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<journal-id journal-id-type="publisher-id">Front. Public Health</journal-id>
<journal-title-group>
<journal-title>Frontiers in Public Health</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Public Health</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2296-2565</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fpubh.2025.1653981</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Burden and trend of cardiovascular diseases in youths aged 0&#x2013;19&#x202F;years in China, Asia, and the world, with forecasts to 2036: a systematic analysis of the global burden of disease study 2021</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Yu</surname><given-names>Jin</given-names></name>
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<name><surname>Zhu</surname><given-names>Shanshan</given-names></name>
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<contrib contrib-type="author">
<name><surname>Wang</surname><given-names>Zeping</given-names></name>
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<contrib contrib-type="author">
<name><surname>Shen</surname><given-names>Yongyao</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name><surname>Ji</surname><given-names>Sheng</given-names></name>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
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<contrib contrib-type="author" corresp="yes" equal-contrib="yes">
<name><surname>Guo</surname><given-names>Yongjin</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author" corresp="yes" equal-contrib="yes">
<name><surname>Jiang</surname><given-names>Liying</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
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<aff id="aff1"><label>1</label><institution>Graduate School, Shanghai University of Traditional Chinese Medicine</institution>, <city>Shanghai</city>, <country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>College of Public Health, Shanghai University of Medicine &#x0026; Health Sciences</institution>, <city>Shanghai</city>, <country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Department of Human Resources, Jiangyan District Center for Disease Control and Prevention</institution>, <city>Taizhou</city>, <state>Jiangsu</state>, <country country="cn">China</country></aff>
<aff id="aff4"><label>4</label><institution>Department of Human Resources, Jiangyan District Health Commission</institution>, <city>Taizhou</city>, <state>Jiangsu</state>, <country country="cn">China</country></aff>
<aff id="aff5"><label>5</label><institution>School of Nursing and Health Management, Shanghai University of Medicine &#x0026; Health Sciences</institution>, <city>Shanghai</city>, <country country="cn">China</country></aff>
<aff id="aff6"><label>6</label><institution>Jiading Central Hospital, Shanghai University of Medicine &#x0026; Health Sciences</institution>, <city>Shanghai</city>, <country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>&#x002A;</label>Correspondence: Yongjin Guo, <email xlink:href="mailto:yongjinguo11@outlook.com">yongjinguo11@outlook.com</email>; Liying Jiang, <email xlink:href="mailto:Jiangly@sumhs.edu.cn">Jiangly@sumhs.edu.cn</email></corresp>
<fn fn-type="equal" id="fn0002"><label>&#x2020;</label><p>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-01-23">
<day>23</day>
<month>01</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>13</volume>
<elocation-id>1653981</elocation-id>
<history>
<date date-type="received">
<day>25</day>
<month>06</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>02</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>31</day>
<month>12</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2026 Yu, Zhu, Wang, Shen, Ji, Guo and Jiang.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Yu, Zhu, Wang, Shen, Ji, Guo and Jiang</copyright-holder>
<license>
<ali:license_ref start_date="2026-01-23">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Objective</title>
<p>To combat the harmful impacts of cardiovascular diseases (CVDs) on public health, it is crucial to understand the epidemiologic features and disease burden. The study aims to identify the trends of CVDs burden in youths aged 0&#x2013;19&#x202F;years from 1990 to 2021 across the global, Asian, and Chinese contexts to inform more effective strategies and actions.</p>
</sec>
<sec>
<title>Methods</title>
<p>The data from the Global Burden of Diseases and Risk Factors Study (GBD) 2021 were analyzed, stratified by sex, age, the socio-demographic index (SDI) regions, disease subtypes, respectively. We also employed the Auto-Regressive Integrated Moving Average (ARIMA) to predict the future burden of CVDs up to 2036.</p>
</sec>
<sec>
<title>Results</title>
<p>In 2021, DALY rate of CVDs among children and adolescents were 303.140 (268.480&#x2013;343.800) globally, 268.730 (242.730&#x2013;298.350) in Asia and 148.800 (126.510&#x2013;173.620) in China. It exhibited significant declined trends from 1990 to 2021, with average annual percentage changes (AAPCs) of &#x2212;2.447, &#x2212;2.665, and &#x2212;4.379, respectively (all <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001). Compared with males, females had relatively higher prevalence of CVDs globally (797.060 vs. 791.140 per 100,000). Rheumatic heart disease served as the most prominent subtype across the world. Non-optimal temperatures emerged as the primary risk factor for CVDs-related disability-adjusted life years (DALYs). The CVDs incidence rate is predicted to rise to 108.861 per 100,000 globally, while the rate in Asia remains steady and decreases in China in 2036.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>A substantial global burden of CVDs in youths aged 0&#x2013;19&#x202F;years remains the pressing public health issue in 2021. The burden of overall and type-specific CVDs varies by age, sex, SDI, regions and countries. Current and future challenges in CVDs prevention for youngsters implied by the epidemiologic features are highlighted in this study.</p>
</sec>
</abstract>
<kwd-group>
<kwd>cardiovascular diseases</kwd>
<kwd>children and adolescents</kwd>
<kwd>disease burden</kwd>
<kwd>world</kwd>
<kwd>Asia</kwd>
<kwd>China</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This study was supported by Local High-level University (cultivation) project in Shanghai (A1-2601-25-203001-8), Research on the Policy Effect Evaluation of Family Doctor Contract Service Fees in Shanghai (2025HP05), Shanghai University of Medicine and Health Sciences&#x2019; Active Health for Everyone Science Popularization and Collaborative Governance Platform (No. A1-0200-25-201007-7).</funding-statement>
</funding-group>
<counts>
<fig-count count="5"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="32"/>
<page-count count="12"/>
