Global, Regional, and National Burden of Ischemic Heart Disease Due to High LDL Cholesterol, Systolic Blood Pressure, and Fasting Glucose in Young Adults (1990–2021)

Global, Regional, and National Burden of Ischemic Heart Disease Due to High LDL Cholesterol, Systolic Blood Pressure, and Fasting Glucose in Young Adults (1990–2021) | 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, Regional, and National Burden of Ischemic Heart Disease Due to High LDL Cholesterol, Systolic Blood Pressure, and Fasting Glucose in Young Adults (1990–2021) Tiecheng Liu, Dawei Fu, Fujiang Cui, Congcong Cheng, Xiaochen Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6862760/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Young adults aged 25–39 are at high risk for early-onset ischemic heart disease (IHD), with a steadily rising prevalence of metabolic risk factors, including high low-density lipoprotein cholesterol (LDL-C), high systolic blood pressure(SBP), and high fasting plasma glucose (FPG). This study aims to assess the global, regional, and national burden of IHD attributable to these factors among young adults from 1990 to 2021. Methods Using data from the Global Burden of Disease Study 2021 (GBD 2021), we analyzed age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate(ASDR) due to IHD attributable to high LDL-C, high SBP, and high FPG among young adults. Pearson correlation and log-linear regression were used to examine trends and associations with the Sociodemographic Index (SDI). Results From 1990 to 2021, the global burden of IHD attributable to high LDL-C, high SBP, and high FPG increased in absolute terms, but age-standardized rates varied. LDL-C-related burden decreased, SBP remained stable, and FPG-related burden significantly increased. Men had higher burdens, with low-middle SDI regions experiencing the highest burden and high-SDI regions the lowest. Disease burden was negatively associated with SDI. Oceania had the highest burden, while high-income Asia-Pacific and Western Europe had the lowest. Central Europe saw the greatest reduction in LDL-C burden, Western Europe in SBP burden, and East Asia in high glucose-related burden. Nauru and the Marshall Islands had the highest burden, while Sweden had the lowest. India, China, and Pakistan, with large populations, contributed significantly to global deaths and disability-adjusted life years(DALYs). Conclusions Between 1990 and 2021, the global burden of IHD attributable to high LDL-C, high SBP, and high FPG among individuals aged 25–39 showed significant variation across time, genders, regions, and countries. While progress has been made in managing some metabolic risk factors, the overall trend remains concerning, highlighting the urgent need for enhanced, multi-level, targeted interventions. Ischemic Heart Disease Global burden of disease LDL-C SBP FPG Aged 25–39 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Ischemic Heart Disease (IHD), also known as coronary artery disease (CAD), occurs due to insufficient coronary blood supply, leading to myocardial ischemia, hypoxia, and even necrosis. Its clinical manifestations include stable angina, acute coronary syndrome, and chronic ischemic cardiomyopathy. IHD is one of the leading causes of death globally and represents a major disease burden, with significant regional and income-related disparities. Hypertension, hypercholesterolemia, and hyperglycemia are traditional risk factors for IHD. Chronic hypertension damages the vascular endothelium, promotes lipid deposition and plaque formation, increases cardiac afterload, accelerates left ventricular hypertrophy, and increases myocardial oxygen consumption, thereby significantly raising the risk of IHD. Studies have shown that for every 10 mmHg reduction in systolic blood pressure, the overall risk of cardiovascular events can be reduced by about 20%, with coronary heart disease risk decreasing by approximately 17%( 1 ). The key mechanism behind hypercholesterolemia is elevated low-density lipoprotein cholesterol (LDL-C). When LDL-C infiltrates the vascular endothelium and becomes oxidized, it triggers an inflammatory response, forming foam cells and atherosclerotic plaques. Meta-analyses have shown that reducing LDL-C, regardless of baseline levels, significantly lowers the risk of cardiovascular events( 2 ). Diabetes is an important risk factor for IHD, with individuals with type 2 diabetes having 2 to 3 times the risk of developing IHD compared to those without diabetes( 3 ). Individuals aged 25–39 years are relatively mature in terms of physical and mental development, social responsibility, and economic ability, making them crucial pillars of society. However, this age group is also at high risk for early-onset cardiovascular diseases. In recent years, the risk of atherosclerotic cardiovascular disease (ASCVD) among young adults has been steadily increasing( 4 ). At the same time, they face a growing burden of cardiovascular risk factors, including elevated low-density lipoprotein cholesterol (LDL-C), hypertension, obesity, and insulin resistance( 5 , 6 ). Among young adults who have not received antihypertensive treatment, those with systolic blood pressure (SBP) ≥ 130 mmHg have a significantly higher risk of cardiovascular events compared to those with SBP < 120 mmHg( 7 ). Additionally, elevated LDL-C levels in young individuals increase the risk of developing ASCVD in the future( 8 – 10 ). Young adults are a key group for cardiovascular disease prevention and control, yet the growing prevalence of risk factors in this population poses a serious threat to the global disease burden. Therefore, it is urgent to strengthen early screening, optimize intervention strategies, and enhance policy support to curb the rise in IHD incidence and complications, and alleviate the health and economic burden of early-onset cardiovascular events. This study aims to systematically assess the global, regional, and national burden of IHD attributable to high LDL-C, high systolic blood pressure, and high fasting plasma glucose among young adults (aged 25–39) from 1990 to 2021. 2. Methods This study utilized data from the Global Burden of Disease (GBD) 2021 dataset to assess the burden of ischemic heart disease (IHD) attributable to high fasting plasma glucose (FPG), high low-density lipoprotein cholesterol (LDL-C), and high systolic blood pressure (SBP) among individuals aged 25–39 years across 204 countries and territories from 1990 to 2021. All data were obtained from the Global Health Data Exchange (GHDx) platform ( https://vizhub.healthdata.org/gbd-results/ ). We extracted age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) attributable to the three metabolic risk factors to quantify the IHD burden. Countries and territories were classified into five categories (low, low-middle, middle, high-middle, and high) based on the Socio-demographic Index (SDI) to examine the relationship between disease burden and socioeconomic development. Age-standardized rates (ASRs) were calculated using the World Health Organization’s standard population (WHO 2000–2025) ( 11 )with the following formula: Age Standardized Rate= \(\:\frac{{\sum\:}_{i=1}^{A}{a}_{i}{w}_{i}}{{\sum\:}_{i=1}^{A}{w}_{i}}\times\:\text{100,000}\) where \(\:{a}_{i}\) is the age-specific rate in group i, \(\:{w}_{i}\) is the corresponding weight from the standard population, and A is the total number of age groups. To assess temporal trends, estimated annual percentage change (EAPC) was calculated using a log-linear regression model( 12 ): EAPC = 100×( e β −1) where β is the regression coefficient for the year in the natural logarithmic model. An EAPC with a 95% confidence interval (CI) entirely above 0 was considered an increasing trend; entirely below 0 indicated a decreasing trend; and a CI that included 0 indicated a stable trend. Pearson correlation analysis was used to assess the relationship between SDI and disease burden. A two-sided P-value of < 0.05 was considered statistically significant. 3. Results 3.1 Global Burden of IHD 3.1.1 Global Burden of Ischemic Heart Disease Attributable to High LDL Cholesterol Among Adults Aged 25–39 Years, 1990–2021 In 2021, among individuals aged 25–39 years, high LDL cholesterol contributed to an estimated 101,126 deaths globally (95% CI: 76,854–124,749) and 5,736,646 DALYs (95% CI: 4,349,511–7,061,996). In comparison, the corresponding figures in 1990 were 77,995 deaths (95% CI: 59,855–94,899) and 4,416,391 DALYs (95% CI: 3,385,524–5,383,908). Although the absolute burden increased over time, the age-standardized rates declined. The age-standardized mortality rate (ASMR) decreased from 6.78 per 100,000 population in 1990 (95% CI: 5.21–8.26) to 5.71 in 2021 (95% CI: 4.33–7.02), with an estimated annual percentage change (EAPC) of -0.62 (95% CI: -0.69 to -0.55). Similarly, the age-standardized DALY rate (ASDR) fell from 382.62 (95% CI: 293.58–466.75) to 324.26 (95% CI: 245.54–398.46), with an EAPC of -0.60 (95% CI: -0.67 to -0.52). 3.1.2 Global Burden of Ischemic Heart Disease Attributable to High Systolic Blood Pressure Among Adults Aged 25–39 Years, 1990–2021 In 2021, the global number of ischemic heart disease (IHD) deaths attributable to high systolic blood pressure among individuals aged 25–39 years was 56,129 (95% CI: 39,437–73,387), with a corresponding DALY count of 3,166,599(95% CI: 2,220,211–4,145,104).In comparison to 1990 (with 36,798 deaths [95% CI: 24,964–48,326] and DALYs of 2,070,608 [95% CI: 1,400,809–2,723,230]), the absolute burden of IHD increased significantly in this population. However, after adjusting for age and time structure, the overall burden remained stable. The age-standardized mortality rate (ASMR) decreased from 3.21 per 100,000 in 1990 (95% CI: 2.17–4.26) to 3.17 in 2021 (95% CI: 2.19–4.17), with an estimated annual percentage change (EAPC) of -0.08 (95% CI: -0.18 to 0.02).Similarly, the age-standardized DALY rate (ASDR) decreased from 180.08 (95% CI: 121.46–239.09) in 1990 to 178.77 (95% CI: 123.60–235.87) in 2021, with an EAPC of -0.05 (95% CI: -0.15 to 0.05). 3.1.3 Global Burden of Ischemic Heart Disease Attributable to High Fasting Plasma Glucose Among Adults Aged 25–39 Years, 1990–2021 In 2021, the global number of ischemic heart disease (IHD) deaths attributable to high fasting plasma glucose among individuals aged 25–39 years was 6,724 (95% CI: 5,356–8,016), with a DALY count of 375,435 (95% CI: 299,758–447,882).In comparison, the figures in 1990 were 3,222 deaths (95% CI: 2,542–3,984) and 179,710 DALYs (95% CI: 141,821–222,623), indicating a marked increase in the absolute burden. After adjusting for age and temporal factors, the burden continued to show an upward trend. The age-standardized mortality rate (ASMR) rose from 0.28 per 100,000 in 1990 (95% CI: 0.22–0.36) to 0.38 in 2021 (95% CI: 0.29–0.47). Similarly, the age-standardized DALY rate (ASDR) increased from 15.72 (95% CI: 12.12–19.78) to 21.14 (95% CI: 16.38–26.29).From 1990 to 2021, the estimated annual percentage change (EAPC) was 1.02 (95% CI: 0.96–1.09) for ASMR and 1.03 (95% CI: 0.97–1.10) for ASDR. Detailed results are provided in Tables 1 . Table 1 Global Burden of Ischemic Heart Disease Attributable to The Three Metabolic Risk Factors Among Adults Aged 25–39 Years, 1990–2021 Risk Factors 1990 2021 ASR (per 100000) 1990 2021 EAPC High LDL cholesterol Deaths (95% CI) 77995 (59855,94899) 101126 (76854,124749) ASMR (95% CI) 6.78 (5.21,8.26) 5.71 (4.33,7.02) -0.62 (-0.69,-0.55) DALYs (95% CI) 4416391 (3385524,5383908) 5736646 (4349511,7061996) ASDR (95% CI) 382.62 (293.58,466.75) 324.26 (245.54,398.46) -0.6 (-0.67,-0.52) High systolic blood pressure Deaths (95% CI) 36798 (24964,48326) 56129 (39437,73387) ASMR (95% CI) 3.21 (2.17,4.26) 3.17 (2.19,4.17) -0.08 (-0.18,0.02) DALYs (95% CI) 2070608 (1400809,2723230) 3166599 (2220211,4145104) ASDR (95% CI) 180.08 (121.46,239.09) 178.77 (123.60,235.87) -0.05 (-0.15,0.05) High Fasting plasma glucose Deaths (95% CI) 3222 (2542,3984) 6724 (5356,8016) ASMR (95% CI) 0.28 (0.22,0.36) 0.38 (0.29,0.47) 1.02 (0.96,1.09) DALYs (95% CI) 179710 (141821,222623) 375435 (299758,447882) ASDR (95% CI) 15.72 (12.12,19.78) 21.14 (16.38,26.29) 1.03 (0.97,1.1) ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years rate; EAPC, estimated annual percentage change; DALYs, disability-adjusted life years; ASR, Age-standardized Rate. 3.2 Sex Differences 3.2.1 Sex Differences in the Global Burden of Ischemic Heart Disease Attributable to High LDL Cholesterol Among Individuals Aged 25–39 Years, 1990–2021 In 2021, the global burden of ischemic heart disease (IHD) attributable to high low-density lipoprotein cholesterol (LDL-C) among individuals aged 25–39 years was significantly higher in males than in females. The number of deaths was 72,475 (95% CI: 55,264–89,924) in males and 28,651 (95% CI: 21,865–35,300) in females. Corresponding DALYs were 4,097,221 (95% CI: 3,119,757–5,080,221) for males and 1,639,425 (95% CI: 1,250,286–2,014,632) for females. After adjusting for age and time, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) per 100,000 population were 8.10 (95% CI: 6.17–10.02) and 458.29 (95% CI: 349.19–566.26) for males, and 3.27 (95% CI: 2.49–4.05) and 187.49 (95% CI: 142.58–231.32) for females. From 1990 to 2021, the global burden of IHD attributable to high LDL-C in this age group declined for both sexes, with a more pronounced decrease observed in females. The estimated annual percentage change (EAPC) in ASMR and ASDR was − 1.02 (95% CI: −1.09 to − 0.95) and − 1.01 (95% CI: −1.08 to − 0.94) for females, and − 0.43 (95% CI: −0.51 to − 0.35) and − 0.40 (95% CI: −0.49 to − 0.32) for males. 3.2.2 Sex Differences in the Global Burden of Ischemic Heart Disease Attributable to High Systolic Blood Pressure Among Individuals Aged 25–39 Years, 1990–2021 In 2021, the global burden of ischemic heart disease (IHD) attributable to high systolic blood pressure was significantly higher in males than in females among individuals aged 25–39 years. The number of deaths was 43,803 (95% CI: 31,280–56,807) in males and 12,327 (95% CI: 7,972–16,479) in females. Corresponding DALYs were 2,468,611 (95% CI: 1,765,188–3,197,373) for males and 697,988 (95% CI: 450,715–934,661) for females. After adjusting for age and time, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) per 100,000 population were 4.89 (95% CI: 3.39–6.45) and 275.91 (95% CI: 191.06–364.08) for males, and 1.41 (95% CI: 0.90–1.93) and 79.64 (95% CI: 50.56–109.50) for females. From 1990 to 2021, the global burden of IHD attributable to high systolic blood pressure remained stable in males but declined in females. The estimated annual percentage change (EAPC) in ASMR and ASDR for females was − 0.52 (95% CI: −0.59 to − 0.44) and − 0.51 (95% CI: −0.58 to − 0.44), respectively; for males, it was 0.07 (95% CI: −0.05 to 0.19) and 0.10 (95% CI: −0.02 to 0.22). 3.2.3 Sex Differences in the Global Burden of Ischemic Heart Disease Attributable to High Fasting Plasma Glucose Among Individuals Aged 25–39 Years, 1990–2021 In 2021, the global burden of ischemic heart disease (IHD) attributable to high fasting plasma glucose (FPG) among individuals aged 25–39 years was significantly higher in males than in females. The number of deaths was 4,945 (95% CI: 3,892–6,076) in males and 1,779 (95% CI: 1,354–2,280) in females; corresponding DALYs were 275,412 (95% CI: 216,542–338,051) for males and 100,024 (95% CI: 76,286–127,813) for females. After adjusting for age and time, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) per 100,000 population were 0.55 (95% CI: 0.42–0.70) and 30.70 (95% CI: 23.48–39.19) for males, and 0.20 (95% CI: 0.15–0.26) and 11.39 (95% CI: 8.45–14.73) for females. From 1990 to 2021, the IHD burden attributable to high FPG increased in both sexes, with a more pronounced rise in males. The estimated annual percentage change (EAPC) in ASMR and ASDR was 0.68 (95% CI: 0.63–0.74) and 0.69 (95% CI: 0.64–0.74) for females, and 1.16 (95% CI: 1.08–1.25) and 1.18 (95% CI: 1.09–1.27) for males. Detailed results are provided in Tables 2 Table 2 Sex Differences in the Global Burden of Ischemic Heart Disease Attributable to The Three Metabolic Risk Factors Among Adults Aged 25–39 Years, 1990–2021 1990 2021 ASR (per 100000) 1990 2021 EAPC High LDL cholesterol Female Deaths (95% CI) 25132 (18652,31165) 28651 (21865,35300) ASMR (95% CI) 4.4 (3.27,5.50) 3.27 (2.49,4.05) -1.02 (-1.09,-0.95) DALYs (95% CI) 1437424 (1064827,1784135) 1639425(1250286,2014632) ASDR (95% CI) 250.82(186.13,313.59) 187.49(142.58,231.32) -1.01 (-1.08,-0.94) Male Deaths (95% CI) 52864 (40455,64539) 72475 (55264,89924) ASMR (95% CI) 9.09 (6.95,11.10) 8.10 (6.17,10.02) -0.43 (-0.51,-0.35) DALYs (95% CI) 2978966(2281286,3641197) 4097221(3119757,5080221) ASDR (95% CI) 510.87(390.44,624.31) 458.29(349.19,566.26) -0.4 (-0.49,-0.32) High Systolic blood pressure Female Deaths (95% CI) 9167 (5828,13127) 12327 (7972,16479) ASMR (95% CI) 1.61 (0.99,2.36) 1.41 (0.90,1.93) -0.52 (-0.59,-0.44) DALYs (95% CI) 519505 (329747,740181) 697988 (450715,934661) ASDR (95% CI) 91.23 (55.70,133.49) 79.64 (50.56,109.50) -0.51 (-0.58,-0.44) Male Deaths (95% CI) 27631 (18770,36024) 43802 (31280,56807) ASMR (95% CI) 4.76 (3.18,6.35) 4.89 (3.39,6.45) 0.07 (-0.05,0.19) DALYs (95% CI) 1551103 (1056646,2026643) 2468611 (1765187,3197373) ASDR (95% CI) 266.58 (177.79,355.25) 275.91 (191.06,364.08) 0.1 (-0.02,0.22) High Fasting plasma glucose Female Deaths (95% CI) 935 (706,1166) 1779 (1354,2280) ASMR (95% CI) 0.17 (0.12,0.22) 0.20 (0.15,0.26) 0.68 (0.63,0.74) DALYs (95% CI) 52600 (39687,65764) 100024 (76286,127813) ASDR (95% CI) 9.29 (6.80,12.220) 11.39 (8.45,14.73) 0.69 (0.64,0.74) Male Deaths (95% CI) 2287 (1813,2872) 4945 (3892,6076) ASMR (95% CI) 0.40 (0.30,0.51) 0.55 (0.42,0.70) 1.16 (1.08,1.25) DALYs (95% CI) 127110 (100855,159364) 275412 (216542,338051) ASDR (95% CI) 21.97 (16.65,28.43) 30.70 (23.48,39.19) 1.18 (1.09,1.27) ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change; DALYs, disability-adjusted life years; ASR, Age-standardized Rate. 3.3 SDI Regional Differences 3.3.1 Differences in the Burden of IHD Associated with High LDL Cholesterol Among Individuals Aged 25–39 Years Across Different SDI Regions in 2021 In 2021, among individuals aged 25–39 years, the burden of ischemic heart disease (IHD) attributable to high low-density lipoprotein cholesterol (LDL-C) was highest in low-middle SDI regions, with an ASMR of 7.97 per 100,000 (95% CI: 5.90–9.93) and an ASDR of 450.71 per 100,000 (95% CI: 334.16–562.45). In contrast, high SDI regions reported the lowest burden, with an ASMR of 2.41 per 100,000 (95% CI: 1.86–3.02) and an ASDR of 137.91 per 100,000 (95% CI: 106.58–172.82).From 1990 to 2021, the IHD burden attributable to high LDL-C declined in four of the five SDI regions, except for low SDI regions, which remained stable. The most significant decline was observed in high-middle SDI regions, with EAPCs of -1.91 (95% CI: -2.14 to -1.68) for ASMR and − 1.85 (95% CI: -2.08 to -1.63) for ASDR. Pearson correlation analysis revealed a significant negative association between SDI and both ASMR and ASDR of LDL-C-related IHD, indicating that higher SDI levels were associated with lower disease burden (ASMR: R = − 0.5562, p < 0.001; ASDR: R = − 0.5623, p < 0.001). 