Recently updated global diabetic retinopathy screening guidelines: commonalities, differences, and future possibilities

Individualised screening for diabetic retinopathy with proliferative retinopathy and macular oedema as separate end points

PURPOSE

A number of algorithms have been developed to calculate screening intervals for diabetic retinopathy on the basis of individual risk factors. However, these approaches have not considered proliferative diabetic retinopathy (PDR) and diabetic macular oedema (DME) as separate end points and death as competing risk.

METHODS

A multi-state survival model with death as competing risk was used to predict the screening interval for diabetic retinopathy based on information about all 2446 patients from a well-defined population who had started treatment for either PDR or DME during 25 years. The performance of the model was tested on the existing database and at seven screening sites on patients who had not developed a treatment requiring condition.

RESULTS

Testing on the existing database showed that at a risk level of 2% the algorithm could predict a screening interval with a success rate higher than 90% and a 1.75 times average prolongation of the screening interval without failing to detect the development of verified PDR og DME. The model was limited to a diabetes duration shorter than 40 years and depended on knowledge of relevant risk factors. At the other participating screening sites the algorithm predicted shorter intervals than the screener.

CONCLUSIONS

Algorithms for individualised screening for diabetic retinopathy can prolong screening intervals without losing patients who develop a vision threatening condition. The calculation of screening intervals requires access to relevant risk factors and should be developed on large data sets that reflect the population in which the algorithm should be used.

Health transition and eye care policy planning for people with diabetic retinopathy in south Asia

The prevalence of type 2 diabetes (T2D), associated systemic disorders, diabetic retinopathy (DR) and current health policies in south Asian countries were analysed to assess country-specific preparedness to meet the 2030 Sustainable Development Goals. The south Asian countries were classified by human development index, socio-demographic index, multidimensional poverty indices, and eye health resources for epidemiological resource-level analysis. In south Asia, the prevalence of diagnosed and undiagnosed T2D in adults aged 40 years or above, was higher in Pakistan (26.3%) and Afghanistan (71.4%), respectively; India has the highest absolute number of people with DR, and Afghanistan has the highest prevalence of DR (50.6%). In this region, out-of-pocket spending is high (∼77%). This Health Policy is a situational analysis of data available on the prevalence of DR and common eye diseases in people with T2D in south Asia and available resources to suggest tailored health policies to local needs. The common issues in the region are insufficient human resources for eye health, unequal distribution of available workforce, and inadequate infrastructure. Addressing these challenges of individuals with T2D and DR, a 10-point strategy is suggested to improve infrastructure, augment human resources, reduce out-of-pocket spending, employ targeted screening, and encourage public-private partnerships.

Ocular image-based deep learning for predicting refractive error: A systematic review

Background

Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors.

Main text

We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n ​= ​5), OCT-based (n ​= ​1), and external ocular photo-based (n ​= ​3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R 2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79-0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%-68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%-84.00% and specificity of 74.00%-86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models.

Conclusions

The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.

Association between intuitive eating and health outcomes in outpatients with type 2 diabetes: a cross-sectional study

PURPOSE

To evaluate the association between intuitive eating and health outcomes in patients with type 2 diabetes in a cross-sectional study.

METHODS

Consecutively, outpatients attending at university hospital underwent clinical, laboratory, lifestyle, and eating behavior evaluations. Intuitive eating was assessed using the Intuitive Eating Scale-2 (IES-2), and the Three Factor Eating Questionnaire-21 was adopted as a confirmatory tool for disordered eating behavior. Optimized health outcomes were considered according to the American Diabetes Association criteria for BMI, HbA1c, lipid profile, and blood pressure values, and the International Diabetes Federation criteria for waist circumference. Considering the answers of the IES-2 items, patients were grouped by latent class analysis, and their characteristics were compared by appropriate tests.

RESULTS

In total, 267 patients were evaluated: 62.2% women, with 60 (53-65) years, BMI 31.9 ± 5.4 kg/m², diabetes duration of 16 ± 9 years, HbA1c 8.5 ± 1.5%, and an IES-2 total score of 58 (50-67)%. Three intuitive eating groups were identified: higher intuitive eating, nonemotional-oriented coping, and lower intuitive eating. Patients with higher intuitive eating have higher chances of having optimized BMI and serum triglycerides values compared to patients with lower intuitive eating. Also, the 10-point increase on IES-2 was associated with a 0.62 kg/m² reduction on BMI values (95%CI -1.18;-0.06), 1.90 cm on waist circumference (95%CI -3.26;-0.54), and 23 mg/dL in serum triglycerides values (95%CI -38.27;-7.40) after adjustment for age, sex, psychotropic drug use, medication effect score, smoking, and BMI.

CONCLUSION

Intuitive eating seems to be associated with optimized health outcomes and may contribute to better personalized interventions in nutritional treatment that promote adaptive behaviors in diabetes management, but should be tested.