<word-count count="6328"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Children and Health</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality, inflicting substantial social and economic costs (<xref ref-type="bibr" rid="ref1">1</xref>, <xref ref-type="bibr" rid="ref2">2</xref>). According to the Global Burden of Disease (GBD) 2021 study and the World Heart Report 2023, CVDs are responsible for 20.5 million deaths globally, accounting for approximately one-third of total deaths (<xref ref-type="bibr" rid="ref3">3</xref>, <xref ref-type="bibr" rid="ref4">4</xref>). The Asia exhibits tremendous disparities and adversities in wealth status, rendering it a critical focal point for CVDs prevention and control efforts. According to the China Cardiovascular Health and Disease Report 2023, CVDs serve as the leading cause of death, accounting for 48.980% of deaths in rural areas and 47.350% in urban regions in 2021 (<xref ref-type="bibr" rid="ref5">5</xref>, <xref ref-type="bibr" rid="ref6">6</xref>).</p>
<p>CVDs mainly appear in the middle-age adults and older adults. CVDs in adults encompass a range of disorders affecting the blood vessels, including coronary artery disease (CAD), cerebrovascular disease (CeVD), and peripheral vascular disease (PVD). However, some CVDs tend to be triggered in younger population in recent decades. For example, the prevalence of hypertension in youths in China was 13.000% in 2019, 13.200% in girls (12.700% in boys), also 14.100% in the rural (11.900% in the urban), representing an upward trend with age (<xref ref-type="bibr" rid="ref7">7</xref>).</p>
<p>As a major cause of CVDs mortality in youths in developing countries, rheumatic heart disease can cause approximately 250,000 deaths every year and reaches a peak between the 20&#x2013;29 age. DALYs for alcoholic cardiomyopathy rises rapidly from the age of 25 (<xref ref-type="bibr" rid="ref8">8</xref>). Moreover, infective endocarditis in youths without congenital heart disease was really similar to that in adults and caused short-term deaths, combined with acute heart failure, compared with those with congenital heart disease (<xref ref-type="bibr" rid="ref9">9</xref>, <xref ref-type="bibr" rid="ref10">10</xref>).</p>
<p>CVDs in youths present chronic and complex conditions inflicted by biological contexts and environmental factors (<xref ref-type="bibr" rid="ref7">7</xref>). Adolescence is an important period for physical and mental development being more neglected for health coverage (<xref ref-type="bibr" rid="ref11">11</xref>, <xref ref-type="bibr" rid="ref12">12</xref>). Previous studies suggested that CVDs in youths presented a substantial share in mortality burden with that of adults (<xref ref-type="bibr" rid="ref13">13</xref>). Atherosclerosis can begin early in lifetime and develop undetected for an extended time before going into an advanced phase with clinically presentable manifestations (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref15">15</xref>). The underlying mechanisms and features of pediatric CVDs exhibit specific patterns and differ commonly compared with adults (<xref ref-type="bibr" rid="ref6">6</xref>, <xref ref-type="bibr" rid="ref16">16</xref>). Most importantly, the pathological changes from childhood to adulthood can predict fatal or nonfatal CVD in later middle lifetime.</p>
<p>Obesity, high blood pressure, and abnormal plasma lipids are well-established CVDs risk factors appearing in childhood and adolescents (<xref ref-type="bibr" rid="ref17">17</xref>). If these risk factors, being inherited or acquired, continue to be unmodified, high-risk conditions are prone to develop the adverse outcomes when approaching adults. Previous research focuses on clinical factors such as obesity, blood pressure, and cholesterol that align with CVDs in adults, while researches on pediatric CVDs tend to focus on lifestyle-related factor, such as dietary quality, physical activity and psychological characteristics (<xref ref-type="bibr" rid="ref18">18</xref>). Critical barriers to early prevention are the difficulty in accurately evaluating CVDs risk in youths due to the subclinical nature of CVDs. Evidence on potential risk factors for CVDs among young population globally is limited. These gaps might be involved in informing effective strategies and prevention efforts to reduce CVDs burden in young population worldwide.</p>
<p>There is an urgent need to develop and implement effective and targeted strategies for the primary prevention of CVDs. Understanding the temporal trends in the epidemiological trends of overall CVDs in youths and its attributable risk factors is crucial worldwide. To the best of our knowledge, the features of CVDs epidemics in the young population across the world have yet to be presented, also a comprehensive profile in Asia and China. Therefore, we aimed to report CVDs epidemics and burdens and how they changed from 1990 to 2021 using newly updated estimates of total and specific CVDs in young population aged 0&#x2013;19 at global, regional, and national levels, stratified by age, sex, regions, countries, and sociodemographic index (SDI), also along with predictions to the 2036&#x202F;year.</p>
</sec>
<sec sec-type="materials|methods" id="sec2">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec3">
<label>2.1</label>
<title>Study data</title>
<p>The data were obtained from the Global Burden of Disease (GBD) 2021 database. To ensure robustness, the GBD study employs DisMod-MR and Spatiotemporal Gaussian Process Regression to address data gaps and missing data and applies standardized case definitions and inclusion criteria for variable selection. The detailed methods for the GBD have been described elsewhere (<xref ref-type="bibr" rid="ref3">3</xref>, <xref ref-type="bibr" rid="ref19">19</xref>). The parameters, including fatal metrics (incidence rates, prevalence estimates, and YLLs), as well as non-fatal metrics (DALYs, mortality, and YLDs) were extracted from the GBD 2021 repository<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> published by the Institute for Health Metrics and Evaluation, which synthesizes data by sex, age and SDI, from 204 countries through 12,765 data sources (<xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref20">20</xref>). Ethical approval and informed consent were waived because the GBD is publicly available and no identifiable information was included in the study. The study incorporated projected trajectories of these metrics to 2036 employing the modeling frameworks to forecast temporal trends.</p>
<p>YLL quantifies premature mortality by calculating the difference between age at death and the standard life expectancy, using demographic-specific reference standards. YLD measures non-fatal health loss through disability weight (DW) assignments (0&#x202F;=&#x202F;full health, 1&#x202F;=&#x202F;death). As the principal composite metric in burden of disease studies, DALY integrates mortality and morbidity through: DALY&#x202F;=&#x202F;YLL&#x202F;+&#x202F;YLD.</p>
<p>SDI is a composite measure of a country&#x2019;s lag distributed income per capita, average years of schooling, and the fertility rate in females younger than 25&#x202F;years. This study categorized countries into five SDI levels to explore the correlation between burden and socioeconomic development.</p>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>Definitions of CVDs in children and adolescents</title>
