{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T22:26:55Z","timestamp":1794263215946,"version":"4.9.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Capital Health Development Research Project","award":["2024-3-1181"],"award-info":[{"award-number":["2024-3-1181"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82372304"],"award-info":[{"award-number":["82372304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Med Res"],"DOI":"10.1186\/s40001-025-02442-5","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T15:48:51Z","timestamp":1742312931000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms"],"prefix":"10.1186","volume":"30","author":[{"given":"Xiaohua","family":"Wan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruihuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanwei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"issue":"11","key":"2442_CR1","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1038\/s41433-024-03101-5","volume":"38","author":"Vision Loss Expert Group of the Global Burden of Disease Study; GBD","year":"2019","unstructured":"Vision Loss Expert Group of the Global Burden of Disease Study; GBD. Blindness and Vision Impairment Collaborators (2024) Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020. Eye (Lond). 2019;38(11):2047\u201357. https:\/\/doi.org\/10.1038\/s41433-024-03101-5.","journal-title":"Eye (Lond)."},{"issue":"1","key":"2442_CR2","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.1038\/s41467-023-39864-w","volume":"14","author":"X Hou","year":"2023","unstructured":"Hou X, Wang L, Zhu D, Guo L, Weng J, Zhang M, et al. Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China. Nat Commun. 2023;14(1):4296. https:\/\/doi.org\/10.1038\/s41467-023-39864-w.","journal-title":"Nat Commun"},{"issue":"11","key":"2442_CR3","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","volume":"128","author":"ZL Teo","year":"2021","unstructured":"Teo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology. 2021;128(11):1580\u201391. https:\/\/doi.org\/10.1016\/j.ophtha.2021.04.027.","journal-title":"Ophthalmology"},{"issue":"4","key":"2442_CR4","doi-asserted-by":"publisher","first-page":"315","DOI":"10.3760\/cma.j.cn115791-20210221-00095","volume":"13","author":"Chinese Diabetes Society","year":"2021","unstructured":"Chinese Diabetes Society. Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition). Chin J Diabetes Mellit. 2021;13(4):315\u2013409. https:\/\/doi.org\/10.3760\/cma.j.cn115791-20210221-00095.","journal-title":"Chin J Diabetes Mellit"},{"issue":"2","key":"2442_CR5","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3760\/cma.j.cn511434-20230110-00018","volume":"39","author":"Fundus Disease Group of Ophthalmological Society of Chinese Medical Association, Fundus Disease Group of Ophthalmologist Branch of Chinese Medical Doctor Association","year":"2023","unstructured":"Fundus Disease Group of Ophthalmological Society of Chinese Medical Association, Fundus Disease Group of Ophthalmologist Branch of Chinese Medical Doctor Association. Evidence-based guidelines for diagnosis and treatment of diabetic retinopathy in China (2022). Chin J Ocul Fundus Dis. 2023;39(2):99\u2013124. https:\/\/doi.org\/10.3760\/cma.j.cn511434-20230110-00018.","journal-title":"Chin J Ocul Fundus Dis."},{"key":"2442_CR6","first-page":"949","volume-title":"2022 IEEE 18th international conference on automation science and engineering (CASE), Mexico","author":"SH Chen","year":"2022","unstructured":"Chen SH, Wang ZK, Yao B, Liu TM. Prediction of diabetic retinopathy using longitudinal electronic health records. In: Chen SH, editor. 2022 IEEE 18th international conference on automation science and engineering (CASE), Mexico. Mexico: IEEE; 2022. p. 949\u201354."},{"issue":"3","key":"2442_CR7","doi-asserted-by":"publisher","first-page":"412","DOI":"10.2337\/dc16-2641","volume":"40","author":"SD Solomon","year":"2017","unstructured":"Solomon SD, Chew E, Duh EJ, Sobrin L, Sun JK, VanderBeek BL, et al. Diabetic retinopathy: a position statement by the American Diabetes Association. Diabetes Care. 2017;40(3):412\u20138. https:\/\/doi.org\/10.2337\/dc16-2641.","journal-title":"Diabetes Care"},{"issue":"1","key":"2442_CR8","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/1056-8727(94)00005-9","volume":"9","author":"J Wylie-Rosett","year":"1995","unstructured":"Wylie-Rosett J, Basch C, Walker EA, Zybert P, Shamoon H, Engel S, et al. Ophthalmic referral rates for patients with diabetes in primary-care clinics located in disadvantaged urban communities. J Diabetes Complic. 