Comprehensive insights into a decade-long journey: The evolution, impact, and human factors of an asynchronous telemedicine program for diabetic retinopathy screening in Pennsylvania, United States

Diabetic Retinopathy stands as a leading cause of irreversible blindness, necessitating frequent examinations, especially in the early stages where effective treatments are available. However, current examination rates vary widely, ranging from 25-60%. This study scrutinizes the Point-of-Care Diabetic Retinopathy Examination Program at the University of Pittsburgh/UPMC, delving into its composition, evolution, challenges, solutions, and improvement opportunities. Employing a narrative approach, insights are gathered from key stakeholders, including ophthalmologists and staff from primary care clinics. A quantitative analysis from 2008 to 2020 provides a comprehensive overview of program outcomes, covering 94 primary care offices with 51 retinal cameras. Program components feature automated non-mydriatic 45° retinal cameras, a dedicated coordinator, rigorous training, and standardized workflows. Over this period, the program conducted 21,960 exams in 16,458 unique individuals, revealing a diverse population with an average age of 58.5 and a balanced gender distribution. Average body mass index (33.96±8.02 kg/m2) and hemoglobin A1c (7.58%±1.88%) surpassed normal ranges, indicating prevalent risk factors for diabetes-related complications. Notably, 24.2% of patients underwent more than one exam, emphasizing program engagement. Findings indicated that 86.3% of exams were gradable, with 59.0% within normal limits, 12.1% showing some evidence of diabetic retinopathy, and 6.4% exhibiting vision-threatening diabetic retinopathy. Follow-up appointments with ophthalmologists were recommended in 31.5% of exams due to indeterminate results, positive diabetic retinopathy (≥moderate or macular exudate), or other findings like age-related macular degeneration or suspected glaucoma. The program demonstrated high reproducibility across diverse healthcare settings, featuring a sustainable model with minimal camera downtime, standardized workflows, and financial support from grants, health systems, and clinical revenues. Despite COVID-19 pandemic challenges, this research emphasizes the program's reproducibility, user-friendly evolution, and promising outcomes. Beyond technical contributions, it highlights human factors influencing program success. Future research could explore adherence to follow-up ophthalmological recommendations and its associated factors.

Diabetic retinopathy screening integrated in a multidisciplinary diabetes care clinic: a pilot project

OBJECTIVE

To characterize patients referred for diabetic retinopathy (DR) screening in a unique multidisciplinary diabetes care clinic at a tertiary care centre.

METHODS

A retrospective study was conducted involving patients who were referred to the Cardiac and Renal Endocrine Clinic at a tertiary care centre (University Health Network) for DR screening between April 2019-March 2020 and November 2020-August 2021. Patients' demographics; micro- and macrovascular disease measurements; visual acuity, intraocular pressure, fundus imaging, and optical coherence tomography results were collected and analyzed.

RESULTS

Of the 64 patients who attended the clinic, 21 patients (33%) with type 2 diabetes had on-site DR screening. The remaining 43 patients had DR screening within 6 months of the appointment or were under ophthalmology care with annual screening visits elsewhere. Of the 21 patients who underwent retinopathy screening, 7 patients (33%) had DR: 4 had mild nonproliferative DR, 2 had moderate nonproliferative DR, 1 had proliferative DR, and 1 had macular edema. Patients with DR had a significantly longer diabetes duration than patients without DR (24.5 ± 10.2 years vs 12.5 ± 5.8 years; p = 0.0247). No significant differences were observed in glycemic control, blood pressure, lipid profiles, kidney function, visual acuity, or intraocular pressure.

CONCLUSIONS

Our analysis suggests a potential benefit of integrated DR screening in patients with long-standing diabetes as part of a multidisciplinary diabetes care clinic to diagnose and manage DR. Future work is needed to further develop such clinics and investigate their long-term effect on patient outcomes.

CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading

Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR. Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR.

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables

OBJECTIVE

Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario.

METHODS

In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction.

RESULTS

The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively.

CONCLUSION

The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Glucose tolerance and insulin resistance/sensitivity associate with retinal layer characteristics: the LIFE-Adult-Study

AIMS/HYPOTHESIS

As the prevalence of insulin resistance and glucose intolerance is increasing throughout the world, diabetes-induced eye diseases are a global health burden. We aim to identify distinct optical bands which are closely related to insulin and glucose metabolism, using non-invasive, high-resolution spectral domain optical coherence tomography (SD-OCT) in a large, population-based dataset.

METHODS

The LIFE-Adult-Study randomly selected 10,000 participants from the population registry of Leipzig, Germany. Cross-sectional, standardised phenotyping included the assessment of various metabolic risk markers and ocular imaging, such as SD-OCT-derived thicknesses of ten optical bands of the retina. Global and Early Treatment Diabetic Retinopathy Study (ETDRS) subfield-specific optical retinal layer thicknesses were investigated in 7384 healthy eyes of 7384 participants from the LIFE-Adult-Study stratified by normal glucose tolerance, prediabetes (impaired fasting glucose and/or impaired glucose tolerance and/or HbA1c 5.7-6.4% [39-47 mmol/mol]) and diabetes. The association of optical retinal band characteristics with different indices of glucose tolerance (e.g. fasting glucose, area under the glucose curve), insulin resistance (e.g. HOMA2-IR, triglyceride glucose index), or insulin sensitivity (e.g. estimated glucose disposal rate [eGDR], Stumvoll metabolic clearance rate) was determined using multivariable linear regression analyses for the individual markers adjusted for age, sex and refraction. Various sensitivity analyses were performed to validate the observed findings.

