2 ). However, as most patients cannot seek medical attention within this time window, this delay greatly affects disease prognosis. Hypertension, diabetes, smoking, hypercholesterolemia, obesity, heart disease, and atrial fibrillation are significant risk factors for ischemic stroke( 3 , 4 ). The cumulative effect and overlap of these risk factors significantly increase the incidence of stroke. Effective weight management and control of blood glucose, blood pressure, and other risk factors are crucial for preventing ischemic stroke and reducing the global disease burden. High body mass index (BMI) increases ischemic stroke risk through multiple pathways( 5 , 6 ), including metabolic disorders, inflammatory responses, and synergistic effects with other risk factors. An abnormal increase in BMI increases the release of inflammatory factors and puts the blood in a hypercoagulable state( 7 ), increasing the likelihood of ischemic stroke. Individuals with elevated BMI frequently develop visceral adiposity, which contributes to insulin resistance and disturbances in lipid metabolism, exacerbating atherosclerosis progression( 8 ). A large-scale clinical study in Japan found that patients with obesity were more likely to suffer from hypertension( 9 ), increasing ischemic stroke risk. Additionally, high BMI affects ischemic stroke prognosis; one study revealed that patients with high BMI who undergo arterial thrombectomy had a postoperative bleeding risk( 10 ). High BMI affects ischemic stroke, a disease with high disability and mortality rates, in multiple ways. Therefore, further observations and research on ischemic stroke burden caused by high BMI are crucial. Presently, Global Burden of Disease (GBD)-based academic research on the disease burden of ischemic stroke and high BMI remains limited, particularly with respect to age-period-cohort (APC) analyses and future predictions. In this study, we analyzed the global disease burden of ischemic stroke caused by high BMI and conducted specific analyses from multiple perspectives, such as region, economy, period, sex, and age, and predicted future trends in disease burden. The purpose of this study was to emphasize the serious consequences of high BMI on ischemic stroke burden and provide a strong basis for public health organizations and governments to formulate targeted medical policies and intervention measures. 2. Methods a. Data Sources For this survey, we obtained updated data for 2021 from the GBD database. The GBD systematically evaluates health losses attributable to more than 370 diseases, injuries, and risk factors (including a high BMI) in more than 200 countries and regions. The core indicator is the disability-adjusted life years (DALYs), which combines premature death and disability to reveal health trends. It also evaluates policy effectiveness and guides priority allocation for global public health resources(11,12). During the data download process, we specified a high BMI in the risk factor option and ischemic stroke in the disease option, and downloaded relevant disease burden indicator data. We compiled data regarding fatalities attributable to ischemic stroke related to high BMI, age-standardized rates (ASR), and DALYs across 204 nations, 21 GBD regions, and five Socio-Demographic Index (SDI) regions. Furthermore, we obtained pertinent information concerning SDI and the Human-Development Index (HDI) for each nation to investigate their possible correlations with disease burden. b. Definition Ischemic stroke was defined according to the 2021 version of the International Classification of Diseases and Related Health Problems, Tenth Revision, which defines it as an acute episode of focal cerebral, spinal, or retinal dysfunction because of an infarction of the central nervous system tissue. Acute infarction can be confirmed through one of two recognized criteria: (a) symptoms persisting for >24 h, or (b) neuroimaging or other diagnostic evaluations of clinically relevant brain regions. High BMI refers to a BMI >25 kg/m² in the population aged ≥20. The GBD evaluates various countries and regions from three perspectives: the fertility rate of individuals aged 15, to obtain the SDI, which is calibrated on a continuum ranging from 0–1. Countries were divided into five different levels based on their SDI values: low (0.81). The HDI is obtained by evaluating life expectancy at birth, mean years of schooling, expected years of schooling, and Gross National Income per capita of various countries and regions. The values of this index were defined as low, medium, high, and extremely high levels in the following ranges: <0.55, 0.55–0.69, 0.70–0.79, and 0.80–1.00, respectively. c. Statistical Analysis ⅰ. Burden Description In this study, we examined ischemic stroke disease burden caused by high BMI using mortality rates, DALYs, age-standardized mortality rate (ASMR), and age-standardized disability rate (ASDR) as the primary measures. These metrics effectively illustrate the disease burden across diverse geographical areas and nations. To assess changes in disease burden between 1990 and 2021, we computed the Estimated Average Percent Change (EAPC) for ASMR and ASDR. The EAPC was determined by applying a linear regression model to the natural logarithm of the rates, represented by the equation: ln(rate) = α + β × calendar year + ε, where β signifies the overarching trend. Subsequently, the EAPC was calculated using the formula: 100 × (exp(β) − 1). A positive EAPC accompanied by a 95% uncertainty interval (UI) that does not include zero indicates a rising trend, whereas a negative EAPC indicates a declining trend. We also calculated the percentage change over the entire study duration. ⅱ. Cross-National Inequality Analysis As recommended by the World Health Organization (WHO), we used the Slope Index of Inequality (SII) and Concentration Index (CI) to assess the disease burden imbalance among countries(13). The SII quantifies absolute inequality via a regression analysis on the correlation between ASMR/ASDR and the relative SDI standing. Conversely, the CI assesses relative inequality by aligning the cumulative percentages of ASMR and ASDR with the cumulative population distribution organized using the SDI, utilizing numerical integration via the Lorenz curve. Both indices provide significant insight into health inequality magnitude and direction within and among countries. The SII is particularly instrumental in comprehending disparities in absolute terms, whereas CI illustrates relative inequalities, underscoring the uneven health outcome distribution across socioeconomic layers. To mitigate bias and variability, we adopted robust regression models instead of traditional linear regression models, effectively minimizing the impact of data heterogeneity or outliers, offering a more precise health inequality depiction. We further examined health disparity evolution from 1990 to 2021 across 204 nations and territories, providing an in-depth global inequality exploration. ⅲ. Frontier Analysis Building on the burden description, we applied frontier analysis, a method that compares different regions’ and countries’ performance data against the best-performing ones, identifying the optimal benchmarks. Using frontier analysis, we identified the most suitable benchmark for evaluating the prevalence of ischemic stroke in populations with elevated BMI. We used quantitative difference measures to determine the difference between a country's disease burden and the minimum disease burden within its corresponding SDI. ⅳ. APC Study In this study, we employed an APC framework to assess the impact of age, temporal context, and birth cohort on disease burden. APC analysis was conducted using an APC modeling framework based on Poisson regression. The number of deaths was set as the outcome variable, with the log of population size as the offset. Categorical predictors were used to incorporate the effects of age, study period, and cohort type. To solve the collinearity problem (where age, period, and cohort are linearly dependent), we used the Intrinsic Estimator method. This approach applies a unique data-driven constraint aimed at minimizing the squared norms of the estimated coefficients, yielding a solution that remains orthogonal to the null vector. The results were presented as relative risks with 95% UIs, which were exponentiated from the model coefficients. The reference groups were set as the period average for period effects and the 1955 birth cohort for cohort effects, following common practices in the APC literature. Statistical analyses were conducted using two-tailed tests, with a significance threshold of p < 0.05. All APC analyses were conducted using the "apc" package within the R programming environment. ⅴ. Predictive Analysis To predict the incidence of ischemic stroke associated with a high BMI, we used the Nordpred package in R software to analyze the number of deaths in different age groups from 2019 to 2036 and calculated the ASR by sex. To verify prediction results, we used the Bayesian APC (BAPC) and INLA packages in the R software for analysis. All data in this study were analyzed using R software, with p<0.05 indicating statistical significance. The BAPC model forecasts age-specific mortality rates for ischemic stroke by unraveling the distinct influences of age, time period, and birth cohort. This characteristic makes it exceptionally effective for predicting trends in populations experiencing significant demographic transformations, as it accommodates the changing mortality risks associated with various age brackets and generations. ⅵ. Statistics ASMR and ASDR indicators were reported per 100,000 individuals, with 95% UIs. Data were analyzed and visualized using R version 4.2.1. 3. Results a. Global and Regional Burden of Ischemic Stroke Attributed to High BMI The data show the number of deaths rose from 88,075 in 1990 (95% confidence interval: 12,636–176,367) to 172,391 in 2021 (95% confidence interval: 25,065–347,913), representing a 95.7% increase (80.9–109.7%) globally. Meanwhile, the number of DALYs increased from 2,125,883 (95% confidence interval: 305,139–4,176,471) to 4,439,186 (95% confidence interval: 649,030–8,647,485), a 108.8% increase (93.8–123.9%). Trend analysis showed a decline in ASMR and ASDR. EAPCs were −1.103 (95% confidence interval: −1.241 to −0.965) for ASMR and −0.583 (95% confidence interval: −0.705 to −0.46) for ASDR. By 2021, these rates reached 7.365 (95% confidence interval: 1.074–14.654) and 51.516 (95% confidence interval: 7.522–100.277), respectively. b. National Burden of Ischemic Stroke Due to High BMI The disease burden of ischemic stroke associated with high BMI varies across countries. China (44,263 deaths, 95% confidence interval: 6,069–91,658), Russia (19,327 deaths, 95% confidence interval: 2,815–39,602), the United States (8,371 deaths, 95% confidence interval: 1,192–17,710), and Egypt (7,875 deaths, 95% confidence interval: 1,359–15,228) had the highest death burdens in 2021. Regarding DALYs, China, Russia, the United States, and Egypt ranked in the top four, with approximately 1.19 million DALYs (95% confidence interval: 166,008–2,395,996), 417,061 DALYs (95% confidence interval: 61,375–815,070), 226,759 DALYs (95% confidence interval: 33,749–429,106), and 218,926 DALYs (95% confidence interval: 39,537–405,525), respectively. This could be attributed to the substantial demographic foundation, increasing elderly population, and nutritional practices prevalent in the four aforementioned nations. Owing to its high BMI, Egypt had the highest ASMR and ASDR in 2021 at 15.806 (95% confidence interval: 2.578–31.828) and 346.945 (95% confidence interval: 59.755–665.416), respectively. The