<p>Subtypes included hypertensive heart disease, ischemic disease, non-rheumatic valvular heart disease, rheumatic heart disease, stroke, aortic aneurysm, cardiomyopathy and myocarditis, endocarditis, and other cardiovascular and circulatory diseases. Age-specific burden of CVDs was being categorized into four age groups: &#x2264;5&#x202F;years, 5&#x2013;9&#x202F;years, 10&#x2013;14&#x202F;years, and 15&#x2013;19&#x202F;years.</p>
<p>This study focused on the potential risk factors associated with CVDs in children and adolescents under 20&#x202F;years old, as identified in the database. All causes were analyzed via the GBD hierarchical framework to identify predominant contributors to health loss. We categorized diseases into three level groups: (1) communicable, maternal, neonatal, and nutritional diseases; (2) non-communicable diseases (NCDs); and (3) injuries. For granular analysis of chronic disease patterns, we exclusively focused on level 2 and level 3 causes within the NCDs category (<xref ref-type="bibr" rid="ref21">21</xref>). The definitions of all risk factor were abstracted from the Global Health Data Exchange (GHDx) platform.</p>
</sec>
<sec id="sec5">
<label>2.3</label>
<title>Statistical analyses</title>
<p>Descriptive analyses were used to characterize the burden of CVDs separated by gender, age group, disease subtypes at three hierarchical levels: global, Asia, and China. The trends across 1990&#x2013;2021 were evaluated using average annual percent change (AAPC). The future trends (2021&#x2013;2036) were predicted using the ARIMA calculated by the Joinpoint Regression Program (Version 5.4.0) and R studio (Version V4.4.1) (<xref ref-type="bibr" rid="ref22">22</xref>). The predictive validity of the ARIMA model was secured by achieving stationarity and optimizing its core parameters, including the autoregressive (<italic>p</italic>), differencing (<italic>d</italic>), and moving-average (<italic>q</italic>), respectively, via Akaike information criterion value, followed by rigorous diagnostic checks for residual white noise tests and a battery of forecast error measures. All uncertainty intervals were generated through 1,000 posterior draws from ARIMA, with final estimates representing the 2.5th&#x2013;97.5th percentile ranges. Statistical significance was defined as non-overlapping 95%UIs for <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05 in regression models.</p>
</sec>
</sec>
<sec sec-type="results" id="sec6">
<label>3</label>
<title>Results</title>
<p>In 2021, the number of CVDs among children and adolescents were 20.928 million (95%CI: 16.652&#x2013;25.860) globally, 8.862 million (95%CI: 7.018&#x2013;10.910) in Asia, and 2.131 million (95%CI: 1.697&#x2013;2.667) in China, with prevalence rate of 794.020 (95%CI: 631.780&#x2013;981.130), 614.030 (95%CI: 486.320&#x2013;755.900), 637.670 (95%CI: 507.670&#x2013;797.820) per 100,000 population, respectively. While metrics showed downward trends (all AAPC &#x003C;0), excepted for four exceptions: global incidence (0.314, 95%CI: 0.271&#x2013;0.357), global prevalence (0.506, 95%CI: 0.439&#x2013;0.574), global YLDs (0.203, 95%CI: 0.172&#x2013;0.234), and Asian prevalence (0.163, 95%CI: 0.080&#x2013;0.245) (<xref ref-type="table" rid="tab1">Table 1</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Absolute numbers, rates and annual percentage changes in the incidence, prevalence, death, DALYs, YLLs and YLDs of CVDs in China, Asia and the world from 1990 to 2021 (rate: per 100,000 people).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th rowspan="2">Measures</th>
<th rowspan="2">Metrics</th>
<th align="center" valign="middle" colspan="2">Global</th>
<th align="center" valign="middle" colspan="2">Asian</th>
<th align="center" valign="middle" colspan="2">China</th>
</tr>
<tr>
<th align="center" valign="middle">1990</th>
<th align="center" valign="middle">2021</th>
<th align="center" valign="middle">1990</th>
<th align="center" valign="middle">2021</th>
<th align="center" valign="middle">1990</th>
<th align="center" valign="middle">2021</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" rowspan="3">Incidence (95%UI)</td>
<td align="left" valign="middle">Number</td>
<td align="center" valign="middle">2134448.334 (1667532.634, 2715350.961)</td>
<td align="center" valign="middle">2738540.036 (2074960.889, 3589001.341)</td>
<td align="center" valign="middle">1260791.653 (983610.881, 1610107.923)</td>
<td align="center" valign="middle">1245604.688 (949258.181, 1631122.914)</td>
<td align="center" valign="middle">542194.445 (426129.901, 692967.785)</td>
<td align="center" valign="middle">311749.978 (241405.915, 394797.853)</td>
</tr>
<tr>
<td align="left" valign="middle">Rate</td>
<td align="center" valign="middle">94.565 (73.834, 120.223)</td>
<td align="center" valign="middle">103.966 (78.722, 136.168)</td>
<td align="center" valign="middle">90.873 (70.895, 116.051)</td>
<td align="center" valign="middle">86.304 (65.771, 113.015)</td>
<td align="center" valign="middle">121.830 (95.750, 155.708)</td>
<td align="center" valign="middle">93.252 (72.219, 118.168)</td>
</tr>
<tr>
<td align="left" valign="middle">AAPC</td>
<td align="center" valign="middle" colspan="2">0.314 (0.271, 0.357)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;0.158 (&#x2212;0.245, &#x2212;0.072)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;0.866 (&#x2212;0.963, &#x2212;0.769)&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">Prevalence (95%UI)</td>
<td align="left" valign="middle">Number</td>
<td align="center" valign="middle">15345812.633 (12390966.030, 18569761.065)</td>
<td align="center" valign="middle">20928983.821 (16652804.846, 25860941.238)</td>
<td align="center" valign="middle">8122755.943 (6502245.077, 9980430.563)</td>
<td align="center" valign="middle">8862193.531 (7018989.524, 10910163.870)</td>
<td align="center" valign="middle">3389825.062 (2658247.591, 4252937.392)</td>
<td align="center" valign="middle">2131751.776 (1697153.960, 2667155.732)</td>
</tr>
<tr>
<td align="left" valign="middle">Rate</td>
<td align="center" valign="middle">679.451 (548.623, 822.192)</td>
<td align="center" valign="middle">794.028 (631.785, 981.137)</td>
<td align="center" valign="middle">585.462 (468.661, 719.357)</td>
<td align="center" valign="middle">614.034 (486.324, 755.932)</td>
<td align="center" valign="middle">761.687 (597.303, 955.626)</td>
<td align="center" valign="middle">637.673 (507.679, 797.827)</td>
</tr>
<tr>
<td align="left" valign="middle">AAPC</td>
<td align="center" valign="middle" colspan="2">0.506 (0.439, 0.574)&#x002A;</td>
<td align="center" valign="middle" colspan="2">0.163 (0.080, 0.245)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;0.554 (&#x2212;0.666, &#x2212;0.441)&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">DALYs(95%UI)</td>
<td align="left" valign="middle">Number</td>
<td align="center" valign="middle">14791657.733 (13368075.392, 16966494.143)</td>
<td align="center" valign="middle">7990383.383 (7076648.615, 9061976.187)</td>
<td align="center" valign="middle">8493415.375 (7649410.780, 9723420.846)</td>
<td align="center" valign="middle">3878556.524 (3503271.467, 4305985.699)</td>