3.3.2 Differences in the Burden of IHD Associated with High Systolic Blood Pressure Among Individuals Aged 25–39 Years Across Different SDI Regions in 2021 In 2021, the global burden of ischemic heart disease (IHD) attributable to high systolic blood pressure (SBP) in the 25–39 age group was most severe in low-middle SDI regions, with ASMR and ASDR of 4.73 (95% CI, 3.20, 6.23) and 266.41 (95% CI, 179.69, 351.62) per 100,000, respectively. Conversely, the burden was lightest in high SDI regions, with ASMR and ASDR of 1.10 (95% CI, 0.69, 1.52) and 62.63 (95% CI, 38.91, 86.18) per 100,000. From 1990 to 2021, the disease burden decreased in high and high-middle SDI regions. The EAPC for ASMR and ASDR in high SDI regions was − 1.86 (95% CI, -2.12, -1.60) and − 1.80 (95% CI, -2.06, -1.54), respectively. In high-middle SDI regions, the EAPC for ASMR and ASDR was − 1.44 (95% CI, -1.73, -1.15) and − 1.38 (95% CI, -1.67, -1.09), respectively. In contrast, disease burden increased in middle SDI, low-middle SDI, and low SDI regions. The EAPC for ASMR and ASDR in middle SDI regions was 0.23 (95% CI, 0.12, 0.34) and 0.25 (95% CI, 0.14, 0.36), respectively; in low-middle SDI regions, the EAPC was 0.48 (95% CI, 0.35, 0.61) and 0.47 (95% CI, 0.35, 0.60); and in low SDI regions, the EAPC was 0.20 (95% CI, 0.03, 0.38) and 0.20 (95% CI, 0.03, 0.37).Pearson correlation analysis showed a negative correlation between the IHD burden related to high SBP (ASMR and ASDR) and SDI in the 25–39 age group. As SDI increased, the IHD burden decreased, with ASMR (R = -0.4326, p < 0.001) and ASDR (R = -0.4369, p < 0.001). 3.3.3 Differences in the Burden of IHD Associated with High Fasting Plasma Glucose Among Individuals Aged 25–39 Years Across Different SDI Regions in 2021 In 2021, the burden of ischemic heart disease (IHD) attributable to high fasting plasma glucose in adults aged 25–39 was highest in low-middle SDI regions, with ASMR and ASDR of 0.54 (95% CI, 0.41, 0.70) and 29.92 (95% CI, 22.64, 38.88) per 100,000, respectively. In contrast, the burden was lowest in high SDI regions, with ASMR and ASDR of 0.22 (95% CI, 0.16, 0.30) and 12.38 (95% CI, 8.86, 17.02) per 100,000.Between 1990 and 2021, the burden increased in all SDI regions except for high-middle SDI regions, where the trend remained relatively stable. The most notable increase occurred in low-middle SDI regions, with EAPCs for ASMR and ASDR both at 1.24 (95% CI, ASMR: 1.13, 1.36; ASDR: 1.12, 1.35).Pearson correlation analysis showed a significant negative correlation between IHD burden (both ASMR and ASDR) attributable to high fasting plasma glucose and SDI across this age group. Specifically, as SDI increased, the disease burden decreased: ASMR (R = − 0.6461, p < 0.001) and ASDR (R = − 0.6486, p < 0.001). Detailed results are provided in Tables 3 . Table 3 Differences in the Burden of Ischemic Heart Disease Attributable to The Three Metabolic Risk Factors Among Individuals Aged 25–39 Years Across Different SDI Regions, 1990–2021 ASDR(per 100000) ASMR(per 100000) Characteristics 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) High LDL cholesterol Low SDI 260.16 (181.89,347.80) 253.26 (181.87,330.09) -0.12 (-0.3,0.06) 4.61 (3.23,6.16) 4.47 (3.21,5.83) -0.11 (-0.29,0.06) Low-middle SDI 512.52(379.27,645.51) 450.71 (334.16,562.45) -0.31 (-0.41,-0.21) 9.06 (6.70,11.41) 7.97 (5.90,9.93) -0.3 (-0.4,-0.2) Middle SDI 399.64 (303.95,490.53) 363.16 (278.27,444.68) -0.25 (-0.3,-0.2) 7.06 (5.37,8.66) 6.38 (4.90,7.82) -0.27 (-0.32,-0.22) High-middle SDI 401.10 (316.92,481.59) 262.27 (205.25,318.37) -1.85 (-2.08,-1.63) 7.15(5.65,8.57) 4.62 (3.62,5.61) -1.91 (-2.14,-1.68) High SDI 240.97 (192.64,282.37) 137.91 (106.58,172.82) -1.62 (-1.77,-1.46) 4.27 (3.42,5.01) 2.41(1.86,3.02) -1.68 (-1.84,-1.52) High systolic blood pressure Low SDI 138.05 (84.71,196.47) 147.15 (96.95,196.15) 0.2 (0.03,0.37) 2.46 (1.51,3.49) 2.61 (1.72,3.47) 0.2 (0.03,0.38) Low-middle SDI 246.08 (160.40,335.18) 266.41 (179.69,351.62) 0.47 (0.35,0.6) 4.37(2.85,5.94) 4.73 (3.20,6.23) 0.48 (0.35,0.61) Middle SDI 179.47 (114.77,245.61) 187.90 (124.81,256.39) 0.25 (0.14,0.36) 3.19 (2.04,4.36) 3.32 (2.21,4.53) 0.23 (0.12,0.34) High-middle SDI 191.70(124.33,264.42) 147.99 (92.79,207.46) -1.38 (-1.67,-1.09) 3.44 (2.24,4.74) 2.62 (1.65,3.67) -1.44 (-1.73,-1.15) High SDI 113.87 (78.56,147.91) 62.63 (38.91,86.18) -1.8 (-2.06,-1.54) 2.03 (1.40,2.63) 1.10 (0.69,1.52) -1.86 (-2.12,-1.6) High fasting plasma glucose Low SDI 11.71 (8.49,15.74) 14.57 (11.03,18.56) 0.74 (0.58,0.9) 0.21 (0.15,0.28) 0.26 (0.20,0.33) 0.75 (0.59,0.91) Low-middle SDI 21.80 (16.13,29.05) 29.92 (22.64,38.88) 1.24 (1.12,1.35) 0.39 (0.29,0.52) 0.54 (0.41,0.70) 1.24 (1.13,1.36) Middle SDI 17.69 (13.30,22.99) 23.48 (17.71,29.85) 1.04 (0.98,1.09) 0.32 (0.24,0.41) 0.42 (0.32,0.53) 1.02 (0.97,1.08) High-middle SDI 14.00 (10.24,18.41) 15.70 (11.62,20.92) 0.06 (-0.1,0.22) 0.25 (0.18,0.33) 0.28 (0.21,0.37) 0.01 (-0.16,0.17) High SDI 9.69 (7.42,12.30) 12.38 (8.86,17.02) 1.08 (0.9,1.27) 0.17 (0.13,0.22) 0.22 (0.16,0.30) 1.04 (0.86,1.23) ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change; SDI, Sociodemographic Index. 3.4 Geographic Variation 3.4.1 Geographic Variation in the Burden of IHD Attributable to High LDL Cholesterol Among Adults Aged 25–39 in 2021 In 2021, among the 21 global regions, Oceania had the highest burden of ischemic heart disease (IHD) attributable to high low-density lipoprotein cholesterol (LDL-C) in adults aged 25–39, with an ASMR of 12.18 (95% CI, 8.06–17.65) and an ASDR of 681.85 (95% CI, 451.24–988.91) per 100,000. In contrast, the burden was lowest in the high-income Asia Pacific region, where ASMR and ASDR were 1.10 (95% CI, 0.86–1.33) and 64.28 (95% CI, 50.58–78.04), respectively. Among the 21 regions, six showed a relatively stable trend in IHD burden from 1990 to 2021, including Oceania, Central Latin America, Eastern and Western Sub-Saharan Africa, the Caribbean, and South Asia. The estimated annual percentage change (EAPC) in ASMR and ASDR for these regions were as follows: Oceania: 0.03 (95% CI, -0.04 to 0.10) and 0.04 (95% CI, -0.03 to 0.11); Central Latin America: 0.14 (95% CI, -0.32 to 0.60) and 0.16 (95% CI, -0.29 to 0.61); Eastern Sub-Saharan Africa: 0.05 (95% CI, -0.07 to 0.17) and 0.06 (95% CI, -0.06 to 0.18); Western Sub-Saharan Africa: 0.12 (95% CI, -0.03 to 0.27) and 0.15 (95% CI, 0.00 to 0.30); Caribbean: -0.34 (95% CI, -0.71 to 0.04) and − 0.33 (95% CI, -0.70 to 0.04); South Asia: -0.14 (95% CI, -0.35 to 0.08) for both ASMR and ASDR. The remaining 15 regions experienced a declining trend in IHD burden over time, with the most significant decrease observed in Central Europe, where ASMR and ASDR had EAPCs of -4.59 (95% CI, -4.82 to -4.35) and − 4.49 (95% CI, -4.72 to -4.25), respectively. Detailed results are provided in Tables 4 . 3.4.2 Geographic Variation in the Burden of IHD Attributable to High Systolic Blood Pressure Among Adults Aged 25–39 in 2021 In 2021, among the 21 global regions, Oceania had the highest burden of ischemic heart disease (IHD) attributable to high systolic blood pressure (SBP) in adults aged 25–39, with an ASMR of 5.49 (95% CI, 2.55–9.68) and an ASDR of 292.75 (95% CI, 134.72–518.81) per 100,000. In contrast, the burden was lowest in the high-income Asia Pacific region, where ASMR and ASDR were 0.46 (95% CI, 0.23–0.70) and 26.62 (95% CI, 13.38–40.16), respectively. Among the 21 regions, 12 showed a declining trend in IHD burden over time, including the high-income Asia Pacific, high-income North America, Western Europe, Australasia, Tropical Latin America, Southern Latin America, Central Europe, Eastern Europe, Central Asia, North Africa and the Middle East, and Sub-Saharan Africa regions. The most significant decrease was observed in Western Europe, where the EAPCs for ASMR and ASDR were − 4.31 (95% CI, -4.41 to -4.21) and − 4.21 (95% CI, -4.31 to -4.12), respectively. In contrast, 7 regions experienced an increase in IHD burden, with the most notable increase in Western Sub-Saharan Africa, where the EAPCs for ASMR and ASDR were 1.44 (95% CI, 1.21–1.67) and 1.47 (95% CI, 1.24–1.69), respectively. These regions included Andean Latin America, the Caribbean, Southeast Asia, East Asia, Oceania, Western Sub-Saharan Africa, and Eastern Sub-Saharan Africa. The IHD burden in South Asia and Central Latin America remained stable over time, with no significant change. Detailed results are provided in Tables 5 . 3.4.3 Geographic Variation in the Burden of IHD Attributable to High Fasting Plasma Glucose Among Adults Aged 25–39 in 2021 In 2021, Oceania had the highest burden of IHD attributable to high fasting plasma glucose among individuals aged 25–39, with an age-standardized mortality rate (ASMR) of 1.17 per 100,000 (95% CI: 0.83–1.64) and an age-standardized disability-adjusted life year rate (ASDR) of 64.27 (95% CI: 45.68–90.76). In contrast, Western Europe had the lowest burden, with an ASMR of 0.06 (95% CI: 0.04–0.07) and an ASDR of 3.16 (95% CI: 2.49–3.92) per 100,000.Over time, IHD burden decreased in nine regions: High-income Asia Pacific, Western Europe, Australasia, Andean Latin America, Tropical Latin America, Southern Latin America, Central Europe, Eastern Europe, and Central Asia. The most significant decline was observed in Central Europe, where the estimated annual percentage changes (EAPCs) for ASMR and ASDR were − 3.61 (95% CI: -3.89 to -3.34) and − 3.52 (95% CI: -3.80 to -3.25), respectively. Conversely, nine regions experienced an increasing burden, including High-income North America, Central Latin America, the Caribbean, North Africa and the Middle East, South Asia, East Asia, Oceania, and sub-Saharan Africa (West and East). East Asia showed the most rapid increase, with EAPCs for ASMR and ASDR at 1.37 (95% CI: 1.25 to 1.50) and 1.41 (95% CI: 1.29 to 1.53), respectively. Three regions—Southeast Asia, Central sub-Saharan Africa, and sub-Saharan Africa—remained stable over time, with no significant changes in disease burden observed. Detailed results are provided in Tables 6 . Table 4 Geographic Variation in the Burden of Ischemic Heart Disease Attributable to High LDL Cholesterol Among Adults Aged 25–39, 1990–2021 ASDR(per 100000) ASMR(per 100000) Location 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) East Asia 259.56 (192.90,327.45) 247.44 (180.88,322.18) -0.16 (-0.25,-0.07) 4.59 (3.42,5.80) 4.34 (3.16,5.67) -0.2 (-0.29,-0.1) Southeast Asia 486.67 (364.20,615.27) 441.27 (330.45,568.33) -0.32 (-0.4,-0.25) 8.59 (6.43,10.87) 7.79 (5.82,10.04) -0.32 (-0.39,-0.24) Oceania 674.37 (456.33,960.90) 681.85 (451.24,988.91) 0.04 (-0.03,0.11) 12.09 (8.19,17.20) 12.18 (8.06,17.65) 0.03 (-0.04,0.1) Central Asia 675.04(513.77,824.29) 405.39 (307.01,510.67) -2.6 (-3.08,-2.1) 12.07 (9.19,14.74) 7.17 (5.42,9.03) -2.62 (-3.11,-2.13) Central Europe 594.05 (479.38,691.86) 162.18 (129.22,193.65) -4.49 (-4.72,-4.25) 10.65 (8.59,12.39) 2.83 (2.25,3.37) -4.59 (-4.82,-4.35) Eastern Europe 638.78 (518.97,753.09) 434.75 (344.61,530.18) -2.29 (-3.05,-1.53) 11.52 (9.36,13.58) 7.78 (6.17,9.50) -2.32 (-3.07,-1.56) High-income Asia Pacific 133.82 (102.54,165.32) 64.28 (50.58,78.04) -2.67 (-2.92,-2.41) 2.33 (1.79,2.89) 1.10 (0.86,1.33) -2.74 (-3,-2.47) Australasia 193.62 (153.94,233.19) 73.87 (57.66,91.18) -3.23 (-3.49,-2.97) 3.42 (2.71,4.12) 1.29 (1.00,1.59) -3.27 (-3.53,-3.02) Western Europe 224.11 (182.16,261.66) 65.37 (52.22,76.70) -3.89 (-3.98,-3.79) 3.96 (3.23,4.61) 1.12(0.89,1.32) -3.99 (-4.08,-3.89) Southern Latin America 265.46 (209.14,326.61) 108.82 (85.44,132.96) -2.68 (-3.05,-2.3) 4.71 (3.70,5.79) 1.89 (1.48,2.32) -2.73 (-3.11,-2.35) High-income North America 230.80 (185.74,267.94) 138.92 (108.25,167.60) -1.59 (-1.7,-1.48) 4.10(3.30,4.76) 2.44 (1.91,2.93) -1.63 (-1.74,-1.52) Caribbean 392.71 (295.56,487.33) 313.77 (223.84,420.40) -0.33 (-0.7,0.04) 6.94 (5.22,8.62) 5.52 (3.94,7.42) -0.34 (-0.71,0.04) Andean Latin America 300.97 (224.60,387.61) 187.99 (137.33,250.59) -1.67 (-2.04,-1.3) 5.25 (3.91,6.77) 3.23 (2.35,4.32) -1.73 (-2.1,-1.35) Central Latin America 290.65 (226.58,349.92) 291.03 (227.41,352.86) 0.16 (-0.29,0.61) 5.10 (3.97,6.13) 5.08 (3.96,6.17) 0.14 (-0.32,0.6) Tropical Latin America 398.55 (317.30,472.54) 230.29 (184.50,272.01) -1.74 (-1.97,-1.52) 7.09 (5.65,8.41) 4.03 (3.23,4.76) -1.81 (-2.03,-1.58) North Africa and Middle East 787.84 (593.74,980.06) 500.71 (378.10,631.07) -1.45 (-1.51,-1.38) 13.92 (10.49,17.32) 8.77 (6.62,11.08) -1.48 (-1.54,-1.41) South Asia 533.68 (383.16,685.16) 492.06 (362.69,625.25) -0.14 (-0.35,0.08) 9.44 (6.78,12.13) 8.70 (6.42,11.05) -0.12 (-0.34,0.09) Central Sub-Saharan Africa 147.87 (89.37,224.05) 139.14 (86.96,209.30) -0.24 (-0.31,-0.17) 2.63 (1.58,3.99) 2.47 (1.54,3.71) -0.25 (-0.32,-0.18) Eastern Sub-Saharan Africa 138.22(94.60,193.31) 150.20 (99.87,207.98) 0.06 (-0.06,0.18) 2.43 (1.66,3.40) 2.63 (1.75,3.64) 0.05 (-0.07,0.17) Southern Sub-Saharan Africa 278.58 (205.73,361.23) 160.28 (117.40,211.16) -1.94 (-2.89,-0.97) 4.95 (3.65,6.43) 2.83 (2.07,3.73) -1.96 (-2.92,-0.99) Western Sub-Saharan Africa 128.53 (93.05,172.09) 132.27(92.83,178.31) 0.15 (0,0.3) 2.28 (1.64,3.06) 2.33 (1.63,3.15) 0.12 (-0.03,0.27) ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change. Table 5 Geographic Variation in the Burden of Ischemic Heart Disease Attributable to High Systolic Blood Pressure Among Adults Aged 25–39, 1990–2021 ASDR(per 100000) ASMR(per 100000) Location 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) High-income Asia Pacific 68.42 (45.04,94.76) 26.62 (13.38,40.16) -3.74 (-3.98,-3.51) 1.20 (0.79,1.66) 0.46 (0.23,0.67) -3.79 (-4.03,-3.56) High-income North America 82.18 (47.51,115.77) 59.00 (28.47,90.56) -1 (-1.27,-0.73) 1.47 (0.85,2.07) 1.04 (0.50,1.60) -1.03 (-1.3,-0.76) Western Europe 123.55 (90.13,154.98) 33.49 (22.54,43.91) -4.21 (-4.31,-4.12) 2.20 (1.61,2.76) 0.58 (0.39,0.76) -4.31 (-4.41,-4.21) Australasia 87.80 (59.22,119.42) 32.34 (16.75,48.68) -3.69 (-4.05,-3.32) 1.56 (1.05,2.11) 0.57 (0.30,0.86) -3.73 (-4.09,-3.36) Andean Latin America 59.47 (19.38,114.45) 73.95 (35.38,117.45) 1.32 (0.63,2.02) 1.05 (0.35,2.02) 1.27 (0.61,2.02) 1.22 (0.52,1.92) Tropical Latin America 190.64 (122.62,258.88) 115.05 (79.15,150.22) -1.54 (-1.75,-1.32) 3.40 (2.20,4.62) 2.02 (1.40,2.64) -1.6 (-1.82,-1.39) Central Latin America 130.13 (77.79,182.16) 136.03 (83.38,196.39) 0.34 (-0.2,0.88) 2.30(1.38,3.21) 2.39 (1.46,3.44) 0.31 (-0.24,0.85) Southern Latin America 80.65 (36.17,134.03) 52.70 (26.86,79.33) -0.96 (-1.26,-0.66) 1.44 (0.65,2.39) 0.92 (0.47,1.38) -1.03 (-1.33,-0.73) Caribbean 157.91(79.10,244.37) 165.20 (92.54,249.40) 0.73 (0.33,1.13) 2.81 (1.41,4.35) 2.92 (1.64,4.41) 0.71 (0.3,1.12) Central Europe 323.11 (231.00,413.47) 95.73 (67.04,123.34) -4.2 (-4.38,-4.01) 5.83 (4.18,7.45) 1.68 (1.17,2.16) -4.29 (-4.48,-4.1) Eastern Europe 378.90 (271.80,479.81) 266.11(185.22,346.39) -2.21 (-2.93,-1.48) 6.86 (4.92,8.68) 4.78 (3.33,6.22) -2.23 (-2.96,-1.5) Central Asia 386.28 (258.25,515.75) 256.31(171.11,341.02) -2.19 (-2.59,-1.79) 6.95 (4.65,9.27) 4.55 (3.05,6.06) -2.23 (-2.63,-1.82) North Africa and Middle East 370.62 (228.18,528.66) 272.94(177.57,373.43) -0.89 (-0.97,-0.81) 6.57 (4.05,9.37) 4.80 (3.13,6.56) -0.92 (-1,-0.84) South Asia 278.37 (182.32,377.20) 277.09(184.88,371.91) 0.1 (-0.16,0.36) 4.95 (3.25,6.69) 4.93 (3.29,6.61) 0.11 (-0.15,0.38) Southeast Asia 220.45(138.36,302.89) 241.97(158.69,334.92) 0.55 (0.45,0.66) 3.92 (2.46,5.38) 4.45 (2.94,6.14) 0.55 (0.45,0.66) East Asia 84.03 (24.88,168.29) 126.36 (44.08,217.30) 1.29 (1.13,1.46) 1.50 (0.45,2.99) 2.31(0.82,3.96) 1.25 (1.08,1.41) Oceania 208.38 (105.73,332.57) 292.75(134.72,518.81) 1.31 (1.11,1.51) 3.76 (1.91,6.00) 5.49 (2.55,9.68) 1.3 (1.1,1.49) Western Sub-Saharan Africa 67.29 (42.21,96.91) 94.57 (62.23,132.03) 1.47 (1.24,1.69) 1.20(0.75,1.73) 1.67 (1.10,2.35) 1.44 (1.21,1.67) Eastern Sub-Saharan Africa 65.20 (39.81,95.20) 102.08 (63.31,140.33) 1.36 (1.19,1.54) 1.15(0.70,1.69) 1.80 (1.11,2.47) 1.35 (1.17,1.53) Central Sub-Saharan Africa 110.95 (58.87,178.55) 91.91 (49.21,148.69) -0.81 (-0.89,-0.73) 1.98 (1.05,3.19) 1.64 (0.87,2.65) -0.82 (-0.9,-0.73) Southern Sub-Saharan Africa 198.04 (114.15,286.50) 112.96 (67.77,157.16) -2.03 (-2.92,-1.14) 3.55(2.05,5.12) 2.00 (1.20,2.79) -2.06 (-2.96,-1.16) ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change. Table 6 Geographic Variation in the Burden of Ischemic Heart Disease Attributable to High Fasting Plasma Glucose Among Adults Aged 25–39, 1990–2021 ASDR(per 100000) ASMR(per 100000) Location 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) 1990 (95% CI) 2021 (95% CI) EAPC (95% CI) High-income Asia Pacific 6.31 (4.53,8.72) 3.87(2.81,5.23) -2.04 (-2.22,-1.85) 0.11 (0.08,0.15) 0.07 (0.05,0.09) -2.1 (-2.3,-1.91) High-income North America 9.77 (6.56,14.51) 12.80 (8.63,18.50) 1.13 (0.91,1.34) 0.18(0.12,0.26) 0.23 (0.15,0.33) 1.09 (0.87,1.31) Western Europe 7.46 (5.78,9.52) 3.16 (2.49,3.92) -2.74 (-2.84,-2.64) 0.13 (0.10,0.17) 0.06 (0.04,0.07) -2.84 (-2.94,-2.73) Australasia 6.57 (4.11,10.33) 4.32 (2.60,7.12) -1.42 (-1.69,-1.16) 0.12 (0.07,0.18) 0.08 (0.05,0.13) -1.46 (-1.72,-1.19) Andean Latin America 7.14 (4.76,10.06) 6.54 (4.50,9.10) -0.58 (-1.03,-0.12) 0.13 (0.08,0.18) 0.11 (0.08,0.16) -0.63 (-1.09,-0.16) Tropical Latin America 16.62 (11.16,24.65) 13.52(8.83,19.27) -0.63 (-0.86,-0.4) 0.30 (0.20,0.44) 0.24 (0.16,0.34) -0.69 (-0.92,-0.45) Central Latin America 15.23 (11.41,19.96) 21.37 (15.55,29.41) 1.24 (0.81,1.67) 0.27 (0.20,0.36) 0.38 (0.28,0.52) 1.23 (0.79,1.66) Southern Latin America 7.49 (4.88,11.11) 5.30 (3.67,7.39) -0.79 (-1.07,-0.51) 0.13 (0.09,0.20) 0.09 (0.06,0.13) -0.84 (-1.12,-0.55) Caribbean 16.35 (12.23,21.06) 17.66 (12.48,24.01) 0.66 (0.24,1.07) 0.29 (0.22,0.38) 0.32(0.22,0.43) 0.65 (0.23,1.07) Central Europe 19.65 (14.38,27.13) 7.31 (5.46,9.37) -3.52 (-3.8,-3.25) 0.36 (0.26,0.49) 0.13 (0.10,0.17) -3.61 (-3.89,-3.34) Eastern Europe 15.14 (10.92,20.89) 13.65 (10.03,18.32) -1.29 (-2.03,-0.55) 0.28 (0.20,0.38) 0.25 (0.18,0.33) -1.31 (-2.05,-0.57) Central Asia 17.81 (13.31,22.83) 17.11 (12.95,22.02) -0.96 (-1.4,-0.51) 0.32 (0.24,0.41) 0.31 (0.23,0.40) -0.98 (-1.42,-0.53) North Africa and Middle East 27.52 (20.61,36.33) 33.28 (25.36,43.64) 0.66 (0.6,0.72) 0.49 (0.37,0.65) 0.59 (0.45,0.78) 0.64 (0.58,0.71) South Asia 28.20 (19.56,40.80) 38.28 (27.26,54.01) 1.23 (1.01,1.45) 0.51(0.35,0.73) 0.69 (0.49,0.97) 1.24 (1.02,1.46) Southeast Asia 14.27 (10.85,18.10) 16.17 (12.04,21.04) 0.14 (0,0.27) 0.26 (0.20,0.33) 0.29 (0.22,0.38) 0.14 (0,0.28) East Asia 12.63 (8.04,19.23) 18.48 (12.42,27.23) 1.41 (1.29,1.53) 0.23 (0.14,0.34) 0.33 (0.22,0.48) 1.37 (1.25,1.5) Oceania 50.55 (35.10,73.06) 64.27 (45.68,90.76) 0.74 (0.64,0.84) 0.92 (0.64,1.33) 1.17(0.83,1.64) 0.73 (0.63,0.83) Western Sub-Saharan Africa 4.34 (3.11,5.76) 6.33 (4.59,8.52) 1.3 (1.15,1.45) 0.08(0.06,0.10) 0.11 (0.08,0.15) 1.29 (1.13,1.44) Eastern Sub-Saharan Africa 3.94 (2.87,5.28) 4.93 (3.63,6.52) 0.5 (0.42,0.59) 0.07 (0.05,0.09) 0.09(0.06,0.12) 0.49 (0.4,0.57) Central Sub-Saharan Africa 11.33 (6.12,20.28) 11.87 (7.34,19.04) -0.05 (-0.22,0.12) 0.20 (0.11,0.37) 0.21 (0.13,0.34) -0.05 (-0.23,0.12) Southern Sub-Saharan Africa 8.58 (5.95,11.92) 6.90 (4.99,9.53) -0.86 (-1.85,0.14) 0.15 (0.11,0.21) 0.12 (0.09,0.17) -0.88 (-1.88,0.13) ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change. 