second to fourth highest ASMRs were observed in North Macedonia, Iraq, and Bulgaria, whereas the second to fourth highest ASDRs were observed in Iraq, North Macedonia, and Nauru. Between 1990 and 2021, Zimbabwe experienced the largest and fastest increase in disease burden, with EAPCs of 5.107 (95% confidence interval: 4.413–5.805) for ASMR and 4.785 (95% confidence interval: 4.145–5.429) for ASDR. PCs were 265.1% (152.6–466%) and 246.7% (159.2–395.8%), respectively. In the Czech Republic, the ASMR and ASDR caused by high BMI showed the greatest decline, with percentage changes of −78.9% (−82.3% to −75.2%) and −74.5% (−77.9% to −70.8%), respectively, whereas Estonia had the fastest annual declines, with EAPCs of −6.369 (95% confidence interval: −6.970 to −5.764) and −5.529 (95% confidence interval: −6.021 to −5.034), respectively (Figures 2, 3; Additional Files 1–4). c. The Impact of Ischemic Stroke Linked to Elevated BMI Across Age Groups and Genders Globally, in 2021, mortality rates associated with elevated BMI increased progressively with age in both sexes. Similarly, the pattern observed in DALYs revealed a comparable increase with advancing age, with the exception of males in the 70–79 age category. It is important to note that, irrespective of sex, the highest mortality rates were recorded in the 70–74 age group. DALYs peaked at ages 70–74 in women and 65–69 in men. Global data analysis revealed that among the population aged ≥65, females exhibited higher DALY values and mortality counts than their male counterparts across all age groups beyond 70 years (Figure 4). The mortality rate of ischemic stroke associated with high BMI generally increases with age across all age groups and sexes. However, this trend did not apply to males aged >90 in moderate SDI regions. Across most age categories within all SDI regions, an upward trend in DALY rates was observed with age. Exceptions included women in the middle SDI region and men and women in the low SDI and low-middle SDI regions, who showed declining DALYs between the ages of 70 and 85 years. Additionally, men in the middle SDI region showed a decline in DALYs between the ages of 75–85 and >90 years. The prevalence of fatalities and DALYs attributable to ischemic stroke linked to elevated BMI exhibited a notable trajectory across all SDI regions: it increased significantly with advancing age before gradually tapering off. In regions classified as high-middle and high SDI, the age cohort with the highest mortality for women transitioned from the 70–74 age bracket, which is noted in other regions, to the 80–84 and 85–90 age groups, respectively. Conversely, in the low- and low-middle SDI regions, the mortality figures for women surpassed those of men across nearly all age categories (Figures 5 and 6). d. Burden of Ischemic Stroke Related to High BMI and Its Relation to SDI and HDI Between 1990 and 2021, ASMR and ASDR associated with a high BMI for ischemic stroke exhibited an upward trend when the SDI was below 0.7, after which they began to decline. Notably, the ASMR and ASDR figures recorded in Eastern Europe, Central Europe, Central Asia, North Africa, and the Middle East exceeded the expected levels based on developmental indicators (Figure 7). At the national level, the disease burden of different SDI countries presented a curve similar to that of the previous 21 GBD regions. The disease burden increased up to an SDI of approximately 0.77 and then started to decline. Countries such as Bulgaria, Serbia, North Macedonia, and Latvia had higher burdens than expected (Figure 8). In our analysis, we found a negative correlation between the EAPC (ASMR and ASDR) and ASR in 1990 (R = −0.431, −0.380, p < 0.001) (Figure 9). Furthermore, in 2021, there was a negative correlation between EAPC (ASMR and ASDR) and HDI (R = −0.738, −0.7346, p < 0.001), which is consistent with the previous downward trend in disease burden in high-income areas (Figure 9). e. Analysis of Inequality Across Countries in the Burden of Ischemic Stroke Due to High BMI To evaluate the existence of substantial relative and absolute inequalities associated with SDI concerning the incidence of ischemic stroke attributable to elevated BMI, we examined the disparities among different countries. Our findings revealed considerable relative and absolute inequalities linked to SDI in 1990, indicating that regions with elevated SDI scores experienced a higher ischemic stroke burden related to increased BMI. The SII value of ASDR decreased from 47.74 (95% confidence interval: 31.28–64.20) in 1990 to −0.05 (95% confidence interval: −19.16 to 19.05) in 2021. In a similar fashion, the SII value for ASMR declined from 2.29 (95% confidence interval: 1.61–2.96) in 1990 to 0.15 (95% confidence interval: −0.65 to 0.96) in 2021. The SII confidence intervals for ASMR and ASDR in 2021 were zero, suggesting a lack of a substantial correlation between socioeconomic status and ischemic stroke impact. These changes indicate a significant reduction in inequality compared with those of 1990. In 1990, the CI value was 0.14 (95% confidence interval: 0.08–0.19), showing ischemic stroke burden concentration in higher socioeconomic groups. By 2021, the average CI dropped to −0.02 (95% confidence interval: −0.07 to 0.04), suggesting a gradual shift of disease burden toward lower socioeconomic groups, although the confidence interval of zero indicates limited statistical significance (Figures 10, 11). f. Frontier Examination of the Influence of Elevated BMI on the Incidence of Ischemic Stroke We investigated the possible increase in the incidence of ischemic stroke associated with elevated BMI in connection with the SDI by employing frontier analysis. By comparing the effective differences (ef-df), we have listed the top 15 countries (ef-df range: 151.214–341.814), including Egypt (ef-df: 341.814); Iraq (ef-df:235.276); North Macedonia (ef-df:231.377); Nauru (ef-df:228.975); Bulgaria (ef-df:213.084); Saudi Arabia (ef-df:182.172); Turkmenistan (ef-df:176.210); Serbia (ef-df:173.340); Russia (ef-df:169.827); Palestine (ef-df:162.004); Syria (ef-df:160.556); Libya (ef-df:158.749); Kazakhstan (ef-df:155.134); Sudan (ef-df:152.969); and Eswatini (ef-df:151.214). Among countries with SDI below 0.5, Ethiopia (ef-df: 5.585), Somalia (ef-df: 5.746), South Sudan (ef-df: 6.462), Timor-Leste (ef-df: 8.364), and Nepal (ef-df: 10.666) have the lowest ischemic stroke burden caused by high BMI. Conversely, among countries with an SDI above 0.85, Lithuania (ef-df: 93.925), the United States (ef-df: 36.120), Monaco (ef-df: 33.022), Germany (ef-df: 24.201), and Finland (ef-df: 21.581) exhibited considerable promise for enhancement in relation to their developmental phase (Figure 12). g. APC Study To ascertain the crucial determinants contributing to ischemic stroke prevalence because of elevated BMI, we conducted a study utilizing an APC framework. Age effects are represented that mortality rates increased with age. Between 1990 and 2021, the annual change rate of mortality for individuals aged <30 is positive, indicating an increasing trend in the disease burden for this age group. The highest mortality rate was observed between 1995 and 2005. Additionally, the mortality risk for different birth cohorts showed a U-shaped pattern, with those born between 1950 and 1980 at the lowest, followed by a significant upward trend in disease mortality (Figure 13). Analysis of mortality rate fluctuations across various age cohorts between 1990 and 2021 revealed an increasing trajectory in mortality rates linked to elevated BMI within the 20–39 age bracket (Figure 15). Meanwhile, the APC trends in DALYs were generally consistent with those of mortality rates (Figures 14 and 15). h. Prognostic Evaluation of the Effect of Increased BMI on the Occurrence of Ischemic Stroke BAPC model analysis of deaths attributed to high BMI ischemic stroke in different sexes and age groups predicts the development trend of disease burden over the next 15 years. As illustrated in Figure 16, ASMR associated with elevated BMI showed an upward trend. By 2036, it is anticipated that there will be an approximate value of 2.252 (with a 95% confidence interval ranging from 1.896–2.608) for male and female patients globally. It is anticipated that the ASMR for male patients will increase significantly, reaching 2.414 (95% confidence interval: 2.078–2.750) per 100,000 individuals by 2036. In contrast, the ASMR for female patients with a high BMI is expected to increase slightly initially and then gradually decline between 2022 and 2036. The predicted ASMR for females in 2036 is 2.078 (95% confidence interval: 1.731–2.424). In this study, we forecast anticipated fatalities and mortality statistics categorized by gender and age demographics. Between 2022 and 2036, an increase in male fatalities was projected across all age groups. Females will show an upward trend in deaths in all age groups, except for those aged 25–44. Mortality rates for males and the overall population are expected to rise in the 20–79 age group but decline in those aged ≥80. Female mortality rates were predicted to increase slowly in the 20–24 and 45–64 age groups (see Additional files 1-6). 4. Discussion a. Global Trends in the Burden of Ischemic Stroke Related to High BMI Since 1990, the global prevalence of high BMI (obesity and overweight) has continuously increased(14). This increase has led to a greater burden of various related diseases and has affected human health, attracting widespread attention. The disease burden of ischemic stroke associated with high BMI also shows an increasing trend. In this study, we found different temporal trends across regions and different levels of economic development worldwide. Notable differences in the distribution were observed in the analyses according to age and sex. In this study, we aimed to emphasize the importance of high BMI as a risk factor for ischemic stroke and assist public health organizations in developing more targeted health measures by analyzing factors related to disease burden. Studies have revealed that populations with a persistently high average BMI have a 30% higher risk of stroke than those with normal weight. A high BMI may increase the likelihood of ischemic stroke through several pathways, including inflammation and thrombosis (e.g., increased levels of C-reactive protein, which facilitates atherosclerotic plaque development)(7), insulin resistance, hypertension, and dyslipidemia, accelerating vascular endothelial damage. Compared with normal patients, in patients with diabetes, an increased BMI causes a more significant increase in the risk of ischemic stroke(15). Additionally, patients with obesity have elevated fibrinogen levels and enhanced platelet aggregation, which exacerbate thrombosis(7). Evidence suggests a minor reduction in ASMR and ASDR burden worldwide, which is attributed to ischemic stroke associated with elevated BMI. The number of deaths and DALY cases was nearly twice that of 1990. This increase will inevitably lead to significant medical and socioeconomic burdens. Therefore, in-depth and multilevel analyses are required to provide a foundation for governments to formulate relevant healthcare policies. b. Socioeconomic and Age/Gender Disparities Globally, the disease burden (deaths and DALY) of high BMI-related ischemic stroke significantly increased in 2021. The ASDR and ASMR associated with increased BMI for ischemic stroke showed a declining pattern in regions with high-middle and high SDI. Conversely, in areas categorized as middle-, low-middle-, or low-SDI, these rates experienced an upward trajectory. This conclusion is further supported by correlation analysis: countries