Investigating physical activity levels in adults who are blind and vision impaired

BACKGROUND

It is well established that meeting physical activity (PA) guidelines has a range of physical and mental health benefits. For people who are blind and vision impaired (BVI) there may be additional benefits in terms of social inclusion and the prevention of sight deterioration.

OBJECTIVE

This study aimed to quantify PA levels, barriers to and motivators for PA in adults who are BVI.

METHODS

PA levels, perceived barriers to, and motivators for PA were measured via questionnaire of 310 self-identifying BVI adults (n = 310 mean age = 29.77 ± 11.37, 55.8% male).

RESULTS

PA levels were low, with 21.7% meeting PA guidelines. Median PA levels were not statistically significantly different between different age groups. There was no significant difference between genders, though mean days of PA for males was 0.382 days lower than for females. There was a significant difference between PA levels between the "no vision" (B1) and "useful vision" (B3) groups (p = 0.027), and the "no vision" (B1) and the "low vision" (B2) groups (p = 0.003). Transport (54.8%) and lack of access to enjoyable activities (47.0%) were the most commonly cited barriers, while "to relax" (36.4%) and "to have fun" (35.6%) were most commonly cited as very important motivators.

CONCLUSIONS

This study provides a valuable insight into the low levels of PA that persist amongst adults with BVI. Future research should seek to gain a deeper understanding of the PA barriers, motivators and facilitators in this cohort.

Improving diabetic retinopathy screening at the point of care: integrating telemedicine to overcome current challenges

OBJECTIVE

To investigate the utility of point of care screening of diabetic retinopathy (DR) and the impact of a telemedicine program to overcome current challenges.

METHODS

This was a retrospective study on people with type 2 diabetes mellitus (T2DM) who were screened for DR using the single-field non-mydriatic fundus photography at the point of care during routine follow-up visits at endocrinology clinic. Retinal images were uploaded and sent to a retina specialist for review. Reports indicating retinopathy status and the need for direct retinal examination were transmitted back to the endocrinology clinic. All patients were informed about DR status and, if needed, referred to the retina specialist for direct retinal examination.

RESULTS

Of the 1159 individuals screened for DR, 417 persons (35.98%) were screen-positive and referred to the retina specialist for direct retinal examination. A total of 121 individuals (29.01%) underwent direct retinal examination by the specialist. Diabetes macular edema (DME) was detected in 12.1%. In addition, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) were detected in 53.4% and 2.6% of the patients, respectively.

CONCLUSION

Integrating DR screening program at the point of care at the secondary care services improves the rate of DR screening as well as detection of sight threatening retinopathy and provides the opportunity for timely intervention in order to prevent advanced retinopathy in people with T2DM.

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy

OBJECTIVES

To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers.

METHODS

A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry.

RESULTS

Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively.

CONCLUSIONS

Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

BACKGROUND

Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes.

OBJECTIVE

This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors.

METHODS

We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI.

RESULTS

Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR).

CONCLUSIONS

This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study

BACKGROUND

The Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants' baseline characteristics, and preliminary DR progression rates at the first annual follow-up.

METHODS

DRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics. Patients with referable DR/DME (> mild NPDR) or ungradable fundus images were excluded. Sociodemographic, lifestyle, medical and clinical information was obtained from medical records and interviewer-administered questionnaires at baseline. These data are extracted from medical records at 12, 24 and 36 months post-enrollment. Baseline descriptive characteristics stratified by DR severity at baseline and rates of progression to referable DR at 12-month follow-up were calculated.

RESULTS

Of 5,840 eligible patients, 78.3% (n = 4,570, median [interquartile range [IQR] age 61.0 [55-67] years; 54.7% male; 68.0% Chinese) completed the baseline assessment. At baseline, 97.4% and 2.6% had none and mild NPDR (worse eye), respectively. Most participants had hypertension (79.2%) and dyslipidemia (92.8%); and almost half were obese (43.4%, BMI ≥ 27.5 kg/m2). Participants without DR (vs mild DR) reported shorter DM duration, and had lower haemoglobin A1c, triglycerides and urine albumin/creatinine ratio (all p < 0.05). To date, we have extracted 41.8% (n = 1909) of the 12-month follow-up data. Of these, 99.7% (n = 1,904) did not progress to referable DR. Those who progressed to referable DR status (0.3%) had no DR at baseline.

CONCLUSIONS

In our prospective study of patients with T2DM and non-referable DR attending polyclinics, we found extremely low annual DR progression rates. These preliminary results suggest that extending screening intervals beyond 12 months may be viable and safe for most participants, although our 3-year follow up data are needed to substantiate this claim and develop the risk stratification model to identify low-risk patients with T2DM who can be assigned biennial or triennial screening intervals.

An Economic Analysis for the Use of Artificial Intelligence in Screening for Diabetic Retinopathy in Trinidad and Tobago

This is a systematic review of 25 publications on the topics of the prevalence and cost of diabetic retinopathy (DR) in Trinidad and Tobago, the cost of traditional methods of screening for DR, and the use and cost of artificial intelligence (AI) in screening for DR. Analysis of these publications was used to identify and make estimates for how resources allocated to ophthalmology in public health systems in Trinidad and Tobago can be more efficiently utilized by employing AI in diagnosing treatable DR. DR screening was found to be an effective method of detecting the disease. Screening was found to be a universally cost-effective method of disease prevention and for altering the natural history of the disease in the spectrum of low-middle to high-income economies, such as Rwanda, Thailand, China, South Korea, and Singapore. AI and deep learning systems were found to be clinically superior to, or as effective as, human graders in areas where they were deployed, indicating that the systems are clinically safe. They have been shown to improve access to diabetic retinal screening, improve compliance with screening appointments, and prove to be cost-effective, especially in rural areas. Trinidad and Tobago, which is estimated to be disproportionately more affected by the burden of DR when projected out to the mid-21st century, stands to save as much as US$60 million annually from the implementation of an AI-based system to screen for DR versus conventional manual grading.

Diabetic retinopathy: Knowledge, awareness, and practices in primary-care centers in the Kingdom of Bahrain

Background

With the growing incidence of diabetic-related complications such as retinopathy and the well-understood role of screening and telemedicine around the world in preventing and managing this potentially blinding disease, there comes the importance of awareness toward this silent eye condition. Awareness ideally should be raised both from the patients' and physicians' perspectives. This study aims to assess the awareness of primary care physicians (PCPs) towards diabetic retinopathy (DR) and its management.

Materials and Methods

A survey considering professional background, knowledge, and practices was distributed electronically to all PCPs in the Kingdom of Bahrain and analyzed using SPSS Vs 21.

Results

A total of 83 physicians completed the survey; 81.9% were female and 96.4% were Bahraini. More than 60% of them accurately knew screening guidelines for type 1 and 2 DR. Of the total, 72.5% would refer patients to the ophthalmologist even if asymptomatic, and 97.6% correctly stated that a dilated eye exam is the method of choice most efficient for assessing DR. Nearly 60% knew about the current treatment modalities for DR and 91.3% knew about the presence of a national screening program in the Kingdom.

Conclusion

The overall knowledge of DR was good with some potential gaps in information about screening. PCPs could benefit from regular refresher courses for more precise practices on screening and referral of retinopathy.

Integration of dietary nutrition and TRIB3 action into diabetes mellitus

Despite intensive studies for decades, the common mechanistic correlations among the underlying pathology of diabetes mellitus (DM), its complications, and effective clinical treatments remain poorly characterized. High-quality diets and nutrition therapy have played an indispensable role in the management of DM. More importantly, tribbles homolog 3 (TRIB3), a nutrient-sensing and glucose-responsive regulator, might be an important stress-regulatory switch, linking glucose homeostasis and insulin resistance. Therefore, this review aimed to introduce the latest research progress on the crosstalk between dietary nutrition intervention and TRIB3 in the development and treatment of DM. This study also summarized the possible mechanisms involved in the signaling pathways of TRIB3 action in DM, in order to gain an in-depth understanding of dietary nutrition intervention and TRIB3 in the pathogenesis of DM at the organism level.