3.5 Differences Between Countries Worldwide In 2021, substantial disparities in the burden of ischemic heart disease (IHD) attributable to three major metabolic risk factors were observed among individuals aged 25–39 years across countries. For IHD attributable to elevated low-density lipoprotein cholesterol (LDL-C), Kingdom of Sweden exhibited the lowest age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life year rate (ASDR), at 0.42 (95% CI: 0.32–0.54) and 27.31 (95% CI: 20.85–34.75) per 100,000 population, respectively. In contrast, Republic of Nauru reported the highest burden, with ASMR and ASDR of 45.41 (95% CI: 30.17–65.93) and 2529.20 (95% CI: 1678.50–3673.91), representing approximately 108.0-fold and 92.6-fold differences. Regarding IHD attributable to high systolic blood pressure, Kingdom of Sweden again had the lowest ASMR and ASDR (0.15 [95% CI: 0.05–0.27] and 9.83 [95% CI: 3.27–17.58] per 100,000), while Republic of Nauru reported the highest values (30.96 [95% CI: 15.21–50.03] and 1718.49 [95% CI: 840.35–2780.75]), indicating differences of 200.4-fold and 174.8-fold, respectively. For high fasting plasma glucose, the lowest ASMR and ASDR were observed in Kingdom of Sweden (0.02 [95% CI: 0.02–0.04] and 1.58 [95% CI: 1.00–2.59] per 100,000), whereas the highest were in the Republic of the Marshall Islands (4.58 [95% CI: 2.41–8.12] and 253.64 [95% CI: 133.73–447.88] per 100,000), corresponding to approximately 229.0-fold and 160.7-fold differences. Although India, China, and Pakistan did not rank among the countries with the highest ASMR or ASDR, their large population sizes resulted in the greatest absolute numbers of IHD-related deaths and DALYs attributable to these metabolic risk factors globally. Detailed results are provided in Supplementary materials(Tables 7–12). 4. Discussion 4.1 Global Burden of Ischemic Heart Disease (IHD) From 1990 to 2021, the absolute burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose increased significantly, primarily driven by global population growth and aging. After adjusting for age structure and temporal changes, the burden of IHD attributable to high LDL cholesterol showed a declining trend from 1990 to 2021. This reduction is likely due to advances in lipid-lowering therapies and improved cardiovascular management. Since the 1990s, the widespread use of statins has significantly reduced the risk of cardiovascular events among individuals with elevated LDL-C(13). In recent years, the development of novel oral agents and combination therapies has further enhanced treatment efficacy(14). Simultaneously, many countries have implemented lipid screening programs and clinical guidelines, improving intervention coverage among high-risk populations. Public health education has raised awareness of dyslipidemia, while the promotion of low-fat diets and regular physical activity has encouraged healthier lifestyles. After adjusting for age and temporal trends, the burden of IHD attributable to high systolic blood pressure remained largely unchanged. This stability may be due to the limited coverage and adherence to hypertension prevention and management strategies among young adults. In low-income regions, hypertension screening rates are low, and many individuals remain undiagnosed or untreated(15). Moreover, unhealthy lifestyles—such as physical inactivity, obesity, excessive alcohol consumption, and high-salt diets—undermine the effects of antihypertensive therapies and public health education. Inequitable healthcare resource distribution and inadequate primary care services further hinder effective intervention, contributing to the stagnation in disease burden(15, 16). From 1990 to 2021, the burden of IHD attributable to high fasting plasma glucose increased significantly in both absolute numbers and age-standardized rates. This trend likely reflects the growing prevalence of diabetes among younger populations, coupled with inadequate glycemic management, including limited access to lifestyle interventions and pharmacotherapy. The widespread consumption of high-sugar and high-calorie foods—such as sugary beverages and fast food—has led to rising rates of obesity and insulin resistance(17, 18), while physical inactivity has further exacerbated metabolic dysfunction(19). Delayed screening has resulted in late diagnosis and treatment, accelerating vascular damage. Moreover, disparities in healthcare access have limited the availability of effective glucose-lowering therapies in low- and middle-income regions. In summary, among the three metabolic risk factors analyzed in this study, high LDL cholesterol was associated with the greatest burden of ischemic heart disease (IHD) among adults aged 25–39. While effective control has been achieved, continued investment in lipid-lowering therapies—such as expanding the use of statins and novel agents—is essential. The burden attributable to high systolic blood pressure has remained stable without notable decline, indicating a need to strengthen hypertension screening and management in young adults, including the promotion of affordable antihypertensive medications. The burden linked to high fasting plasma glucose has continued to rise and should be prioritized through enhanced efforts in diabetes prevention, screening, and glycemic control policies. 4.2 Sex Differences Among adults aged 25–39, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose is significantly higher in men than in women. From 1990 to 2021, the decline in IHD burden related to high LDL-C and high systolic pressure was less pronounced in men, while the increase related to high fasting glucose was more substantial in men. These sex-based differences may be explained by the following factors: Estrogen exerts protective effects on the cardiovascular system, potentially delaying atherosclerosis in women(20); Men have greater arterial stiffness, making blood pressure harder to control(21); Visceral fat accumulation and insulin resistance are more common in men(22); Unhealthy behaviors such as smoking, alcohol consumption, high-salt and high-sugar diets are more prevalent among men, along with lower physical activity levels; Women are more proactive in health screening and chronic disease management, with better adherence to medical interventions; Pregnancy-related health checks facilitate early detection and management of metabolic risk factors; Men are more frequently exposed to occupational stress and sleep deprivation, which may further elevate cardiovascular risk. 4.3 SDI Regional Differences and Geographic Variation The Sociodemographic Index (SDI) is a composite indicator used to assess the level of social development and demographic structure across countries and regions. It is widely employed in global health research to evaluate disparities in disease burden and their underlying socioeconomic drivers. SDI is calculated based on three key dimensions: per capita income (reflecting economic development), average years of education (indicating educational attainment), and total fertility rate (TFR), which reflects demographic structure and modernization—lower TFR generally indicates a higher level of development. While SDI primarily reflects overall socioeconomic development, geographic regions are shaped by distinct factors such as culture, dietary patterns, and environmental conditions. Together, these factors influence regional variations in the risk and burden of ischemic heart disease (IHD). Overall, among individuals aged 25–39, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose is inversely associated with Sociodemographic Index (SDI) levels. This trend is largely driven by differences in socioeconomic status and healthcare resources. In low-SDI regions, underdeveloped economies and limited resources contribute to low health expenditure, inadequate primary healthcare infrastructure, insufficient health education, and limited access to early screening and long-term treatment. In contrast, high-SDI regions benefit from more advanced healthcare systems, broader insurance coverage, greater availability of early detection programs, and improved access to essential medications. Furthermore, these regions often implement comprehensive public health interventions, have higher levels of health education, and foster stronger health awareness and earlier disease intervention among the population. During the transition from low to high SDI levels, economic growth and improved healthcare resources contribute to better health outcomes. However, rapid urbanization simultaneously introduces new challenges. Increased consumption of processed foods high in salt, saturated fats, sugar and calories, along with the erosion of traditional diets rich in fiber and low in fat, elevates metabolic risks(23, 24). Additionally, shifts in work and lifestyle(23), and delayed chronic disease management may offset some of the health gains associated with rising SDI. Based on the analysis of 21 regions, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose is most pronounced in Oceania among individuals aged 25–39. Several factors may contribute to this pattern: Dietary transition: Traditional diets are increasingly replaced by high-fat, high-sugar, and high-salt foods(25); Rising youth obesity: Obesity rates among adults aged 25–39 are markedly elevated(26, 27), contributing to elevated LDL cholesterol, insulin resistance, and hypertension, all of which increase IHD risk; Lifestyle changes: Accelerated urbanization has led to reduced physical activity, disrupting energy balance(28); Genetic predisposition: Pacific Islanders, such as Polynesians, may carry gene polymorphisms associated with a higher susceptibility to lipid metabolism disorders and insulin resistance under modern dietary conditions(29, 30); Inadequate healthcare systems: Weak chronic disease control, limited health education, and poor access to essential medications hinder effective prevention and management; Socioeconomic and environmental challenges(31): In some island nations, healthy foods are unaffordable for low-income families, who rely more on cheap; meanwhile, rising sea levels and extreme weather damage local agriculture, exacerbating food insecurity and increasing reliance on imported unhealthy food. Among individuals aged 25–39, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol and high systolic blood pressure is the lowest in high-income Asia Pacific countries. For IHD attributable to high fasting plasma glucose, the lowest burden is observed in Western Europe, which also exhibits the fastest decline in IHD burden related to high systolic blood pressure in this age group. These trends reflect both the general advantages of high-SDI regions—such as strong economies, well-developed healthcare systems, and comprehensive public health interventions—and region-specific factors. These include: (1) the continuation of traditional healthy dietary habits (low in saturated fat and high in dietary fiber), supported by robust nutrition policies; and (2) widespread health education, strong public awareness, and high coverage of early screening programs, enabling timely detection and intervention. Additionally, Central Europe has shown the most significant reductions in IHD burden attributable to high LDL cholesterol and high fasting plasma glucose. This progress likely results from a combination of economic development, improvements in healthcare access, effective public health strategies, and regional sociocultural influences. In summary, from 1990 to 2021, the global burden of ischemic heart disease attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose among young adults aged 25–39 demonstrated complex spatiotemporal trends, with marked disparities by sex, region, and level of sociodemographic development. Although progress has been made in controlling certain metabolic risk factors, the overall trend remains concerning. There is an urgent need for multi-level interventions, particularly targeting high-risk populations, resource-limited settings, and countries with rapidly increasing burdens. Effective prevention and control of metabolic risks in young adults is critical and requires a shift from a treatment-centered model to a comprehensive approach, integrating policy guidance, environmental improvements, and behavioral interventions. Furthermore, leveraging the resource allocation advantages brought by social development is essential to advancing precision and efficiency in chronic disease control and meeting the ongoing global challenge of noncommunicable disease epidemics. Abbreviations IHD Ischemic Heart Disease SBP Systolic Blood Pressure FPG Fasting Plasma Glucose LDL-C Low-Density Lipoprotein Cholesterol GBD Global Burden of Disease ASMR Age-Standardized Mortality Rate ASDR Age-Standardized Disability-adjusted life years Rate SDI Sociodemographic Index DALYs Disability-Adjusted Life Years CAD Coronary Artery Disease ASCVD Atherosclerotic Cardiovascular Disease ASRs Age-Standardized Rates EAPC Estimated Annual Percentage Change CI Confidence Interval TFR Total Fertility Rate Declarations Ethics Approval: Ethics approval and consent to participate were not required. Consent for publication: All authors of the manuscript have read and agreed to its content and are accountable for all aspects of the accuracy and integrity of the manuscript in accordance with ICMJE (International Committee of Medical Journal Editors) criteria. Funding: None. Competing Interests: The authors declare no conflict of interest. Informed Consent: This study used data exclusively from the Global Burden of Disease (GBD) study. No individual participants were directly involved. Data Availability: The GBD Study 2021 data are publicly available online through the Global Health Data Exchange (GHDx) query tool ( http://ghdx.healthdata.org/gbd-results-tool ). All data generated or analyzed during this study are included in this article and the supplementary material. Author Contribution T. L: Conceptualization, Methodology, Formal Analysis, Data Curation, Writing-Original Draft, Writing-Review & Editing, Supervision, Validation, Project Administration. D.F: Validation, Investigation.F. C: Resources, Data Curation.C. C: Writing-Review & Editing.X.L: Supervision. Acknowledgement We thank all the collaborators and team members of the Global Burden of Disease study for their valuable contributions and efforts. We also sincerely acknowledge the Institute for Health Metrics and Evaluation (IHME) for providing the data. Special thanks to the GBD Study 2021 collaborators for their invaluable contributions and dedication. References Ettehad D, Emdin CA, Kiran A, Anderson SG, Callender T, Emberson J, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet. 2016;387(10022):957–67. Sabatine MS, Wiviott SD, Im K, Murphy SA, Giugliano RP. Efficacy and Safety of Further Lowering of Low-Density Lipoprotein Cholesterol in Patients Starting With Very Low Levels: A Meta-analysis. JAMA Cardiol. 2018;3(9):823–8. Gerstein HC, Miller ME, Ismail-Beigi F, Largay J, McDonald C, Lochnan HA, et al. Effects of intensive glycaemic control on ischaemic heart disease: analysis of data from the randomised, controlled ACCORD trial. Lancet. 2014;384(9958):1936–41. Vasan RS, Song RJ, van den Heuvel ER. Temporal Trends in Incidence of Premature Cardiovascular Disease Over the Past 7 Decades: The Framingham Heart Study. J Am Heart Association. 2022;11(19):e026497. Aggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011–2020. JAMA. 2024;331(21):1858–60. Aggarwal R, Yeh RW, Joynt Maddox KE, Wadhera RK. Cardiovascular Risk Factor Prevalence, Treatment, and Control in US Adults Aged 20 to 44 Years, 2009 to March 2020. JAMA. 2023;329(11):899–909. Lee H, Yano Y, Cho SMJ, Park JH, Park S, Lloyd-Jones DM, et al. Cardiovascular Risk of Isolated Systolic or Diastolic Hypertension in Young Adults. Circulation. 2020;141(22):1778–86. Allen NB, Siddique J, Wilkins JT, Shay C, Lewis CE, Goff DC, et al. Blood pressure trajectories in early adulthood and subclinical atherosclerosis in middle age. JAMA. 2014;311(5):490–7. Navar-Boggan AM, Peterson ED, D'Agostino RB, Sr., Neely B, Sniderman AD, Pencina MJ. Hyperlipidemia in early adulthood increases long-term risk of coronary heart disease. Circulation. 2015;131(5):451–8. Pletcher MJ, Vittinghoff E, Thanataveerat A, Bibbins-Domingo K, Moran AE. Young Adult Exposure to Cardiovascular Risk Factors and Risk of Events Later in Life: The Framingham Offspring Study. PLoS ONE. 2016;11(5):e0154288. (WHO) TWHOs. World (WHO 2000–2025) Standard [Available from: https://seer.cancer.gov/stdpopulations/world.who.html Zhang L, Tong Z, Han R, Guo R, Zang S, Zhang X, et al. Global, Regional, and National Burdens of Ischemic Heart Disease Attributable to Smoking From 1990 to 2019. J Am Heart Association. 2023;12(3):e028193. Last AR, Ference JD, Menzel ER, Hyperlipidemia. Drugs for Cardiovascular Risk Reduction in Adults. Am Family Phys. 2017;95(2):78–87. Michaeli DT, Michaeli JC, Albers S, Boch T, Michaeli T. Established and Emerging Lipid-Lowering Drugs for Primary and Secondary Cardiovascular Prevention. American journal of cardiovascular drugs: drugs, devices, and other interventions. 2023;23(5):477–95. Perkovic V, Huxley R, Wu Y, Prabhakaran D, MacMahon S. The burden of blood pressure-related disease: a neglected priority for global health. Hypertension (Dallas, Tex: 1979). 2007;50(6):991-7. Patel P, Ordunez P, DiPette D, Escobar MC, Hassell T, Wyss F, et al. Improved Blood Pressure Control to Reduce Cardiovascular Disease Morbidity and Mortality: The Standardized Hypertension Treatment and Prevention Project. J Clin Hypertens (Greenwich Conn). 2016;18(12):1284–94. Malik VS, Hu FB. The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nat reviews Endocrinol. 2022;18(4):205–18. Nguyen M, Jarvis SE, Tinajero MG, Yu J, Chiavaroli L, Mejia SB, et al. Sugar-sweetened beverage consumption and weight gain in children and adults: a systematic review and meta-analysis of prospective cohort studies and randomized controlled trials. Am J Clin Nutr. 2023;117(1):160–74. Małkowska P. Positive Effects of Physical Activity on Insulin Signaling. Curr Issues Mol Biol. 2024;46(6):5467–87. Fontaine C, Morfoisse F, Tatin F, Zamora A, Zahreddine R, Henrion D et al. The Impact of Estrogen Receptor in Arterial and Lymphatic Vascular Diseases. Int J Mol Sci. 2020;21(9). Ogola BO, Zimmerman MA, Clark GL, Abshire CM, Gentry KM, Miller KS, et al. New insights into arterial stiffening: does sex matter? Am J Physiol Heart Circ Physiol. 2018;315(5):H1073–87. Machann J, Thamer C, Schnoedt B, Stefan N, Stumvoll M, Haring HU, et al. Age and gender related effects on adipose tissue compartments of subjects with increased risk for type 2 diabetes: a whole body MRI/MRS study. Volume 18. New York, NY): Magma; 2005. pp. 128–37. 3. Fleischer NL, Diez Roux AV, Alazraqui M, Spinelli H, De Maio F. Socioeconomic gradients in chronic disease risk factors in middle-income countries: evidence of effect modification by urbanicity in Argentina. Am J Public Health. 2011;101(2):294–301. Goryakin Y, Rocco L, Suhrcke M. The contribution of urbanization to non-communicable diseases: Evidence from 173 countries from 1980 to 2008. Econ Hum Biol. 2017;26:151–63. Santos JA, McKenzie B, Trieu K, Farnbach S, Johnson C, Schultz J, et al. Contribution of fat, sugar and salt to diets in the Pacific Islands: a systematic review. Public Health Nutr. 2019;22(10):1858–71. Global regional. national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet. 2025;405(10481):813–38. Statistics ABo, Overweight. and obesity. 2017-18 [Available from: ABS. https://www.abs.gov.au/statistics/health/health-conditions-and-risks/waist-circumference-and-bmi/2017-18 Tong TJ, Mohammadnezhad M, Alqahtani NS. Determinants of overweight and obesity and preventive strategies in Pacific countries: a systematic review. Global Health J. 2022;6(3):122–8. Hanson RL, Safabakhsh S, Curtis JM, Hsueh WC, Jones LI, Aflague TF, et al. Association of CREBRF variants with obesity and diabetes in Pacific Islanders from Guam and Saipan. Diabetologia. 