with an SDI 0.7 have the opposite effect. Additionally, the higher the HDI, the smaller the EAPCs, with countries with an HDI >0.75 showing negative EAPCs. These trends are influenced by multiple socioeconomic, healthcare, and lifestyle factors. First, governments in higher SDI regions should pay more attention to and intervene in high BMI earlier and more comprehensively, formulating relevant policies and strong medical insurance systems, from diet, physical examination(16), and community intervention to clinical therapeutics and robust economic support(17). Comprehensive management of metabolic risk factors partially offsets the vascular injury effects of high BMI(7). Additionally, well-established stroke treatment systems and optimized coverage of new anticoagulants(18) have improved disease prognosis. In contrast, obesity rates are surging in the middle- and low-SDI regions(19), where effective interventions are lacking. Studies have shown that the average BMI among young and middle-aged individuals in middle SDI regions is approximately 24.4 kg/m²(20), yet grassroots weight management programs remain insufficient. Furthermore, the prevalence of diabetes is rapidly increasing in these regions(21), amplifying the synergistic diabetes and obesity effect. Important prognostic markers such as serum albumin and LDL-C are not routinely monitored, which delays the identification of high-risk populations(22). Moreover, limited treatment options in low- and middle-income regions exacerbate the disease burden. Strengthening low-income countries’ healthcare systems, expanding and optimizing international aid, and enhancing international public health policies and measures (including medical technology transfer and knowledge-sharing) are of great importance. By 2021, regions classified with a high-middle SDI continued to exhibit the highest incidence of fatalities and DALYs attributed to ischemic strokes associated with elevated BMI. Furthermore, ASDR and ASMR persisted at elevated levels. This pattern is related to the historical accumulation of cases resulting from previously elevated obesity and aging rates in these regions, along with their greater disease diagnostic capabilities. Global data indicate that the prevalence of ischemic stroke associated with elevated BMI is notably greater among women than among men aged ≥65. This disparity may stem from the typically longer life expectancy of women than that of their male counterparts. Within this age bracket, the total count and proportion of the female population surpassed those of males. This indicates that more women survive to an age when chronic diseases are prevalent, resulting in a larger population being exposed to the risks of chronic diseases. Another crucial physiological factor is a decline in the protective effect of estrogen after menopause. This loss increases cardiovascular disease risk(23), and a high BMI worsens the associated metabolic disturbances. These factors call for global attention to weight management among women aged > 65 to reduce the incidence rate of ischemic stroke. It is significant that in regions characterized by low-to-middle and low SDI, mortality rates among women exceed those of men in nearly every age category. This can be linked to the intensification of biological susceptibility resulting from social and sex-based inequalities. Gender inequality is more prevalent in regions with a low SDI, where women usually have fewer opportunities for education and less access to health information(24,25), which leads to a lack of awareness of stroke symptoms, hypertension management, and the significance of a healthy diet. In resource-constrained households, women usually resort to consuming more affordable, carbohydrate-rich, and sodium-laden foods, which increase the likelihood of obesity and hypertension. Medical resources may be prioritized for male family members, and the use of preventive healthcare services among women is also lower. These societal influences cumulatively contribute to an increase in mortality rates among women of all age groups in regions characterized by low and low-middle SDI. In contrast, within regions characterized by high-middle and high SDI, the maximum mortality rate among females has transitioned to older age brackets, particularly those aged 80–84 and 85–90. This shift is notable when compared with the 70–74 age group observed in regions with middle and low-middle SDI. This is attributed to better education, preventive awareness, superior economic conditions, and healthcare measures for women in regions with a high SDI. These factors greatly compensate for the biological disadvantages in postmenopausal women(26). In many low-income countries, improvements in women's education and socioeconomic status are limited by multiple factors. Advancements in women's education and socioeconomic standing in low-income nations have encountered various hurdles; however, these elements have had considerably beneficial influences on women's health. Therefore, prioritizing these factors is imperative for the WHO, alongside local governments and health authorities. Health inequality analysis revealed that, in 1990, disease burden indicators were highly positively correlated with economic development levels. At that time, high-income countries bore the heaviest burdens. However, by 2021, this correlation had significantly weakened and even became "not significantly unbalanced.” This shift is not coincidental; it is the result of a series of global "push" and "pull" forces, where "push" forces exacerbate disparities and "pull" forces work to reduce them. The success of high SDI countries in controlling disease burden represents a "pull" force that narrows the gap; specifically, high-income countries have effectively managed risk factors and improved disease outcomes through multiple approaches, thereby reducing disparities with low-income countries. Since the late 20th century, high-income countries have recognized hypertension, high cholesterol levels, smoking, and obesity as significant contributors to the onset of cardiovascular diseases. Public health campaigns aimed at reducing salt intake, banning trans fats, providing warnings about saturated fats, controlling tobacco use, and promoting physical exercise have been implemented for decades, gradually yielding visible effects(27). High-income countries have established comprehensive secondary prevention protocols for ischemic stroke and complete emergency response systems. They also use advanced techniques such as intravenous thrombolysis and arterial thrombectomy, which reduce mortality and disability rates(28). Even when a stroke occurs, survival rates and prognoses significantly improve. In low SDI countries, there is a "risk growth," which acts as a "push" force, exacerbating the gap. In low- to middle-income countries, nutritional and epidemiological transitions have occurred. These shifts mirror the misguided trajectories observed in affluent nations, particularly the "Westernization" of dietary patterns. This phenomenon encompasses readily available ultra-processed foods with high sugar, fat, and salt, alongside sugary drinks, which align with the increasingly rapid lifestyle. With economic development, physical activity has decreased in low-income countries, which has resulted in a significant decline in energy expenditure. Consequently, these lifestyle changes increase obesity and ischemic stroke risk(29-31). Additionally, the constraints of healthcare system resources, inadequate access to diagnostic and therapeutic services, and a shortage of qualified professionals specializing in stroke prevention and management are insufficient to address the rising disease burden. Low-income regions require support and assistance in medical technology and the opportunity to learn from the experiences of high-income countries with high obesity rates. This analysis is essential for formulating effective intervention strategies to promote healthier diets and lifestyle habits. c. The Rising Burden in the Young Population APC analyses revealed several significant findings. First, the disease burden among individuals aged 20–34 years increased over the past 31 years, suggesting that ischemic stroke caused by high BMI occurs at a younger age. In addition to high BMI, this age group may face other modern lifestyle risks such as extreme lack of exercise, late nights, and insufficient sleep(32), causing metabolic disorders, high stress levels, and excessive consumption of sugary energy drinks. This population plays a significant role in families and society, and an increase in the disease burden inevitably creates substantial family and social challenges. This trend necessitates corresponding shifts in healthcare policies and increased attention from governments and health insurance organizations. The highest mortality rate occurred between 1995 and 2005. An analysis of social development during this time indicated a mismatch between the accumulation of disease risk and the adequacy of healthcare services. By the 1990s, the dietary changes and lifestyle alterations brought about by postwar economic prosperity led to a significant increase in populations affected by obesity and metabolic syndrome, reaching a critical risk point. At the time, healthcare resources and treatment measures were limited. The observed reduction in mortality rates post-2005 can be primarily linked to strides in medical technology and enhancements in emergency response systems, including the implementation of streamlined protocols for stroke intervention, advocacy of thrombolytic and thrombectomy techniques, and the prevalent application of statins and antihypertensive treatment(33). The disease burden among individuals born after 1980 significantly increased with later birth years in their lifetime. Those born after 1980 have been exposed to increasingly prevalent unhealthy diets characterized by high-sugar and high-fat processed foods and sugary beverages, as well as environments marked by the proliferation of electronic products, reduced physical activity, and increased sedentary behavior since childhood or adolescence(34). They were exposed to risk factors for obesity for longer durations and at higher intensities. This suggests that in the absence of efficient intervention strategies, the age at onset of ischemic stroke is likely to decline in the forthcoming decades, and middle-aged populations will become a key focus for stroke prevention and treatment in the future. Frontier analysis indicated that countries with an SDI <0.5, including Ethiopia, Somalia, South Sudan, Timor-Leste, and Nepal, have the lowest disease burden caused by their economic development levels. This is primarily due to the lower proportion of high-BMI populations and younger population demographics. Such a low disease burden under low-risk exposure conditions is a consequence of socioeconomic underdevelopment. This demonstrates the impact of high BMI on ischemic stroke from another perspective. We also found that high SDI regions, such as Lithuania, the United States, Monaco, Germany, and Finland, have a heavier disease burden compared with what would be expected based on their economic development levels. This is attributed to the prevalence of highly processed foods; a diet rich in meat and dairy products, red meat, processed meat products, butter, and dairy products(35), along with insufficient physical activity and a sedentary lifestyle. Additionally, cold climates in countries such as Lithuania and Finland significantly limit outdoor physical activity. Currently, the overall disease burden in the high-SDI regions is decreasing. However, the elimination of historically accumulated