RESULTS

In the study cohort, nine out of ten optical bands of the retina showed significant sex- and glucose tolerance-dependent differences in band thicknesses. Multivariable linear regression analyses revealed a significant, independent, and inverse association between markers of glucose intolerance and insulin resistance (e.g. HOMA2-IR) with the thickness of the optical bands representing the anatomical retinal outer nuclear layer (ONL, standardised β=-0.096; p<0.001 for HOMA2-IR) and myoid zone (MZ; β=-0.096; p<0.001 for HOMA2-IR) of the photoreceptors. Conversely, markers of insulin sensitivity (e.g. eGDR) positively and independently associated with ONL (β=0.090; p<0.001 for eGDR) and MZ (β=0.133; p<0.001 for eGDR) band thicknesses. These global associations were confirmed in ETDRS subfield-specific analyses. Sensitivity analyses further validated our findings when physical activity, neuroanatomical cell/tissue types and ETDRS subfield categories were investigated after stratifying the cohort by glucose homeostasis.

CONCLUSIONS/INTERPRETATION

An impaired glucose homeostasis associates with a thinning of the optical bands of retinal ONL and photoreceptor MZ. Changes in ONL and MZ thicknesses might predict early metabolic retinal alterations in diabetes.

Present and future screening programs for diabetic retinopathy: a narrative review

Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed. In low-income countries, only one out of 29, partially implemented a DESP, while 21 out of 50 lower-middle-income countries have started the DR policy cycle. Among upper-middle-income countries, a third of 59 nations have advanced in DR agenda-setting, with five having a comprehensive national DESP and 11 in the early stages of implementation.Many nations use 2-4 fields fundus images, proven effective with 80-98% sensitivity and 86-100% specificity compared to the traditional seven-field evaluation for DR. A cell phone based screening with a hand held retinal camera presents a potential low-cost alternative as imaging device. While this method in low-resource settings may not entirely match the sensitivity and specificity of seven-field stereoscopic photography, positive outcomes are observed.Individualized DR screening intervals are the standard in many high-resource nations. In countries that lacks a national DESP and resources, screening are more sporadic, i.e. screening intervals are not evidence-based and often less frequently, which can lead to late recognition of treatment required DR.The rising global prevalence of DR poses an economic challenge to nationwide screening programs AI-algorithms have showed high sensitivity and specificity for detection of DR and could provide a promising solution for the future screening burden.In summary, this narrative review enlightens on the epidemiology of DR and the necessity for effective DR screening programs. Worldwide evolution in existing approaches for DR screening has showed promising results but has also revealed limitations. Technological advancements, such as handheld imaging devices, tele ophthalmology and artificial intelligence enhance cost-effectiveness, but also the accessibility of DR screening in countries with low resources or where distance to or a shortage of ophthalmologists exists.

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.

Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios

PURPOSE

To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases.

METHODS

Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the "majority clinician response" (defined as the maximum number of identical responses provided by the clinicians) and "majority AI-platform" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned.

RESULTS

Clinicians, AI platforms, and the "majority clinician response" and "majority AI response" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately.

CONCLUSION

Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations.

CLINICAL TRIAL REGISTRATION

Not applicable.

Accurate Segmentation and Tracking of Chorda Tympani in Endoscopic Middle Ear Surgery with Artificial Intelligence

Objective: We introduce a novel endoscopic middle ear surgery dataset specifically designed for evaluating deep learning (DL)-based semantic segmentation of chorda tympani. Methods: We curated a dataset comprising 8240 images from 25 patients, divided into a training set (20%, 1648 images), validation set (5%, 412 images), and test set (75%, 6180 images). We employed data enhancement techniques to expand the picture size of the training and validation sets by 5 times (training set: 8240 images, verification set: 2060 images). Subsequently, we employed a multistage transfer learning training method to establish, train, and validate various convolutional neural networks. Results: On the validation set of 2060 labeled images, our proposed network achieved good results, with the U-net exhibiting the highest effectiveness (mIOU = 0.8737, mPA = 0.9263). Furthermore, when applied to the test dataset of 6180 raw images and contrasted with the prediction of otologists, the overall performance of the U-net was excellent (accuracy = 0.911, precision = 0.9823, sensitivity = 0.8777, specificity = 0.9714). Conclusions: Our findings demonstrate that DL can be successfully employed for automatic segmentation of chorda tympani in endoscopic middle ear surgery, yielding high-performance results. This study validates the potential feasibility of future intelligent navigation technologies to assist in endoscopic middle ear surgery.