2019;62(9):1647–52. Myles S, Hradetzky E, Engelken J, Lao O, Nürnberg P, Trent RJ, et al. Identification of a candidate genetic variant for the high prevalence of type II diabetes in Polynesians. Eur J Hum genetics: EJHG. 2007;15(5):584–9. Davila F, Burkhart S, O’Connell T. State of Food and Nutrition Security in the Pacific. In: Dansie A, Alleway HK, Böer B, editors. The Water, Energy, and Food Security Nexus in Asia and the Pacific: The Pacific. Cham: Springer International Publishing; 2024. pp. 85–106. Tables Table 7 to 12 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table712.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor invited by journal 17 Jun, 2025 Editor assigned by journal 16 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 10 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6862760","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483688600,"identity":"546c81f9-258a-4f60-b65e-f09ee5a397cf","order_by":0,"name":"Tiecheng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACPmYgkQBGDAwHP1TY8PCzN+DXwsbMwNgA1cJ4WOJMmoxkzwECWoAqGyAWMTAf4G05bGNww4GAFnYe8wcPKuzy+KXbLxyQbDjPw3CDgfHDxxx8DuMxbEg4k1wsOedMwYHCHbd5GGc3MEvO3EZAS2LbgcQNN3ISDkieuc3DLHOAjZmXoJZ/BxL3g7Twtp3jYZNIIEZLA9AWifQDQC0HeHgIa2ErnJFwLDlxxo0cBmAgJ/NI8BxsxusXfv7DGz7+qLFL7J+R/vjjhwo7e/vjzQc/fMSjBQnwGEAZ4IgiCrA/IFblKBgFo2AUjDAAAOsRVhfxhGvzAAAAAElFTkSuQmCC","orcid":"","institution":"Cangzhou People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tiecheng","middleName":"","lastName":"Liu","suffix":""},{"id":483688601,"identity":"85ec7d99-a071-437d-88a9-0dded166442c","order_by":1,"name":"Dawei Fu","email":"","orcid":"","institution":"Cangzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Fu","suffix":""},{"id":483688602,"identity":"eaf0eacc-e895-42d9-94f5-33c255e62986","order_by":2,"name":"Fujiang Cui","email":"","orcid":"","institution":"Cangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Fujiang","middleName":"","lastName":"Cui","suffix":""},{"id":483688603,"identity":"48613ccf-4dd1-4612-aed6-03654e9c5d87","order_by":3,"name":"Congcong Cheng","email":"","orcid":"","institution":"Cangzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Congcong","middleName":"","lastName":"Cheng","suffix":""},{"id":483688604,"identity":"821a4777-9108-4d62-a123-0d32d72632cb","order_by":4,"name":"Xiaochen Liu","email":"","orcid":"","institution":"Cangzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-10 11:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6862760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6862760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86708645,"identity":"6265cf99-6eef-4e49-ab51-3500eaa00090","added_by":"auto","created_at":"2025-07-14 18:00:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":254998,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal Trends in the Burden of Ischemic Heart Disease Associated with Three Metabolic Risk Factors by Gender Among Adults Aged 25-39 (1990-2021). (A)(D)The global trends in the burden of ischemic heart disease associated with high LDL cholesterol,(B)(E) The global trends in the burden of ischemic heart disease associated with high systolic blood pressure,(C)(F) The global trends in the burden of ischemic heart disease associated with High fasting plasma glucose. ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; DALYs, disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6862760/v1/f28052ebb59b2acd52368c51.jpeg"},{"id":86708062,"identity":"e7c10fd8-1683-49e8-b9ec-61ef5fdd006f","added_by":"auto","created_at":"2025-07-14 17:52:35","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":280260,"visible":true,"origin":"","legend":"\u003cp\u003eThe EAPC of The Burden‘s from 1990 to 2021 of IHD Attributable to Three Metabolic Risk Factors in global and 5SDI Regions. (A)(B) The EAPC of the burden‘s from 1990 to 2021 of IHD attributable to high LDL cholesterol,(C)(D) The EAPC of the burden‘s from 1990 to 2021 of IHD attributable to high systolic blood pressure,(E)(F) The EAPC of the burden‘s from 1990 to 2021 of IHD attributable to high fasting plasma glucose. EAPC, estimated annual percentage change; IHD，ischemic heart disease; SDI，sociodemographic index; ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6862760/v1/cfd70e4f1a2448f052429b1d.jpeg"},{"id":86707627,"identity":"19f60ad5-61b9-4f2e-8bf8-ce05a81a7b27","added_by":"auto","created_at":"2025-07-14 17:44:35","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":399848,"visible":true,"origin":"","legend":"\u003cp\u003ePearson Correlation Analysis between IHD‘s Burden Attributable to Three Metabolic Risk Factors and SDI Scores in 2021 Across the Globe and 21 Regions.(A)(D) Pearson Correlation Analysis between IHD‘s Burden Attributable to high LDL cholesterol and SDI Scores,(B)(E) Pearson Correlation Analysis between IHD‘s Burden Attributable to high systolic blood pressure and SDI Scores,(C)(F)Pearson Correlation Analysis between IHD‘s Burden Attributable to high systolic blood pressure and SDI Scores. IHD, ischemic heart disease; SDI, sociodemographic index; ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6862760/v1/33230a3bf01eb9346226341d.jpeg"},{"id":86708064,"identity":"89e3e178-baee-4792-a337-9a3245a2f79a","added_by":"auto","created_at":"2025-07-14 17:52:35","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327894,"visible":true,"origin":"","legend":"\u003cp\u003eThe EAPC of The Burden‘s From 1990 to 2021 of IHD Attributable to Three Metabolic Risk Factors in global and 21 Regions.(A)(D) The EAPC of the burden‘s from 1990 to 2021 of IHD attributable to high LDL cholesterol in global and 21 Regions,(B)(E) The EAPC of the burden‘s from 1990 to 2021 of IHD attributable to high systolic blood pressure in global and 21 Regions,(C)(F) The EAPC of the burden‘s from 1990 to 2021 of IHD attributable to high fasting plasma glucose in global and 21 Regions.EAPC，estimated annual percentage change; IHD，ischemic heart disease; SDI，sociodemographic index; ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6862760/v1/e99b799be7238fa6db459f81.jpeg"},{"id":86708065,"identity":"ed6bca3e-7bb4-4560-bbe0-b0eeabe43a9c","added_by":"auto","created_at":"2025-07-14 17:52:35","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":334939,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of ischemic heart disease burden in 2021 attributed to three metabolic risk factors.(A)(D) Distribution of ischemic heart disease burden in 2021 attributed to high LDL cholesterol,(B)(E) Distribution of ischemic heart disease burden in 2021 attributed to high systolic blood pressure,(C)(F) Distribution of ischemic heart disease burden in 2021 attributed to high fasting plasma glucose.ASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6862760/v1/32933a83df39fbcb032a97b5.jpeg"},{"id":86709600,"identity":"9a498140-6ce6-47b8-980f-33c58bed66bc","added_by":"auto","created_at":"2025-07-14 18:08:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3494326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6862760/v1/b844bdee-8938-4c2e-b212-bce682f0ea6c.pdf"},{"id":86707621,"identity":"96515f33-f8de-47af-855a-2a85de5ca243","added_by":"auto","created_at":"2025-07-14 17:44:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":250027,"visible":true,"origin":"","legend":"","description":"","filename":"Table712.docx","url":"https://assets-eu.researchsquare.com/files/rs-6862760/v1/7546ee0593af41c7f8b5caa3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global, Regional, and National Burden of Ischemic Heart Disease Due to High LDL Cholesterol, Systolic Blood Pressure, and Fasting Glucose in Young Adults (1990–2021)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIschemic Heart Disease (IHD), also known as coronary artery disease (CAD), occurs due to insufficient coronary blood supply, leading to myocardial ischemia, hypoxia, and even necrosis. Its clinical manifestations include stable angina, acute coronary syndrome, and chronic ischemic cardiomyopathy. IHD is one of the leading causes of death globally and represents a major disease burden, with significant regional and income-related disparities.\u003c/p\u003e\u003cp\u003eHypertension, hypercholesterolemia, and hyperglycemia are traditional risk factors for IHD. Chronic hypertension damages the vascular endothelium, promotes lipid deposition and plaque formation, increases cardiac afterload, accelerates left ventricular hypertrophy, and increases myocardial oxygen consumption, thereby significantly raising the risk of IHD. Studies have shown that for every 10 mmHg reduction in systolic blood pressure, the overall risk of cardiovascular events can be reduced by about 20%, with coronary heart disease risk decreasing by approximately 17%(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The key mechanism behind hypercholesterolemia is elevated low-density lipoprotein cholesterol (LDL-C). When LDL-C infiltrates the vascular endothelium and becomes oxidized, it triggers an inflammatory response, forming foam cells and atherosclerotic plaques. Meta-analyses have shown that reducing LDL-C, regardless of baseline levels, significantly lowers the risk of cardiovascular events(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Diabetes is an important risk factor for IHD, with individuals with type 2 diabetes having 2 to 3 times the risk of developing IHD compared to those without diabetes(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIndividuals aged 25\u0026ndash;39 years are relatively mature in terms of physical and mental development, social responsibility, and economic ability, making them crucial pillars of society. However, this age group is also at high risk for early-onset cardiovascular diseases. In recent years, the risk of atherosclerotic cardiovascular disease (ASCVD) among young adults has been steadily increasing(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). At the same time, they face a growing burden of cardiovascular risk factors, including elevated low-density lipoprotein cholesterol (LDL-C), hypertension, obesity, and insulin resistance(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Among young adults who have not received antihypertensive treatment, those with systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg have a significantly higher risk of cardiovascular events compared to those with SBP\u0026thinsp;\u0026lt;\u0026thinsp;120 mmHg(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, elevated LDL-C levels in young individuals increase the risk of developing ASCVD in the future(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eYoung adults are a key group for cardiovascular disease prevention and control, yet the growing prevalence of risk factors in this population poses a serious threat to the global disease burden. Therefore, it is urgent to strengthen early screening, optimize intervention strategies, and enhance policy support to curb the rise in IHD incidence and complications, and alleviate the health and economic burden of early-onset cardiovascular events. This study aims to systematically assess the global, regional, and national burden of IHD attributable to high LDL-C, high systolic blood pressure, and high fasting plasma glucose among young adults (aged 25\u0026ndash;39) from 1990 to 2021.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study utilized data from the Global Burden of Disease (GBD) 2021 dataset to assess the burden of ischemic heart disease (IHD) attributable to high fasting plasma glucose (FPG), high low-density lipoprotein cholesterol (LDL-C), and high systolic blood pressure (SBP) among individuals aged 25\u0026ndash;39 years across 204 countries and territories from 1990 to 2021. All data were obtained from the Global Health Data Exchange (GHDx) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe extracted age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) attributable to the three metabolic risk factors to quantify the IHD burden. Countries and territories were classified into five categories (low, low-middle, middle, high-middle, and high) based on the Socio-demographic Index (SDI) to examine the relationship between disease burden and socioeconomic development.\u003c/p\u003e\u003cp\u003eAge-standardized rates (ASRs) were calculated using the World Health Organization\u0026rsquo;s standard population (WHO 2000\u0026ndash;2025) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)with the following formula:\u003c/p\u003e\u003cp\u003eAge Standardized Rate=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\sum\\:}_{i=1}^{A}{a}_{i}{w}_{i}}{{\\sum\\:}_{i=1}^{A}{w}_{i}}\\times\\:\\text{100,000}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the age-specific rate in group i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the corresponding weight from the standard population, and \u003cem\u003eA\u003c/em\u003e is the total number of age groups.\u003c/p\u003e\u003cp\u003eTo assess temporal trends, estimated annual percentage change (EAPC) was calculated using a log-linear regression model(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e):\u003c/p\u003e\u003cp\u003eEAPC\u0026thinsp;=\u0026thinsp;100\u0026times;(\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/sup\u003e\u0026minus;1)\u003c/p\u003e\u003cp\u003ewhere β is the regression coefficient for the year in the natural logarithmic model. An EAPC with a 95% confidence interval (CI) entirely above 0 was considered an increasing trend; entirely below 0 indicated a decreasing trend; and a CI that included 0 indicated a stable trend. Pearson correlation analysis was used to assess the relationship between SDI and disease burden. A two-sided P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Global Burden of IHD\u003c/h2\u003e\u003cp\u003e\u003cb\u003e3.1.1 Global Burden of Ischemic Heart Disease Attributable to High LDL Cholesterol Among Adults Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, among individuals aged 25\u0026ndash;39 years, high LDL cholesterol contributed to an estimated 101,126 deaths globally (95% CI: 76,854\u0026ndash;124,749) and 5,736,646 DALYs (95% CI: 4,349,511\u0026ndash;7,061,996). In comparison, the corresponding figures in 1990 were 77,995 deaths (95% CI: 59,855\u0026ndash;94,899) and 4,416,391 DALYs (95% CI: 3,385,524\u0026ndash;5,383,908). Although the absolute burden increased over time, the age-standardized rates declined. The age-standardized mortality rate (ASMR) decreased from 6.78 per 100,000 population in 1990 (95% CI: 5.21\u0026ndash;8.26) to 5.71 in 2021 (95% CI: 4.33\u0026ndash;7.02), with an estimated annual percentage change (EAPC) of -0.62 (95% CI: -0.69 to -0.55). Similarly, the age-standardized DALY rate (ASDR) fell from 382.62 (95% CI: 293.58\u0026ndash;466.75) to 324.26 (95% CI: 245.54\u0026ndash;398.46), with an EAPC of -0.60 (95% CI: -0.67 to -0.52).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.1.2 Global Burden of Ischemic Heart Disease Attributable to High Systolic Blood Pressure Among Adults Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the global number of ischemic heart disease (IHD) deaths attributable to high systolic blood pressure among individuals aged 25\u0026ndash;39 years was 56,129 (95% CI: 39,437\u0026ndash;73,387), with a corresponding DALY count of 3,166,599(95% CI: 2,220,211\u0026ndash;4,145,104).In comparison to 1990 (with 36,798 deaths [95% CI: 24,964\u0026ndash;48,326] and DALYs of 2,070,608 [95% CI: 1,400,809\u0026ndash;2,723,230]), the absolute burden of IHD increased significantly in this population. However, after adjusting for age and time structure, the overall burden remained stable. The age-standardized mortality rate (ASMR) decreased from 3.21 per 100,000 in 1990 (95% CI: 2.17\u0026ndash;4.26) to 3.17 in 2021 (95% CI: 2.19\u0026ndash;4.17), with an estimated annual percentage change (EAPC) of -0.08 (95% CI: -0.18 to 0.02).Similarly, the age-standardized DALY rate (ASDR) decreased from 180.08 (95% CI: 121.46\u0026ndash;239.09) in 1990 to 178.77 (95% CI: 123.60\u0026ndash;235.87) in 2021, with an EAPC of -0.05 (95% CI: -0.15 to 0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.1.3 Global Burden of Ischemic Heart Disease Attributable to High Fasting Plasma Glucose Among Adults Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the global number of ischemic heart disease (IHD) deaths attributable to high fasting plasma glucose among individuals aged 25\u0026ndash;39 years was 6,724 (95% CI: 5,356\u0026ndash;8,016), with a DALY count of 375,435 (95% CI: 299,758\u0026ndash;447,882).In comparison, the figures in 1990 were 3,222 deaths (95% CI: 2,542\u0026ndash;3,984) and 179,710 DALYs (95% CI: 141,821\u0026ndash;222,623), indicating a marked increase in the absolute burden. After adjusting for age and temporal factors, the burden continued to show an upward trend. The age-standardized mortality rate (ASMR) rose from 0.28 per 100,000 in 1990 (95% CI: 0.22\u0026ndash;0.36) to 0.38 in 2021 (95% CI: 0.29\u0026ndash;0.47). Similarly, the age-standardized DALY rate (ASDR) increased from 15.72 (95% CI: 12.12\u0026ndash;19.78) to 21.14 (95% CI: 16.38\u0026ndash;26.29).From 1990 to 2021, the estimated annual percentage change (EAPC) was 1.02 (95% CI: 0.96\u0026ndash;1.09) for ASMR and 1.03 (95% CI: 0.97\u0026ndash;1.10) for ASDR.\u003c/p\u003e\u003cp\u003eDetailed results are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGlobal Burden of Ischemic Heart Disease Attributable to The Three Metabolic Risk Factors Among Adults Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASR\u003c/p\u003e\u003cp\u003e(per 100000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEAPC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eLDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77995\u003c/p\u003e\u003cp\u003e(59855,94899)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101126\u003c/p\u003e\u003cp\u003e(76854,124749)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.78\u003c/p\u003e\u003cp\u003e(5.21,8.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.71\u003c/p\u003e\u003cp\u003e(4.33,7.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.62\u003c/p\u003e\u003cp\u003e(-0.69,-0.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4416391\u003c/p\u003e\u003cp\u003e(3385524,5383908)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5736646\u003c/p\u003e\u003cp\u003e(4349511,7061996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e382.62\u003c/p\u003e\u003cp\u003e(293.58,466.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e324.26\u003c/p\u003e\u003cp\u003e(245.54,398.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.6\u003c/p\u003e\u003cp\u003e(-0.67,-0.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003esystolic\u003c/p\u003e\u003cp\u003eblood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36798\u003c/p\u003e\u003cp\u003e(24964,48326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56129\u003c/p\u003e\u003cp\u003e(39437,73387)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003cp\u003e(2.17,4.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003cp\u003e(2.19,4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003cp\u003e(-0.18,0.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2070608\u003c/p\u003e\u003cp\u003e(1400809,2723230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3166599\u003c/p\u003e\u003cp\u003e(2220211,4145104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e180.08\u003c/p\u003e\u003cp\u003e(121.46,239.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e178.77\u003c/p\u003e\u003cp\u003e(123.60,235.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003cp\u003e(-0.15,0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eFasting\u003c/p\u003e\u003cp\u003eplasma glucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3222\u003c/p\u003e\u003cp\u003e(2542,3984)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6724\u003c/p\u003e\u003cp\u003e(5356,8016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003cp\u003e(0.22,0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003cp\u003e(0.29,0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003cp\u003e(0.96,1.