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Purohit N, Gupta PC, Buttan S, Chauhan AS, Choudhury RK, Gupta V, Kotwal A, Prinja S. Optimizing Diabetic Retinopathy Screening at Primary Health Centres in India: A Cost-Effectiveness Analysis. PHARMACOECONOMICS - OPEN 2025:10.1007/s41669-025-00572-4. [PMID: 40205319 DOI: 10.1007/s41669-025-00572-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/04/2025] [Indexed: 04/11/2025]
Abstract
BACKGROUND The eye care package under the Ayushman Bharat comprehensive primary healthcare programme includes annual population-based screening for diabetic retinopathy (DR) using non-mydriatic fundus cameras at the primary health centres (PHCs) in India. However, there can be several implementation models for introduction of a systematic screening programme for DR. OBJECTIVES This study aims to assess the cost effectiveness of screening for DR in comparison with the usual-care scenario without a DR screening programme, and to determine cost-effective approaches for implementation of annual population-based screening for DR by optometrists at PHCs in India in terms of screening modalities (face-to-face vs tele-supported screening [screening followed by transfer and remote grading of images by ophthalmologists] vs artificial intelligence [AI]-supported screening) and target population groups for screening. METHODS A mathematical model comprising a decision tree and Markov model was developed. An extensive review of published literature was undertaken to obtain model parameters. Primary data collection was done to derive quality-of-life values. We used a lifetime horizon, abridged societal perspective, and discounted future costs and consequences at an annual rate of 3%. The incremental cost-effectiveness ratio (ICER) was computed for alternative screening strategies. A willingness-to-pay equal to gross domestic product per capita equal to ₹171,498 (US$2182) was used to determine the cost-effective choice. Sensitivity analyses were performed to assess the impact of variation in input parameters on the ICER values. RESULTS All the annual screening strategies were found to have lower ICERs relative to usual care. Among the screening strategies, annual tele-supported screening in the population with diabetes duration ≥5 years was the most cost-effective strategy with an ICER value of ₹57,408 (US$730) per quality-adjusted life year (QALY) gained. At the national level, this strategy is likely to reduce the annual incidence of vision-threatening DR and blindness by 17.3%, and 38.5%, respectively, and would result in higher benefits in Indian states with higher epidemiological transition. Sensitivity analyses showed that if adequate glycaemic control is achieved in 79% of the diabetic population, annual AI-supported screening in individuals with a diabetes' duration of 10 years or more becomes the most cost-effective strategy. CONCLUSION The results of the study suggest the prioritization of an annual tele-supported DR screening programme in India. They also highlight the importance of the adoption of an integrated approach and functional linkage between eye care and diabetes care, to intensify efforts directed at improving glycaemic control, and to facilitate early DR detection and management.
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Affiliation(s)
- Neha Purohit
- Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012, India
| | - Parul Chawla Gupta
- Department of Ophthalmology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | - Akashdeep Singh Chauhan
- Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012, India
- Public Health Foundation of India, Indian Institute of Public Health, New Delhi, India
| | | | - Vishali Gupta
- Department of Ophthalmology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Atul Kotwal
- National Health Systems Resource Centre, New Delhi, India
| | - Shankar Prinja
- Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012, India.
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Hernández-Teixidó C, Barrot de la Puente J, Miravet Jiménez S, Fernández-Camins B, Mauricio D, Romero Aroca P, Vlacho B, Franch-Nadal J. Incidence of Diabetic Retinopathy in Individuals with Type 2 Diabetes: A Study Using Real-World Data. J Clin Med 2024; 13:7083. [PMID: 39685542 DOI: 10.3390/jcm13237083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2024] [Revised: 11/18/2024] [Accepted: 11/20/2024] [Indexed: 12/18/2024] Open
Abstract
Background/Objectives: This study aimed to assess the incidence of diabetic retinopathy (DR) in patients with type 2 diabetes (T2DM) treated in primary-care settings in Catalonia, Spain, and identify key risk factors associated with DR development. Methods: A retrospective cohort study was conducted using the SIDIAP (System for Research and Development in Primary Care) database. Patients aged 30-90 with T2DM who underwent retinal screening between 2010 and 2015 were included. Multivariable Cox regression analysis was used to assess the impact of clinical variables, including HbA1c levels, diabetes duration, and comorbidities, on DR incidence. Results: This study included 146,506 patients, with a mean follow-up time of 6.96 years. During this period, 4.7% of the patients developed DR, resulting in an incidence rate of 6.99 per 1000 person-years. Higher HbA1c levels were strongly associated with an increased DR risk, with patients with HbA1c > 10% having more than four times the risk compared to those with HbA1c levels < 7% (hazard ratio: 4.23; 95% CI: 3.90-4.58). Other significant risk factors for DR included greater diabetes duration, male sex, ex-smoker status, macrovascular disease, and chronic kidney disease. In contrast, obesity appeared to be a protective factor against DR, with an HR of 0.93 (95% CI: 0.89-0.98). Conclusions: In our real-world setting, the incidence rate of DR was 6.99 per 1000 person-years. Poor glycemic control, especially HbA1c > 10%, and prolonged diabetes duration were key risk factors. Effective management of these factors is crucial in preventing DR progression. Regular retinal screenings in primary care play a vital role in early detection and reducing the DR burden for T2DM patients.
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Affiliation(s)
- Carlos Hernández-Teixidó
- Primary Health Care Centre Burguillos del Cerro, Servicio Extremeño de Salud, 06370 Badajoz, Spain
- RedGDPS Foundation, 08204 Sabadell, Spain
- Departament of Medicine, Universitat de Barcelona, 08036 Barcelona, Spain
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
| | - Joan Barrot de la Puente
- RedGDPS Foundation, 08204 Sabadell, Spain
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Primary Health Care Center Dr. Jordi Nadal i Fàbregas (Salt), Gerència d'Atenció Primària, Institut Català de la Salut, 17007 Girona, Spain
| | - Sònia Miravet Jiménez
- RedGDPS Foundation, 08204 Sabadell, Spain
- Primary Health Care Center Martorell, Gerència d'Atenció Primària Baix Llobregat, Institut Català de la Salut, 08007 Barcelona, Spain
| | - Berta Fernández-Camins
- RedGDPS Foundation, 08204 Sabadell, Spain
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Institut de Recerca Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain
| | - Didac Mauricio
- Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau, 08025 Barcelona, Spain
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM, ID CB15/00071), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
- Department of Medicine, University of Vic-Central University of Catalonia, 08500 Vic, Spain
| | - Pedro Romero Aroca
- Ophthalmology Service, University Hospital Sant Joan, 43202 Reus, Spain
- Institut de Investigacio Sanitaria Pere Virgili (IISPV), University of Rovira and Virgili, 43002 Tarragona, Spain
| | - Bogdan Vlacho
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Institut de Recerca Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM, ID CB15/00071), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
| | - Josep Franch-Nadal
- RedGDPS Foundation, 08204 Sabadell, Spain
- Departament of Medicine, Universitat de Barcelona, 08036 Barcelona, Spain
- DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM, ID CB15/00071), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
- Primary Health Care Center Raval Sud, Gerència d'Àmbit d'Atenció Primària Barcelona Ciutat, Institut Català de la Salut, 08007 Barcelona, Spain
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Raghu K, Surya R J, Rani PK, Sharma T, Raman R. Incidence and Progression of Diabetic Retinopathy in Urban India: Sankara Nethralaya Diabetic Retinopathy Epidemiology and Molecular Genetics Study, 15yr Follow up. Ophthalmic Epidemiol 2024:1-9. [PMID: 39531593 DOI: 10.1080/09286586.2024.2419015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 09/26/2024] [Accepted: 10/07/2024] [Indexed: 11/16/2024]
Abstract
Purpose: To evaluate the 15 year incidence and progression of Diabetic Retinopathy (DR) and identify risk factors among Indian population.Methods: From a cross-sectional study of 1425 subjects, 911 participants took part in the 4-year follow-up. Out of these 911 participants, 140 returned for the 15-year follow-up, with baseline examinations conducted between 2003 and 2006, and subsequent follow-ups occurring from 2007 to 2011 and the current 15-year follow-up from 2018 to 2021. Of the 140 participants, 112 were eligible for analysis after excluding individuals with ungradable fundus photographs.Results: The 15-year incidence of any diabetic retinopathy (DR) was 5%, with mild NPDR and moderate NPDR at 1.57% and 2.7%, respectively. Proliferative DR was observed in 0.71% of cases, while diabetic macular edema (DME) and sight-threatening diabetic retinopathy (STDR) rates were 0.48% and 1.10%, respectively. Age-standardized rates revealed a significant association with increasing age and incident any DR and STDR. DR progression over 15 years included 7.5% one-step and 1.75% two-step progressions, while regression was limited to 1.75% one-step regression. Multiple logistic regression analyses revealed that baseline duration of diabetes, systolic blood pressure, HbA1c levels, and the presence of anemia influenced the incidence of any DR, DME, and STDR. Smoking and higher HbA1c were identified as risk factors for one-step progression of DR.Conclusion: This study provides crucial insights into the long-term incidence, progression, and regression of DR among individuals with Type 2 diabetes in India.
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Affiliation(s)
- Keerthana Raghu
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Janani Surya R
- Department of Biostatistics, National Institute of Epidemiology, Chennai, India
| | - Padmaja Kumari Rani
- Anand Bajaj Retina Institute, Srimati Kannuri Santhamma Centre for Vitreoretinal Diseases, LV Prasad Eye Institute, Hyderabad, India
| | - Tarun Sharma
- Department of Ophthalmology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
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Mohapatra A, Sudharshan S, Majumder PD, Sreenivasan J, Raman R. Clinical Profile and Ocular Morbidities in Patients with Both Diabetic Retinopathy and Uveitis. OPHTHALMOLOGY SCIENCE 2024; 4:100511. [PMID: 39139545 PMCID: PMC11321282 DOI: 10.1016/j.xops.2024.100511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/20/2024] [Accepted: 03/05/2024] [Indexed: 08/15/2024]
Abstract
Purpose To describe the clinical profile and complications of diabetic retinopathy (DR) and uveitis in patients with coexisting conditions and to derive associations based on site of primary inflammation, stage of DR, and complications of each. Design Single-center, cross-sectional observational study. Participants Sixty-six patients with coexisting DR and uveitis. Methods Electronic medical records of 66 such cases were evaluated. The demographic data, diabetic status, clinical characteristics, and complications of DR and uveitis on the final follow-up were recorded. Main Outcome Measures Associations between best corrected visual acuity (BCVA), prevalence of various stages, and complications of DR among eyes with and without uveitis, and correlation between the intensity and primary sites of inflammation among eyes with proliferative and nonproliferative changes. Results Of the 132 eyes, all had DR and 97 eyes had uveitis (35 unilateral and 31 bilateral cases). Mean age of patients was 53.4 ± 8.7 years, duration of diabetes was 10.5 ± 6.9 years, and duration of uveitis was 61.3 ± 68.8 months. Of uveitis patients, 54.6% had anterior uveitis (AU), 20.6% had intermediate, 10.3% posterior, and 14.4% panuveitis. Forty-nine point five percent of eyes had proliferative DR (PDR) changes. There was a higher proportion PDR cases among anterior (56.6%), posterior (70%), and panuveitis (64.3%), with difference in AU cases approaching statistical significance (P = 0.067). Conversely, significant (P < 0.001) intermediate uveitis cases had nonproliferative changes (80%). Final BCVA was significantly poorer in the group with uveitis (P = 0.045). The proportion of fibrovascular proliferations, tractional detachments. and iris neovascularization among proliferative retinopathy eyes with uveitis (14.6%, 18.8%, and 12.5% respectively) was higher than those without uveitis (5.3%, 10.5%, and 5.3%). Among uveitis cases, 58.5% eyes developed cataracts, 44.3% had posterior synechiae, 12.3% developed secondary glaucoma, 4.1% had epiretinal membrane, 4.1% had band-shaped keratopathy, and 1.0% developed macular neovascularization. Conclusions Eyes with coexisting DR and uveitis have a higher prevalence of neovascular and uveitis complications along with a risk of poorer visual outcomes. Treatment should aim at limiting the duration and intensity of inflammation. Strict glycemic control is essential for inflammation control and preventing the progression of DR to more advanced stages. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Ayushi Mohapatra
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | | | | | - Janani Sreenivasan
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
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5
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Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, Lim CC, Ruamviboonsuk P, Raman R, Corsino L, Echouffo-Tcheugui JB, Luk AOY, Chen LJ, Sun X, Hamzah H, Wu Q, Wang X, Liu R, Wang YX, Chen T, Zhang X, Yang X, Yin J, Wan J, Du W, Quek TC, Goh JHL, Yang D, Hu X, Nguyen TX, Szeto SKH, Chotcomwongse P, Malek R, Normatova N, Ibragimova N, Srinivasan R, Zhong P, Huang W, Deng C, Ruan L, Zhang C, Zhang C, Zhou Y, Wu C, Dai R, Koh SWC, Abdullah A, Hee NKY, Tan HC, Liew ZH, Tien CSY, Kao SL, Lim AYL, Mok SF, Sun L, Gu J, Wu L, Li T, Cheng D, Wang Z, Qin Y, Dai L, Meng Z, Shu J, Lu Y, Jiang N, Hu T, Huang S, Huang G, Yu S, Liu D, Ma W, Guo M, Guan X, Yang X, Bascaran C, Cleland CR, Bao Y, Ekinci EI, Jenkins A, Chan JCN, Bee YM, Sivaprasad S, Shaw JE, Simó R, Keane PA, Cheng CY, Tan GSW, Jia W, Tham YC, Li H, Sheng B, Wong TY. Integrated image-based deep learning and language models for primary diabetes care. Nat Med 2024; 30:2886-2896. [PMID: 39030266 PMCID: PMC11485246 DOI: 10.1038/s41591-024-03139-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 06/18/2024] [Indexed: 07/21/2024]
Abstract
Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.