<td align="center" valign="middle">2625984.855 (2335722.414, 2924035.846)</td>
<td align="center" valign="middle">497456.256 (422933.349, 580424.943)</td>
</tr>
<tr>
<td align="left" valign="middle">Rate</td>
<td align="center" valign="middle">654.912 (591.883, 751.249)</td>
<td align="center" valign="middle">303.148 (268.489, 343.806)</td>
<td align="center" valign="middle">612.18 (551.354, 700.835)</td>
<td align="center" valign="middle">268.736 (242.734, 298.353)</td>
<td align="center" valign="middle">590.055 (524.838, 657.039)</td>
<td align="center" valign="middle">148.8 (126.518, 173.622)</td>
</tr>
<tr>
<td align="left" valign="middle">AAPC</td>
<td align="center" valign="middle" colspan="2">&#x2212;2.447 (&#x2212;2.496, &#x2212;2.399)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;2.665 (&#x2212;2.737, &#x2212;2.592)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;4.379 (&#x2212;4.512, &#x2212;4.246)&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">YLDs (95%UI)</td>
<td align="left" valign="middle">Number</td>
<td align="center" valign="middle">1133510.583 (795222.227, 1557997.379)</td>
<td align="center" valign="middle">1407909.419 (963907.123, 1962293.482)</td>
<td align="center" valign="middle">642339.183 (455903.992, 879745.772)</td>
<td align="center" valign="middle">644440.810 (451162.786, 886073.245)</td>
<td align="center" valign="middle">271284.828 (189858.458, 375527.229)</td>
<td align="center" valign="middle">160950.488 (112157.612, 222007.532)</td>
</tr>
<tr>
<td align="left" valign="middle">Rate</td>
<td align="center" valign="middle">50.190 (35.211, 68.982)</td>
<td align="center" valign="middle">53.419 (36.572, 74.459)</td>
<td align="center" valign="middle">46.3 (32.863, 63.419)</td>
<td align="center" valign="middle">44.655 (31.263, 61.394)</td>
<td align="center" valign="middle">60.964 (42.669, 84.383)</td>
<td align="center" valign="middle">48.143 (33.556, 66.414)</td>
</tr>
<tr>
<td align="left" valign="middle">AAPC</td>
<td align="center" valign="middle" colspan="2">0.203 (0.172, 0.234)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;0.115 (&#x2212;0.180, &#x2212;0.050)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;0.762 (&#x2212;0.826, &#x2212;0.698)&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">Deaths (95%UI)</td>
<td align="left" valign="middle">Number</td>
<td align="center" valign="middle">163751.281(147009.56, 190128.867)</td>
<td align="center" valign="middle">81419.723(71596.254, 91661.148)</td>
<td align="center" valign="middle">94993.86(84680.12, 109562.09)</td>
<td align="center" valign="middle">40649.273(36507.624, 45073.963)</td>
<td align="center" valign="middle">28503.035(25206.187, 32175.626)</td>
<td align="center" valign="middle">4271.156(3611.389, 4919.947)</td>
</tr>
<tr>
<td align="left" valign="middle">Rate</td>
<td align="center" valign="middle">7.253(6.519, 8.428)</td>
<td align="center" valign="middle">3.098(2.726, 3.488)</td>
<td align="center" valign="middle">6.852(6.124, 7.923)</td>
<td align="center" valign="middle">2.824(2.535, 3.162)</td>
<td align="center" valign="middle">6.409(5.66389, 7.232)</td>
<td align="center" valign="middle">1.284(1.085, 1.473)</td>
</tr>
<tr>
<td align="left" valign="middle">AAPC</td>
<td align="center" valign="middle" colspan="2">&#x2212;2.627 (&#x2212;2.712, &#x2212;2.541)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;2.840 (&#x2212;2.926, &#x2212;2.754)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;3.921 (&#x2212;4.048, &#x2212;3.793)&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">YLLs (95%UI)</td>
<td align="left" valign="middle">Number</td>
<td align="center" valign="middle">13658147.150 (122280479.82, 15924522.575)</td>
<td align="center" valign="middle">6582473.965 (5754595.45, 7443538.696)</td>
<td align="center" valign="middle">7851076.195 (6979047.653, 9091512.534)</td>
<td align="center" valign="middle">3234115.776 (2896610.96, 3611770.84)</td>
<td align="center" valign="middle">2354700.039 (2080801.974, 2666536.078)</td>
<td align="center" valign="middle">336505.776 (285264.269, 388332.137)</td>
</tr>
<tr>
<td align="left" valign="middle">Rate</td>
<td align="center" valign="middle">604.723 (541.412, 705.071)</td>
<td align="center" valign="middle">249.73 (218.32, 282.490)</td>
<td align="center" valign="middle">565.882 (503.034, 655.298)</td>
<td align="center" valign="middle">224.085 (200.776, 250.250)</td>
<td align="center" valign="middle">529.165 (467.557, 599.178)</td>
<td align="center" valign="middle">100.669 (85.330, 116.169)</td>
</tr>
<tr>
<td align="left" valign="middle">AAPC</td>
<td align="center" valign="middle" colspan="2">&#x2212;2.753 (&#x2212;2.896, &#x2212;2.611)&#x002A;</td>
<td align="center" valign="middle" colspan="2">&#x2212;2.996&#x002A; (&#x2212;3.081, &#x2212;2.910)</td>
<td align="center" valign="middle" colspan="2">&#x2212;3.971 (&#x2212;4.101, &#x2212;3.841)&#x002A;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;<italic>p</italic>&#x003C;0.001.</p>
</table-wrap-foot>
</table-wrap>
<p><xref ref-type="fig" rid="fig1">Figure 1</xref> mapped the global CVDs burden for children and adolescents synthetically. Briefly, DALY rates were predominantly concentrated in African in both 1990 and 2021. In 2021, the three highest DALY rates were in Republic of Niue: 1513.120 (95%CI: 1336.220&#x2013;1721.500); Tokelau: 1371.700 (95%CI: 1055.120&#x2013;1624.180); State of Libya: 1264.530 (95%CI: 986.530&#x2013;1600.860) per 100,000 population, respectively. Conversely, the lowest DALY rates predominantly clustered in high-SDI nations: Republic of Cyprus: 42.600 (95%CI: 35.300&#x2013;51.420); State of Israel: 42.610 (95%CI: 36.040&#x2013;51.050); Swiss Confederation:45.090 (95%CI: 38.350&#x2013;53.070). A strong inverse correlation linking higher SDI with lower DALYs rates existed (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figures 1, 2</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Burden of CVDs across 204 countries/territories among children and adolescents, in 1990 and 2021.</p>
</caption>
<graphic xlink:href="fpubh-13-1653981-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Map illustrations show global patterns of disease prevalence, Disability-Adjusted Life Years (DALYs), and deaths in 1990 and 2021. Color gradients represent rates, with darker colors indicating higher rates. Inset maps detail regions like the Caribbean, Persian Gulf, and Southeast Asia. The images compare regional changes over time, highlighting areas with significant health impacts.</alt-text>
</graphic>
</fig>
<sec id="sec7">
<label>3.1</label>
<title>Age-specific CVDs metrics in children and adolescents</title>
<p>Globally, CVDs burden among children and adolescents exhibited a clear age-dependent divergence between fatal and non-fatal outcomes (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>). With age advancing, DALYs and mortality gradually declined, while the prevalence and incidence showed gradual increase slowly. These trends were roughly same in China, Asia and the world (<xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Temporal trends in prevalence, DALYs, and mortality rate of CVDs in children and adolescents by age in China, the Asia, and the world, from 1990 to 2019.</p>