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e179710\u003c/p\u003e\u003cp\u003e(141821,222623)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e375435\u003c/p\u003e\u003cp\u003e(299758,447882)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.72\u003c/p\u003e\u003cp\u003e(12.12,19.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.14\u003c/p\u003e\u003cp\u003e(16.38,26.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003cp\u003e(0.97,1.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years rate; EAPC, estimated annual percentage change; DALYs, disability-adjusted life years; ASR, Age-standardized Rate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Sex Differences\u003c/h2\u003e\u003cp\u003e\u003cb\u003e3.2.1 Sex Differences in the Global Burden of Ischemic Heart Disease Attributable to High LDL Cholesterol Among Individuals Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the global burden of ischemic heart disease (IHD) attributable to high low-density lipoprotein cholesterol (LDL-C) among individuals aged 25\u0026ndash;39 years was significantly higher in males than in females. The number of deaths was 72,475 (95% CI: 55,264\u0026ndash;89,924) in males and 28,651 (95% CI: 21,865\u0026ndash;35,300) in females. Corresponding DALYs were 4,097,221 (95% CI: 3,119,757\u0026ndash;5,080,221) for males and 1,639,425 (95% CI: 1,250,286\u0026ndash;2,014,632) for females. After adjusting for age and time, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) per 100,000 population were 8.10 (95% CI: 6.17\u0026ndash;10.02) and 458.29 (95% CI: 349.19\u0026ndash;566.26) for males, and 3.27 (95% CI: 2.49\u0026ndash;4.05) and 187.49 (95% CI: 142.58\u0026ndash;231.32) for females. From 1990 to 2021, the global burden of IHD attributable to high LDL-C in this age group declined for both sexes, with a more pronounced decrease observed in females. The estimated annual percentage change (EAPC) in ASMR and ASDR was \u0026minus;\u0026thinsp;1.02 (95% CI: \u0026minus;1.09 to \u0026minus;\u0026thinsp;0.95) and \u0026minus;\u0026thinsp;1.01 (95% CI: \u0026minus;1.08 to \u0026minus;\u0026thinsp;0.94) for females, and \u0026minus;\u0026thinsp;0.43 (95% CI: \u0026minus;0.51 to \u0026minus;\u0026thinsp;0.35) and \u0026minus;\u0026thinsp;0.40 (95% CI: \u0026minus;0.49 to \u0026minus;\u0026thinsp;0.32) for males.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2.2 Sex Differences in the Global Burden of Ischemic Heart Disease Attributable to High Systolic Blood Pressure Among Individuals Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the global burden of ischemic heart disease (IHD) attributable to high systolic blood pressure was significantly higher in males than in females among individuals aged 25\u0026ndash;39 years. The number of deaths was 43,803 (95% CI: 31,280\u0026ndash;56,807) in males and 12,327 (95% CI: 7,972\u0026ndash;16,479) in females. Corresponding DALYs were 2,468,611 (95% CI: 1,765,188\u0026ndash;3,197,373) for males and 697,988 (95% CI: 450,715\u0026ndash;934,661) for females. After adjusting for age and time, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) per 100,000 population were 4.89 (95% CI: 3.39\u0026ndash;6.45) and 275.91 (95% CI: 191.06\u0026ndash;364.08) for males, and 1.41 (95% CI: 0.90\u0026ndash;1.93) and 79.64 (95% CI: 50.56\u0026ndash;109.50) for females. From 1990 to 2021, the global burden of IHD attributable to high systolic blood pressure remained stable in males but declined in females. The estimated annual percentage change (EAPC) in ASMR and ASDR for females was \u0026minus;\u0026thinsp;0.52 (95% CI: \u0026minus;0.59 to \u0026minus;\u0026thinsp;0.44) and \u0026minus;\u0026thinsp;0.51 (95% CI: \u0026minus;0.58 to \u0026minus;\u0026thinsp;0.44), respectively; for males, it was 0.07 (95% CI: \u0026minus;0.05 to 0.19) and 0.10 (95% CI: \u0026minus;0.02 to 0.22).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2.3 Sex Differences in the Global Burden of Ischemic Heart Disease Attributable to High Fasting Plasma Glucose Among Individuals Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the global burden of ischemic heart disease (IHD) attributable to high fasting plasma glucose (FPG) among individuals aged 25\u0026ndash;39 years was significantly higher in males than in females. The number of deaths was 4,945 (95% CI: 3,892\u0026ndash;6,076) in males and 1,779 (95% CI: 1,354\u0026ndash;2,280) in females; corresponding DALYs were 275,412 (95% CI: 216,542\u0026ndash;338,051) for males and 100,024 (95% CI: 76,286\u0026ndash;127,813) for females. After adjusting for age and time, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) per 100,000 population were 0.55 (95% CI: 0.42\u0026ndash;0.70) and 30.70 (95% CI: 23.48\u0026ndash;39.19) for males, and 0.20 (95% CI: 0.15\u0026ndash;0.26) and 11.39 (95% CI: 8.45\u0026ndash;14.73) for females. From 1990 to 2021, the IHD burden attributable to high FPG increased in both sexes, with a more pronounced rise in males. The estimated annual percentage change (EAPC) in ASMR and ASDR was 0.68 (95% CI: 0.63\u0026ndash;0.74) and 0.69 (95% CI: 0.64\u0026ndash;0.74) for females, and 1.16 (95% CI: 1.08\u0026ndash;1.25) and 1.18 (95% CI: 1.09\u0026ndash;1.27) for males.\u003c/p\u003e\u003cp\u003eDetailed results are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSex Differences in the Global Burden of Ischemic Heart Disease Attributable to The Three Metabolic Risk Factors Among Adults Aged 25\u0026ndash;39 Years, 1990\u0026ndash;2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASR\u003c/p\u003e\u003cp\u003e(per 100000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eEAPC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c2\" namest=\"c1\" rowspan=\"4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eLDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25132\u003c/p\u003e\u003cp\u003e(18652,31165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28651\u003c/p\u003e\u003cp\u003e(21865,35300)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003cp\u003e(3.27,5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.27\u003c/p\u003e\u003cp\u003e(2.49,4.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.02\u003c/p\u003e\u003cp\u003e(-1.09,-0.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1437424 (1064827,1784135)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1639425(1250286,2014632)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e250.82(186.13,313.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e187.49(142.58,231.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.01\u003c/p\u003e\u003cp\u003e(-1.08,-0.94)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52864\u003c/p\u003e\u003cp\u003e(40455,64539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e72475\u003c/p\u003e\u003cp\u003e(55264,89924)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003cp\u003e(6.95,11.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.10\u003c/p\u003e\u003cp\u003e(6.17,10.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.43\u003c/p\u003e\u003cp\u003e(-0.51,-0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2978966(2281286,3641197)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4097221(3119757,5080221)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e510.87(390.44,624.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e458.29(349.19,566.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.4\u003c/p\u003e\u003cp\u003e(-0.49,-0.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9167\u003c/p\u003e\u003cp\u003e(5828,13127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12327\u003c/p\u003e\u003cp\u003e(7972,16479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003cp\u003e(0.99,2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003cp\u003e(0.90,1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.52\u003c/p\u003e \u003cp\u003e(-0.59,-0.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e519505\u003c/p\u003e\u003cp\u003e(329747,740181)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e697988\u003c/p\u003e\u003cp\u003e(450715,934661)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e91.23 (55.70,133.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e79.64 (50.56,109.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003cp\u003e(-0.58,-0.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27631\u003c/p\u003e\u003cp\u003e(18770,36024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e43802\u003c/p\u003e\u003cp\u003e(31280,56807)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.76\u003c/p\u003e\u003cp\u003e(3.18,6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003cp\u003e(3.39,6.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(-0.05,0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1551103\u003c/p\u003e\u003cp\u003e(1056646,2026643)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2468611 (1765187,3197373)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e266.58 (177.79,355.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e275.91 (191.06,364.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003cp\u003e(-0.02,0.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eFasting plasma glucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e935\u003c/p\u003e\u003cp\u003e(706,1166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1779\u003c/p\u003e\u003cp\u003e(1354,2280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e(0.12,0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003cp\u003e(0.15,0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.68 (0.63,0.74)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52600\u003c/p\u003e\u003cp\u003e(39687,65764)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100024\u003c/p\u003e\u003cp\u003e(76286,127813)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003cp\u003e(6.80,12.220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.39\u003c/p\u003e\u003cp\u003e(8.45,14.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.69 (0.64,0.74)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2287\u003c/p\u003e\u003cp\u003e(1813,2872)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4945\u003c/p\u003e\u003cp\u003e(3892,6076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003cp\u003e(0.30,0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003cp\u003e(0.42,0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.16 (1.08,1.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e127110\u003c/p\u003e\u003cp\u003e(100855,159364)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e275412\u003c/p\u003e\u003cp\u003e(216542,338051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.97\u003c/p\u003e\u003cp\u003e(16.65,28.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e30.70\u003c/p\u003e\u003cp\u003e(23.48,39.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.18 (1.09,1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change; DALYs, disability-adjusted life years; ASR, Age-standardized Rate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 SDI Regional Differences\u003c/h2\u003e\u003cp\u003e\u003cb\u003e3.3.1 Differences in the Burden of IHD Associated with High LDL Cholesterol Among Individuals Aged 25\u0026ndash;39 Years Across Different SDI Regions in 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, among individuals aged 25\u0026ndash;39 years, the burden of ischemic heart disease (IHD) attributable to high low-density lipoprotein cholesterol (LDL-C) was highest in low-middle SDI regions, with an ASMR of 7.97 per 100,000 (95% CI: 5.90\u0026ndash;9.93) and an ASDR of 450.71 per 100,000 (95% CI: 334.16\u0026ndash;562.45). In contrast, high SDI regions reported the lowest burden, with an ASMR of 2.41 per 100,000 (95% CI: 1.86\u0026ndash;3.02) and an ASDR of 137.91 per 100,000 (95% CI: 106.58\u0026ndash;172.82).From 1990 to 2021, the IHD burden attributable to high LDL-C declined in four of the five SDI regions, except for low SDI regions, which remained stable. The most significant decline was observed in high-middle SDI regions, with EAPCs of -1.91 (95% CI: -2.14 to -1.68) for ASMR and \u0026minus;\u0026thinsp;1.85 (95% CI: -2.08 to -1.63) for ASDR. Pearson correlation analysis revealed a significant negative association between SDI and both ASMR and ASDR of LDL-C-related IHD, indicating that higher SDI levels were associated with lower disease burden (ASMR: R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.5562, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ASDR: R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.5623, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3.2 Differences in the Burden of IHD Associated with High Systolic Blood Pressure Among Individuals Aged 25\u0026ndash;39 Years Across Different SDI Regions in 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the global burden of ischemic heart disease (IHD) attributable to high systolic blood pressure (SBP) in the 25\u0026ndash;39 age group was most severe in low-middle SDI regions, with ASMR and ASDR of 4.73 (95% CI, 3.20, 6.23) and 266.41 (95% CI, 179.69, 351.62) per 100,000, respectively. Conversely, the burden was lightest in high SDI regions, with ASMR and ASDR of 1.10 (95% CI, 0.69, 1.52) and 62.63 (95% CI, 38.91, 86.18) per 100,000. From 1990 to 2021, the disease burden decreased in high and high-middle SDI regions. The EAPC for ASMR and ASDR in high SDI regions was \u0026minus;\u0026thinsp;1.86 (95% CI, -2.12, -1.60) and \u0026minus;\u0026thinsp;1.80 (95% CI, -2.06, -1.54), respectively. In high-middle SDI regions, the EAPC for ASMR and ASDR was \u0026minus;\u0026thinsp;1.44 (95% CI, -1.73, -1.15) and \u0026minus;\u0026thinsp;1.38 (95% CI, -1.67, -1.09), respectively. In contrast, disease burden increased in middle SDI, low-middle SDI, and low SDI regions. The EAPC for ASMR and ASDR in middle SDI regions was 0.23 (95% CI, 0.12, 0.34) and 0.25 (95% CI, 0.14, 0.36), respectively; in low-middle SDI regions, the EAPC was 0.48 (95% CI, 0.35, 0.61) and 0.47 (95% CI, 0.35, 0.60); and in low SDI regions, the EAPC was 0.20 (95% CI, 0.03, 0.38) and 0.20 (95% CI, 0.03, 0.37).Pearson correlation analysis showed a negative correlation between the IHD burden related to high SBP (ASMR and ASDR) and SDI in the 25\u0026ndash;39 age group. As SDI increased, the IHD burden decreased, with ASMR (R = -0.4326, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ASDR (R = -0.4369, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3.3 Differences in the Burden of IHD Associated with High Fasting Plasma Glucose Among Individuals Aged 25\u0026ndash;39 Years Across Different SDI Regions in 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, the burden of ischemic heart disease (IHD) attributable to high fasting plasma glucose in adults aged 25\u0026ndash;39 was highest in low-middle SDI regions, with ASMR and ASDR of 0.54 (95% CI, 0.41, 0.70) and 29.92 (95% CI, 22.64, 38.88) per 100,000, respectively. In contrast, the burden was lowest in high SDI regions, with ASMR and ASDR of 0.22 (95% CI, 0.16, 0.30) and 12.38 (95% CI, 8.86, 17.02) per 100,000.Between 1990 and 2021, the burden increased in all SDI regions except for high-middle SDI regions, where the trend remained relatively stable. The most notable increase occurred in low-middle SDI regions, with EAPCs for ASMR and ASDR both at 1.24 (95% CI, ASMR: 1.13, 1.36; ASDR: 1.12, 1.35).Pearson correlation analysis showed a significant negative correlation between IHD burden (both ASMR and ASDR) attributable to high fasting plasma glucose and SDI across this age group. Specifically, as SDI increased, the disease burden decreased: ASMR (R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.6461, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ASDR (R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.6486, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eDetailed results are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences in the Burden of Ischemic Heart Disease Attributable to The Three Metabolic Risk Factors Among Individuals Aged 25\u0026ndash;39 Years Across Different SDI Regions, 1990\u0026ndash;2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eASDR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eASMR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCharacteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eLDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260.16 (181.89,347.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253.26 (181.87,330.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.12 (-0.3,0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.61 (3.23,6.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.47 (3.21,5.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.11 (-0.29,0.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e512.52(379.27,645.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e450.71 (334.16,562.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.31 (-0.41,-0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.06 (6.70,11.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.97 (5.90,9.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.3 (-0.4,-0.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e399.64 (303.95,490.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e363.16 (278.27,444.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.25 (-0.3,-0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.06 (5.37,8.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.38 (4.90,7.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.27 (-0.32,-0.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e401.10 (316.92,481.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e262.27 (205.25,318.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.85 (-2.08,-1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.15(5.65,8.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.62 (3.62,5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.91 (-2.14,-1.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240.97 (192.64,282.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137.91 (106.58,172.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.62 (-1.77,-1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.27 (3.42,5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.41(1.86,3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.68 (-1.84,-1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHigh systolic\u003c/p\u003e\u003cp\u003eblood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.05 (84.71,196.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147.15 (96.95,196.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2 (0.03,0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.46 (1.51,3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.61 (1.72,3.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.2 (0.03,0.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e246.08 (160.40,335.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e266.41 (179.69,351.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.47 (0.35,0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.37(2.85,5.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.73 (3.20,6.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.48 (0.35,0.61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e179.47 (114.77,245.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e187.90 (124.81,256.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25 (0.14,0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.19 (2.04,4.