Ischemic Heart Disease Mortality and Metabolic Risk Factors: A Global Analysis of Gender Disparities and Regional Variations (1980–2021)

Ischemic Heart Disease Mortality and Metabolic Risk Factors: A Global Analysis of Gender Disparities and Regional Variations (1980–2021) | 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 Ischemic Heart Disease Mortality and Metabolic Risk Factors: A Global Analysis of Gender Disparities and Regional Variations (1980–2021) Huili Li, Fei Xiao, Jiaying Chen, Jiarui Wang, Yuanlu Chen, Zhenyu Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5820382/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The mortality rate and the number of deaths from ischemic heart disease (IHD) in developing regions have surpassed those in developed regions. Since 1980, the global number of IHD deaths, disability-adjusted life years (DALY), years of life lost (YLL), and years lived with disability (YLD) have significantly increased, particularly in regions with medium and high Social Development Index (SDI). Although the overall mortality rate has declined, developing countries, especially those in the medium SDI and low-medium SDI regions, are facing a greater burden of death. In the 1990s, while the number of IHD deaths increased, the socio-economic development level (SDI) also improved. However, over the past 30 years, the number of deaths in medium SDI regions, especially in China and Central Asia, has risen sharply, with these regions experiencing a faster increase in mortality rates. In contrast, the number of deaths in high SDI regions has steadily declined. The IHD mortality rate among men is generally higher than that of women, and the gender gap may continue to widen. Globally, the main risk factors for IHD deaths include high systolic blood pressure, high low-density lipoprotein (LDL), smoking, and high blood sugar. With improvements in health management in developed regions, deaths caused by metabolic risk factors have significantly declined. However, risk factors in developing countries, particularly in low-income and middle-income regions, remain significant. Air pollution, smoking, and other factors continue to be major health threats. Ischemic heart disease metabolic mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Despite a 34.9% decrease in the global age-standardized mortality rate for cardiovascular diseases (CVDs) (from 358.4 per 100,000 in 1990 to 233.2 per 100,000 in 2022), the actual number of deaths from CVDs has risen—from 12.4 million in 1990 to 19.8 million in 2022. This increase reflects the impacts of global population growth, aging, and modifiable risk factors related to metabolism, behavior, and the environment. To date, ischemic heart disease (IHD) remains the primary cause of CVD mortality worldwide, with an age-standardized death rate of 108.8 per 100,000. Hemorrhagic and ischemic strokes follow as other leading causes. The 2023 Global Burden of Cardiovascular Diseases report attributes the rising number of CVD deaths largely to preventable factors, such as high blood pressure, elevated cholesterol, unhealthy diet, and air pollution 1 . Given the profound role that metabolic risk factors such as hypertension, hyperlipidemia, and obesity play in the development and progression of ischemic heart disease (IHD), understanding how these risk factors have influenced IHD mortality over time is critical for devising effective public health interventions. Large-scale epidemiological studies, such as the Global Burden of Disease (GBD) study, have consistently identified metabolic syndrome and its components as significant contributors to IHD mortality, with rising trends in many regions attributed to increasing rates of obesity and type 2 diabetes 2 – 3 . These risk factors not only increase the likelihood of developing IHD but also complicate its management and prognosis, underscoring the need for targeted strategies that address these modifiable risks. Targeted programs focusing on managing metabolic risks, such as hypertension, diabetes, and obesity, in addition to environmental and lifestyle changes, can significantly reduce the burden of IHD in these populations, where resources for health care are often limited. These insights will aid policymakers in developing effective strategies for both prevention and treatment, particularly as cardiovascular disease rates rise globally 3 . A paper explored the global burden of cardiovascular diseases (CVDs) and their risk factors from 1990 to 2019, based on estimates from the Global Burden of Disease Study (GBD 2019). The burden of CVDs is notably higher in low- and middle-income countries compared to high-income countries. The article discusses various disease conditions across different regions, including rheumatic heart disease and congenital heart disease, as well as the impact of risk factors such as elevated fasting plasma glucose, body mass index (BMI), and hypertension on cardiovascular health 4 . The cardiovascular risk factors and burden in adolescents and young adults have also been studied, based on data from the Global Burden of Disease Study (GBD 2019). It primarily discusses the prevalence, incidence, disability-adjusted life years (DALYs), and mortality rates of overall and specific types of cardiovascular diseases, with stratified analyses by gender, age group, region, and the Socio-Demographic Index (SDI). High systolic blood pressure, high body mass index (BMI), and elevated low-density lipoprotein (LDL) cholesterol are identified as major contributors to CVD worldwide, particularly in low-income and lower-middle-income countries. Additionally, household air pollution from solid fuels is highlighted as a significant risk factor in low-income countries 5 . An article also discusses the environmental factors influencing cardiovascular health. Münzel T et al. primarily discuss the impact of environmental risk factors on cardiovascular diseases (CVDs), identifying environmental pollutants such as air pollution, noise pollution, light pollution, and climate change as major contributors to CVDs. These non-communicable diseases (NCDs) significantly contribute to the global disease burden, particularly in low- and middle-income countries. The article summarizes the pathophysiological mechanisms through which these environmental stressors affect the cardiovascular system and suggests potential solutions to mitigate these risks 6 . A recent article, based on data from the Global Burden of Disease Study 2019, focuses on the impact of metabolic risk factors on the burden of ischemic heart disease (IHD). The article analyzes the burden of IHD across various countries and regions worldwide from 1990 to 2019, identifying metabolic risk factors—such as high blood pressure, elevated low-density lipoprotein (LDL) cholesterol, high fasting blood glucose, and high body mass index—as major drivers of the global mortality burden of IHD 7 . These studies provide a foundation for understanding the shifting disease patterns and underscore the importance of addressing modifiable risk factors in developing regions. Building on this foundation, we conducted an updated analysis of the impact of metabolic factors on ischemic heart disease. Our study reveals significant differences in ischemic heart disease (IHD) across regions and between genders. While global IHD mortality rates have decreased, the burden in developing countries continues to rise, particularly in middle and low-middle SDI regions, where IHD-related deaths have notably increased. Male mortality rates are higher than those of females, and this gender gap has widened over time. Metabolic risk factors such as hypertension, low-density lipoprotein cholesterol, and hyperglycemia dominate the IHD mortality rates across SDI regions, while smoking and environmental pollution remain significant risk factors in low-SDI regions. Overall, the cardiovascular burden in developing countries is intensifying, especially among younger populations, highlighting a major challenge for global health management. Result The number of IHD-related deaths and mortality rates in developing regions surpass those in developed regions. The overall health impact of ischemic heart disease (IHD) is measured in terms of deaths and disability-adjusted life years (DALYs), which comprise years of life lost (YLL) and years lived with disability (YLD). From 1980 to 2021, there was a significant rise in IHD-related deaths, DALYs, YLL, and YLD. For instance, IHD deaths rose steadily from 2.54 million (95% uncertainty interval [UI]: 2.41–2.65) in 1980 to 8.99 million (95% UI: 8.26–9.53) by 2021 (Table 1 ; Fig. 1 A). Similarly, the number of DALYs increased from 119.16 (95% UI: 114.55, 123.45) million in 1990 to 188.36 million (95% UI: 177.03–198.15) in 2021 (Table S1 ). Both YLL and YLD attributed to IHD showed a corresponding upward trend similar to DALYs (Tables S2 and S3). Despite the rise in absolute numbers, global age-standardized rates for deaths, DALYs, YLL, and YLD showed a gradual decline between 1990 and 2021 (Tables 1 and S1–S3). This upward trend in IHD incidence aligns with the broader increase in the disease burden of all cardiovascular diseases. Table 1 The death cases and age-standardized death rates of ischemic heart disease in 1990 and 2021, and their temporal trends from 1990 to 2021 Characteristics 1990 2021 1990–2021 Number of DALYs No. × 10 3 (95% UI) ASR-DALYs per 100, 000 No. (95% UI) Number of DALYs No. × 10 3 (95% UI) ASR-DALYs per 100, 000 No. (95% UI) EAPC of ASR-YLL No. (95% CI) Global 5367.14 (5076.40-5562.77) 158.90 (148.14–165.30) 8991.64 (8264.12-9531.13) 108.73 (99.60-115.38) -1.23 Sex Male 2804.95 (2673.80-2921.93) 187.66 (177.77-195.35) 5002.68 (4679.27-5336.25) 136.84 (127.37–145.90) -1.01 Female 2562.19 (2357.41-2702.95) 134.50 (122.46-142.23) 3988.96 (3540.38-4317.97) 85.32 (75.90-92.31) -1.46 Sociodemographic index Low SDI 228.81 (202.39-255.18) 119.50 (106.26-132.45) 493.48 (448.13-543.12) 116.41 (105.21-127.69) -0.08 Low-middle SDI 753.13 (698.70-804.55) 140.99 (129.76-151.24) 1834.65 (1699.51-1964.08) 142.10 (131.30-151.87) 0.03 Middle SDI 1038.97 (978.52-1100.28) 127.06 (118.35-134.97) 2811.12 (2564.66-3023.43) 118.71 (107.23–127.80) -0.22 High-middle SDI 1604.86 (1527.81-1657.94) 193.94 (182.11–200.60) 2450.43 (2218.84-2647.30) 127.50 (115.03-137.72) -1.34 High SDI 1732.39 (1591.27-1797.85) 157.59 (144.19-163.92) 1392.37 (1217.17-1489.07) 58.45 (52.18–61.92) -3.15 Location High-income Asia Pacific 117.62 (107.06-122.95) 67.04 (59.85–70.50) 152.70 (124.00-168.79) 25.56 (21.78–27.65) -3.06 Central Asia 131.77 (124.69-136.25) 320.47 (299.83-332.36) 175.39 (159.20-192.41) 265.51 (240.67-290.42) -0.61 High-income North America 644.90 (580.45-675.67) 177.72 (160.27-186.07) 534.77 (468.40-571.53) 75.85 (67.17–80.60) -2.71 Western Sub-Saharan Africa 75.12 (64.54–87.13) 105.29 (90.24-121.56) 161.33 (140.03-185.44) 105.97 (92.83-120.17) 0.02 Tropical Latin America 107.55 (102.01-110.64) 135.91 (126.39-140.82) 162.30 (149.05-170.32) 64.49 (58.98–67.84) -2.38 Australasia 40.16 (37.29–41.66) 176.76 (162.82-184.02) 28.64 (24.37–30.97) 46.67 (40.23–50.21) -4.2 Western Europe 879.29 (811.26-910.87) 148.22 (136.54-153.85) 543.04 (463.99-584.34) 47.27 (41.45–50.42) -3.62 East Asia 570.43 (505.99-639.93) 93.92 (83.87–105.30) 2008.01 (1683.97-2335.23) 108.90 (91.18-125.79) 0.48 Eastern Europe 786.27 (751.13–803.30) 323.17 (305.26-331.76) 903.62 (811.06-990.58) 252.89 (226.96-277.15) -0.79 Southern Sub-Saharan Africa 17.90 (15.56–19.84) 75.96 (64.95–84.84) 39.82 (36.78–43.19) 83.44 (76.93–90.19) 0.3 Caribbean 44.60 (42.48–46.17) 188.97 (179.08-195.79) 61.45 (54.94–69.04) 112.50 (100.51-126.54) -1.66 Central Sub-Saharan Africa 23.27 (18.25–29.48) 134.90 (107.86–167.20) 49.85 (38.89–63.65) 119.34 (93.70-150.29) -0.39 Oceania 4.53 (3.75–5.49) 182.55 (155.47-217.42) 11.14 (9.33–13.32) 170.89 (145.43-201.15) -0.21 Andean Latin America 17.09 (15.55–18.72) 92.95 (84.56-101.24) 32.96 (28.04–39.37) 58.17 (49.56–69.33) -1.5 Southeast Asia 252.65 (229.37-275.07) 114.72 (103.39-125.65) 638.70 (575.91-694.08) 110.92 (100.18–120.20) -0.11 Central Europe 364.09 (349.91-371.87) 272.56 (259.22-279.18) 331.28 (299.88–352.60) 139.98 (126.84-148.91) -2.13 Eastern Sub-Saharan Africa 43.99 (39.10-50.52) 69.44 (61.05–78.85) 101.22 (87.73-117.27) 72.16 (62.09–82.99) 0.12 Central Latin America 88.86 (84.86–90.85) 125.72 (118.69-129.14) 247.05 (221.19-273.15) 103.69 (92.48-114.59) -0.62 South Asia 708.22 (643.04-771.05) 136.39 (122.87-149.55) 1990.11 (1824.49-2155.70) 110.92 (100.18–120.20) 0.29 Southern Latin America 62.79 (59.93–64.74) 149.43 (141.12-154.54) 49.10 (45.02–51.73) 54.41 (50.08–57.26) -3.21 North Africa and Middle East 386.04 (357.95-418.21) 275.18 (253.62-299.12) 769.14 (685.36-858.25) 202.85 (180.59-223.68) -0.98 Since changes in the overall disease burden are primarily attributed to variations in disease-related mortality (Roth et al., 2020), it is essential to focus on the risk factors that influence IHD mortality and exacerbate the global cardiovascular burden. Understanding the global and regional trends of these risk factors is crucial. Globally, the age-standardized death rate (ASDR) has gradually declined over the past 30 years (Fig. 1 C), with an estimated annual percentage change (EAPC) of 1.23. These trends are similar across both male and female populations, with an EAPC of 1.01 for males and 1.46 for females. During the 1990s, the total number of deaths related to ischemic heart disease (IHD) increased alongside a gradual improvement in the Socio-Demographic Index (SDI). However, over the past three decades, the number of IHD-related deaths surged in regions with middle SDI and high-middle SDI, reaching 2.58 million (95% UI: 2.31–2.81) and 2.24 million (95% UI: 1.98–2.46) in 2021 (Table 1 ; Fig. 1 B). This trend can be illustrated in a line chart (Fig. 1 C+). As a result, the IHD burden in middle SDI region surpassed that of high-middle SDI areas, accounting for the largest share of global IHD-related deaths in the most recent year analyzed. In contrast, high SDI regions experienced a consistent decline in IHD-related deaths, from 1.61 million (95% UI: 1.44–1.71) in 1990 to 1.24 million (95% UI: 1.04–1.36) in 2021. Furthermore, the rate of decline in the age-standardized death rate (ASDR) was slower in low-middle SDI (EAPC = 0.03 ) and middle SDI regions (EAPC = 0.22) compared to high-middle SDI (EAPC = 1.34 ) and high SDI (EAPC = 3.15) regions (Table 1 ; Fig. 1 C). Consequently, the IHD burden in developing regions has increased, surpassing that of developed regions over the last three decades. According to region-specific data, the absolute number of deaths rose over time in most Global Burden of Disease (GBD) regions. However, five regions saw reductions: Central Europe, Western Europe, Australasia, high-income North America, and Southern Latin America, all characterized by relatively high SDIs. The age-standardized death rates (ASDRs) for IHD decreased in 17 GBD regions, with Australasia showing the steepest decline (EAPC = 4.20), followed by Western Europe (EAPC = 3.63 [95% UI: 3.80–3.45]) and high-income Asia Pacific (EAPC = 3.62). By the end of the study period, high-income Asia Pacific had the lowest ASDR at 25.56 [95% UI: 21.78–27.65]. Conversely, increases in ASDR were noted in East Asia and Southern Sub-Saharan Africa, with Central Asia having the highest ASDR globally in 2021at 265.51 [95% UI: 240.67–290.42]. At the national level, Timor-Leste, Honduras, Djibouti, and Kenya experienced the largest increases in IHD-related deaths, with percentage changes ranging from 280–500%. In contrast, Denmark, Norway, Georgia, and the United Kingdom saw the most significant declines, with reductions between 60% and 40% (Figure S1 ). Nauru recorded an exceptionally high ASDR (432.64 [95% UI: 361.02–517.42]), far surpassing any other country (Fig. 1 D). Meanwhile, the lowest ASDRs were observed in Japan, the Republic of Korea, San marino and France, where rates ranged from 30 to 40 per 100,000 population in 2021. Despite the general downward trend in ASDRs across most countries, some developing Asian nations witnessed relatively rapid increases (Fig. 1 E). For instance, the ASDR increase in China, as measured by the EAPC, was greater than that of 95% of other countries, even though China's current ASDR remains comparatively low. Additionally, several Central Asian countries, including Uzbekistan, Tajikistan, and Kyrgyzstan, experienced both high ASDRs and sharp increases over the decades, signaling a significant healthcare challenge in the region. Uzbekistan, in particular, faced the most critical situation, with the highest ASDR globally and the fastest increase. In contrast, developed countries in higher SDI quintiles, such as the Ireland, Israel, Norway, Estonia and Denmark, saw the most substantial ASDR reductions, with EAPCs ranging from 5.25 to 4.50. Men exhibit higher mortality rates from ischemic heart disease (IHD) compared to women, and the disparity in IHD-related death burden between sexes may continue to widen. Overall, men experienced a greater burden of ischemic heart disease (IHD) than women, with sex differences likely to widen in the future. Globally, the number of deaths from IHD rose for both genders over the past few decades, reaching 4.60 million (95% UI: 4.14–4.99) for males and 3.63 million (95% CI: 3.14–3.99) for females in 2021 (Table 1 ). Additionally, men consistently exhibited higher IHD mortality rates compared to women throughout the entire study period, with the gap appearing to expand over time (Fig. 1 A). Similarly, men exhibited higher age-standardized death rates (ASDRs) for IHD compared to women, both globally and regionally. In 2021, the highest ASDRs were observed in Central Asia for men (336.56) and Eastern Europe for men (328.68), while the lowest rates were recorded in high-income Asia Pacific, with males at 36.12 and females at 16.64 and Australasia with female at 31.64 (Fig. 2 A). Despite a global decline in ASDRs for both sexes from 1990 to 2021, this decrease was largely driven by improvements in high-middle- and high-SDI regions (Fig. 2 B). Furthermore, the reduction in ASDR was more pronounced in women than in men during the study period (Fig. 2 C), suggesting that if this trend continues, the gender gap in IHD mortality may widen. From 1990 to 2021, the male-to-female ASDR ratio remained between 1.40 and 1.50 across middle-SDI to high-SDI regions, but in high-SDI regions, the ratio reached around 1.80, highlighting an increasing disparity in the IHD death burden between genders in more developed areas (Fig. 2 D). ARIMA (Auto-Regressive Integrated Moving Average) was used to analyze time series data and predict future values. We predicted the ASDR for the next 30 years based on existing data and made separate predictions by sex(Figure XX). Premature mortality from ischemic heart disease (IHD) is more severe in developing regions compared to developed regions. Consistent with the 2019 study, the global burden of ischemic heart disease (IHD) showed a rising trend with age, and premature mortality (between ages 30–70) remains a significant issue in developing nations. As illustrated in Fig. 3 A, the number of IHD-related deaths globally increased with age, with older adults, particularly those over 70, representing the largest share of deaths. In 2021, the age groups 70–74, 75–79, 80–84, and 85–89 had the highest absolute death counts in low-, low-middle-, middle-, high-middle-, and high-SDI regions, respectively. However, the growth in death numbers across all age groups was faster in lower-SDI and low-middle-regions compared to others between 1990 and 2021. By the end of the study period, the middle-SDI region had the highest number of IHD deaths among all SDI regions. We categorized the population into four age groups: 15–49 years, 50–69 years, 70–74 years, and 75–94 years. We then assessed and compared the percent changes in deaths across these groups in each SDI region from 1990 to 2021. Globally, the largest rise in mortality was observed in the 75–94 years group during the study period, followed by the 70–74 years group (Fig. 3 B). In other regions, all age groups experienced significant increases in death rates, while high-SDI regions showed an overall decline.