{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:18:46Z","timestamp":1787811526196,"version":"build-2784847793"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"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>The mechanisms underlying why some individuals with diabetes develop severe diabetic retinopathy (DR), whereas others remain free of retinal complications despite long-standing disease, remain incompletely understood. We aimed to identify systemic susceptibility and resilience signatures associated with severe DR using an extreme phenotype-derived machine learning framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An extreme phenotype cohort was established comprising 712 individuals with diabetes mellitus, including 437 patients with proliferative diabetic retinopathy (PDR; susceptible phenotype) and 275 patients with diabetes duration \u226510 years without retinopathy (resilient phenotype). Clinical and biochemical variables were integrated to develop interpretable machine learning models. The optimal model was further interpreted using SHapley Additive exPlanations (SHAP). An independent community-based diabetic cohort (n=673) was used to evaluate the distribution of susceptibility signatures in real-world populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LASSO regression identified 21 phenotype-associated features for model development. Among the evaluated algorithms, LightGBM demonstrated the strongest ability to discriminate susceptible and resilient phenotypes, achieving an area under the receiver operating characteristic curve (AUC) of 0.90 (95% CI, 0.84\u20130.95) in the internal validation cohort. SHAP analysis identified urinary albumin excretion rate (UAER), diabetes duration, serum creatinine, total protein, age, and hypertension duration as the dominant phenotype-defining features. Notably, UAER exhibited a pronounced nonlinear association with susceptibility scores, suggesting a close link between renal microvascular injury and vulnerability to severe DR. When applied to the community cohort, the susceptibility signature showed limited discrimination in the overall population (AUC\u00a0=\u00a00.54) but became progressively enriched among individuals with greater metabolic burden, reaching an AUC of 0.71 in participants with fasting blood glucose \u22659.0 mmol\/L.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using an extreme phenotype-derived machine learning framework, we identified systemic susceptibility and resilience signatures associated with severe diabetic retinopathy. Renal dysfunction, albuminuria, glycemic burden, and disease duration emerged as key phenotype-defining characteristics. These signatures became increasingly enriched in metabolically stressed individuals, supporting the concept that severe diabetic retinopathy arises through the interaction between intrinsic biological susceptibility and cumulative metabolic exposure. This framework may provide new insights into disease heterogeneity and facilitate future precision risk stratification strategies in diabetic eye disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1933878","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:39:36Z","timestamp":1787809176000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Extreme phenotype-derived machine learning reveals susceptibility and resilience signatures for severe diabetic retinopathy"],"prefix":"10.3389","volume":"17","author":[{"given":"Jing-Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]},{"name":"Beijing Diabetes Institute,Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue-Ming","family":"Shu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang-Yuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]},{"name":"Beijing Diabetes Institute,Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si-Wei","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong-Rong","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]},{"name":"Beijing Diabetes Institute,Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Tian","sequence":"additional","affiliation":[{"name":"Outpatient Department, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Rong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]},{"name":"Healthy Management Center, Beijing Tongren Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-024-03101-5","article-title":"Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020","volume":"38","year":"2024","journal-title":"Eye (Lond)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3390\/cells14050376","article-title":"Diabetic retinopathy (DR): mechanisms, current therapies, and emerging strategies","volume":"14","author":"Seo","year":"2025","journal-title":"Cells"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines12071405","article-title":"Advances in structural and functional retinal imaging and biomarkers for early detection of diabetic retinopathy","volume":"12","author":"Zhang","year":"2024","journal-title":"Biomedicines"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2008.08.023","article-title":"The Wisconsin Epidemiologic Study of Diabetic Retinopathy: XXII the twenty-five-year progression of retinopathy in persons with type 1 diabetes","volume":"115","author":"Klein","year":"2008","journal-title":"Ophthalmology"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115152","article-title":"An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from chest X-ray","volume":"180","author":"Tamal","year":"2021","journal-title":"Expert Syst Appl"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2018.04.007","article-title":"Guidelines on diabetic eye care: the International Council of Ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings","volume":"125","author":"Wong","year":"2018","journal-title":"Ophthalmology"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/eye.2000.235","article-title":"Development of proliferative retinopathy in patients with long-standing insulin-dependent diabetes mellitus","volume":"14","author":"Styles","year":"2000","journal-title":"Eye (Lond)"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.2337\/db18-0567","article-title":"Multiethnic genome-wide association study of diabetic retinopathy using liability threshold modeling of duration of diabetes and glycemic control","volume":"68","author":"Pollack","year":"2019","journal-title":"Diabetes"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2024.101271","article-title":"Resilience to diabetic retinopathy","volume":"101","author":"Serikbaeva","year":"2024","journal-title":"Prog