cases in certain countries urgently requires more efficient optimization, health policy reform, and implementation systems. d. Prediction of Future Disease Burden Our research analysis revealed that without intervention, the ischemic stroke mortality rate caused by a high BMI will increase over the next 15 years. There was a significant increase in ASMR among men across various age groups. Men usually neglect their health awareness and effective weight management. Additionally, they may face specific occupational pressures and social environments that contribute to irregular diets, excessive alcohol consumption, and insufficient sleep. Smoking and drinking rates are typically higher among men than women(36), and these risk factors, combined with a high BMI, significantly increase stroke risk. This pattern suggests that global healthcare policies should focus on implementing targeted and effective measures for weight control and lifestyle guidance aimed at reducing the risk of ischemic stroke and alleviating the socioeconomic burden in men. e. Limitations of the research This study has certain limitations. First, the GBD 2021 data offer an extensive overview; however, they may fall short in capturing epidemiological figures for certain low-income nations, which could result in an underappreciation of the ischemic stroke burden caused by elevated BMI in these areas. Second, the categorization of high BMI was not delineated into subdivisions, such as overweight and various classes of obesity; thus, further investigation is required to elucidate the effects of varying levels of high BMI on the ischemic stroke burden. Finally, the GBD 2021 database lacks detailed classifications of the various ischemic stroke subtypes. This absence may hinder the clarity of burden patterns and the elevated BMI impact across these distinct subtypes. Additionally, this study was based on the most comprehensive GBD 2021 data currently available. Notably, the GBD database is constantly being updated and revised (e.g., GBD 2023 data are expected to be released in the near future). While forthcoming data may adjust the absolute values, the fundamental trends identified, such as the impact pattern associated with age, period, and cohort effects, the inverted U-shaped correlation between disease burden and SDI, and the notable rise in risk among younger populations, are expected to remain consistent. 5. Conclusions Between 1990 and 2021, deaths and DALYs from ischemic stroke caused by high BMI increased substantially. In terms of economic development, the disease burden has shifted, with ASMR and ASDR rising in low- and middle-income areas. From the perspective of population characteristics, the disease burden among young people has been increasing annually; however, 65-year-old women bear a heavier disease burden than men do. The results obtained offer critical and timely insights into the ongoing global efforts in stroke prevention and management. Future investigations should further validate and enhance the predictive outcomes of this study using updated data. Abbreviations BMI Body mass index GBD Global burden of disease APC Age-period-cohort DALY Disease-adjusted life years ASR Age-standardized rate SDI Socio-demographic index HDI Human-development index ASMR Age-standardized mortality rate ASDR Age-standardized disability adjusted life year rate EAPC Estimated annual percentage changes UIs Uncertainty intervals SII Slope Index of Inequality CI confidence interval Declarations Ethics approval and consent to participate: This study used anonymized, aggregated data from the GBD database, which is publicly available and does not require additional ethical approval; therefore, it does not involve any ethical issues. Consent for publication: All contributors have granted their approval for the dissemination of this research. Availability of data and materials: All data used in this study are publicly available from the GBD 2021, accessible via the IHME GBD Results Tool. https://vizhub.healthdata.org/gbd-results. Use of these data is subject to the GBD Data Use Agreement. Competing interests: The authors declare that they have no competing interests Funding: Liaoning Province Science and Technology Project: An Intelligent Medical Technology-Driven Cross-Theoretical Model: Study on the Effect of Collaborative Traditional Chinese and Western Medical Technologies in Early Detection and Intervention for Cognitive Frailty‌ in the Elderly. Project Number: 2025-MS-330. Authors’ contributions: F.P. performed analysis and visualization and prepared the first draft of the manuscript. M.B. contributed to data curation and analysis and assisted in drafting the manuscript. Y.L. managed project administration, provided resources, and contributed to drafting the manuscript. S.M. contributed to data curation, participated in drafting, and performed proofreading. B.L. supervised the research project, oversaw the publication process, and served as the corresponding author responsible for communication. All authors read and approved the final manuscript. Acknowledgments: The authors wish to express their gratitude to the Institute for Health Metrics and Evaluation (IHME) and its financial supporters, the Bill & Melinda Gates Foundation, for rendering the data from the GBD accessible to the public.We would like to thank Editage ( www.editage.cn ) for English language editing. References Su PW, Zhai Z, Wang T, Zhang YN, Wang Y, Ma K, et al. Research progress on astrocyte autophagy in ischemic stroke. Front Neurol. 2022;13:951536. https://doi.org/10.3389/fneur.2022.951536 . Velioglu M, Onal Y, Agackiran A, Dogan Ak P, Karakas HM. Initial experience with the CatchView thrombectomy device for acute ischemic stroke. J Neurointerv Surg. 2021;13(10):946–50. https://doi.org/10.1136/neurintsurg-2020-016784 . Naz F, Malik S, Asif K, Mahsood M, Rehman S, Rehman N, UNRAVELLING ATRIAL FIBRILLATION AETIOLOGY, AND ANTICOAGULATION TRENDS IN STROKE. WHERE DO WE STAND? A STUDY FROM NORTHERN PAKISTAN. 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BMC Public Health. 2019;19(1):1466. https://doi.org/10.1186/s12889-019-7826-6 . Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files TABLE1.xlsx TABLE2.xlsx SupplementaryFigure1.pdf SupplementaryFigure2.pdf SupplementaryFigure3.pdf SupplementaryFigure4.pdf SupplementaryFigure5.pdf SupplementaryFigure6.pdf SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable4.xlsx Additionalfiles.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-7557435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514270835,"identity":"d714f3ca-5ca7-4d6d-9bc6-8f3c056bb5de","order_by":0,"name":"Fei Peng","email":"","orcid":"","institution":"Jinqiu Hospital of Liaoning Province","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Peng","suffix":""},{"id":514270836,"identity":"fa4d9aba-5737-4ad3-b556-7301968faed4","order_by":1,"name":"Mingyan Bian","email":"","orcid":"","institution":"Jinqiu Hospital of Liaoning 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19:23:34","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":18920,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-7557435/v1/26b7413c1bfc924dfc2dacf8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Ischemic Stroke Burden Due to High BMI, 1990–2021: An Age-Period-Cohort Study and Future Risk Prediction Using GBD Data","fulltext":[{"header":"1. Background","content":"\u003cp\u003eIschemic stroke refers to an acute cerebrovascular event caused by the blockage of cerebral blood flow to the brain by thrombosis or embolism, resulting in local brain tissue ischemia and hypoxic necrosis. It accounts for approximately 70–80% of all instances of stroke and is one of the leading causes of mortality and disability among adults globally(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). With advances in diagnostic technologies, early ischemic stroke diagnosis is more feasible. Current treatments focus on rapidly restoring cerebral blood flow to minimize infarct size, primarily through intravenous thrombolysis and arterial thrombectomy(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, as most patients cannot seek medical attention within this time window, this delay greatly affects disease prognosis. Hypertension, diabetes, smoking, hypercholesterolemia, obesity, heart disease, and atrial fibrillation are significant risk factors for ischemic stroke(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The cumulative effect and overlap of these risk factors significantly increase the incidence of stroke. Effective weight management and control of blood glucose, blood pressure, and other risk factors are crucial for preventing ischemic stroke and reducing the global disease burden.\u003c/p\u003e\u003cp\u003eHigh body mass index (BMI) increases ischemic stroke risk through multiple pathways(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), including metabolic disorders, inflammatory responses, and synergistic effects with other risk factors. An abnormal increase in BMI increases the release of inflammatory factors and puts the blood in a hypercoagulable state(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), increasing the likelihood of ischemic stroke. Individuals with elevated BMI frequently develop visceral adiposity, which contributes to insulin resistance and disturbances in lipid metabolism, exacerbating atherosclerosis progression(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A large-scale clinical study in Japan found that patients with obesity were more likely to suffer from hypertension(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), increasing ischemic stroke risk. Additionally, high BMI affects ischemic stroke prognosis; one study revealed that patients with high BMI who undergo arterial thrombectomy had a postoperative bleeding risk(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). High BMI affects ischemic stroke, a disease with high disability and mortality rates, in multiple ways. Therefore, further observations and research on ischemic stroke burden caused by high BMI are crucial.\u003c/p\u003e\u003cp\u003ePresently, Global Burden of Disease (GBD)-based academic research on the disease burden of ischemic stroke and high BMI remains limited, particularly with respect to age-period-cohort (APC) analyses and future predictions. In this study, we analyzed the global disease burden of ischemic stroke caused by high BMI and conducted specific analyses from multiple perspectives, such as region, economy, period, sex, and age, and predicted future trends in disease burden. The purpose of this study was to emphasize the serious consequences of high BMI on ischemic stroke burden and provide a strong basis for public health organizations and governments to formulate targeted medical policies and intervention measures.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003ea. Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this survey, we obtained updated data for 2021 from the GBD database. The GBD systematically evaluates health losses attributable to more than 370 diseases, injuries, and risk factors (including\u0026nbsp;a high BMI) in more than 200 countries and regions.\u0026nbsp;The core indicator is the disability-adjusted life years (DALYs), which combines premature death and disability to reveal health trends. It also evaluates policy effectiveness and guides priority allocation for global public health resources(11,12). During the data download process, we specified\u0026nbsp;a high BMI in\u0026nbsp;the risk factor\u0026nbsp;option and ischemic stroke in the disease option, and downloaded relevant disease burden indicator data.