</caption>
<graphic xlink:href="fpubh-13-1653981-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Nine line graphs compare the success rates of yeast protein domain linker predictions across different regions: Global, Asia, and China. Each row represents a region, and columns differentiate various prediction tools. The y-axis shows percentages, with x-axis detailing the methods. Color-coded lines and bars illustrate success rates and confidence intervals.</alt-text>
</graphic>
</fig>
<p>From 1990 to 2021, age-specific disparities revealed the distinct patterns as follows: for children&#x003C;5&#x202F;years, most metrics such as the Chinese DALY rate (1236.893&#x2013;140.450) exhibited uniform declines, comparing with the rising metric of global and Asian prevalence/YLDs rate (global prevalence 242.532&#x2013;287.023); for those 5&#x2013;9&#x202F;years: global incidence, prevalence, YLDs and Asian prevalence rate (89.082&#x2013;96.250, 543.190&#x2013;611.981, 41.613&#x2013;42.840, 444.327&#x2013;459.865 respectively) all increased, whereas other metrics declined significantly; for 10&#x2013;14&#x202F;years children: the rate of global incidence, prevalence, YLDs and Asian prevalence (109.256&#x2013;122.716, 864.346&#x2013;989.620, 62.032&#x2013;64.990, 1062.133&#x2013;767.640, respectively per 100,000 population) have shown an increasing trend, whereas the other Asian and Chinese metrics exhibited stable; among 15&#x2013;19&#x202F;years children, only global incidence (124.814&#x2013;140.525), prevalence (1163.296&#x2013;1320.248), and YLDs (83.510&#x2013;86.055) continued to rise through the period (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>).</p>
<p>Notably, DALYs, mortality, and YLLs demonstrated a unique age hierarchy: the highest value [&#x2212;3.412 (95%CI: &#x2212;3.301 to &#x2212;3.524); &#x2212;3.526 (95%CI: &#x2212;3.425 to &#x2212;3.626); &#x2212;3.522 (95%CI: &#x2212;3.422 to &#x2212;3.622)] existed in children under 5&#x202F;years, followed by 15&#x2013;19, 10&#x2013;14, and 5&#x2013;9&#x202F;years at the global level. In Asia and China, accelerated reductions in mortality aged 5&#x2013;9&#x202F;years repositioned this specific group from the highest to the second-highest by 2021 (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>).</p>
</sec>
<sec id="sec8">
<label>3.2</label>
<title>Sex-specific CVDs and SDI-specific CVDs metrics in children and adolescents</title>
<p><xref ref-type="fig" rid="fig3">Figure 3</xref> revealed that there was a significant declining trend as for DALYs, mortality, across the time, while prevalence presented a gradual upward trend since 2016. These indicators were strongly associated with SDI levels, that is, the lower SDI groups consistently exhibited the higher values, indicating the more severe disease burden. At the global, Asian, and China levels, all the values generally ranged among low-middle and middle SDI regions. In 2021, globally, the prevalence rate of CVDs remained higher in females compared with males (797.060 vs. 791.140 per 100,000). Similarly, the prevalence was higher in females in Asia (625.720 vs. 603.310), also similar pattern in China (646.200 vs. 630.230), per 100,000 population. The prevalence in the global presented the highest upward trend, females: (0.509, 95%CI: 0.417&#x2013;0.601), males: (0.498, 95%CI: 0.446&#x2013;0.550), whereas the YLLs in China had the largest downward trend females: (&#x2212;4.597, 95%CI: &#x2212;4.767 to &#x2212;4.427), males: (&#x2212;4.812, 95%CI: &#x2212;4.982 to &#x2212;4.642) (<xref ref-type="table" rid="tab2">Table 2</xref>).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Temporal trends in prevalence, DALYs, and mortality rate of CVDs in children and adolescents by sex and SDI from 1990 to 2021.</p>
</caption>
<graphic xlink:href="fpubh-13-1653981-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Nine line graphs are arranged in a three-by-three grid, showing data labeled "Both", "Male", and "Female". The top row presents trends over time, possibly in metrics like BMI. The middle and bottom rows depict declines over age for various measurements and age groups. Each graph uses different colored lines for distinct data sets, with all having consistent downward or upward trends.</alt-text>
</graphic>
</fig>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Mean annual percentage changes, rate in incidence, prevalence, death, DALYs, YLLs, and YLDs due to premature death in China, Asia and the world population under 20&#x202F;years of age, 1990&#x2013;2021.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Measures</th>
<th align="center" valign="middle" colspan="4">Male</th>
<th align="center" valign="middle" colspan="4">Female</th>
</tr>
<tr>
<th/>
<th align="center" valign="middle">1990</th>
<th align="center" valign="middle">2021</th>
<th align="center" valign="middle">AAPC 95%CI (%)</th>
<th align="center" valign="middle"><italic>p</italic></th>
<th align="center" valign="middle">1990</th>
<th align="center" valign="middle">2021</th>
<th align="center" valign="middle">AAPC 95%CI (%)</th>
<th align="center" valign="middle"><italic>p</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="9">Global</td>
</tr>
<tr>
<td align="left" valign="middle">Incidence</td>
<td align="center" valign="middle">91.462 (71.583, 115.556)</td>
<td align="center" valign="middle">99.522 (75.873, 129.457)</td>
<td align="center" valign="middle">0.278 (0.232, 0.328)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">97.719 (76.718, 125.320)</td>
<td align="center" valign="middle">108.559 (81.789, 143.809)</td>
<td align="center" valign="middle">0.341 (0.303, 0.385)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Prevalence</td>
<td align="center" valign="middle">678.511 (550.493, 821.914)</td>
<td align="center" valign="middle">791.152 (632.033, 971.938)</td>
<td align="center" valign="middle">0.499 (0.455, 0.550)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">680.435 (552.540, 826.638)</td>
<td align="center" valign="middle">797.072 (634.283, 985.584)</td>
<td align="center" valign="middle">0.518 (0.422, 0.601)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">DALYs</td>
<td align="center" valign="middle">651.022 (586.975, 732.696)</td>
<td align="center" valign="middle">315.786 (277.038, 359.558)</td>
<td align="center" valign="middle">&#x2212;2.291 (&#x2212;2.352, &#x2212;2.243)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">659.003 (583.072, 783.371)</td>
<td align="center" valign="middle">289.716 (256.688, 326.77)</td>
<td align="center" valign="middle">&#x2212;2.633 (&#x2212;2.684, &#x2212;2.595)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">YLDs</td>
<td align="center" valign="middle">47.717 (33.789, 66.062)</td>
<td align="center" valign="middle">51.492 (34.711, 72.746)</td>
<td align="center" valign="middle">0.256 (0.238, 0.276)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">52.791 (37.472, 71.963)</td>
<td align="center" valign="middle">55.462 (38.803, 77.043)</td>
<td align="center" valign="middle">0.162 (0.137, 0.197)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Deaths</td>
