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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Endocrinol.</journal-id>
<journal-title>Frontiers in Endocrinology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Endocrinol.</abbrev-journal-title>
<issn pub-type="epub">1664-2392</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fendo.2025.1647064</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Endocrinology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Epidemiological research on diabetic nephropathy at global, regional, and national levels from 1990 to 2021: an analysis derived from the global burden of disease 2021 study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Zhang</surname>
<given-names>Lu</given-names>
</name>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3093237/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Jiang</surname>
<given-names>Liangliang</given-names>
</name>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Xu</surname>
<given-names>Rong</given-names>
</name>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhang</surname>
<given-names>Xuemei</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhang</surname>
<given-names>Boxun</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/962042/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Yue</surname>
<given-names>Rensong</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1261010/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<institution>Hospital of Chengdu University of Traditional Chinese Medicine</institution>, <addr-line>Chengdu, Sichuan</addr-line>,&#xa0;<country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1003085/overview">Cem Haymana</ext-link>, University of Health Sciences, T&#xfc;rkiye</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2952595/overview">Levent Ozsari</ext-link>, University of Health Sciences, T&#xfc;rkiye</p>
<p>Murat Da&#x11f;deviren, Ankara Etlik City Hospital, T&#xfc;rkiye</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Xuemei Zhang, <email xlink:href="mailto:zxm18708402325@yeah.net">zxm18708402325@yeah.net</email>; Boxun Zhang, <email xlink:href="mailto:1243876560@qq.com">1243876560@qq.com</email>; Rensong Yue, <email xlink:href="mailto:songrenyue@cdutcm.edu.cn">songrenyue@cdutcm.edu.cn</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work and share first authorship</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>27</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1647064</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>06</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>13</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Zhang, Jiang, Xu, Zhang, Zhang and Yue.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Zhang, Jiang, Xu, Zhang, Zhang and Yue</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Objective</title>
<p>A comprehensive assessment of the disease burden is essential for developing effective strategies to address diabetic nephropathy. This study investigates the long-term global trends and epidemiological characteristics of diabetic nephropathy.</p>
</sec>
<sec>
<title>Methods</title>
<p>Data on diabetic nephropathy from the Global Burden of Disease (GBD) 2021 were utilized to evaluate morbidity, mortality, disability-adjusted life years (DALYs), and the impact of the Socio-Demographic Index (SDI). Global risk attribution was assessed, and the Bayesian Age&#x2013;Period&#x2013;Cohort (BAPC) model was applied to forecast the future burden of diabetic nephropathy.</p>
</sec>
<sec>
<title>Results</title>
<p>In 2021, there were 107.6 million prevalent cases of diabetic nephropathy globally (95% UI: 99.2&#x2013;116.0), with an age-standardized prevalence rate of 1,259.6 per 100,000 population (95% UI: 1,162.0&#x2013;1,359.9), representing a 5.1% decline since 1990. Global deaths attributed to diabetic nephropathy in 2021 reached 477.3 thousand (95% UI: 401.5&#x2013;566.0), with an age-standardized mortality rate of 5.7 per 100,000 (95% UI: 4.8&#x2013;6.8), reflecting a 37.8% increase since 1990. The number of DALYs attributable to diabetic nephropathy was 11,278.9 thousand (95% UI: 9,682.8&#x2013;13,103.9), with an age-standardized DALY rate of 131.1 per 100,000 (95% UI: 112.8&#x2013;152.5), indicating a 24% rise since 1990.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>Over the past three decades, the global age-standardized prevalence of diabetic nephropathy has declined, while age-standardized mortality and DALY rates have increased. Significant disparities exist in prevalence, incidence, and DALY rates across regions and countries. The SDI exerts a notable influence on diabetic nephropathy prevalence, underscoring the importance of sustained and enhanced management of risk factors to prevent and treat this condition. Diabetic nephropathy remains a critical global health challenge moving forward.</p>
</sec>
</abstract>
<kwd-group>
<kwd>diabetic nephropathy</kwd>
<kwd>global disease burden</kwd>
<kwd>epidemiology</kwd>
<kwd>GBD2021</kwd>
<kwd>forecasting</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="1"/>
<equation-count count="0"/>
<ref-count count="41"/>
<page-count count="10"/>