A Narrative Review of the Advances in Screening Methods for Diabetic Retinopathy: Enhancing Early Detection and Vision Preservation

Diabetes mellitus (DM) is putting a great burden worldwide. This rise in DM cases, both type 1 and 2, significantly impacts public health. India has grappled with a diabetes epidemic for several years, leading to many misdiagnosed and untreated diabetes cases. Diabetes remains a significant factor in adult-onset blindness despite improvements in diabetes management. This increases the danger of diabetic retinopathy (DR) with permanent loss of sight for those affected. The screening for DR aims to identify those persons with complications arising from diabetes or DR, which could potentially result in blindness, so that treatment can be started immediately and blindness can be avoided. A comprehensive health system approach is required to ensure that the public sector in India effectively screens for DR. Improving patient outcomes and avoiding visual loss depends significantly on early identification and treatment. This article discusses the actions that should be implemented to establish a national effort for systematic DR screening. It also highlights the importance of screening in DR and its impact on treatment effectiveness. Regular screenings enable the early detection of retinopathy, allowing for timely intervention and treatment. Early screening helps prevent complications associated with DR, such as macular edema or retinal detachment. Screening also assists healthcare providers in planning, optimizing treatment approaches, and monitoring treatment effectiveness. Meanwhile, early intervention is essential for enhancing treatment outcomes, thus enhancing the chances of preserving vision and preventing further progression of the disease. This helps in improving the overall management of this sight-threatening complication.

What is the coverage of retina screening services for people with diabetes? Protocol for a systematic review and meta-analysis

INTRODUCTION

Diabetic retinopathy is a leading cause of vision impairment globally. Vision loss from diabetic retinopathy can generally be prevented by early detection and timely treatment. The WHO included a measure of service access for diabetic retinopathy as a core indicator in the Eye Care Indicator Menu launched in 2022: retina screening coverage for people with diabetes. The aim of this review is to provide a comprehensive global and regional summary of the available information on retina screening coverage for people with diabetes.

METHODS AND ANALYSIS

A search will be conducted in five databases without language restrictions for studies from any country reporting retina screening coverage for adults with any type of diabetes at the national or subnational level using data collected since 1 January 2000 until the search date. We will also seek reports and coverage statistics from government websites of all WHO member states. Two investigators will independently screen studies, extract relevant data and assess risk of bias of included studies. The results of the review will be reported using the Preferred Reporting Items for Systematic Review and Meta-Analysis guideline. We will summarise the range of coverage definitions reported across included studies and present the median retina screening coverage in WHO regions and by World Bank country income level. Depending on the availability of data, we will conduct meta-analysis to assess disparities in retina screening coverage for people with diabetes by factors in the PROGRESS framework (Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status and Social capital).

ETHICS AND DISSEMINATION

This review will only include published data thus no ethical approval will be sought. The findings of this review will be published in a peer-reviewed journal and presented at relevant conferences.

PROTOCOL REGISTRATION NUMBER

OSF registration 17/10/2023: https://osf.io/k5p69.

Unveiling Diabetes: Categories, Genetics, Diagnostics, Treatments, and Future Horizons

Diabetes mellitus is a global epidemic affecting millions of individuals worldwide. This comprehensive review aims to provide a thorough understanding of the categorization, disease identity, genetic architecture, diagnosis, and treatment of diabetes. The categorization of diabetes is discussed, with a focus on type 1 and type 2 diabetes, as well as the lesser-known types, type 3 and type 4 diabetes. The geographical variation, age, gender, and ethnic differences in the prevalence of type 1 and type 2 diabetes are explored. The impact of disease identity on disease management and the role of autoimmunity in diabetes are examined. The genetic architecture of diabetes, including the interplay between genotype and phenotype, is discussed to enhance our understanding of the underlying mechanisms. The importance of insulin injection sites and the insulin signalling pathway in diabetes management are highlighted. The diagnostic techniques for diabetes are reviewed, along with advancements for improved differentiation between types. Treatment and management approaches, including medications used in diabetes management are presented. Finally, future perspectives are discussed, emphasizing the need for further research and interventions to address the global burden of diabetes. This review serves as a valuable resource for healthcare professionals, researchers, and policymakers, providing insights to develop targeted strategies for the prevention, diagnosis, and management of this complex disease.

Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.

Optical coherence tomography angiography in diabetic retinopathy

There remain many unanswered questions on how to assess and treat the pathology and complications that arise from diabetic retinopathy (DR). Optical coherence tomography angiography (OCTA) is a novel and non-invasive three-dimensional imaging method that can visualize capillaries in all retinal layers. Numerous studies have confirmed that OCTA can identify early evidence of microvascular changes and provide quantitative assessment of the extent of diseases such as DR and its complications. A number of informative OCTA metrics could be used to assess DR in clinical trials, including measurements of the foveal avascular zone (FAZ; area, acircularity, 3D para-FAZ vessel density), vessel density, extrafoveal avascular zones, and neovascularization. Assessing patients with DR using a full-retinal slab OCTA image can limit segmentation errors and confounding factors such as those related to center-involved diabetic macular edema. Given emerging data suggesting the importance of the peripheral retinal vasculature in assessing and predicting DR progression, wide-field OCTA imaging should also be used. Finally, the use of automated methods and algorithms for OCTA image analysis, such as those that can distinguish between areas of true and false signals, reconstruct images, and produce quantitative metrics, such as FAZ area, will greatly improve the efficiency and standardization of results between studies. Most importantly, clinical trial protocols should account for the relatively high frequency of poor-quality data related to sub-optimal imaging conditions in DR and should incorporate time for assessing OCTA image quality and re-imaging patients where necessary.

Implementation of Diabetic Retinopathy Screening in Adult Patients With Type 2 Diabetes in a Primary Care Setting

Diabetic retinopathy (DR) is a microvascular complication of type 2 diabetes and the leading cause of blindness globally. Although diabetes-related eye exams are widely recognized as an effective method for early detection of DR, which can help to prevent eventual vision loss, adherence to screening exams in the United States is suboptimal. This article describes a quality improvement project to increase DR screening rates and increase knowledge and awareness of DR in adults with type 2 diabetes in a primary care setting using mobile DR screening units. This project addressed gaps of care and demonstrated that primary care settings can increase access to DR screening through a patient-centered process and thereby help to prevent irreversible outcomes of DR and improve quality of life.

Prevalence and associated factors of diabetic retinopathy among people with diabetes screened using fundus photography at a community diabetic retinopathy screening program in Nepal

BACKGROUND

This study aimed to assess the prevalence and associated factors of diabetic retinopathy (DR) and vision threatening DR (VTDR) among people with diabetes screened using fundus photography in Nepal.

METHODS

This is a retrospective study among people with diabetes presented for DR screening using fundus photography from 2013 to 2019. Detailed demographics, duration of diabetes, medical history, visual acuity, and grading of DR on fundus photography were analyzed. Fundus camera used in the study were;Topcon digital fundus camera 900 CXR and digital portable fundus cameras (Nidek-10 portable non-mydriatric fundus camera; Versacam & Trade & Alpha, France), and a Zeiss portable fundus camera (Zeiss Visucout 100). Macula centred and disc centred 45 degree two images were taken from each eye. Pupil were dilated in cases where there was media haze in un-dilated cases. DR was graded using early treatment diabetic retinopathy study criteria. The images were graded by fellowship trained retina specialist. DR prevalence included any DR changes in one or both eyes.

RESULTS

Total of 25,196 patients with diabetes were enrolled. Mean age was 54.2 years with Standard Deviation (S.D):12.9 years, ranging from 6 years to 97 years. Type 1 and type 2 diabetes comprised of 451 people (1.79%) and 24,747 (98.21%) respectively. Overall, 1.8% of the images were un-gradable. DR prevalence was 19.3% (95% Confidence Interval (CI): 18.8 - 19.7%). DR prevalence in type 1 and type 2 diabetes was 15.5% (95% CI: 12.5 - 18.6%) and 19.3% (CI: 18.8 - 19.8%) respectively. Clinically significant macular edema (CSME) was found in 5.9% (95% CI: 5.6-6.2%) and VTDR in 7.9% (95% CI: 7.7-8.3%). In multivariate analysis, our study revealed strong evidence to suggest that there is meaningful association between DR and VTDR with duration of diabetes, diabetic foot, diabetic neuropathy, agriculture occupation, those under oral hypoglycaemic agents or insulin or both as compared to those under diet only, and presenting visual acuity > 0.3LogMAR.

CONCLUSION

Prevalence and associated factors for DR and VTDR were similar to other DR screening programs in the region. Emphasis on wider coverage of DR screening could help for timely detection and treatment of STDR to avoid irreversible blindness.