\u0026nbsp;We compiled data regarding fatalities attributable to ischemic stroke related to high BMI, age-standardized rates (ASR), and DALYs across 204 nations, 21 GBD regions, and five Socio-Demographic Index (SDI) regions. Furthermore, we obtained pertinent information concerning SDI and the Human-Development Index (HDI) for each nation to investigate their possible correlations with disease burden.\u003c/p\u003e\n\u003cp id=\"_Toc19163\"\u003e\u003cstrong\u003eb. Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIschemic stroke was defined according to the 2021 version of\u0026nbsp;the International Classification of Diseases and Related Health Problems, Tenth Revision, which defines it as an acute episode of focal cerebral, spinal, or retinal dysfunction because of an infarction of\u0026nbsp;the central nervous system tissue. Acute infarction can be confirmed through one of two recognized criteria: (a) symptoms persisting for\u0026nbsp;\u0026gt;24\u0026nbsp;h, or (b) neuroimaging or other diagnostic evaluations of clinically relevant brain regions. High BMI refers to a BMI\u0026nbsp;\u0026gt;25 kg/m\u0026sup2;\u0026nbsp;in the population aged\u0026nbsp;\u0026ge;20. The GBD evaluates various countries and regions from three perspectives: the fertility rate of individuals aged\u0026nbsp;\u0026lt;25, education level, and per capita income of the population aged\u0026nbsp;\u0026gt;15, to obtain the SDI, which is calibrated on a continuum ranging from 0\u0026ndash;1. Countries\u0026nbsp;were divided into five different levels based on\u0026nbsp;their SDI values: low (\u0026lt;0.46), low-middle (0.46\u0026ndash;0.60), middle (0.61\u0026ndash;0.69), high-middle (0.70\u0026ndash;0.81), and high (\u0026gt;0.81). The HDI is obtained by evaluating\u0026nbsp;life expectancy at birth, mean years of schooling, expected years of schooling, and Gross National Income per capita of various countries and regions. The values of this index were defined as low, medium, high, and extremely high levels in the following ranges: \u0026lt;0.55, 0.55\u0026ndash;0.69, 0.70\u0026ndash;0.79, and 0.80\u0026ndash;1.00, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eⅰ. Burden Description\u003c/p\u003e\n\u003cp\u003eIn this study, we examined ischemic stroke disease burden caused by high BMI using mortality rates, DALYs, age-standardized mortality rate (ASMR), and age-standardized disability rate (ASDR) as the primary measures. These metrics effectively illustrate the disease burden across diverse geographical areas and nations. To assess\u0026nbsp;changes in disease burden between 1990 and 2021, we computed the Estimated Average Percent Change (EAPC) for ASMR and ASDR. The EAPC was determined by applying a linear regression model to the natural logarithm of the rates, represented by the equation: ln(rate) =\u0026nbsp;\u0026alpha;\u0026nbsp;+\u0026nbsp;\u0026beta;\u0026nbsp;\u0026times;\u0026nbsp;calendar year +\u0026nbsp;\u0026epsilon;, where\u0026nbsp;\u0026beta;\u0026nbsp;signifies the overarching trend. Subsequently, the EAPC was calculated using the formula: 100\u0026nbsp;\u0026times;\u0026nbsp;(exp(\u0026beta;)\u0026nbsp;\u0026minus;\u0026nbsp;1). A positive EAPC accompanied by a 95% uncertainty interval (UI) that does not include zero indicates a rising trend, whereas a negative EAPC indicates a declining trend. We also calculated the percentage change over the entire\u0026nbsp;study duration.\u003c/p\u003e\n\u003cp\u003eⅱ. Cross-National Inequality Analysis\u003c/p\u003e\n\u003cp\u003eAs recommended by the World Health Organization (WHO), we used\u0026nbsp;the Slope Index of Inequality (SII) and Concentration Index (CI) to assess the disease burden imbalance among countries(13). The SII quantifies absolute inequality via\u0026nbsp;a regression analysis on the correlation between\u0026nbsp;ASMR/ASDR and the relative SDI standing. Conversely, the CI assesses relative inequality by aligning the cumulative percentages of\u0026nbsp;ASMR and ASDR with the cumulative population distribution organized using\u0026nbsp;the SDI, utilizing numerical integration via the Lorenz curve. Both indices provide significant insight\u0026nbsp;into health inequality magnitude and direction within and among countries. The SII is particularly instrumental in comprehending disparities in absolute terms, whereas CI illustrates relative inequalities, underscoring the uneven health outcome distribution across socioeconomic layers. To mitigate bias and variability, we adopted robust regression models instead of traditional linear regression models, effectively minimizing the impact of data heterogeneity or outliers, offering a more precise health inequality depiction. We further examined health disparity evolution from 1990 to 2021 across 204 nations and territories, providing an in-depth global inequality exploration.\u003c/p\u003e\n\u003cp\u003eⅲ. Frontier Analysis\u003c/p\u003e\n\u003cp\u003eBuilding on the burden description, we applied frontier analysis, a method that compares different regions\u0026rsquo; and countries\u0026rsquo; performance data against the best-performing ones, identifying\u0026nbsp;the optimal benchmarks. Using frontier analysis, we\u0026nbsp;identified the most suitable benchmark for evaluating the prevalence of ischemic stroke in populations with elevated BMI. We used quantitative difference measures to\u0026nbsp;determine the difference between a country\u0026apos;s disease burden and the minimum disease burden within its corresponding SDI.\u003c/p\u003e\n\u003cp\u003eⅳ. APC Study\u003c/p\u003e\n\u003cp\u003eIn this study, we employed an APC framework to assess the impact of age, temporal context, and birth cohort on disease burden. APC analysis was conducted using an APC modeling framework based on Poisson regression. The number of deaths was set as the outcome variable, with the log of population size as the offset. Categorical predictors were used to incorporate the effects of\u0026nbsp;age, study period, and cohort type. To\u0026nbsp;solve the collinearity problem (where age, period, and cohort are linearly dependent), we used the Intrinsic Estimator method. This approach applies a unique data-driven constraint aimed at minimizing the squared norms of the estimated coefficients, yielding a solution that remains orthogonal to the null vector. The results were presented as relative risks with 95% UIs, which were exponentiated from the model coefficients. The reference groups were set as the period average for period effects and the 1955 birth cohort for cohort effects, following common practices in the APC literature.\u0026nbsp;Statistical analyses were conducted using two-tailed tests, with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All APC analyses were conducted using the \u0026quot;apc\u0026quot; package within the R programming environment.\u003c/p\u003e\n\u003cp\u003eⅴ. Predictive Analysis\u003c/p\u003e\n\u003cp\u003eTo predict the incidence of ischemic stroke associated with\u0026nbsp;a high BMI, we used the Nordpred package in R software to analyze the number of deaths in different age groups from 2019 to 2036\u0026nbsp;and calculated\u0026nbsp;the ASR by\u0026nbsp;sex. To verify prediction results, we used the Bayesian APC (BAPC) and INLA packages in the R software for analysis. All data in this study were analyzed using R software, with p\u0026lt;0.05 indicating statistical significance.\u0026nbsp;The BAPC model forecasts age-specific mortality rates for ischemic stroke by unraveling the distinct influences of age, time period, and birth cohort. This characteristic makes it exceptionally effective for predicting trends in populations experiencing significant demographic transformations, as it accommodates the changing mortality risks associated with various age brackets and generations.\u003c/p\u003e\n\u003cp\u003eⅵ. Statistics\u003c/p\u003e\n\u003cp\u003eASMR and ASDR indicators were reported per 100,000 individuals, with 95% UIs. Data were analyzed and visualized using R version 4.2.1.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003ea. Global and Regional Burden of Ischemic Stroke Attributed to High BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data show the number of deaths rose from 88,075 in 1990 (95% confidence interval: 12,636\u0026ndash;176,367) to 172,391 in 2021 (95% confidence interval: 25,065\u0026ndash;347,913), representing a 95.7% increase (80.9\u0026ndash;109.7%) globally. Meanwhile, the number of DALYs increased from 2,125,883 (95% confidence interval: 305,139\u0026ndash;4,176,471) to 4,439,186 (95% confidence interval: 649,030\u0026ndash;8,647,485), a 108.8% increase (93.8\u0026ndash;123.9%). Trend analysis showed a decline in ASMR and ASDR. EAPCs were\u0026nbsp;\u0026minus;1.103 (95% confidence interval:\u0026nbsp;\u0026minus;1.241 to\u0026nbsp;\u0026minus;0.965) for ASMR and\u0026nbsp;\u0026minus;0.583 (95% confidence interval:\u0026nbsp;\u0026minus;0.705 to\u0026nbsp;\u0026minus;0.46) for ASDR. By 2021, these rates\u0026nbsp;reached 7.365 (95% confidence interval: 1.074\u0026ndash;14.654) and 51.516 (95% confidence interval: 7.522\u0026ndash;100.277), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. National Burden of Ischemic Stroke Due to High BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe disease burden of ischemic stroke associated with high BMI varies across countries. China (44,263 deaths, 95% confidence interval: 6,069\u0026ndash;91,658), Russia (19,327 deaths, 95% confidence interval: 2,815\u0026ndash;39,602), the United States (8,371 deaths, 95% confidence interval: 1,192\u0026ndash;17,710), and Egypt (7,875 deaths, 95% confidence interval: 1,359\u0026ndash;15,228) had the highest death burdens in 2021. Regarding DALYs, China, Russia, the United States, and Egypt ranked\u0026nbsp;in the top four, with approximately 1.19 million DALYs (95% confidence interval: 166,008\u0026ndash;2,395,996), 417,061 DALYs (95% confidence interval: 61,375\u0026ndash;815,070), 226,759 DALYs (95% confidence interval: 33,749\u0026ndash;429,106), and 218,926 DALYs (95% confidence interval: 39,537\u0026ndash;405,525), respectively. This could be attributed to the substantial demographic foundation,\u0026nbsp;increasing elderly population, and nutritional practices prevalent in the\u0026nbsp;four aforementioned\u0026nbsp;nations. Owing to\u0026nbsp;its high BMI, Egypt had the highest ASMR and ASDR in 2021 at 15.806 (95% confidence interval: 2.578\u0026ndash;31.828) and 346.945 (95% confidence interval: 59.755\u0026ndash;665.416), respectively. The second to fourth highest ASMRs were observed in North Macedonia, Iraq, and Bulgaria, whereas the second to fourth highest ASDRs were observed in Iraq, North Macedonia, and Nauru. Between 1990 and 2021, Zimbabwe experienced the largest and fastest increase in disease burden,\u0026nbsp;with\u0026nbsp;EAPCs of 5.107 (95% confidence interval: 4.413\u0026ndash;5.805) for ASMR and 4.785 (95% confidence interval: 4.145\u0026ndash;5.429) for ASDR. PCs were 265.1% (152.6\u0026ndash;466%) and 246.7% (159.2\u0026ndash;395.8%), respectively. In the Czech Republic, the ASMR and ASDR caused by high BMI showed the greatest decline, with percentage changes of\u0026nbsp;\u0026minus;78.9% (\u0026minus;82.3% to \u0026minus;75.2%) and\u0026nbsp;\u0026minus;74.5% (\u0026minus;77.9% to\u0026nbsp;\u0026minus;70.8%), respectively, whereas Estonia had the fastest annual declines, with EAPCs of \u0026minus;6.369 (95% confidence interval: \u0026minus;6.970 to \u0026minus;5.764) and \u0026minus;5.529 (95% confidence interval: \u0026minus;6.021 to \u0026minus;5.034), respectively (Figures 2, 3; Additional Files 1\u0026ndash;4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. The Impact of Ischemic Stroke Linked to Elevated BMI Across Age Groups and