<word-count count="4170"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Clinical Diabetes</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Diabetic nephropathy (DN), also referred to as diabetic kidney disease (DKD), is a major microvascular complication of diabetes mellitus and a leading cause of chronic kidney disease and end-stage renal disease (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>). Approximately 40% of individuals with diabetes develop DN (<xref ref-type="bibr" rid="B3">3</xref>). The pathogenesis of DN is complex, involving metabolic disturbances driven by chronic inflammation, oxidative stress, and persistent hyperglycemia (<xref ref-type="bibr" rid="B4">4</xref>, <xref ref-type="bibr" rid="B5">5</xref>). Clinically, DN is characterized by a progressive decline in glomerular filtration rate, thickening of the glomerular basement membrane, worsening proteinuria, glomerular hypertrophy, podocyte loss, and hyperplasia of associated membranes (<xref ref-type="bibr" rid="B6">6</xref>&#x2013;<xref ref-type="bibr" rid="B8">8</xref>). According to the International Diabetes Federation, more than 460 million people worldwide are currently living with diabetes, and DN is projected to become a major global public health challenge (<xref ref-type="bibr" rid="B9">9</xref>). Patients with DN often require lifelong dialysis or kidney transplantation, resulting in substantial socioeconomic burden (<xref ref-type="bibr" rid="B10">10</xref>).</p>
<p>The Global Burden of Disease (GBD) study provides comprehensive epidemiological data that offer critical insights for public health policymaking (<xref ref-type="bibr" rid="B11">11</xref>). The GBD database contains extensive information on prevalence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2021, serving as an essential resource for assessing global trends in DN. This study aimed to leverage GBD data to analyze epidemiological patterns of DN over this period and to project future trends in its burden, thereby providing evidence to inform public health strategies and clinical interventions.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Sources of data</title>
<p>The GBD 2021 database encompasses the most recent global and regional epidemiological data on 371 diseases and injuries, along with 88 associated risk factors (<xref ref-type="bibr" rid="B12">12</xref>). These data are publicly accessible through the Global Health Data Exchange (GHDx) query tool (<ext-link ext-link-type="uri" xlink:href="https://vizhub.healthdata.org/gbd-results/">https://vizhub.healthdata.org/gbd-results/</ext-link>). For this study, we extracted global data on DN, including information on age, incidence, mortality, and DALYs.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Statistical analysis and visualization</title>
<p>This study employed prevalence, incidence, mortality, disability-adjusted life years (DALYs), and estimated annual percentage change (EAPC) to assess epidemiological trends in DN. Global burden data for DN from 1990 to 2021 were compiled in Excel 2024 and primarily analyzed using the Bayesian Age&#x2013;Period&#x2013;Cohort (BAPC) prediction model. This model applies Bayesian inference to integrate age, period, and cohort effects, enabling the projection of long-term trends. The strength of the BAPC model lies in its capacity to address data sparsity and heterogeneity, thereby producing more robust predictive estimates. Model construction and inference were performed using the BAPC package in RStudio, with parameter estimation conducted via the Markov Chain Monte Carlo (MCMC) method. The influence of the socio-demographic index (SDI) on DN was also evaluated. All statistical analyses and data visualizations were performed using R software, version 4.4.1.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>International scale</title>
<p>In 2021, the global number of prevalent cases of DN was estimated at 107.6 million (95% UI: 99.2&#x2013;116.0), corresponding to an age-standardized prevalence rate of 1,259.6 per 100,000 population (95% UI: 1,162.0&#x2013;1,359.9), representing a 5.1% decrease since 1990. In the same year, global deaths attributable to DN reached 477.3 thousand (95% UI: 401.5&#x2013;566.0), with an age-standardized mortality rate of 5.7 per 100,000 population (95% UI: 4.8&#x2013;6.8), marking a 37.8% increase since 1990. The global DALYs associated with DN in 2021 totaled 11,278.9 thousand (95% UI: 9,682.8&#x2013;13,103.9), with an age-standardized rate of 131.1 per 100,000 population (95% UI: 112.8&#x2013;152.5), reflecting a 24.0% increase since 1990 (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>The prevalence, mortality, and DALYs of DN in 2021, as well as the percentage change in age-standardized rates (ASR) per 100,000 people in global disease burden regions from 1990 to 2021.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">
<break/>
</th>
<th valign="middle" rowspan="2" align="left">Location</th>
<th valign="middle" align="left">Prevalence (95% UI)</th>
<th valign="middle" rowspan="2" align="left">
<break/>ASRs per 100000 (95% UI)</th>
<th valign="middle" rowspan="2" align="left">
<break/>Percentage change in ASRs from 1990 to 2021</th>
<th valign="middle" align="left">Deaths (95% UI)</th>
<th valign="middle" rowspan="2" align="left">
<break/>ASRs per 100000 (95% UI)</th>
<th valign="middle" rowspan="2" align="left">
<break/>Percentage change in ASRs from 1990 to 2021</th>
<th valign="middle" align="left">DALYs (95% UI)</th>
<th valign="middle" rowspan="2" align="left">
<break/>ASRs per 100000 (95% UI)</th>
<th valign="middle" rowspan="2" align="left">