1995;9(1):49\u201354. https:\/\/doi.org\/10.1016\/1056-8727(94)00005-9.","journal-title":"J Diabetes Complic"},{"key":"2442_CR9","doi-asserted-by":"publisher","first-page":"ooab066","DOI":"10.1093\/jamiaopen\/ooab066","volume":"4","author":"OI Ogunyemi","year":"2021","unstructured":"Ogunyemi OI, Gandhi M, Lee M, Teklehaimanot S, Daskivich LP, Hindman D, et al. Detecting diabetic retinopathy through machine learning on electronic health record data from an urban, safety net healthcare system. JAMA Open. 2021;4:ooab066. https:\/\/doi.org\/10.1093\/jamiaopen\/ooab066.","journal-title":"JAMA Open"},{"issue":"Suppl 9","key":"2442_CR10","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1186\/s12859-018-2277-0","volume":"19","author":"HY Tsao","year":"2018","unstructured":"Tsao HY, Chan PY, Su EC. Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms. BMC Bioinform. 2018;19(Suppl 9):283. https:\/\/doi.org\/10.1186\/s12859-018-2277-0.","journal-title":"BMC Bioinform"},{"key":"2442_CR11","doi-asserted-by":"publisher","first-page":"1136653","DOI":"10.3389\/fmed.2023.1136653","volume":"10","author":"H Pan","year":"2023","unstructured":"Pan H, Sun J, Luo X, Ai H, Zeng J, Shi R, et al. A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management. Front Med (Lausanne). 2023;10:1136653. https:\/\/doi.org\/10.3389\/fmed.2023.1136653.","journal-title":"Front Med (Lausanne)"},{"key":"2442_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.773881","volume":"8","author":"C Yang","year":"2021","unstructured":"Yang C, Liu Q, Guo H, Zhang M, Zhang L, Zhang G, et al. Usefulness of machine learning for identification of referable diabetic retinopathy in a large-scale population-based study. Front Med (Lausanne). 2021;8: 773881. https:\/\/doi.org\/10.3389\/fmed.2021.773881.","journal-title":"Front Med (Lausanne)"},{"issue":"22","key":"2442_CR13","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"DSW Ting","year":"2017","unstructured":"Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318(22):2211\u201323. https:\/\/doi.org\/10.1001\/jama.2017.18152.","journal-title":"JAMA"},{"key":"2442_CR14","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.dss.2014.01.005","volume":"59","author":"A Bourouis","year":"2014","unstructured":"Bourouis A, Feham M, Hossain MA, Zhang L. An intelligent mobile based decision support system for retinal disease diagnosis. Decis Support Syst. 2014;59:341\u201350. https:\/\/doi.org\/10.1016\/j.dss.2014.01.005.","journal-title":"Decis Support Syst"},{"key":"2442_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2022.100343","volume":"26","author":"A Homayouni","year":"2022","unstructured":"Homayouni A, Liu T, Thieu T. Diabetic retinopathy prediction using progressive ablation feature selection: a comprehensive classifier evaluation. Smart Health. 2022;26: 100343. https:\/\/doi.org\/10.1016\/j.smhl.2022.100343.","journal-title":"Smart Health"},{"issue":"11","key":"2442_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdiacomp.2022.108313","volume":"36","author":"H Yang","year":"2022","unstructured":"Yang H, Xia M, Liu Z, Xing Y, Zhao W, Li Y, et al. Nomogram for prediction of diabetic retinopathy in patients with type 2 diabetes mellitus: a retrospective study. J Diabetes Complic. 2022;36(11): 108313. https:\/\/doi.org\/10.1016\/j.jdiacomp.2022.108313.","journal-title":"J Diabetes Complic"},{"key":"2442_CR17","doi-asserted-by":"publisher","first-page":"8114049","DOI":"10.1155\/2022\/8114049","volume":"2022","author":"G Cardozo","year":"2022","unstructured":"Cardozo G, Pintarelli GB, Andreis GR, Lopes ACW, Marques JLB. Use of machine learning and routine laboratory tests for diabetes mellitus screening. Biomed Res Int. 2022;2022:8114049. https:\/\/doi.org\/10.1155\/2022\/8114049.","journal-title":"Biomed Res Int"},{"key":"2442_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.876559","volume":"13","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Li X, Li S, Dong M, Yu H, Zhang M, et al. Using machine learning techniques to develop risk prediction models for the risk of incident diabetic retinopathy among patients with type 2 diabetes mellitus: a cohort study. Front Endocrinol (Lausanne). 