Genders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobally, in 2021, mortality rates associated with elevated BMI increased progressively with age in both sexes. Similarly, the pattern observed in DALYs revealed a comparable increase with advancing age, with the exception of males in the 70\u0026ndash;79 age category. It is important to note that, irrespective of sex, the highest mortality rates were recorded in the 70\u0026ndash;74 age group. DALYs peaked at ages 70\u0026ndash;74 in women and 65\u0026ndash;69 in men. Global data analysis revealed that among the population aged\u0026nbsp;\u0026ge;65, females exhibited higher DALY values and mortality counts\u0026nbsp;than their male counterparts across all age groups beyond 70\u0026nbsp;years (Figure 4). The mortality rate of ischemic stroke associated with high BMI generally increases with age across all age groups and\u0026nbsp;sexes. However, this trend did not apply to males aged\u0026nbsp;\u0026gt;90 in moderate SDI regions. Across most age categories within all SDI regions, an upward trend in DALY rates was observed\u0026nbsp;with age. Exceptions included women in the middle SDI region and men and women in the low SDI and low-middle SDI regions,\u0026nbsp;who showed declining DALYs between\u0026nbsp;the ages of 70 and 85 years. Additionally, men in the middle SDI region\u0026nbsp;showed a decline in DALYs between\u0026nbsp;the ages of 75\u0026ndash;85 and\u0026nbsp;\u0026gt;90 years. The prevalence of fatalities and DALYs attributable to ischemic stroke linked to elevated BMI exhibited a notable trajectory across all SDI regions: it\u0026nbsp;increased significantly with advancing age before gradually tapering off. In regions classified as high-middle and high SDI, the age cohort with the highest mortality for women transitioned from the 70\u0026ndash;74 age bracket, which is noted in other regions, to the 80\u0026ndash;84 and 85\u0026ndash;90 age groups, respectively. Conversely, in the low- and low-middle SDI regions, the mortality figures for women surpassed those of men across nearly all age categories (Figures 5 and 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. Burden of Ischemic Stroke Related to High BMI and Its Relation to SDI and HDI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween 1990 and 2021, ASMR and ASDR associated with\u0026nbsp;a high BMI for ischemic stroke exhibited an upward trend when the SDI was below 0.7, after\u0026nbsp;which they began to decline. Notably, the ASMR and ASDR figures recorded in Eastern Europe, Central Europe, Central Asia, North Africa, and the Middle East exceeded the expected\u0026nbsp;levels based on developmental indicators (Figure 7). At the national level, the disease burden of different SDI countries presented a\u0026nbsp;curve similar\u0026nbsp;to that of the previous 21 GBD regions. The disease burden increased up to an SDI of approximately 0.77 and then started to decline. Countries such as Bulgaria, Serbia, North Macedonia, and Latvia had\u0026nbsp;higher burdens\u0026nbsp;than expected (Figure 8). In our analysis, we found a negative correlation between\u0026nbsp;the EAPC (ASMR and ASDR) and ASR in 1990 (R = \u0026minus;0.431, \u0026minus;0.380, p \u0026lt; 0.001) (Figure 9). Furthermore, in\u0026nbsp;2021, there was a negative correlation between EAPC (ASMR and ASDR) and HDI (R = \u0026minus;0.738, \u0026minus;0.7346, p \u0026lt; 0.001), which is consistent with the previous downward trend in disease burden in high-income areas (Figure 9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. Analysis of Inequality Across Countries in the Burden of Ischemic Stroke Due to High BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the existence of substantial relative and absolute inequalities associated with SDI concerning the incidence of ischemic stroke attributable to elevated BMI, we examined the disparities among different countries. Our findings revealed considerable relative and absolute inequalities linked to SDI in 1990, indicating that regions with elevated SDI\u0026nbsp;scores experienced a higher ischemic stroke burden related to increased BMI. The SII value of ASDR decreased from 47.74 (95% confidence interval: 31.28\u0026ndash;64.20) in 1990 to \u0026minus;0.05 (95% confidence interval: \u0026minus;19.16 to 19.05) in 2021. In a similar fashion, the SII value for ASMR declined from 2.29 (95% confidence interval: 1.61\u0026ndash;2.96) in 1990 to 0.15 (95% confidence interval: \u0026minus;0.65 to 0.96) in 2021. The SII confidence intervals for ASMR and ASDR in 2021\u0026nbsp;were zero, suggesting a lack of\u0026nbsp;a substantial correlation between socioeconomic status and ischemic stroke impact. These changes indicate a significant reduction in inequality compared\u0026nbsp;with those of 1990. In 1990, the CI value was 0.14 (95% confidence interval: 0.08\u0026ndash;0.19), showing ischemic stroke burden concentration in higher socioeconomic groups. By 2021, the average CI dropped to \u0026minus;0.02 (95% confidence interval: \u0026minus;0.07 to 0.04), suggesting a gradual shift of disease burden toward lower socioeconomic groups, although the confidence interval of zero indicates limited statistical significance (Figures 10, 11).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef. Frontier Examination of the Influence of Elevated BMI on the Incidence of Ischemic Stroke\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the possible increase in the incidence of ischemic stroke associated with elevated BMI in connection with the SDI by employing frontier analysis. By comparing the effective differences (ef-df), we have listed the top 15 countries (ef-df range: 151.214\u0026ndash;341.814), including Egypt (ef-df: 341.814); Iraq (ef-df:235.276); North Macedonia (ef-df:231.377); Nauru (ef-df:228.975); Bulgaria (ef-df:213.084); Saudi Arabia (ef-df:182.172); Turkmenistan (ef-df:176.210); Serbia (ef-df:173.340); Russia (ef-df:169.827); Palestine (ef-df:162.004); Syria (ef-df:160.556); Libya (ef-df:158.749); Kazakhstan (ef-df:155.134); Sudan (ef-df:152.969); and Eswatini (ef-df:151.214). Among countries with SDI below 0.5, Ethiopia (ef-df: 5.585), Somalia (ef-df: 5.746), South Sudan (ef-df: 6.462), Timor-Leste (ef-df: 8.364), and Nepal (ef-df: 10.666) have the lowest ischemic stroke burden caused by high BMI. Conversely, among countries with an SDI above 0.85, Lithuania (ef-df: 93.925), the United States (ef-df: 36.120), Monaco (ef-df: 33.022), Germany (ef-df: 24.201), and Finland (ef-df: 21.581) exhibited considerable promise for enhancement in relation to their developmental phase (Figure 12).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg. APC Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ascertain the crucial determinants contributing to ischemic stroke prevalence because of elevated BMI, we conducted a study utilizing an APC framework. Age effects are represented that mortality rates increased with age. Between 1990 and 2021, the annual change rate of mortality for individuals aged\u0026nbsp;\u0026lt;30\u0026nbsp;is positive, indicating an increasing trend in\u0026nbsp;the disease burden for this age group. The highest mortality rate\u0026nbsp;was observed between 1995 and 2005. Additionally,\u0026nbsp;the mortality risk for different birth cohorts showed a U-shaped pattern, with those born between 1950 and 1980 at the\u0026nbsp;lowest, followed by a significant upward trend in disease mortality (Figure 13). Analysis of mortality rate fluctuations across various age cohorts between 1990 and 2021 revealed an increasing trajectory in mortality rates linked to elevated BMI within the 20\u0026ndash;39 age bracket (Figure 15). Meanwhile, the APC trends in DALYs were generally consistent with those of mortality rates (Figures 14 and 15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh. Prognostic Evaluation of the Effect of Increased BMI on the Occurrence of Ischemic Stroke\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBAPC model analysis of deaths attributed to high BMI ischemic stroke in different sexes and age groups predicts the development trend of disease burden over the next 15 years. As illustrated in Figure 16, ASMR associated with elevated BMI showed an upward trend. By 2036, it is anticipated that there will be an approximate value of 2.252 (with a 95% confidence interval ranging from 1.896\u0026ndash;2.608) for male and female patients globally. It is anticipated that the ASMR for male patients will increase significantly, reaching 2.414 (95% confidence interval: 2.078\u0026ndash;2.750) per 100,000 individuals by 2036. In contrast, the ASMR for female patients with a high BMI is expected to increase slightly initially and then gradually decline between 2022 and 2036. The predicted ASMR for females in 2036 is 2.078 (95% confidence interval: 1.731\u0026ndash;2.424). In this study, we forecast anticipated fatalities and mortality statistics categorized by gender and age demographics. Between 2022 and 2036, an increase in male fatalities was projected across all age groups. Females will show an upward trend in deaths in all age groups, except for those aged 25\u0026ndash;44. Mortality rates for males and the overall population are expected to rise in the 20\u0026ndash;79 age group but decline in those aged \u0026ge;80. Female mortality rates were predicted to increase slowly in the 20\u0026ndash;24 and 45\u0026ndash;64 age groups (see Additional files 1-6).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003ea.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGlobal Trends in the Burden of Ischemic Stroke Related to High BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince 1990, the global prevalence of high BMI (obesity and overweight) has continuously increased(14). This increase has led to a greater burden of various related diseases and\u0026nbsp;has affected human health, attracting widespread attention.\u0026nbsp;The disease burden of ischemic stroke associated with high BMI also shows an increasing trend. In this study, we found different temporal trends across regions and\u0026nbsp;different levels of economic development worldwide. Notable\u0026nbsp;differences in the distribution\u0026nbsp;were observed in\u0026nbsp;the analyses\u0026nbsp;according to age and sex. In this study, we aimed to emphasize the importance of high BMI as a risk factor for ischemic stroke and assist public health organizations in developing more targeted health measures by analyzing factors related to disease burden.\u003c/p\u003e\n\u003cp\u003eStudies have revealed that populations with\u0026nbsp;a persistently high average BMI have a 30% higher risk of stroke\u0026nbsp;than those with normal weight. A high BMI may increase the likelihood of ischemic stroke through several pathways, including inflammation and thrombosis (e.g., increased levels of C-reactive protein, which facilitates atherosclerotic plaque development)(7), insulin resistance, hypertension, and dyslipidemia, accelerating vascular endothelial damage. Compared with normal patients, in patients with diabetes, an increased BMI causes a more significant increase in the risk of ischemic stroke(15). Additionally, patients with obesity have elevated fibrinogen levels and enhanced platelet aggregation,\u0026nbsp;which exacerbate thrombosis(7).\u003c/p\u003e\n\u003cp\u003eEvidence suggests a minor reduction in ASMR and ASDR burden worldwide, which is attributed to ischemic stroke associated with elevated BMI. The number of deaths and DALY cases was nearly twice that of 1990. This increase will inevitably lead to significant medical and socioeconomic burdens. Therefore, in-depth and multilevel analyses are required to provide a foundation for governments to formulate relevant healthcare policies.