Bibliometric analysis of diabetic retinopathy therapy based on Web of Science database_Chinese Journal of Ocular Fundus Diseases

Authors：

Man Shulei , Zhang Yifan , Xu Hanyue , Chen Qing ,  Zhang Ming

Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu 610041, China;

Corresponding author：

Zhang Ming, Email: mingzhangscu@163.com

Keywords：

Diabetic retinopathy; Bibliometric analysis; Visualization; Co-citation network

DOI：

10.3760/cma.j.cn511434-20220516-00303

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To analyze the trend, hotspots and frontiers of diabetic retinopathy (DR) therapy by bibliometric method. Methods Data were taken from the Web of Science website of Science Citation Index. Articles from 2017 to 2021, which were related to the therapy of diabetic retinopathy (DR), were included. The bibliometric analysis softwares, VOSviewer and CiteSpace were used to generate and analyze visual representations of the complex data input, including high-frequency keywords, keywords with the strongest citation bursts and co-occurrence networks of keywords. Results A total of 3,845 articles were included. The amounts of papers published from 2017 to 2021 is 633, 651, 708, 893, and 960 respectively, increasing over years. Chinese scholars published the most articles, followed by the United States. The number of articles funded by the National Natural Science Foundation of China ranks third. There were 47 high-frequency keywords clustered into DR treatment, pathogenesis of DR, diagnosis of DR, Oxidative stress, diabetic macular edema (DME), type 2 diabetes, optical coherence tomography and deep learning. Those keywords were research hotspots and new keywords were constantly emerging. Among the top 11 burst words, the burst values of “intravitreal bevacizumab”, “vascular endothelial growth factor (VEGF)”, “choroidal neovascularization”, “inhibition”, and “receptors” were all over 10. Highly cited references showed a significant clustering tendency, which were treatment of DME, review of DR, clinical research of anti-VEGF drug therapy. Conclusions The amount of paper related to DR therapy is on the rise; the specific treatment methods for the pathogenesis of DR are constantly research hotspots. In addition, formulating treatment strategies to reduce macular edema and other complications of diabetes, applying optical coherence tomography, deep learning and other technologies to improve the efficiency of DR diagnosis and treatment, improve targeted drug delivery systems, and finding new target points were research frontiers.

Citation： Man Shulei, Zhang Yifan, Xu Hanyue, Chen Qing, Zhang Ming. Bibliometric analysis of diabetic retinopathy therapy based on Web of Science database. Chinese Journal of Ocular Fundus Diseases, 2023, 39(3): 238-248. doi: 10.3760/cma.j.cn511434-20220516-00303 Copy