The hsa_circ_0004805/hsa_miR-149-5p/TGFB2 axis plays critical roles in the pathophysiology of diabetic retinopathy in vitro and in vivo

The aim of this study was to investigate the mechanism underlying the role of a recently identified hsa_circ_0004805/hsa_miR-149-5p/transforming growth factor beta 2 (TGFB2) axis in the progression of diabetic retinopathy (DR). Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis suggested that hsa_circ_0004805 was highly expressed in aqueous humor samples of patients with DR, whereas hsa_miR-149-5p showed the opposite trend. Meanwhile, the results of a dual-luciferase reporter assay indicated that hsa_miR-149-5p directly interacted with both hsa_circ_0004805 and TGFB2. Using a variety of assays (Cell Counting Kit-8, EdU-labeling, Transwell, flow cytometric, wound healing, tube formation assays), we found that the overexpression of hsa_circ_0004805 significantly downregulated the level of hsa_miR-149-5p and promoted DNA synthesis, proliferation, migration, and tube formation in human retinal microvascular epithelial cells (hRECs) cultivated in a high-glucose environment. In contrast, hsa_miR-149-5p mimics inhibited DNA synthesis, proliferation, migration, and tube formation in hRECs by reducing the expression of its downstream target TGFB2 as well as the levels of phosphorylated SMAD2; however, these effects were reversed by the overexpression of hsa_circ_0004805. In a streptozotocin-induced Sprague-Dawley rat model of DR, retinal vascular leakage, capillary decellularization, loss of pericytes, fibrosis, and gliosis were evident, which could be reversed by vitreous microinjection of rat miR-149-5p mimics (rno-miR-149-5p agomir). Combined, our findings indicated that, under hyperglycemia, the hsa_circ_0004805/hsa_miR-149-5p/TGFB2 axis plays a critical role in the retinal pathophysiology associated with the development of DR, and has potential as a therapeutic target in the treatment of this condition.

Protective effect of evolocumab on Müller cells in the rat retina under hyperglycaemic and hypoxic conditions

AIMS

In this study, rat retinal Müller cells (RMCs) were cultured in vitro to investigate the protective mechanism of evolocumab on rat RMCs in diabetes mellitus (DM) and the expression of relevant inflammatory factors.

METHODS

The expression of proprotein convertase subtilisin/kexin type 9 (PCSK9) in the retinal tissues of diabetic rats was detected by immunohistochemistry. Sprague-Dawley (SD) rats at 5-7 d of life were selected as the source of RMCs and divided equally into three groups of 12 rats/24 eyes each. The effect of CoCl2 and evolocumab on the cellular activity of RMCs was determined by CCK-8 assay. The effect of CoCl2 and evolocumab on the migration level of RMCs after 72 h was measured by scratch test and the expression of various proteins after 72 h was measured by Western blot.

RESULTS

In STZ rats, the expression of PCSK9 was significantly upregulated in the retina, especially in the inner nuclear layer, which is mainly composed of RMCs. High glucose and CoCl2 stimulation markedly elevated PCSK9 and GFAP expression at the protein level in RMCs (P < 0.05). Evolocumab treatment (100 μg/ml) reduced the expression and secretion of inflammatory factors in stimulated RMCs (P < 0.05). Furthermore, evolocumab downregulates toll-like receptor-4 (TLR-4) levels and inhibited nuclear transcription factor-κB (NF-κB) phosphorylation in RMCs (P < 0.05).

CONCLUSIONS

Evolocumab protects against inflammation in RMCs, at least in part, by negatively regulating the activation of the TLR-4/NF-κB signalling pathway. Evolocumab may be a promising anti-inflammatory therapy for ocular fundus diseases, such as DR.

The interface targeting hnRNPA2B1 regulates the repression of transthyretin against human retinal microvascular endothelial cells in high-glucose environment

BACKGROUND

The interaction between transthyretin (TTR) and heterogeneous nuclear ribonucleoprotein (hnRNP)A2B1 is involved in the neovascularization of human retinal microvascular endothelial cells (hRECs) under hyperglycemic conditions. However, whether the TTR-hnRNPA2B1 interface can be altered and how this protein-protein interaction and associated downstream pathways are regulated is unclear.

METHODS

We performed homologous sequential analysis and binding energy assays using Discovery Studio and designed substitution targeting three fragments of the interface (fragment 1: aa 34-39, -RKAADD-; fragment 2, aa 61-68, -EEEFVEGI-; and fragment 3, aa 96-102, -TANDSGP-) to disrupt or stabilize the TTR-hnRNPA2B1 complex and were subjected to Co-immunoprecipitation analysis. To investigate the effect of TTR-hnRNPA2B1 interface alterations on the physiological properties of hRECs, we performed CCK-8, EdU, migration, wound healing and tube formation assays. To study the downstream genes, we performed qRT-PCR and western blot.

RESULTS

Nineteen TTR substitutions were recombinantly expressed in soluble form, results indicated that reducing the binding energy stabilized the TTR-hnRNPA2B1, while increasing the binding energy had the opposite effect. The native TTR significantly prohibited the proliferation, DNA synthesis, migration and tube formation capacities of hRECs, while fragment 1 always reduced these effects. However, the I68R and D99R substitutions in fragments 2 and 3, respectively, increased the inhibitory effect of TTR. Furthermore, our qRT-PCR and western blot results showed that the expression and protein levels of STAT-4, miR-223-3p and FBXW7 were also regulated by the alteration of the TTR-hnRNPA2B1 interface.

CONCLUSION

This work suggests that the formation of the TTR-hnRNPA2B1 complex plays vital role in hyperglycemia, and modification of this interface regulates the TTR-mediated inhibition of hREC neovascularization via the STAT-4/miR-223-3p/FBXW7 pathway. This mechanism could have important implications for diabetic retinopathy treatment.

Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review

OBJECTIVES

To systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening.

METHODS

We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively.

RESULTS

Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was <80% for all ML systems and in 6/31 studies evaluating DL systems. Studies reported high accuracy for detection of ungradable images, but the latter were analysed and reported inconsistently. Seven studies reported that AI was more sensitive but less specific than human graders.

CONCLUSIONS

AI-based systems are more sensitive than human graders and could be safe to use in clinical practice but have variable specificity. However, for many systems evidence is limited, at high risk of bias and may not generalise across settings. Therefore, pre-implementation assessment in the target clinical pathway is essential to obtain reliable and applicable accuracy estimates.

'I don`t need an eye check-up'. A qualitative study using a behavioural model to understand treatment-seeking behaviour of patients with sight threatening diabetic retinopathy (STDR) in India

Diabetic Retinopathy (DR) affects about 27% of patients with diabetes globally. According to the World Health Organization (WHO), DR is responsible for37 million cases of blindness worldwide. The SMART India study (October 2020-August 2021) documented the prevalence of diabetes, and DR in people40 years and above across ten Indian states and one Union Territory by conducting community screening. About 90% of people with sight threatening diabetic retinopathy (STDR) were referred from this screening study to eye hospitals for management, but failed to attend. This qualitative study, a component of the SMART India study, explored perceptions of referred patients regarding their susceptibility to eye related problems in diabetes and the benefits/barriers to seeking care. Perceived barriers from the viewpoint of ophthalmologists were also explored. Guided by the Health Beliefs Model (HBM), 20 semi structured interviews were carried out with consenting patients diagnosed with STDR. They included nine patients who had sought care recruited from eight eye hospitals across different states in India and eleven patients who did not seek care. Eleven ophthalmologists also participated. Four themes of analysis based on the HBM were, understanding of DR and its treatment, perceptions about susceptibility and severity, perceived barriers, perceived benefits and cues to action. Findings revealed poor understanding of the effects of diabetes on the eye contributing to low risk perception. Prohibitive costs of treatment, difficulties in accessing care services and poor social support were major barriers to seeking care. Ophthalmologists acknowledged that the absence of symptoms and the slow progressive nature of the disease deluded patients into thinking that they were fine. The study attests to the need for greater health literacy around diabetes, DR and STDR; for making treatment more affordable and accessible and for the development of effective patient education and communication strategies towards increasing compliance.

Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies

Aims

To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness.

Materials and methods

A search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm.

Results

Finally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR.

Conclusion

AI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.

Quantification of Microvascular Lesions in the Central Retinal Field: Could It Predict the Severity of Diabetic Retinopathy?

Diabetic retinopathy (DR) is a neurodegenerative disease characterized by the presence of microcirculatory lesions. Among them, microaneurysms (MAs) are the first observable hallmark of early ophthalmological changes. The present work aims to study whether the quantification of MAs, hemorrhages (Hmas) and hard exudates (HEs) in the central retinal field could have a predictive value on DR severity. These retinal lesions were quantified in a single field NM-1 of 160 retinographies of diabetic patients from the IOBA's reading center. Samples included different disease severity levels and excluded proliferating forms: no DR (n = 30), mild non-proliferative (n = 30), moderate (n = 50) and severe (n = 50). Quantification of MAs, Hmas, and HEs revealed an increasing trend as DR severity progresses. Differences between severity levels were statistically significant, suggesting that the analysis of the central field provides valuable information on severity level and could be used as a clinical tool to assess DR grading in the eyecare routine. Even though further validation is needed, counting microvascular lesions in a single retinal field can be proposed as a rapid screening system to classify DR patients with different stages of severity according to the international classification.