A systematic review of diabetic retinopathy screening intervals

The current evidence on whether annual diabetic retinopathy (DR) screening intervals can be extended was reviewed. A systematic review protocol was followed (PROSPERO ID: CRD42022359590). Original longitudinal articles that specifically assessed DR screening intervals were in English and collected data after 2000 were included. Two reviewers independently conducted the search and reviewed the articles for quality and relevant information. The heterogeneity of the data meant that a meta-analysis was not appropriate. Twelve publications were included. Studies were of good quality and many used data from DR screening programs. Studies fit into three categories; those that assessed specific DR screening intervals, those that determined optimal DR screening intervals and those that developed/assessed DR screening risk equations. For those with type 2 diabetes, extending screening intervals to 3- to 4-yearly in those with no baseline DR appeared safe. DR risk equations considered clinical factors and allocated those at lower risk of DR progression screening intervals of up to five years. Those with baseline DR or type 1 diabetes appeared to have a higher risk of progression to STDR and needed more frequent screening. DR screening intervals can be extended to 3-5 yearly in certain circumstances. These include patients with type 2 diabetes and no current DR, and those who have optimal management of other risk factors such as glucose and blood pressure.

Convolutional Neural Networks Accurately Identify Ungradable Images in a Diabetic Retinopathy Telemedicine Screening Program

Purpose: Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM). Standard of care for patients with DM is an annual eye examination or retinal imaging to assess for DR, the latter of which may be completed through telemedicine approaches. One significant issue is poor-quality images that prevent adequate screening and are thus ungradable. We used artificial intelligence to enable point-of-care (at time of imaging) identification of ungradable images in a DR screening program. Methods: Nonmydriatic retinal images were gathered from patients with DM imaged during a primary care or endocrinology visit from September 1, 2017, to June 1, 2021. The Topcon TRC-NW400 retinal camera (Topcon Corp., Tokyo, Japan) was used. Images were interpreted by 5 ophthalmologists for gradeability, presence and stage of DR, and presence of non-DR pathologies. A convolutional neural network with Inception V3 network architecture was trained to assess image gradeability. Images were divided into training and test sets, and 10-fold cross-validation was performed. Results: A total of 1,377 images from 537 patients (56.1% female, median age 58) were analyzed. Ophthalmologists classified 25.9% of images as ungradable. Of gradable images, 18.6% had DR of varying degrees and 26.5% had non-DR pathology. 10 fold cross-validation produced an average area under receiver operating characteristic curve (AUC) of 0.922 (standard deviation: 0.027, range: 0.882 to 0.961). The final model exhibited similar test set performance with an AUC of 0.924. Conclusions: This model accurately assesses gradeability of nonmydriatic retinal images. It could be used for increasing the efficiency of DR screening programs by enabling point-of-care identification of poor-quality images.

Knowledge, Attitude, and Practice Regarding Diabetic Retinopathy Screening and Eye Management Among Diabetics in Saudi Arabia

Background Globally, one of the leading causes of blindness is diabetic retinopathy (DR). However, many patients do not participate in DR screening because of a lack of awareness. This study aims to assess the knowledge, attitude, and practice (KAP) level regarding DR screening and eye management among diabetic patients in Saudi Arabia. Methodology This cross-sectional study was conducted among diabetic patients aged 18 years or older in Saudi Arabia between October 2022 and February 2023. A validated online KAP-36 questionnaire collected information on sociodemographic data, diabetes profile, diabetes-related complications, and KAP regarding DR screening and management. Results Of the 1,391 diabetic patients, 736 (52.9%) had good knowledge about DR screening and care, while 655 (47.1%) had poor knowledge. A positive attitude toward eye examination for the early detection of DR was noticed among 1,124 (80.8%) participants. Regarding the participants' practice of regular fundus examination, 1,000 (71.9%) participants had good practice. Significant relationships were found between education level (p = 0.017), diabetes mellitus (DM) type and duration (p= 0.01, 0.02), type of treatment (p = 0.001), and a high degree of knowledge. Significant determinants of patients' favorable attitudes included their type of diabetes (p = 0.003), region of residence (p = 0.038), and work or education outside the medical field (p = 0.001). Age (p = 0.001), location of residence (p = 0.015), educational attainment (p= 0.041), and type of diabetes (p = 0.045) were the factors that determined good practice. Conclusions Many diabetic patients supported DR screening and engaged in it regularly. Unfortunately, only around half of the participants had a good understanding of DR. Type 2 diabetes mellitus (T2DM), a longer history of DM, and being highly educated were factors associated with a diabetic patient's higher level of knowledge. Positive attitudes were significantly higher among those living in the central Saudi region, employed outside of the medical field, and those with T2DM. Finally, regarding the practice of eye screening and management among diabetic patients, elderly patients living in the southern Saudi region and those with T2DM were adherent to their regular eye examinations. Consequently, the key to ensuring adequate adherence to DR screening may be intervention techniques and focused education to increase patients' knowledge of DR.

Comparison of Color Fundus Photography and Multicolor Fundus Imaging for Detection of Lesions in Diabetic Retinopathy and Retinal Vein Occlusion

Purpose

To evaluate the agreement between conventional fundus photography (CFP) and multicolor fundus imaging (MFI) for the detection of lesions of diabetic retinopathy (DR) and retinal vein occlusion (RVO).

