{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T01:05:46Z","timestamp":1788915946684,"version":"build-2803163510"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:00:00Z","timestamp":1746403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81972372, 82272993"],"award-info":[{"award-number":["81972372, 82272993"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:p>Diabetic retinopathy (DR) and diabetic kidney disease (DKD) are the most common microvascular complications associated with type 2 diabetes mellitus (T2DM). However, the occurrence of DR and DKD is not parallel. The aim of our study is to identify the risk factors for combining DKD in T2DM patients with pre-existing DR and construct a nomogram predictive model to identify high-risk patients with DR combined with DKD. We retrospectively reviewed 683 T2DM patients with DR from March 2017 to March 2023. The patients were divided into the DR group and the DR combined with DKD group. The hold-out method was used to randomly divide all subjects into a training set (70%) and a validation set (30%). Using multivariate logistic regression, we identified eight independent risk factors: fibrinogen (FIB), albumin (ALB), atherogenic index of plasma (AIP), low-density lipoprotein cholesterol (LDL-C), body mass index (BMI), classification of DR, gender, and history of hypertension. These factors were used to construct the nomogram prediction model. The model\u2019s discriminative ability was assessed using receiver operating characteristic (ROC) curve analysis, yielding an area under the curve (AUC) of 0.780 (95% CI: 0.736-0.823) in the training set and 0.739 (95% CI: 0.668-0.809) in the validation set. Calibration curves and decision curve analysis (DCA) further demonstrated the model\u2019s clinical utility. Additionally, to explore potential genetic predisposition, single nucleotide polymorphism (SNP) genotyping analysis was conducted on a subset of 50 randomly selected patients (25 from each group). The results suggested that the rs6591190 and rs12146493 loci of the AP5B1 gene might be associated with an increased susceptibility to DKD in patients with DR, warranting further investigation. In summary, our nomogram represents a valuable tool for identifying T2DM patients with DR who are at high risk for developing DKD.<\/jats:p>","DOI":"10.3389\/fendo.2025.1499866","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T04:10:35Z","timestamp":1746418235000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Development and validation of a risk prediction model for diabetic kidney disease in patients with diabetic retinopathy"],"prefix":"10.3389","volume":"16","author":[{"given":"Mengsha","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenke","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linan","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guixia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokun","family":"Gang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","article-title":"IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040","volume":"128","author":"Ogurtsova","year":"2017","journal-title":"Diabetes Res Clin Pract"},{"key":"B2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/nrendo.2017.151","article-title":"Global aetiology and epidemiology of type 2 diabetes mellitus and its complications","volume":"14","author":"Zheng","year":"2018","journal-title":"Nat Rev Endocrinol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.03700417","article-title":"Diabetes and CKD in the United States population, 2009-2014","volume":"12","author":"Zelnick","year":"2017","journal-title":"Clin J Am Soc Nephrol"},{"key":"B4","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":"B5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0161897","article-title":"Diabetic retinopathy in patients with diabetic nephropathy: development and progression","volume":"11","author":"Jeng","year":"2016","journal-title":"PloS One"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsx.2020.08.026","article-title":"Relationship between diabetic retinopathy and diabetic nephropathy; A longitudinal follow-up study from a tertiary care unit of Karachi, Pakistan","volume":"14","author":"Butt","year":"2020","journal-title":"Diabetes Metab Syndr"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1111\/jdi.12953","article-title":"Discordance in risk factors for the progression of diabetic retinopathy and diabetic nephropathy in patients with type 2 diabetes mellitus","volume":"10","author":"Song","year":"2019","journal-title":"J Diabetes Investig"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e3514","DOI":"10.1002\/dmrr.3514","article-title":"C-peptide, glycaemic control, and diabetic complications in type 2 diabetes mellitus: A real-world study","volume":"38","author":"Huang","year":"2022","journal-title":"Diabetes Metab Res Rev"},{"key":"B9","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s11892-019-1186-6","article-title":"Genetics of diabetic retinopathy","volume":"19","author":"Han","year":"2019","journal-title":"Curr Diabetes Rep"},{"key":"B10","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s40246-019-0205-7","article-title":"Genome-wide association study identifies novel loci for type 2 diabetes-attributed end-stage kidney disease in African Americans","volume":"13","author":"M. 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