1.	Cheung N, Mitchell P, Wong TY. Diabetic retinopathy[J]. Lancet, 2010, 376(9735): 124-136. DOI: 10.1016/s0140-6736(09)62124-3.
2.	郑志. 糖尿病视网膜病变临床防治: 进展、挑战与展望[J]. 中华眼底病杂志, 2012, 28(3): 209-214. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.Zheng Z. Prevention and treatment of diabetic retinopathy: progress, challenges and future prospects[J]. Chin J Ocul Fundus Dis, 2012, 28(3): 209-214. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.
3.	Stitt AW, Curtis TM, Chen M, et al. The progress in understanding and treatment of diabetic retinopathy[J]. Prog Retin Eye Res, 2016, 51: 156-186. DOI: 10.1016/j.preteyeres.2015.08.001.
4.	Heng LZ, Comyn O, Peto T, et al. Diabetic retinopathy: pathogenesis, clinical grading, management and future developments[J]. Diabet Med, 2013, 30(6): 640-650. DOI: 10.1111/dme.12089.
5.	Hou J. Exploration into the evolution and historical roots of citation analysis by referenced publication year spectroscopy[J]. Scientometrics, 2017, 110(3): 1437-1452. DOI: 10.1007/s11192-016-2206-9.
6.	胡臻, 龙兴跃. 基于普赖斯定律与综合指数法的核心作者和扩展核心作者分析——以《四川图书馆学报》为例[J]. 四川图书馆学报, 2016(3): 74-76. DOI: 10.3969/j.issn.1003-7136.2016.03.020.Hu Z, Long XY. Analysis of the core authors and the extended core authors based on price law and comprehensive index method——take the journal of the library science society of Sichuan as an example[J]. J Lib Sci Soc Sichuan, 2016(3): 74-76. DOI: 10.3969/j.issn.1003-7136.2016.03.020.
7.	Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping[J]. Scientometrics, 2010, 84(2): 523-538. DOI: 10.1007/s11192-009-0146-3.
8.	Small H. Co-citation in the scientific literature: a new measure of the relationship between two documents[J]. Journal of the American Society for Information Science, 1973, 24(4): 265-269. DOI: 10.1002/asi.4630240406.
9.	张良友. 期刊共被引分析的若干问题及其实证研究[D]. 广州: 中山大学, 2010.Zhang LY. The empirical research on problems of journal co-citation analysis[D]. Gudngzhou: Sun Yat-sen University, 2010.
10.	Mane KK, Börner K. Mapping topics and topic bursts in PNAS[J]. Proc Natl Acad Sci U S A, 2004, 101(Suppl 1): S5287-5290. DOI: 10.1073/pnas.0307626100.
11.	李杰, 陈超美. CiteSpace 的诞生[M]//李杰, 陈超美. Citespace科技文本挖掘及可视化(第2版). 北京: 首都经济贸易大学出版社, 2017: 5.Li J, Chen CM. The birth of CiteSpace[M]//Li J, Chen CM. CiteSpace: text mining and visualization in scientific literature (Version 2). Beijing: Capital Economic And Trade University Press, 2017: 5.
12.	肖云扬, 王红. 基于文献计量的中美农业经济发展分析[J]. 科学观察, 2020, 15(5): 52-62. DOI: 10.15978/j.cnki.1673-5668.202005008.Xiao YY, Wang H. Analysis on development of China-U. S. A gricultural economy based on bibliometrics[J]. Sci Focus, 2020, 15(5): 52-62. DOI: 10.15978/j.cnki.1673-5668.202005008.
13.	Synnestvedt MB, Chen C, Holmes JH. CiteSpace Ⅱ: visualization and knowledge discovery in bibliographic databases[J]. AMIA Annu Symp Proc, 2005, 2005: 724-728.
14.	Perc M. The Matthew effect in empirical data[J/OL]. J R Soc Interface, 2014, 11(98): 20140378[2014-09-06]. https://europepmc.org/article/MED/24990288. DOI: 10.1098/rsif.2014.0378.
15.	Ilyasova NY, Shirokanev AS, Demin NS, et al. Information technology for decision-making support for personalized parameter selection in retinal laser treatment and photocoagulation outcome prognostication[J]. Optical Memory and Neural Networks, 2020, 29(4): 358-367. DOI: 10.3103/S1060992X20040098.