2022;13: 876559. https:\/\/doi.org\/10.3389\/fendo.2022.876559.","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"9403","key":"2442_CR19","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/S0140-6736(03)15268-3","volume":"363","author":"WHO Expert Consultation","year":"2004","unstructured":"WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157\u201363. https:\/\/doi.org\/10.1016\/S0140-6736(03)15268-3.","journal-title":"Lancet"},{"issue":"9","key":"2442_CR20","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1016\/S0161-6420(03)00475-5","volume":"110","author":"CP Wilkinson","year":"2003","unstructured":"Wilkinson CP, Ferris FL 3rd, Klein RE, Lee PP, Agardh CD, Davis M, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110(9):1677\u201382. https:\/\/doi.org\/10.1016\/S0161-6420(03)00475-5.","journal-title":"Ophthalmology"},{"issue":"7","key":"2442_CR21","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1002\/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S","volume":"15","author":"KG Alberti","year":"1998","unstructured":"Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539\u201353. https:\/\/doi.org\/10.1002\/(SICI)1096-9136(199807)15:7%3c539::AID-DIA668%3e3.0.CO;2-S.","journal-title":"Diabet Med."},{"issue":"6","key":"2442_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/dmrr.3158","volume":"35","author":"W Jia","year":"2019","unstructured":"Jia W, Weng J, Zhu D, Ji L, Lu J, Zhou Z, et al. Standards of medical care for type 2 diabetes in China 2019. Diabetes Metab Res Rev. 2019;35(6): e3158. https:\/\/doi.org\/10.1002\/dmrr.3158.","journal-title":"Diabetes Metab Res Rev"},{"issue":"12","key":"2442_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsx.2023.102919","volume":"17","author":"MM Islam","year":"2023","unstructured":"Islam MM, Rahman MJ, Rabby MS, Alam MJ, Pollob SMAI, Ahmed NAMF, et al. Predicting the risk of diabetic retinopathy using explainable machine learning algorithms. Diabetes Metab Syndr. 2023;17(12): 102919. https:\/\/doi.org\/10.1016\/j.dsx.2023.102919.","journal-title":"Diabetes Metab Syndr"},{"issue":"1","key":"2442_CR24","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1186\/s12911-023-02343-9","volume":"23","author":"S Shi","year":"2023","unstructured":"Shi S, Gao L, Zhang J, Zhang B, Xiao J, Xu W, et al. The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients. BMC Med Inform Decis Mak. 2023;23(1):241. https:\/\/doi.org\/10.1186\/s12911-023-02343-9.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"12","key":"2442_CR25","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1515\/cclm-2022-1015","volume":"60","author":"A Padoan","year":"2022","unstructured":"Padoan A, Plebani M. Artificial intelligence: is it the right time for clinical laboratories? Clin Chem Lab Med. 2022;60(12):1859\u201361. https:\/\/doi.org\/10.1515\/cclm-2022-1015.","journal-title":"Clin Chem Lab Med"},{"key":"2442_CR26","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","volume-title":"KDD '16: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","author":"T Chen","year":"2016","unstructured":"Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Chen T, editor. KDD \u201916: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. New York: Association for Computing Machinery; 2016. p. 785\u201394. https:\/\/doi.org\/10.1145\/2939672.2939785."},{"key":"2442_CR27","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.neunet.2022.02.007","volume":"149","author":"PY Hao","year":"2022","unstructured":"Hao PY, Chiang JH, Chen YD. Possibilistic classification by support vector networks. Neural Netw. 2022;149:40\u201356. https:\/\/doi.org\/10.1016\/j.neunet.2022.02.007.","journal-title":"Neural Netw"},{"issue":"1","key":"2442_CR28","doi-asserted-by":"publisher","first-page":"15889","DOI":"10.1038\/s41598-022-20149-z","volume":"12","author":"H Seto","year":"2022","unstructured":"Seto H, Oyama A, Kitora S, Toki H, Yamamoto R, Kotoku J, et al. Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Sci Rep. 2022;12(1):15889. https:\/\/doi.org\/10.1038\/s41598-022-20149-z.","journal-title":"Sci Rep"},{"issue":"6","key":"2442_CR29","doi-asserted-by":"publisher","first-page":"661","DOI":"10.3390\/pr8060661","volume":"8","author":"NB Shaik","year":"2020","unstructured":"Shaik NB, Pedapati SR, Taqvi SA, Othman AR, Dzubir FA. A feed-forward back propagation neural network approach to predict the life condition of crude oil pipeline. Processes. 