<td align="center" valign="middle">7.241 (6.473, 8.242)</td>
<td align="center" valign="middle">3.275 (2.828, 3.679)</td>
<td align="center" valign="middle">&#x2212;2.465 (&#x2212;2.518, &#x2212;2.429)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">7.269 (6.397, 8.721)</td>
<td align="center" valign="middle">2.905 (2.545, 3.256)</td>
<td align="center" valign="middle">&#x2212;2.852(&#x2212;2.938, &#x2212;2.782)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">YLLs</td>
<td align="center" valign="middle">603.311 (536.545, 686.187)</td>
<td align="center" valign="middle">264.291 (227.073, 298.978)</td>
<td align="center" valign="middle">&#x2212;2.552 (&#x2212;2.597, &#x2212;2.518)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">606.21 (531.71, 732.09)</td>
<td align="center" valign="middle">234.247 (204.359, 263.807)</td>
<td align="center" valign="middle">&#x2212;2.951(&#x2212;3.026, &#x2212;2.889)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="9">Asia</td>
</tr>
<tr>
<td align="left" valign="middle">Incidence</td>
<td align="center" valign="middle">87.415 (68.647, 111.278)</td>
<td align="center" valign="middle">81.471 (61.718, 105.739)</td>
<td align="center" valign="middle">&#x2212;0.230 (&#x2212;0.297, &#x2212;0.168)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">94.573 (74.124, 120.905)</td>
<td align="center" valign="middle">91.585 (69.553, 121.442)</td>
<td align="center" valign="middle">&#x2212;0.193 (&#x2212;0.180, &#x2212;0.018)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Prevalence</td>
<td align="center" valign="middle">573.952 (453.031, 700.632)</td>
<td align="center" valign="middle">603.321 (482.077, 741.539)</td>
<td align="center" valign="middle">0.176 (0.058, 0.293)</td>
<td align="center" valign="middle">0.005</td>
<td align="center" valign="middle">597.766 (477.448, 738.479)</td>
<td align="center" valign="middle">625.733 (496.075, 777.166)</td>
<td align="center" valign="middle">0.153 (&#x2212;0.010, 0.317)</td>
<td align="center" valign="middle">0.0630</td>
</tr>
<tr>
<td align="left" valign="middle">DALYs</td>
<td align="center" valign="middle">603.815 (546.637, 676.919)</td>
<td align="center" valign="middle">271.635 (242.742, 303.898)</td>
<td align="center" valign="middle">&#x2212;2.562 (&#x2212;2.666, &#x2212;2.462)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">621.110 (543.81, 740.483)</td>
<td align="center" valign="middle">265.577 (236.918, 296.509)</td>
<td align="center" valign="middle">&#x2212;2.762 (&#x2212;2.854, &#x2212;2.688)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">YLDs</td>
<td align="center" valign="middle">42.692 (29.945, 58.978)</td>
<td align="center" valign="middle">41.923 (28.648, 58.580)</td>
<td align="center" valign="middle">&#x2212;0.052 (&#x2212;0.115, &#x2212;0.016)</td>
<td align="center" valign="middle">0.030</td>
<td align="center" valign="middle">50.152 (35.874, 67.811)</td>
<td align="center" valign="middle">47.630 (33.942, 65.006)</td>
<td align="center" valign="middle">&#x2212;0.162 (&#x2212;0.226, &#x2212;0.109)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Deaths</td>
<td align="center" valign="middle">6.803 (6.115, 7.677)</td>
<td align="center" valign="middle">2.882 (2.586, 3.209)</td>
<td align="center" valign="middle">&#x2212;2.718 (&#x2212;2.819, &#x2212;2.610)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">6.908 (6.029, 8.310)</td>
<td align="center" valign="middle">2.741 (2.423, 3.068)</td>
<td align="center" valign="middle">&#x2212;2.972 (&#x2212;3.163, &#x2212;2.787)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">YLLs</td>
<td align="center" valign="middle">561.125 (502.907, 633.938)</td>
<td align="center" valign="middle">229.712 (204.066, 256.089)</td>
<td align="center" valign="middle">&#x2212;2.86 (&#x2212;2.975, &#x2212;2.766)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">570.971 (496.773, 690.708)</td>
<td align="center" valign="middle">217.948 (191.412, 244.469)</td>
<td align="center" valign="middle">&#x2212;3.102 (&#x2212;3.245, &#x2212;2.958)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="9">China</td>
</tr>
<tr>
<td align="left" valign="middle">Incidence</td>
<td align="center" valign="middle">120.229 (94.720, 154.218)</td>
<td align="center" valign="middle">91.734 (70.732, 116.351)</td>
<td align="center" valign="middle">&#x2212;0.865 (&#x2212;0.957, &#x2212;0.785)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">123.572 (97.436, 157.598)</td>
<td align="center" valign="middle">95.002 (73.615, 120.769)</td>
<td align="center" valign="middle">&#x2212;0.843(&#x2212;0.956, &#x2212;0.735)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Prevalence</td>
<td align="center" valign="middle">742.442 (579.361, 932.333)</td>
<td align="center" valign="middle">630.232 (497.688, 792.819)</td>
<td align="center" valign="middle">&#x2212;0.537 (&#x2212;0.619, &#x2212;0.456)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">782.529 (614.030, 982.482)</td>
<td align="center" valign="middle">646.207 (516.326, 807.053)</td>
<td align="center" valign="middle">&#x2212;0.622 (&#x2212;0.738, &#x2212;0.516)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">DALYs</td>
<td align="center" valign="middle">617.658 (541.611, 695.269)</td>
<td align="center" valign="middle">167.222 (140.958, 197.121)</td>
<td align="center" valign="middle">&#x2212;4.162 (&#x2212;4.275, &#x2212;4.047)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">560.183 (489.926, 639.618)</td>
<td align="center" valign="middle">127.662 (105.587, 148.629)</td>
<td align="center" valign="middle">&#x2212;4.737 (&#x2212;4.993, &#x2212;4.467)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">YLDs</td>
<td align="center" valign="middle">54.902 (37.898, 76.220)</td>
<td align="center" valign="middle">44.362 (30.366, 61.839)</td>
<td align="center" valign="middle">&#x2212;0.698 (&#x2212;0.775, &#x2212;0.617)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">67.516 (47.629, 91.836)</td>
<td align="center" valign="middle">52.496 (37.252, 71.826)</td>
<td align="center" valign="middle">&#x2212;0.829 (&#x2212;0.902, &#x2212;0.756)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Deaths</td>
<td align="center" valign="middle">6.871 (6.018, 7.892)</td>
<td align="center" valign="middle">1.572 (1.305, 1.868)</td>
<td align="center" valign="middle">&#x2212;3.512 (&#x2212;3.643, &#x2212;3.387)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">5.902 (5.124, 6.788)</td>
<td align="center" valign="middle">0.947 (0.788, 1.119)</td>
<td align="center" valign="middle">&#x2212;4.559 (&#x2212;4.722, &#x2212;4.388)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">YLLs</td>
<td align="center" valign="middle">562.757 (490.454, 645.666)</td>
<td align="center" valign="middle">122.868 (101.463, 144.552)</td>
<td align="center" valign="middle">&#x2212;4.817 (&#x2212;4.988, &#x2212;4.641)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">492.679 (425.376, 568.739)</td>
<td align="center" valign="middle">75.173 (61.887, 88.828)</td>
<td align="center" valign="middle">&#x2212;4.609 (&#x2212;4.772, &#x2212;4.436)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec9">