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Affiliation(s)
- Jiajia Li
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Jing Wang
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Cynthia Ciwei Lim
- Department of Renal Medicine, Singapore General Hospital, SingHealth-Duke Academic Medical Centre, Singapore, Singapore
| | - Paisan Ruamviboonsuk
- Faculty of Medicine, Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Leonor Corsino
- Department of Medicine, Division of Endocrinology, Metabolism and Nutrition, and Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Justin B Echouffo-Tcheugui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Andrea O Y Luk
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Asia Diabetes Foundation, Hong Kong Special Administrative Region, China
| | - Li Jia Chen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Qiang Wu
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruhan Liu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
| | - Tingli Chen
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Xiao Zhang
- The People's Hospital of Sixian County, Anhui, China
| | - Xiaolong Yang
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Jun Yin
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Jing Wan
- Department of Endocrinology and Metabolism, Shanghai Eighth People's Hospital, Shanghai, China
| | - Wei Du
- Department of Endocrinology and Metabolism, Shanghai Eighth People's Hospital, Shanghai, China
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Jocelyn Hui Lin Goh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiaoyan Hu
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Truong X Nguyen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Simon K H Szeto
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peranut Chotcomwongse
- Faculty of Medicine, Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - Rachid Malek
- Department of Internal Medicine, Setif University Ferhat Abbas, Setif, Algeria
| | - Nargiza Normatova
- Ophthalmology Department at Tashkent Advanced Training Institute for Doctors, Tashkent, Uzbekistan
| | - Nilufar Ibragimova
- Charity Union of Persons with Disabilities and People with Diabetes UMID, Tashkent, Uzbekistan
| | - Ramyaa Srinivasan
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Pingting Zhong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Chenxin Deng
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Ruan
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chenxi Zhang
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Zhou
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chan Wu
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Rongping Dai
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Sky Wei Chee Koh
- National University Polyclinics, National University Health System, Department of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Adina Abdullah
- Department of Primary Care Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | - Hong Chang Tan
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Zhong Hong Liew
- Department of Renal Medicine, Singapore General Hospital, SingHealth-Duke Academic Medical Centre, Singapore, Singapore
| | - Carolyn Shan-Yeu Tien
- Department of Renal Medicine, Singapore General Hospital, SingHealth-Duke Academic Medical Centre, Singapore, Singapore
| | - Shih Ling Kao
- Division of Endocrinology, University Medicine Cluster, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Amanda Yuan Ling Lim
- Division of Endocrinology, University Medicine Cluster, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Shao Feng Mok
- Division of Endocrinology, University Medicine Cluster, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Lina Sun
- Department of Internal Medicine, Huadong Sanatorium, Wuxi, China
| | - Jing Gu
- Department of Internal Medicine, Huadong Sanatorium, Wuxi, China
| | - Liang Wu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Tingyao Li
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Di Cheng
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Zheyuan Wang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiming Qin
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Dai
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziyao Meng
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jia Shu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuwei Lu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Nan Jiang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Tingting Hu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Shan Huang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Gengyou Huang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shujie Yu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Dan Liu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Weizhi Ma
- Institute for AI Industry Research, Tsinghua University, Beijing, China
| | - Minyi Guo
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Xinping Guan
- Department of Automation and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Covadonga Bascaran
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - Charles R Cleland
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - Yuqian Bao
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Elif I Ekinci
- Department of Endocrinology, Austin Health, Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne (Austin Health), Melbourne, Victoria, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, Victoria, Australia
| | - Alicia Jenkins
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, Victoria, Australia
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Asia Diabetes Foundation, Hong Kong Special Administrative Region, China
| | - Yong Mong Bee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Sobha Sivaprasad
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Jonathan E Shaw
- Department of Medicine, The University of Melbourne (Austin Health), Melbourne, Victoria, Australia
| | - Rafael Simó
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
- Diabetes and Metabolism Research Unit, Vall d'Hebron Research Institut, Autonomous University of Barcelona, Barcelona, Spain
| | - Pearse A Keane
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Center for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Center for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore.
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Bin Sheng
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
- Beijing Tsinghua Changgung Hospital, Beijing, China.
- Zhongshan Ophthalmic Center, Guangzhou, China.
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6
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Sharif A, Smith DR, Hellgren KJ, Jendle J. Diabetic retinopathy among the elderly with type 2 diabetes: A Nationwide longitudinal registry study. Acta Ophthalmol 2024; 102:e883-e892. [PMID: 38339879 DOI: 10.1111/aos.16659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
PURPOSE To investigate the prevalence, incidence and risk factors of DR in elderly people living with type 2 diabetes. METHODS Individuals >80 years, in the Swedish National Diabetes Register (NDR) between 2008 and 2017, were included. Prevalence and incidence were calculated and stratified by age. Estimates were assessed by longitudinal binary logistic regression models. RESULTS One hundred forty-one thousand, one hundred fifty-eight individuals with type 2 diabetes were included, median age 83 years, 53.3% females and with a median HbA1c 52 mmol/mol. The DR prevalence was stable at 336.2 cases/1000 patients in 2008 (95% CI, 330.2-342.3), with no significant changes during the 10-year period. Crude DR incidence rate: 88.5 cases/1000 patient years (95% CI, 87.6-89.4). The incidence rate was lower at higher ages. The effect of age on incident DR varied by sex, with females having an increasingly higher risk than males from 83 years of age, OR 1.25 (1.11-1.42) at age 90 years. The risk of incident DR with longer diabetes duration increased more rapidly at worse glycaemic control. CONCLUSION The growing population of elderly with type 2 diabetes shows a stable proportion of DR and proposes an increased need for DR screening and eye care. Established risk factors for DR, such as diabetes duration and level of glycaemic control, are also important in the elderly; however, age and sex should be considered.
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Affiliation(s)
- Ali Sharif
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Ophthalmology, Örebro University Hospital, Örebro, Sweden
| | - Daniel R Smith
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Karl-Johan Hellgren
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Ophthalmology, Karlstad Hospital, Karlstad, Sweden
| | - Johan Jendle
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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7
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Inouye K, Petrosyan A, Moskalensky L, Thankam FG. Artificial intelligence in therapeutic management of hyperlipidemic ocular pathology. Exp Eye Res 2024; 245:109954. [PMID: 38838975 DOI: 10.1016/j.exer.2024.109954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 04/09/2024] [Accepted: 06/02/2024] [Indexed: 06/07/2024]
Abstract
Hyperlipidemia has many ocular manifestations, the most prevalent being retinal vascular occlusion. Hyperlipidemic lesions and occlusions to the vessels supplying the retina result in permanent blindness, necessitating prompt detection and treatment. Retinal vascular occlusion is diagnosed using different imaging modalities, including optical coherence tomography angiography. These diagnostic techniques obtain images representing the blood flow through the retinal vessels, providing an opportunity for AI to utilize image recognition to detect blockages and abnormalities before patients present with symptoms. AI is already being used as a non-invasive method to detect retinal vascular occlusions and other vascular pathology, as well as predict treatment outcomes. As providers see an increase in patients presenting with new retinal vascular occlusions, the use of AI to detect and treat these conditions has the potential to improve patient outcomes and reduce the financial burden on the healthcare system. This article comprehends the implications of AI in the current management strategies of retinal vascular occlusion (RVO) in hyperlipidemia and the recent developments of AI technology in the management of ocular diseases.
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Affiliation(s)
- Keiko Inouye
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Aelita Petrosyan
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Liana Moskalensky
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Finosh G Thankam
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA.
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8
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Madit W, Harnirattisai T, Hain D, Gaudio PA. Effect of a self-care promoting program on engagement in self-care behaviors and health-related outcomes among persons with type 2 diabetes and diabetic retinopathy: A single-blind randomized controlled trial. BELITUNG NURSING JOURNAL 2024; 10:272-284. [PMID: 38947309 PMCID: PMC11211747 DOI: 10.33546/bnj.3360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/04/2024] [Accepted: 06/03/2024] [Indexed: 07/02/2024] Open
Abstract
Background Diabetic retinopathy (DR) is the most common microvascular complication of diabetes, leading to visual impairment and eventual blindness. Promoting self-care behaviors is crucial in controlling DR progression and preventing blindness. Objective This study aimed to investigate the effects of a Self-Care Promoting Program (SCPP) on engagement in self-care behaviors, HbA1c levels, visual acuity (VA), severity of DR, and vision-related quality of life (VRQoL) among individuals with type 2 diabetes and DR. Methods This study employed a single-blind randomized controlled trial design to compare SCPP with conventional diabetic care interventions (standard care). The SCPP was based on the Self-Care of Chronic Illness Theory, Self-efficacy theory, and the Association of Diabetic Care and Education Specialist (ADCES) guidelines incorporating health education, self-care maintenance, monitoring, and management skills training over 12 weeks. Ninety-eight participants were randomly allocated to the experimental or control group (n = 49 per group). While the experimental group received SCPP alongside standard care, the control group received standard care alone. Data collection occurred between May 2022 and March 2023 and included demographic information, the Self-Care of Diabetes Index questionnaire (SCODI), the self-care for diabetes eye care questionnaire (SCFDE), the impact of visual impairment questionnaire (IVI-Thai version), and retinal images for DR severity grading. Data analysis utilized descriptive statistics, Chi-Square tests, t-tests, and MANOVA. Results Following 8 and 16 weeks of SCPP, the experimental group had significantly higher mean scores in engagement with self-care and eye-care behaviors compared to the control group (p <0.001). The highest scores were observed in self-care and eye-care confidence behaviors, followed by maintenance, monitoring, and management. Furthermore, HbA1c levels and VRQoL significantly decreased and were lower than those of the control group at week 16 (p <0.001 and p <0.05, respectively). However, there were no significant differences in VA, and DR severity increased in both groups by week 16. Conclusion SCPP benefits individuals with DR, enhancing their confidence and ability to perform, monitor, and manage self-care behaviors. These strategies contribute to improved diabetes management, enhanced quality of life, and reduced DR-related blindness. Integrating SCPP into routine DR management is recommended, with nurses playing a pivotal role in overseeing and driving this integration, highlighting the critical role of nurses in managing this widespread global disease. Trial Registry Number Thai Clinical Trials Registration (TCTR20230302002).