\u003c/p\u003e\n\u003cp id=\"_Toc8423\"\u003e\u003cstrong\u003eb. Socioeconomic and Age/Gender Disparities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobally, the disease burden (deaths and DALY) of high BMI-related ischemic stroke significantly increased in 2021. The ASDR and ASMR associated with increased BMI for ischemic stroke showed\u0026nbsp;a declining pattern in regions with high-middle and high SDI. Conversely, in areas categorized as middle-, low-middle-,\u0026nbsp;or low-SDI, these rates experienced an upward trajectory. This conclusion is further supported by correlation analysis: countries with\u0026nbsp;an SDI\u0026nbsp;\u0026lt;0.7 have an increasing\u0026nbsp;ischemic stroke burden caused by high BMI as\u0026nbsp;the SDI increases, whereas countries with an SDI\u0026nbsp;\u0026gt;0.7 have the opposite effect. Additionally, the higher the HDI, the smaller the EAPCs, with countries with an HDI\u0026nbsp;\u0026gt;0.75 showing negative EAPCs. These trends are influenced by multiple socioeconomic, healthcare, and lifestyle factors. First, governments in higher SDI regions\u0026nbsp;should pay more attention to and intervene in high BMI earlier and more comprehensively, formulating relevant policies and strong medical insurance systems, from diet, physical examination(16), and community intervention to clinical therapeutics and robust economic support(17). Comprehensive management of metabolic risk factors partially offsets the vascular injury effects of high BMI(7). Additionally, well-established stroke treatment systems and optimized coverage of new anticoagulants(18)\u0026nbsp;have improved disease prognosis. In contrast, obesity rates are surging in\u0026nbsp;the middle- and low-SDI regions(19), where effective interventions are lacking. Studies have shown that the average BMI among young and middle-aged individuals in middle SDI regions is approximately 24.4 kg/m\u0026sup2;(20), yet grassroots weight management programs remain insufficient. Furthermore, the prevalence of diabetes is rapidly increasing in these regions(21), amplifying the synergistic\u0026nbsp;diabetes and obesity effect. Important prognostic markers\u0026nbsp;such as serum albumin and LDL-C are not routinely monitored, which delays the identification of high-risk populations(22). Moreover, limited treatment options in low- and middle-income regions exacerbate the disease burden. Strengthening low-income countries\u0026rsquo; healthcare systems, expanding and optimizing international aid, and enhancing international public health policies and measures (including medical technology transfer and knowledge-sharing) are of great importance. By 2021, regions classified with a high-middle SDI\u0026nbsp;continued to exhibit the highest incidence of fatalities and DALYs attributed to ischemic strokes associated with elevated BMI. Furthermore, ASDR and ASMR persisted at elevated levels. This pattern is related to the historical accumulation of cases resulting from previously elevated obesity and aging rates in these regions, along with their greater disease diagnostic capabilities.\u003c/p\u003e\n\u003cp\u003eGlobal data indicate that the prevalence of ischemic stroke associated with elevated\u0026nbsp;BMI is notably greater among women than among men\u0026nbsp;aged\u0026nbsp;\u0026ge;65. This disparity may stem from the typically longer life expectancy of women than that of their male counterparts. Within this age bracket, the total count and proportion of the female population surpassed those of males. This\u0026nbsp;indicates that more women survive to an age when chronic diseases are prevalent, resulting in a larger population\u0026nbsp;being exposed to the risks of chronic diseases. Another crucial physiological factor is\u0026nbsp;a decline in the protective effect of estrogen after menopause. This loss increases cardiovascular disease risk(23), and\u0026nbsp;a high BMI worsens the associated metabolic disturbances. These factors call for global attention to weight management among women\u0026nbsp;aged \u0026gt; 65 to reduce the incidence rate of ischemic stroke. It is significant that in regions characterized by low-to-middle and low SDI, mortality rates among women exceed those of men in nearly every age category. This can be linked to the intensification of biological susceptibility resulting from social and sex-based inequalities. Gender inequality is more prevalent in\u0026nbsp;regions with a low SDI, where women usually have fewer opportunities for education and less access to health information(24,25), which leads to a lack of awareness of stroke symptoms, hypertension management, and the significance of a healthy diet. In resource-constrained households, women usually resort to consuming more affordable, carbohydrate-rich, and sodium-laden foods, which increase the likelihood of obesity and hypertension. Medical resources may be prioritized for male family members, and the use of preventive healthcare services among women is also lower. These societal influences cumulatively contribute to an\u0026nbsp;increase in mortality rates among women of all age groups in regions characterized by low and low-middle SDI. In contrast, within regions characterized by high-middle and high SDI, the maximum mortality rate among females has transitioned to older age brackets, particularly those aged 80\u0026ndash;84 and 85\u0026ndash;90. This shift is notable when compared with the 70\u0026ndash;74 age group observed in regions with middle and low-middle SDI. This is attributed to better education, preventive awareness, superior economic conditions, and healthcare measures for women in regions with a high SDI. These factors greatly compensate for the biological disadvantages in postmenopausal women(26). In many low-income countries, improvements in women\u0026apos;s education and socioeconomic status are limited by multiple factors. Advancements in women\u0026apos;s education and socioeconomic standing in low-income nations\u0026nbsp;have encountered various hurdles; however, these elements\u0026nbsp;have had considerably beneficial influences on women\u0026apos;s health. Therefore, prioritizing these factors is imperative for the WHO, alongside local governments and health authorities.\u003c/p\u003e\n\u003cp\u003eHealth inequality analysis revealed that, in 1990, disease burden indicators were highly positively correlated with economic development levels. At that time, high-income countries bore the heaviest burdens. However, by 2021, this correlation had significantly weakened and even became \u0026quot;not significantly unbalanced.\u0026rdquo; This shift is not coincidental; it is the result of a series of global \u0026quot;push\u0026quot; and \u0026quot;pull\u0026quot; forces, where \u0026quot;push\u0026quot; forces exacerbate disparities and \u0026quot;pull\u0026quot; forces work to reduce them. The success of high\u0026nbsp;SDI countries in controlling disease burden represents a \u0026quot;pull\u0026quot; force that narrows the gap; specifically, high-income countries have effectively managed risk factors and improved disease outcomes through multiple approaches, thereby reducing disparities with low-income countries. Since the late 20th century, high-income countries have recognized hypertension, high cholesterol\u0026nbsp;levels, smoking, and obesity as significant contributors to the onset of cardiovascular diseases. Public health campaigns aimed at reducing salt intake, banning trans fats,\u0026nbsp;providing warnings about saturated fats, controlling tobacco use, and promoting physical exercise have been implemented for decades,\u0026nbsp;gradually\u0026nbsp;yielding visible\u0026nbsp;effects(27). High-income countries have established comprehensive secondary prevention protocols for ischemic stroke and complete emergency response systems. They also use advanced techniques such as intravenous thrombolysis and arterial thrombectomy, which reduce mortality and disability rates(28). Even when a stroke occurs, survival rates and prognoses\u0026nbsp;significantly improve. In low SDI countries, there is a \u0026quot;risk growth,\u0026quot; which acts as a \u0026quot;push\u0026quot; force, exacerbating the gap. In low-\u0026nbsp;to middle-income countries, nutritional and epidemiological transitions\u0026nbsp;have occurred. These shifts mirror the misguided trajectories observed in affluent nations, particularly the \u0026quot;Westernization\u0026quot; of dietary patterns. This phenomenon encompasses readily available ultra-processed foods with high sugar, fat, and salt, alongside sugary drinks, which align with the increasingly rapid lifestyle. With economic development, physical activity has decreased in low-income countries,\u0026nbsp;which has resulted in a significant decline in energy expenditure. Consequently, these lifestyle\u0026nbsp;changes increase\u0026nbsp;obesity and ischemic stroke risk(29-31). Additionally, the constraints of healthcare system resources, inadequate access to diagnostic and therapeutic services, and a shortage of qualified professionals specializing in stroke prevention and management are insufficient to address the rising disease burden. Low-income regions require support and assistance in medical technology and the opportunity to learn from the experiences of high-income countries with high obesity rates. This analysis is essential for formulating effective intervention strategies\u0026nbsp;to promote healthier diets and lifestyle habits.\u003c/p\u003e\n\u003cp id=\"_Toc27366\"\u003e\u003cstrong\u003ec. The Rising Burden in the Young Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAPC analyses revealed several significant findings. First, the disease burden among individuals aged 20\u0026ndash;34 years increased over the past 31 years, suggesting that ischemic stroke caused by high BMI occurs at\u0026nbsp;a younger age. In addition to high BMI, this age group may face other modern lifestyle risks such as extreme lack of exercise, late nights, and insufficient sleep(32), causing metabolic disorders, high stress levels, and excessive consumption of sugary energy drinks. This population plays a significant role in families and society, and an increase in the disease burden inevitably creates substantial family and social challenges. This trend necessitates corresponding shifts in healthcare policies and increased attention from governments and health insurance organizations. The highest mortality rate occurred between 1995 and 2005. An analysis of social development during this time indicated a mismatch between the accumulation of disease risk and the adequacy of healthcare services. By the 1990s, the dietary changes and lifestyle alterations brought about by postwar economic prosperity led to a significant increase in populations affected by obesity and metabolic syndrome, reaching a critical risk point. At the time, healthcare resources and treatment measures were limited. The observed reduction in mortality rates post-2005 can be primarily linked to strides in medical technology and enhancements in emergency response systems, including the implementation of streamlined protocols for stroke intervention, advocacy of thrombolytic and thrombectomy techniques, and the prevalent application of statins and antihypertensive treatment(33). The disease burden among individuals born after 1980 significantly increased with later birth years in their lifetime. Those born after 1980 have been exposed to increasingly prevalent unhealthy diets characterized by high-sugar and high-fat processed foods and sugary beverages, as well as environments marked by the proliferation of electronic products, reduced physical activity, and increased sedentary behavior since childhood or adolescence(34). They were exposed to risk factors\u0026nbsp;for obesity for longer durations and at higher intensities. This suggests that in the absence of efficient intervention strategies, the\u0026nbsp;age at onset of ischemic stroke is likely to decline in the forthcoming decades,\u0026nbsp;and middle-aged populations will become a key focus for stroke prevention and treatment\u0026nbsp;in the future.