{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:54:32Z","timestamp":1781844872124,"version":"3.54.5"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"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 progression of rapidly progressive diabetic retinopathy (PDR) in type 2 diabetes mellitus (T2DM) is characterized by substantial inter-individual variability. To develop and validate a nomogram for individualized risk prediction and stratification of rapidly progressive PDR in T2DM by incorporating diabetes duration, glycated hemoglobin (HbA1c), 24-hour urinary protein quantification, growth differentiation factor 15 (GDF15), Diabetic Retinopathy Severity Scale (DRSS) grade, and foveal avascular zone area.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study enrolled 342 patients with T2DM (1999 WHO criteria), randomly assigned to training (n=240) and validation (n=102) sets (7:3 ratio). Baseline demographic, clinical, metabolic, renal, inflammatory biomarker, and ophthalmic imaging data were collected. Predictive variables were selected via univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Independent predictors identified by multivariable logistic regression were incorporated into a nomogram. For comparison, Random Forest, multivariable logistic regression, and Gradient Boosting Machine models were also developed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), calibration curves, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Univariate analysis identified six significant factors (all\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05): diabetes duration, HbA1c, 24-hour urinary protein quantification, GDF15, DRSS grade, and foveal avascular zone area. LASSO regression retained all six, and multivariable logistic regression confirmed them as independent risk factors for rapidly progressive DR in T2DM (all\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05). Three machine learning models were constructed. The Random Forest model achieved the numerically highest validation AUC (0.780) compared with Gradient Boosting Machine (0.741) and multivariable logistic regression (0.698), though the DeLong test showed no statistically significant difference between Random Forest and Gradient Boosting Machine (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.38). Calibration curves showed good consistency between predicted and observed probabilities. DCA indicated high clinical net benefit of the model at 0.1-0.8 threshold probability vs other models and extreme strategies.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A novel risk prediction model for rapidly progressive PDR in T2DM was developed and validated by integrating multidimensional parameters. Demonstrating favorable discrimination, calibration, and clinical utility, this model provides a promising tool for early identification of high-risk individuals and optimization of personalized intervention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1837957","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:31:08Z","timestamp":1781843468000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a risk stratification tool for rapidly progressive diabetic retinopathy in type 2 diabetes"],"prefix":"10.3389","volume":"17","author":[{"given":"Aiping","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichao","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanying","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renlong","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodi","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengke","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.958218","article-title":"Gut microbiota: a new target for T2DM prevention and treatment","volume":"13","author":"Liu","year":"2022","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2024.1367376","article-title":"Association of the systemic immuno-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with diabetic microvascular complications","volume":"15","author":"Li","year":"2024","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B3","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1186\/s12933-024-02535-1","article-title":"Metabolomic studies reveal and validate potential biomarkers of diabetic retinopathy in two Chinese datasets with type 2 diabetes: a cross-sectional study","volume":"23","author":"Zhou","year":"2024","journal-title":"Cardiovasc Diabetol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13237083","article-title":"Incidence of diabetic retinopathy in individuals with type 2 diabetes: a study using real-world data","volume":"13","author":"Hern\u00e1ndez-Teixid\u00f3","year":"2024","journal-title":"J Clin Med"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1093\/ajcn\/nqac154","article-title":"Effect of a ketogenic diet versus Mediterranean diet on glycated hemoglobin in individuals with prediabetes and type 2 diabetes mellitus: the interventional Keto-Med randomized crossover trial","volume":"116","author":"Gardner","year":"2022","journal-title":"Am J Clin Nutr"},{"key":"B6","doi-asserted-by":"publisher","first-page":"17508","DOI":"10.1038\/s41598-021-96999-0","article-title":"Urine protein-to-creatinine ratio is a predictor of diabetic retinopathy progression and regression in type 2 diabetes","volume":"11","author":"Jeng","year":"2021","journal-title":"Sci Rep"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.1033485","article-title":"Circulating growth differentiation factor 15 is a potential biomarker for diabetic retinopathy: a meta-analysis","volume":"13","author":"Li","year":"2022","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2019.03.011","article-title":"Baseline retinal characteristics and progression of diabetic retinopathy: 2-year results from the DRCR.net Protocol T","volume":"126","author":"Sun","year":"2019","journal-title":"Ophthalmology"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2021.05.014","article-title":"Optical coherence tomography angiography parameters and diabetic retinopathy progression: a 3-year longitudinal study","volume":"231","author":"Kim","year":"2021","journal-title":"Am J Ophthalmol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.2196\/45976","article-title":"Machine learning-based risk prediction for diabetic retinopathy: a systematic review and meta-analysis","volume":"25","author":"Tsao","year":"2023","journal-title":"J Med Internet Res"},{"key":"B11","first-page":"1","article-title":"Definition, diagnosis and classification of diabetes mellitus and its complications: report of a WHO consultation. Part 1: Diagnosis and classification of diabetes mellitus","year":"1999"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1002\/dmrr.3158","article-title":"Standards of medical care for type 2 diabetes in China 2019","volume":"35","author":"Jia","year":"2019","journal-title":"Diabetes Metab Res Rev"},{"key":"B13","first-page":"1","article-title":"Diabetic retinopathy preferred practice pattern\u00ae","year":"2019"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0318226","article-title":"Development and validation of predictive models for diabetic retinopathy using machine learning","volume":"20","author":"Yang","year":"2025","journal-title":"PloS One"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2025.1556049","article-title":"Risk prediction models for diabetic retinopathy: a systematic review","volume":"16","author":"Huang","year":"2025","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1159\/000541294","article-title":"Comparison of machine learning algorithms and nomogram construction for diabetic retinopathy prediction in type 2 diabetes mellitus patients","volume":"67","author":"Jiang","year":"2024","journal-title":"Ophthalmic Res"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2025.100859","article-title":"Predicting progression to vision-threatening complications in diabetic retinopathy","volume":"5","author":"Davis","year":"2025","journal-title":"Ophthalmol Sci"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1001\/archopht.1994.01090210105023","article-title":"The Wisconsin Epidemiologic Study of diabetic retinopathy. XIV. Ten-year incidence and progression of diabetic retinopathy","volume":"112","author":"Klein","year":"1994","journal-title":"Arch Ophthalmol"},{"key":"B19","article-title":"Fundus photographic risk factors for progression of diabetic retinopathy. ETDRS report number 12","volume":"98","year":"1991","journal-title":"Ophthalmology"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/S0161-6420(03)00475-5","article-title":"Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales","volume":"110","author":"Wilkinson","year":"2003","journal-title":"Ophthalmology"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1837957\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:31:11Z","timestamp":1781843471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1837957\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,19]]},"references-count":21,"alternative-id":["10.3389\/fendo.2026.1837957"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2026.1837957","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,19]]},"article-number":"1837957"}}