<break/>Percentage change in ASRs from1990 to 2021</th>
</tr>
<tr>
<th valign="middle" align="left">No, in millions (95% UI)</th>
<th valign="middle" align="left">No, in thousands (95% UI)</th>
<th valign="middle" align="left">No, in thousands (95% UI)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">Global</td>
<td valign="middle" align="left">107.6 (99.2,116)</td>
<td valign="middle" align="left">1259.6 (1162,1359.9)</td>
<td valign="middle" align="left">-5.1 (-7.5,-3)</td>
<td valign="middle" align="left">477.3 (401.5,566)</td>
<td valign="middle" align="left">5.7 (4.8,6.8)</td>
<td valign="middle" align="left">37.8 (19.2,49.6)</td>
<td valign="middle" align="left">11278.9 (9682.8,13103.9)</td>
<td valign="middle" align="left">131.1 (112.8,152.5)</td>
<td valign="middle" align="left">24 (9.3,33)</td>
</tr>
<tr>
<td valign="middle" align="left">2</td>
<td valign="middle" align="left">High-income Asia Pacific</td>
<td valign="middle" align="left">4.6 (4.2,4.9)</td>
<td valign="middle" align="left">1275.4 (1168.2,1377.4)</td>
<td valign="middle" align="left">-11.8 (-13.6,-10)</td>
<td valign="middle" align="left">23.6 (17.3,30.4)</td>
<td valign="middle" align="left">3.6 (2.8,4.5)</td>
<td valign="middle" align="left">-18.3 (-25.2,-13)</td>
<td valign="middle" align="left">394.5 (318.7,468.5)</td>
<td valign="middle" align="left">75.2 (62.2,87.2)</td>
<td valign="middle" align="left">-18.9 (-23.2,-15.1)</td>
</tr>
<tr>
<td valign="middle" align="left">3</td>
<td valign="middle" align="left">High-income North America</td>
<td valign="middle" align="left">6.2 (5.8,6.7)</td>
<td valign="middle" align="left">1056.5 (979.6,1135.3)</td>
<td valign="middle" align="left">4.9 (3.5,6.4)</td>
<td valign="middle" align="left">57.2 (49.3,64.5)</td>
<td valign="middle" align="left">8.1 (7,9.1)</td>
<td valign="middle" align="left">259.9 (210.8,316.3)</td>
<td valign="middle" align="left">1153.7 (1037.9,1260.9)</td>
<td valign="middle" align="left">174 (157,190.9)</td>
<td valign="middle" align="left">168.4 (142.9,199.9)</td>
</tr>
<tr>
<td valign="middle" align="left">4</td>
<td valign="middle" align="left">Western Europe</td>
<td valign="middle" align="left">5.9 (5.4,6.3)</td>
<td valign="middle" align="left">737.4 (683,790.1)</td>
<td valign="middle" align="left">-10.7 (-14.4,-8)</td>
<td valign="middle" align="left">22.8 (16.6,30.9)</td>
<td valign="middle" align="left">1.8 (1.4,2.4)</td>
<td valign="middle" align="left">30.6 (16.1,44.3)</td>
<td valign="middle" align="left">428.6 (343.3,532.2)</td>
<td valign="middle" align="left">40.9 (32.9,49.7)</td>
<td valign="middle" align="left">5.6 (-1.4,12.7)</td>
</tr>
<tr>
<td valign="middle" align="left">5</td>
<td valign="middle" align="left">Australasia</td>
<td valign="middle" align="left">0.4 (0.3,0.4)</td>
<td valign="middle" align="left">768.6 (695,843.2)</td>
<td valign="middle" align="left">-7.1 (-12.3,-3.6)</td>
<td valign="middle" align="left">0.5 (0.4,0.7)</td>
<td valign="middle" align="left">0.8 (0.6,1.1)</td>
<td valign="middle" align="left">62.5 (42.3,85.2)</td>
<td valign="middle" align="left">13 (10.1,16.2)</td>
<td valign="middle" align="left">23.6 (18.3,29.5)</td>
<td valign="middle" align="left">29 (15.6,44.9)</td>
</tr>
<tr>
<td valign="middle" align="left">6</td>
<td valign="middle" align="left">Andean Latin America</td>
<td valign="middle" align="left">0.6 (0.5,0.7)</td>
<td valign="middle" align="left">957.2 (862.9,1056.2)</td>
<td valign="middle" align="left">-2.7 (-5.8,0.3)</td>
<td valign="middle" align="left">8.4 (6.5,10.8)</td>
<td valign="middle" align="left">14.9 (11.5,19.2)</td>
<td valign="middle" align="left">42.8 (15.1,76.5)</td>
<td valign="middle" align="left">165.5 (127.5,211.1)</td>
<td valign="middle" align="left">286.1 (220.7,365.1)</td>
<td valign="middle" align="left">36.4 (11.6,68.7)</td>
</tr>
<tr>
<td valign="middle" align="left">7</td>
<td valign="middle" align="left">Tropical Latin America</td>
<td valign="middle" align="left">3 (2.7,3.2)</td>
<td valign="middle" align="left">1145.8 (1053.3,1238.7)</td>
<td valign="middle" align="left">-9.5 (-11.9,-7.5)</td>
<td valign="middle" align="left">17.9 (15,21.2)</td>
<td valign="middle" align="left">7.2 (6,8.6)</td>
<td valign="middle" align="left">16.4 (10.2,21.5)</td>
<td valign="middle" align="left">397.8 (335.7,457.7)</td>
<td valign="middle" align="left">155.9 (131.5,178.9)</td>
<td valign="middle" align="left">8.6 (3.6,13)</td>
</tr>
<tr>
<td valign="middle" align="left">8</td>
<td valign="middle" align="left">Central Latin America</td>
<td valign="middle" align="left">3.4 (3.1,3.7)</td>
<td valign="middle" align="left">1327.6 (1225,1427.2)</td>
<td valign="middle" align="left">-5.7 (-8.3,-3.3)</td>
<td valign="middle" align="left">24.4 (19.3,30.5)</td>
<td valign="middle" align="left">10.1 (8,12.5)</td>
<td valign="middle" align="left">49.9 (37,64.6)</td>
<td valign="middle" align="left">582.7 (462.7,718.5)</td>
<td valign="middle" align="left">232.6 (185.5,287.1)</td>
<td valign="middle" align="left">56.6 (41.8,72)</td>
</tr>
<tr>
<td valign="middle" align="left">9</td>
<td valign="middle" align="left">Southern Latin America</td>
<td valign="middle" align="left">0.8 (0.7,0.8)</td>
<td valign="middle" align="left">911.8 (819.8,1021.1)</td>
<td valign="middle" align="left">3 (-1,6.6)</td>
<td valign="middle" align="left">4.6 (3.6,5.8)</td>
<td valign="middle" align="left">5 (3.9,6.3)</td>