<label>3.3</label>
<title>Subtypes-specific CVDs metrics in children and adolescents</title>
<p><xref ref-type="fig" rid="fig4">Figure 4</xref> presented a comparative analysis of CVDs subtypes among children and adolescents across the global, Asian, and Chinese populations. In terms of global, Asian, and Chinese prevalence indicators, rheumatic heart disease (RHD), other cardiovascular/circulatory diseases, stroke, and cardiomyopathy and myocarditis ranked as the top four position, obviously exceeding 5% proportions. Notably, RHD remained the leading contributor in both 1990 and 2021, accounting for over 50% of prevalent cases. Regarding DALYs, the top four conditions were stroke, cardiomyopathy and myocarditis, non-rheumatic valvular heart disease, and RHD at the three levels, respectively. While stroke remained the primary DALYs burden in both 1990 and 2021, its proportional contribution gradually declined over time. The other three conditions, however, exhibited continuous increases.</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Contribution of prevalence, DALYs, and mortality in China, the Asia, and the world from 1990 to 2021.</p>
</caption>
<graphic xlink:href="fpubh-13-1653981-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Nine circular charts comparing cardiovascular disease data between 1990 (inner circle) and 2021 (outer circle) globally, in Asia, and in China. The charts cover prevalence, disability-adjusted life years, and deaths, highlighting shifts in disease types such as ischemic heart disease and stroke across each region and timeframe. Each chart is color-coded for different cardiovascular conditions.</alt-text>
</graphic>
</fig>
<p>In mortality metrics, the top four diseases are stroke, other cardiovascular/circulatory diseases, cardiomyopathy/myocarditis, and RHD across three levels. Similar to the DALYs indicators, stroke continued to serve as the leading mortality factor demonstrating a distinct decline (by at least more than 10%) in 2021 compared to 2019. Interestingly, despite ischemic heart disease was comparatively lower in 1990, it displayed the most significant increase among all the diseases.</p>
</sec>
<sec id="sec10">
<label>3.4</label>
<title>Risk factors of children and adolescents CVDs</title>
<p>As evidenced by DALYs, the attributed risk factors for age-specific CVD included high systolic blood pressure, alcohol use, non-optimal ambient temperatures, and metabolic/behavioral/environmental risks. From the global perspective, non-optimal temperatures, hypertension, and metabolic risks served as the primary diseases.</p>
</sec>
<sec id="sec11">
<label>3.5</label>
<title>Predictions of children and adolescents CVDs to 2036</title>
<p><xref ref-type="fig" rid="fig5">Figure 5</xref> showed the long-term trends DALYs for CVDs among individuals under 20 ages from 1990 to 2036. At the global and Asia levels, the incidence and prevalence exhibited the gradual rising trend. For China, the incidence presented a gradual decreasing trend, while the rising trend existed for prevalence. Other indicators, such as DALYs, mortality, and YLLs presented a sharp decreased trend except for YLDs in both Asia and China.</p>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Predicted trends in the burden of CVDs among children and adolescents up to 2036.</p>
</caption>
<graphic xlink:href="fpubh-13-1653981-g005.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">A series of graphs displaying health predictions globally, in Asia, and in China. Each column corresponds to a region, and each row represents a different health metric: incidence, prevalence, Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLDs), Years of Life Lost (YLLs), and deaths. Red lines indicate actual data, while dotted lines in yellow highlight predictions. Overall, the graphs show trends from past data with forecasts extending into the future.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec12">
<label>4</label>
<title>Discussion</title>
<p>Our study used the best available and robust estimates from the GBD 2021 to explicitly report CVDs epidemics and burden in children and adolescents. In the past three decades, CVDs burden still presented a substantial decline across the world, but it is projected to increase to 2036. Also, a critical public health paradox existed: fatal metrics (mortality and YLLs) have declined significantly, while non-fatal metrics (prevalence and YLDs) have risen substantially. This divergence underscores an ongoing epidemiological transition in pediatric CVDs, that is, from acute and fatal manifestations toward chronic and disabling conditions requiring long-term attention and management.</p>
<p>In terms of births number, China and some Asian countries are experiencing the most significant declining trend (<xref ref-type="bibr" rid="ref23">23</xref>). This demographic shift may, to some extent, influence the evaluation of the disease epidemic. However, some indicators, including mortality and DALYs, presented the downward trends, aligning with advancements in pediatric healthcare and precision prevention worldwide. The results highlight the urgent need for better understanding of the mediators of cardiometabolic dysfunction in youths for prevention strategies of CVDs in early life. Moreover, the incidence, prevalence and YLDs indicators were higher for females than males, while the other three indicators (mortality, YLLs, and DALYs) showed the opposite direction worldwide. Females also presented a more pronounced decline in AAPC for mortality, YLLs, and DALYs compared to male counterparts. These disparities could be attributable to the combination of physiological properties, daily lifestyle and healthcare-seeking behaviors (<xref ref-type="bibr" rid="ref24">24</xref>). Those social factors, such as military service participation and sports engagement for males, even enlarge the disparities in CVDs manifestations between genders. Consideration of gender differences is important for prevention, diagnosis, treatment and management of CVDs even for youngsters. The observed characteristics of the epidemics in different regions are of long-term importance, not only in guiding current national policies and strategies in CVDs prevention, but also in predicting future challenges.</p>
<p>In our study, negative associations between SDI and key metrics (DALYs, mortality, and YLLs) for children and adolescents reinforce the evidence that links socioeconomic status with CVDs epidemics, which highlights the imperative of taking actions to address the unique needs of this vulnerable population. Previous studies suggest that low-SDI regions exhibit disproportionately contributions to CVDs mortality from household air pollution (18.480% vs. 0.090%) and lead exposure (2.570% vs. 0.360%) compared with high-SDI regions (<xref ref-type="bibr" rid="ref4">4</xref>, <xref ref-type="bibr" rid="ref19">19</xref>). Targeted interventions for disadvantaged populations through enhancing maternal health and early time nutritional state are essential to prevent CVDs for kids (<xref ref-type="bibr" rid="ref25 ref26 ref27">25&#x2013;27</xref>). Also, RHD and stroke have remained the key contributions to CVDs burden in this young population. Meanwhile, high systolic blood pressure and non-optimal temperature have emerged as critical risk factors for pediatric CVDs (<xref ref-type="bibr" rid="ref1">1</xref>, <xref ref-type="bibr" rid="ref7">7</xref>). Future researches are needed to prioritize accessibility and equality of health workers and parental practices to promote health dynamics.</p>