我是不是没有按文章中给的年龄分组 The percentage change in deaths among the 15–49 years group decreased as SDI levels increased, with the highest changes seen in low-SDI regions, while high-SDI regions showed negative changes. Consequently, IHD-related deaths in developing regions not only increased more rapidly than in developed areas but also impacted younger populations. These rising trends among young and middle-aged individuals are likely to persist, contributing to a growing long-term burden of IHD. Between 1990 and 2021, the age distribution of individuals who died from ischemic heart disease (IHD) showed a shift toward older age groups. In high-SDI regions, a larger proportion of elderly individuals accounted for IHD-related deaths compared to regions with lower SDI (Fig. 3 C). Oceania had the youngest age distribution of IHD mortality among all GBD regions, with the 15–49 years groups contributing to 20.5% of deaths in 1990 and 19.5% in 2021. In contrast, Western Europe had the oldest distribution, with elderly individuals making up 63.5% of IHD deaths in 1990 and rising to 76.1% in 2021. Central Asia was unique in showing a decline in the age at death, with older populations comprising 51% of IHD deaths in 1990, decreasing to 49% in 2021. Meanwhile, East Asia saw the sharpest rise in age at death, as the proportion of elderly deaths grew from 38.5% in 1990 to 62.3% in 2021. The inadequate management of metabolic risk factors in lower-SDI regions has led to a significant shift in the majority of IHD-related deaths from developed nations to developing countries. The burden of ischemic heart disease (IHD) is primarily linked to metabolic, behavioral, environmental, and occupational risk factors (Yusuf et al., 2020). Following decades of cardiovascular disease management efforts, the death burden associated with these risk factors significantly declined between 1990 and 2021. The top five risk factors contributing to age-standardized death rates (ASDRs) in 1990—high systolic blood pressure (SBP), elevated LDL cholesterol, smoking, kidney dysfunction, and Ambient particulate matter pollution (Figure S2 A). By 2021, due to varying degrees of reduction, metabolic risk factors had become the leading contributors to the rise in ASDR. High SBP and high LDL cholesterol remained the top two risk factors, while high FPG rose to the fifth, respectively. In contrast, ASDRs attributable to smoking declines over the past years. In all SDI regions, high systolic blood pressure (SBP) and elevated LDL cholesterol (LDL-c) were the leading contributors to age-standardized death rates (ASDRs) (Fig. 4 A). From 1990 to 2021, these two risk factors declined rapidly in high-middle- and high-SDI regions, while they remained persistently high in low-, low-middle-, and middle-SDI regions. In low- and lower-middle-SDI regions, household air pollution from solid fuels was the primary factor contributing to age-standardized death rates (ASDRs). The ASDR associated with high fasting plasma glucose (FPG) ranked just behind high SBP and high LDL-c; however, its reduction in high-middle- and high-SDI regions was much slower compared to the latter two. In lower, low-middle-, and middle-SDI regions, FPG levels even showed a continuous upward trend. Over the past decades, death rates attributed to high BMI decreased in high-middle- and high-SDI regions but significantly rose in low-, low-middle-, and middle-SDI regions, leading to a relatively stable global trend. Geographically, Asian countries exhibited the fastest growth in high BMI-related death rates, followed by Eastern and Western Sub-Saharan Africa (Figure S2 B). In stark contrast, Australasia, Western Europe, Central Europe, High SDI and high-income Asia Pacific regions experienced the most rapid declines in high BMI-related deaths. By 2021, Eastern Europe had the highest BMI-related death rates, while high-income Asia Pacific recorded the lowest (Figure S2 C). Compared to changes in high BMI, shifts in IHD mortality were more substantial across all SDI regions. Metabolic risk factors, therefore, pose significant challenges for reducing IHD-related deaths worldwide, especially in developing regions. Despite a significant decline in smoking-related mortality, it remained a major risk factor in 2021, with an attributable age-standardized death rate (ASDR) of 15.76 (95% UI: 13.33–18.39) per 100,000 (Figure S2 A). In 1990, smoking-attributed ASDR was notably high in high-middle- and high-SDI regions. Over the past 30 years, these regions saw a sharp decline in smoking-related ASDR, whereas the reductions in lower-SDI and low-middle SDI regions were more moderate (Figure S2 D). By the end of the study period, the high-SDI region recorded the lowest ASDR from smoking, while the high-middle-SDI region had the highest (Figure S2 E).) Geographically, smoking-related deaths decreased in 19 of the GBD regions studied. However, East Asia experienced an increased burden over this time. In contrast to the 2019 article, which noted an increase in Eastern Europe during this period, a comparison between 2021 and 1990 shows that the ASDR due to smoking in Eastern Europe has decreased. High-income Asia Pacific had the lowest smoking-related ASDR in 2019. The contributions of environmental risk factors to IHD-related mortality varied across SDI regions. In low- to middle-SDI regions, the death burden from ambient particulate matter pollution increased over the study period, while it declined in high-middle- and high-SDI regions. Additionally, although the global burden from household air pollution due to solid fuels significantly decreased, it remained a major issue in low-SDI regions. The trends in risk factors associated with death burdens across different geographic regions mirrored those seen at the SDI level (Figure S2 F). In 2021, metabolic risk factors, such as elevated SBP, LDL-c, FPG, and BMI, were the primary contributors to IHD mortality in most regions. ASDRs related to metabolic risk factors were lower in regions with an SDI below 0.644 (From Eastern SubSaharan Africa to Southeast Asia), but these rates remained steady or increased slightly over time. In contrast, higher-SDI regions (SDI > 0.788) experienced a substantial decline in ASDRs linked to metabolic risk factors, which were higher in 1990. The geographical disparities in death burden aligned with the regional differences in metabolic risk factor impacts, and a country-level analysis produced similar findings (Figures S3–S5A). In 1990, the death rate from ischemic heart disease (IHD) in 33 low-SDI countries was the lowest among all SDI regions, with an ASDR of 119.50 per 100,000 (Table 1 ). Over the past three decades, this rate remained relatively unchanged (Figure S3A). By 2021, these countries had the second-lowest average death rate, with an ASDR of 116.41 per 100,000 (Table 1 ). However, nations such as the Afghanistan, Solomon Islands, Yemen, Haiti, Guinea-Bissauand, Central African Republic, Papua New Guinea, Gambia, Equatorial Guinea, Nepal, Sierra Leone and Pakistan exhibited death rates above the regional average (Figure S3A). The rise in death rates in these countries was primarily linked to the growing burden of high SBP, high LDL-c, high FPG, household air pollution, and smoking over the study period, while other risk factors remained stable. In 1990, the IHD death rate in 43 low-middle-SDI countries ranked third among all SDI regions. Over the past years, the mortality rate has slightly increased (Figure S3B). By 2021, these countries recorded the highest average death rate, with an ASDR of 142.10 per 100,000 (Table 1 ). Nations such as Tajikistan, Kyrgyzstan, Mongolia, Marshall Islands, Tuvalu, Kiribati, Palestine, Lao People's Democratic Republic, Honduras, Congo, Vanuatu, Morocco, and Sudan had death rates exceeding the regional average (Figure S3B). Like in low-SDI countries, rising death rates in these nations were linked to the growing burden of high SBP, high LDL-c, high FPG, household air pollution, and smoking in 1990. However, by 2021, household air pollution had become less of a factor in mortality. In addition to the impact of high FPG, high BMI became a more significant contributor to the death rate compared to the low-SDI region. Notably, Tajikistan experienced the fastest growth in mortality, driven by rapidly increasing burdens of high SBP, household air pollution from solid fuels and high LDL-c, and high FPG. In 1990, the 41 countries classified within the middle-SDI region had the fourth-highest average death rate among all SDI regions. Similar to the trends in low- and low-middle-SDI regions, the change in mortality rate over the past years has been minimal (Figure S4A). By 2021, these countries rose to third place in terms of average death rate, with an ASDR of 118.71 per 100,000 (Table 1 ). Countries such as Uzbekistan, Azerbaijan, the Syrian Arab Republic, Egypt, Turkmenistan, Fiji, Iraq, Samoa, Tokelau, Nauru, Algeria, Tunisia, Albania, Philippines, Indonesia, Tonga, Guyana and Armenia experienced death rates exceeding the middle-SDI regional average (Figure S4A). The higher mortality in these countries was largely attributed to the burdens of high SBP, elevated LDL-c, high FPG, high BMI, and ambient particulate matter pollution throughout the study period. Additionally, the death burden from ambient particulate matter pollution was notably high in Uzbekistan, Azerbaijan, the Syrian Arab Republic, Egypt, Iraq, Turkmenistan, Algeria, and Armenia. Nauru had the highest mortality rate and the steepest increase among all middle-SDI countries by 2021, primarily driven by rapid growth in the burdens of high SBP, high LDL-c, high FPG, high BMI, and particulate matter pollution. Other countries, such as China, had relatively lower death rates in 2021, but the burden from high SBP and Ambient particulate matter pollution increased over time. In 1990, the 48 countries in the high-middle-SDI region had the highest average death rate among all SDI regions. Unlike the low-, low-middle-, and middle-SDI regions, these countries experienced a significant decline in mortality rates from 1990 to 2021 (Figure S4B). By 2021, the high-middle-SDI region had the second-highest average death rate, with an ASDR of 127.50 per 100,000 (Table 1 ). The main contributors to the death burden were high SBP, elevated LDL-c, high FPG, high BMI, smoking, and ambient particulate matter pollution. Over the past three decades, death rates associated with these risk factors decreased significantly in most high-middle-SDI countries (Figure S4B). However, in countries like Argentina, Ukraine, Sri Lanka, Libya, Niue, American Samoa, Malaysia, Palau, North Macedonia, the Cook Islands, the Northern Mariana Islands, Oman, Montenegro, the United States Virgin Islands, and Saudi Arabia, the death burdens from these risk factors have not significantly declined or have slightly increased. In 1990, the 39 high-SDI countries had the second-highest IHD death rate among all SDI regions. Over the past years, the death rate significantly declined (Figure S5A), and by 2021, these countries had the lowest average death rate, with an ASDR of 54.41 per 100,000 (Table 1 ). The primary contributors to the death burden in these nations were high SBP, elevated LDL-c, high FPG, high BMI, and smoking(Figure S5A). Except for United Arab Emirates, all countries in the high-SDI region saw significant reductions in death rates from these risk factors. In the United States, high SBP and LDL-c were the leading causes of mortality in 1990, but by 2019, the death rate had decreased by 50%. However, ASDRs related to high FPG and high BMI showed little to no improvement during the study period. In summary, despite the decline in many high-middle- and high-SDI countries, high SBP, elevated LDL-c, and high FPG remained the top three risk factors for IHD deaths across all regions and countries by 2021. The death burden associated with household air pollution from solid fuels decreased over time as SDI levels increased, while the death burden from ambient particulate matter pollution rose in low- to middle-SDI regions (Fig. 4 B). However, in high-middle- and high-SDI regions, the opposite trend was observed. A similar pattern was seen in the death burden linked to high BMI, which followed the same trajectory as household air pollution from solid fuels. The national-level trends in death burdens from metabolic risk factors mirrored the patterns at the SDI level. Countries in low-, low-middle-, and middle-SDI regions experienced rising death rates from metabolic factors, while those in high-middle- and high-SDI regions saw significant reductions in death rates linked to these risk factors. Globally, men had a higher ASDR than women, which can be linked to all the aforementioned risk factors (Figure S5B). The sex ratio for smoking (male to female > 3.0) was significantly higher compared to other risk factors, highlighting smoking as a key contributor to the sex-based disparity in IHD-related deaths (Fig. 4 B).Over the past years, the mortality rate has slightly increased (Figure S3B). Notable differences between men and women were also observed in ASDRs related to high sodium intake and ambient particulate matter pollution. Especially in low-SDI regions, of the nine risk factors associated with IHD deaths, only the mortality gap due to household air pollution from solid fuels decreased, suggesting that the overall gender disparity in IHD mortality may widen in the future. Discussion The health burden of ischemic heart disease (IHD) is increasing globally, particularly in developing regions, where the sharp rise in related mortality rates and death counts has raised widespread concern 9 . According to research data, from 1980 to 2021, the number of IHD-related deaths surged from 2.5 million to 8.99 million, reflecting the complexity and severe challenges that cardiovascular disease poses to global health systems. Although the overall age-standardized death rate (ASDR) has decreased, this masks significant disparities across regions, particularly in countries with a low or middle Socio-Demographic Index (SDI), where the rising trend in IHD deaths is concerning. The study results indicate that both the number of deaths and the mortality rate from ischemic heart disease (IHD) are significantly higher in developing regions compared to developed ones. This phenomenon is driven by various complex factors, including levels of economic development, lifestyle choices, accessibility of healthcare resources, and socio-cultural influences 2 , 10 – 11 . Firstly, socio-economic factors in developing regions are key contributors to the increasing burden of IHD. According to the study by Marmot et al. (2008), improvements in economic levels are generally associated with lifestyle improvements 12 ; however, in many developing countries, the rapid pace of urbanization has led to lifestyle changes, such as increased consumption of high-fat, high-salt diets, and reduced physical activity. These factors are likely direct causes of the rising incidence of IHD. Additionally, economic disparities result in a lack of healthcare resources in some areas, making it difficult for residents to access timely and effective medical interventions 13 . In developed regions, the IHD mortality rate has gradually declined due to sufficient healthcare resources and effective implementation of public health policies 14 . However, healthcare systems in developing regions continue to face significant challenges, including a lack of infrastructure, inadequate public health interventions, and limited capacity for chronic disease management 15 , 16 . These factors greatly restrict IHD prevention and control efforts, particularly in middle-SDI and upper-middle-SDI regions, where IHD-related deaths have risen significantly. In fact, by 2021, the number of deaths in middle-SDI regions surpassed those in high-SDI regions. This trend underscores the urgent need for more balanced global cardiovascular health strategies to ensure that appropriate healthcare services are accessible in all regions. Secondly, gender differences are also an essential factor when discussing the burden of IHD. Data show that the proportion of IHD-related deaths among men is significantly higher than among women, and this gap is widening. In 2021, the number of IHD deaths was 4.6 million for men and 3.63 million for women, with mortality rates consistently higher in men. Studies indicate that male mortality rates are generally higher than those of females 17 , closely linked to physiological factors (such as hormone levels) and social behaviors (like smoking and drinking habits). Men are more prone to unhealthy lifestyles, while women often maintain healthier habits over a longer period, which may account for this difference. Meanwhile, cardiovascular risks for women rise after menopause, which could further increase the gender mortality gap in the future 18 . This gender disparity may be influenced by multiple factors, including lifestyle, health behaviors, and healthcare access differences 19 – 20 . To effectively address this issue, future research should explore the key factors influencing gender differences and develop targeted intervention