2020;8(6):661. https:\/\/doi.org\/10.3390\/pr8060661.","journal-title":"Processes"},{"issue":"12","key":"2442_CR30","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1016\/j.pmrj.2014.10.006","volume":"6","author":"KL Sainani","year":"2014","unstructured":"Sainani KL. Logistic regression. PM R. 2014;6(12):1157\u201362. https:\/\/doi.org\/10.1016\/j.pmrj.2014.10.006.","journal-title":"PM R"},{"key":"2442_CR31","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1109\/ic2ie53219.2021.9649207","volume-title":"2021 4th International conference of computer and informatics engineering (IC2IE)","author":"D Kartini","year":"2021","unstructured":"Kartini D, Nugrahadi DT, Farmadi A. Hyperparameter tuning using GridsearchCV on the comparison of the activation function of the ELM method to the classification of pneumonia in toddlers. In: Kartini D, editor. 2021 4th International conference of computer and informatics engineering (IC2IE). Depok: IEEE; 2021. p. 390\u20135. https:\/\/doi.org\/10.1109\/ic2ie53219.2021.9649207."},{"key":"2442_CR32","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/REEDCON57544.2023.10151392","volume-title":"2023 international conference on recent advances in electrical, electronics and digital healthcare technologies (REEDCON)","author":"KS Gill","year":"2023","unstructured":"Gill KS, Gupta R. Chronic kidney disease detection using GridSearchCV cross validation method. In: Gill KS, editor. 2023 international conference on recent advances in electrical, electronics and digital healthcare technologies (REEDCON). New Delhi: IEEE; 2023. p. 318\u201322. https:\/\/doi.org\/10.1109\/REEDCON57544.2023.10151392."},{"key":"2442_CR33","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.2291","volume":"10","author":"S Singamsetty","year":"2024","unstructured":"Singamsetty S, Ghanta S, Biswas S, Pradhan A. Enhancing machine learning-based forecasting of chronic renal disease with explainable AI. PeerJ Comput Sci. 2024;10: e2291. https:\/\/doi.org\/10.7717\/peerj-cs.2291.","journal-title":"PeerJ Comput Sci"},{"key":"2442_CR34","first-page":"983","volume":"2015","author":"O Ogunyemi","year":"2015","unstructured":"Ogunyemi O, Kermah D. Machine learning approaches for detecting diabetic retinopathy from clinical and public health records. AMIA Annu Symp Proc. 2015;2015:983\u201390.","journal-title":"AMIA Annu Symp Proc"},{"key":"2442_CR35","doi-asserted-by":"publisher","first-page":"3990035","DOI":"10.1155\/2023\/3990035","volume":"2023","author":"GW Zong","year":"2023","unstructured":"Zong GW, Wang WY, Zheng J, Zhang W, Luo WM, Fang ZZ, et al. A metabolism-based interpretable machine learning prediction model for diabetic retinopathy risk: a cross-sectional study in Chinese patients with type 2 diabetes. J Diabetes Res. 2023;2023:3990035. https:\/\/doi.org\/10.1155\/2023\/3990035.","journal-title":"J Diabetes Res"},{"issue":"11","key":"2442_CR36","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-050989","volume":"11","author":"W Li","year":"2021","unstructured":"Li W, Song Y, Chen K, Ying J, Zheng Z, Qiao S, et al. Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China. BMJ Open. 2021;11(11): e050989. https:\/\/doi.org\/10.1136\/bmjopen-2021-050989.","journal-title":"BMJ Open"},{"key":"2442_CR37","volume-title":"Vision 2020: Eye health in China, Chinese english comparison","author":"Nation Committee for the Prevention of Blindness","year":"2023","unstructured":"Nation Committee for the Prevention of Blindness. Vision 2020: Eye health in China, Chinese english comparison. Beijing: People\u2019s Medical Publishing House; 2023."},{"issue":"48","key":"2442_CR38","doi-asserted-by":"publisher","first-page":"3831","DOI":"10.3760\/cma.j.cn112137-20200825-02468","volume":"100","author":"Y Mayinuer","year":"2020","unstructured":"Mayinuer Y, Wang NL. Vision 2020: the progress of blindness prevention and eye health in China. Zhonghua Yi Xue Za Zhi. 2020;100(48):3831\u20134. https:\/\/doi.org\/10.3760\/cma.j.cn112137-20200825-02468.","journal-title":"Zhonghua Yi Xue Za Zhi."