Inactive matrix Gla protein in relation to diabetic retinopathy in type 2 diabetes

Background and Aims

The contribution of inactive Matrix Gla protein (MGP) to ectopic vascular calcification associated with type 2 diabetes mellitus (T2DM) is well recognized. However, its role in diabetic microvascular complications remains unknown. The study aim was to identify any association between inactive MGP and diabetic retinopathy (DR). Its relation to insulin resistance was also explored.

Methods

The study included 90 participants, 65 Type 2 diabetic patients (25 without DR and 40 with DR) and 25 healthy controls. Serum inactive MGP was measured using ELISA. HOMA-IR was also assessed.

Results

Inactive MGP was significantly higher in both diabetic groups compared to controls (P < 0.001), as well as in Type 2 diabetic patients with retinopathy compared to Type 2 diabetes without retinopathy (P = 0.002). Inactive MGP was positively correlated with HbA1c, HOMA-IR, LDL-C and triglycerides (P < 0.001), and negatively correlated with HDL-C (P = 0.008) and eGFR (P < 0.001). Logistic Regression Analysis showed that inactive MGP was one of the most associated factors with DR.

Conclusions

Inactive MGP was found to be related to DR, insulin resistance and other dysmetabolic risk factors. These findings highlight that inactive MGP may be a significant contributor to the pathogenesis, evolution, and progression of DR.

Dynamic nomogram prediction model for diabetic retinopathy in patients with type 2 diabetes mellitus

BACKGROUND

To develop a dynamic prediction model for diabetic retinopathy (DR) using systemic risk factors.

METHODS

This retrospective study included type 2 diabetes mellitus (T2DM) patients discharged from the Second Affiliated Hospital of Kunming Medical University between May 2020 and February 2022. The early patients (80%) were used for the training set and the late ones (20%) for the validation set.

RESULTS

Finally, 1257 patients (1049 [80%] in the training set and 208 [20%] in the validation set) were included; 360 (28.6%) of them had DR. The areas under the curves (AUCs) for the multivariate regression (MR), least absolute shrinkage and selection operator regression (LASSO), and backward elimination stepwise regression (BESR) models were 0.719, 0.727, and 0.728, respectively. The Delong test showed that the BESR model had a better predictive value than the MR (p = 0.04899) and LASSO (P = 0.04999) models. The DR nomogram risk model was established according to the BESR model, and it included disease duration, age at onset, treatment method, total cholesterol, urinary albumin to creatinine ratio (UACR), and urine sugar. The AUC, kappa coefficient, sensitivity, specificity, and compliance of the nomogram risk model in the validation set were 0.79, 0.48, 71.2%, 78.9%, and 76.4%, respectively.

CONCLUSIONS

A relatively reliable DR nomogram risk model was established based on the BESR model.

Global trends and performances in diabetic retinopathy studies: A bibliometric analysis

Objective

The objective of this study is to conduct a comprehensive bibliometric analysis to identify and evaluate global trends in diabetic retinopathy (DR) research and visualize the focus and frontiers of this field.

Methods

Diabetic retinopathy-related publications from the establishment of the Web of Science (WOS) through 1 November 2022 were retrieved for qualitative and quantitative analyses. This study analyzed annual publication counts, prolific countries, institutions, journals, and the top 10 most cited literature. The findings were presented through descriptive statistics. VOSviewer 1.6.17 was used to exhibit keywords with high frequency and national cooperation networks, while CiteSpace 5.5.R2 displayed the timeline and burst keywords for each term.

Results

A total of 10,709 references were analyzed, and the number of publications continuously increased over the investigated period. America had the highest h-index and citation frequency, contributing to the most influence. China was the most prolific country, producing 3,168 articles. The University of London had the highest productivity. The top three productive journals were from America, and Investigative Ophthalmology Visual Science had the highest number of publications. The article from Gulshan et al. (2016; co-citation counts, 2,897) served as the representative and symbolic reference. The main research topics in this area were incidence, pathogenesis, treatment, and artificial intelligence (AI). Deep learning, models, biomarkers, and optical coherence tomography angiography (OCTA) of DR were frontier hotspots.

Conclusion

Bibliometric analysis in this study provided valuable insights into global trends in DR research frontiers. Four key study directions and three research frontiers were extracted from the extensive DR-related literature. As the incidence of DR continues to increase, DR prevention and treatment have become a pressing public health concern and a significant area of research interest. In addition, the development of AI technologies and telemedicine has emerged as promising research frontiers for balancing the number of doctors and patients.

Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program

INTRODUCTION

Deep learning (DL) for screening diabetic retinopathy (DR) has the potential to address limited healthcare resources by enabling expanded access to healthcare. However, there is still limited health economic evaluation, particularly in low- and middle-income countries, on this subject to aid decision-making for DL adoption.

METHODS

In the context of a middle-income country (MIC), using Thailand as a model, we constructed a decision tree-Markov hybrid model to estimate lifetime costs and outcomes of Thailand's national DR screening program via DL and trained human graders (HG). We calculated the incremental cost-effectiveness ratio (ICER) between the two strategies. Sensitivity analyses were performed to probe the influence of modeling parameters.

RESULTS

From a societal perspective, screening with DL was associated with a reduction in costs of ~ US$ 2.70, similar quality-adjusted life-years (QALY) of + 0.0043, and an incremental net monetary benefit of ~ US$ 24.10 in the base case. In sensitivity analysis, DL remained cost-effective even with a price increase from US$ 1.00 to US$ 4.00 per patient at a Thai willingness-to-pay threshold of ~ US$ 4.997 per QALY gained. When further incorporating recent findings suggesting improved compliance to treatment referral with DL, our analysis models effectiveness benefits of ~ US$ 20 to US$ 50 depending on compliance.

CONCLUSION

DR screening using DL in an MIC using Thailand as a model may result in societal cost-savings and similar health outcomes compared with HG. This study may provide an economic rationale to expand DL-based DR screening in MICs as an alternative solution for limited availability of skilled human resources for primary screening, particularly in MICs with similar prevalence of diabetes and low compliance to referrals for treatment.

Disease perception and experience in people with diabetic retinopathy: A qualitative study

AIM

The aim of this study was to investigate the lived experience of people with diabetic retinopathy and to understand the impact of the disease on them including the practical problems faced in the day-to-day life.

DESIGN

Descriptive qualitative research.

METHODS

A convenience sample of 11 patients with diabetic retinopathy who were hospitalized in a tertiary hospitals in Shandong province of China were enrolled. Data were collected using semi-structured in-depth interviews. The six-stage thematic analysis of Braun and Clarke was used for data analysis.

RESULTS

Three themes and 12 sub-themes were extracted, namely: countdown to darkness (lack of disease knowledge, bystander's perspective, distrust of grassroots hospitals); Endless abyss (action restrictions, social isolation, stigma, lost of meaning in life, catastrophizing explanations, a heavy burden); Light chaser (craving for light, turning points in behaviour, self-adjustment).

Salivary Exosomes in Health and Disease: Future Prospects in the Eye

Exosomes are a group of vesicles that package and transport DNA, RNA, proteins, and lipids to recipient cells. They can be derived from blood, saliva, urine, and/or other biological tissues. Their impact on several diseases, such as neurodegenerative, autoimmune, and ocular diseases, have been reported, but not fully unraveled. The exosomes that are derived from saliva are less studied, but offer significant advantages over exosomes from other sources, due to their accessibility and ease of collection. Thus, their role in the pathophysiology of diseases is largely unknown. In the context of ocular diseases, salivary exosomes have been under-utilized, thus creating an enormous gap in the literature. The current review discusses the state of exosomes research on systemic and ocular diseases and highlights the role and potential of salivary exosomes as future ocular therapeutic vehicles.

Quantification of risk factors for diabetic retinopathy progression

AIMS

To determine the importance of blood sugar control, blood pressure, and other key systemic factors on the risk of progression from no retinopathy to various stages of diabetic retinopathy.