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.32 (2.21,4.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.23 (0.12,0.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191.70(124.33,264.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147.99 (92.79,207.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.38 (-1.67,-1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.44 (2.24,4.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.62 (1.65,3.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.44 (-1.73,-1.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.87 (78.56,147.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.63 (38.91,86.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.8 (-2.06,-1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.03 (1.40,2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.10 (0.69,1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.86 (-2.12,-1.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHigh fasting\u003c/p\u003e\u003cp\u003eplasma glucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.71 (8.49,15.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.57 (11.03,18.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.74 (0.58,0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21 (0.15,0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26 (0.20,0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75 (0.59,0.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.80 (16.13,29.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.92 (22.64,38.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.24 (1.12,1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.39 (0.29,0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.54 (0.41,0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.24 (1.13,1.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.69 (13.30,22.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.48 (17.71,29.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04 (0.98,1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.32 (0.24,0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42 (0.32,0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.02 (0.97,1.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.00 (10.24,18.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.70 (11.62,20.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06 (-0.1,0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25 (0.18,0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28 (0.21,0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01 (-0.16,0.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.69 (7.42,12.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.38 (8.86,17.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08 (0.9,1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.17 (0.13,0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22 (0.16,0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.04 (0.86,1.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change; SDI, Sociodemographic Index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Geographic Variation\u003c/h2\u003e\u003cp\u003e\u003cb\u003e3.4.1 Geographic Variation in the Burden of IHD Attributable to High LDL Cholesterol Among Adults Aged 25\u0026ndash;39 in 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, among the 21 global regions, Oceania had the highest burden of ischemic heart disease (IHD) attributable to high low-density lipoprotein cholesterol (LDL-C) in adults aged 25\u0026ndash;39, with an ASMR of 12.18 (95% CI, 8.06\u0026ndash;17.65) and an ASDR of 681.85 (95% CI, 451.24\u0026ndash;988.91) per 100,000. In contrast, the burden was lowest in the high-income Asia Pacific region, where ASMR and ASDR were 1.10 (95% CI, 0.86\u0026ndash;1.33) and 64.28 (95% CI, 50.58\u0026ndash;78.04), respectively. Among the 21 regions, six showed a relatively stable trend in IHD burden from 1990 to 2021, including Oceania, Central Latin America, Eastern and Western Sub-Saharan Africa, the Caribbean, and South Asia. The estimated annual percentage change (EAPC) in ASMR and ASDR for these regions were as follows:\u003c/p\u003e\u003cp\u003eOceania: 0.03 (95% CI, -0.04 to 0.10) and 0.04 (95% CI, -0.03 to 0.11);\u003c/p\u003e\u003cp\u003eCentral Latin America: 0.14 (95% CI, -0.32 to 0.60) and 0.16 (95% CI, -0.29 to 0.61);\u003c/p\u003e\u003cp\u003eEastern Sub-Saharan Africa: 0.05 (95% CI, -0.07 to 0.17) and 0.06 (95% CI, -0.06 to 0.18);\u003c/p\u003e\u003cp\u003eWestern Sub-Saharan Africa: 0.12 (95% CI, -0.03 to 0.27) and 0.15 (95% CI, 0.00 to 0.30);\u003c/p\u003e\u003cp\u003eCaribbean: -0.34 (95% CI, -0.71 to 0.04) and \u0026minus;\u0026thinsp;0.33 (95% CI, -0.70 to 0.04);\u003c/p\u003e\u003cp\u003eSouth Asia: -0.14 (95% CI, -0.35 to 0.08) for both ASMR and ASDR.\u003c/p\u003e\u003cp\u003eThe remaining 15 regions experienced a declining trend in IHD burden over time, with the most significant decrease observed in Central Europe, where ASMR and ASDR had EAPCs of -4.59 (95% CI, -4.82 to -4.35) and \u0026minus;\u0026thinsp;4.49 (95% CI, -4.72 to -4.25), respectively.\u003c/p\u003e\u003cp\u003eDetailed results are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.4.2 Geographic Variation in the Burden of IHD Attributable to High Systolic Blood Pressure Among Adults Aged 25\u0026ndash;39 in 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, among the 21 global regions, Oceania had the highest burden of ischemic heart disease (IHD) attributable to high systolic blood pressure (SBP) in adults aged 25\u0026ndash;39, with an ASMR of 5.49 (95% CI, 2.55\u0026ndash;9.68) and an ASDR of 292.75 (95% CI, 134.72\u0026ndash;518.81) per 100,000. In contrast, the burden was lowest in the high-income Asia Pacific region, where ASMR and ASDR were 0.46 (95% CI, 0.23\u0026ndash;0.70) and 26.62 (95% CI, 13.38\u0026ndash;40.16), respectively. Among the 21 regions, 12 showed a declining trend in IHD burden over time, including the high-income Asia Pacific, high-income North America, Western Europe, Australasia, Tropical Latin America, Southern Latin America, Central Europe, Eastern Europe, Central Asia, North Africa and the Middle East, and Sub-Saharan Africa regions. The most significant decrease was observed in Western Europe, where the EAPCs for ASMR and ASDR were \u0026minus;\u0026thinsp;4.31 (95% CI, -4.41 to -4.21) and \u0026minus;\u0026thinsp;4.21 (95% CI, -4.31 to -4.12), respectively. In contrast, 7 regions experienced an increase in IHD burden, with the most notable increase in Western Sub-Saharan Africa, where the EAPCs for ASMR and ASDR were 1.44 (95% CI, 1.21\u0026ndash;1.67) and 1.47 (95% CI, 1.24\u0026ndash;1.69), respectively. These regions included Andean Latin America, the Caribbean, Southeast Asia, East Asia, Oceania, Western Sub-Saharan Africa, and Eastern Sub-Saharan Africa. The IHD burden in South Asia and Central Latin America remained stable over time, with no significant change.\u003c/p\u003e\u003cp\u003eDetailed results are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.4.3 Geographic Variation in the Burden of IHD Attributable to High Fasting Plasma Glucose Among Adults Aged 25\u0026ndash;39 in 2021\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2021, Oceania had the highest burden of IHD attributable to high fasting plasma glucose among individuals aged 25\u0026ndash;39, with an age-standardized mortality rate (ASMR) of 1.17 per 100,000 (95% CI: 0.83\u0026ndash;1.64) and an age-standardized disability-adjusted life year rate (ASDR) of 64.27 (95% CI: 45.68\u0026ndash;90.76). In contrast, Western Europe had the lowest burden, with an ASMR of 0.06 (95% CI: 0.04\u0026ndash;0.07) and an ASDR of 3.16 (95% CI: 2.49\u0026ndash;3.92) per 100,000.Over time, IHD burden decreased in nine regions: High-income Asia Pacific, Western Europe, Australasia, Andean Latin America, Tropical Latin America, Southern Latin America, Central Europe, Eastern Europe, and Central Asia. The most significant decline was observed in Central Europe, where the estimated annual percentage changes (EAPCs) for ASMR and ASDR were \u0026minus;\u0026thinsp;3.61 (95% CI: -3.89 to -3.34) and \u0026minus;\u0026thinsp;3.52 (95% CI: -3.80 to -3.25), respectively. Conversely, nine regions experienced an increasing burden, including High-income North America, Central Latin America, the Caribbean, North Africa and the Middle East, South Asia, East Asia, Oceania, and sub-Saharan Africa (West and East). East Asia showed the most rapid increase, with EAPCs for ASMR and ASDR at 1.37 (95% CI: 1.25 to 1.50) and 1.41 (95% CI: 1.29 to 1.53), respectively. Three regions\u0026mdash;Southeast Asia, Central sub-Saharan Africa, and sub-Saharan Africa\u0026mdash;remained stable over time, with no significant changes in disease burden observed.\u003c/p\u003e\u003cp\u003eDetailed results are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeographic Variation in the Burden of Ischemic Heart Disease Attributable to High LDL Cholesterol Among Adults Aged 25\u0026ndash;39, 1990\u0026ndash;2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eASDR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eASMR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259.56 (192.90,327.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247.44 (180.88,322.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.16 (-0.25,-0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.59 (3.42,5.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.34 (3.16,5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.2 (-0.29,-0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e486.67 (364.20,615.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e441.27 (330.45,568.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.32 (-0.4,-0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.59 (6.43,10.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.79 (5.82,10.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.32 (-0.39,-0.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e674.37 (456.33,960.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e681.85 (451.24,988.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04 (-0.03,0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.09 (8.19,17.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.18 (8.06,17.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03 (-0.04,0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e675.04(513.77,824.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e405.39 (307.01,510.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.6 (-3.08,-2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.07 (9.19,14.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.17 (5.42,9.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.62 (-3.11,-2.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e594.05 (479.38,691.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162.18 (129.22,193.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.49 (-4.72,-4.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.65 (8.59,12.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.83 (2.25,3.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-4.59 (-4.82,-4.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e638.78 (518.97,753.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e434.75 (344.61,530.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.29 (-3.05,-1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.52 (9.36,13.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.78 (6.17,9.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.32 (-3.07,-1.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133.82 (102.54,165.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.28 (50.58,78.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.67 (-2.92,-2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.33 (1.79,2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10 (0.86,1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.74 (-3,-2.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralasia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193.62 (153.94,233.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.87 (57.66,91.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.23 (-3.49,-2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.42 (2.71,4.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.29 (1.00,1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.27 (-3.53,-3.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e224.11 (182.16,261.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.37 (52.22,76.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.89 (-3.98,-3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.96 (3.23,4.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12(0.89,1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.99 (-4.08,-3.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e265.46 (209.14,326.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.82 (85.44,132.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.68 (-3.05,-2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.71 (3.70,5.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.89 (1.48,2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.73 (-3.11,-2.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e230.80 (185.74,267.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.92 (108.25,167.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.59 (-1.7,-1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.10(3.30,4.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.44 (1.91,2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.63 (-1.74,-1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e392.71 (295.56,487.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e313.77 (223.84,420.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.33 (-0.7,0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.94 (5.22,8.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.52 (3.94,7.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.34 (-0.71,0.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndean Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300.97 (224.60,387.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187.99 (137.33,250.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.67 (-2.04,-1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.25 (3.91,6.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.23 (2.35,4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.73 (-2.1,-1.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e290.65 (226.58,349.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e291.03 (227.41,352.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16 (-0.29,0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.10 (3.97,6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.08 (3.96,6.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.14 (-0.32,0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e398.55 (317.30,472.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230.29 (184.50,272.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.74 (-1.97,-1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.09 (5.65,8.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.03 (3.23,4.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.81 (-2.03,-1.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e787.84 (593.74,980.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e500.71 (378.10,631.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.45 (-1.51,-1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.92 (10.49,17.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.77 (6.62,11.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.48 (-1.54,-1.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e533.68 (383.16,685.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e492.06 (362.69,625.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.14 (-0.35,0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.44 (6.78,12.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.70 (6.42,11.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.12 (-0.34,0.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e147.87 (89.37,224.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.14 (86.96,209.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.24 (-0.31,-0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.63 (1.58,3.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.47 (1.54,3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.25 (-0.32,-0.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.22(94.60,193.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150.20 (99.87,207.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06 (-0.06,0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.43 (1.66,3.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.63 (1.75,3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05 (-0.07,0.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e278.58 (205.73,361.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160.28 (117.40,211.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.94 (-2.89,-0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.95 (3.65,6.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.83 (2.07,3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.96 (-2.92,-0.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128.53 (93.05,172.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132.27(92.83,178.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15 (0,0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.28 (1.64,3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.33 (1.63,3.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12 (-0.03,0.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeographic Variation in the Burden of Ischemic Heart Disease Attributable to High Systolic Blood Pressure Among Adults Aged 25\u0026ndash;39, 1990\u0026ndash;2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eASDR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eASMR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.42 (45.04,94.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.62 (13.38,40.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.74 (-3.98,-3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.20 (0.79,1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.46 (0.23,0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.79 (-4.03,-3.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.18 (47.51,115.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.00 (28.47,90.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1 (-1.27,-0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.47 (0.85,2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04 (0.50,1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.03 (-1.3,-0.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123.55 (90.13,154.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.49 (22.54,43.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.21 (-4.31,-4.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.20 (1.61,2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58 (0.39,0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-4.31 (-4.41,-4.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralasia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.80 (59.22,119.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.34 (16.75,48.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.69 (-4.05,-3.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.56 (1.05,2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57 (0.30,0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.73 (-4.09,-3.