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Affiliation(s)
- Wimol Madit
- Faculty of Nursing, Thammasat University, Pathum Thani, Thailand
| | | | - Debra Hain
- Christine E. Lynn College of Nursing, Florida Atlantic University, United States
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9
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Abou Taha A, Dinesen S, Vergmann AS, Grauslund J. Present and future screening programs for diabetic retinopathy: a narrative review. Int J Retina Vitreous 2024; 10:14. [PMID: 38310265 PMCID: PMC10838429 DOI: 10.1186/s40942-024-00534-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 01/19/2024] [Indexed: 02/05/2024] Open
Abstract
Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed. In low-income countries, only one out of 29, partially implemented a DESP, while 21 out of 50 lower-middle-income countries have started the DR policy cycle. Among upper-middle-income countries, a third of 59 nations have advanced in DR agenda-setting, with five having a comprehensive national DESP and 11 in the early stages of implementation.Many nations use 2-4 fields fundus images, proven effective with 80-98% sensitivity and 86-100% specificity compared to the traditional seven-field evaluation for DR. A cell phone based screening with a hand held retinal camera presents a potential low-cost alternative as imaging device. While this method in low-resource settings may not entirely match the sensitivity and specificity of seven-field stereoscopic photography, positive outcomes are observed.Individualized DR screening intervals are the standard in many high-resource nations. In countries that lacks a national DESP and resources, screening are more sporadic, i.e. screening intervals are not evidence-based and often less frequently, which can lead to late recognition of treatment required DR.The rising global prevalence of DR poses an economic challenge to nationwide screening programs AI-algorithms have showed high sensitivity and specificity for detection of DR and could provide a promising solution for the future screening burden.In summary, this narrative review enlightens on the epidemiology of DR and the necessity for effective DR screening programs. Worldwide evolution in existing approaches for DR screening has showed promising results but has also revealed limitations. Technological advancements, such as handheld imaging devices, tele ophthalmology and artificial intelligence enhance cost-effectiveness, but also the accessibility of DR screening in countries with low resources or where distance to or a shortage of ophthalmologists exists.
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Affiliation(s)
- Andreas Abou Taha
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark.
| | - Sebastian Dinesen
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Anna Stage Vergmann
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jakob Grauslund
- Department of Ophthalmology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
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10
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Dai L, Sheng B, Chen T, Wu Q, Liu R, Cai C, Wu L, Yang D, Hamzah H, Liu Y, Wang X, Guan Z, Yu S, Li T, Tang Z, Ran A, Che H, Chen H, Zheng Y, Shu J, Huang S, Wu C, Lin S, Liu D, Li J, Wang Z, Meng Z, Shen J, Hou X, Deng C, Ruan L, Lu F, Chee M, Quek TC, Srinivasan R, Raman R, Sun X, Wang YX, Wu J, Jin H, Dai R, Shen D, Yang X, Guo M, Zhang C, Cheung CY, Tan GSW, Tham YC, Cheng CY, Li H, Wong TY, Jia W. A deep learning system for predicting time to progression of diabetic retinopathy. Nat Med 2024; 30:584-594. [PMID: 38177850 PMCID: PMC10878973 DOI: 10.1038/s41591-023-02702-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 11/10/2023] [Indexed: 01/06/2024]
Abstract
Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.
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Grants
- the National Key Research and Development Program of China (2022YFA1004804), the Shanghai Municipal Key Clinical Specialty, Shanghai Research Center for Endocrine and Metabolic Diseases (2022ZZ01002), and the Chinese Academy of Engineering (2022-XY-08)
- the General Program of NSFC (62272298), the National Key Research and Development Program of China (2022YFC2407000), the Interdisciplinary Program of Shanghai Jiao Tong University (YG2023LC11 and YG2022ZD007), National Natural Science Foundation of China (62272298 and 62077037), the College-level Project Fund of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital (ynlc201909), and the Medical-industrial Cross-fund of Shanghai Jiao Tong University (YG2022QN089)
- the Clinical Special Program of Shanghai Municipal Health Commission (20224044) and Three-year action plan to strengthen the construction of public health system in Shanghai (GWVI-11.1-28)
- the National Natural Science Foundation of China (82100879)
- the National Key Research and Development Program of China (2022YFA1004804), Excellent Young Scientists Fund of NSFC (82022012), General Fund of NSFC (81870598), Innovative research team of high-level local universities in Shanghai (SHSMU-ZDCX20212700)
- the National Key R & D Program of China (2022YFC2502800) and National Natural Science Fund of China (8238810007)
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Affiliation(s)
- Ling Dai
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Sheng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Tingli Chen
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Qiang Wu
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruhan Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chun Cai
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Liang Wu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Yuexing Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Shujie Yu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Tingyao Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziqi Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Anran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haoxuan Che
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Jia Shu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shan Huang
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chan Wu
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shiqun Lin
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Dan Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Jiajia Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zheyuan Wang
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziyao Meng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Shen
- Medical Records and Statistics Office, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuhong Hou
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Chenxin Deng
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Ruan
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Lu
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Miaoli Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ramyaa Srinivasan
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Laboratory, Beijing, China
| | - Jiarui Wu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- Center for Excellence in Molecular Science, Chinese Academy of Sciences, Shanghai, China
| | - Hai Jin
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Rongping Dai
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Dinggang Shen
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
- Shanghai United Imaging Intelligence, Shanghai, China
- Shanghai Clinical Research and Trial Center, Shanghai, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Minyi Guo
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
- Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Tsinghua Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
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Tan H, Fu X, Chen Y, Wang Y, Chen D. Hyperlipidemia and lipid-lowering therapy in diabetic retinopathy (DR): A bibliometric study and visualization analysis in 1993-2023. Heliyon 2023; 9:e21109. [PMID: 37916126 PMCID: PMC10616351 DOI: 10.1016/j.heliyon.2023.e21109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/04/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023] Open
Abstract
Background Diabetic retinopathy (DR) is a common complication in diabetic patients. DR is also a neurodegenerative disease. Patients with hyperglycemia, hyperlipidemia, and hypertension are vulnerable to retinopathy development. While the roles of blood glucose and blood pressure in the development of retinopathy have been extensively studied, the relationship between body fat and DR pathogenesis and the impact of lipid-reducing drugs on DR has just emerged as a research hotspot in DR study. We aim to visualize the contributions and cooperation of reporters, organizations, and nations, in addition to the research hotspots and trends in DR-related lipid research from 1993 to 2023, by bibliometric analysis. Methods We extracted all publications about DR-related lipid research from 1993 to 2023 from the Web of Science Core Collection, and bibliometric features were studied using VOSviewer and the CiteSpace program. Results 1402 documents were retrieved. The number of studies has risen consistently for three decades, from an average of 16.8/year in the 1990s to 28.8/year in the 2000s, 64.5/year in 2010s, and reached 112/year in 2020-2022, confirming they are hot research topic in the field. These reports were from 93 nations/regions, with the USA, China, Japan, Australia, and England taking the leading positions. Diabetes Research and Clinical Practice was the journal that published the most studies, and Diabetes Care was the most quoted. We identified 6979 authors, with Wong TY having the most papers and being the most commonly co-cited. The most popular keyword, according to our research, is diabetic retinopathy. Oxidative stress, diabetic macular edema (DME), lipid peroxidation, and other topics have often been investigated. Conclusion DR-related lipid research is conducted mainly in North America, Asia, Oceania, and Europe. Much study has centered on the relationship between lipid-lowering therapy and DR pathogenesis. These studies strongly support using lipid-reducing medications (fenofibrate, statins, and omega-3 PUFAs), combined with hyperglycemia and hypertension therapy, to prevent and treat DR. However, the impact of fenofibrate or statin on retinopathy is not correlated with their action on blood lipid profiles. Thus, more randomized clinical trials with primary endpoints related to DR in T1D or T2D are merited. In addition, the lipid biomarker for DR (lipid aldehydes, ALEs, and cholesterol crystals), the action of lipid-reducing medicines on retinopathy, the mechanism of lipid-lowering medications preventing or curing DR, and ocular delivery of lipid-lowering drugs to diabetic patients are predicted as the research focus in the future in the DR-related lipid research field.
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Affiliation(s)
- Haishan Tan
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xiangyu Fu
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yongjiang Chen
- The School of Optometry and Vision Science, University of Waterloo, 200 University Ave. W., Waterloo, ON, N2L 3G1, Canada
| | - Yujiao Wang
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Danian Chen
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Surya J, Kashyap H, Nadig RR, Raman R. Developing a Risk Stratification Model Based on Machine Learning for Targeted Screening of Diabetic Retinopathy in the Indian Population. Cureus 2023; 15:e45853. [PMID: 37881381 PMCID: PMC10595397 DOI: 10.7759/cureus.45853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2023] [Indexed: 10/27/2023] Open
Abstract
OBJECTIVE This study aimed to develop a predictive risk score model based on deep learning (DL) independent of fundus photography, totally reliant on systemic data through targeted screening from a population-based study to diagnose diabetic retinopathy (DR) in the Indian population. METHODS It involved machine learning application on datasets of a cross-sectional population-based study. A total of 1425 subjects (1175 subjects with known diabetes and 250 with newly diagnosed diabetes) were included in the study. We applied five machine learning algorithms, random forest (RF), logistic regression (LR), support vector machines (SVM), artificial neural networks (ANN), and decision trees (DT), to predict diabetic retinopathy in our datasets. We incorporated a percentage split in the first experiment and randomly divided our data set into 80% as a training set and 20% as a test set. We performed a three-way data split in the second experiment to prevent overestimating predictive performance. We randomly divided our data set into 60% as a training set, 20% as a validation set, and 20% as the test set. Furthermore, we integrated five-fold cross-validation to split the percentage to evaluate our method. We judged the predictive performance based on the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy (Acc), sensitivity, and specificity. RESULTS The RF classifier achieved the best prediction performance with AUC, Acc, and sensitivity values of 0.91, 0.89, and 0.90, respectively, in the percentage split. Similarly, a three-way data split attained an outcome of 0.86 and 0.85 in AUC and Acc. Likewise, the five-fold cross-validation performed the best with results of 0.90, 0.97, 0.91, and 0.75 in AUC, Acc, sensitivity, and specificity, respectively. CONCLUSION Since the RF classifier achieved the best performance, we propose it to identify diabetic retinopathy for targeted screening in the general population.
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Affiliation(s)
- Janani Surya
- Epidemiology and Biostatistics, National Institute of Epidemiology, Chennai, IND
| | - Himanshu Kashyap
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, IND
| | - Ramya R Nadig
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, IND
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, IND
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Correlating the patterns of diabetic macular edema, optical coherence tomography biomarkers and grade of diabetic retinopathy with stage of renal disease. Int Ophthalmol 2022; 42:3333-3343. [PMID: 35633427 DOI: 10.1007/s10792-022-02332-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/18/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE To correlate optical coherence tomography (OCT)-based morphological patterns of diabetic macular edema (DME), biomarkers and grade of diabetic retinopathy (DR) in patients with various stages of chronic kidney disease (CKD) secondary to diabetes. DESIGN Multicentric retrospective cross-sectional study was conducted at seven centers across India. METHODS Data from medical records of patients with DME and CKD were entered in a common excel sheet across all seven centers. Staging of CKD was based on estimated glomerular filtration rate (eGFR). RESULTS The most common morphological pattern of DME was cystoid pattern (42%) followed by the mixed pattern (31%). The proportion of different morphological patterns did not significantly vary across various CKD stages (p = 0.836). The presence of external limiting membrane-ellipsoid zone (ELM-EZ) defects (p < 0.001) and foveal sub-field thickness (p = 0.024) showed a direct correlation with the stage of CKD which was statistically significant. The presence of hyperreflective dots (HRD) and disorganization of inner retinal layers (DRIL) showed no significant correlation with the stage of CKD. Sight threatening DR was found to increase from 70% in CKD stage 3 to 82% in stages 4 and 5 of CKD, and this was statistically significant (p = 0.03). CONCLUSION Cystoid morphological pattern followed by mixed type was the most common pattern of DME on OCT found in patients suffering from stage 3 to 5 of CKD. However, the morphological patterns of DME did not significantly vary across various CKD stages. ELM-EZ defects may be considered as an important OCT biomarker for advanced stage of CKD.