METHODS

Restrospective cohort analysis of patients (N = 99, 280) in the Kaiser Permanente Northern California healthcare system with a baseline retina photographic screening showing no evidence of retinopathy and a minimum follow-up surveillance period of 3 years from 2008 to 2019. We gathered longitudinal data on diabetic retinopathy progression provided by subsequent screening fundus photographs and data captured in the electronic medical record over a mean surveillance of 7.3 ± 2.2 (mean ± SD) years. Progression from an initial state of no diabetic retinopathy to any of four outcomes was determined: (1) any incident retinopathy, (2) referable (moderate or worse) retinopathy, (3) diabetic macular edema, and (4) proliferative diabetic retinopathy. Multiple predictors, including age, race, gender, glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), cholesterol, chronic renal disease, and type of diabetes were investigated.

RESULTS

Among modifiable risk factors, the average HbA1c had the strongest impact on the progression of diabetic retinopathy, followed by average SBP control and total cholesterol. Patients with an average HbA1c of 10.0% or greater (≥ 97 mmol/mol) had a risk ratio of 5.72 (95% CI 5.44-6.02) for progression to any retinopathy, 18.84 (95% CI 17.25-20.57) for referable retinopathy, 22.85 (95% CI 18.87-27.68) for diabetic macular edema, and 25.96 (95% CI 18.75-36.93) for proliferative diabetic retinopathy compared to those with an average HbA1c of 7.0% (53 mmol/mol) or less. Non-white patients generally had a higher risk of progression to all forms of diabetic retinopathy, while Asian patients were less likely to develop diabetic macular edema (HR 0.76, 95% CI 0.66-0.87).

CONCLUSIONS

We confirm the critical importance of glucose control as measured by HbA1c on the risk of development of diabetic retinopathy.

Ocular Pharmacokinetic Properties of Intravitreally Injected Aflibercept in Rabbits After Using Brinzolamide/Timolol Eye Drops

Purpose: To investigate the ocular pharmacokinetic properties of intravitreally injected aflibercept in rabbits after using brinzolamide 1%/timolol maleate 0.5% fixed-combination eye drops. Methods: The right eye of 5 rabbits was topically administered 30 μL of brinzolamide and timolol maleate eye drops twice a day (q12h). The 2 eyes of each rabbit were injected with 1.0 mg (0.025 cc) of aflibercept on the 2nd day after instilling the eye drops. The intraocular pressure of the rabbits was measured before injection and sampling. The aqueous humor was drawn at 1, 3, 7, 14, 21, and 28 days. Aflibercept concentrations in aqueous humor and vitreous humor (28 days) were measured by enzyme-linked immunosorbent assay. Results: The aflibercept aqueous concentrations in the right eye at days 7, 14, 21, and 28 after injection were all significantly higher than those in the left eye (P > 0.05, n = 5). The peak aqueous concentrations of aflibercept in right eyes (49.5 μg/mL) and left eyes (50.9 μg/mL) were both observed at 1 day after injection. The elimination half-life of aflibercept in the aqueous humor of the right eye (4.70 days) was 1 day longer than that of the left eye (3.65 days). The average percentage of residual aflibercept in the vitreous humor of the right eye (3.35%) was also significantly higher than that of the left eye (0.63%). Conclusions: Brinzolamide 1%/timolol maleate 0.5% fixed-combination eye drops can significantly extend the ocular residence time of intravitreally injected aflibercept.

Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications-risks and mitigation

Proliferative diabetic retinopathy (PDR) the sequel of diabetic retinopathy (DR), a frequent complication of diabetes mellitus (DM), is the leading cause of blindness in the working-age population. The current screening process for the DR risk is not sufficiently effective such that often the disease is undetected until irreversible damage occurs. Diabetes-associated small vessel disease and neuroretinal changes create a vicious cycle resulting in the conversion of DR into PDR with characteristic ocular attributes including excessive mitochondrial and retinal cell damage, chronic inflammation, neovascularisation, and reduced visual field. PDR is considered an independent predictor of other severe diabetic complications such as ischemic stroke. A "domino effect" is highly characteristic for the cascading DM complications in which DR is an early indicator of impaired molecular and visual signaling. Mitochondrial health control is clinically relevant in DR management, and multi-omic tear fluid analysis can be instrumental for DR prognosis and PDR prediction. Altered metabolic pathways and bioenergetics, microvascular deficits and small vessel disease, chronic inflammation, and excessive tissue remodelling are in focus of this article as evidence-based targets for a predictive approach to develop diagnosis and treatment algorithms tailored to the individual for a cost-effective early prevention by implementing the paradigm shift from reactive medicine to predictive, preventive, and personalized medicine (PPPM) in primary and secondary DR care management.

Extracellular vesicles derived from different sources play various roles in diabetic retinopathy

Extracellular vesicles (EVs) are present in almost all biological fluids and secreted by almost all cell types. A growing number of studies have revealed the potential roles of EVs in the diagnosis and treatment of the diabetic retinopathy (DR). Changes in the quantity and content of EVs may serve as biomarkers of cause or consequence of pathological status of DR, such as inflammation, neovascularization and epithelial-mesenchymal transition. In addition, as natural, safe and efficient drug carrier, EVs have been reported to play important roles in intercellular communication by acting for essential cell-specific information to target cells. In this review, we summarize the roles of EVs, secreted by various types of cells and participated in various biological processes, in the pathogenesis, diagnosis, and treatment of DR.

Single-Field Fundus Photography for Screening of Diabetic Retinopathy: The Prevalence and Associated Factors in a Population-Based Study

INTRODUCTION

We aimed to determine the prevalence and risk factors for diabetic retinopathy (DR) in a multi-primary healthcare facilities-based DR screening project by analyzing single-field fundus photographs among patients with diabetes in Rafsanjan City, Iran, based on the Rafsanjan Cohort Study, as a part of the prospective epidemiological research studies in IrAN (PERSIAN).

METHODS

Of all participants in the Rafsanjan Cohort Study (performed in four primary healthcare facilities across Rafsanjan City from August 2015 to December 2017), patients with diabetes were recruited in this study. All participants underwent a standardized interview and clinical and paraclinical examinations for demographic characteristics, and medical conditions according to the PERSIAN's protocols. In addition, digital fovea-centered and single-field fundus photography was performed for DR identification and grading. For assessment of agreement, a subgroup of participants underwent fundus examination, randomly. DR was graded as nonproliferative (NPDR) or proliferative (PDR).

RESULTS

Of 8414 screened participants, 1889 had diabetes. The total prevalence of DR was 6.93% [131 individuals including 110 (5.82%) with NPDR, and 21 (1.11%) with PDR] based on single-field fundus photographs, with almost perfect agreement with fundus examinations (κ = 0.82). On adjusted multivariate analysis, duration of diabetes (OR 1.16, 95% CI 1.13-1.19), positive family history for diabetes (OR 1.73, 95% CI 1.09-2.75), fasting plasma glucose (FPG) ≥ 126 mg/dL (OR 1.98, 95% CI 1.16-3.39), and serum creatinine level (OR 1.79, 95% CI 1.08-2.98) were associated with DR. Factors including age, education level, physical activity, body mass index, hypertension, and cardiovascular and renal diseases did not have association with DR on adjusted multivariate analysis.

CONCLUSIONS

Single-field fundus photography can be used for screening of DR in primary healthcare facilities. In individuals with diabetes, duration of diabetes, positive family history for diabetes, FPG ≥ 126 mg/dL, and serum creatinine level may be associated with DR.

Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis

PURPOSE

Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection.

METHODS

Embase, Pubmed, the Cochrane Library, and IEEE Xplore were searched up to August 14, 2021. We included studies using deep learning algorithms to detect DME from OCT images. Two reviewers extracted the data independently, and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the risk of bias. The study is reported according to Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA).

RESULTS

Ninteen studies involving 41005 subjects were included. The pooled sensitivity and specificity were 96.0% (95% confidence interval (CI): 93.9% to 97.3%) and 99.3% (95% CI: 98.2% to 99.7%), respectively. Subgroup analyses found that data set selection, sample size of training set and the choice of OCT devices contributed to the heterogeneity (all P < 0.05). While there was no association between the diagnostic accuracy and transfer learning adoption or image management (all P > 0.05).

CONCLUSIONS

Deep learning methods, particularly the convolutional neural networks (CNNs) could effectively detect clinically significant DME, which can provide referral suggestions to the patients.

Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban-rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study

BACKGROUND

National and subnational estimates of the prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy (VTDR) are needed to inform the stepwise implementation of systematic retinal screening for people with diabetes in India to decrease the rate of blindness. We aimed to assess these national and subnational estimates and to stratify the prevalence of diabetic retinopathy and VTDR on the basis of people with known versus undiagnosed diabetes, urban versus rural residence, and epidemiological transition level (ETL) and Socio-demographic Index (SDI) categories of states.