16.	Muc R, Saracen A, Grabska-Liberek I. Associations of diabetic retinopathy with retinal neurodegeneration on the background of diabetes mellitus. Overview of recent medical studies with an assessment of the impact on healthcare systems[J]. Open Med (Wars), 2018, 13: 130-136. DOI: 10.1515/med-2018-0008.
17.	Peng JJ, Xiong SQ, Ding LX, et al. Diabetic retinopathy: focus on NADPH oxidase and its potential as therapeutic target[J]. Eur J Pharmacol, 2019, 853: 381-387. DOI: 10.1016/j.ejphar.2019.04.038.
18.	Cavan D, Makaroff L, da Rocha Fernandes J, et al. The Diabetic Retinopathy Barometer Study: global perspectives on access to and experiences of diabetic retinopathy screening and treatment[J]. Diabetes Res Clin Pract, 2017, 129: 16-24. DOI: 10.1016/j.diabres.2017.03.023.
19.	Relhan N, Flynn HW Jr. The Early Treatment Diabetic Retinopathy Study historical review and relevance to today's management of diabetic macular edema[J]. Curr Opin Ophthalmol, 2017, 28(3): 205-212. DOI: 10.1097/icu.0000000000000362.
20.	Sinclair SH, Schwartz SS. Diabetic retinopathy-an underdiagnosed and undertreated inflammatory, neuro-vascular complication of diabetes[J]. Front Endocrinol (Lausanne), 2019, 10: 843. DOI: 10.3389/fendo.2019.00843.
21.	Choi W, Waheed NK, Moult EM, et al. Ultrahigh speed swept source optical coherence tomography angiography of retinal and choriocapillaris alterations in diabetic patients with and without retinopathy[J]. Retina, 2017, 37(1): 11-21. DOI: 10.1097/iae.0000000000001250.
22.	Regatieri CV, Branchini L, Carmody J, et al. Choroidal thickness in patients with diabetic retinopathy analyzed by spectral-domain optical coherence tomography[J]. Retina, 2012, 32(3): 563-568. DOI: 10.1097/IAE.0b013e31822f5678.
23.	Oh K, Kang HM, Leem D, et al. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images[J]. Sci Rep, 2021, 11(1): 1897. DOI: 10.1038/s41598-021-81539-3.
24.	Zhao L, Ren H, Zhang J, et al. Diabetic retinopathy, classified using the lesion-aware deep learning system, predicts diabetic end-stage renal disease in chinese patients[J]. Endocr Pract, 2020, 26(4): 429-443. DOI: 10.4158/ep-2019-0512.
25.	Dai L, Wu L, Li H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum[J]. Nat Commun, 2021, 12(1): 3242. DOI: 10.1038/s41467-021-23458-5.
26.	Keum DH, Kim SK, Koo J, et al. Wireless smart contact lens for diabetic diagnosis and therapy[J/OL]. Sci Adv, 2020, 6(17): eaba3252[2020-04-24]. https://europepmc.org/article/MED/32426469. DOI: 10.1126/sciadv.aba3252.
27.	Kutlutürk Karagöz I, Allahverdiyev A, Bağırova M, et al. Current approaches in treatment of diabetic retinopathy and future perspectives[J]. J Ocul Pharmacol Ther, 2020, 36(7): 487-496. DOI: 10.1089/jop.2019.0137.
28.	Ebrahimi M, Balibegloo M, Rezaei N. Monoclonal antibodies in diabetic retinopathy[J]. Expert Rev Clin Immunol, 2022, 18(2): 163-178. DOI: 10.1080/1744666x.2022.2037420.
29.	Jiang Q, Liu C, Li CP, et al. Circular RNA-ZNF532 regulates diabetes-induced retinal pericyte degeneration and vascular dysfunction[J]. J Clin Invest, 2020, 130(7): 3833-3847. DOI: 10.1172/jci123353.
30.	Tomita Y, Cagnone G, Fu Z, et al. Vitreous metabolomics profiling of proliferative diabetic retinopathy[J]. Diabetologia, 2021, 64(1): 70-82. DOI: 10.1007/s00125-020-05309-y.

1. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy[J]. Lancet, 2010, 376(9735): 124-136. DOI: 10.1016/s0140-6736(09)62124-3.
2. 郑志. 糖尿病视网膜病变临床防治: 进展、挑战与展望[J]. 中华眼底病杂志, 2012, 28(3): 209-214. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.Zheng Z. Prevention and treatment of diabetic retinopathy: progress, challenges and future prospects[J]. Chin J Ocul Fundus Dis, 2012, 28(3): 209-214. DOI: 10.3760/cma.j.issn.1005-1015.2012.03.001.
3. Stitt AW, Curtis TM, Chen M, et al. The progress in understanding and treatment of diabetic retinopathy[J]. Prog Retin Eye Res, 2016, 51: 156-186. DOI: 10.1016/j.preteyeres.2015.08.001.
4. Heng LZ, Comyn O, Peto T, et al. Diabetic retinopathy: pathogenesis, clinical grading, management and future developments[J]. Diabet Med, 2013, 30(6): 640-650. DOI: 10.1111/dme.12089.
5. Hou J. Exploration into the evolution and historical roots of citation analysis by referenced publication year spectroscopy[J]. Scientometrics, 2017, 110(3): 1437-1452. DOI: 10.1007/s11192-016-2206-9.
6. 胡臻, 龙兴跃. 基于普赖斯定律与综合指数法的核心作者和扩展核心作者分析——以《四川图书馆学报》为例[J]. 四川图书馆学报, 2016(3): 74-76. DOI: 10.3969/j.issn.1003-7136.2016.03.020.Hu Z, Long XY. Analysis of the core authors and the extended core authors based on price law and comprehensive index method——take the journal of the library science society of Sichuan as an example[J]. J Lib Sci Soc Sichuan, 2016(3): 74-76. DOI: 10.3969/j.issn.1003-7136.2016.03.020.
7. Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping[J]. Scientometrics, 2010, 84(2): 523-538. DOI: 10.1007/s11192-009-0146-3.
8. Small H. Co-citation in the scientific literature: a new measure of the relationship between two documents[J]. Journal of the American Society for Information Science, 1973, 24(4): 265-269. DOI: 10.1002/asi.4630240406.
9. 张良友. 期刊共被引分析的若干问题及其实证研究[D]. 广州: 中山大学, 2010.Zhang LY. The empirical research on problems of journal co-citation analysis[D]. Gudngzhou: Sun Yat-sen University, 2010.
10. Mane KK, Börner K. Mapping topics and topic bursts in PNAS[J]. Proc Natl Acad Sci U S A, 2004, 101(Suppl 1): S5287-5290. DOI: 10.1073/pnas.0307626100.
11. 李杰, 陈超美. CiteSpace 的诞生[M]//李杰, 陈超美. Citespace科技文本挖掘及可视化(第2版). 北京: 首都经济贸易大学出版社, 2017: 5.Li J, Chen CM. The birth of CiteSpace[M]//Li J, Chen CM. CiteSpace: text mining and visualization in scientific literature (Version 2). Beijing: Capital Economic And Trade University Press, 2017: 5.
12. 肖云扬, 王红. 基于文献计量的中美农业经济发展分析[J]. 科学观察, 2020, 15(5): 52-62. DOI: 10.15978/j.cnki.1673-5668.202005008.Xiao YY, Wang H. Analysis on development of China-U. S. A gricultural economy based on bibliometrics[J]. Sci Focus, 2020, 15(5): 52-62. DOI: 10.15978/j.cnki.1673-5668.202005008.
13. Synnestvedt MB, Chen C, Holmes JH. CiteSpace Ⅱ: visualization and knowledge discovery in bibliographic databases[J]. AMIA Annu Symp Proc, 2005, 2005: 724-728.
14. Perc M. The Matthew effect in empirical data[J/OL]. J R Soc Interface, 2014, 11(98): 20140378[2014-09-06]. https://europepmc.org/article/MED/24990288. DOI: 10.1098/rsif.2014.0378.
15. Ilyasova NY, Shirokanev AS, Demin NS, et al. Information technology for decision-making support for personalized parameter selection in retinal laser treatment and photocoagulation outcome prognostication[J]. Optical Memory and Neural Networks, 2020, 29(4): 358-367. DOI: 10.3103/S1060992X20040098.