strategies. Regional disparities are equally important. Although IHD mortality rates are gradually declining in high-SDI regions, certain low- and middle-SDI areas, such as Central Asia and East Asia, have seen an increase in IHD mortality rates. This trend may be related to rapid urbanization and lifestyle changes in these regions, particularly the Westernization of dietary habits and a more sedentary lifestyle 21 , 22 . For instance, in China, economic growth has led to improved healthcare conditions, yet rising obesity rates and the incidence of cardiovascular diseases have not decreased as expected, posing serious challenges to public health policies. In developed regions, adequate healthcare resources and effective public health policies have contributed to a gradual reduction in IHD mortality rates. However, healthcare systems in developing regions still face numerous challenges, including a lack of infrastructure, insufficient public health interventions, and limited chronic disease management capacity 13 . These issues severely restrict IHD prevention and control efforts, especially in middle- and upper-middle-SDI regions, where IHD-related deaths have risen significantly. In fact, by 2021, IHD deaths in middle-SDI regions surpassed those in high-SDI regions. This trend highlights the urgent need for more balanced global cardiovascular health strategies to ensure access to adequate healthcare services in all regions. In addition, the impact of population aging on IHD is becoming increasingly significant. Data show a sharp increase in IHD-related deaths among those aged 70 and above, particularly in low- and middle-SDI regions. With the global aging process accelerating, IHD will pose an even greater threat to public health, making prevention and intervention measures for the elderly a critical focus for policymakers 23 . Premature mortality is especially severe in developing countries. Among younger populations, mortality rates are high between ages 30 and 70, indicating that IHD not only affects the elderly but also threatens the working-age population. This issue not only adds to the financial burden on families but also negatively impacts national economic development. Consequently, health interventions targeting younger populations are essential, and governments should strengthen health education and promotion activities to improve cardiovascular health in this demographic. Aging-related mitochondrial dysfunction, leading to imbalances in reactive oxygen species production, results in endothelial and smooth muscle dysfunction, with clinical implications for major adverse cardiac events and mortality 24 . Lastly, understanding country-specific circumstances is crucial to discussing the global burden of IHD. For example, in countries like Uzbekistan and Tajikistan, ASDR levels remain high, indicating an urgent need for improvements in their healthcare systems. In contrast, high-income countries such as Denmark and Norway have shown notable declines in mortality rates, reflecting the effectiveness of health policies and the importance of sustained investment. Therefore, countries should consider their unique situations and draw from global best practices to advance targeted public health policies to effectively address the challenges posed by IHD. Modifiable global risk factors significantly impact cardiovascular disease (CVD) and all-cause mortality. A study consolidates and standardizes individual-level data to analyze the contribution of five major modifiable risk factors—body mass index (BMI), systolic blood pressure (SBP), non-high-density lipoprotein cholesterol, smoking, and diabetes—to the incidence and mortality of CVD. The findings indicate that these risk factors have varying effects across different regions and genders. Overall, approximately 57.2% of cardiovascular events in women and 52.6% in men, as well as 22.2% of all-cause mortality in women and 19.1% in men, can be attributed to these five modifiable risk factors. Although there is some uncertainty in estimates, international studies show that between 1990 and 2015, the prevalence of elevated SBP significantly increased, along with the DALY and mortality associated with higher SBP. The global burden of hypertension was analyzed, and its impact on cardiovascular mortality was evaluated 25 . Hypertension is one of the primary risk factors for cardiovascular disease. Studies suggest that sustained hypertension leads to structural changes in the heart, including left ventricular hypertrophy and arterial stiffness, which increase the risk of heart attack and stroke. According to a large prospective study, targeting a systolic blood pressure below 120 mmHg in high-risk cardiovascular patients without diabetes resulted in lower rates of both fatal and non-fatal major cardiovascular events and all-cause mortality compared to those with an SBP below 140 mmHg. Related research emphasizes that early screening and management of hypertension are crucial for reducing IHD-related mortality 26 . LDL-c is considered a key factor in atherosclerosis. Literature shows that for each unit increase in LDL-c, the risk of IHD rises by about 10% 27 . Endothelial dysfunction and inflammatory responses triggered by high cholesterol levels play a crucial role in the formation of atherosclerosis 28 . Both pharmacological interventions (such as statins) and lifestyle modifications (such as a low-saturated-fat diet) have proven effective in lowering LDL-c levels, thereby reducing the incidence of IHD 29 . Studies indicate that each 1 mmol/L increase in fasting glucose raises the mortality risk of IHD by approximately 20% 30 . Advanced glycation end-products (AGEs) resulting from high blood glucose levels play a key role in vascular damage and atherosclerosis. Therefore, controlling blood glucose levels is a critical measure to reduce the risk of IHD. Obesity is closely associated with multiple metabolic abnormalities, including insulin resistance and chronic inflammation. A large meta-analysis of 239 prospective studies examined the impact of BMI on all-cause mortality and cardiovascular disease across four continents, revealing a consistent association between overweight/obesity and higher all-cause mortality. Obesity not only increases the risk of hypertension and high cholesterol but also directly impacts heart function. Weight control through improved diet and increased physical activity has been shown to significantly reduce IHD incidence 31 . Diets high in salt, sugar, and saturated fats are closely linked to cardiovascular disease. Studies show that reducing salt intake can effectively lower hypertension rates, while diets rich in dietary fiber, such as the Mediterranean diet, are proven to improve cardiovascular health. Promoting healthy dietary habits can help reduce the risk of IHD 32 . Summary Ischemic heart disease (IHD) remains a major global public health concern, though IHD-related mortality has gradually shifted from developed to developing countries. This shift is mainly due to insufficient control over various risk factors, especially cardiometabolic risks, in low-SDI regions. Additionally, mortality rates among men are higher than among women, and the gender gap may continue to widen due to the more substantial impact of major risk factors on men. While older age groups remain a primary focus for IHD management, the rapidly increasing premature mortality in low- to middle-SDI regions is equally concerning. Countries should adopt national strategies to reverse this unfavorable trend. Effective prevention and control measures for risk factors can significantly reduce IHD-related mortality. Developed countries have made substantial progress in reducing IHD risks, and these efforts should continue in the future. In developing countries, where economic and social development lag behind, focus should be placed on controlling critical and urgent risk factors, such as cardiometabolic risks, smoking, and air pollution, to reduce IHD-related mortality. Based on specific data on the local burden of IHD, each country should independently assess its disease burden to set strategic priorities. Periodic updates on disease conditions under the Global Burden of Disease (GBD) framework are highly beneficial for providing timely assessments and offer ongoing guidance for addressing this serious global health issue. limitation This study primarily relies on secondary data from the Global Burden of Disease (GBD) database, which is based on death statistics and health indicators reported by various countries. However, the quality of health data varies across countries and regions, particularly in developing countries. Data from some low- and middle-income countries may be incomplete and subject to reporting bias, which could lead to an underestimation of the ischemic heart disease (IHD) death burden in these regions. The study does not thoroughly explore the differences in heart disease management across regions, such as accessibility to treatment, disparities in healthcare infrastructure, and other factors that influence the IHD death burden. While the study mentions a significant decline in mortality rates in high-income countries, these changes may be partly attributed to the stronger cardiovascular disease management systems in these countries. In contrast, in many low- and middle-income countries, the level of cardiovascular disease management remains limited, leading to inadequate control of mortality rates in these regions. Declarations Acknowledgements None. Authors’ contributions Huili Li and Fei Xiao designed the study and wrote the manuscript. Huili Li extracted, collected and analyzed data. Jiarui Wang, Yuanlu Chen, and Zhenyu Li prepared tables and figures. Peirong Lin and Sheng Wang reviewed the results and interpreted data. All authors have made an intellectual contribution to the manuscript and approved the submission. Funding This work was supported by the National Natural Science Foundation of China (No. 82160157 and No. 81970290) , the Joint Funds of the National Natural Science Foundation of China (No. U20A2018) , and the Natural Science Foundation of Beijing (No. 7242046 and No. 7222044). Data and Code Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. The data were sourced from the GBD database. The code used in this article can be obtained by contacting the corresponding author, Sheng Wang. Ethics approval and consent to participate The study was performed according to the guidelines of the Helsinki Declaration. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Competing interests The authors declare that they have no competing interests. STAR Methods Key resources table STAR Methods Key resources table RESOURCE SOURCE IDENTIFIER R software version 4.1.3 Data: GBD http://www.rstudios.co https://vizhub.healthdata.org/gbd N/A N/A Resource availability Data availability All data and requests for resources should be directed to and will be fulfilled by the lead contact, Sheng Wang ( [email protected] ). Code availability All data for resources should be directed to and will be fulfilled by the lead contact, Sheng Wang ( [email protected] ). Other relevant items information availability Other relevant items information should be directed to and will be fulfilled by the lead contact, Sheng Wang ( [email protected] ). Lead contact Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Sheng Wang ( [email protected] ). Materials availability This study did not generate new unique reagents. Experimental model and study participant details The experimental models and participant details in this study were all obtained from the MIMIC database. Method details Data source Data on the annual incidence and age-standardized rates (ASRs) of IHD-related deaths, DALYs, YLD, and YLL from these year, categorized by sex, age, region, and country, were sourced from the Global Health Data Exchange (GHD) query tool (http://ghdx.healthdata.org/gbd-results-tool), which is supported by ongoing multinational collaboration. This tool offers accessible epidemiological data on 369 diseases and 87 risk factors from 204 countries and territories, tracked over time and by location 8 . Countries are grouped by the Sociodemographic Index (SDI) into five quintiles: low, low-middle, middle, high-middle, and high SDI regions. Additionally, they are classified into 21 regions based on their geographical locations. Definitions Detailed methods for diagnosis and confirmation of GBD 2021 can be found on the website (https://doi.org/10.1016/j.jacc.2020.11.010). Metabolic risk factors were defined as high systolic blood pressure (SBP), high low-density lipoprotein cholesterol (LDL-c), high fasting plasma glucose (FPG), and high body mass index (BMI). Ischemic heart disease (IHD) was defined based on standard case definitions, including acute myocardial infarction, chronic stable angina, chronic IHD, and its related heart failure. Myocardial infarction was defined following the Fourth Universal Definition of Myocardial Infarction and was adjusted to account for out-of-hospital sudden cardiac deaths. Stable angina was defined using the Rose Angina Questionnaire. Mortality data were sourced from vital registration records coded in the International Classification of Diseases (ICD) system or from household mortality surveys. Data representation and statistical analysis The formulas for calculating ASDR and EAPC are referenced from other articles 7 . ARIMA (Auto-Regressive Integrated Moving Average) was used to analyze time series data and predict future values. Quantification and statistical analysis For data analysis, we utilized R software version 4.1.3, provided by the R Foundation for Statistical Computing in Vienna, Austria. We considered a P-value of less than 0.05 on a two-tailed test to be indicative of statistical significance across all tests conducted. References Mensah GA, Fuster V, Roth GA. A Heart-Healthy and Stroke-Free World: Using Data to Inform Global Action. J Am Coll Cardiol 2023; 82 :2343–2349. Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, Ahmed M, Aksut B, Alam T, Alam K, Alla F, Alvis-Guzman N, Amrock S, Ansari H, Ärnlöv J, Asayesh H, Atey TM, Avila-Burgos L, Awasthi A, Banerjee A, Barac A, Bärnighausen T, Barregard L, Bedi N, Belay Ketema E, Bennett D, Berhe G, Bhutta Z, Bitew S, Carapetis J, Carrero JJ, Malta DC, Castañeda-Orjuela CA, Castillo-Rivas J, Catalá-López F, Choi J-Y, Christensen H, Cirillo M, Cooper L, Criqui M, Cundiff D, Damasceno A, Dandona L, Dandona R, Davletov K, Dharmaratne S, Dorairaj P, Dubey M, Ehrenkranz R, El Sayed Zaki M, Faraon EJA, Esteghamati A, Farid T, Farvid M, Feigin V, Ding EL, Fowkes G, Gebrehiwot T, Gillum R, Gold A, Gona P, Gupta R, Habtewold TD, Hafezi-Nejad N, Hailu T, Hailu GB, Hankey G, Hassen HY, Abate KH, Havmoeller R, Hay SI, Horino M, Hotez PJ, Jacobsen K, James S, Javanbakht M, Jeemon P, John D, Jonas J, Kalkonde Y, Karimkhani C, Kasaeian A, Khader Y, Khan A, Khang Y-H, Khera S, Khoja AT, Khubchandani J, Kim D, Kolte D, Kosen S, Krohn KJ, Kumar GA, Kwan GF, Lal DK, Larsson A, Linn S, Lopez A, Lotufo PA, El Razek HMA, Malekzadeh R, Mazidi M, Meier T, Meles KG, Mensah G, Meretoja A, Mezgebe H, Miller T, Mirrakhimov E, Mohammed S, Moran AE, Musa KI, Narula J, Neal B, Ngalesoni F, Nguyen G, Obermeyer CM, Owolabi M, Patton G, Pedro J, Qato D, Qorbani M, Rahimi K, Rai RK, Rawaf S, Ribeiro A, Safiri S, Salomon JA, Santos I, Santric Milicevic M, Sartorius B, Schutte A, Sepanlou S, Shaikh MA, Shin M-J, Shishehbor M, Shore H, Silva DAS, Sobngwi E, Stranges S, Swaminathan S, Tabarés-Seisdedos R, Tadele Atnafu N, Tesfay F, Thakur JS, Thrift A, Topor-Madry R, Truelsen T, Tyrovolas S, Ukwaja KN, Uthman O, Vasankari T, Vlassov V, Vollset SE, Wakayo T, Watkins D, Weintraub R, Werdecker A, Westerman R, Wiysonge CS, Wolfe C, Workicho A, Xu G, Yano Y, Yip P, Yonemoto N, Younis M, Yu C, Vos T, Naghavi M, Murray C. Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015. J Am Coll Cardiol 2017; 70 :1–25. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, Brauer M, Kutty VR, Gupta R, Wielgosz A, AlHabib KF, Dans A, Lopez-Jaramillo P, Avezum A, Lanas F, Oguz A, Kruger IM, Diaz R, Yusoff K, Mony P, Chifamba J, Yeates K, Kelishadi R, Yusufali A, Khatib R, Rahman O, Zatonska K, Iqbal R, Wei L, Bo H, Rosengren A, Kaur M, Mohan V, Lear SA, Teo KK, Leong D, O’Donnell M, McKee M, Dagenais G. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet 2020; 395 :795–808. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, Bonny A, Brauer M, Brodmann M, Cahill TJ, Carapetis J, Catapano AL, Chugh SS, Cooper LT, Coresh J, Criqui M, DeCleene N, Eagle KA, Emmons-Bell S, Feigin VL, Fernández-Solà J, Fowkes G, Gakidou E, Grundy SM, He FJ, Howard G, Hu F, Inker L, Karthikeyan G, Kassebaum N, Koroshetz W, Lavie C, Lloyd-Jones D, Lu HS, Mirijello A, Temesgen AM, Mokdad A, Moran AE, Muntner P, Narula J, Neal B, Ntsekhe M, Moraes de Oliveira G, Otto C, Owolabi M, Pratt M, Rajagopalan S, Reitsma M, Ribeiro ALP, Rigotti N, Rodgers A, Sable C, Shakil S, Sliwa-Hahnle K, Stark B, Sundström J, Timpel P, Tleyjeh IM, Valgimigli M, Vos T, Whelton PK, Yacoub M, Zuhlke L, Murray C, Fuster V, GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol 2020; 76 :2982–3021. Sun J, Qiao Y, Zhao M, Magnussen CG, Xi B. Global, regional, and national burden of cardiovascular diseases in youths and young adults aged 15-39 years in 204 countries/territories, 1990-2019: a systematic analysis of Global Burden of Disease Study 2019. BMC Med 2023; 21 :222. Münzel T, Hahad O, Sørensen M, Lelieveld J, Duerr GD, Nieuwenhuijsen M, Daiber A. Environmental risk factors and cardiovascular diseases: a comprehensive expert review. Cardiovasc Res 2022; 118 :2880–2902. Wang W, Hu M, Liu H, Zhang X, Li H, Zhou F, Liu Y-M, Lei F, Qin J-J, Zhao Y-C, Chen Z, Liu W, Song X, Huang X, Zhu L, Ji Y-X, Zhang P, Zhang X-J, She Z-G, Yang J, Yang H, Cai J, Li H. Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease. Cell Metab 2021; 33 :1943-1956.e2. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396 :1204–1222. Vogel B, Acevedo M, Appelman Y, Bairey Merz CN, Chieffo A, Figtree GA, Guerrero M, Kunadian V, Lam CSP, Maas AHEM, Mihailidou AS, Olszanecka A, Poole JE, Saldarriaga C, Saw J, Zühlke L, Mehran R. The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030. Lancet 2021; 397 :2385–2438. Álvarez-Gálvez J, Ortega-Martín E, Carretero-Bravo J, Pérez-Muñoz C, Suárez-Lledó V, Ramos-Fiol B. Social determinants of multimorbidity patterns: A systematic review. Front Public Health 2023; 11 :1081518. Mente A, Dehghan M, Rangarajan S, McQueen M, Dagenais G, Wielgosz A, Lear S, Li W, Chen H, Yi S, Wang Y, Diaz R, Avezum A, Lopez-Jaramillo P, Seron P, Kumar R, Gupta R, Mohan V, Swaminathan S, Kutty R, Zatonska K, Iqbal R, Yusuf R, Mohammadifard N, Khatib R, Nasir NM, Ismail N, Oguz A, Rosengren A, Yusufali A, Wentzel-Viljoen E, Puoane T, Chifamba J, Teo K, Anand SS, Yusuf S, Prospective Urban Rural Epidemiology (PURE) study investigators. Association of dietary nutrients with blood lipids and blood pressure in 18 countries: a cross-sectional analysis from the PURE study. Lancet Diabetes Endocrinol 2017; 5 :774–787. Marmot M, Friel S, Bell R, Houweling TAJ, Taylor S, Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet 2008; 372 :1661–1669. Sliwa K, Acquah L, Gersh BJ, Mocumbi AO. Impact of Socioeconomic Status, Ethnicity, and Urbanization on Risk Factor Profiles of Cardiovascular Disease in Africa. Circulation 2016; 133 :1199–1208. Timmis A, Townsend N, Gale CP, Torbica A, Lettino M, Petersen SE, Mossialos EA, Maggioni AP, Kazakiewicz D, May HT, De Smedt D, Flather M, Zuhlke L, Beltrame JF, Huculeci R, Tavazzi L, Hindricks G, Bax J, Casadei B, Achenbach S, Wright L, Vardas P, European Society of Cardiology. European Society of Cardiology: Cardiovascular Disease Statistics 2019. Eur Heart J 2020; 41 :12–85. Timmis A, Aboyans V, Vardas P, Townsend N, Torbica A, Kavousi M, Boriani G, Huculeci R, Kazakiewicz D, Scherr D, Karagiannidis E, Cvijic M, Kapłon-Cieślicka A, Ignatiuk B, Raatikainen P, De Smedt D, Wood A, Dudek D, Van Belle E, Weidinger F, ESC National Cardiac Societies. European Society of Cardiology: the 2023 Atlas of Cardiovascular Disease Statistics. Eur Heart J 2024; 45 :4019–4062. Hanson MA, Gluckman PD. Developmental origins of health and disease--global public health implications. Best Pract Res Clin Obstet Gynaecol 2015; 29 :24–31. Wong ND, Cordola Hsu AR, Rozanski A, Shaw LJ, Whelton SP, Budoff MJ, Nasir K, Miedema MD, Rumberger J, Blaha MJ, Berman DS. Sex Differences in Coronary Artery Calcium and Mortality From Coronary Heart Disease, Cardiovascular Disease, and All Causes in Adults With Diabetes: The Coronary Calcium Consortium. Diabetes Care 2020; 43 :2597–2606. Fonseca MIH, Silva IT da, Ferreira SRG. Impact of menopause and diabetes on atherogenic lipid profile: is it worth to analyse lipoprotein subfractions to assess cardiovascular risk in women? Diabetol Metab Syndr 2017; 9 :22. Sullivan S, Young A, Hammadah M, Lima BB, Levantsevych O, Ko Y-A, Pearce BD, Shah AJ, Kim JH, Moazzami K, Driggers EG, Haffar A, Ward L, Herring I, Hankus A, Lewis TT, Mehta PK, Bremner JD, Raggi P, Quyyumi A, Vaccarino V. Sex differences in the inflammatory response to stress and risk of adverse cardiovascular outcomes among patients with coronary heart disease. Brain Behav Immun 2020; 90 :294–302. Patwardhan V, Gil GF, Arrieta A, Cagney J, DeGraw E, Herbert ME, Khalil M, Mullany EC, O’Connell EM, Spencer CN, Stein C, Valikhanova A, Gakidou E, Flor LS. Differences across the lifespan between females and males in the top 20 causes of disease burden globally: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Public Health 2024; 9 :e282–e294. Karam G, Agarwal A, Sadeghirad B, Jalink M, Hitchcock CL, Ge L, Kiflen R, Ahmed W, Zea AM, Milenkovic J, Chedrawe MA, Rabassa M, El Dib R, Goldenberg JZ, Guyatt GH, Boyce E, Johnston BC. Comparison of seven popular structured dietary programmes and risk of mortality and major cardiovascular events in patients at increased cardiovascular risk: systematic review and network meta-analysis. BMJ 2023; 380 :e072003. Montone RA, Camilli M, Calvieri C, Magnani G, Bonanni A, Bhatt DL, Rajagopalan S, Crea F, Niccoli G. Exposome in ischaemic heart disease: beyond traditional risk factors. Eur Heart J 2024; 45 :419–438. Zhang Y, Zhang J, Butler J, Yang X, Xie P, Guo D, Wei T, Yu J, Wu Z, Gao Y, Han X, Zhang X, Wen S, Anker SD, Filippatos G, Fonarow GC, Gan T, Zhang R, China-HF Investigators. Contemporary Epidemiology, Management, and Outcomes of Patients Hospitalized for Heart Failure in China: Results From the China Heart Failure (China-HF) Registry. J Card Fail 2017; 23 :868–875. Tracy EP, Hughes W, Beare JE, Rowe G, Beyer A, LeBlanc AJ. Aging-Induced Impairment of Vascular Function: Mitochondrial Redox Contributions and Physiological/Clinical Implications. Antioxid Redox Signal 2021; 35 :974–1015. Forouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, Alexander L, Estep K, Hassen Abate K, Akinyemiju TF, Ali R, Alvis-Guzman N, Azzopardi P, Banerjee A, Bärnighausen T, Basu A, Bekele T, Bennett DA, Biadgilign S, Catalá-López F, Feigin VL, Fernandes JC, Fischer F, Gebru AA, Gona P, Gupta R, Hankey GJ, Jonas JB, Judd SE, Khang Y-H, Khosravi A, Kim YJ, Kimokoti RW, Kokubo Y, Kolte D, Lopez A, Lotufo PA, Malekzadeh R, Melaku YA, Mensah GA, Misganaw A, Mokdad AH, Moran AE, Nawaz H, Neal B, Ngalesoni FN, Ohkubo T, Pourmalek F, Rafay A, Rai RK, Rojas-Rueda D, Sampson UK, Santos IS, Sawhney M, Schutte AE, Sepanlou SG, Shifa GT, Shiue I, Tedla BA, Thrift AG, Tonelli M, Truelsen T, Tsilimparis N, Ukwaja KN, Uthman OA, Vasankari T, Venketasubramanian N, Vlassov VV, Vos T, Westerman R, Yan LL, Yano Y, Yonemoto N, Zaki MES, Murray CJL. Global Burden of Hypertension and Systolic Blood Pressure of at Least 110 to 115 mm Hg, 1990-2015. JAMA 2017; 317 :165–182. SPRINT Research Group, Wright JT, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC, Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med 2015; 373 :2103–2116. Cholesterol Treatment Trialists’ (CTT) Collaboration, Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, Bhala N, Peto R, Barnes EH, Keech A, Simes J, Collins R. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet 2010; 376 :1670–1681. Libby P. The changing landscape of atherosclerosis. Nature 2021; 592 :524–533. Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, Braunwald E, Sabatine MS. Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions: A Systematic Review and Meta-analysis. JAMA 2016; 316 :1289–1297. Emerging Risk Factors Collaboration, Sarwar N, Gao P, Seshasai SRK, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, Stampfer M, Stehouwer CDA, Lewington S, Pennells L, Thompson A, Sattar N, White IR, Ray KK, Danesh J. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 2010; 375 :2215–2222. Global BMI Mortality Collaboration null, Di Angelantonio E, Bhupathiraju S, Wormser D, Gao P, Kaptoge S, Berrington de Gonzalez A, Cairns B, Huxley R, Jackson C, Joshy G, Lewington S, Manson J, Murphy N, Patel A, Samet J, Woodward M, Zheng W, Zhou M, Bansal N, Barricarte A, Carter B, Cerhan J, Smith G, Fang X, Franco O, Green J, Halsey J, Hildebrand J, Jung K, Korda R, McLerran D, Moore S, O’Keeffe L, Paige E, Ramond A, Reeves G, Rolland B, Sacerdote C, Sattar N, Sofianopoulou E, Stevens J, Thun M, Ueshima H, Yang L, Yun Y, Willeit P, Banks E, Beral V, Chen Z, Gapstur S, Gunter M, Hartge P, Jee S, Lam T-H, Peto R, Potter J, Willett W, Thompson S, Danesh J, Hu F. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet 2016; 388 :776–786. Micha R, Khatibzadeh S, Shi P, Andrews KG, Engell RE, Mozaffarian D, Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide. BMJ Open 2015; 5 :e008705. Additional Declarations No competing interests reported. Supplementary Files Supplementarydocument.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5820382","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402114744,"identity":"bac6367e-7b63-4700-b970-d2e57ca57450","order_by":0,"name":"Huili Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huili","middleName":"","lastName":"Li","suffix":""},{"id":402114745,"identity":"c24d1914-b754-476e-a661-863782dc596c","order_by":1,"name":"Fei Xiao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Xiao","suffix":""},{"id":402114746,"identity":"8a375788-be43-4e22-b8b1-ee4639682adf","order_by":2,"name":"Jiaying Chen","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaying","middleName":"","lastName":"Chen","suffix":""},{"id":402114747,"identity":"3d64d197-cc6d-492e-8796-56729a2bc495","order_by":3,"name":"Jiarui Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiarui","middleName":"","lastName":"Wang","suffix":""},{"id":402114748,"identity":"c0bc63e5-9c93-4794-85a4-dde94df9ae38","order_by":4,"name":"Yuanlu Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of University of Science and Technology of China （USTC）","correspondingAuthor":false,"prefix":"","firstName":"Yuanlu","middleName":"","lastName":"Chen","suffix":""},{"id":402114749,"identity":"29cd77fd-5758-482b-ae4f-4291e18f5e4e","order_by":5,"name":"Zhenyu Li","email":"","orcid":"","institution":"Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Li","suffix":""},{"id":402114750,"identity":"1eb2f125-b357-4001-9e03-b9d1f19032d3","order_by":6,"name":"Peirong Lin","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peirong","middleName":"","lastName":"Lin","suffix":""},{"id":402114751,"identity":"9b458cc4-4de0-46a2-b781-e094f720f6dd","order_by":7,"name":"Sheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYDACCRiDvYGBIaEAxmMjRgvPAaAWA5K0SCQACWK0yM9uPvbwa5tdnnzk68QPDwwY5PmnnTFg+FB2mIF/dgNWLQZ3jqUby7YlFxvezt0sAXSY4YzbOQaMM84dZpC4cwC7FokcM2nJbcyJG2fnbgBpSTCQzjFg5m07DJRKwO6wGfnfgFrqEzfOPLv5B1zLXzxaGG7ksEl+3HY4cb4E7zaELYx4tBjcSDOTZvx3PHEDT+42iwQDCaBf0goO9pxL55G4gcthyc8kf5ypTpzffnbzzR8VNvL8s5M3PvhRZi3HPwOHw4CAmQdkHSR4INEEYvPgVA8EjD9A1jXgUzIKRsEoGAUjGgAArJlc4+p7xmEAAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-13 13:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5820382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5820382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73934477,"identity":"f4f9a0fc-2b5a-49b5-af3b-ae6058cc697b","added_by":"auto","created_at":"2025-01-16 06:46:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137374,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal death cases and death rates of IHD from 1990 to 2021\u003c/p\u003e\n\u003cp\u003e(A) Deaths by sex from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e(B) Deaths in territories with low to high SDIs in 1990 and 2021.\u003c/p\u003e\n\u003cp\u003e(C) The ASDRs in different SDI regions from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e(D) The ASDRs in 204 countries and territories in 2021.\u003c/p\u003e\n\u003cp\u003e(E) The EAPCs in ASDRs in 204 countries and territories from 1990 to 2021.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5820382/v1/e04b5503bc9a6a051c48c5ea.png"},{"id":73935471,"identity":"4aec654e-83a4-4dc9-a2d8-b80aa840c571","added_by":"auto","created_at":"2025-01-16 06:54:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153846,"visible":true,"origin":"","legend":"\u003cp\u003eSex differences and trends in IHD death rates in different regions\u003c/p\u003e\n\u003cp\u003e(A) The ASDRs in males and females globally, in territories with low to high SDIs and in 21 GBD regions in 2021.\u003c/p\u003e\n\u003cp\u003e(B) The ASDRs globally and in territories with low to high SDIs by sex from 1990 to 2021. (C) The percent changes in ASDRs in males and females between 1990 and 2021.\u003c/p\u003e\n\u003cp\u003e(D) Male to female ratios of ASDRs globally and in territories with low to high SDIs from 1990 to 2021.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5820382/v1/154159548a36ff5675e7be14.png"},{"id":73934478,"identity":"2345fbef-5786-4c87-9a6b-adb935d94050","added_by":"auto","created_at":"2025-01-16 06:46:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260795,"visible":true,"origin":"","legend":"\u003cp\u003eDeath cases of IHD and their changes by age groups in different regions\u003c/p\u003e\n\u003cp\u003e(A) The contribution of each age group (15–94 years, 5-year intervals) to total deaths between 1990 and 2019 globally and in territories with low to high SDIs.\u003c/p\u003e\n\u003cp\u003e(B) The percent changes in deaths in four age groups (15–49 years, 50–69 years, 70–74 years, and 75–94 years) between 1990 and 2021 globally and in territories with low to high SDIs.\u003c/p\u003e\n\u003cp\u003e(C) The four age groups as percentages of total deaths globally, in territories with low to high SDIs and in 21 GBD regions in 1990 and 2021.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5820382/v1/b06d768e93b6fb100074de2d.png"},{"id":73935473,"identity":"48563d2c-b1d2-4587-a7bf-2d29bc4eef1d","added_by":"auto","created_at":"2025-01-16 06:54:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":337262,"visible":true,"origin":"","legend":"\u003cp\u003ePredominant contribution of metabolic risk factors to IHD-related deaths by SDI, sex, and age groups\u003c/p\u003e\n\u003cp\u003e(A) The ASDRs attributable to main risk factors by SDI region from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e(B) Male to female ratios of ASDRs attributable to main risk factors from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e(C) The percent changes in deaths attributable to four metabolic risk factors by age group and SDI region between 1990 and 2021.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5820382/v1/db840ba3da7c70e13b3bf651.png"},{"id":73935772,"identity":"1f988529-985f-416c-848f-d4915c1d2692","added_by":"auto","created_at":"2025-01-16 07:02:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68136,"visible":true,"origin":"","legend":"\u003cp\u003eForecast trends of ASDR 30 years from now\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5820382/v1/c7fc2c0d29a22fe0d43636e5.png"},{"id":73964716,"identity":"ee42fc0c-ee22-46eb-b934-7c304f96e03a","added_by":"auto","created_at":"2025-01-16 12:32:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2017713,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5820382/v1/5045e09f-f13c-4406-8c98-c041e86f589e.pdf"},{"id":73934481,"identity":"a72de356-17e3-429b-91bc-6bb3ab23741f","added_by":"auto","created_at":"2025-01-16 06:46:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1358889,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydocument.docx","url":"https://assets-eu.researchsquare.com/files/rs-5820382/v1/0476d50e6199e506a23ac30d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ischemic Heart Disease Mortality and Metabolic Risk Factors: A Global Analysis of Gender Disparities and Regional Variations (1980–2021)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite a 34.9% decrease in the global age-standardized mortality rate for cardiovascular diseases (CVDs) (from 358.4 per 100,000 in 1990 to 233.2 per 100,000 in 2022), the actual number of deaths from CVDs has risen\u0026mdash;from 12.4\u0026nbsp;million in 1990 to 19.8\u0026nbsp;million in 2022. This increase reflects the impacts of global population growth, aging, and modifiable risk factors related to metabolism, behavior, and the environment. To date, ischemic heart disease (IHD) remains the primary cause of CVD mortality worldwide, with an age-standardized death rate of 108.8 per 100,000. Hemorrhagic and ischemic strokes follow as other leading causes. The 2023 Global Burden of Cardiovascular Diseases report attributes the rising number of CVD deaths largely to preventable factors, such as high blood pressure, elevated cholesterol, unhealthy diet, and air pollution\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the profound role that metabolic risk factors such as hypertension, hyperlipidemia, and obesity play in the development and progression of ischemic heart disease (IHD), understanding how these risk factors have influenced IHD mortality over time is critical for devising effective public health interventions. Large-scale epidemiological studies, such as the Global Burden of Disease (GBD) study, have consistently identified metabolic syndrome and its components as significant contributors to IHD mortality, with rising trends in many regions attributed to increasing rates of obesity and type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These risk factors not only increase the likelihood of developing IHD but also complicate its management and prognosis, underscoring the need for targeted strategies that address these modifiable risks.\u003c/p\u003e \u003cp\u003eTargeted programs focusing on managing metabolic risks, such as hypertension, diabetes, and obesity, in addition to environmental and lifestyle changes, can significantly reduce the burden of IHD in these populations, where resources for health care are often limited. These insights will aid policymakers in developing effective strategies for both prevention and treatment, particularly as cardiovascular disease rates rise globally\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA paper explored the global burden of cardiovascular diseases (CVDs) and their risk factors from 1990 to 2019, based on estimates from the Global Burden of Disease Study (GBD 2019). The burden of CVDs is notably higher in low- and middle-income countries compared to high-income countries. The article discusses various disease conditions across different regions, including rheumatic heart disease and congenital heart disease, as well as the impact of risk factors such as elevated fasting plasma glucose, body mass index (BMI), and hypertension on cardiovascular health\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The cardiovascular risk factors and burden in adolescents and young adults have also been studied, based on data from the Global Burden of Disease Study (GBD 2019). It primarily discusses the prevalence, incidence, disability-adjusted life years (DALYs), and mortality rates of overall and specific types of cardiovascular diseases, with stratified analyses by gender, age group, region, and the Socio-Demographic Index (SDI). High systolic blood pressure, high body mass index (BMI), and elevated low-density lipoprotein (LDL) cholesterol are identified as major contributors to CVD worldwide, particularly in low-income and lower-middle-income countries. Additionally, household air pollution from solid fuels is highlighted as a significant risk factor in low-income countries\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn article also discusses the environmental factors influencing cardiovascular health. M\u0026uuml;nzel T et al. primarily discuss the impact of environmental risk factors on cardiovascular diseases (CVDs), identifying environmental pollutants such as air pollution, noise pollution, light pollution, and climate change as major contributors to CVDs. These non-communicable diseases (NCDs) significantly contribute to the global disease burden, particularly in low- and middle-income countries. The article summarizes the pathophysiological mechanisms through which these environmental stressors affect the cardiovascular system and suggests potential solutions to mitigate these risks\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A recent article, based on data from the Global Burden of Disease Study 2019, focuses on the impact of metabolic risk factors on the burden of ischemic heart disease (IHD). The article analyzes the burden of IHD across various countries and regions worldwide from 1990 to 2019, identifying metabolic risk factors\u0026mdash;such as high blood pressure, elevated low-density lipoprotein (LDL) cholesterol, high fasting blood glucose, and high body mass index\u0026mdash;as major drivers of the global mortality burden of IHD\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These studies provide a foundation for understanding the shifting disease patterns and underscore the importance of addressing modifiable risk factors in developing regions.