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Susarla G, Rizza AN, Li A, Han S, Khan R, Chan W, Lains I, Apivatthakakul A, Brustoski K, Khetan V, Raman R, Igo RP, Iyengar SK, Mathavan S, Sobrin L. Younger Age and Albuminuria are Associated with Proliferative Diabetic Retinopathy and Diabetic Macular Edema in the South Indian GeNetics of DiAbeTic Retinopathy (SIGNATR) Study. Curr Eye Res 2022; 47:1389-1396. [PMID: 35815717 PMCID: PMC9637383 DOI: 10.1080/02713683.2022.2091148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/06/2022] [Accepted: 06/10/2022] [Indexed: 11/03/2022]
Abstract
Purpose: The purpose of the South Indian GeNetics of DiAbeTic Retinopathy (SIGNATR) Study is to identify non-genetic and genetic risk factors associated with diabetic retinopathy (DR). This report examines the non-genetic risk factors for DR in South Indian patients.Methods: Participants with South Indian ancestry and type 2 diabetes (T2D) were included from two sources: the Sankara Nethralaya Diabetic Retinopathy and Molecular Genetics Study (SN-DREAMS) and prospective recruitment at Sankara Nethralaya affiliates. Fundus photography and optical coherence tomography (OCT) were obtained on participants. Fundus images were graded for DR severity and OCTs were graded for center-involved diabetic macular edema (ciDME). Multivariate analyses were performed using stepwise logistic regression to assess effects of the demographic and clinical factors on proliferative DR (PDR) and DME.Results: Among the 2941 participants with DR grading, participants with PDR were more likely to be younger [odds ratio (OR)=0.95], men (OR = 1.83), have a longer duration of diabetes (OR = 1.10), have a higher hemoglobin A1c (OR = 1.12), have albuminuria (OR = 5.83), have hypertension (OR = 1.69), have a higher HDL (OR = 1.02) and a lower total cholesterol (OR = 0.99) (all p < 0.05). Among the 483 participants with gradable OCT scans, participants who had ciDME were more likely to be younger (OR = 0.97), men (OR = 2.80), have a longer duration of diabetes (OR = 1.06), have lower triglycerides (OR = 0.99), and have albuminuria (OR = 3.12) (all p < 0.05).Conclusions: Younger age, male sex, longer duration of diabetes, higher HbA1c, and presence of albuminuria were identified as risk factors for PDR and DME in a South Indian population with T2D.
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Affiliation(s)
- Gayatri Susarla
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - A N Rizza
- Vision Research Foundation, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Ashley Li
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Samuel Han
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Rehana Khan
- Vision Research Foundation, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Weilin Chan
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Ines Lains
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Atitaya Apivatthakakul
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Kim Brustoski
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Vikas Khetan
- Vision Research Foundation, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Rajiv Raman
- Vision Research Foundation, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Robert P Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | | | - Lucia Sobrin
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
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Jeong IS, Kang CM. Time to Diagnosis and Treatment of Diabetes Mellitus among Korean Adults with Hyperglycemia: Using a Community-Based Cohort Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12090. [PMID: 36231389 PMCID: PMC9566253 DOI: 10.3390/ijerph191912090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVES To identify the time from hyperglycemia to diabetes mellitus (DM) diagnosis and treatment, the risk factors for diabetes development, and the prevalence of comorbidities/complications in patients > 40 years of age. METHODS This secondary data analysis study used data from the Korean Genome and Epidemiology Study. The participants comprised 186 patients who did not have diabetes at baseline, but developed hyperglycemia at the first follow-up. The average and median periods until DM diagnosis and treatment were calculated using Kaplan-Meier survival analysis. RESULTS Of the 186 participants, 57.0% were men and 35.5% were 40-49 years old. The average time to DM diagnosis and treatment was 10.87 years and 11.34 years, respectively. The risk factors for the duration of DM were current smoking, body mass index (BMI), fasting blood sugar (FBS), and postprandial 2-hour glucose (PP2). The risk factors for the duration of diabetes treatment were current smoking, hypertension, BMI, FBS, and PP2. The development of one or more comorbidities or diabetes complications was identified at the time of DM diagnosis (36.5%) and DM treatment (41.4%). CONCLUSIONS As diabetes complications occur at the time of DM, and early treatment can impact the development of diabetes complications or mortality, it is necessary to establish a referral program so that participants presenting with high blood sugar levels in the screening program can be diagnosed and treated in a timely manner.
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Affiliation(s)
- Ihn-Sook Jeong
- College of Nursing, Pusan National University, Yangsan 50612, Korea
| | - Chan-Mi Kang
- Department of Nursing, Dong-Eui Institute of Technology, Busan 47230, Korea
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Khan R, Saha SK, Frost S, Kanagasingam Y, Raman R. The Longitudinal Assessment of Vascular Parameters of the Retina and Their Correlations with Systemic Characteristics in Type 2 Diabetes-A Pilot Study. Vision (Basel) 2022; 6:vision6030045. [PMID: 35893762 PMCID: PMC9326718 DOI: 10.3390/vision6030045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/18/2022] [Accepted: 06/30/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of the study was to assess various retinal vessel parameters of diabetes mellitus (DM) patients and their correlations with systemic factors in type 2 DM. A retrospective exploratory study in which 21 pairs of baseline and follow-up images of patients affected by DM were randomly chosen from the Sankara Nethralaya−Diabetic Retinopathy Study (SN DREAMS) I and II datasets. Patients’ fundus was photographed, and the diagnosis was made based on Klein classification. Vessel thickness parameters were generated using a web-based retinal vascular analysis platform called VASP. The thickness changes between the baseline and follow-up images were computed and normalized with the actual thicknesses of baseline images. The majority of parameters showed 10~20% changes over time. Vessel width in zone C for the second vein was significantly reduced from baseline to follow-up, which showed positive correlations with systolic blood pressure and serum high-density lipoproteins. Fractal dimension for all vessels in zones B and C and fractal dimension for vein in zones A, B and C showed a minimal increase from baseline to follow-up, which had a linear relationship with diastolic pressure, mean arterial pressure, serum triglycerides (p < 0.05). Lacunarity for all vessels and veins in zones A, B and C showed a minimal decrease from baseline to follow-up which had a negative correlation with pulse pressure and positive correlation with serum triglycerides (p < 0.05). The vessel widths for the first and second arteries significantly increased from baseline to follow-up and had an association with high-density lipoproteins, glycated haemoglobin A1C, serum low-density lipoproteins and total serum cholesterol. The central reflex intensity ratio for the second artery was significantly decreased from baseline to follow-up, and positive correlations were noted with serum triglyceride, serum low-density lipoproteins and total serum cholesterol. The coefficients for branches in zones B and C artery and the junctional exponent deviation for the artery in zone A decreased from baseline to follow-up showed positive correlations with serum triglycerides, serum low-density lipoproteins and total serum cholesterol. Identifying early microvascular changes in diabetic patients will allow for earlier intervention, improve visual outcomes and prevent vision loss.
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Affiliation(s)
- Rehana Khan
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai 600006, Tamil Nadu, India;
| | - Sajib K Saha
- Australian e-Health Research Centre, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA 6151, Australia; (S.K.S.); (S.F.)
| | - Shaun Frost
- Australian e-Health Research Centre, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA 6151, Australia; (S.K.S.); (S.F.)
| | - Yogesan Kanagasingam
- Digital Health and Telemedicine, The University of Notre Dame, Fremantle, WA 6160, Australia;
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai 600006, Tamil Nadu, India;
- Correspondence: ; Tel.: +91-44-28271616
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Lin Z, Wen L, Wang Y, Li D, Zhai G, Moonasar N, Wang F, Liang Y. Incidence, progression and regression of diabetic retinopathy in a northeastern Chinese population. Br J Ophthalmol 2022; 107:bjophthalmol-2022-321384. [PMID: 35864776 DOI: 10.1136/bjo-2022-321384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/27/2022] [Indexed: 11/04/2022]
Abstract
AIM To determine the incidence, progression and regression of diabetic retinopathy (DR), with corresponding risk factors, in a northeastern Chinese population of patients with type 2 diabetes. METHODS Among 2006 patients who completed baseline examinations in 2012-2013 and underwent re-examination after a mean interval of 21.2 months, 1392 patients with gradable fundus photographs for both baseline and follow-up examinations were included. Incidence was defined as new development of any DR among patients without DR at baseline. An increase of ≥2 scales (concatenating Early Treatment Diabetic Retinopathy Study levels of both eyes) in eyes with DR at baseline was defined as progression, while a reduction of ≥2 scales was defined as regression. RESULTS The age- and sex-standardised incidence, progression and regression were 5.8% (95% CI 4.7% to 6.9%), 26.8% (95% CI 24.8% to 28.8%) and 10.0% (95% CI 8.6% to 11.3%), respectively. In addition to poor blood glucose control, wider central retinal venular equivalent was associated with both incidence (relative risk (RR) 2.17, 95% CI 1.09 to 4.32, for ≥250 µm vs <210 µm) and progression (RR 2.00, 95% CI 1.02 to 3.96, for ≥250 µm vs <210 µm). Patients without insulin therapy (RR 0.64, 95% CI 0.43 to 0.97) and patients with wider central retinal arteriolar equivalent (RR 1.14, 95% CI 1.02 to 1.26, per 10 µm increase) were likely to exhibit DR regression. CONCLUSION We determined the incidence, progression and regression of DR among northeastern Chinese patients with type 2 diabetes. Retinal vessel diameters, in addition to blood glucose level, influence the natural evolution of DR.
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Affiliation(s)
- Zhong Lin
- The Eye Hospital, School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China
- National Clinical Research Center for Ocular Diseases, Wenzhou, Zhejiang, China
| | - Liang Wen
- Department of Ophthalmology, Fushun Eye Hospital, Fushun, Liaoning, China
| | - Yu Wang
- Department of Ophthalmology, Fushun Eye Hospital, Fushun, Liaoning, China
| | - Dong Li
- Department of Ophthalmology, Fushun Eye Hospital, Fushun, Liaoning, China
| | - Gang Zhai
- Department of Ophthalmology, Fushun Eye Hospital, Fushun, Liaoning, China
| | | | - Fenghua Wang
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yuanbo Liang
- The Eye Hospital, School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China
- National Clinical Research Center for Ocular Diseases, Wenzhou, Zhejiang, China
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Haghpanah S, Zekavat OR, Safaei S, Ashraf MA, Parand S, Ashraf H. Optical coherence tomography findings in patients with transfusion-dependent β-thalassemia. BMC Ophthalmol 2022; 22:279. [PMID: 35751049 PMCID: PMC9233398 DOI: 10.1186/s12886-022-02490-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Structural ophthalmologic findings have been reported in patients with β-thalassemia due to chronic anemia, iron overload, and iron chelation therapy toxicity in few previous studies. We aimed to investigate structural ocular findings and their relationship with hematological parameters in patients with transfusion-dependent β-thalassemia (TDT). METHODS In this cross-sectional study, from January 2018 to January 2019, 39 patients with TDT over the age of 18 participated. Multicolor fundus imaging, optical coherence tomography (OCT), and blue light fundus autofluorescence imaging were performed for all patients and 27 age- and sex-matched controls. RESULTS The mean age of patients was 28.6 ± 6.2 years. The central macular thickness and macular thicknesses in all quadrants were significantly thinner in patients than controls (P<0.05). None of the retinal nerve fiber layer (RNFL) measurements were significantly different between TDT patients and controls. There was a significantly negative correlation between hemoglobin with central macula thickness (r=-0.439, P=0.005). All measurements of macular subfield thickness were insignificantly thinner in patients with diabetes mellitus (DM) compared to the non-DM subgroup. CONCLUSIONS Macular thickness was significantly thinner in central macula and entire quadrants in TDT patients compared to healthy individuals; however, all RNFL measurement thicknesses were comparable between the two groups. Close monitoring of TDT patients by periodic ophthalmologic examinations with more focus on diabetic patients, patients with severe anemia and iron overload should be warranted.