16. Muc R, Saracen A, Grabska-Liberek I. Associations of diabetic retinopathy with retinal neurodegeneration on the background of diabetes mellitus. Overview of recent medical studies with an assessment of the impact on healthcare systems[J]. Open Med (Wars), 2018, 13: 130-136. DOI: 10.1515/med-2018-0008.
17. Peng JJ, Xiong SQ, Ding LX, et al. Diabetic retinopathy: focus on NADPH oxidase and its potential as therapeutic target[J]. Eur J Pharmacol, 2019, 853: 381-387. DOI: 10.1016/j.ejphar.2019.04.038.
18. Cavan D, Makaroff L, da Rocha Fernandes J, et al. The Diabetic Retinopathy Barometer Study: global perspectives on access to and experiences of diabetic retinopathy screening and treatment[J]. Diabetes Res Clin Pract, 2017, 129: 16-24. DOI: 10.1016/j.diabres.2017.03.023.
19. Relhan N, Flynn HW Jr. The Early Treatment Diabetic Retinopathy Study historical review and relevance to today's management of diabetic macular edema[J]. Curr Opin Ophthalmol, 2017, 28(3): 205-212. DOI: 10.1097/icu.0000000000000362.
20. Sinclair SH, Schwartz SS. Diabetic retinopathy-an underdiagnosed and undertreated inflammatory, neuro-vascular complication of diabetes[J]. Front Endocrinol (Lausanne), 2019, 10: 843. DOI: 10.3389/fendo.2019.00843.
21. Choi W, Waheed NK, Moult EM, et al. Ultrahigh speed swept source optical coherence tomography angiography of retinal and choriocapillaris alterations in diabetic patients with and without retinopathy[J]. Retina, 2017, 37(1): 11-21. DOI: 10.1097/iae.0000000000001250.
22. Regatieri CV, Branchini L, Carmody J, et al. Choroidal thickness in patients with diabetic retinopathy analyzed by spectral-domain optical coherence tomography[J]. Retina, 2012, 32(3): 563-568. DOI: 10.1097/IAE.0b013e31822f5678.
23. Oh K, Kang HM, Leem D, et al. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images[J]. Sci Rep, 2021, 11(1): 1897. DOI: 10.1038/s41598-021-81539-3.
24. Zhao L, Ren H, Zhang J, et al. Diabetic retinopathy, classified using the lesion-aware deep learning system, predicts diabetic end-stage renal disease in chinese patients[J]. Endocr Pract, 2020, 26(4): 429-443. DOI: 10.4158/ep-2019-0512.
25. Dai L, Wu L, Li H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum[J]. Nat Commun, 2021, 12(1): 3242. DOI: 10.1038/s41467-021-23458-5.
26. Keum DH, Kim SK, Koo J, et al. Wireless smart contact lens for diabetic diagnosis and therapy[J/OL]. Sci Adv, 2020, 6(17): eaba3252[2020-04-24]. https://europepmc.org/article/MED/32426469. DOI: 10.1126/sciadv.aba3252.
27. Kutlutürk Karagöz I, Allahverdiyev A, Bağırova M, et al. Current approaches in treatment of diabetic retinopathy and future perspectives[J]. J Ocul Pharmacol Ther, 2020, 36(7): 487-496. DOI: 10.1089/jop.2019.0137.
28. Ebrahimi M, Balibegloo M, Rezaei N. Monoclonal antibodies in diabetic retinopathy[J]. Expert Rev Clin Immunol, 2022, 18(2): 163-178. DOI: 10.1080/1744666x.2022.2037420.
29. Jiang Q, Liu C, Li CP, et al. Circular RNA-ZNF532 regulates diabetes-induced retinal pericyte degeneration and vascular dysfunction[J]. J Clin Invest, 2020, 130(7): 3833-3847. DOI: 10.1172/jci123353.
30. Tomita Y, Cagnone G, Fu Z, et al. Vitreous metabolomics profiling of proliferative diabetic retinopathy[J]. Diabetologia, 2021, 64(1): 70-82. DOI: 10.1007/s00125-020-05309-y.

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Bibliometric analysis of diabetic retinopathy therapy based on Web of Science database

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