<p>The global epidemic of childhood obesity and its precipitous upsurge have led to the emergence of CVDs in youngsters, including &#x201C;adult-onset&#x201D; diseases at an early age (<xref ref-type="bibr" rid="ref28">28</xref>). Notably, most children are born with Ideal Cardiovascular Health (ICVH) with specific genetic conditions. The loss of ICVH is a gradual and continuous process, often preceded by prolonged, asymptomatic periods characterized by subclinical changes that eventually manifest as CVDs (<xref ref-type="bibr" rid="ref29">29</xref>). Approximately 80% of overweight children aged 10&#x2013;15 develop obesity as approaching adults, perpetuating cardiometabolic risks across the lifespan. Maternal health during lactation and early-life nutritional intake shape the trajectories of CVDs development (<xref ref-type="bibr" rid="ref30">30</xref>). Early-life interventions, including maternal nutrition optimization and prenatal screening for fetal CVDs risks could prevent the development of CVDs (<xref ref-type="bibr" rid="ref16">16</xref>). Such measures are vital to further reduce the disease burden in low-resource settings, where disparities in healthcare access exacerbate the burden of non-fatal outcomes. The medical advancements in treating CVDs have not been paralleled in adolescents and young adults, highlighting an urgent need to enhance the management of CVDs in this population, particularly in low-income countries. As for absolute case numbers trajectories of the CVDs epidemiologic transition, the future pattern remain stable, while prediction model to 2036 for the prevalence presents the declining trend as expected. The pathogenesis of CVDs originating in childhood could progressively worsen without timely interventions. Most importantly, monitoring the CVDs burden and promoting the progress in the availability of effective and safe prevention strategies worldwide are really important at early time.</p>
<p>However, this study has several limitations need to be considered. First, as civil registration systems serve as primary sources for mortality statistics, population coverage remains insufficient, especially in those low and middle-income countries. Incomplete medically reporting systems may compromise the quality and the representativeness of CVDs-related mortality data. Second, as children and adolescents CVDs exhibited complex factors with psychological adversity and nutritional factors, these dimensions could not be systematically evaluated due to the unavailability of the data. Moreover, the original data sources from specific countries and periods might influence the accuracy of the GBD estimates and account for the quality of the predictive models (<xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref22">22</xref>, <xref ref-type="bibr" rid="ref31">31</xref>, <xref ref-type="bibr" rid="ref32">32</xref>). Countries and regions with scarce input data should establish high quality databases to help conduct more comprehensive and rigorous research.</p>
<p>In summary, the CVDs burden among children and adolescents is the continuously decreasing feature across the world during the last three decades and continues to rise up to 2036. The burden of overall and type-specific CVDs varied by age, sex, SDI, region, and country, and the marked geographic differences in CVDs epidemics are due to the combined effects of age and other determinants. Given the necessity of devising timely strategies to prevent and control CVDs, concerted efforts of the health workforce in the targeted implementation of effective primary prevention strategies to mitigate the disease burden.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec13">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">Supplementary material</xref>, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec sec-type="ethics-statement" id="sec14">
<title>Ethics statement</title>
<p>Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and the institutional requirements.</p>
</sec>
<sec sec-type="author-contributions" id="sec15">
<title>Author contributions</title>
<p>JY: Investigation, Writing &#x2013; original draft. SZ: Validation, Visualization, Writing &#x2013; original draft. ZW: Formal analysis, Investigation, Writing &#x2013; original draft. YS: Conceptualization, Data curation, Writing &#x2013; original draft. SJ: Formal analysis, Investigation, Methodology, Writing &#x2013; original draft. YG: Project administration, Writing &#x2013; review &#x0026; editing. LJ: Conceptualization, Data curation, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The authors would like to thank all of the participants.</p>
</ack>
<sec sec-type="COI-statement" id="sec16">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec17">
<title>Generative AI statement</title>
<p>The author(s) declared that Generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec18">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec19">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fpubh.2025.1653981/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fpubh.2025.1653981/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Data_Sheet_1.pdf" id="SM2" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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<fn-group>
<fn fn-type="custom" custom-type="edited-by" id="fn0003">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/267973/overview">Dragos Cretoiu</ext-link>, Carol Davila University of Medicine and Pharmacy, Romania</p></fn>
<fn fn-type="custom" custom-type="reviewed-by" id="fn0004">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/103656/overview">Eugenia M. Bastos</ext-link>, Independent Researcher, Sommerville, MA, United States</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2946021/overview">Cecilia Salzillo</ext-link>, University of Campania Luigi Vanvitelli, Italy</p></fn>
</fn-group>
<fn-group>
<fn id="fn0001"><label>1</label><p><ext-link xlink:href="http://ghdx.healthdata.org" ext-link-type="uri">http://ghdx.healthdata.org</ext-link></p></fn>
</fn-group>
<fn-group>
<fn fn-type="abbr" id="abbrev1">
<label>Abbreviations:</label>
<p>AAPC, average annual percentage change; CI, confidence interval; CVDs, cardiovascular diseases; DALY, disability-adjusted life year; DisMod-MR, disease modeling meta-regression; GBD, global burden of disease; GHDx, Global Health Data Exchange; ICVH, Ideal Cardiovascular Health; NCDs, non-communicable diseases; PAFs, population-attributable fraction; RHD, rheumatic heart disease; SDI, socio-demographic index; UIs, uncertainty intervals; YLD, years lived with disability; YLL, years of life lost.</p>
</fn>
</fn-group>
</back>
</article>