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Affiliation(s)
- Sezaneh Haghpanah
- Hematology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Omid Reza Zekavat
- Hematology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Sanaz Safaei
- Hematology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Ali Ashraf
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shirin Parand
- Hematology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hossein Ashraf
- Poostchi Ophthalmology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
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Bryl A, Mrugacz M, Falkowski M, Zorena K. The Effect of Hyperlipidemia on the Course of Diabetic Retinopathy—Literature Review. J Clin Med 2022; 11:jcm11102761. [PMID: 35628887 PMCID: PMC9146710 DOI: 10.3390/jcm11102761] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 04/29/2022] [Accepted: 05/11/2022] [Indexed: 12/16/2022] Open
Abstract
Diabetes mellitus is a very important social issue, and its retinal complications continue to be one of the major causes of blindness worldwide. The effect of glucose level on the development of retinal retinopathy has been the subject of numerous studies and is well understood. Hypertension and hyperlipidemia have been known to be important risk factors in the development of diabetes complications. However, the mechanisms of this effect have not been fully explained and raise a good deal of controversy. The latest research results suggest that some lipoproteins are closely correlated with the incidence of diabetic retinopathy and that by exerting an impact on their level the disease course can be modulated. Moreover, pharmacotherapy which reduces the level of lipids, particularly by means of statins and fibrate, has been shown to alleviate diabetic retinopathy. Therefore, we have decided to review the latest literature on diabetic retinopathy with respect to the impact of hyperlipidemia and possible preventive measures
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Affiliation(s)
- Anna Bryl
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, Waszyngtona 17, 15-274 Bialystok, Poland;
- Correspondence:
| | - Małgorzata Mrugacz
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, Waszyngtona 17, 15-274 Bialystok, Poland;
| | - Mariusz Falkowski
- PhD Studies, Medical University of Bialystok, 15-089 Bialystok, Poland;
| | - Katarzyna Zorena
- Department of Immunobiology and Environmental Microbiology, Medical University of Gdansk, 80-211 Gdansk, Poland;
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20
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Wang W, Li L, Wang J, Chen Y, Kun X, Gong X, Wei D, Wang D, Liang X, Liu H, Huang W. Macular Choroidal Thickness and the Risk of Referable Diabetic Retinopathy in Type 2 Diabetes: A 2-Year Longitudinal Study. Invest Ophthalmol Vis Sci 2022; 63:9. [PMID: 35420642 PMCID: PMC9034727 DOI: 10.1167/iovs.63.4.9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 02/19/2022] [Indexed: 11/27/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the associations between choroidal thickness (CT) and the 2-year incidence of referable diabetic retinopathy (RDR). Methods This was a prospective cohort study. Patients with type 2 diabetes in Guangzhou, China, aged 30 to 80 years underwent comprehensive examinations, including standard 7-field fundus photography. Macular CT was measured using a commercial swept-source optical coherence tomography (SS-OCT) device (DRI OCT Triton; Topcon, Tokyo, Japan). The relative risk (RR) with 95% confidence intervals (CIs) was used to quantify the association between CT and new-onset RDR. The prognostic value of CT was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results A total of 1345 patients with diabetes were included in the study, and 120 (8.92%) of them had newly developed RDR at the 2-year follow-up. After adjusting for other factors, the increased RDR risk was associated with greater HbA1c (RR = 1.35, 95% CI = 1.17-1.55, P < 0.001), higher systolic blood pressure (SBP; RR = 1.02, 95% CI = 1.01-1.03, P = 0.005), lower triglyceride (TG) level (RR = 0.81, 95% CI = 0.69-0.96, P = 0.015), presence of diabetic retinopathy (DR; RR = 8.16, 95% CI = 4.47-14.89, P < 0.001), and thinner average CT (RR = 0.903, 95% CI = 0.871-0.935, P < 0.001). The addition of average CT improved NRI (0.464 ± 0.096, P < 0.001) and IDI (0.0321 ± 0.0068, P < 0.001) for risk of RDR, and it also improved the AUC from 0.708 (95% CI = 0.659-0.757) to 0.761 (95% CI = 0.719-0.804). Conclusions CT thinning measured by SS-OCT is an early imaging biomarker for the development of RDR, suggesting that alterations in CT play an essential role in DR occurrence.
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Affiliation(s)
- Wei Wang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Longyue Li
- School of Medicine, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jun Wang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Yifan Chen
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Xiong Kun
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Xia Gong
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Daheng Wei
- Institute of Eyes, Jinzhou Medical University, Jinzhou, Liaoning, People's Republic of China
| | - Dongning Wang
- Institute of Eyes, Jinzhou Medical University, Jinzhou, Liaoning, People's Republic of China
| | - Xiaolin Liang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Hua Liu
- Institute of Eyes, Jinzhou Medical University, Jinzhou, Liaoning, People's Republic of China
| | - Wenyong Huang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, People's Republic of China
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21
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The Effect of Diet and Lifestyle on the Course of Diabetic Retinopathy-A Review of the Literature. Nutrients 2022; 14:nu14061252. [PMID: 35334909 PMCID: PMC8955064 DOI: 10.3390/nu14061252] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/10/2022] [Accepted: 03/14/2022] [Indexed: 12/23/2022] Open
Abstract
Diabetes is a major social problem. As shown by epidemiological studies, the world incidence of diabetes is increasing and so is the number of people suffering from its complications. Therefore, it is important to determine possible preventive tools. In the prevention of diabetic retinopathy, it is essential to control glycemia, lipid profile and blood pressure. This can be done not only by pharmacological treatment, but first of all by promoting a healthy lifestyle, changing dietary habits and increasing physical activity. In our work, we present a review of the literature to show that physical exercise and an adequate diet can significantly reduce the risk of diabetes and diabetic retinopathy.
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22
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Gange WS, Lopez J, Xu BY, Lung K, Seabury SA, Toy BC. Incidence of Proliferative Diabetic Retinopathy and Other Neovascular Sequelae at 5 Years Following Diagnosis of Type 2 Diabetes. Diabetes Care 2021; 44:2518-2526. [PMID: 34475031 PMCID: PMC8546279 DOI: 10.2337/dc21-0228] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 08/05/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To determine the incidence and risk factors for developing proliferative diabetic retinopathy (PDR), tractional retinal detachment (TRD), and neovascular glaucoma (NVG) at 5 years after the initial diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS Insured patients aged ≥18 years with newly diagnosed type 2 diabetes and 5 years of continuous enrollment were identified from a nationwide commercial claims database containing data from 2007 to 2015. The incidences of PDR, TRD, and NVG were computed at 5 years following the index diagnosis of type 2 diabetes. Associations between these outcomes and demographic, socioeconomic, and medical factors were tested with multivariable logistic regression. RESULTS At 5 years following the initial diagnosis of type 2 diabetes, 1.74% (1,249 of 71,817) of patients had developed PDR, 0.25% of patients had developed TRD, and 0.14% of patients had developed NVG. Insulin use (odds ratio [OR] 3.59, 95% CI 3.16-4.08), maximum HbA1c >9% or >75 mmol/mol (OR 2.10, 95% CI 1.54-2.69), renal disease (OR 2.68, 95% CI 2.09-3.42), peripheral circulatory disorders (OR 1.88, 95% CI 1.25-2.83), neurological disease (OR 1.62, 95% CI 1.24-2.11), and older age (age 65-74 years) at diagnosis (OR 1.62, 95% CI 1.28-2.03) were identified as risk factors for development of PDR at 5 years. Young age (age 18-23 years) at diagnosis (OR 0.46, 95% CI 0.29-0.74), Medicare insurance (OR 0.60, 95% CI 0.70-0.76), morbid obesity (OR 0.72, 95% CI 0.59-0.87), and smoking (OR 0.84, 95% CI 0.70-1.00) were identified as protective factors. CONCLUSIONS A subset of patients with type 2 diabetes develop PDR and other neovascular sequelae within the first 5 years following the diagnosis with type 2 diabetes. These patients may benefit from increased efforts for screening and early intervention.
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Affiliation(s)
- William S Gange
- Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Jennifer Lopez
- Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Benjamin Y Xu
- Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Khristina Lung
- Keck-Shaeffer Initiative for Population Health Policy, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Seth A Seabury
- Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Keck-Shaeffer Initiative for Population Health Policy, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Brian C Toy
- Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA
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23
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Delhiwala K, Khamar B. Commentary: Oral management of diabetic retinopathy. Indian J Ophthalmol 2021; 69:3327-3328. [PMID: 34708797 PMCID: PMC8725083 DOI: 10.4103/ijo.ijo_2088_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Kushal Delhiwala
- Department of Vitreo Retina, Netralaya Superspeciality Eye Hospital, Ahmedabad, Gujarat, India
| | - Bakulesh Khamar
- Department of Vitreo Retina, Netralaya Superspeciality Eye Hospital, Ahmedabad, Gujarat, India
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24
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Parameswarappa DC, Rajalakshmi R, Mohamed A, Kavya S, Munirathnam H, Manayath G, Kumar MA, Raman R, Vignesh TP, Ramasamy K, Mani S, Muralidhar A, Agarwal M, Anantharaman G, Bijlani N, Chawla G, Sen A, Kulkarni S, Behera UC, Sivaprasad S, Das T, Rani PK. Severity of diabetic retinopathy and its relationship with age at onset of diabetes mellitus in India: A multicentric study. Indian J Ophthalmol 2021; 69:3255-3261. [PMID: 34708783 PMCID: PMC8725142 DOI: 10.4103/ijo.ijo_1459_21] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Purpose: To present clinical profile and risk factors of sight-threatening diabetic retinopathy (STDR) among people with age of onset of diabetes (AOD) <25 versus ≥25 years. Methods: A retrospective chart analysis of consecutive patients with diabetic retinopathy (DR) n = 654) treated at 14 eye care centers across India between 2018 and 2019 was performed. Patients were divided into two groups, Group 1: AOD <25 years and Group 2: AOD ≥25 years. DR and diabetic macular edema (DME) were classified using the International Clinical Classification of DR severity scale. STDR included severe nonproliferative DR (NPDR), proliferative DR (PDR), and moderate to severe DME. A multilevel mixed-effects model was used for comparison between two groups: 1) Patients with DR and AOD <25 years and 2) Patients with DR and AOD ≥25 years. Bivariate and multivariate regression analyses were used to evaluate risk factors between the two groups. Results: A total of 654 patients were included, 161 (307 eyes) in AOD <25 and 493 (927 eyes) in AOD >25 group. There was a higher prevalence of PDR with high-risk characteristics in AOD <25 group (24% vs. 12%) at baseline and 12-month follow-up (25% vs. 6%); P < 0.001. Systolic hypertension and poor glycemic control were risk factors in both groups, with no difference in these modifiable risk factors between groups. Conclusion: People with youth-onset DM are likely to present with severer form of STDR despite similar modifiable risk factors. Therefore, strict control of systolic blood pressure, glycemic status, and regular screening for DR are recommended to reduce the risk of STDR irrespective of the age of onset of diabetes.
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Affiliation(s)
- Deepika C Parameswarappa
- Smt Kanuri Santhamma Center for Vitreo-aRetina Diseases, L V Prasad Eye Institute, Hyderabad, Telangana, India
| | - Ramachandran Rajalakshmi
- Department of Ophthalmology, Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Chennai, Tamil Nadu, India
| | - Ashik Mohamed
- Ophthalmic Biophysics, L V Prasad Eye Institute, Hyderabad, Telangana, India
| | - Sanagavarapu Kavya
- Smt Kanuri Santhamma Center for Vitreo-aRetina Diseases, L V Prasad Eye Institute, Hyderabad, Telangana, India
| | | | | | | | - Rajiv Raman
- Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - T P Vignesh
- Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Kim Ramasamy
- Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Sheena Mani
- Dr. Tony Fernandez Eye Hospital, Aluva, Kerala, India
| | | | | | | | - Neha Bijlani
- Vision Care And Research Centre, Bhopal, Madhya Pradesh, India
| | - Gajendra Chawla
- Vision Care And Research Centre, Bhopal, Madhya Pradesh, India
| | - Alok Sen
- Sadguru Netra Chikitsalaya, Chitrakot, Madhya Pradesh, India
| | | | - Umesh C Behera
- Retina Vitreous Service, Mithu Tulsi Chanrai campus, L V Prasad Eye Institute, Bhubaneswar, Odisha, India
| | - Sobha Sivaprasad
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust; Vision Sciences, UCL Institute of Ophthalmology, London, UK
| | - Taraprasad Das
- Smt Kanuri Santhamma Center for Vitreo-aRetina Diseases, L V Prasad Eye Institute, Hyderabad, Telangana, India
| | - Padmaja Kumari Rani
- Smt Kanuri Santhamma Center for Vitreo-aRetina Diseases, L V Prasad Eye Institute, Hyderabad, Telangana, India
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25
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Kohli P, Babu N, Mishra C, Damodaran S, Bhavani S, Kumar M, Ramasamy K. Incidence of ocular and systemic diseases affecting visual function among state bus drivers. Indian J Ophthalmol 2021; 69:2625-2628. [PMID: 34571600 PMCID: PMC8597470 DOI: 10.4103/ijo.ijo_76_21] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Purpose: To evaluate the incidence of ocular and systemic disease affecting visual function among state transport corporation bus drivers in a south Indian district. Methods: This retrospective study analysed the records of all the drivers who presented to a south Indian tertiary-care eye hospital in 2019 for their mandatory annual ocular check-up. Details reviewed included demographic details; refraction; presence of systemic and ocular diseases with vision-threatening potential; presence of ocular conditions responsible for visual loss and the treatment administered. Results: 3042 drivers (mean age, 47.0 ± 5.7 years) were evaluated. Visual function-threatening systemic diseases were present in 25.0% drivers, out of which diabetes mellitus (18.7%) was the most common pathology. The most common ocular problem was refractive error (45.0%). Visual function-threatening ocular diseases were present in 9.5% drivers. Diabetic retinopathy, visually-significant cataract, glaucoma and central serous chorioretinopathy were noted in 4.0%, 1.9%, 1.7% and 0.8% drivers. Surgical intervention was required in 2.2% drivers. Thirteen drivers were temporarily deemed unfit for driving heavy-weight vehicles. Conclusion: Several bus drivers suffer from vision-threatening systemic and ocular diseases. Some of them require surgical intervention to retain fitness. A complete ocular and systemic evaluation of diseases with vision-threatening potential should be performed at the time of renewal of the driving license. The drivers should be educated about the systemic diseases which can affect their driving skills and must be encouraged to seek medical help at an early stage.