Retin Eye Res"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"B12","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Abramoff","year":"2018","journal-title":"NPJ Digit Med"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.2337\/dc11-1909","article-title":"Global prevalence and major risk factors of diabetic retinopathy","volume":"35","author":"Yau","year":"2012","journal-title":"Diabetes Care"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/nrg2779","article-title":"Uncovering the roles of rare variants in common disease through whole-genome sequencing","volume":"11","author":"Cirulli","year":"2010","journal-title":"Nat Rev Genet"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.3027314","article-title":"A survey on explainable artificial intelligence (XAI): Toward medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3390\/genes11090987","article-title":"A systematic review of extreme phenotype strategies to search for rare variants in genetic studies of complex disorders","volume":"11","author":"Amanat","year":"2020","journal-title":"Genes (Basel)"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1007\/s00592-020-01602-9","article-title":"Different retinopathy phenotypes in type 2 diabetes predict retinopathy progression","volume":"58","author":"Marques","year":"2021","journal-title":"Acta Diabetol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125461","article-title":"XAI-RACapsNet: Relevance aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation","volume":"261","author":"Ahmed","year":"2025","journal-title":"Expert Syst Appl"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1142\/s0218348x23401023","article-title":"DCNNBT: A novel deep convolution neural network-based brain tumor classification model","volume":"31","author":"Haq","year":"2023","journal-title":"Fractals"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2026.102696","article-title":"HEAL-LLMF: A hierarchical explainable AI framework using LLM fusion for multi-modal and personalized healthcare decision making","volume":"140","author":"Haq","year":"2026","journal-title":"Inf Syst"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.129372","article-title":"Tri-STANet: An advanced framework integrating dual axis transformer and morphological encoding for enhanced AS detection","volume":"297","author":"Haq","year":"2026","journal-title":"Expert Syst Appl"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2022.021968","article-title":"SMOTEDNN: A novel model for air pollution forecasting and AQI classification","volume":"71","author":"Haq","year":"2021","journal-title":"Computers Mater Continua"},{"key":"B23","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s12874-019-0681-4","article-title":"Machine learning in medicine: a practical introduction","volume":"19","author":"Sidey-Gibbons","year":"2019","journal-title":"BMC Med Res Methodol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s41746-022-00712-8","article-title":"Multimodal machine learning in precision health: a scoping review","volume":"5","author":"Kline","year":"2022","journal-title":"NPJ Digit Med"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1038\/s41581-022-00562-3","article-title":"Artificial intelligence-enabled decision support in nephrology","volume":"18","author":"Loftus","year":"2022","journal-title":"Nat Rev Nephrol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.3390\/ijms252413491","article-title":"Comprehensive evaluation of advanced imputation methods for proteomic data acquired via the label-free approach","volume":"25","author":"Wryk","year":"2024","journal-title":"Int J Mol Sci"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2026.1776188","article-title":"An interpretable machine learning model for detecting vision-threatening diabetic retinopathy among patients with diabetic retinopathy: a web-based cross-sectional study","volume":"17","author":"Song","year":"2026","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1007\/s12094-010-0487-7","article-title":"Selection of extreme phenotypes: the role of clinical observation in translational research","volume":"12","author":"P\u00e9rez-Gracia","year":"2010","journal-title":"Clin Transl Oncol"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.816400","article-title":"Mechanistic pathogenesis of endothelial dysfunction in diabetic nephropathy and retinopathy","volume":"13","author":"Yang","year":"2022","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21249456","article-title":"Molecular mechanisms in early diabetic kidney disease: glomerular endothelial cell dysfunction","volume":"21","author":"Lass\u00e9n","year":"2020","journal-title":"Int J Mol Sci"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.3390\/medsci13030087","article-title":"Pathophysiological mechanisms of diabetes-induced macrovascular and microvascular complications: the role of oxidative stress","volume":"13","author":"Roy","year":"2025","journal-title":"Med Sci (Basel)"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-8587(18)30128-1","article-title":"Incidence and progression of diabetic retinopathy: a systematic review","volume":"7","author":"Sabanayagam","year":"2019","journal-title":"Lancet Diabetes Endocrinol"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-023-05964-x","article-title":"Genetics of diabetes-associated microvascular complications","volume":"66","author":"Lyssenko","year":"2023","journal-title":"Diabetologia"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.2337\/dc10-1675","article-title":"Protection from retinopathy and other complications in patients with type 1 diabetes of extreme duration: the Joslin 50-year medalist study","volume":"34","author":"Sun","year":"2011","journal-title":"Diabetes Care"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2024.1416924","article-title":"Genetic insights and emerging therapeutics in diabetic retinopathy: From molecular pathways to personalized medicine","volume":"15","author":"Pei","year":"2024","journal-title":"Front Genet"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1933878\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:39:37Z","timestamp":1787809177000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1933878\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,27]]},"references-count":36,"alternative-id":["10.3389\/fendo.2026.1933878"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2026.1933878","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,27]]},"article-number":"1933878"}}