{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T13:59:08Z","timestamp":1790863148122,"version":"4.1.0"},"reference-count":93,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diabetes mellitus poses a major global health challenge, yet its intrinsic heterogeneity has been poorly captured by traditional classification systems. The international classification of diseases, tenth revision (ICD-10), built on a phenotype-based framework, lacks the granularity needed to incorporate contemporary molecular insights into diabetes subtypes, hampering both clinical precision and public health surveillance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main body<\/jats:title>\n                    <jats:p>This review examines how the recently implemented international classification of diseases, eleventh revision (ICD-11) addresses these limitations through its ontology-based architecture. We analyze three key areas. First, ICD-11 reclassifies prediabetes as a distinct reportable entity with formal subtyping of impaired fasting glucose and impaired glucose tolerance, which may support more precise epidemiological tracking and evidence-based prevention. Second, its Uniform Resource Identifier (URI)-based taxonomy provides granular classification across the diabetes spectrum, from type 1 and type 2 diabetes to monogenic forms such as maturity-onset diabetes of the young (MODY), with etiological stratification that can inform both clinical decision-making and population-level surveillance. Third, its postcoordination mechanism allows multidimensional documentation of complications, drug-induced diabetes, and unstable disease states, generating structured, machine-readable data with potential for real-world evidence generation and health system performance assessment. However, early implementation experiences across multiple countries reveal substantial challenges\u2014including heterogeneous adoption timelines, coding inconsistencies, and workforce training gaps\u2014that may constrain the realization of these benefits in practice. We also discuss the implications of these advances for health systems, including value-based reimbursement models and the production of actionable evidence for policy makers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ICD-11 repositions diabetes classification toward a framework that offers substantially enhanced capacity for etiological and granular representation of the disease. By generating standardized, granular population-level data, it offers a potential infrastructure for precision public health. Yet realizing this potential will depend on health system capacity, workforce training, and sustained investment in implementation\u2014particularly in resource-limited settings, where the risk of widening disparities is most acute. Future refinements should integrate genomic descriptors and dynamic update mechanisms to keep pace with rapid advances in diabetes research, with continued attention to health equity and global applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1944007","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T13:17:17Z","timestamp":1790860637000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ICD-11 and diabetes: opportunities for precision public health through enhanced classification"],"prefix":"10.3389","volume":"14","author":[{"given":"Jingya","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Medical Records, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","place":["Beijing, China"]},{"name":"Collaborating Center for the WHO Family of International Classifications in China","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Xia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Severe and Rare Diseases, Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanmeng","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Endocrinology of National Health Commission of People's Republic of China, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naishi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medical Records, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","place":["Beijing, China"]},{"name":"Collaborating Center for the WHO Family of International Classifications in China","place":["Beijing, China"]},{"name":"Key Laboratory of Endocrinology of National Health Commission of People's Republic of China, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/S2213-8587(25)00299-2","article-title":"11th edition of the IDF diabetes atlas: global, regional, and national diabetes prevalence estimates for 2024 and projections for 2050","volume":"14","author":"Genitsaridi","year":"2026","journal-title":"Lancet Diabetes Endocrinol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"490","DOI":"10.2337\/dc25-2583","article-title":"Global, regional, and national estimates of undiagnosed diabetes in adults: findings from the 2025 IDF diabetes atlas","volume":"49","author":"Teufel","year":"2026","journal-title":"Diabetes Care."},{"key":"B3","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1001\/jama.2024.15563","article-title":"Leading causes of death in the US, 2019\u20132023","volume":"332","author":"Ahmad","year":"2024","journal-title":"JAMA."},{"key":"B4","doi-asserted-by":"crossref","first-page":"2100","DOI":"10.1016\/S0140-6736(24)00367-2","article-title":"Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990\u20132021: a systematic analysis for the global burden of disease study 2021","volume":"403","year":"2024","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"963","DOI":"10.2337\/dc17-1962","article-title":"Global economic burden of diabetes in adults: projections from 2015 to 2030","volume":"41","author":"Bommer","year":"2018","journal-title":"Diabetes Care."},{"key":"B6","doi-asserted-by":"publisher","first-page":"S20","DOI":"10.2337\/dc24-S002","volume":"47","year":"2024","journal-title":"Diabetes Care"},{"key":"B7","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.2337\/dbi20-0017","article-title":"Management of latent autoimmune diabetes in adults: a consensus statement from an international expert panel","volume":"69","author":"Buzzetti","year":"2020","journal-title":"Diabetes."},{"key":"B8","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.2337\/dci20-0065","article-title":"Monogenic diabetes: from genetic insights to population-based precision in care. 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