METHODS

We did a multicentre cross-sectional screening study for diabetic retinopathy using a complex cluster sampling design in people aged 40 years or older in ten Indian states and one union territory between Dec 20, 2018, and March 20, 2020. We did non-mydriatic retinal screening and assessed risk factor burden for people with diabetes. We estimated nationally weighted prevalence of diabetic retinopathy and VTDR for individuals with known and undiagnosed diabetes by urban versus rural residence, and by state categorisation by ETL and SDI. We also assessed adjusted risk factors.

FINDINGS

From 42 146 participants screened, 7910 (18·8%) were identified to have diabetes. Of these, 6133 (77·5%; 4350 with known diabetes and 1783 with undiagnosed diabetes) had gradable retinal images. 3411 (56%) participants were women and 2722 (44%) were men, and the median age was 56 years (IQR 49-65). The estimated national prevalence was 12·5% (95% CI 11·0-14·2) for diabetic retinopathy and 4·0% (3·4-4·8) for VTDR, with no significant differences between urban and rural residence for diabetic retinopathy. Compared with individuals with undiagnosed diabetes, we observed a higher prevalence of diabetic retinopathy (15·5% [13·4-17·8] vs 8·0% [6·3-10·1]) and VTDR (5·3% [4·5-6·3] vs 2·4% [1·6-3·6]) in individuals with known diabetes. The prevalence was significantly lower in low ETL-SDI states compared with high and middle ETL-SDI states for diabetic retinopathy (by 7·0%, 1·9-12·2, p=0·024) and VTDR (by 4·8%, 3·0-6·6, p<0·0001). Hyperglycaemia was the strongest modifiable risk factor.

INTERPRETATION

We estimate that, in absolute numbers, approximately 3 million people aged 40 years or older have VTDR in India, with a higher prevalence in those with known diabetes residing in high and middle ETI-SDI states.

FUNDING

UKRI Global Challenge Research Fund.

Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting: The RAIDERS Randomized Trial

PURPOSE

This trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda.

DESIGN

The Rwanda Artificial Intelligence for Diabetic Retinopathy Screening (RAIDERS) study was an investigator-masked, parallel-group randomized controlled trial.

PARTICIPANTS

Patients ≥ 18 years of age with known diabetes who required referral for DR based on AI interpretation.

METHODS

The RAIDERS study screened for DR using retinal imaging with AI interpretation implemented at 4 facilities from March 2021 through July 2021. Eligible participants were assigned randomly (1:1) to immediate feedback of AI grading (intervention) or communication of referral advice after human grading was completed 3 to 5 days after the initial screening (control).

MAIN OUTCOME MEASURES

Difference between study groups in the rate of presentation for referral services within 30 days of being informed of the need for a referral visit.

RESULTS

Of the 823 clinic patients who met inclusion criteria, 275 participants (33.4%) showed positive findings for referable DR based on AI screening and were randomized for inclusion in the trial. Study participants (mean age, 50.7 years; 58.2% women) were randomized to the intervention (n = 136 [49.5%]) or control (n = 139 [50.5%]) groups. No significant intergroup differences were found at baseline, and main outcome data were available for analyses for 100% of participants. Referral adherence was statistically significantly higher in the intervention group (70/136 [51.5%]) versus the control group (55/139 [39.6%]; P = 0.048), a 30.1% increase. Older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.05; P < 0.0001), male sex (OR, 2.07; 95% CI, 1.22-3.51; P = 0.007), rural residence (OR, 1.79; 95% CI, 1.07-3.01; P = 0.027), and intervention group (OR, 1.74; 95% CI, 1.05-2.88; P = 0.031) were statistically significantly associated with acceptance of referral in multivariate analyses.

CONCLUSIONS

Immediate feedback on referral status based on AI-supported screening was associated with statistically significantly higher referral adherence compared with delayed communications of results from human graders. These results provide evidence for an important benefit of AI screening in promoting adherence to prescribed treatment for diabetic eye care in sub-Saharan Africa.

Retinal Thickness and Morphology Changes on OCT in Youth with Type 2 Diabetes: Findings from the TODAY Study

Objective

To evaluate changes in retinal thickness and morphology using OCT in youth with type 2 diabetes (T2D) and to identify systemic biomarkers correlating with these changes.

Design

Retrospective subgroup analysis of a prospective study.

Participants

Participants who underwent OCT imaging in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) trial and its follow-up study TODAY2.

Methods

In 2010-2011 (TODAY) and 2017-2018 (TODAY2), 6 × 6-mm macular volume OCT scans were acquired, segmented, and analyzed to generate total retinal thickness, inner retinal thickness, and outer retinal thickness. The main retinal morphologies graded were intraretinal cystoid spaces, subretinal fluid, and posterior vitreous detachment (PVD).

Main Outcome Measures

Changes in total and individual retinal layer thickness and development of abnormal vitreomacular morphology between TODAY and TODAY2.

Results

Participants had a mean age of 17.9 ± 2.4 years and glycated hemoglobin (HbA1c) of 8.2 ± 2.8% in TODAY and a mean age of 25.0 ± 2.4 years and mean HbA1c of 9.5 ± 2.8% in TODAY2. Longitudinally between assessments, there were overall decreases in outer retinal thickness from 167.2 ± 11.5 microns to 158.4 ± 12.8 microns (P < 0.001) and in photoreceptor thickness from 30.3 ± 2.9 microns to 29.8 ± 4.1 microns (P = 0.04) in the central subfield, while in the inner subfield, we noted a decrease in outer retinal thickness from 150.5 ± 10.1 microns to 144.9 ± 10.5 microns (P < 0.001) and an increase in inner retinal thickness from 136.9 ± 11.5 microns to 137.4 ± 12.6 microns (P = 0.01). Multivariate analysis showed that in the center subfield, HbA1c increases were associated with increases in total retinal thickness (r: 0.67, P = 0.001), whereas fasting glucose was positively correlated with inner retinal thickness (r: 0.02, P = 0.02). In the inner subfield, both systolic (r: -0.22, P < 0.001) and diastolic (r: -0.22, P = 0.003) blood pressures were negatively correlated with total retinal thickness. There was an increase in PVD (18.9%) and cystoid spaces (4.2%).

Conclusions

Youth with T2D develop retinal thickness changes on OCT, including increases in total retinal and inner retinal thickness in the center subfield that correlate with HbA1c and fasting glucose, respectively. Taken together with the increased prevalence of abnormal vitreomacular morphology in this cohort at risk, these findings emphasize the importance of controlling risk factors to prevent the development of sight-threatening retinal complications.

The scientific progress and prospects of artificial intelligence in digestive endoscopy: A comprehensive bibliometric analysis

Artificial intelligence (AI) has been used for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention and shown promising and stimulating results. This study aimed to determine the development trends and research hotspots of AI in digestive endoscopy by visualizing articles. Publications on AI in digestive endoscopy research were retrieved from the Web of Science Core Collection on April 25, 2022. VOSviewer and CiteSpace were used to assess and plot the research outputs. This analytical research was based on original articles and reviews. A total of 524 records of AI research in digestive endoscopy, published between 2005 and 2022, were retrieved. The number of articles has increased 27-fold from 2017 to 2021. Fifty-one countries and 994 institutions contributed to all publications. Asian countries had the highest number of publications. China, the USA, and Japan were consistently the leading driving forces and mainly contributed (26%, 21%, and 14.31%, respectively). With a solid academic reputation in this area, Japan has the highest number of citations per article. Tada Tomohiro published the most articles and received the most citations.. Gastrointestinal endoscopy published the largest number of publications, and 4 of the top 10 cited papers were published in this journal. "The Classification," "ulcerative colitis," "capsule endoscopy," "polyp detection," and "early gastric cancer" were the leading research hotspots. Our study provides systematic elaboration for researchers to better understand the development of AI in gastrointestinal endoscopy.

Are we overlooking the neurodegeneration in the early stage of Type 1 Diabetes Mellitus without Visual Impairment or Diabetic Retinopathy: is it probably occurred before retinal vasculature dysfunction?

PURPOSE

Using electrophysiology (ERG) to investigate the early alterations of retinal function in diabetic children and adolescents without diabetic retinopathy (DR) or visual impairment (VI).

METHODS

We recorded and compared the data of full-field flicker ERGs between 59 normal subjects and 60 children and adolescents with type 1 diabetes mellitus (T1DM) from the Children's Hospital of Fudan University in Shanghai.