<td valign="middle" align="left">-2.8 (-10.5,4.9)</td>
<td valign="middle" align="left">88.5 (71.1,108.3)</td>
<td valign="middle" align="left">99.6 (80.2,121.6)</td>
<td valign="middle" align="left">-8.3 (-13.9,-2.3)</td>
</tr>
<tr>
<td valign="middle" align="left">10</td>
<td valign="middle" align="left">Caribbean</td>
<td valign="middle" align="left">0.6 (0.5,0.6)</td>
<td valign="middle" align="left">1069.9 (978.5,1169.7)</td>
<td valign="middle" align="left">-4.1 (-6.9,-1.4)</td>
<td valign="middle" align="left">6.2 (5.2,7.5)</td>
<td valign="middle" align="left">11.4 (9.5,13.7)</td>
<td valign="middle" align="left">43 (24,61.8)</td>
<td valign="middle" align="left">131.4 (108.8,159.2)</td>
<td valign="middle" align="left">242.9 (200.8,294.5)</td>
<td valign="middle" align="left">40.1 (21.4,59)</td>
</tr>
<tr>
<td valign="middle" align="left">11</td>
<td valign="middle" align="left">Central Europe</td>
<td valign="middle" align="left">1.7 (1.6,1.8)</td>
<td valign="middle" align="left">855.7 (792.6,922.2)</td>
<td valign="middle" align="left">-8.8 (-12.5,-6.3)</td>
<td valign="middle" align="left">2.8 (2.1,3.5)</td>
<td valign="middle" align="left">1.1 (0.9,1.4)</td>
<td valign="middle" align="left">5.4 (-4.8,16.6)</td>
<td valign="middle" align="left">76 (62.1,92.5)</td>
<td valign="middle" align="left">33.5 (27.3,40.6)</td>
<td valign="middle" align="left">-2 (-9,5.6)</td>
</tr>
<tr>
<td valign="middle" align="left">12</td>
<td valign="middle" align="left">Eastern Europe</td>
<td valign="middle" align="left">4.3 (4,4.7)</td>
<td valign="middle" align="left">1390.3 (1277.4,1520.4)</td>
<td valign="middle" align="left">-9.6 (-13.9,-6.5)</td>
<td valign="middle" align="left">2.8 (2.2,3.6)</td>
<td valign="middle" align="left">0.8 (0.6,1)</td>
<td valign="middle" align="left">142.7 (115.2,168.5)</td>
<td valign="middle" align="left">95.8 (76.3,118.8)</td>
<td valign="middle" align="left">26.7 (21.4,33)</td>
<td valign="middle" align="left">25.7 (13.4,40)</td>
</tr>
<tr>
<td valign="middle" align="left">13</td>
<td valign="middle" align="left">Central Asia</td>
<td valign="middle" align="left">1.3 (1.2,1.4)</td>
<td valign="middle" align="left">1494 (1381.4,1617.5)</td>
<td valign="middle" align="left">-4.5 (-7.8,-1.7)</td>
<td valign="middle" align="left">1.3 (1,1.7)</td>
<td valign="middle" align="left">1.8 (1.3,2.3)</td>
<td valign="middle" align="left">177.9 (122.1,241.4)</td>
<td valign="middle" align="left">54.9 (43.8,67.8)</td>
<td valign="middle" align="left">67.5 (54.1,82.9)</td>
<td valign="middle" align="left">52.9 (35.6,72.5)</td>
</tr>
<tr>
<td valign="middle" align="left">14</td>
<td valign="middle" align="left">North Africa and Middle East</td>
<td valign="middle" align="left">8 (7.3,8.8)</td>
<td valign="middle" align="left">1505.9 (1369,1642.9)</td>
<td valign="middle" align="left">-4 (-6.4,-1.7)</td>
<td valign="middle" align="left">31 (23.7,39.8)</td>
<td valign="middle" align="left">8.2 (6.3,10.5)</td>
<td valign="middle" align="left">28.9 (-18.2,60.8)</td>
<td valign="middle" align="left">739.7 (577.4,932.7)</td>
<td valign="middle" align="left">170.2 (133.9,214.5)</td>
<td valign="middle" align="left">22.3 (-18.8,49.4)</td>
</tr>
<tr>
<td valign="middle" align="left">15</td>
<td valign="middle" align="left">South Asia</td>
<td valign="middle" align="left">25.5 (23.2,28)</td>
<td valign="middle" align="left">1547.3 (1418.4,1687.6)</td>
<td valign="middle" align="left">-9.7 (-12,-7.5)</td>
<td valign="middle" align="left">70.3 (54.7,89.9)</td>
<td valign="middle" align="left">5.3 (4.1,6.6)</td>
<td valign="middle" align="left">28.5 (-0.7,55.5)</td>
<td valign="middle" align="left">1976.8 (1609.6,2453.2)</td>
<td valign="middle" align="left">134.4 (110.5,166)</td>
<td valign="middle" align="left">22.1 (-0.6,43.7)</td>
</tr>
<tr>
<td valign="middle" align="left">16</td>
<td valign="middle" align="left">Southeast Asia</td>
<td valign="middle" align="left">12.3 (11.3,13.4)</td>
<td valign="middle" align="left">1739.3 (1595.7,1883.9)</td>
<td valign="middle" align="left">-3 (-5.5,-0.7)</td>
<td valign="middle" align="left">59 (48.6,70.6)</td>
<td valign="middle" align="left">10.4 (8.6,12.5)</td>
<td valign="middle" align="left">31.4 (5.6,52.1)</td>
<td valign="middle" align="left">1535.4 (1287.5,1818.9)</td>
<td valign="middle" align="left">237.7 (199.8,276.1)</td>
<td valign="middle" align="left">23.5 (2.9,40.5)</td>
</tr>
<tr>
<td valign="middle" align="left">17</td>
<td valign="middle" align="left">East Asia</td>
<td valign="middle" align="left">21.7 (19.9,23.4)</td>
<td valign="middle" align="left">1054.1 (972.3,1140.1)</td>
<td valign="middle" align="left">-13.1 (-15.7,-10.9)</td>
<td valign="middle" align="left">115.1 (91.6,141.5)</td>
<td valign="middle" align="left">5.8 (4.7,7.2)</td>
<td valign="middle" align="left">-16.6 (-35.7,1.5)</td>
<td valign="middle" align="left">2697.3 (2197.7,3240.4)</td>
<td valign="middle" align="left">125.6 (103.4,149.7)</td>
<td valign="middle" align="left">-20.9 (-35.6,-5.9)</td>
</tr>
<tr>
<td valign="middle" align="left">18</td>
<td valign="middle" align="left">Oceania</td>
<td valign="middle" align="left">0.1 (0.1,0.1)</td>
<td valign="middle" align="left">1337.2 (1193.9,1478.5)</td>
<td valign="middle" align="left">-4.3 (-7.6,-1.3)</td>
<td valign="middle" align="left">0.8 (0.6,1)</td>
<td valign="middle" align="left">13.6 (11.2,17.3)</td>
<td valign="middle" align="left">27.3 (-10.2,80.5)</td>