\u003c/p\u003e \u003cp\u003eBuilding on this foundation, we conducted an updated analysis of the impact of metabolic factors on ischemic heart disease. Our study reveals significant differences in ischemic heart disease (IHD) across regions and between genders. While global IHD mortality rates have decreased, the burden in developing countries continues to rise, particularly in middle and low-middle SDI regions, where IHD-related deaths have notably increased. Male mortality rates are higher than those of females, and this gender gap has widened over time. Metabolic risk factors such as hypertension, low-density lipoprotein cholesterol, and hyperglycemia dominate the IHD mortality rates across SDI regions, while smoking and environmental pollution remain significant risk factors in low-SDI regions. Overall, the cardiovascular burden in developing countries is intensifying, especially among younger populations, highlighting a major challenge for global health management.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e \u003cb\u003eThe number of IHD-related deaths and mortality rates in developing regions surpass those in developed regions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe overall health impact of ischemic heart disease (IHD) is measured in terms of deaths and disability-adjusted life years (DALYs), which comprise years of life lost (YLL) and years lived with disability (YLD). From 1980 to 2021, there was a significant rise in IHD-related deaths, DALYs, YLL, and YLD. For instance, IHD deaths rose steadily from 2.54\u0026nbsp;million (95% uncertainty interval [UI]: 2.41\u0026ndash;2.65) in 1980 to 8.99\u0026nbsp;million (95% UI: 8.26\u0026ndash;9.53) by 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Similarly, the number of DALYs increased from 119.16 (95% UI: 114.55, 123.45) million in 1990 to 188.36\u0026nbsp;million (95% UI: 177.03\u0026ndash;198.15) in 2021 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Both YLL and YLD attributed to IHD showed a corresponding upward trend similar to DALYs (Tables S2 and S3). Despite the rise in absolute numbers, global age-standardized rates for deaths, DALYs, YLL, and YLD showed a gradual decline between 1990 and 2021 (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and S1\u0026ndash;S3). This upward trend in IHD incidence aligns with the broader increase in the disease burden of all cardiovascular diseases.\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\u003eThe death cases and age-standardized death rates of ischemic heart disease in 1990 and 2021, and their temporal trends from 1990 to 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of DALYs No. \u0026times; 10\u003csup\u003e3\u003c/sup\u003e (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASR-DALYs per 100, 000 No. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of DALYs No. \u0026times; 10\u003csup\u003e3\u003c/sup\u003e (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASR-DALYs per 100, 000 No. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAPC of ASR-YLL No. (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5367.14 (5076.40-5562.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158.90 (148.14\u0026ndash;165.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8991.64 (8264.12-9531.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.73 (99.60-115.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2804.95 (2673.80-2921.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187.66 (177.77-195.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5002.68 (4679.27-5336.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136.84 (127.37\u0026ndash;145.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2562.19 (2357.41-2702.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134.50 (122.46-142.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3988.96 (3540.38-4317.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.32 (75.90-92.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.81 (202.39-255.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119.50 (106.26-132.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e493.48 (448.13-543.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.41 (105.21-127.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e753.13 (698.70-804.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140.99 (129.76-151.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1834.65 (1699.51-1964.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142.10 (131.30-151.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1038.97 (978.52-1100.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.06 (118.35-134.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2811.12 (2564.66-3023.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.71 (107.23\u0026ndash;127.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1604.86 (1527.81-1657.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193.94 (182.11\u0026ndash;200.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2450.43 (2218.84-2647.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127.50 (115.03-137.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1732.39 (1591.27-1797.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157.59 (144.19-163.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1392.37 (1217.17-1489.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.45 (52.18\u0026ndash;61.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e117.62 (107.06-122.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.04 (59.85\u0026ndash;70.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152.70 (124.00-168.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.56 (21.78\u0026ndash;27.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.06\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\u003e131.77 (124.69-136.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e320.47 (299.83-332.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175.39 (159.20-192.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e265.51 (240.67-290.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.61\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\u003e644.90 (580.45-675.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177.72 (160.27-186.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e534.77 (468.40-571.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.85 (67.17\u0026ndash;80.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.71\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\u003e75.12 (64.54\u0026ndash;87.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105.29 (90.24-121.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.33 (140.03-185.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.97 (92.83-120.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\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\u003e107.55 (102.01-110.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135.91 (126.39-140.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162.30 (149.05-170.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.49 (58.98\u0026ndash;67.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.38\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\u003e40.16 (37.29\u0026ndash;41.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176.76 (162.82-184.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.64 (24.37\u0026ndash;30.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.67 (40.23\u0026ndash;50.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.2\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\u003e879.29 (811.26-910.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148.22 (136.54-153.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e543.04 (463.99-584.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.27 (41.45\u0026ndash;50.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.62\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\u003e570.43 (505.99-639.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.92 (83.87\u0026ndash;105.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008.01 (1683.97-2335.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.90 (91.18-125.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\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\u003e786.27 (751.13\u0026ndash;803.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e323.17 (305.26-331.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e903.62 (811.06-990.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e252.89 (226.96-277.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.79\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\u003e17.90 (15.56\u0026ndash;19.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.96 (64.95\u0026ndash;84.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.82 (36.78\u0026ndash;43.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.44 (76.93\u0026ndash;90.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\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\u003e44.60 (42.48\u0026ndash;46.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188.97 (179.08-195.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.45 (54.94\u0026ndash;69.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.50 (100.51-126.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.66\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\u003e23.27 (18.25\u0026ndash;29.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134.90 (107.86\u0026ndash;167.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.85 (38.89\u0026ndash;63.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119.34 (93.70-150.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.39\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\u003e4.53 (3.75\u0026ndash;5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182.55 (155.47-217.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.14 (9.33\u0026ndash;13.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170.89 (145.43-201.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.21\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\u003e17.09 (15.55\u0026ndash;18.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.95 (84.56-101.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.96 (28.04\u0026ndash;39.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.17 (49.56\u0026ndash;69.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.5\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\u003e252.65 (229.37-275.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.72 (103.39-125.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e638.70 (575.91-694.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.92 (100.18\u0026ndash;120.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.11\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\u003e364.09 (349.91-371.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272.56 (259.22-279.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331.28 (299.88\u0026ndash;352.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139.98 (126.84-148.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.13\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\u003e43.99 (39.10-50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.44 (61.05\u0026ndash;78.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.22 (87.73-117.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.16 (62.09\u0026ndash;82.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\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\u003e88.86 (84.86\u0026ndash;90.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125.72 (118.69-129.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e247.05 (221.19-273.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.69 (92.48-114.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.62\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\u003e708.22 (643.04-771.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136.39 (122.87-149.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1990.11 (1824.49-2155.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.92 (100.18\u0026ndash;120.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\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\u003e62.79 (59.93\u0026ndash;64.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149.43 (141.12-154.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.10 (45.02\u0026ndash;51.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.41 (50.08\u0026ndash;57.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.21\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\u003e386.04 (357.95-418.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275.18 (253.62-299.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e769.14 (685.36-858.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202.85 (180.59-223.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.98\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\u003eSince changes in the overall disease burden are primarily attributed to variations in disease-related mortality (Roth et al., 2020), it is essential to focus on the risk factors that influence IHD mortality and exacerbate the global cardiovascular burden. Understanding the global and regional trends of these risk factors is crucial. Globally, the age-standardized death rate (ASDR) has gradually declined over the past 30 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), with an estimated annual percentage change (EAPC) of 1.23. These trends are similar across both male and female populations, with an EAPC of 1.01 for males and 1.46 for females.\u003c/p\u003e \u003cp\u003eDuring the 1990s, the total number of deaths related to ischemic heart disease (IHD) increased alongside a gradual improvement in the Socio-Demographic Index (SDI). However, over the past three decades, the number of IHD-related deaths surged in regions with middle SDI and high-middle SDI, reaching 2.58\u0026nbsp;million (95% UI: 2.31\u0026ndash;2.81) and 2.24\u0026nbsp;million (95% UI: 1.98\u0026ndash;2.46) in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This trend can be illustrated in a line chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eC+). As a result, the IHD burden in middle SDI region surpassed that of high-middle SDI areas, accounting for the largest share of global IHD-related deaths in the most recent year analyzed. In contrast, high SDI regions experienced a consistent decline in IHD-related deaths, from 1.61\u0026nbsp;million (95% UI: 1.44\u0026ndash;1.71) in 1990 to 1.24\u0026nbsp;million (95% UI: 1.04\u0026ndash;1.36) in 2021. Furthermore, the rate of decline in the age-standardized death rate (ASDR) was slower in low-middle SDI (EAPC\u0026thinsp;=\u0026thinsp;0.03 ) and middle SDI regions (EAPC\u0026thinsp;=\u0026thinsp;0.22) compared to high-middle SDI (EAPC\u0026thinsp;=\u0026thinsp;1.34 ) and high SDI (EAPC\u0026thinsp;=\u0026thinsp;3.15) regions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Consequently, the IHD burden in developing regions has increased, surpassing that of developed regions over the last three decades.\u003c/p\u003e \u003cp\u003eAccording to region-specific data, the absolute number of deaths rose over time in most Global Burden of Disease (GBD) regions. However, five regions saw reductions: Central Europe, Western Europe, Australasia, high-income North America, and Southern Latin America, all characterized by relatively high SDIs. The age-standardized death rates (ASDRs) for IHD decreased in 17 GBD regions, with Australasia showing the steepest decline (EAPC\u0026thinsp;=\u0026thinsp;4.20), followed by Western Europe (EAPC\u0026thinsp;=\u0026thinsp;3.63 [95% UI: 3.80\u0026ndash;3.45]) and high-income Asia Pacific (EAPC\u0026thinsp;=\u0026thinsp;3.62). By the end of the study period, high-income Asia Pacific had the lowest ASDR at 25.56 [95% UI: 21.78\u0026ndash;27.65]. Conversely, increases in ASDR were noted in East Asia and Southern Sub-Saharan Africa, with Central Asia having the highest ASDR globally in 2021at 265.51 [95% UI: 240.67\u0026ndash;290.42].\u003c/p\u003e \u003cp\u003eAt the national level, Timor-Leste, Honduras, Djibouti, and Kenya experienced the largest increases in IHD-related deaths, with percentage changes ranging from 280\u0026ndash;500%. In contrast, Denmark, Norway, Georgia, and the United Kingdom saw the most significant declines, with reductions between 60% and 40% (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Nauru recorded an exceptionally high ASDR (432.64 [95% UI: 361.02\u0026ndash;517.42]), far surpassing any other country (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Meanwhile, the lowest ASDRs were observed in Japan, the Republic of Korea, San marino and France, where rates ranged from 30 to 40 per 100,000 population in 2021. Despite the general downward trend in ASDRs across most countries, some developing Asian nations witnessed relatively rapid increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). For instance, the ASDR increase in China, as measured by the EAPC, was greater than that of 95% of other countries, even though China's current ASDR remains comparatively low. Additionally, several Central Asian countries, including Uzbekistan, Tajikistan, and Kyrgyzstan, experienced both high ASDRs and sharp increases over the decades, signaling a significant healthcare challenge in the region. Uzbekistan, in particular, faced the most critical situation, with the highest ASDR globally and the fastest increase. In contrast, developed countries in higher SDI quintiles, such as the Ireland, Israel, Norway, Estonia and Denmark, saw the most substantial ASDR reductions, with EAPCs ranging from 5.25 to 4.50.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMen exhibit higher mortality rates from ischemic heart disease (IHD) compared to women, and the disparity in IHD-related death burden between sexes may continue to widen.