RESULTS

In both groups, patients with diabetes and healthy controls were matched for age, gender, weight, height, BMI, intraocular pressure (IOP), and best-corrected visual acuity (BCVA). Among the parameters of the outcomes of ERG, the implicit time in eyes in DM patients was significantly prolonged compared to normal eyes (p = .008, 16 Td-s; p = .000, 32 Td-s). In the case group, we found significantly positive correlation between implicit time and BMI (p < .05), as well as implicit time and axial length (AL).

CONCLUSIONS

The study reveals that the dysfunction of retina in DM children can be detected with ERGs. It also shows that hyperglycemia has an impact on the occurrence of neurodegeneration in the early stage of DM.

An Assessment on the Awareness of Diabetic Retinopathy Among Participants Attending the Diabetes Awareness Camp in Saudi Arabia

Aim The objective of this study is to assess the level of awareness of the effect of diabetes and diabetic retinopathy (DR) on the eye among a sample of the Jeddah community. Methods A cross-sectional study was conducted among those attending a diabetes awareness camp in Jeddah, Saudi Arabia, in November 2021. Participants were asked to answer questions in a structured questionnaire that was already used in a previous study. Responses were analyzed using Statistical Package for the Social Sciences (SPSS) version 25 (IBM SPSS Statistics, Armonk, NY, USA). Results A total of 352 participants were included in this study, 184 (52.3%) of them were females. Of the participants, only 74 (21%) had diabetes mellitus (DM). The vast majority (94%) of the participants believed that diabetes could affect the eyes, and 94.3% believed that maintaining the level of blood sugar could maintain the eye and the level of vision. Moreover, 77.3% were aware that diabetes could lead to visual impairment and blindness. Around one-third of the total participants and less than half of the diabetic group were found familiar with DR. Although 96% of diabetic participants reported the need for diabetics to get their eyes checked annually, only 70% did so. Lack of awareness of the effect of diabetes on the retina was the main barrier preventing diabetic groups from getting their eye checked. Conclusion Despite the good level of awareness among the community and diabetics about diabetes and its effect on the eyes, there is less awareness that DR is one of the most dangerous complications that lead to visual impairments. These findings assure the importance to raise awareness of DR among the community and diabetics and increase awareness of the importance of annual eye examinations.

Retinal and Choroidal Neovascularization Antivascular Endothelial Growth Factor Treatments: The Role of Gene Therapy

Neovascularization in ocular vessels causes a major disease burden. The most common causes of choroidal neovascularization (CNV) are age-related macular degeneration and diabetic retinopathy, which are the leading causes of irreversible vision loss in the adult population. Vascular endothelial growth factor (VEGF) is critical for the formation of new vessels and is the main regulator in ocular angiogenesis and vascular permeability through its receptors. Laser therapy and antiangiogenic factors have been used for CNV treatment. Bevacizumab, ranibizumab, and aflibercept are commonly used anti-VEGF agents; however, high costs and the need for frequent intraocular injections are major drawbacks of anti-VEGF drugs. Gene therapy, given the potency of one-time treatment and no need for frequent injections offers the real possibility of such a lasting treatment, with fewer adverse effects and higher patient quality of life. Herein, we reviewed the role of gene therapy in the CNV treatment. In addition, we discuss the advantages and challenges of current treatments compared with gene therapy.

Educational quality and content of YouTube videos on diabetic macular edema

PURPOSE

Diabetic macular edema (DME) is a vision-threatening complication of diabetes mellitus due to increased vascular permeability. Patients are increasingly using YouTube videos to educate themselves about DME. This study analyzes the content and quality of YouTube videos about DME.

METHODS

Videos were searched in December 2021 for "diabetic macular edema." The first 100 videos sorted by both relevance and view count were reviewed (n = 200). Quantitative metrics and content were collected. Two reviewers assessed videos using the JAMA (0-4), modified DISCERN (1-5), and Global Quality Scale (GQS, 1-5). Videos were sorted into author groups: 1 (academic institutions/organizations), 2 (private practices/organizations), and 3 (independent users; ophthalmologist users noted). Statistical analyses were deemed significant at a = 0.05.

RESULTS

One hundred four videos were included after applying exclusion criteria. Overall mean + standard deviations were 2.25 ± 0.83 (JAMA), 3.47 ± 0.55 (DISCERN), and 3.95 ± 0.95 (GQS). 51.9% of videos stated a definition, 32.7% mentioned screening, and 50% mentioned any DME risk factor. Healthcare professional-targeted videos had higher JAMA and DISCERN scores than patient-targeted videos (p < 0.05). Videos using ophthalmologists had higher JAMA and DISCERN scores than those lacking their presence (p < 0.05). JAMA scores significantly varied between author groups; within group 3, ophthalmologist-authored videos had higher DISCERN scores (p < 0.05).

CONCLUSION

Videos without ophthalmologists or targeted toward patients had poor quality and content coverage. The rising prevalence of diabetes, coupled with increased internet use for acquiring medical information, creates a strong need for high-quality information about DME.

Effect of troxerutin in counteracting hyperglycemia-induced VEGF upregulation in endothelial cells: a new option to target early stages of diabetic retinopathy?

Diabetic retinopathy (DR), one of the most common complications of diabetes mellitus, is characterized by degeneration of retinal neurons and neoangiogenesis. Until today, the pharmacological approaches for DR are limited and focused on counteracting the end-stage of this neurodegenerative disease, therefore efforts should be carried out to discover novel pharmacological targets useful to prevent DR development. Hyperglycemia is a major risk factor for endothelial dysfunction and vascular complication, which subsequently may trigger neurodegeneration. We previously demonstrated that, in the rat retina, hyperglycemia activates a new molecular cascade implicating, up-stream, protein kinase C βII (PKC βII), which in turn leads to a higher expression of vascular endothelial growth factor (VEGF), via the mRNA-binding Hu-antigen R (HuR) protein. VEGF is a pivotal mediator of neovascularization and a well-known vasopermeability factor. Blocking the increase of VEGF via modulation of this cascade can thus represent a new pharmacological option to prevent DR progression. To this aim, proper in vitro models are crucial for drug discovery, as they allow to better identify promising effective molecules. Considering that endothelial cells are key elements in DR and that hyperglycemia triggers the PKCβII/HuR/VEGF pathway, we set up two distinct in vitro models applying two different stimuli. Namely, human umbilical vein endothelial cells were exposed to phorbol 12-myristate 13-acetate, which mimics diacylglycerol whose synthesis is triggered by diabetic hyperglycemia, while human retinal endothelial cells were treated with high glucose for different times. After selecting the optimal experimental conditions able to determine an increased VEGF production, in search of molecules useful to prevent DR development, we investigated the capability of troxerutin, an antioxidant flavonoid, to counteract not only the rise of VEGF but also the activation of the PKCβII/HuR cascade in both in vitro models. The results show the capability of troxerutin to hinder the hyperglycemia-induced increase in VEGF in both models through PKCβII/HuR pathway modulation. Further, these data confirm the key engagement of this cascade as an early event triggered by hyperglycemia to promote VEGF expression. Finally, the present findings also suggest the potential use of troxerutin as a preventive treatment during the early phases of DR.

Extracellular vesicles derived from human umbilical cord mesenchymal stem cells relieves diabetic retinopathy through a microRNA-30c-5p-dependent mechanism

AIMS

Extracellular vesicle (EV)-transferred microRNAs (miRNAs) are proved to be potentially therapeutic candidates. Here, we attempted to unveil the role of delivery of miR-30c-5p by human umbilical cord mesenchymal stem cells (hUCMSCs)-derived EVs in diabetic retinopathy (DR).

METHODS

miR-30c-5p and PLCG1 expression in streptozotocin-induced diabetes mellitus (DM) rats and high glucose (HG)-treated human retinal endothelial cells (HRECs) was quantified, followed by analysis on their interaction. EVs were isolated from hUCMSCs and co-cultured with HRECs. Through gain- and loss-of-function assays, the role of hUCMSCs-derived EV containing miR-30c-5p in DR involving PLCG1 and NF-κB pathway was analyzed in vitro and in vivo.

RESULTS

Elevated PLCG1 was found in DM rats and HG-treated HRECs where miR-30c-5p was reduced while increased in hUCMSC-derived EVs. PLCG1 was pinpointed as a target gene of miR-30c-5p, which consequently disrupted the PKC/NF-κB pathway. hUCMSC-derived EVs decreased inflammation reaction by transferring miR-30c-5p in DM rats and HG-treated HRECs. Furthermore, similar changing tendency was observed in HG-treated HRECs induced by overexpressed miR-30c-5p through downregulation of PLCG1 in vivo.

CONCLUSION

Overall, our findings underlined delivery of miR-30c-5p by hUCMSC-derived EVs as a novel suppressor in the inflammatory response following DR.