},{"issue":"4","key":"2442_CR39","doi-asserted-by":"publisher","first-page":"536","DOI":"10.4103\/2141-9248.122087","volume":"3","author":"R Karoli","year":"2013","unstructured":"Karoli R, Fatima J, Shukla V, Garg P, Ali A. Predictors of diabetic retinopathy in patients with type 2 diabetes who have normoalbuminuria. Ann Med Health Sci Res. 2013;3(4):536\u201340. https:\/\/doi.org\/10.4103\/2141-9248.122087.","journal-title":"Ann Med Health Sci Res"},{"issue":"7","key":"2442_CR40","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.1038\/eye.2009.108","volume":"23","author":"TM Curtis","year":"2009","unstructured":"Curtis TM, Gardiner TA, Stitt AW. Microvascular lesions of diabetic retinopathy: clues towards understanding pathogenesis? Eye (Lond). 2009;23(7):1496\u2013508. https:\/\/doi.org\/10.1038\/eye.2009.108.","journal-title":"Eye (Lond)"},{"issue":"1","key":"2442_CR41","doi-asserted-by":"publisher","DOI":"10.7189\/jogh.08.010803","volume":"8","author":"P Song","year":"2018","unstructured":"Song P, Yu J, Chan KY, Theodoratou E, Rudan I. Prevalence, risk factors and burden of diabetic retinopathy in China: a systematic review and meta-analysis. J Glob Health. 2018;8(1): 010803. https:\/\/doi.org\/10.7189\/jogh.08.010803.","journal-title":"J Glob Health"},{"issue":"3","key":"2442_CR42","doi-asserted-by":"publisher","first-page":"556","DOI":"10.2337\/dc11-1909","volume":"35","author":"JW Yau","year":"2012","unstructured":"Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556\u201364. https:\/\/doi.org\/10.2337\/dc11-1909.","journal-title":"Diabetes Care"},{"issue":"1","key":"2442_CR43","doi-asserted-by":"publisher","DOI":"10.1136\/bmjdrc-2013-000005","volume":"2","author":"R Raman","year":"2014","unstructured":"Raman R, Ganesan S, Pal SS, Kulothungan V. Sharma T (2014) Prevalence and risk factors for diabetic retinopathy in rural India. Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetic Study III (SN-DREAMS III), report no 2. BMJ Open Diabetes Res Care. 2014;2(1): e000005. https:\/\/doi.org\/10.1136\/bmjdrc-2013-000005.","journal-title":"BMJ Open Diabetes Res Care."},{"issue":"Suppl 1","key":"2442_CR44","doi-asserted-by":"publisher","first-page":"S17","DOI":"10.2337\/dc22-S002","volume":"45","author":"American Diabetes Association Professional Practice Committee","year":"2022","unstructured":"American Diabetes Association Professional Practice Committee. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2022. Diabetes Care. 2022;45(Suppl 1):S17\u201338. https:\/\/doi.org\/10.2337\/dc22-S002.","journal-title":"Diabetes Care."},{"issue":"10","key":"2442_CR45","first-page":"6083","volume":"15","author":"Y Liu","year":"2023","unstructured":"Liu Y, Zhou Z, Wang Z, Yang H, Zhang F, Wang Q. Construction and clinical validation of a nomogram-based predictive model for diabetic retinopathy in type 2 diabetes. Am J Transl Res. 2023;15(10):6083\u201394.","journal-title":"Am J Transl Res"},{"key":"2442_CR46","doi-asserted-by":"publisher","first-page":"477","DOI":"10.2147\/DMSO.S39093","volume":"6","author":"B Kowall","year":"2013","unstructured":"Kowall B, Rathmann W. HbA1c for diagnosis of type 2 diabetes. Is there an optimal cut point to assess high risk of diabetes complications, and how well does the 6.5% cutoff perform? Diabetes Metab Syndr Obes. 2013;6:477\u201391. https:\/\/doi.org\/10.2147\/DMSO.S39093.","journal-title":"Diabetes Metab Syndr Obes."}],"container-title":["European Journal of Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40001-025-02442-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40001-025-02442-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40001-025-02442-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T15:49:02Z","timestamp":1742312942000},"score":1,"resource":{"primary":{"URL":"https:\/\/eurjmedres.biomedcentral.com\/articles\/10.1186\/s40001-025-02442-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2442"],"URL":"https:\/\/doi.org\/10.1186\/s40001-025-02442-5","relation":{},"ISSN":["2047-783X"],"issn-type":[{"value":"2047-783X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,18]]},"assertion":[{"value":"10 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the ethics committee of Beijing Tongren Hospital, Capital Medical University (No. TREC2024-KY040).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Every human participant agreed to participate in the study and signed an informed consent form.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human ethics and consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"183"}}