<td valign="middle" align="left">22.6 (18.3,27.9)</td>
<td valign="middle" align="left">309.8 (257.3,383.9)</td>
<td valign="middle" align="left">23.8 (-10.4,71.1)</td>
</tr>
<tr>
<td valign="middle" align="left">19</td>
<td valign="middle" align="left">Western Sub-Saharan Africa</td>
<td valign="middle" align="left">3.2 (3,3.5)</td>
<td valign="middle" align="left">1276.3 (1183.6,1373.8)</td>
<td valign="middle" align="left">-3.9 (-6,-2)</td>
<td valign="middle" align="left">8.1 (6.1,10.9)</td>
<td valign="middle" align="left">5.5 (4.1,7.5)</td>
<td valign="middle" align="left">11.4 (-7.2,29.3)</td>
<td valign="middle" align="left">222.8 (175,283.1)</td>
<td valign="middle" align="left">124.3 (97.5,159.9)</td>
<td valign="middle" align="left">7.6 (-6.5,22.6)</td>
</tr>
<tr>
<td valign="middle" align="left">20</td>
<td valign="middle" align="left">Eastern Sub-Saharan Africa</td>
<td valign="middle" align="left">2.2 (2,2.4)</td>
<td valign="middle" align="left">942.8 (861.6,1032.9)</td>
<td valign="middle" align="left">-1.1 (-4.1,1.1)</td>
<td valign="middle" align="left">15.1 (12.1,18.9)</td>
<td valign="middle" align="left">12 (9.7,15)</td>
<td valign="middle" align="left">-4.9 (-18.5,9.5)</td>
<td valign="middle" align="left">344.1 (277.4,431.6)</td>
<td valign="middle" align="left">230.4 (187.1,284.8)</td>
<td valign="middle" align="left">-13.5 (-23.9,-1.4)</td>
</tr>
<tr>
<td valign="middle" align="left">21</td>
<td valign="middle" align="left">Central Sub-Saharan Africa</td>
<td valign="middle" align="left">1 (0.9,1.1)</td>
<td valign="middle" align="left">1377.5 (1262.9,1506.7)</td>
<td valign="middle" align="left">-6.9 (-9.8,-4.1)</td>
<td valign="middle" align="left">3.6 (2.5,5)</td>
<td valign="middle" align="left">9 (6.1,12.9)</td>
<td valign="middle" align="left">4.8 (-22.4,36.5)</td>
<td valign="middle" align="left">97.6 (69.6,134.1)</td>
<td valign="middle" align="left">196.1 (139.9,268.7)</td>
<td valign="middle" align="left">2.4 (-20.6,31.3)</td>
</tr>
<tr>
<td valign="middle" align="left">22</td>
<td valign="middle" align="left">Southern Sub-Saharan Africa</td>
<td valign="middle" align="left">0.9 (0.8,1)</td>
<td valign="middle" align="left">1362.7 (1254.9,1471)</td>
<td valign="middle" align="left">-3.9 (-6.4,-1.6)</td>
<td valign="middle" align="left">2.1 (1.6,2.8)</td>
<td valign="middle" align="left">4.4 (3.3,5.8)</td>
<td valign="middle" align="left">49.3 (10.3,73.8)</td>
<td valign="middle" align="left">60.3 (47.5,78.2)</td>
<td valign="middle" align="left">108.9 (85.5,140)</td>
<td valign="middle" align="left">36.9 (11.3,55)</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Regional tier</title>
<p>In 2021, the highest age-standardized prevalence rates (per 100,000 population) of DN were observed in Southeast Asia (1,739.3), South Asia (1,547.3), North Africa and the Middle East (1,505.9), and Central Asia (1,494.0). In contrast, Western Europe (737.4), Australasia (768.6), and Central Europe (855.7) reported comparatively lower prevalence rates (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). During the same year, Andean Latin America (14.9), Oceania (13.6), and Eastern Sub-Saharan Africa (12.0) recorded the highest age-standardized mortality rates (per 100,000), whereas Eastern Europe (0.8), Australasia (0.8), and Central Europe (1.1) had the lowest (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). Similarly, Oceania (309.8), Andean Latin America (286.1), and the Caribbean (242.9) exhibited the highest age-standardized DALY rates (per 100,000), while Central Europe (33.5), Western Europe (40.9), and Central Asia (67.5) recorded the lowest (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
<p>From 1990 to 2021, the increase in age-standardized prevalence of DN was most pronounced in High-income North America (4.9%) and Southern Latin America (3.0%), whereas the largest declines were observed in East Asia (&#x2212;13.1%) and High-income Asia Pacific (&#x2212;11.8%) (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). During the same period, age-standardized mortality rates rose substantially in High-income North America (259.9%), Central Asia (177.9%), and Eastern Europe (142.7%), while notable decreases were recorded in East Asia (&#x2212;16.6%), High-income Asia Pacific (&#x2212;18.3%), and Eastern Asia (&#x2212;16.3%). Age-standardized DALY rates increased markedly in High-income North America (168.4%), Central Latin America (56.6%), and Central Asia (52.9%), but declined in East Asia (&#x2212;20.9%), High-income Asia Pacific (&#x2212;18.9%), and Eastern Sub-Saharan Africa (&#x2212;13.5%) (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>National level</title>
<p>In 2021, countries with the highest incidence rates included Japan (82.96; 95% UI: 74.83&#x2013;90.76), Puerto Rico (68.94; 95% UI: 60.32&#x2013;78.53), and Bermuda (67.59; 95% UI: 58.21&#x2013;76.43) (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1A</bold>
</xref>). The highest age-standardized DALY rates (per 100,000 population) were recorded in Mauritius (1,071.04; 95% UI: 918.15&#x2013;1,229.15), American Samoa (978.03; 95% UI: 748.02&#x2013;1,221.06), and Niue (950.07; 95% UI: 567.49&#x2013;1,434.78) (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1B</bold>
