{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:00:49Z","timestamp":1781067649211,"version":"3.54.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Using the 2023 Global Burden of Disease (GBD) database, we systematically compared the burden of chronic kidney disease due to type 2 diabetes mellitus (T2DM-CKD) between China and G20 countries from 1990 to 2023, and projected trends in the disease burden over the next 26 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used the Joinpoint model to analyze trends in the disease burden over time, applied the Das Gupta decomposition method, and utilized the concentration index and the slope inequality index to assess health inequalities. Finally, we employed ARIMA (Auto Regressive Integrated Moving Average) and BAPC (Bayesian Age-Period-Cohort) models to forecast trends for the period 2024\u20132050.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Over the past 33 years, China\u2019s age-standardized incidence, death, and DALY rates (ASIR, ASDR, ASDALYR) all showed a downward trend, with the average annual percentage change (AAPC) of \u20130.72%, \u20132.02%, and \u20131.45%, but aging has driven a steady rise in the absolute burden. The burden of ASIR is concentrated in low Socio-demographic Index (SDI) countries (Concentration Index (CI): \u20130.168 to \u20130.277), while the burden of ASDR is shifting toward high-SDI countries (CI: \u20130.312 to \u20130.161). The BAPC model projected a 23.2% increase in age-standardized incidence to 2050 (the ARIMA model yielded an unrealistic over-estimation and is not emphasized).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>China must prepare for a future with a growing population living with chronic conditions and focus on reducing incidence rates among the elderly. Meanwhile, G20 countries require tailored interventions and joint efforts to reduce health inequalities across SDI levels.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1853478","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T04:30:04Z","timestamp":1781065804000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Burden and trends of chronic kidney disease due to type 2 diabetes mellitus in China and G20 countries, 1990\u20132023: a comparative analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Jincheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtao","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Biotherapy, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine","place":["Chengdu, 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