Methods

Cross-sectional analysis of eyes with DR or RVO who underwent CFP and MFI. All images were independently analyzed by two observers (O1 and O2), and the evaluated lesions were classified as "present" or "absent". Then, a paired comparison between both exams of the same eye was performed, to assess which made it easier to detect the lesions.

Results

Considering DR, the agreement was substantial for cotton wool spots and photocoagulation scars for both observers (O1: κ=0.75 and κ=0.67; O2: κ=0.71 and κ=0.64, respectively) and for hard exudates for O1 (κ=0.80). These lesions were detected more frequently on MFI. Regarding RVO, the agreement was considered substantial for venous sheathing by O1 (κ=0.64) and moderate for optociliary shunts by O2 (κ=0.60). Optociliary shunts were detected more frequently in CPF by both observers and venous sheathing on MFI by O1. For microaneurysms, retinal hemorrhages, retinal neovascularization, and proliferative membranes, in DR, and retinal hemorrhages, venous engorgement, and retinal neovascularization in RVO, the agreement was almost perfect (κ>0.82). In the paired analysis, both observers considered that, in DR, microaneurysms and retinal hemorrhages were easier to detect on CFP and that retinal neovascularization, cotton wool spots, and photocoagulation scars were easier to identify on MFI. Regarding RVO, optocilliary shunts were easier to identify on CFP and venous engorgement on MFI.

Conclusion

The agreement of MFI and CFP was substantial to almost perfect for most lesions. MFI seems better to detect cotton wool spots and photocoagulations scars in DR and venous sheathing in RVO. Optocilliary shunts seem easier to detect on CFP.

Prevalence and predictors for being unscreened for diabetic retinopathy: a population-based study over a decade

OBJECTIVE

To determine the population-level predictors for being unscreened for diabetic retinopathy (DR) among individuals with diabetes in a developed country.

DESIGN

A retrospective population-based repeated-cross-sectional study.

PARTICIPANTS

All individuals with diabetes (types 1 and 2) aged ≥20 years in the universal health care system in Ontario were identified in the 2011-2013 and 2017-2019 time periods.

METHODS

The Mantel-Haenszel test was used for the relative risk (RR) comparison of subcategories stratified by the 2 cross-sectional time periods.

RESULTS

A total of 1 145 645 and 1 346 578 individuals with diabetes were identified in 2011-2013 and 2017-2019, respectively. The proportion of patients unscreened for DR declined very slightly from 35% (n = 405 967) in 2011-2013 to 34% (n = 455 027) in 2017-2019 of the population with diabetes (RR = 0.967; 95% CI, 0.964-0.9693; p < 0.0001). Young adults aged 20-39 years of age had the highest proportion of unscreened patients (62% and 58% in 2011-2013 and 2017-2019, respectively). Additionally, those who had a lower income quintile (RR = 1.039; 95% CI, 1.036-1.044; p < 0.0001), were recent immigrants (RR = 1.286; 95% CI, 1.280-1.293; p < 0.0001), lived in urban areas (RR = 1.149; 95% CI, 1.145-1.154; p < 0.0001), had a mental health history (RR = 1.117; 95% CI, 1.112-1.122; p < 0.0001), or lacked a connection to a primary care provider (RR = 1.656; 95% CI, 1.644-1.668; p < 0.0001) had a higher risk of being unscreened.

CONCLUSIONS

This population-based study suggests that over 1 decade, 33% of individuals with diabetes are unscreened for DR, and young age, low income, immigration, residing in a large city, mental health illness, and no primary care access are the main predictors.

Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors

Despite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algorithm using retinal photographs. The performance of the Reti-CKD score was verified using two longitudinal cohorts of the UK Biobank and Korean Diabetic Cohort. Validation was done in people with preserved kidney function, excluding individuals with eGFR <90 mL/min/1.73 m² or proteinuria at baseline. In the UK Biobank, 720/30,477 (2.4%) participants had CKD events during the 10.8-year follow-up period. In the Korean Diabetic Cohort, 206/5014 (4.1%) had CKD events during the 6.1-year follow-up period. When the validation cohorts were divided into quartiles of Reti-CKD score, the hazard ratios for CKD development were 3.68 (95% Confidence Interval [CI], 2.88-4.41) in the UK Biobank and 9.36 (5.26-16.67) in the Korean Diabetic Cohort in the highest quartile compared to the lowest. The Reti-CKD score, compared to eGFR based methods, showed a superior concordance index for predicting CKD incidence, with a delta of 0.020 (95% CI, 0.011-0.029) in the UK Biobank and 0.024 (95% CI, 0.002-0.046) in the Korean Diabetic Cohort. In people with preserved kidney function, the Reti-CKD score effectively stratifies future CKD risk with greater performance than conventional eGFR-based methods.

Associations of concomitant retinopathy and depression with mortality in a nationally representative population

OBJECTIVE

To evaluate the interaction effects between retinopathy and depression on mortality risks in genral population and subpopulation with diabetes.