</xref>).The highest age-standardized prevalence rates (per 100,000 population) of DN were observed in Japan (2,764.19; 95% UI: 2,539.65&#x2013;2,976.44), Thailand (2,716.64; 95% UI: 2,417.13&#x2013;3,019.55), Mauritius (2,569.61; 95% UI: 2,256.73&#x2013;2,900.06), and the Republic of Moldova (2,533.64; 95% UI: 2,262.28&#x2013;2,826.01) (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1C</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>In 2021, the burden of diabetic nephropathy was estimated in 204 countries/territories. <bold>(A)</bold> incidence, <bold>(B)</bold> DALY rates and <bold>(C)</bold> prevalence.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1647064-g001.tif">
<alt-text content-type="machine-generated">Three world maps labeled A, B, and C display data on cervical cancer. Map A shows cervical cancer incidence rates, map B shows death rates, and map C shows prevalence rates per 100,000 people. High rates are marked in red, medium in orange, and lower in yellow or green. Gray indicates no data available.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Demographic patterns of age and gender</title>
<p>
<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref> presents the global number of prevalent cases, incident cases, and DALYs for DN by age group in 2021. The highest number of prevalent cases was observed in individuals aged 65&#x2013;69 years, while the highest number of incident cases occurred in the 70&#x2013;74-year age group. <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref> depicts the global age-specific prevalence, incidence, and DALY rates of DN in 2021. Males demonstrated a higher susceptibility to DN than females. The disease burden increases progressively after the age of 45, reaching its peak between 75 and 79 years.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Global <bold>(A)</bold> Number of DALYs, <bold>(B)</bold> Number of Prevalences, and <bold>(C)</bold> Number of Incidences of diabetic nephropathy in 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1647064-g002.tif">
<alt-text content-type="machine-generated">Three side-by-side population pyramids show data by age and sex. (A) Displays Disability-Adjusted Life Years (DALYs) with more impact on older females. (B) Shows prevalence rates, indicating higher numbers in older females. (C) Illustrates incidence rates with a similar trend, higher in older females. Each pyramid categorizes data by age group with red for females and blue for males.</alt-text>
</graphic>
</fig>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Global <bold>(A)</bold> age-standardized DALY rate, <bold>(B)</bold> age-standardized incidence rate, and <bold>(C)</bold> age-standardized prevalence rate of diabetic nephropathy in 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1647064-g003.tif">
<alt-text content-type="machine-generated">Three line graphs display health-related data by age and sex. Graph A shows Disability-Adjusted Life Years (DALYs) per 100,000, with a notable increase for both sexes after age 60. Graph B illustrates incidence rates per 100,000, peaking between ages 70 and 74. Graph C presents prevalence per 100,000, peaking around ages 70 to 79. Red lines represent males and blue lines represent females, with shaded areas indicating confidence intervals.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Correlation with SDI</title>
<p>At the regional level, an S-shaped association was observed between the SDI and the prevalence of DN from 1990 to 2021, with prevalence increasing sharply as the SDI rose. South Asia, Southeast Asia, Eastern Europe, and High-income Asia Pacific exhibited prevalence rates exceeding those expected based on SDI (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). At the national level, a V-shaped relationship was identified between DN prevalence and SDI, with rates surpassing expectations in Thailand, Mauritania, Latvia, Japan, and Lithuania (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>
<bold>(A)</bold> Association of prevalence with SDI by region, 1990-2021. <bold>(B)</bold> Association of prevalence with SDI by country in 2021. (B&amp;L represents expected prevalence based on SDI only).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1647064-g004.tif">
<alt-text content-type="machine-generated">Graph A shows changes in GDP per capita across different regions from 1990 to 2015, plotted against the Socio-Demographic Index. Graph B displays GDP per capita for various countries in 2016, also plotted against the Socio-Demographic Index. Different colors and shapes represent regions and countries, with a black trend line indicating the overall relationship.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_6">
<label>3.6</label>
<title>Risk factors</title>
<p>
<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref> depicts the proportions of risk factors contributing to DALYs attributable to DN globally and across 21 regions in 2021. The leading risk factors include renal impairment, elevated fasting plasma glucose, and increased body mass index. Diets high in processed meat predominantly affect Western Europe and other high-income regions, while diets rich in red meat mainly impact Australasia. Elevated systolic blood pressure is a primary risk factor in Central Asia, Central Europe, and Eastern Europe.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>DALYs for diabetic nephropathy and risk factor shares in 21 regions in 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1647064-g005.tif">