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndean Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.47 (19.38,114.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.95 (35.38,117.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32 (0.63,2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05 (0.35,2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27 (0.61,2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.22 (0.52,1.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190.64 (122.62,258.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.05 (79.15,150.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.54 (-1.75,-1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.40 (2.20,4.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.02 (1.40,2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.6 (-1.82,-1.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130.13 (77.79,182.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.03 (83.38,196.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34 (-0.2,0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.30(1.38,3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.39 (1.46,3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31 (-0.24,0.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.65 (36.17,134.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.70 (26.86,79.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.96 (-1.26,-0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.44 (0.65,2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92 (0.47,1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.03 (-1.33,-0.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157.91(79.10,244.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165.20 (92.54,249.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73 (0.33,1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.81 (1.41,4.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.92 (1.64,4.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71 (0.3,1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e323.11 (231.00,413.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.73 (67.04,123.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.2 (-4.38,-4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.83 (4.18,7.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.68 (1.17,2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-4.29 (-4.48,-4.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e378.90 (271.80,479.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e266.11(185.22,346.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.21 (-2.93,-1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.86 (4.92,8.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.78 (3.33,6.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.23 (-2.96,-1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e386.28 (258.25,515.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e256.31(171.11,341.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.19 (-2.59,-1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.95 (4.65,9.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.55 (3.05,6.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.23 (-2.63,-1.82)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e370.62 (228.18,528.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e272.94(177.57,373.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.89 (-0.97,-0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.57 (4.05,9.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.80 (3.13,6.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.92 (-1,-0.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e278.37 (182.32,377.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e277.09(184.88,371.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1 (-0.16,0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.95 (3.25,6.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.93 (3.29,6.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11 (-0.15,0.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220.45(138.36,302.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e241.97(158.69,334.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55 (0.45,0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.92 (2.46,5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.45 (2.94,6.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.55 (0.45,0.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.03 (24.88,168.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126.36 (44.08,217.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29 (1.13,1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.50 (0.45,2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.31(0.82,3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.25 (1.08,1.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e208.38 (105.73,332.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e292.75(134.72,518.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.31 (1.11,1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.76 (1.91,6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.49 (2.55,9.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.3 (1.1,1.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.29 (42.21,96.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.57 (62.23,132.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47 (1.24,1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.20(0.75,1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.67 (1.10,2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.44 (1.21,1.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.20 (39.81,95.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.08 (63.31,140.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36 (1.19,1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15(0.70,1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.80 (1.11,2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.35 (1.17,1.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110.95 (58.87,178.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.91 (49.21,148.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.81 (-0.89,-0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.98 (1.05,3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.64 (0.87,2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.82 (-0.9,-0.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198.04 (114.15,286.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112.96 (67.77,157.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.03 (-2.92,-1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.55(2.05,5.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.00 (1.20,2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.06 (-2.96,-1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeographic Variation in the Burden of Ischemic Heart Disease Attributable to High Fasting Plasma Glucose Among Adults Aged 25\u0026ndash;39, 1990\u0026ndash;2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eASDR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eASMR(per 100000)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1990\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.31 (4.53,8.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.87(2.81,5.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.04 (-2.22,-1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11 (0.08,0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.07 (0.05,0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.1 (-2.3,-1.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.77 (6.56,14.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.80 (8.63,18.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13 (0.91,1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18(0.12,0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23 (0.15,0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.09 (0.87,1.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.46 (5.78,9.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.16 (2.49,3.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.74 (-2.84,-2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13 (0.10,0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06 (0.04,0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.84 (-2.94,-2.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralasia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.57 (4.11,10.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.32 (2.60,7.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.42 (-1.69,-1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12 (0.07,0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08 (0.05,0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.46 (-1.72,-1.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndean Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.14 (4.76,10.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.54 (4.50,9.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.58 (-1.03,-0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13 (0.08,0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11 (0.08,0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.63 (-1.09,-0.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.62 (11.16,24.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.52(8.83,19.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.63 (-0.86,-0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30 (0.20,0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.24 (0.16,0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.69 (-0.92,-0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.23 (11.41,19.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.37 (15.55,29.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24 (0.81,1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27 (0.20,0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38 (0.28,0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.23 (0.79,1.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.49 (4.88,11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.30 (3.67,7.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.79 (-1.07,-0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13 (0.09,0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09 (0.06,0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.84 (-1.12,-0.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.35 (12.23,21.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.66 (12.48,24.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66 (0.24,1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29 (0.22,0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.32(0.22,0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65 (0.23,1.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.65 (14.38,27.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.31 (5.46,9.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.52 (-3.8,-3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36 (0.26,0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13 (0.10,0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.61 (-3.89,-3.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.14 (10.92,20.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.65 (10.03,18.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.29 (-2.03,-0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28 (0.20,0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25 (0.18,0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.31 (-2.05,-0.57)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.81 (13.31,22.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.11 (12.95,22.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.96 (-1.4,-0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32 (0.24,0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.31 (0.23,0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.98 (-1.42,-0.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.52 (20.61,36.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.28 (25.36,43.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66 (0.6,0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49 (0.37,0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.59 (0.45,0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64 (0.58,0.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.20 (19.56,40.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.28 (27.26,54.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23 (1.01,1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51(0.35,0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69 (0.49,0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.24 (1.02,1.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.27 (10.85,18.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.17 (12.04,21.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14 (0,0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26 (0.20,0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.29 (0.22,0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.14 (0,0.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.63 (8.04,19.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.48 (12.42,27.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41 (1.29,1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23 (0.14,0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33 (0.22,0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.37 (1.25,1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.55 (35.10,73.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.27 (45.68,90.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74 (0.64,0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.92 (0.64,1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.17(0.83,1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73 (0.63,0.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.34 (3.11,5.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.33 (4.59,8.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3 (1.15,1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08(0.06,0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11 (0.08,0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.29 (1.13,1.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.94 (2.87,5.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.93 (3.63,6.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5 (0.42,0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07 (0.05,0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09(0.06,0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.49 (0.4,0.57)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.33 (6.12,20.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.87 (7.34,19.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.05 (-0.22,0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.20 (0.11,0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21 (0.13,0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.05 (-0.23,0.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.58 (5.95,11.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.90 (4.99,9.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.86 (-1.85,0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15 (0.11,0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12 (0.09,0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.88 (-1.88,0.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASMR, age-standardized mortality rate; ASDR, age-standardized disability-adjusted life years; EAPC, estimated annual percentage change.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Differences Between Countries Worldwide\u003c/h2\u003e\u003cp\u003eIn 2021, substantial disparities in the burden of ischemic heart disease (IHD) attributable to three major metabolic risk factors were observed among individuals aged 25\u0026ndash;39 years across countries.\u003c/p\u003e\u003cp\u003eFor IHD attributable to elevated low-density lipoprotein cholesterol (LDL-C), Kingdom of Sweden exhibited the lowest age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life year rate (ASDR), at 0.42 (95% CI: 0.32\u0026ndash;0.54) and 27.31 (95% CI: 20.85\u0026ndash;34.75) per 100,000 population, respectively. In contrast, Republic of Nauru reported the highest burden, with ASMR and ASDR of 45.41 (95% CI: 30.17\u0026ndash;65.93) and 2529.20 (95% CI: 1678.50\u0026ndash;3673.91), representing approximately 108.0-fold and 92.6-fold differences.\u003c/p\u003e\u003cp\u003eRegarding IHD attributable to high systolic blood pressure, Kingdom of Sweden again had the lowest ASMR and ASDR (0.15 [95% CI: 0.05\u0026ndash;0.27] and 9.83 [95% CI: 3.27\u0026ndash;17.58] per 100,000), while Republic of Nauru reported the highest values (30.96 [95% CI: 15.21\u0026ndash;50.03] and 1718.49 [95% CI: 840.35\u0026ndash;2780.75]), indicating differences of 200.4-fold and 174.8-fold, respectively.\u003c/p\u003e\u003cp\u003eFor high fasting plasma glucose, the lowest ASMR and ASDR were observed in Kingdom of Sweden (0.02 [95% CI: 0.02\u0026ndash;0.04] and 1.58 [95% CI: 1.00\u0026ndash;2.59] per 100,000), whereas the highest were in the Republic of the Marshall Islands (4.58 [95% CI: 2.41\u0026ndash;8.12] and 253.64 [95% CI: 133.73\u0026ndash;447.88] per 100,000), corresponding to approximately 229.0-fold and 160.7-fold differences.\u003c/p\u003e\u003cp\u003eAlthough India, China, and Pakistan did not rank among the countries with the highest ASMR or ASDR, their large population sizes resulted in the greatest absolute numbers of IHD-related deaths and DALYs attributable to these metabolic risk factors globally.\u003c/p\u003e\u003cp\u003eDetailed results are provided in Supplementary materials(Tables\u0026nbsp;7\u0026ndash;12).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Global Burden of Ischemic Heart Disease (IHD)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, the absolute burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose increased significantly, primarily driven by global population growth and aging.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for age structure and temporal changes, the burden of IHD attributable to high LDL cholesterol showed a declining trend from 1990 to 2021. This reduction is likely due to advances in lipid-lowering therapies and improved cardiovascular management. Since the 1990s, the widespread use of statins has significantly reduced the risk of cardiovascular events among individuals with elevated LDL-C(13). In recent years, the development of novel oral agents and combination therapies has further enhanced treatment efficacy(14). Simultaneously, many countries have implemented lipid screening programs and clinical guidelines, improving intervention coverage among high-risk populations. Public health education has raised awareness of dyslipidemia, while the promotion of low-fat diets and regular physical activity has encouraged healthier lifestyles.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for age and temporal trends, the burden of IHD attributable to high systolic blood pressure remained largely unchanged. This stability may be due to the limited coverage and adherence to hypertension prevention and management strategies among young adults. In low-income regions, hypertension screening rates are low, and many individuals remain undiagnosed or untreated(15). Moreover, unhealthy lifestyles\u0026mdash;such as physical inactivity, obesity, excessive alcohol consumption, and high-salt diets\u0026mdash;undermine the effects of antihypertensive therapies and public health education. Inequitable healthcare resource distribution and inadequate primary care services further hinder effective intervention, contributing to the stagnation in disease burden(15, 16).