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Affiliation(s)
- Piyush Kohli
- Department of Vitreo-Retinal Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Madurai, Tamil Nadu, India
| | - Naresh Babu
- Department of Vitreo-Retinal Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Madurai, Tamil Nadu, India
| | - Chitaranjan Mishra
- Department of Vitreo-Retinal Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Madurai, Tamil Nadu, India
| | - Sourav Damodaran
- Department of Vitreo-Retinal Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Madurai, Tamil Nadu, India
| | - S Bhavani
- Department of Vitreo-Retinal Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Madurai, Tamil Nadu, India
| | - Mahesh Kumar
- Department of Vitreo-Retinal Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Madurai, Tamil Nadu, India
| | - Kim Ramasamy
- Department of Vitreo-Retinal Services, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Madurai, Tamil Nadu, India
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26
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Diabetic retinopathy and diabetic macular oedema pathways and management: UK Consensus Working Group. Eye (Lond) 2021; 34:1-51. [PMID: 32504038 DOI: 10.1038/s41433-020-0961-6] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The management of diabetic retinopathy (DR) has evolved considerably over the past decade, with the availability of new technologies (diagnostic and therapeutic). As such, the existing Royal College of Ophthalmologists DR Guidelines (2013) are outdated, and to the best of our knowledge are not under revision at present. Furthermore, there are no other UK guidelines covering all available treatments, and there seems to be significant variation around the UK in the management of diabetic macular oedema (DMO). This manuscript provides a summary of reviews the pathogenesis of DR and DMO, including role of vascular endothelial growth factor (VEGF) and non-VEGF cytokines, clinical grading/classification of DMO vis a vis current terminology (of centre-involving [CI-DMO], or non-centre involving [nCI-DMO], systemic risks and their management). The excellent UK DR Screening (DRS) service has continued to evolve and remains world-leading. However, challenges remain, as there are significant variations in equipment used, and reproducible standards of DMO screening nationally. The interphase between DRS and the hospital eye service can only be strengthened with further improvements. The role of modern technology including optical coherence tomography (OCT) and wide-field imaging, and working practices including virtual clinics and their potential in increasing clinic capacity and improving patient experiences and outcomes are discussed. Similarly, potential roles of home monitoring in diabetic eyes in the future are explored. The role of pharmacological (intravitreal injections [IVT] of anti-VEGFs and steroids) and laser therapies are summarised. Generally, IVT anti-VEGF are offered as first line pharmacologic therapy. As requirements of diabetic patients in particular patient groups may vary, including pregnant women, children, and persons with learning difficulties, it is important that DR management is personalised in such particular patient groups. First choice therapy needs to be individualised in these cases and may be intravitreal steroids rather than the standard choice of anti-VEGF agents. Some of these, but not all, are discussed in this document.
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Pradhana D, Priya M N S, Surya J, Bhende M, Laxmi G, Sharma T, Raman R. Optical Coherence Tomography-Based Prevalence of Diabetic Macular Edema and its Associated Risk Factors in Urban South India: A Population-Based Study. Ophthalmic Epidemiol 2021; 29:149-155. [PMID: 33856942 DOI: 10.1080/09286586.2021.1907846] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Background: To estimate the prevalence of optical coherence tomography (OCT)-defined diabetic macular oedema (DME) in urban South Indian population and to elucidate their associated risk factors.Methods: Of 911 participants from the Sankara Nethralaya Diabetic Retinopathy and Molecular Genetics Study-II (SN-DREAMS-), 759 who underwent OCT were analysed. The participants underwent a comprehensive examination and retinal photography following a standard protocol for diabetic retinopathy (DR) grading. The subjects were categorized into centre-involving DME (CI-DME), non-centre involving DME (NCI-DME), and No-DME based on the mean retinal thickness at the central 1 mm, inner and outer ETDRS subfields.Results: The prevalence of CI-DME and NCI-DME in the Chennai population was 3.03% (95% CI: 3.01-3.05) and 10.80% (95% CI: 10.7-11.02). NCI-DME was found to be higher by 9.5% (95% CI: 0.07-0.11) in the early stages of DR. A greater number of subjects with CI DME were aged >60 years and had diabetes mellitus (DM) for >10 years. The significant risk factors for NCI-DME are diastolic blood pressure, serum total cholesterol, serum triglyceride, insulin use and neuropathy (OR (95% CI): 0.97 (0.94-100), 1.00 (1.00-1.01), 0.99 (0.98-0.99), 2.32 (1.15-4.68) and 4.24 (1.22-14.69), respectively) and for CI DME are duration of diabetes, anaemia, neuropathy and insulin use (OR (95% CI): 2.49 (0.96-6.40), 3.41 (1.34-8.65), 10.58 (1.68-66.56) and 3.51 (1.12-10.95), respectively).Conclusions: The prevalence of NCI-DME was found to be higher than that of CI-DME in patients with DR.
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Affiliation(s)
- Divya Pradhana
- Department of Vitreoretina, Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | | | - Janani Surya
- Vision Research Foundation, Sankara Nethralaya, Chennai, India
| | - Muna Bhende
- Department of Vitreoretina, Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Gella Laxmi
- Department of Vitreoretina, Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Tarun Sharma
- Department of Vitreoretina, Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Rajiv Raman
- Department of Vitreoretina, Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
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28
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Raman R, Ramasamy K, Rajalakshmi R, Sivaprasad S, Natarajan S. Diabetic retinopathy screening guidelines in India: All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement. Indian J Ophthalmol 2021; 69:678-688. [PMID: 33269742 PMCID: PMC7942107 DOI: 10.4103/ijo.ijo_667_20] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/13/2020] [Accepted: 07/14/2020] [Indexed: 12/15/2022] Open
Abstract
Diabetic retinopathy (DR) is an emerging preventable cause of blindness in India. All India Ophthalmology Society (AIOS) and Vitreo-Retinal Society of India (VRSI) have initiated several measures to improve of DR screening in India. This article is a consensus statement of the AIOS DR task force and VRSI on practical guidelines of DR screening in India. Although there are regional variations in the prevalence of diabetes in India at present, all the States in India should screen their population for diabetes and its complications. The purpose of DR screening is to identify people with sight-threatening DR (STDR) so that they are treated promptly to prevent blindness. This statement provides strategies for the identification of people with diabetes for DR screening, recommends screening intervals in people with diabetes with and without DR, and describes screening models that are feasible in India. The logistics of DR screening emphasizes the need for dynamic referral pathways with feedback mechanisms. It provides the clinical standards required for DR screening and treatment of STDR and addresses the governance and quality assurance (QA) standards for DR screening in Indian settings. Other aspects incorporate education and training, recommendations on Information technology (IT) infrastructure, potential use of artificial intelligence for grading, data capture, and requirements for maintenance of a DR registry. Finally, the recommendations include public awareness and the need to work with diabetologists to control the risk factors so as to have a long-term impact on prevention of diabetes blindness in India.
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Affiliation(s)
- Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Chennai, Tamil Nadu, India
| | - Kim Ramasamy
- Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Ramachandran Rajalakshmi
- Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
| | | | - S Natarajan
- Aditya Jyot Eye Hospital Pvt. Ltd., Mumbai, Maharashtra, India
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Limwattanayingyong J, Nganthavee V, Seresirikachorn K, Singalavanija T, Soonthornworasiri N, Ruamviboonsuk V, Rao C, Raman R, Grzybowski A, Schaekermann M, Peng LH, Webster DR, Semturs C, Krause J, Sayres R, Hersch F, Tiwari R, Liu Y, Ruamviboonsuk P. Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders. J Diabetes Res 2020; 2020:8839376. [PMID: 33381600 PMCID: PMC7758133 DOI: 10.1155/2020/8839376] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/06/2020] [Accepted: 11/30/2020] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR. METHODS We randomly selected patients with diabetes screened twice, two years apart within a nationwide screening program. The reference standard was established via adjudication by retina specialists. Each patient's color fundus photographs were graded, and a patient was considered as having sight-threatening DR (STDR) if the worse eye had severe nonproliferative DR, proliferative DR, or diabetic macular edema. We compared DR screening via two modalities: DL and HG. For each modality, we simulated treatment referral by excluding patients with detected STDR from the second screening using that modality. RESULTS There were 5,738 patients (12.3% STDR) in the first screening. DL and HG captured different numbers of STDR cases, and after simulated referral and excluding ungradable cases, 4,148 and 4,263 patients remained in the second screening, respectively. The STDR prevalence at the second screening was 5.1% and 6.8% for DL- and HG-based screening, respectively. Along with the prevalence decrease, the sensitivity for both modalities decreased from the first to the second screening (DL: from 95% to 90%, p = 0.008; HG: from 74% to 57%, p < 0.001). At both the first and second screenings, the rate of false negatives for the DL was a fifth that of HG (0.5-0.6% vs. 2.9-3.2%). CONCLUSION On 2-year longitudinal follow-up of a DR screening cohort, STDR prevalence decreased for both DL- and HG-based screening. Follow-up screenings in longitudinal DR screening can be more difficult and induce lower sensitivity for both DL and HG, though the false negative rate was substantially lower for DL. Our data may be useful for health-economics analyses of longitudinal screening settings.
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Affiliation(s)
- Jirawut Limwattanayingyong
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Variya Nganthavee
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Kasem Seresirikachorn
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Tassapol Singalavanija
- Department of Ophthalmology, Chulabhorn Hospital, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | | | - Varis Ruamviboonsuk
- Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chetan Rao
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | | | | | | | | | | | | | | | - Richa Tiwari
- Work done at Google via Optimum Solutions Pte Ltd, Singapore
| | - Yun Liu
- Google Health, Palo Alto, CA, USA
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
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30
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Chou Y, Ma J, Su X, Zhong Y. Emerging insights into the relationship between hyperlipidemia and the risk of diabetic retinopathy. Lipids Health Dis 2020; 19:241. [PMID: 33213461 PMCID: PMC7677820 DOI: 10.1186/s12944-020-01415-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/03/2020] [Indexed: 02/06/2023] Open
Abstract
Hyperlipidemia is correlated with a series of health problems. Notably, aside from its established role in promoting cardiovascular morbidity and mortality, hyperlipidemia has also been considered for modulating the risk and the severity of multiple metabolic disorders. According to the results of epidemiologic investigations, several certain circulating lipoprotein species are correlated with the prevalence of diabetic retinopathy, suggesting that the physiological and pathological role of these lipoproteins is analogous to that observed in cardiovascular diseases. Furthermore, the lipid-lowering treatments, particularly using statin and fibrate, have been demonstrated to ameliorate diabetic retinopathy. Thereby, current focus is shifting towards implementing the protective strategies of diabetic retinopathy and elucidating the potential underlying mechanisms. However, it is worth noting that the relationship between major serum cholesterol species and the development of diabetic retinopathy, published by other studies, was inconsistent and overall modest, revealing the relationship is still not clarified. In this review, the current understanding of hyperlipidemia in pathogenesis of diabetic retinopathy was summarized and the novel insights into the potential mechanisms whereby hyperlipidemia modulates diabetic retinopathy were put forward.