<alt-text content-type="machine-generated">A horizontal bar chart displays the percentage of disability-adjusted life years (DALYs) attributable to various risk factors across different GBD regions. Risk factors include diets high in processed meat, red meat, sodium, and sugar-sweetened beverages, as well as diets low in fruits, vegetables, and whole grains. Other factors are high body mass index, high fasting plasma glucose, high systolic blood pressure, high and low temperatures, kidney dysfunction, lead exposure, and low physical activity. The color-coded bars reflect the impact of each risk factor regionally.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_7">
<label>3.7</label>
<title>Projections for the future</title>
<p>The age-standardized prevalence of DN is declining globally from 2022 to 2036 (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Temporal trends in global age-standardized prevalence from 1990 to 2036.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1647064-g006.tif">
<alt-text content-type="machine-generated">Line graph depicting the age-standardized rate per 100,000 from 1990 to 2030. The rate shows a gradual decline until 2020, then forecasts with increasing uncertainty displayed as a widening cone of blue gradients.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<sec id="s4_1">
<label>4.1</label>
<title>Principal discoveries</title>
<p>This study utilizes data from the Global Burden of Disease Study 2021 to provide comprehensive estimates of the prevalence, mortality, DALYs, and age-standardized rates of DN from 1990 to 2021. In 2021, an estimated 107.6 million cases, 477,300 deaths, and 11.28 million DALYs were attributed to DN worldwide. The burden of DN was analyzed at global, regional, and national levels using contemporary epidemiological approaches and risk factor assessment. These findings offer valuable insights into the impact of varying SDI levels on DN, thereby supporting policymakers in developing targeted prevention and management strategies.</p>
</sec>
<sec id="s4_2">
<label>4.2</label>
<title>Comparison with alternative research</title>
<p>A 2021 study reported a 0.81% increase in the age-standardized DALY rate for DN (<xref ref-type="bibr" rid="B13">13</xref>). From 1990 to 2021, the global age-standardized prevalence of DN decreased by 5.1%, whereas the age-standardized mortality rate increased by 37.8%. Our study provides additional critical insights. Although a previous analysis did not assess risk factors or stratify by age and sex, its findings on mortality, prevalence, and DALYs generally correspond with ours (<xref ref-type="bibr" rid="B14">14</xref>). This underscores that our study offers the most comprehensive and precise data currently available.</p>
<p>From 1990 to 2021, global mortality and DALY rates for DN steadily increased, highlighting its emergence as a significant public health concern. Despite advances, the global management of DN faces ongoing challenges. Key strategies to mitigate its impact include stringent glycemic control (<xref ref-type="bibr" rid="B15">15</xref>, <xref ref-type="bibr" rid="B16">16</xref>), lipid management (<xref ref-type="bibr" rid="B17">17</xref>), blood pressure regulation (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>), dietary modifications (<xref ref-type="bibr" rid="B20">20</xref>), and careful use of nephrotoxic medications (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Recent studies into the pathophysiology of DN have elucidated mechanisms such as renal structural alterations (<xref ref-type="bibr" rid="B23">23</xref>), glomerular hyperfiltration (<xref ref-type="bibr" rid="B24">24</xref>), inflammation (<xref ref-type="bibr" rid="B25">25</xref>), lipotoxicity (<xref ref-type="bibr" rid="B26">26</xref>), organelle dysfunction (<xref ref-type="bibr" rid="B27">27</xref>), and vascular abnormalities (<xref ref-type="bibr" rid="B28">28</xref>). Further comprehensive research is essential to improve therapeutic approaches and patient outcomes in DN.</p>
<p>At the regional level, Southeast Asia exhibits the highest prevalence of DN. This predominantly developing region is marked by uneven economic development and accounts for approximately 1% of global health expenditure (<xref ref-type="bibr" rid="B29">29</xref>). Southeast Asia currently faces significant societal challenges, including rising obesity rates, rapid population aging, and urbanization. Sedentary lifestyles and dietary patterns prevalent in the region further contribute to the increasing burden of DN (<xref ref-type="bibr" rid="B30">30</xref>). In contrast, Western Europe, characterized by the lowest prevalence,ranks among the wealthiest regions globally and benefits from an advanced healthcare system with well-established renal care protocols that support effective prevention and management of DN (<xref ref-type="bibr" rid="B31">31</xref>). The density of healthcare professionals in Western Europe considerably exceeds the global average (<xref ref-type="bibr" rid="B32">32</xref>), thereby improving the quality of DN care. Additionally, Western European governments provide substantial financial support for renal disease-related healthcare costs, which are markedly higher than the global average (<xref ref-type="bibr" rid="B33">33</xref>).</p>