\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, the burden of IHD attributable to high fasting plasma glucose increased significantly in both absolute numbers and age-standardized rates. This trend likely reflects the growing prevalence of diabetes among younger populations, coupled with inadequate glycemic management, including limited access to lifestyle interventions and pharmacotherapy. The widespread consumption of high-sugar and high-calorie foods\u0026mdash;such as sugary beverages and fast food\u0026mdash;has led to rising rates of obesity and insulin resistance(17, 18), while physical inactivity has further exacerbated metabolic dysfunction(19). Delayed screening has resulted in late diagnosis and treatment, accelerating vascular damage. Moreover, disparities in healthcare access have limited the availability of effective glucose-lowering therapies in low- and middle-income regions.\u003c/p\u003e\n\u003cp\u003eIn summary, among the three metabolic risk factors analyzed in this study, high LDL cholesterol was associated with the greatest burden of ischemic heart disease (IHD) among adults aged 25\u0026ndash;39. While effective control has been achieved, continued investment in lipid-lowering therapies\u0026mdash;such as expanding the use of statins and novel agents\u0026mdash;is essential. The burden attributable to high systolic blood pressure has remained stable without notable decline, indicating a need to strengthen hypertension screening and management in young adults, including the promotion of affordable antihypertensive medications. The burden linked to high fasting plasma glucose has continued to rise and should be prioritized through enhanced efforts in diabetes prevention, screening, and glycemic control policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Sex Differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong adults aged 25\u0026ndash;39, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose is significantly higher in men than in women. From 1990 to 2021, the decline in IHD burden related to high LDL-C and high systolic pressure was less pronounced in men, while the increase related to high fasting glucose was more substantial in men. These sex-based differences may be explained by the following factors:\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eEstrogen exerts protective effects on the cardiovascular system, potentially delaying atherosclerosis in women(20);\u003c/li\u003e\n \u003cli\u003eMen have greater arterial stiffness, making blood pressure harder to control(21);\u003c/li\u003e\n \u003cli\u003eVisceral fat accumulation and insulin resistance are more common in men(22);\u003c/li\u003e\n \u003cli\u003eUnhealthy behaviors such as smoking, alcohol consumption, high-salt and high-sugar diets are more prevalent among men, along with lower physical activity levels;\u003c/li\u003e\n \u003cli\u003eWomen are more proactive in health screening and chronic disease management, with better adherence to medical interventions;\u003c/li\u003e\n \u003cli\u003ePregnancy-related health checks facilitate early detection and management of metabolic risk factors;\u003c/li\u003e\n \u003cli\u003eMen are more frequently exposed to occupational stress and sleep deprivation, which may further elevate cardiovascular risk.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 SDI Regional Differences and Geographic Variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Sociodemographic Index (SDI) is a composite indicator used to assess the level of social development and demographic structure across countries and regions. It is widely employed in global health research to evaluate disparities in disease burden and their underlying socioeconomic drivers. SDI is calculated based on three key dimensions: per capita income (reflecting economic development), average years of education (indicating educational attainment), and total fertility rate (TFR), which reflects demographic structure and modernization\u0026mdash;lower TFR generally indicates a higher level of development. While SDI primarily reflects overall socioeconomic development, geographic regions are shaped by distinct factors such as culture, dietary patterns, and environmental conditions. Together, these factors influence regional variations in the risk and burden of ischemic heart disease (IHD).\u003c/p\u003e\n\u003cp\u003eOverall, among individuals aged 25\u0026ndash;39, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose is inversely associated with Sociodemographic Index (SDI) levels. This trend is largely driven by differences in socioeconomic status and healthcare resources.\u003c/p\u003e\n\u003cp\u003eIn low-SDI regions, underdeveloped economies and limited resources contribute to low health expenditure, inadequate primary healthcare infrastructure, insufficient health education, and limited access to early screening and long-term treatment. In contrast, high-SDI regions benefit from more advanced healthcare systems, broader insurance coverage, greater availability of early detection programs, and improved access to essential medications. Furthermore, these regions often implement comprehensive public health interventions, have higher levels of health education, and foster stronger health awareness and earlier disease intervention among the population.\u003c/p\u003e\n\u003cp\u003eDuring the transition from low to high SDI levels, economic growth and improved healthcare resources contribute to better health outcomes. However, rapid urbanization simultaneously introduces new challenges. Increased consumption of processed foods high in salt, saturated fats, sugar and calories, along with the erosion of traditional diets rich in fiber and low in fat, elevates metabolic risks(23, 24). Additionally, shifts in work and lifestyle(23), and delayed chronic disease management may offset some of the health gains associated with rising SDI.\u003c/p\u003e\n\u003cp\u003eBased on the analysis of 21 regions, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose is most pronounced in Oceania among individuals aged 25\u0026ndash;39. Several factors may contribute to this pattern:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eDietary transition: Traditional diets are increasingly replaced by high-fat, high-sugar, and high-salt foods(25);\u003c/li\u003e\n \u003cli\u003eRising youth obesity: Obesity rates among adults aged 25\u0026ndash;39 are markedly elevated(26, 27), contributing to elevated LDL cholesterol, insulin resistance, and hypertension, all of which increase IHD risk;\u003c/li\u003e\n \u003cli\u003eLifestyle changes: Accelerated urbanization has led to reduced physical activity, disrupting energy balance(28);\u003c/li\u003e\n \u003cli\u003eGenetic predisposition: Pacific Islanders, such as Polynesians, may carry gene polymorphisms associated with a higher susceptibility to lipid metabolism disorders and insulin resistance under modern dietary conditions(29, 30);\u003c/li\u003e\n \u003cli\u003eInadequate healthcare systems: Weak chronic disease control, limited health education, and poor access to essential medications hinder effective prevention and management;\u003c/li\u003e\n \u003cli\u003eSocioeconomic and environmental challenges(31): In some island nations, healthy foods are unaffordable for low-income families, who rely more on cheap; meanwhile, rising sea levels and extreme weather damage local agriculture, exacerbating food insecurity and increasing reliance on imported unhealthy food.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAmong individuals aged 25\u0026ndash;39, the burden of ischemic heart disease (IHD) attributable to high LDL cholesterol and high systolic blood pressure is the lowest in high-income Asia Pacific countries. For IHD attributable to high fasting plasma glucose, the lowest burden is observed in Western Europe, which also exhibits the fastest decline in IHD burden related to high systolic blood pressure in this age group. These trends reflect both the general advantages of high-SDI regions\u0026mdash;such as strong economies, well-developed healthcare systems, and comprehensive public health interventions\u0026mdash;and region-specific factors. These include: (1) the continuation of traditional healthy dietary habits (low in saturated fat and high in dietary fiber), supported by robust nutrition policies; and (2) widespread health education, strong public awareness, and high coverage of early screening programs, enabling timely detection and intervention. Additionally, Central Europe has shown the most significant reductions in IHD burden attributable to high LDL cholesterol and high fasting plasma glucose. This progress likely results from a combination of economic development, improvements in healthcare access, effective public health strategies, and regional sociocultural influences.\u003c/p\u003e\n\u003cp\u003eIn summary, from 1990 to 2021, the global burden of ischemic heart disease attributable to high LDL cholesterol, high systolic blood pressure, and high fasting plasma glucose among young adults aged 25\u0026ndash;39 demonstrated complex spatiotemporal trends, with marked disparities by sex, region, and level of sociodemographic development. Although progress has been made in controlling certain metabolic risk factors, the overall trend remains concerning. There is an urgent need for multi-level interventions, particularly targeting high-risk populations, resource-limited settings, and countries with rapidly increasing burdens. Effective prevention and control of metabolic risks in young adults is critical and requires a shift from a treatment-centered model to a comprehensive approach, integrating policy guidance, environmental improvements, and behavioral interventions. Furthermore, leveraging the resource allocation advantages brought by social development is essential to advancing precision and efficiency in chronic disease control and meeting the ongoing global challenge of noncommunicable disease epidemics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIHD Ischemic Heart Disease\u003c/p\u003e\u003cp\u003eSBP Systolic Blood Pressure\u003c/p\u003e\u003cp\u003eFPG Fasting Plasma Glucose\u003c/p\u003e\u003cp\u003eLDL-C Low-Density Lipoprotein Cholesterol\u003c/p\u003e\u003cp\u003eGBD Global Burden of Disease\u003c/p\u003e\u003cp\u003eASMR Age-Standardized Mortality Rate\u003c/p\u003e\u003cp\u003eASDR Age-Standardized Disability-adjusted life years Rate\u003c/p\u003e\u003cp\u003eSDI Sociodemographic Index\u003c/p\u003e\u003cp\u003eDALYs Disability-Adjusted Life Years\u003c/p\u003e\u003cp\u003eCAD Coronary Artery Disease\u003c/p\u003e\u003cp\u003eASCVD Atherosclerotic Cardiovascular Disease\u003c/p\u003e\u003cp\u003eASRs Age-Standardized Rates\u003c/p\u003e\u003cp\u003eEAPC Estimated Annual Percentage Change\u003c/p\u003e\u003cp\u003eCI Confidence Interval\u003c/p\u003e\u003cp\u003eTFR Total Fertility Rate\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors of the manuscript have read and agreed to its content and are accountable for all aspects of the accuracy and integrity of the manuscript in accordance with ICMJE (International Committee of Medical Journal Editors) criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests: \u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e This study used data exclusively from the Global Burden of Disease (GBD) study. No individual participants were directly involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability: \u003c/strong\u003eThe GBD Study 2021 data are publicly available online through the Global Health Data Exchange (GHDx) query tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/span\u003e\u003c/span\u003e). All data generated or analyzed during this study are included in this article and the supplementary material.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eT. L: Conceptualization, Methodology, Formal Analysis, Data Curation, Writing-Original Draft, Writing-Review \u0026amp; Editing, Supervision, Validation, Project Administration. D.F: Validation, Investigation.F. C: Resources, Data Curation.C. C: Writing-Review \u0026amp; Editing.X.L: Supervision.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank all the collaborators and team members of the Global Burden of Disease study for their valuable contributions and efforts. We also sincerely acknowledge the Institute for Health Metrics and Evaluation (IHME) for providing the data. Special thanks to the GBD Study 2021 collaborators for their invaluable contributions and dedication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEttehad D, Emdin CA, Kiran A, Anderson SG, Callender T, Emberson J, et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet. 2016;387(10022):957\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabatine MS, Wiviott SD, Im K, Murphy SA, Giugliano RP. Efficacy and Safety of Further Lowering of Low-Density Lipoprotein Cholesterol in Patients Starting With Very Low Levels: A Meta-analysis. JAMA Cardiol. 2018;3(9):823\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerstein HC, Miller ME, Ismail-Beigi F, Largay J, McDonald C, Lochnan HA, et al. Effects of intensive glycaemic control on ischaemic heart disease: analysis of data from the randomised, controlled ACCORD trial. Lancet. 2014;384(9958):1936\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVasan RS, Song RJ, van den Heuvel ER. Temporal Trends in Incidence of Premature Cardiovascular Disease Over the Past 7 Decades: The Framingham Heart Study. J Am Heart Association. 2022;11(19):e026497.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011\u0026ndash;2020. 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Am J Clin Nutr. 2023;117(1):160\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMałkowska P. Positive Effects of Physical Activity on Insulin Signaling. Curr Issues Mol Biol. 2024;46(6):5467\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFontaine C, Morfoisse F, Tatin F, Zamora A, Zahreddine R, Henrion D et al. The Impact of Estrogen Receptor in Arterial and Lymphatic Vascular Diseases. Int J Mol Sci. 2020;21(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOgola BO, Zimmerman MA, Clark GL, Abshire CM, Gentry KM, Miller KS, et al. New insights into arterial stiffening: does sex matter? Am J Physiol Heart Circ Physiol. 2018;315(5):H1073\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMachann J, Thamer C, Schnoedt B, Stefan N, Stumvoll M, Haring HU, et al. 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Determinants of overweight and obesity and preventive strategies in Pacific countries: a systematic review. Global Health J. 2022;6(3):122\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHanson RL, Safabakhsh S, Curtis JM, Hsueh WC, Jones LI, Aflague TF, et al. Association of CREBRF variants with obesity and diabetes in Pacific Islanders from Guam and Saipan. Diabetologia. 2019;62(9):1647\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMyles S, Hradetzky E, Engelken J, Lao O, N\u0026uuml;rnberg P, Trent RJ, et al. Identification of a candidate genetic variant for the high prevalence of type II diabetes in Polynesians. Eur J Hum genetics: EJHG. 2007;15(5):584\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavila F, Burkhart S, O\u0026rsquo;Connell T. State of Food and Nutrition Security in the Pacific. In: Dansie A, Alleway HK, B\u0026ouml;er B, editors. The Water, Energy, and Food Security Nexus in Asia and the Pacific: The Pacific. Cham: Springer International Publishing; 2024. pp. 85\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 7 to 12 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ischemic Heart Disease, Global burden of disease, LDL-C, SBP, FPG, Aged 25–39","lastPublishedDoi":"10.21203/rs.3.rs-6862760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6862760/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\u003eYoung adults aged 25–39 are at high risk for early-onset ischemic heart disease (IHD), with a steadily rising prevalence of metabolic risk factors, including high low-density lipoprotein cholesterol (LDL-C), high systolic blood pressure(SBP), and high fasting plasma glucose (FPG). This study aims to assess the global, regional, and national burden of IHD attributable to these factors among young adults from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing data from the Global Burden of Disease Study 2021 (GBD 2021), we analyzed age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate(ASDR) due to IHD attributable to high LDL-C, high SBP, and high FPG among young adults. Pearson correlation and log-linear regression were used to examine trends and associations with the Sociodemographic Index (SDI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, the global burden of IHD attributable to high LDL-C, high SBP, and high FPG increased in absolute terms, but age-standardized rates varied. LDL-C-related burden decreased, SBP remained stable, and FPG-related burden significantly increased. Men had higher burdens, with low-middle SDI regions experiencing the highest burden and high-SDI regions the lowest. Disease burden was negatively associated with SDI. Oceania had the highest burden, while high-income Asia-Pacific and Western Europe had the lowest. Central Europe saw the greatest reduction in LDL-C burden, Western Europe in SBP burden, and East Asia in high glucose-related burden. Nauru and the Marshall Islands had the highest burden, while Sweden had the lowest. India, China, and Pakistan, with large populations, contributed significantly to global deaths and disability-adjusted life years(DALYs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween 1990 and 2021, the global burden of IHD attributable to high LDL-C, high SBP, and high FPG among individuals aged 25–39 showed significant variation across time, genders, regions, and countries. While progress has been made in managing some metabolic risk factors, the overall trend remains concerning, highlighting the urgent need for enhanced, multi-level, targeted interventions.\u003c/p\u003e","manuscriptTitle":"Global, Regional, and National Burden of Ischemic Heart Disease Due to High LDL Cholesterol, Systolic Blood Pressure, and Fasting Glucose in Young Adults (1990–2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 17:44:30","doi":"10.21203/rs.3.rs-6862760/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-26T01:37:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76821054872333035046181761768648098072","date":"2025-07-14T01:16:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T13:10:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273235206701503357329617684801928476665","date":"2025-07-10T13:08:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T11:47:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-17T11:03:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-16T07:37:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-16T07:34:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-06-10T11:46:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ffe8a2a-2a28-4963-ab17-b05b5325bf83","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T17:44:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 17:44:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6862760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6862760","identity":"rs-6862760","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}