METHODS

Prospective analyses were conducted on data from the National Health and Nutrition Examination Surveys study. Associations of retinopathy, depression and their interaction with all-cause, cardiovascular disease (CVD)-specific, cancer-specific and other-specific mortality risk were estimated using Kaplan-Meier curves and multivariate Cox proportional hazards models.

RESULTS

Among 5367 participants, the weighted prevalence of retinopathy and depression was 9.6 % and 7.1 %, respectively. After a follow-up period of 12.1 years, 1295 deaths (17.3 %) occurred. Retinopathy was associated with an increased risk of all-cause (hazard ratio [HR]; 95 % confidence interval [CI]) (1.47; 1.27-1.71), CVD-specific (1.87; 1.45-2.41), and other-specific (1.43; 1.14-1.79) mortality. Similar relationship was observed between depression and all-cause mortality (1.24; 1.02-1.52). Retinopathy and depression had a positive multiplicative and additive interaction effect on all-cause (P_interaction = 0.015; relative excess risk of interaction [RERI] 1.30; 95 % CI 0.15-2.45) and CVD-specific mortality (P_interaction = 0.042; RERI 2.65; 95 % CI -0.12-5.42). Concomitant retinopathy and depression was more markedly associated with all-cause (2.86; 1.91-4.28), CVD-specific (4.70; 2.57-8.62), and other-specific mortality risks (2.18; 1.14-4.15) compared to those without retinopathy and depression. These associations were more pronounced in the diabetic participants.

CONCLUSIONS

The co-occurrence of retinopathy and depression increases the risk of all-cause and CVD-specific mortality among middle-aged and older adults in the United States, especially in population with diabetes. Focus on diabetic patients and active evaluation and intervention of retinopathy with depression may improve their quality of life and mortality outcomes.

Diabetic vascular diseases: molecular mechanisms and therapeutic strategies

Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.

Intraocular Microbiome in Diabetes and Diabetic Retinopathy: A Pilot Study

INTRODUCTION

The objective of this study was to compare the microbiome in the aqueous humour and gut of people with diabetes mellitus (DM) with and without diabetic retinopathy (DR).

METHODS

This was a prospective controlled study. The study included 17 people undergoing intraocular surgery in their naïve eyes. Stool samples were obtained in the perioperative period; aqueous humour samples of sufficient quantity were obtained in 12 people during intraocular surgery. Dietary information was obtained using a previously validated questionnaire. The gut and aqueous humour samples were assessed for microbiome using 16S rRNA gene sequencing coupled with QIIME and R software.

RESULTS

Aqueous humour was analysed in 12 people: 4 each healthy controls, people with DM, and people with DR. There were minor differences at the phyla levels, but the aqueous humour microbiomes of healthy controls, DM, and DR formed three distinct clusters on heat map analysis with discriminatory genera. This genera-level clustering was more apparent for the intraocular than the gut microbiome. In people with DM and DR, we identified genera unique to the eye or the gut. There was a consistent reduction in the abundance of anti-inflammatory bacteria in people with DR than DM.

CONCLUSIONS

There is a difference in intraocular and gut microbiome regardless of disease or health. Our preliminary findings indicate distinctive features of the intraocular microbiome in people with DR compared with those without it. While this distinctiveness appears more evident in aqueous humour than in the gut, it needs further confirmation with larger studies.

Association of mobile phone usage time with incidence of diabetic retinopathy in type 2 diabetes: a prospective cohort study

We prospectively analyzed the association between mobile phone usage time and the incidence of diabetic retinopathy (DR) in type 2 diabetes (T2D) among participants.We included a total of 4,371 patients with T2D among the participants. Mobile phone usage time was quantified at baseline by summing up the hours spent on mobile phone use. The types of mobile phone usage time in our study include game time, TikTok time, WeChat time, watching movies or reading time, and online shopping time. We categorized patients into four groups according to different mobile phone usage time: ≤1.5 h/day (n = 1,101), 1.6-3.5 h/day (n = 1,098), 3.6-7.5 h/day (n = 1,095), and >7.6 h/day (n = 1,077). Fundus photography was performed every year from January 2012 to January 2020. During a follow-up of 8 years, 1,119 were affected by DR, resulting in an overall incidence of 25.6%. The incidences of mild nonproliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR) were 10.1%, 5.1%, 5.1%, and 5.2%, respectively. In comparisons with participants in the lowest category (≤1.5 h/day), the hazard ratios (HRs) of DR were 1.19 (95% confidence interval [CI] 1.07, 1.31, p = 0.040) for 1.6-3.5 h/day, 1.60 (95% CI 1.40, 1.81, p < 0.001) for 3.6-7.5 h/day, and 1.85 (95% CI 1.61, 2.09, p < 0.001) for >7.6 h/day, respectively. Our results provide the general population with a feasible and practical alternative for the reduction of mobile phone use behavior time and new measures to prevent the occurrence of DR.

Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals

Given the ever-increasing prevalence of type 2 diabetes and obesity, the pressure on global healthcare is expected to be colossal, especially in terms of blindness. Electroretinogram (ERG) has long been perceived as a first-use technique for diagnosing eye diseases, and some studies suggested its use for preventable risk factors of type 2 diabetes and thereby diabetic retinopathy (DR). Here, we show that in a non-evoked mode, ERG signals contain spontaneous oscillations that predict disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome but not yet diabetes, using one single random forest-based model. Classification performance was both internally and externally validated, and correlation analysis showed that the spontaneous oscillations of the non-evoked ERG are altered before oscillatory potentials, which are the current gold-standard for early DR. Principal component and discriminant analysis suggested that the slow frequency (0.4-0.7 Hz) components are the main discriminators for our predictive model. In addition, we established that the optimal conditions to record these informative signals, are 5-minute duration recordings under daylight conditions, using any ERG sensors, including ones working with portative, non-mydriatic devices. Our study provides an early warning system with promising applications for prevention, monitoring and even the development of new therapies against type 2 diabetes.

Retinal Oxygen Metabolism in Patients With Type 2 Diabetes and Different Stages of Diabetic Retinopathy

The aim of this cross-sectional study was to assess retinal oxygen metabolism in patients with type 2 diabetes and different stages of nonproliferative diabetic retinopathy (DR) (n = 67) compared with healthy control subjects (n = 20). Thirty-four patients had no DR, 15 had mild DR, and 18 had moderate to severe DR. Retinal oxygen saturation in arteries and veins was measured using the oxygen module of a retinal vessel analyzer. Total retinal blood flow (TRBF) was measured using a custom-built Doppler optical coherence tomography system. Retinal oxygen extraction was calculated from retinal oxygen saturation and TRBF. Arteriovenous difference in oxygen saturation was highest in healthy subjects (34.9 ± 7.5%), followed by patients with no DR (32.5 ± 6.3%) and moderate to severe DR (30.3 ± 6.5%). The lowest values were found in patients with mild DR (27.3 ± 8.0%, P = 0.010 vs. healthy subjects). TRBF tended to be higher in patients with no DR (40.1 ± 9.2 μL/min) and mild DR (41.8 ± 15.0 μL/min) than in healthy subjects (37.2 ± 5.7 μL/min) and patients with moderate to severe DR (34.6 ± 10.4 μL/min). Retinal oxygen extraction was the highest in healthy subjects (2.24 ± 0.57 μL O2/min), followed by patients with no DR (2.14 ± 0.6 μL O2/min), mild DR (1.90 ± 0.77 μL O2/min), and moderate to severe DR (1.78 ± 0.57 μL O2/min, P = 0.040 vs. healthy subjects). These results indicate that retinal oxygen metabolism is altered in patients with type 2 diabetes. Furthermore, retinal oxygen extraction decreases with increasing severity of DR.

The risk for developing vision threatening diabetic retinopathy is influenced by heredity to diabetes

Purpose: Clustering of vision threatening diabetic retinopathy within specific families can be the result of similarities in life style but may also be due to a common genetic background. An evaluation of the role of heredity for the development of diabetic retinopathy may help adjusting control intervals in screening programmes to each patient's individual risk profile.Methods: Survival analysis was used to study whether family history of diabetes among men and women with type 1 or type 2 diabetes together with the degree of heredity could be added as risk factors to improve the prediction of the risk for developing proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The study was conducted on data from 12,281 patients followed in the Aarhus area, Denmark, from January 1. 2003 to July 1. 2019.Results: The risk for developing PDR was significantly reduced 2-11 years after known onset of diabetes in the presence of female family members with type 1 diabetes, and the risk for developing DME significantly increased 4-24 years after onset of diabetes in the presence of family members with type 2 diabetes of any sex. These hereditary factors were independent of other studied risk factors such as previous cataract surgery, age of onset of diabetes, metabolic regulation and blood pressure.Conclusions: The presence of family members with diabetes affects the risk for developing vision threatening diabetic retinopathy and affects the risk for developing PDR and DME differently. This evidence may help individualizing the control intervals in screening programmes for diabetic retinopathy.

Awareness of diabetic retinopathy among diabetes mellitus patients visiting a hospital of North India

Context:

Diabetic retinopathy (DR) is a major cause of visual disability leading to irreversible blindness. Awareness of diabetes and its ocular co-morbidities may help in preventing vision loss.

Aim:

To assess the awareness of diabetic retinopathy among diabetic mellitus patients visiting a hospital in North India.

Setting and Design:

Cross-sectional study conducted at an outpatient clinic of ophthalmology department of a hospital.

Materials and Methods:

An interviewer-administered, pre-tested, semi-structured questionnaire was used to evaluate the awareness of DR for the period from July 2020 to January 2021.

Results:

A total of 272 patients with diabetes mellitus (44.4% females and 55.5% males) were included in the study. The mean age of the study population was 53.4 ± 10 years. Of the 272 patients, 79% were aware that diabetes can affect the eyes, and 69.5% knew that DR can lead to blindness. Regarding prevention and treatment of DR, 58.1% of patients were aware that good glycemic control prevents DR and 52.6% knew that DR can be treated. Physicians were the main source of information in 47.4% of patients. The participants were not compliant with a routine retinal assessment with only 26.5% of them having previous fundus examination for DR screening.