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Affiliation(s)
- Yuyu Chou
- Department, of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Jin Ma
- Department, of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Xin Su
- Department of Cardiology, The Xiamen Cardiovascular Hospital of Xiamen University, Xiamen, 363001, Fujian, China.
| | - Yong Zhong
- Department, of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
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31
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Chen T, Jin L, Zhu W, Wang C, Zhang G, Wang X, Wang J, Yang K, Cochrane GM, Lamoureux EL, Friedman DS, Gilbert S, Lansingh VC, Resnikoff S, Zhao J, Xiao B, He M, Congdon N. Knowledge, attitudes and eye health-seeking behaviours in a population-based sample of people with diabetes in rural China. Br J Ophthalmol 2020; 105:806-811. [PMID: 32737033 DOI: 10.1136/bjophthalmol-2020-316105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 05/05/2020] [Accepted: 06/12/2020] [Indexed: 12/23/2022]
Abstract
AIMS To assess knowledge of diabetes and acceptance of eye care among people with diabetes in rural China, to improve service uptake. METHODS Population-based study of people in Guangdong, China, with glycosylated haemoglobin A1c≥6.5% and/or known history of diabetes. Between August and November 2014, participants answered a questionnaire (based on Delphi process/previous focus groups) on medical history, demographic characteristics, self-rated health and vision, knowledge about diabetes and diabetic retinopathy, quality of local healthcare, barriers to treatment, likely acceptance of eye exams and treatment, and interventions rated most likely to improve service uptake. Presenting visual acuity was assessed, fundus photography performed and images graded by trained graders. Potential predictors of accepting care were evaluated and confounders adjusted for using logistic regression. RESULTS A total of 562 people (9.6% (256/5825), mean age 66.2±9.84 years, 207 (36.8%) men) had diabetes, 118 (22.3%) previously diagnosed. 'Very likely' or 'likely' acceptance of laser treatment (140/530=26.4%) was lower than for eye exams (317/530=59.8%, p<0.001). Predictors of accepting both exams and laser included younger age (p<.001) and prior awareness of diabetes diagnosis (p=0.004 and p=0.035, respectively). The leading barrier to receiving diabetes treatment was unawareness of diagnosis (409/454, 97.2%), while interventions rated most likely to improve acceptance of eye exams included reimbursement of travel costs (387/562, 73.0%), video or other health education (359/562, 67.7%) and phone call reminders (346/562, 65.3%). CONCLUSIONS Improving diagnosis of diabetes, along with incentives, education and communication strategies, is most likely to enhance poor acceptance of diabetic eye care in this setting.
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Affiliation(s)
- Tingting Chen
- The Ophthalmology Department, Sun Yat-sen University First Affiliated Hospital, Guangzhou, China.,State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Ling Jin
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Wenhui Zhu
- The Ophthalmology Department, Sun Yat-sen University First Affiliated Hospital, Guangzhou, China.,State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Congyao Wang
- The Ophthalmology Department, Sun Yat-sen University First Affiliated Hospital, Guangzhou, China.,State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Guoshan Zhang
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Xiuqin Wang
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Jun Wang
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Ke Yang
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Gillian M Cochrane
- Faculty of Health, School of Medicine (Optometry), Deakin University, Burwood, Australia
| | - Ecosse Luc Lamoureux
- Health Services Research Unit, Singapore Eye Research Institute, Singapore, Singapore
| | - David S Friedman
- Glaucoma Center of Excellence, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Suzanne Gilbert
- Innovation & Sight Programs, Seva Foundation, Berkeley, California, USA
| | | | | | - Jialiang Zhao
- Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Dongcheng-qu,China
| | - Baixiang Xiao
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China.,Centre for Eye Research Australia; Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Nathan Congdon
- State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China .,Centre for Public Health, Queen's University Belfast, Belfast, UK
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Mao XB, Cheng YH, Peng KS, You ZP. Sirtuin (Sirt) 3 Overexpression Prevents Retinopathy in Streptozotocin-Induced Diabetic Rats. Med Sci Monit 2020; 26:e920883. [PMID: 32506069 PMCID: PMC7275642 DOI: 10.12659/msm.920883] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Sirtuin (Sirt) 3 could promote autophagy by downregulating the expression of genes related to neovascularization in retinal endothelial cells. In this study, we aimed to investigate the effect of Sirt3 overexpression on retinopathy in streptozotocin (STZ)-induced diabetic rats, and to assess its mechanisms. MATERIAL AND METHODS Ntraperitoneal injection of STZ in rats was used to produce a diabetic model. The study rats were divided into 4 groups (n=6 for each group): a control group; a model group; a model+scrambled adenovirus group; and a model+Sirt3 overexpression group. Hematoxylin and eosin (H&E) staining determined the pathological changes of retina tissues. Immunohistochemistry, fluorescence quantitative polymerase chain reaction, and western blotting were used to detect the expression of Sirt3, vascular endothelial growth factor (VEGF), and microtubule-associated protein 1A/1B-light chain 3 (LC3). RESULTS In the model group, the inner limiting membrane was swollen, uneven and thickened, and the capillary endothelial cells occasionally protruded into the inner limiting membrane. These abnormalities were prevented by Sirt3 overexpression. Compared with the control group, the expression of Sirt3 at both mRNA and protein levels in the model group was significantly reduced, while the expression of VEGF was increased versus the control group (P.
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Affiliation(s)
- Xin-Bang Mao
- Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (mainland)
| | - Yan-Hua Cheng
- Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (mainland)
| | - Ke-Su Peng
- Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (mainland)
| | - Zhi-Peng You
- Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (mainland)
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33
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Joseph S, Kim R, Ravindran RD, Fletcher AE, Ravilla TD. Effectiveness of Teleretinal Imaging-Based Hospital Referral Compared With Universal Referral in Identifying Diabetic Retinopathy: A Cluster Randomized Clinical Trial. JAMA Ophthalmol 2020; 137:786-792. [PMID: 31070699 PMCID: PMC6512266 DOI: 10.1001/jamaophthalmol.2019.1070] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Question Does screening for diabetic retinopathy by teleretinal imaging in physician offices in India lead to higher adherence to eye hospital referral and a greater yield of diabetic retinopathy cases compared with a strategy of referral of all eligible patients with diabetes? Findings In a cluster randomized clinical trial of 801 patients with diabetes, proportionately more patients in the teleretinal group attended the hospital eye examination and had confirmed diabetic retinopathy compared with the control group. Meaning The results suggest that, in the Indian setting, teleretinal screening is an effective approach for identifying diabetic retinopathy. Importance Studies in high-income countries provide limited evidence from randomized clinical trials on the benefits of teleretinal screening to identify diabetic retinopathy (DR). Objective To evaluate the effectiveness of teleretinal-screening hospital referral (TR) compared with universal hospital referral (UR) in people with diabetes. Design, Setting, and Participants A cluster randomized clinical trial of 8 diabetes clinics within 10 km from Aravind Eye Hospital (AEH), Madurai, India, was conducted. Participants included 801 patients older than 50 years. The study was conducted from May 21, 2014, to February 7, 2015; data analysis was performed from March 12 to June 16, 2015. Interventions In the TR cohort, nonmydriatic, 3-field, 45° retinal images were remotely graded by a retinal specialist and patients with DR, probable DR, or ungradable images were referred to AEH for a retinal examination. In the UR cohort, all patients were referred for a retinal examination at AEH. Main Outcomes and Measures Hospital-diagnosed DR. Results Of the 801 participants, 401 were women (50.1%) (mean [SD] age, 60.0 [7.3] years); mean diabetes duration was 8.6 (6.6) years. In the TR cohort, 96 of 398 patients (24.1%) who underwent teleretinal imaging were referred with probable DR (53 [13.3%]) or nongradable images (43 [10.8%]). Hospital attendance at AEH was proportionately higher with TR (54 of 96 referred [56.3%]) compared with UR (150 of 400 referred [37.5%]). The intention-to-treat analysis based on all patients eligible for referral in each arm showed that proportionately more patients with TR (36 of 96 [37.5]%) were diagnosed with DR compared with UR (50 of 400 [12.5%]) (unadjusted risk ratio [RR], 3.00; 95% CI, 2.01-4.48). These results were little changed by inclusion of covariates (RR, 2.72; 95% CI, 1.90-3.91). The RR was lower in the per-protocol analysis based on all patients who adhered to referral (covariate-adjusted RR, 1.75; 95% CI, 1.12-2.74). Diagnoses of DR were predominantly mild or moderate nonproliferative DR (36 in TR and 43 in UR). In the UR arm, there were 4 cases of severe nonproliferative DR and 2 cases of proliferative DR. Age (RR, 0.98; 95% CI, 0.95-0.99), female sex (RR, 0.79; 95% CI, 0.64-0.98), and hypertension diagnosis (RR, 0.81; 95% CI, 0.68-0.95) were factors associated with lower attendance. Those with higher secondary educational level or more were twice as likely to attend (RR, 2.00; 95% CI, 1.32-3.03). Conclusions and Relevance The proportionate yield of DR cases was higher in the TR arm, confirming the potential benefit, at least in the setting of eye hospitals in India, of a targeted referral approach using teleretinal screening to identify patients with DR. Trial Registration ClinicalTrials.gov identifier: NCT02085681
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Affiliation(s)
- Sanil Joseph
- Lions Aravind Institute of Community Ophthalmology, Aravind Eye Care System, Madurai, India
| | | | | | - Astrid E Fletcher
- Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Thulasiraj D Ravilla
- Lions Aravind Institute of Community Ophthalmology, Aravind Eye Care System, Madurai, India
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34
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Affiliation(s)
- Sobha Sivaprasad
- Moorfields NIHR Biomedical Research Centre, London, United Kingdom
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35
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Wong TY, Sabanayagam C. The War on Diabetic Retinopathy: Where Are We Now? Asia Pac J Ophthalmol (Phila) 2019; 8:448-456. [PMID: 31789647 PMCID: PMC6903323 DOI: 10.1097/apo.0000000000000267] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 09/30/2019] [Indexed: 12/22/2022] Open
Abstract
Diabetic retinopathy (DR), a major cause of blindness in working-age adults, is emerging as a major public health issue worldwide, in particular in low- and middle-income countries (LMIC). Traditionally, the management of DR has been on tertiary-level treatment (eg, laser, anti-VEGF injections and surgery) in specialized settings by highly trained ophthalmologists on individual patients. To win the war on DR, a paradigm shift in strategic focus and resources must be made from such tertiary treatment toward primary and secondary prevention, which are broader, more impactful, and cost-effective for the larger population. These include improving education and awareness of risk of DR among people initially diagnosed with diabetes, promoting behavioral modifications such as physical activity and medication adherence for improving glycemic and blood pressure control, setting up systematic screening programs for DR to detect the onset or progression of the disease, and implementing cost-effective, evidence-based policies and guidelines for managing DR. Additionally, there is a need to leverage on novel technology including the application of digital big data to predict people at risk of diabetes and DR, the use of wearable devices and smart phone apps, behavioral techniques including social media for self-management of diabetes, and telemedicine-based DR screening incorporating artificial intelligence (AI) to broaden access to screening in all settings. To turn the tide on the war on DR, we must reframe DR not only as a specific condition that can be managed by ophthalmologists, but fundamentally, as a preventable condition by shifting the weight of strategies from tertiary to secondary and primary battlegrounds.