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOverall, men experienced a greater burden of ischemic heart disease (IHD) than women, with sex differences likely to widen in the future. Globally, the number of deaths from IHD rose for both genders over the past few decades, reaching 4.60\u0026nbsp;million (95% UI: 4.14\u0026ndash;4.99) for males and 3.63\u0026nbsp;million (95% CI: 3.14\u0026ndash;3.99) for females in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, men consistently exhibited higher IHD mortality rates compared to women throughout the entire study period, with the gap appearing to expand over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eSimilarly, men exhibited higher age-standardized death rates (ASDRs) for IHD compared to women, both globally and regionally. In 2021, the highest ASDRs were observed in Central Asia for men (336.56) and Eastern Europe for men (328.68), while the lowest rates were recorded in high-income Asia Pacific, with males at 36.12 and females at 16.64 and Australasia with female at 31.64 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Despite a global decline in ASDRs for both sexes from 1990 to 2021, this decrease was largely driven by improvements in high-middle- and high-SDI regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, the reduction in ASDR was more pronounced in women than in men during the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), suggesting that if this trend continues, the gender gap in IHD mortality may widen.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2021, the male-to-female ASDR ratio remained between 1.40 and 1.50 across middle-SDI to high-SDI regions, but in high-SDI regions, the ratio reached around 1.80, highlighting an increasing disparity in the IHD death burden between genders in more developed areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eARIMA (Auto-Regressive Integrated Moving Average) was used to analyze time series data and predict future values. We predicted the ASDR for the next 30 years based on existing data and made separate predictions by sex(Figure XX).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePremature mortality from ischemic heart disease (IHD) is more severe in developing regions compared to developed regions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConsistent with the 2019 study, the global burden of ischemic heart disease (IHD) showed a rising trend with age, and premature mortality (between ages 30\u0026ndash;70) remains a significant issue in developing nations. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the number of IHD-related deaths globally increased with age, with older adults, particularly those over 70, representing the largest share of deaths. In 2021, the age groups 70\u0026ndash;74, 75\u0026ndash;79, 80\u0026ndash;84, and 85\u0026ndash;89 had the highest absolute death counts in low-, low-middle-, middle-, high-middle-, and high-SDI regions, respectively. However, the growth in death numbers across all age groups was faster in lower-SDI and low-middle-regions compared to others between 1990 and 2021. By the end of the study period, the middle-SDI region had the highest number of IHD deaths among all SDI regions.\u003c/p\u003e \u003cp\u003eWe categorized the population into four age groups: 15\u0026ndash;49 years, 50\u0026ndash;69 years, 70\u0026ndash;74 years, and 75\u0026ndash;94 years. We then assessed and compared the percent changes in deaths across these groups in each SDI region from 1990 to 2021. Globally, the largest rise in mortality was observed in the 75\u0026ndash;94 years group during the study period, followed by the 70\u0026ndash;74 years group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In other regions, all age groups experienced significant increases in death rates, while high-SDI regions showed an overall decline.我是不是没有按文章中给的年龄分组 The percentage change in deaths among the 15\u0026ndash;49 years group decreased as SDI levels increased, with the highest changes seen in low-SDI regions, while high-SDI regions showed negative changes. Consequently, IHD-related deaths in developing regions not only increased more rapidly than in developed areas but also impacted younger populations. These rising trends among young and middle-aged individuals are likely to persist, contributing to a growing long-term burden of IHD.\u003c/p\u003e \u003cp\u003eBetween 1990 and 2021, the age distribution of individuals who died from ischemic heart disease (IHD) showed a shift toward older age groups. In high-SDI regions, a larger proportion of elderly individuals accounted for IHD-related deaths compared to regions with lower SDI (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Oceania had the youngest age distribution of IHD mortality among all GBD regions, with the 15\u0026ndash;49 years groups contributing to 20.5% of deaths in 1990 and 19.5% in 2021. In contrast, Western Europe had the oldest distribution, with elderly individuals making up 63.5% of IHD deaths in 1990 and rising to 76.1% in 2021. Central Asia was unique in showing a decline in the age at death, with older populations comprising 51% of IHD deaths in 1990, decreasing to 49% in 2021. Meanwhile, East Asia saw the sharpest rise in age at death, as the proportion of elderly deaths grew from 38.5% in 1990 to 62.3% in 2021.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe inadequate management of metabolic risk factors in lower-SDI regions has led to a significant shift in the majority of IHD-related deaths from developed nations to developing countries.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe burden of ischemic heart disease (IHD) is primarily linked to metabolic, behavioral, environmental, and occupational risk factors (Yusuf et al., 2020). Following decades of cardiovascular disease management efforts, the death burden associated with these risk factors significantly declined between 1990 and 2021. The top five risk factors contributing to age-standardized death rates (ASDRs) in 1990\u0026mdash;high systolic blood pressure (SBP), elevated LDL cholesterol, smoking, kidney dysfunction, and Ambient particulate matter pollution (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). By 2021, due to varying degrees of reduction, metabolic risk factors had become the leading contributors to the rise in ASDR. High SBP and high LDL cholesterol remained the top two risk factors, while high FPG rose to the fifth, respectively. In contrast, ASDRs attributable to smoking declines over the past years.\u003c/p\u003e \u003cp\u003eIn all SDI regions, high systolic blood pressure (SBP) and elevated LDL cholesterol (LDL-c) were the leading contributors to age-standardized death rates (ASDRs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). From 1990 to 2021, these two risk factors declined rapidly in high-middle- and high-SDI regions, while they remained persistently high in low-, low-middle-, and middle-SDI regions. In low- and lower-middle-SDI regions, household air pollution from solid fuels was the primary factor contributing to age-standardized death rates (ASDRs). The ASDR associated with high fasting plasma glucose (FPG) ranked just behind high SBP and high LDL-c; however, its reduction in high-middle- and high-SDI regions was much slower compared to the latter two. In lower, low-middle-, and middle-SDI regions, FPG levels even showed a continuous upward trend. Over the past decades, death rates attributed to high BMI decreased in high-middle- and high-SDI regions but significantly rose in low-, low-middle-, and middle-SDI regions, leading to a relatively stable global trend. Geographically, Asian countries exhibited the fastest growth in high BMI-related death rates, followed by Eastern and Western Sub-Saharan Africa (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). In stark contrast, Australasia, Western Europe, Central Europe, High SDI and high-income Asia Pacific regions experienced the most rapid declines in high BMI-related deaths. By 2021, Eastern Europe had the highest BMI-related death rates, while high-income Asia Pacific recorded the lowest (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). Compared to changes in high BMI, shifts in IHD mortality were more substantial across all SDI regions. Metabolic risk factors, therefore, pose significant challenges for reducing IHD-related deaths worldwide, especially in developing regions.\u003c/p\u003e \u003cp\u003eDespite a significant decline in smoking-related mortality, it remained a major risk factor in 2021, with an attributable age-standardized death rate (ASDR) of 15.76 (95% UI: 13.33\u0026ndash;18.39) per 100,000 (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). In 1990, smoking-attributed ASDR was notably high in high-middle- and high-SDI regions. Over the past 30 years, these regions saw a sharp decline in smoking-related ASDR, whereas the reductions in lower-SDI and low-middle SDI regions were more moderate (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD). By the end of the study period, the high-SDI region recorded the lowest ASDR from smoking, while the high-middle-SDI region had the highest (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE).) Geographically, smoking-related deaths decreased in 19 of the GBD regions studied. However, East Asia experienced an increased burden over this time. In contrast to the 2019 article, which noted an increase in Eastern Europe during this period, a comparison between 2021 and 1990 shows that the ASDR due to smoking in Eastern Europe has decreased. High-income Asia Pacific had the lowest smoking-related ASDR in 2019.\u003c/p\u003e \u003cp\u003eThe contributions of environmental risk factors to IHD-related mortality varied across SDI regions. In low- to middle-SDI regions, the death burden from ambient particulate matter pollution increased over the study period, while it declined in high-middle- and high-SDI regions. Additionally, although the global burden from household air pollution due to solid fuels significantly decreased, it remained a major issue in low-SDI regions.\u003c/p\u003e \u003cp\u003eThe trends in risk factors associated with death burdens across different geographic regions mirrored those seen at the SDI level (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eF). In 2021, metabolic risk factors, such as elevated SBP, LDL-c, FPG, and BMI, were the primary contributors to IHD mortality in most regions. ASDRs related to metabolic risk factors were lower in regions with an SDI below 0.644 (From Eastern SubSaharan Africa to Southeast Asia), but these rates remained steady or increased slightly over time. In contrast, higher-SDI regions (SDI\u0026thinsp;\u0026gt;\u0026thinsp;0.788) experienced a substantial decline in ASDRs linked to metabolic risk factors, which were higher in 1990. The geographical disparities in death burden aligned with the regional differences in metabolic risk factor impacts, and a country-level analysis produced similar findings (Figures S3\u0026ndash;S5A).\u003c/p\u003e \u003cp\u003eIn 1990, the death rate from ischemic heart disease (IHD) in 33 low-SDI countries was the lowest among all SDI regions, with an ASDR of 119.50 per 100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Over the past three decades, this rate remained relatively unchanged (Figure S3A). By 2021, these countries had the second-lowest average death rate, with an ASDR of 116.41 per 100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, nations such as the Afghanistan, Solomon Islands, Yemen, Haiti, Guinea-Bissauand, Central African Republic, Papua New Guinea, Gambia, Equatorial Guinea, Nepal, Sierra Leone and Pakistan exhibited death rates above the regional average (Figure S3A). The rise in death rates in these countries was primarily linked to the growing burden of high SBP, high LDL-c, high FPG, household air pollution, and smoking over the study period, while other risk factors remained stable.\u003c/p\u003e \u003cp\u003eIn 1990, the IHD death rate in 43 low-middle-SDI countries ranked third among all SDI regions. Over the past years, the mortality rate has slightly increased (Figure S3B). By 2021, these countries recorded the highest average death rate, with an ASDR of 142.10 per 100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nations such as Tajikistan, Kyrgyzstan, Mongolia, Marshall Islands, Tuvalu, Kiribati, Palestine, Lao People's Democratic Republic, Honduras, Congo, Vanuatu, Morocco, and Sudan had death rates exceeding the regional average (Figure S3B). Like in low-SDI countries, rising death rates in these nations were linked to the growing burden of high SBP, high LDL-c, high FPG, household air pollution, and smoking in 1990. However, by 2021, household air pollution had become less of a factor in mortality. In addition to the impact of high FPG, high BMI became a more significant contributor to the death rate compared to the low-SDI region. Notably, Tajikistan experienced the fastest growth in mortality, driven by rapidly increasing burdens of high SBP, household air pollution from solid fuels and high LDL-c, and high FPG.\u003c/p\u003e \u003cp\u003eIn 1990, the 41 countries classified within the middle-SDI region had the fourth-highest average death rate among all SDI regions. Similar to the trends in low- and low-middle-SDI regions, the change in mortality rate over the past years has been minimal (Figure S4A). By 2021, these countries rose to third place in terms of average death rate, with an ASDR of 118.71 per 100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Countries such as Uzbekistan, Azerbaijan, the Syrian Arab Republic, Egypt, Turkmenistan, Fiji, Iraq, Samoa, Tokelau, Nauru, Algeria, Tunisia, Albania, Philippines, Indonesia, Tonga, Guyana and Armenia experienced death rates exceeding the middle-SDI regional average (Figure S4A). The higher mortality in these countries was largely attributed to the burdens of high SBP, elevated LDL-c, high FPG, high BMI, and ambient particulate matter pollution throughout the study period. Additionally, the death burden from ambient particulate matter pollution was notably high in Uzbekistan, Azerbaijan, the Syrian Arab Republic, Egypt, Iraq, Turkmenistan, Algeria, and Armenia. Nauru had the highest mortality rate and the steepest increase among all middle-SDI countries by 2021, primarily driven by rapid growth in the burdens of high SBP, high LDL-c, high FPG, high BMI, and particulate matter pollution. Other countries, such as China, had relatively lower death rates in 2021, but the burden from high SBP and Ambient particulate matter pollution increased over time.\u003c/p\u003e \u003cp\u003eIn 1990, the 48 countries in the high-middle-SDI region had the highest average death rate among all SDI regions. Unlike the low-, low-middle-, and middle-SDI regions, these countries experienced a significant decline in mortality rates from 1990 to 2021 (Figure S4B). By 2021, the high-middle-SDI region had the second-highest average death rate, with an ASDR of 127.50 per 100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The main contributors to the death burden were high SBP, elevated LDL-c, high FPG, high BMI, smoking, and ambient particulate matter pollution. Over the past three decades, death rates associated with these risk factors decreased significantly in most high-middle-SDI countries (Figure S4B). However, in countries like Argentina, Ukraine, Sri Lanka, Libya, Niue, American Samoa, Malaysia, Palau, North Macedonia, the Cook Islands, the Northern Mariana Islands, Oman, Montenegro, the United States Virgin Islands, and Saudi Arabia, the death burdens from these risk factors have not significantly declined or have slightly increased.\u003c/p\u003e \u003cp\u003eIn 1990, the 39 high-SDI countries had the second-highest IHD death rate among all SDI regions. Over the past years, the death rate significantly declined (Figure S5A), and by 2021, these countries had the lowest average death rate, with an ASDR of 54.41 per 100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The primary contributors to the death burden in these nations were high SBP, elevated LDL-c, high FPG, high BMI, and smoking(Figure S5A). Except for United Arab Emirates, all countries in the high-SDI region saw significant reductions in death rates from these risk factors. In the United States, high SBP and LDL-c were the leading causes of mortality in 1990, but by 2019, the death rate had decreased by 50%. However, ASDRs related to high FPG and high BMI showed little to no improvement during the study period.\u003c/p\u003e \u003cp\u003eIn summary, despite the decline in many high-middle- and high-SDI countries, high SBP, elevated LDL-c, and high FPG remained the top three risk factors for IHD deaths across all regions and countries by 2021. The death burden associated with household air pollution from solid fuels decreased over time as SDI levels increased, while the death burden from ambient particulate matter pollution rose in low- to middle-SDI regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). However, in high-middle- and high-SDI regions, the opposite trend was observed. A similar pattern was seen in the death burden linked to high BMI, which followed the same trajectory as household air pollution from solid fuels. The national-level trends in death burdens from metabolic risk factors mirrored the patterns at the SDI level. Countries in low-, low-middle-, and middle-SDI regions experienced rising death rates from metabolic factors, while those in high-middle- and high-SDI regions saw significant reductions in death rates linked to these risk factors.\u003c/p\u003e \u003cp\u003eGlobally, men had a higher ASDR than women, which can be linked to all the aforementioned risk factors (Figure S5B). The sex ratio for smoking (male to female\u0026thinsp;\u0026gt;\u0026thinsp;3.0) was significantly higher compared to other risk factors, highlighting smoking as a key contributor to the sex-based disparity in IHD-related deaths (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).Over the past years, the mortality rate has slightly increased (Figure S3B). Notable differences between men and women were also observed in ASDRs related to high sodium intake and ambient particulate matter pollution. Especially in low-SDI regions, of the nine risk factors associated with IHD deaths, only the mortality gap due to household air pollution from solid fuels decreased, suggesting that the overall gender disparity in IHD mortality may widen in the future.