\u003c/p\u003e\n\u003cp\u003eFrontier analysis indicated that countries with\u0026nbsp;an\u0026nbsp;SDI\u0026nbsp;\u0026lt;0.5, including Ethiopia, Somalia, South Sudan, Timor-Leste, and Nepal, have the lowest disease burden caused by their economic development levels. This is primarily due to the lower proportion of high-BMI populations and younger population demographics. Such\u0026nbsp;a low disease burden\u0026nbsp;under low-risk exposure conditions is a consequence of socioeconomic underdevelopment. This demonstrates the impact of high BMI on ischemic stroke from another perspective. We also found that high SDI regions, such as Lithuania, the United States, Monaco, Germany, and Finland, have a heavier disease burden compared with what would be expected based on their economic development levels. This is attributed to the prevalence of highly processed foods; a diet rich in meat and dairy products, red meat, processed meat products, butter, and dairy products(35), along with insufficient physical activity and a sedentary lifestyle. Additionally, cold climates in countries such as Lithuania and Finland significantly limit outdoor physical activity. Currently, the overall disease burden in\u0026nbsp;the high-SDI regions is decreasing. However, the elimination of historically accumulated cases in certain countries urgently requires more efficient optimization, health policy reform, and implementation systems.\u003c/p\u003e\n\u003cp id=\"_Toc25890\"\u003e\u003cstrong\u003ed. Prediction of Future Disease Burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research analysis revealed that\u0026nbsp;without intervention, the\u0026nbsp;ischemic stroke mortality rate caused by a high BMI will increase over the next 15 years. There was a significant increase in ASMR among men across various age groups. Men usually neglect\u0026nbsp;their health awareness and effective weight management. Additionally, they may face specific occupational pressures and social environments that contribute to irregular diets, excessive alcohol consumption, and insufficient sleep. Smoking and drinking rates are typically higher among men than women(36), and these risk factors, combined with\u0026nbsp;a high BMI, significantly increase stroke risk. This\u0026nbsp;pattern suggests that global healthcare policies should focus on implementing targeted and effective measures for weight control and lifestyle guidance aimed at reducing the risk of ischemic stroke and alleviating the socioeconomic burden\u0026nbsp;in men.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. Limitations of the research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has certain limitations. First, the GBD 2021 data offer an extensive overview; however, they may fall short in capturing epidemiological figures for certain low-income nations, which could result in an underappreciation of the ischemic stroke burden caused by elevated BMI in these areas. Second, the categorization of high BMI was not delineated into subdivisions, such as overweight and various classes of obesity; thus, further investigation is required to elucidate the effects of varying levels of high BMI on the ischemic stroke burden. Finally, the GBD 2021 database lacks detailed classifications of the various ischemic stroke subtypes. This absence may hinder the clarity of burden patterns and the elevated BMI impact across these distinct subtypes. Additionally, this study was based on the most comprehensive GBD 2021 data currently available. Notably, the GBD database is constantly being updated and revised (e.g., GBD 2023 data are expected to be released in the near future). While forthcoming data may adjust the absolute values, the fundamental trends identified, such as the impact pattern associated with age, period, and cohort effects, the inverted U-shaped correlation between disease burden and SDI, and the notable rise in risk among younger populations, are expected to remain consistent.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eBetween 1990 and 2021, deaths and DALYs from ischemic stroke caused by high BMI increased substantially. In terms of economic development, the disease burden has shifted, with ASMR and ASDR rising in low- and middle-income areas. From the perspective of population characteristics, the disease burden among young people has been increasing annually; however, 65-year-old women bear a heavier disease burden than men do. The results obtained offer critical and timely insights into the ongoing global efforts in stroke prevention and management. Future investigations should further validate and enhance the predictive outcomes of this study using updated data.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal burden of disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-period-cohort\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDALY\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease-adjusted life years\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-standardized rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSocio-demographic index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman-development index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-standardized mortality rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-standardized disability adjusted life year rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstimated annual percentage changes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUncertainty intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSlope Index of Inequality\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study used anonymized, aggregated data from the GBD database, which is publicly available and does not require additional ethical approval; therefore, it does not involve any ethical issues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll contributors have granted their approval for the dissemination of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll data used in this study are publicly available from the GBD 2021, accessible via the IHME GBD Results Tool. https://vizhub.healthdata.org/gbd-results. Use of these data is subject to the GBD Data Use Agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eLiaoning Province Science and Technology Project: An Intelligent Medical Technology-Driven Cross-Theoretical Model: Study on the Effect of Collaborative Traditional Chinese and Western Medical Technologies in Early Detection and Intervention for Cognitive Frailty\u0026zwnj;\u0026nbsp;in the Elderly. Project Number: 2025-MS-330.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eF.P. performed analysis and visualization and prepared the first draft of the manuscript. M.B. contributed to data curation and analysis and assisted in drafting the manuscript. Y.L. managed project administration, provided resources, and contributed to drafting the manuscript. S.M. contributed to data curation, participated in drafting, and performed proofreading. B.L. supervised the research project, oversaw the publication process, and served as the corresponding author responsible for communication. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors wish to express their gratitude to the Institute for Health Metrics and Evaluation (IHME) and its financial supporters, the Bill \u0026amp; Melinda Gates Foundation, for rendering the data from the GBD accessible to the public.We would like to thank Editage ( www.editage.cn ) for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSu PW, Zhai Z, Wang T, Zhang YN, Wang Y, Ma K, et al. Research progress on astrocyte autophagy in ischemic stroke. 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BMC Public Health. 2019;19(1):1466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-019-7826-6\u003c/span\u003e\u003cspan address=\"10.1186/s12889-019-7826-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ischemic stroke, High body mass index, Global Burden of Disease, Age-Period-Cohort Analysis, Health Inequality, Prediction","lastPublishedDoi":"10.21203/rs.3.rs-7557435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7557435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eIn this study, we used Global Burden of Disease (GBD) data to analyze the worldwide burden of ischemic stroke caused by high body mass index (BMI). The objective was to describe the current distribution of the disease burden and predict future trends to support public health organizations and governments of various countries in formulating targeted healthcare policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe analyzed crucial disease burden indicators for ischemic stroke caused by high BMI across five Socio-demographic Index (SDI) regions, 21 GBD regions, and 204 countries. These indicators were compared and visualized by age, sex, and SDI. We performed inequality and frontier analyses of disease burden across different SDI regions and used age-period-cohort (APC) analysis to explore the drivers of the disease burden. The Bayesian APC method was applied to predict future trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Between 1990 and 2021, the number of ischemic stroke deaths and disability-adjusted life years (DALYs) because of high BMI nearly doubled, increasing by 95.7% and 108.8%, respectively. Conversely, the age-standardized mortality rate (ASMR) and age-standardized disability adjusted life year rate(ASDR) declined, with an estimated annual percentage change of −1.103 and −0.583, respectively. Countries with an SDI of approximately 0.7 experienced the greatest burden. Health inequality analysis revealed that the previously observed higher disease burden in high-income areas decreased by 2021. APC analysis revealed a significant increase in risk for birth cohorts after 1980, particularly in individuals aged 20–39. Projections suggest a rise in global ASMR to 2.252 per 100,000 by 2036, with a more pronounced increase among males.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eBetween 1990 and 2021, deaths and DALYs from ischemic stroke caused by high BMI increased substantially. In terms of economic development, the disease burden has shifted, with ASMR and ASDR rising in low- and middle-income areas. From the perspective of population characteristics, the disease burden among young people has been increasing annually; however, 65-year-old women bear a heavier disease burden than men do. These findings are a crucial warning to public health management departments worldwide, highlighting the need for targeted policies to address the growing impact of high BMI on ischemic stroke.\u003c/p\u003e","manuscriptTitle":"Global Ischemic Stroke Burden Due to High BMI, 1990–2021: An Age-Period-Cohort Study and Future Risk Prediction Using GBD Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 18:59:29","doi":"10.21203/rs.3.rs-7557435/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e9c7b9a-f298-481e-9d5a-3464b8ab9e29","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T15:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 18:59:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7557435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7557435","identity":"rs-7557435","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}