<p>In 2021, Japan, characterized by an aging population, exhibited the highest prevalence of DN. Individuals of Japanese descent have been shown to possess a reduced capacity for insulin secretion (<xref ref-type="bibr" rid="B34">34</xref>). Coupled with an aging demographic and increasing adoption of Westernized lifestyles and dietary habits, this has contributed to the rising prevalence of DN. At least three genetic variants have been identified as contributing factors to DN within the Japanese population (<xref ref-type="bibr" rid="B35">35</xref>). As a developed nation equipped with advanced medical technologies, Japan&#x2019;s high prevalence figures likely reflect robust early screening and diagnostic practices. The elevated prevalence of DN in Japan thus reflects the interplay of physiological, genetic, and environmental factors unique to this population.</p>
<p>Research indicates that diets high in processed meat, red meat, salt, and sugar-sweetened beverages are significant risk factors for the development of diabetic kidney disease. Addressing these modifiable risk factors is essential for the prevention and management of DN. Diet plays a critical role in the pathogenesis of DN (<xref ref-type="bibr" rid="B36">36</xref>, <xref ref-type="bibr" rid="B37">37</xref>). Economically advanced regions such as Western Europe and High-income North America typically consume protein-rich diets, which may contribute to disease progression. Limiting protein intake has been shown to support the prevention and management of DN (<xref ref-type="bibr" rid="B36">36</xref>, <xref ref-type="bibr" rid="B38">38</xref>). Conversely, diets rich in fruits and vegetables provide essential vitamins that may confer renal protection (<xref ref-type="bibr" rid="B39">39</xref>). Elevated fasting plasma glucose promotes inflammation, glomerulosclerosis, and renal fibrosis, thereby accelerating DN progression (<xref ref-type="bibr" rid="B40">40</xref>). Future strategies aimed at optimizing protein intake and supplementing with vitamin-rich foods may help alleviate the global burden of DN.</p>
<p>This study also identified a significant association between the burden of DN and the SDI. Our findings demonstrate that the prevalence of DN increases with rising SDI at the regional level, with a higher burden observed in economically developed regions. At the national level, DN prevalence was elevated in countries with medium to high SDI, where advanced medical technologies facilitate accurate diagnosis. Conversely, countries and regions with low SDI face technical limitations and challenges in the detection and diagnosis of DN.</p>
<p>This study&#x2019;s strength lies in its systematic and comprehensive evaluation of the epidemiology of DN at global, regional, and national levels from 1990 to 2021. It enables assessment of the worldwide impact of DN by comparing regions and countries with varying healthcare resources. There is a critical need to enhance systematic, effective, and routine screening and renal function monitoring for DN (<xref ref-type="bibr" rid="B41">41</xref>). Regions and countries with a high prevalence of DN should allocate additional healthcare resources and implement tailored policies to improve prevention and management efforts. However, this study has several limitations. The data used in this study are not original data, but rather GBD data on DN caused by type 2 diabetes. The data were derived from the GBD database, which relies on varying data collection capacities across countries and regions. Inconsistencies in data quality and the absence of source data in some settings may have affected the accuracy of our analysis.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>This study examines the current status and trends in the global epidemiology of DN over the past three decades. Despite a decline in age-standardized prevalence, both age-standardized mortality and DALY rates have increased. Substantial variation exists in prevalence, incidence, and DALY rates across regions and countries. The SDI exerts a significant influence on DN prevalence, and sustained improvements in risk factor management are essential to mitigate its impact. DN is expected to remain a major global health challenge in the future.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>Publicly available datasets were analyzed in this study. This data can be found here: The data for this study are available in the GBD database (<uri xlink:href="https://vizhub.healthdata.org/gbd-results/">https://vizhub.healthdata.org/gbd-results/</uri>).</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>LZ: Writing &#x2013; original draft. LJ: Writing &#x2013; review &amp; editing. RX: Writing &#x2013; review &amp; editing. XZ: Writing &#x2013; review &amp; editing. BZ: Writing &#x2013; review &amp; editing. RY: Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by the National Key Research and Development Program -Cadre Healthcare Research and Development Project of Sichuan Provincial Health Planning Commission (Sichuan Cadre Healthcare Research ZH2024 -501).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We thank the Institute for Health Metrics and Evaluation (IHME) for providing open access.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was 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 id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors&#xa0;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>
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