Economic Aspects in the Management of Diabetic Macular Edema in Italy

Background

Diabetic Macular Edema (DME) is the most common cause of vision loss in diabetic patients. Currently, the Vascular Endothelial Growth Factor inhibitors (anti-VEGFs) are used as the first line of DME treatment and corticosteroid implants are usually used as a second-line treatment. These implants are a safe and effective therapeutic option that can improve the quality of life of DME patients by reducing the intravitreal injections number. We determined the economic impact related to DME, also from the social perspective, and the consequences of the increased use of the dexamethasone implant.

Methods

The analysis compares two scenarios: the first based on the current rate of recourse to the therapeutic alternatives available in the Italian healthcare setting (as is) and the second based on the assumption of an increased recourse to dexamethasone implants (to be). The results are expressed both in terms of the resource absorption associated with the two scenarios and in terms of the cost differential yielded by their comparison.

Results

The increased use of the dexamethasone implant allows considerable savings in terms of healthcare professionals' time, follow-up and productivity lost by patients/caregivers. These savings would reduce healthcare costs for the management of DME patients in Italy by €2,058,238 in 5 years.

Conclusions

To optimize the healthcare resources allocation, it is necessary to implement treatments that yield not only cost reductions but also a clinical benefit for patients. The dexamethasone implant use is an example of DME management that generates value for patients, health system and society.

Association Between Diabetic Retinopathy, Brain Structural Abnormalities, and Cognitive Impairment for Accumulated Evidence in Observational Studies

PURPOSE

To evaluate the association between diabetic retinopathy (DR) and cerebral disease or cognitive impairment.

DESIGN

Systematic review and meta-analysis.

METHOD

The hypothesis was formulated prior to data collection. Cross-sectional studies and cohort studies that assessed the association between any measure of DR and cerebral small vessel disease or any type of cognitive impairment in diabetic participants were included. The data were independently extracted by two investigators. This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology guidelines RESULTS: A total of 27 studies were included. The combined odds ratio of 5 cross-sectional/cohort studies that reported that the associations between DR and cerebral structural changes was 1.75 (95% confidence interval [CI]: 1.36-2.25). The combined hazard ratio of 4 cohort studies that examined the association between DR and cognitive impairment events was 1.47 (95% CI: 1.22-1.78). The combined odds ratio of 14 cross-sectional/cohort studies that examined the association between DR and different cognitive impairment events was 1.43 (95% CI: 1.06-1.93). The overall coefficient (β) of 4 studies that examined the relationship between DR and specific cognitive performance was 0.09 (95% CI: 0.00-0.18). Considering the quality of the data, we have performed subgroup analysis in studies scored >7 and studies scored ≤7, respectively, according to the Newcastle-Ottawa scale.

CONCLUSION

The present meta-analysis suggests that DR is associated with an increased risk of structural abnormalities in the brain and cognitive impairment. This association remained significant after adjusting for blood glucose, and the presence of hypertension, indicating that DR is an important danger signal for cerebral abnormalities.

A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique

Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neural network (CNN) architectures are widely used in multi-label (ML) classification. It helps in diagnosing normal and various DR grades: mild, moderate, and severe non-proliferative DR (NPDR) and proliferative DR (PDR). DR grades are formulated by appearing multiple DR lesions simultaneously on the color retinal fundus images. Many lesion types have various features that are difficult to segment and distinguished by utilizing conventional and hand-crafted methods. Therefore, the practical solution is to utilize an effective CNN model. In this paper, we present a novel hybrid, deep learning technique, which is called E-DenseNet. We integrated EyeNet and DenseNet models based on transfer learning. We customized the traditional EyeNet by inserting the dense blocks and optimized the resulting hybrid E-DensNet model's hyperparameters. The proposed system based on the E-DenseNet model can accurately diagnose healthy and different DR grades from various small and large ML color fundus images. We trained and tested our model on four different datasets that were published from 2006 to 2019. The proposed system achieved an average accuracy (ACC), sensitivity (SEN), specificity (SPE), Dice similarity coefficient (DSC), the quadratic Kappa score (QKS), and the calculation time (T) in minutes (m) equal [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], 0.883, and 3.5m respectively. The experiments show promising results as compared with other systems.

Transcription factor FOXP1 mediates vascular endothelial dysfunction in diabetic retinopathy

BACKGROUND

Diabetic retinopathy (DR) is still the fastest growing cause of blindness in working aged adults, and its typical characteristics are endothelial cell dysfunction and pericytes loss. Transcription factor fork head box P1 (FOXP1) is a member of FOX family involved in diabetes progression and is expressed in endothelial cells. The purpose of this study was to investigate the role and mechanism of FOXP1 in DR.

METHODS

The vitreous of DR patients and non-DR patients were collected, and the expression of FOXP1 was detected by real-time polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). Human umbilical vein endothelial cells (HUVECs) cultured in high glucose simulated DR environment, and the expressions of FOXP1, vascular endothelial growth factor (VEGF), and pigment epithelium derived factor (PEDF) were detected by RT-qPCR and western blot (WB) after transfection of small interfering RNA (siRNA) to knock out FOXP1. At the same time, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay (MTT), 5-ethynyl-2'-deoxyuridine assay (EDU), flow cytometry, Transwell assay, and tube-forming experiment were performed to determine cell proliferation, migration, and tube-forming ability.

RESULTS

We found that FOXP1 was highly expressed in the vitreous of DR patients and HUVECs under high glucose condition. After FOXP1 was decreased, the activation of VEGF expression and inhibition of PEDF expression in HUVECs induced by high glucose were reversed; meanwhile, cell proliferation, migration, and tube formation decreased, and apoptosis was promoted.

CONCLUSION

Generally, FOXP1 is highly expressed in the vitreous of DR patients, and its silence prevented VEGF/PEDF signaling pathway stimulated by high glucose and also reduced the proliferation, migration, and tube formation of endothelial cell, thus improving vascular endothelial dysfunction caused by DR. The results indicate that FOXP1 may be a therapeutic target of DR.

Gene Biomarkers Related to Th17 Cells in Macular Edema of Diabetic Retinopathy: Cutting-Edge Comprehensive Bioinformatics Analysis and In Vivo Validation

Background

Previous studies have shown that T-helper 17 (Th17) cell-related cytokines are significantly increased in the vitreous of proliferative diabetic retinopathy (PDR), suggesting that Th17 cells play an important role in the inflammatory response of diabetic retinopathy (DR), but its cell infiltration and gene correlation in the retina of DR, especially in diabetic macular edema (DME), have not been studied.

Methods

The dataset GSE160306 was downloaded from the Gene Expression Omnibus (GEO) database, which contains 9 NPDR samples and 10 DME samples. ImmuCellAI algorithm was used to estimate the abundance of Th17 cells in 24 kinds of infiltrating immune cells. The differentially expressed Th17 related genes (DETh17RGs) between NPDR and DME were documented by difference analysis and correlation analysis. Through aggregate analyses such as gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis, a protein-protein interaction (PPI) network was constructed to analyze the potential function of DETh17RGs. CytoHubba plug-in algorithm, Lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) were implemented to comprehensively identify Hub DETh17RGs. The expression archetypes of Hub DETh17RGs were further verified in several other independent datasets related to DR. The Th17RG score was defined as the genetic characterization of six Hub DETh17RGs using the GSVA sample score method, which was used to distinguish early and advanced diabetic nephropathy (DN) as well as normal and diabetic nephropathy. Finally, real-time quantitative PCR (qPCR) was implemented to verify the transcription levels of Hub DETh17RGs in the STZ-induced DR model mice (C57BL/6J).

Results

238 DETh17RGs were identified, of which 212 genes were positively correlated while only 26 genes were negatively correlated. Six genes (CD44, CDC42, TIMP1, BMP7, RHOC, FLT1) were identified as Hub DETh17RGs. Because DR and DN have a strong correlation in clinical practice, the verification of multiple independent datasets related to DR and DN proved that Hub DETh17RGs can not only distinguish PDR patients from normal people, but also distinguish DN patients from normal people. It can also identify the initial and advanced stages of the two diseases (NPDR vs DME, Early DN vs Advanced DN). Except for CDC42 and TIMP1, the qPCR transcription levels and trends of other Hub DETh17RGs in STZ-induced DR model mice were consistent with the human transcriptome level in this study.

Conclusion

This study will improve our understanding of Th17 cell-related molecular mechanisms in the progression of DME. At the same time, it also provides an updated basis for the molecular mechanism of Th17 cell crosstalk in the eye and kidney in diabetes.