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe health burden of ischemic heart disease (IHD) is increasing globally, particularly in developing regions, where the sharp rise in related mortality rates and death counts has raised widespread concern\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. According to research data, from 1980 to 2021, the number of IHD-related deaths surged from 2.5\u0026nbsp;million to 8.99\u0026nbsp;million, reflecting the complexity and severe challenges that cardiovascular disease poses to global health systems. Although the overall age-standardized death rate (ASDR) has decreased, this masks significant disparities across regions, particularly in countries with a low or middle Socio-Demographic Index (SDI), where the rising trend in IHD deaths is concerning. The study results indicate that both the number of deaths and the mortality rate from ischemic heart disease (IHD) are significantly higher in developing regions compared to developed ones. This phenomenon is driven by various complex factors, including levels of economic development, lifestyle choices, accessibility of healthcare resources, and socio-cultural influences\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFirstly, socio-economic factors in developing regions are key contributors to the increasing burden of IHD. According to the study by Marmot et al. (2008), improvements in economic levels are generally associated with lifestyle improvements\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e; however, in many developing countries, the rapid pace of urbanization has led to lifestyle changes, such as increased consumption of high-fat, high-salt diets, and reduced physical activity. These factors are likely direct causes of the rising incidence of IHD. Additionally, economic disparities result in a lack of healthcare resources in some areas, making it difficult for residents to access timely and effective medical interventions\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn developed regions, the IHD mortality rate has gradually declined due to sufficient healthcare resources and effective implementation of public health policies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, healthcare systems in developing regions continue to face significant challenges, including a lack of infrastructure, inadequate public health interventions, and limited capacity for chronic disease management\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These factors greatly restrict IHD prevention and control efforts, particularly in middle-SDI and upper-middle-SDI regions, where IHD-related deaths have risen significantly. In fact, by 2021, the number of deaths in middle-SDI regions surpassed those in high-SDI regions. This trend underscores the urgent need for more balanced global cardiovascular health strategies to ensure that appropriate healthcare services are accessible in all regions.\u003c/p\u003e \u003cp\u003eSecondly, gender differences are also an essential factor when discussing the burden of IHD. Data show that the proportion of IHD-related deaths among men is significantly higher than among women, and this gap is widening. In 2021, the number of IHD deaths was 4.6\u0026nbsp;million for men and 3.63\u0026nbsp;million for women, with mortality rates consistently higher in men. Studies indicate that male mortality rates are generally higher than those of females\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, closely linked to physiological factors (such as hormone levels) and social behaviors (like smoking and drinking habits). Men are more prone to unhealthy lifestyles, while women often maintain healthier habits over a longer period, which may account for this difference. Meanwhile, cardiovascular risks for women rise after menopause, which could further increase the gender mortality gap in the future\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This gender disparity may be influenced by multiple factors, including lifestyle, health behaviors, and healthcare access differences\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To effectively address this issue, future research should explore the key factors influencing gender differences and develop targeted intervention strategies.\u003c/p\u003e \u003cp\u003eRegional disparities are equally important. Although IHD mortality rates are gradually declining in high-SDI regions, certain low- and middle-SDI areas, such as Central Asia and East Asia, have seen an increase in IHD mortality rates. This trend may be related to rapid urbanization and lifestyle changes in these regions, particularly the Westernization of dietary habits and a more sedentary lifestyle\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For instance, in China, economic growth has led to improved healthcare conditions, yet rising obesity rates and the incidence of cardiovascular diseases have not decreased as expected, posing serious challenges to public health policies.\u003c/p\u003e \u003cp\u003eIn developed regions, adequate healthcare resources and effective public health policies have contributed to a gradual reduction in IHD mortality rates. However, healthcare systems in developing regions still face numerous challenges, including a lack of infrastructure, insufficient public health interventions, and limited chronic disease management capacity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These issues severely restrict IHD prevention and control efforts, especially in middle- and upper-middle-SDI regions, where IHD-related deaths have risen significantly. In fact, by 2021, IHD deaths in middle-SDI regions surpassed those in high-SDI regions. This trend highlights the urgent need for more balanced global cardiovascular health strategies to ensure access to adequate healthcare services in all regions.\u003c/p\u003e \u003cp\u003eIn addition, the impact of population aging on IHD is becoming increasingly significant. Data show a sharp increase in IHD-related deaths among those aged 70 and above, particularly in low- and middle-SDI regions. With the global aging process accelerating, IHD will pose an even greater threat to public health, making prevention and intervention measures for the elderly a critical focus for policymakers\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Premature mortality is especially severe in developing countries. Among younger populations, mortality rates are high between ages 30 and 70, indicating that IHD not only affects the elderly but also threatens the working-age population. This issue not only adds to the financial burden on families but also negatively impacts national economic development. Consequently, health interventions targeting younger populations are essential, and governments should strengthen health education and promotion activities to improve cardiovascular health in this demographic. Aging-related mitochondrial dysfunction, leading to imbalances in reactive oxygen species production, results in endothelial and smooth muscle dysfunction, with clinical implications for major adverse cardiac events and mortality\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLastly, understanding country-specific circumstances is crucial to discussing the global burden of IHD. For example, in countries like Uzbekistan and Tajikistan, ASDR levels remain high, indicating an urgent need for improvements in their healthcare systems. In contrast, high-income countries such as Denmark and Norway have shown notable declines in mortality rates, reflecting the effectiveness of health policies and the importance of sustained investment. Therefore, countries should consider their unique situations and draw from global best practices to advance targeted public health policies to effectively address the challenges posed by IHD.\u003c/p\u003e \u003cp\u003eModifiable global risk factors significantly impact cardiovascular disease (CVD) and all-cause mortality. A study consolidates and standardizes individual-level data to analyze the contribution of five major modifiable risk factors\u0026mdash;body mass index (BMI), systolic blood pressure (SBP), non-high-density lipoprotein cholesterol, smoking, and diabetes\u0026mdash;to the incidence and mortality of CVD. The findings indicate that these risk factors have varying effects across different regions and genders. Overall, approximately 57.2% of cardiovascular events in women and 52.6% in men, as well as 22.2% of all-cause mortality in women and 19.1% in men, can be attributed to these five modifiable risk factors. Although there is some uncertainty in estimates, international studies show that between 1990 and 2015, the prevalence of elevated SBP significantly increased, along with the DALY and mortality associated with higher SBP. The global burden of hypertension was analyzed, and its impact on cardiovascular mortality was evaluated\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHypertension is one of the primary risk factors for cardiovascular disease. Studies suggest that sustained hypertension leads to structural changes in the heart, including left ventricular hypertrophy and arterial stiffness, which increase the risk of heart attack and stroke. According to a large prospective study, targeting a systolic blood pressure below 120 mmHg in high-risk cardiovascular patients without diabetes resulted in lower rates of both fatal and non-fatal major cardiovascular events and all-cause mortality compared to those with an SBP below 140 mmHg. Related research emphasizes that early screening and management of hypertension are crucial for reducing IHD-related mortality\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLDL-c is considered a key factor in atherosclerosis. Literature shows that for each unit increase in LDL-c, the risk of IHD rises by about 10%\u003csup\u003e27\u003c/sup\u003e. Endothelial dysfunction and inflammatory responses triggered by high cholesterol levels play a crucial role in the formation of atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Both pharmacological interventions (such as statins) and lifestyle modifications (such as a low-saturated-fat diet) have proven effective in lowering LDL-c levels, thereby reducing the incidence of IHD\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Studies indicate that each 1 mmol/L increase in fasting glucose raises the mortality risk of IHD by approximately 20%\u003csup\u003e30\u003c/sup\u003e. Advanced glycation end-products (AGEs) resulting from high blood glucose levels play a key role in vascular damage and atherosclerosis. Therefore, controlling blood glucose levels is a critical measure to reduce the risk of IHD. Obesity is closely associated with multiple metabolic abnormalities, including insulin resistance and chronic inflammation. A large meta-analysis of 239 prospective studies examined the impact of BMI on all-cause mortality and cardiovascular disease across four continents, revealing a consistent association between overweight/obesity and higher all-cause mortality. Obesity not only increases the risk of hypertension and high cholesterol but also directly impacts heart function. Weight control through improved diet and increased physical activity has been shown to significantly reduce IHD incidence\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Diets high in salt, sugar, and saturated fats are closely linked to cardiovascular disease. Studies show that reducing salt intake can effectively lower hypertension rates, while diets rich in dietary fiber, such as the Mediterranean diet, are proven to improve cardiovascular health. Promoting healthy dietary habits can help reduce the risk of IHD\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSummary\u003c/p\u003e \u003cp\u003eIschemic heart disease (IHD) remains a major global public health concern, though IHD-related mortality has gradually shifted from developed to developing countries. This shift is mainly due to insufficient control over various risk factors, especially cardiometabolic risks, in low-SDI regions. Additionally, mortality rates among men are higher than among women, and the gender gap may continue to widen due to the more substantial impact of major risk factors on men. While older age groups remain a primary focus for IHD management, the rapidly increasing premature mortality in low- to middle-SDI regions is equally concerning. Countries should adopt national strategies to reverse this unfavorable trend.\u003c/p\u003e \u003cp\u003eEffective prevention and control measures for risk factors can significantly reduce IHD-related mortality. Developed countries have made substantial progress in reducing IHD risks, and these efforts should continue in the future. In developing countries, where economic and social development lag behind, focus should be placed on controlling critical and urgent risk factors, such as cardiometabolic risks, smoking, and air pollution, to reduce IHD-related mortality.\u003c/p\u003e \u003cp\u003eBased on specific data on the local burden of IHD, each country should independently assess its disease burden to set strategic priorities. Periodic updates on disease conditions under the Global Burden of Disease (GBD) framework are highly beneficial for providing timely assessments and offer ongoing guidance for addressing this serious global health issue.\u003c/p\u003e \u003cp\u003elimitation\u003c/p\u003e \u003cp\u003eThis study primarily relies on secondary data from the Global Burden of Disease (GBD) database, which is based on death statistics and health indicators reported by various countries. However, the quality of health data varies across countries and regions, particularly in developing countries. Data from some low- and middle-income countries may be incomplete and subject to reporting bias, which could lead to an underestimation of the ischemic heart disease (IHD) death burden in these regions. The study does not thoroughly explore the differences in heart disease management across regions, such as accessibility to treatment, disparities in healthcare infrastructure, and other factors that influence the IHD death burden. While the study mentions a significant decline in mortality rates in high-income countries, these changes may be partly attributed to the stronger cardiovascular disease management systems in these countries. In contrast, in many low- and middle-income countries, the level of cardiovascular disease management remains limited, leading to inadequate control of mortality rates in these regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eAuthors’ contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuili Li and Fei Xiao designed the study and wrote the manuscript. Huili Li extracted, collected and analyzed data. Jiarui Wang, Yuanlu Chen, and Zhenyu Li prepared tables and figures. Peirong Lin and Sheng Wang reviewed the results and interpreted data. All authors have made an intellectual contribution to the manuscript and approved the submission.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82160157 and No. 81970290) , the Joint Funds of the National Natural Science Foundation of China (No.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eU20A2018) , and the Natural Science Foundation of Beijing (No. 7242046 and No. 7222044).\u003c/p\u003e\n\u003cp\u003eData and Code Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. The data were sourced from the GBD database. The code used in this article can be obtained by contacting the corresponding author, Sheng Wang.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was performed according to the guidelines of the Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eSTAR Methods\u003c/p\u003e\n\u003cp\u003eKey resources table\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSTAR Methods\u003c/p\u003e\n\u003cp\u003eKey resources table\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRESOURCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSOURCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIDENTIFIER\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR software version 4.1.3\u003c/p\u003e\n \u003cp\u003eData: GBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttp://www.rstudios.co\u003c/p\u003e\n \u003cp\u003ehttps://vizhub.healthdata.org/gbd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResource availability\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll data and requests for resources should be directed to and will be fulfilled by the lead contact, Sheng Wang ([email protected]).\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eAll data for resources should be directed to and will be fulfilled by the lead contact, Sheng Wang ([email protected]).\u003c/p\u003e\n\u003cp\u003eOther relevant items information availability\u003c/p\u003e\n\u003cp\u003eOther relevant items information should be directed to and will be fulfilled by the lead contact, Sheng Wang ([email protected]).\u003c/p\u003e\n\u003cp\u003eLead contact\u003c/p\u003e\n\u003cp\u003eFurther information and requests for resources should be directed to and will be fulfilled by the lead contact, Sheng Wang ([email protected]).\u003c/p\u003e\n\u003cp\u003eMaterials availability\u003c/p\u003e\n\u003cp\u003eThis study did not generate new unique reagents.\u003c/p\u003e\n\u003cp\u003eExperimental model and study participant details\u003c/p\u003e\n\u003cp\u003eThe experimental models and participant details in this study were all obtained from the MIMIC database.\u003c/p\u003e"},{"header":"Method details","content":"\u003cp\u003e\u003cstrong\u003eData source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on the annual incidence and age-standardized rates (ASRs) of IHD-related deaths, DALYs, YLD, and YLL from these year, categorized by sex, age, region, and country, were sourced from the Global Health Data Exchange (GHD) query tool (http://ghdx.healthdata.org/gbd-results-tool), which is supported by ongoing multinational collaboration. This tool offers accessible epidemiological data on 369 diseases and 87 risk factors from 204 countries and territories, tracked over time and by location\u003csup\u003e8\u003c/sup\u003e. Countries are grouped by the Sociodemographic Index (SDI) into five quintiles: low, low-middle, middle, high-middle, and high SDI regions. Additionally, they are classified into 21 regions based on their geographical locations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetailed methods for diagnosis and confirmation of GBD 2021 can be found on the website (https://doi.org/10.1016/j.jacc.2020.11.010). Metabolic risk factors were defined as high systolic blood pressure (SBP), high low-density lipoprotein cholesterol (LDL-c), high fasting plasma glucose (FPG), and high body mass index (BMI). Ischemic heart disease (IHD) was defined based on standard case definitions, including acute myocardial infarction, chronic stable angina, chronic IHD, and its related heart failure. Myocardial infarction was defined following the Fourth Universal Definition of Myocardial Infarction and was adjusted to account for out-of-hospital sudden cardiac deaths. Stable angina was defined using the Rose Angina Questionnaire. Mortality data were sourced from vital registration records coded in the International Classification of Diseases (ICD) system or from household mortality surveys.