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Affiliation(s)
- Tien Y. Wong
- Singapore Eye Research Institute, Singapore, Singapore National Eye Center, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS, Medical School, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore, Singapore National Eye Center, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS, Medical School, Singapore
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Wong T, Sabanayagam C. Strategies to Tackle the Global Burden of Diabetic Retinopathy: From Epidemiology to Artificial Intelligence. Ophthalmologica 2019; 243:9-20. [DOI: 10.1159/000502387] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 07/29/2019] [Indexed: 11/19/2022]
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Sadat Mahmoudi Nezhad G, Razeghinejad R, Janghorbani M, Mohamadian A, Hassan Jalalpour M, Bazdar S, Salehi A, Molavi Vardanjani H. Prevalence, Incidence and Ecological Determinants of Diabetic Retinopathy in Iran: Systematic Review and Meta-analysis. J Ophthalmic Vis Res 2019; 14:321-335. [PMID: 31660112 PMCID: PMC6815336 DOI: 10.18502/jovr.v14i3.4790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 04/06/2019] [Indexed: 12/18/2022] Open
Abstract
Purpose
To estimate the pooled prevalence and incidence of diabetic retinopathy (DR) in Iran and to investigate their correlations with the Human Development Index (HDI), healthcare access (i.e., density of specialists and sub-specialists), and methodological issues. Methods
Electronic databases such as PubMed, Embase, Scopus, Web of Science, Google Scholar, and local databases were searched for cohort and cross-sectional studies published prior to January 2018. Prevalence and incidence rates of DR were extracted from January 2000 to December 2017 and random effects models were used to estimate pooled effect sizes. The Joanna Briggs Institute critical appraisal tool was applied for quality assessment of eligible studies. Results A total of 55,445 participants across 33 studies were included. The pooled prevalence (95% CI) of DR in diabetic clinics (22 studies), eye clinics (4 studies), and general population (7 studies) was 31.8% (24.5 to 39.2), 57.8% (50.2 to 65.3), and 29.6% (22.6 to 36.5), respectively. It was 7.4% (3.9 to 10.8) for proliferative DR and 7.1% (4.9 to 9.4) for clinically significant macular edema. The heterogeneity of individual estimates of prevalence was highly significant. HDI (P < 0.001), density of specialists (P = 0.004), subspecialists (P < 0.001), and sampling site (P = 0.041) were associated with heterogeneity after the adjustment for type of DR, duration of diabetes, study year, and proportion of diabetics with controlled HbA1C. Conclusion Human development and healthcare access were correlated with the prevalence of DR. Data were scarce on the prevalence of DR in less developed provinces. Participant recruitment in eye clinics might overestimate the prevalence of DR.
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Affiliation(s)
- Golnoush Sadat Mahmoudi Nezhad
- MPH Department, Shiraz University of Medical Sciences, Shiraz, Iran.,Poostchi Ophthalmology Research Center, Department of Ophthalmology, Shiraz University of Medical Sciences, Shiraz, Iran.,Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | | | - Mohsen Janghorbani
- Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran.,Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Mohamadian
- Poostchi Ophthalmology Research Center, Department of Ophthalmology, Shiraz University of Medical Sciences, Shiraz, Iran.,Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Hassan Jalalpour
- Poostchi Ophthalmology Research Center, Department of Ophthalmology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Somaye Bazdar
- MPH Department, Shiraz University of Medical Sciences, Shiraz, Iran.,Poostchi Ophthalmology Research Center, Department of Ophthalmology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Alireza Salehi
- MPH Department, Shiraz University of Medical Sciences, Shiraz, Iran
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Jin G, Xiao W, Ding X, Xu X, An L, Congdon N, Zhao J, He M. Prevalence of and Risk Factors for Diabetic Retinopathy in a Rural Chinese Population: The Yangxi Eye Study. Invest Ophthalmol Vis Sci 2019; 59:5067-5073. [PMID: 30357401 DOI: 10.1167/iovs.18-24280] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To investigate the prevalence and determinants of diabetic retinopathy (DR) among older adults in rural Southern China. Methods Using random cluster sampling, persons aged 50 years or older were randomly selected in rural Yangxi County, Guangdong Province, China. All participants underwent a standardized interview, fundus photography, and point of service glycosylated hemoglobin A1c (HbA1c) testing. Diabetes mellitus (DM) was diagnosed based on confirmed medical history or HbA1c ≥6.5%. Fundus photographs were graded for DR and diabetic macular edema (DME) based on the United Kingdom National Diabetic Eye Screening Program guidelines. Prevalence of and risk factors for DR and vision-threatening diabetic retinopathy (VTDR) were evaluated. Results Among 5825 subjects who participated (90.7% response rate) in the Yangxi Eye Study, 562 (9.6%) were diagnosed with DM, including 79 (14.1%) known and 483 new (85.9%) cases. Among DM cases, 476 (84.7%) had gradable fundus photos. The prevalence of any DR and VTDR were 8.19% (95% confidence interval [CI] 5.9-11.0) and 5.25% (95% CI 3.43-7.66), respectively. These figures were 23.9% and 12.7% for known and 5.43% and 3.95% for new DM cases. Risk factors for any DR were higher HbA1c level (OR [odds ratio] per unit 1.34, P < 0.001), longer duration of DM (OR per year = 2.29, P < 0.001) and having previously undergone cataract surgery (OR 4.11, P < 0.030). Conclusions Our study found a lower prevalence of DR among adults 50 years and older than in previously reports. Perhaps this difference can be explained by the short duration of most cases.
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Affiliation(s)
- Guangming Jin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Wei Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaohu Ding
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiao Xu
- Rehabilitation Administration Department, National Institute of Hospital Administration, Chinese National Health and Family Planning Commission, Beijing, China
| | - Lei An
- Rehabilitation Administration Department, National Institute of Hospital Administration, Chinese National Health and Family Planning Commission, Beijing, China
| | - Nathan Congdon
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Translational Research for Equitable Eye Care, Centre for Public Health, Royal Victoria Hospital, Queen's University Belfast, Belfast, United Kingdom.,Orbis International, New York, New York, United States
| | - Jialiang Zhao
- Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
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Jing L, Li H, Zhang T, Lu J, Zhong L. MicroRNA‑4530 suppresses cell proliferation and induces apoptosis by targeting RASA1 in human umbilical vein endothelial cells. Mol Med Rep 2019; 19:3393-3402. [PMID: 30864691 PMCID: PMC6472120 DOI: 10.3892/mmr.2019.10000] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 08/02/2018] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs/miRs) are a class of endogenous and non-coding RNAs that are present in eukaryotes. In previous studies, miRNAs have been revealed to have an important role in cell growth and apoptosis. In the present study, the function of a novel and rarely studied miRNA, miR-4530, was investigated in human umbilical vein endothelial cells (HUVECs). The expression level of miR-4530 in HUVECs was investigated using reverse transcription-quantitative polymerase chain reaction following transfection with miR-4530 precursor plasmids, anti-miR-4530 plasmids and empty vector plasmids. Following this, it was revealed that overexpression of miR-4530 can suppress cell proliferation and enhance cell apoptosis. TargetScan analysis suggested that Ras p21 protein activator 1 (RASA1) is a target gene of miR-4530. The results of a dual-luciferase reporter assay also suggested that miR-4530 targets RASA1. Furthermore, the results of dual-luciferase reporter assay suggested that miR-4530 enhanced luciferase activity of the wild-type reporter, but not the mutant RASA1 reporter activity, thus suggesting that miR-4530 enhances the expression of RASA1. In addition, western blot analysis demonstrated that the protein expression level of RASA1 was enhanced following upregulation of miR-4530. The exact mechanism underlying this process has not yet been determined and requires further investigation. In addition, a RASA1 overexpression plasmid vector was transfected into HUVECs. The results suggest that overexpression of RASA1 suppresses cell growth and promotes apoptosis, which was in agreement with the results regarding the overexpression of miR-4530. To investigate how miRNA-4530 affects cellular function, numerous proteins associated with the extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/AKT serine/threonine kinase pathways were investigated via western blot analysis. The results suggested that miRNA-4530 suppresses cell proliferation and enhances apoptosis by targeting RASA1 via the ERK/MAPK and PI3K/AKT signaling pathways.
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Affiliation(s)
- Li Jing
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, College of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Hong Li
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, College of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Tao Zhang
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, College of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Jianxin Lu
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, College of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
| | - Lianjin Zhong
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, College of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
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40
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Sabanayagam C, Banu R, Chee ML, Lee R, Wang YX, Tan G, Jonas JB, Lamoureux EL, Cheng CY, Klein BEK, Mitchell P, Klein R, Cheung CMG, Wong TY. Incidence and progression of diabetic retinopathy: a systematic review. Lancet Diabetes Endocrinol 2019; 7:140-149. [PMID: 30005958 DOI: 10.1016/s2213-8587(18)30128-1] [Citation(s) in RCA: 283] [Impact Index Per Article: 47.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 04/06/2018] [Accepted: 04/06/2018] [Indexed: 12/23/2022]
Abstract
Diabetic retinopathy is a leading cause of vision impairment and blindness. We systematically reviewed studies published from Jan 1, 1980, to Jan 7, 2018, assessed the methodological quality, and described variations in incidence of diabetic retinopathy by region with a focus on population-based studies that were conducted after 2000 (n=8, including two unpublished studies). Of these eight studies, five were from Asia, and one each from the North America, Caribbean, and sub-Saharan Africa. The annual incidence of diabetic retinopathy ranged from 2·2% to 12·7% and progression from 3·4% to 12·3%. Progression to proliferative diabetic retinopathy was higher in individuals with mild disease compared with those with no disease at baseline. Our Review suggests that more high-quality population-based studies capturing data on the incidence and progression of diabetic retinopathy with stratification by age and sex are needed to consolidate the evidence base. Our data is useful for conceptualisation and development of major public health strategies such as screening programmes for diabetic retinopathy.
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Affiliation(s)
- Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | - Riswana Banu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Miao Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ryan Lee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, and Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China
| | - Gavin Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, and Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China; Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, Mannheim, Germany
| | - Ecosse L Lamoureux
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Barbara E K Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Paul Mitchell
- Centre for Vision Research, University of Sydney, Sydney, NSW, Australia
| | - Ronald Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - C M Gemmy Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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41
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Fu Y, Tang M, Xiang X, Liu K, Xu X. Glucose affects cell viability, migration, angiogenesis and cellular adhesion of human retinal capillary endothelial cells via SPARC. Exp Ther Med 2018; 17:273-283. [PMID: 30651792 PMCID: PMC6307404 DOI: 10.3892/etm.2018.6970] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 09/06/2018] [Indexed: 01/09/2023] Open
Abstract
The expression of secreted protein acidic and rich in cysteine (SPARC) has been recently identified to be associated with the pathology of diabetic retinopathy. Therefore, the present study aimed to evaluate the regulatory role of SPARC in human retinal capillary endothelial cells (HRCECs), following exposure to a high glucose environment in vitro. The cell viability, migration, angiogenesis, permeability and SPARC expression levels of HRCECs were measured following treatment with different concentrations of glucose (25, 50 or 100 mM). Lentiviral vectors (LV185-pL_shRNA_mKate2-SPARC-543; target sequence, GGATGAGGACAACAACCTTCT) that inhibit the expression of SPARC were constructed, and HRCECs were evaluated when infected by viruses carrying the lentiviral vectors. Cell viability was examined using the Cell Counting Kit-8 assay. The expression of SPARC in HRCECs increased as the concentration of glucose in the culture medium increased. Relatively high concentrations of glucose significantly inhibited cell proliferation (P<0.05), migration (P<0.05), angiogenesis (P<0.01), and the expression of ZO, occludin, claudin and JAM1 in tight junctions (P<0.01), gap junctions (Cx37 and Cx43; P<0.01) and adherens junctions (VE-cadherin, CTNNA1 and CTNNB1; P<0.05). However, when SPARC was downregulated by lentiviral vectors, the inhibitions induced by high concentrations of glucose were partially reversed. To conclude, the inhibitory effects on cell viability, migration, angiogenesis and cellular adhesion of HRCECs induced by high concentrations of glucose were reversed once the expression of SPARC was inhibited. These findings suggest that SPARC may serve an important role in pathogenesis of diabetic retinopathy.
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Affiliation(s)
- Yang Fu
- Department of Ophthalmology, Shanghai General Hospital of Nanjing Medical University, Shanghai 200080, P.R. China.,Shanghai Key Laboratory of Fundus Disease, Shanghai 200080, P.R. China
| | - Min Tang
- Department of Ophthalmology, Shanghai General Hospital of Nanjing Medical University, Shanghai 200080, P.R. China.,Shanghai Key Laboratory of Fundus Disease, Shanghai 200080, P.R. China
| | - Xiaoqiong Xiang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
| | - Kun Liu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
| | - Xun Xu
- Department of Ophthalmology, Shanghai General Hospital of Nanjing Medical University, Shanghai 200080, P.R. China.,Shanghai Key Laboratory of Fundus Disease, Shanghai 200080, P.R. China
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