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData representation and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe formulas for calculating ASDR and EAPC are referenced from other articles\u003csup\u003e7\u003c/sup\u003e. ARIMA (Auto-Regressive Integrated Moving Average) was used to analyze time series data and predict future values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor data analysis, we utilized R software version 4.1.3, provided by the R Foundation for Statistical Computing in Vienna, Austria. We considered a P-value of less than 0.05 on a two-tailed test to be indicative of statistical significance across all tests conducted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMensah GA, Fuster V, Roth GA. A Heart-Healthy and Stroke-Free World: Using Data to Inform Global Action. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e 2023;\u003cstrong\u003e82\u003c/strong\u003e:2343\u0026ndash;2349. \u003c/li\u003e\n\u003cli\u003eRoth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, Ahmed M, Aksut B, Alam T, Alam K, Alla F, Alvis-Guzman N, Amrock S, Ansari H, \u0026Auml;rnl\u0026ouml;v J, Asayesh H, Atey TM, Avila-Burgos L, Awasthi A, Banerjee A, Barac A, B\u0026auml;rnighausen T, Barregard L, Bedi N, Belay Ketema E, Bennett D, Berhe G, Bhutta Z, Bitew S, Carapetis J, Carrero JJ, Malta DC, Casta\u0026ntilde;eda-Orjuela CA, Castillo-Rivas J, Catal\u0026aacute;-L\u0026oacute;pez F, Choi J-Y, Christensen H, Cirillo M, Cooper L, Criqui M, Cundiff D, Damasceno A, Dandona L, Dandona R, Davletov K, Dharmaratne S, Dorairaj P, Dubey M, Ehrenkranz R, El Sayed Zaki M, Faraon EJA, Esteghamati A, Farid T, Farvid M, Feigin V, Ding EL, Fowkes G, Gebrehiwot T, Gillum R, Gold A, Gona P, Gupta R, Habtewold TD, Hafezi-Nejad N, Hailu T, Hailu GB, Hankey G, Hassen HY, Abate KH, Havmoeller R, Hay SI, Horino M, Hotez PJ, Jacobsen K, James S, Javanbakht M, Jeemon P, John D, Jonas J, Kalkonde Y, Karimkhani C, Kasaeian A, Khader Y, Khan A, Khang Y-H, Khera S, Khoja AT, Khubchandani J, Kim D, Kolte D, Kosen S, Krohn KJ, Kumar GA, Kwan GF, Lal DK, Larsson A, Linn S, Lopez A, Lotufo PA, El Razek HMA, Malekzadeh R, Mazidi M, Meier T, Meles KG, Mensah G, Meretoja A, Mezgebe H, Miller T, Mirrakhimov E, Mohammed S, Moran AE, Musa KI, Narula J, Neal B, Ngalesoni F, Nguyen G, Obermeyer CM, Owolabi M, Patton G, Pedro J, Qato D, Qorbani M, Rahimi K, Rai RK, Rawaf S, Ribeiro A, Safiri S, Salomon JA, Santos I, Santric Milicevic M, Sartorius B, Schutte A, Sepanlou S, Shaikh MA, Shin M-J, Shishehbor M, Shore H, Silva DAS, Sobngwi E, Stranges S, Swaminathan S, Tabar\u0026eacute;s-Seisdedos R, Tadele Atnafu N, Tesfay F, Thakur JS, Thrift A, Topor-Madry R, Truelsen T, Tyrovolas S, Ukwaja KN, Uthman O, Vasankari T, Vlassov V, Vollset SE, Wakayo T, Watkins D, Weintraub R, Werdecker A, Westerman R, Wiysonge CS, Wolfe C, Workicho A, Xu G, Yano Y, Yip P, Yonemoto N, Younis M, Yu C, Vos T, Naghavi M, Murray C. Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e 2017;\u003cstrong\u003e70\u003c/strong\u003e:1\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eYusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, Brauer M, Kutty VR, Gupta R, Wielgosz A, AlHabib KF, Dans A, Lopez-Jaramillo P, Avezum A, Lanas F, Oguz A, Kruger IM, Diaz R, Yusoff K, Mony P, Chifamba J, Yeates K, Kelishadi R, Yusufali A, Khatib R, Rahman O, Zatonska K, Iqbal R, Wei L, Bo H, Rosengren A, Kaur M, Mohan V, Lear SA, Teo KK, Leong D, O\u0026rsquo;Donnell M, McKee M, Dagenais G. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. \u003cem\u003eLancet\u003c/em\u003e 2020;\u003cstrong\u003e395\u003c/strong\u003e:795\u0026ndash;808. \u003c/li\u003e\n\u003cli\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, Bonny A, Brauer M, Brodmann M, Cahill TJ, Carapetis J, Catapano AL, Chugh SS, Cooper LT, Coresh J, Criqui M, DeCleene N, Eagle KA, Emmons-Bell S, Feigin VL, Fern\u0026aacute;ndez-Sol\u0026agrave; J, Fowkes G, Gakidou E, Grundy SM, He FJ, Howard G, Hu F, Inker L, Karthikeyan G, Kassebaum N, Koroshetz W, Lavie C, Lloyd-Jones D, Lu HS, Mirijello A, Temesgen AM, Mokdad A, Moran AE, Muntner P, Narula J, Neal B, Ntsekhe M, Moraes de Oliveira G, Otto C, Owolabi M, Pratt M, Rajagopalan S, Reitsma M, Ribeiro ALP, Rigotti N, Rodgers A, Sable C, Shakil S, Sliwa-Hahnle K, Stark B, Sundstr\u0026ouml;m J, Timpel P, Tleyjeh IM, Valgimigli M, Vos T, Whelton PK, Yacoub M, Zuhlke L, Murray C, Fuster V, GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e 2020;\u003cstrong\u003e76\u003c/strong\u003e:2982\u0026ndash;3021. \u003c/li\u003e\n\u003cli\u003eSun J, Qiao Y, Zhao M, Magnussen CG, Xi B. Global, regional, and national burden of cardiovascular diseases in youths and young adults aged 15-39 years in 204 countries/territories, 1990-2019: a systematic analysis of Global Burden of Disease Study 2019. \u003cem\u003eBMC Med\u003c/em\u003e 2023;\u003cstrong\u003e21\u003c/strong\u003e:222. \u003c/li\u003e\n\u003cli\u003eM\u0026uuml;nzel T, Hahad O, S\u0026oslash;rensen M, Lelieveld J, Duerr GD, Nieuwenhuijsen M, Daiber A. Environmental risk factors and cardiovascular diseases: a comprehensive expert review. \u003cem\u003eCardiovasc Res\u003c/em\u003e 2022;\u003cstrong\u003e118\u003c/strong\u003e:2880\u0026ndash;2902. \u003c/li\u003e\n\u003cli\u003eWang W, Hu M, Liu H, Zhang X, Li H, Zhou F, Liu Y-M, Lei F, Qin J-J, Zhao Y-C, Chen Z, Liu W, Song X, Huang X, Zhu L, Ji Y-X, Zhang P, Zhang X-J, She Z-G, Yang J, Yang H, Cai J, Li H. Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease. \u003cem\u003eCell Metab\u003c/em\u003e 2021;\u003cstrong\u003e33\u003c/strong\u003e:1943-1956.e2. \u003c/li\u003e\n\u003cli\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet\u003c/em\u003e 2020;\u003cstrong\u003e396\u003c/strong\u003e:1204\u0026ndash;1222. \u003c/li\u003e\n\u003cli\u003eVogel B, Acevedo M, Appelman Y, Bairey Merz CN, Chieffo A, Figtree GA, Guerrero M, Kunadian V, Lam CSP, Maas AHEM, Mihailidou AS, Olszanecka A, Poole JE, Saldarriaga C, Saw J, Z\u0026uuml;hlke L, Mehran R. The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030. \u003cem\u003eLancet\u003c/em\u003e 2021;\u003cstrong\u003e397\u003c/strong\u003e:2385\u0026ndash;2438. \u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez-G\u0026aacute;lvez J, Ortega-Mart\u0026iacute;n E, Carretero-Bravo J, P\u0026eacute;rez-Mu\u0026ntilde;oz C, Su\u0026aacute;rez-Lled\u0026oacute; V, Ramos-Fiol B. Social determinants of multimorbidity patterns: A systematic review. \u003cem\u003eFront Public Health\u003c/em\u003e 2023;\u003cstrong\u003e11\u003c/strong\u003e:1081518. \u003c/li\u003e\n\u003cli\u003eMente A, Dehghan M, Rangarajan S, McQueen M, Dagenais G, Wielgosz A, Lear S, Li W, Chen H, Yi S, Wang Y, Diaz R, Avezum A, Lopez-Jaramillo P, Seron P, Kumar R, Gupta R, Mohan V, Swaminathan S, Kutty R, Zatonska K, Iqbal R, Yusuf R, Mohammadifard N, Khatib R, Nasir NM, Ismail N, Oguz A, Rosengren A, Yusufali A, Wentzel-Viljoen E, Puoane T, Chifamba J, Teo K, Anand SS, Yusuf S, Prospective Urban Rural Epidemiology (PURE) study investigators. Association of dietary nutrients with blood lipids and blood pressure in 18 countries: a cross-sectional analysis from the PURE study. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e 2017;\u003cstrong\u003e5\u003c/strong\u003e:774\u0026ndash;787. \u003c/li\u003e\n\u003cli\u003eMarmot M, Friel S, Bell R, Houweling TAJ, Taylor S, Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. \u003cem\u003eLancet\u003c/em\u003e 2008;\u003cstrong\u003e372\u003c/strong\u003e:1661\u0026ndash;1669. \u003c/li\u003e\n\u003cli\u003eSliwa K, Acquah L, Gersh BJ, Mocumbi AO. Impact of Socioeconomic Status, Ethnicity, and Urbanization on Risk Factor Profiles of Cardiovascular Disease in Africa. \u003cem\u003eCirculation\u003c/em\u003e 2016;\u003cstrong\u003e133\u003c/strong\u003e:1199\u0026ndash;1208. \u003c/li\u003e\n\u003cli\u003eTimmis A, Townsend N, Gale CP, Torbica A, Lettino M, Petersen SE, Mossialos EA, Maggioni AP, Kazakiewicz D, May HT, De Smedt D, Flather M, Zuhlke L, Beltrame JF, Huculeci R, Tavazzi L, Hindricks G, Bax J, Casadei B, Achenbach S, Wright L, Vardas P, European Society of Cardiology. European Society of Cardiology: Cardiovascular Disease Statistics 2019. \u003cem\u003eEur Heart J\u003c/em\u003e 2020;\u003cstrong\u003e41\u003c/strong\u003e:12\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eTimmis A, Aboyans V, Vardas P, Townsend N, Torbica A, Kavousi M, Boriani G, Huculeci R, Kazakiewicz D, Scherr D, Karagiannidis E, Cvijic M, Kapłon-Cieślicka A, Ignatiuk B, Raatikainen P, De Smedt D, Wood A, Dudek D, Van Belle E, Weidinger F, ESC National Cardiac Societies. European Society of Cardiology: the 2023 Atlas of Cardiovascular Disease Statistics. \u003cem\u003eEur Heart J\u003c/em\u003e 2024;\u003cstrong\u003e45\u003c/strong\u003e:4019\u0026ndash;4062. \u003c/li\u003e\n\u003cli\u003eHanson MA, Gluckman PD. Developmental origins of health and disease--global public health implications. \u003cem\u003eBest Pract Res Clin Obstet Gynaecol\u003c/em\u003e 2015;\u003cstrong\u003e29\u003c/strong\u003e:24\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eWong ND, Cordola Hsu AR, Rozanski A, Shaw LJ, Whelton SP, Budoff MJ, Nasir K, Miedema MD, Rumberger J, Blaha MJ, Berman DS. Sex Differences in Coronary Artery Calcium and Mortality From Coronary Heart Disease, Cardiovascular Disease, and All Causes in Adults With Diabetes: The Coronary Calcium Consortium. \u003cem\u003eDiabetes Care\u003c/em\u003e 2020;\u003cstrong\u003e43\u003c/strong\u003e:2597\u0026ndash;2606. \u003c/li\u003e\n\u003cli\u003eFonseca MIH, Silva IT da, Ferreira SRG. Impact of menopause and diabetes on atherogenic lipid profile: is it worth to analyse lipoprotein subfractions to assess cardiovascular risk in women? \u003cem\u003eDiabetol Metab Syndr\u003c/em\u003e 2017;\u003cstrong\u003e9\u003c/strong\u003e:22. \u003c/li\u003e\n\u003cli\u003eSullivan S, Young A, Hammadah M, Lima BB, Levantsevych O, Ko Y-A, Pearce BD, Shah AJ, Kim JH, Moazzami K, Driggers EG, Haffar A, Ward L, Herring I, Hankus A, Lewis TT, Mehta PK, Bremner JD, Raggi P, Quyyumi A, Vaccarino V. Sex differences in the inflammatory response to stress and risk of adverse cardiovascular outcomes among patients with coronary heart disease. \u003cem\u003eBrain Behav Immun\u003c/em\u003e 2020;\u003cstrong\u003e90\u003c/strong\u003e:294\u0026ndash;302. \u003c/li\u003e\n\u003cli\u003ePatwardhan V, Gil GF, Arrieta A, Cagney J, DeGraw E, Herbert ME, Khalil M, Mullany EC, O\u0026rsquo;Connell EM, Spencer CN, Stein C, Valikhanova A, Gakidou E, Flor LS. Differences across the lifespan between females and males in the top 20 causes of disease burden globally: a systematic analysis of the Global Burden of Disease Study 2021. \u003cem\u003eLancet Public Health\u003c/em\u003e 2024;\u003cstrong\u003e9\u003c/strong\u003e:e282\u0026ndash;e294. \u003c/li\u003e\n\u003cli\u003eKaram G, Agarwal A, Sadeghirad B, Jalink M, Hitchcock CL, Ge L, Kiflen R, Ahmed W, Zea AM, Milenkovic J, Chedrawe MA, Rabassa M, El Dib R, Goldenberg JZ, Guyatt GH, Boyce E, Johnston BC. Comparison of seven popular structured dietary programmes and risk of mortality and major cardiovascular events in patients at increased cardiovascular risk: systematic review and network meta-analysis. \u003cem\u003eBMJ\u003c/em\u003e 2023;\u003cstrong\u003e380\u003c/strong\u003e:e072003. \u003c/li\u003e\n\u003cli\u003eMontone RA, Camilli M, Calvieri C, Magnani G, Bonanni A, Bhatt DL, Rajagopalan S, Crea F, Niccoli G. Exposome in ischaemic heart disease: beyond traditional risk factors. \u003cem\u003eEur Heart J\u003c/em\u003e 2024;\u003cstrong\u003e45\u003c/strong\u003e:419\u0026ndash;438. \u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang J, Butler J, Yang X, Xie P, Guo D, Wei T, Yu J, Wu Z, Gao Y, Han X, Zhang X, Wen S, Anker SD, Filippatos G, Fonarow GC, Gan T, Zhang R, China-HF Investigators. Contemporary Epidemiology, Management, and Outcomes of Patients Hospitalized for Heart Failure in China: Results From the China Heart Failure (China-HF) Registry. \u003cem\u003eJ Card Fail\u003c/em\u003e 2017;\u003cstrong\u003e23\u003c/strong\u003e:868\u0026ndash;875. \u003c/li\u003e\n\u003cli\u003eTracy EP, Hughes W, Beare JE, Rowe G, Beyer A, LeBlanc AJ. Aging-Induced Impairment of Vascular Function: Mitochondrial Redox Contributions and Physiological/Clinical Implications. \u003cem\u003eAntioxid Redox Signal\u003c/em\u003e 2021;\u003cstrong\u003e35\u003c/strong\u003e:974\u0026ndash;1015. \u003c/li\u003e\n\u003cli\u003eForouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, Alexander L, Estep K, Hassen Abate K, Akinyemiju TF, Ali R, Alvis-Guzman N, Azzopardi P, Banerjee A, B\u0026auml;rnighausen T, Basu A, Bekele T, Bennett DA, Biadgilign S, Catal\u0026aacute;-L\u0026oacute;pez F, Feigin VL, Fernandes JC, Fischer F, Gebru AA, Gona P, Gupta R, Hankey GJ, Jonas JB, Judd SE, Khang Y-H, Khosravi A, Kim YJ, Kimokoti RW, Kokubo Y, Kolte D, Lopez A, Lotufo PA, Malekzadeh R, Melaku YA, Mensah GA, Misganaw A, Mokdad AH, Moran AE, Nawaz H, Neal B, Ngalesoni FN, Ohkubo T, Pourmalek F, Rafay A, Rai RK, Rojas-Rueda D, Sampson UK, Santos IS, Sawhney M, Schutte AE, Sepanlou SG, Shifa GT, Shiue I, Tedla BA, Thrift AG, Tonelli M, Truelsen T, Tsilimparis N, Ukwaja KN, Uthman OA, Vasankari T, Venketasubramanian N, Vlassov VV, Vos T, Westerman R, Yan LL, Yano Y, Yonemoto N, Zaki MES, Murray CJL. Global Burden of Hypertension and Systolic Blood Pressure of at Least 110 to 115 mm Hg, 1990-2015. \u003cem\u003eJAMA\u003c/em\u003e 2017;\u003cstrong\u003e317\u003c/strong\u003e:165\u0026ndash;182. \u003c/li\u003e\n\u003cli\u003eSPRINT Research Group, Wright JT, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC, Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. \u003cem\u003eN Engl J Med\u003c/em\u003e 2015;\u003cstrong\u003e373\u003c/strong\u003e:2103\u0026ndash;2116. \u003c/li\u003e\n\u003cli\u003eCholesterol Treatment Trialists\u0026rsquo; (CTT) Collaboration, Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, Bhala N, Peto R, Barnes EH, Keech A, Simes J, Collins R. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. \u003cem\u003eLancet\u003c/em\u003e 2010;\u003cstrong\u003e376\u003c/strong\u003e:1670\u0026ndash;1681. \u003c/li\u003e\n\u003cli\u003eLibby P. The changing landscape of atherosclerosis. \u003cem\u003eNature\u003c/em\u003e 2021;\u003cstrong\u003e592\u003c/strong\u003e:524\u0026ndash;533. \u003c/li\u003e\n\u003cli\u003eSilverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, Braunwald E, Sabatine MS. Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA\u003c/em\u003e 2016;\u003cstrong\u003e316\u003c/strong\u003e:1289\u0026ndash;1297. \u003c/li\u003e\n\u003cli\u003eEmerging Risk Factors Collaboration, Sarwar N, Gao P, Seshasai SRK, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, Stampfer M, Stehouwer CDA, Lewington S, Pennells L, Thompson A, Sattar N, White IR, Ray KK, Danesh J. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. \u003cem\u003eLancet\u003c/em\u003e 2010;\u003cstrong\u003e375\u003c/strong\u003e:2215\u0026ndash;2222. \u003c/li\u003e\n\u003cli\u003eGlobal BMI Mortality Collaboration null, Di Angelantonio E, Bhupathiraju S, Wormser D, Gao P, Kaptoge S, Berrington de Gonzalez A, Cairns B, Huxley R, Jackson C, Joshy G, Lewington S, Manson J, Murphy N, Patel A, Samet J, Woodward M, Zheng W, Zhou M, Bansal N, Barricarte A, Carter B, Cerhan J, Smith G, Fang X, Franco O, Green J, Halsey J, Hildebrand J, Jung K, Korda R, McLerran D, Moore S, O\u0026rsquo;Keeffe L, Paige E, Ramond A, Reeves G, Rolland B, Sacerdote C, Sattar N, Sofianopoulou E, Stevens J, Thun M, Ueshima H, Yang L, Yun Y, Willeit P, Banks E, Beral V, Chen Z, Gapstur S, Gunter M, Hartge P, Jee S, Lam T-H, Peto R, Potter J, Willett W, Thompson S, Danesh J, Hu F. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. \u003cem\u003eLancet\u003c/em\u003e 2016;\u003cstrong\u003e388\u003c/strong\u003e:776\u0026ndash;786. \u003c/li\u003e\n\u003cli\u003eMicha R, Khatibzadeh S, Shi P, Andrews KG, Engell RE, Mozaffarian D, Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide. \u003cem\u003eBMJ Open\u003c/em\u003e 2015;\u003cstrong\u003e5\u003c/strong\u003e:e008705. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ischemic heart disease, metabolic, mortality","lastPublishedDoi":"10.21203/rs.3.rs-5820382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5820382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe mortality rate and the number of deaths from ischemic heart disease (IHD) in developing regions have surpassed those in developed regions. Since 1980, the global number of IHD deaths, disability-adjusted life years (DALY), years of life lost (YLL), and years lived with disability (YLD) have significantly increased, particularly in regions with medium and high Social Development Index (SDI). Although the overall mortality rate has declined, developing countries, especially those in the medium SDI and low-medium SDI regions, are facing a greater burden of death. In the 1990s, while the number of IHD deaths increased, the socio-economic development level (SDI) also improved. However, over the past 30 years, the number of deaths in medium SDI regions, especially in China and Central Asia, has risen sharply, with these regions experiencing a faster increase in mortality rates. In contrast, the number of deaths in high SDI regions has steadily declined. The IHD mortality rate among men is generally higher than that of women, and the gender gap may continue to widen. Globally, the main risk factors for IHD deaths include high systolic blood pressure, high low-density lipoprotein (LDL), smoking, and high blood sugar. With improvements in health management in developed regions, deaths caused by metabolic risk factors have significantly declined. However, risk factors in developing countries, particularly in low-income and middle-income regions, remain significant. Air pollution, smoking, and other factors continue to be major health threats.\u003c/p\u003e","manuscriptTitle":"Ischemic Heart Disease Mortality and Metabolic Risk Factors: A Global Analysis of Gender Disparities and Regional Variations (1980–2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-16 06:46:01","doi":"10.21203/rs.3.rs-5820382/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d7ca7ec-cd69-4a6f-add6-0705a6de119e","owner":[],"postedDate":"January 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-16T12:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-16 06:46:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5820382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5820382","identity":"rs-5820382","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}