Conclusion:

Most of the participants were aware of DR but there existed major deficits in the knowledge and behavior of diabetic patients toward management of DR. There is a need to create awareness about DR and emphasize the importance of retinal screening to reduce visual disability caused by diabetes.

Potential Protective Role of TRPM7 and Involvement of PKC/ERK Pathway in Blue Light-Induced Apoptosis in Retinal Pigment Epithelium Cells in Vitro

PURPOSE

Blue light triggers apoptosis of retinal pigment epithelium (RPE) cells and causes retinal damage. The aim of this study was to elucidate the protective role of transient receptor potential melastatin 7 (TRPM7) in photodamaged RPE cells.

METHODS

RPE cells were isolated from Sprague-Dawley (SD) rats and exposed to varying intensities of blue light (500-5000 lux) in vitro. Cell proliferation and metabolic activity were respectively assessed by bromodeoxyuridine (BrdU) incorporation and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays. Real-time polymerase chain reaction (RT-PCR) and western blotting were used to analyze the TRPM7, protein kinase C (PKC), extracellular signal-regulated kinase (ERK) and Bcl2-associated x/B-cell lymphoma 2 (Bax/Bcl-2) messenger RNA (mRNA) and protein expression levels. The cells were transfected with TRPM7 small interfering RNA (siRNA) or transduced with TRPM7-overexpressing lentiviruses and cultured with or without the pigment epithelium-derived factor (PEDF).

RESULTS

Blue light inhibited the proliferation and metabolic activity of RPE cells in an intensity-dependent manner when compared to nonirradiated controls (P < 0.05). Compared to the control, photodamaged RPE cells showed decreased levels of TRPM7, PKC, ERK, and Bax, and an increase in Bcl-2 levels (P < 0.01). Forced expression of TRPM7 partially rescued the proliferative capacity of RPE cells (P < 0.01) and restored the levels of TRPM7, PKC, ERK, and Bax (P < 0.01), whereas TRPM7 knockdown had the opposite effects (P < 0.01). TRPM7 and PEDF synergistically alleviated the damaging effects of blue light.

CONCLUSIONS

Blue light triggers apoptosis of RPE cells, and its deleterious effects can be partially attenuated by the synergistic action of TRPM7 and PEDF via the PKC/ERK signaling pathway.

Bridging the valley of death between research and implementing a systematic diabetic retinopathy screening program in low- and medium-income countries

Translating research findings to routine clinical practice is fraught with obstacles. The gap between the end of a research project and the implementation of its results is often termed the “valley of death.” In this perspective, we highlight the barriers and potential solutions in translating research on diabetic retinopathy care pathways to implementation in the clinic. This gap analysis applies to all countries around the world, though it predominantly applies to low- and middle-income countries.

Associations of new-onset atrial fibrillation and severe visual impairment in type 2 diabetes: A multicenter nationwide study

BACKGROUND

Many studies have demonstrated an association between type 2 diabetes mellitus (T2DM) and atrial fibrillation (AF). However, the potential independent contributions of T2DM and AF to the prevalence of visual impairment have not been evaluated.

AIM

To determine whether such an association between T2DM and incident AF with visual impairment exists, and if so, the prevalence and magnitude of this association.

METHODS

We conducted a nationwide cross-sectional study based on the DM/HT study of the Medical Research Network of the Consortium of Thai Medical Schools. This study had evaluated adult T2DM patients from 831 public hospitals in Thailand in the year 2013. T2DM patients were categorized into two groups: patients without and with incident AF. T2DM patients without AF were selected as the reference group. The association between incident AF and visual impairment among T2DM patients was assessed using multivariate logistic regression.

RESULTS

A total of 27281 T2DM patients with available eye examination data were included in this analysis. The mean age was 60.7 ± 10.5 years, and 31.2% were male. The incident AF was 0.2%. The prevalence of severe visual impairment in all T2DM patients, T2DM patients without AF, and T2DM patients with incident AF were 1.4%, 1.4%, and 6.3%, respectively. T2DM patients with incident AF were associated with an increased OR of 3.89 (95%CI: 1.17-13.38) for severe visual impairment compared with T2DM patients without AF.

CONCLUSION

T2DM patients with incident AF were independently associated with increased severe visual impairment. Therefore, early eye screening should be provided for these high-risk individuals.

Multiple Single Nucleotide Polymorphism Testing Improves the Prediction of Diabetic Retinopathy Risk with Type 2 Diabetes Mellitus

Diabetic retinopathy (DR) is one of the most frequent causes of irreversible blindness, thus prevention and early detection of DR is crucial. The purpose of this study is to identify genetic determinants of DR in individuals with type 2 diabetic mellitus (T2DM). A total of 551 T2DM patients (254 with DR, 297 without DR) were included in this cross-sectional research. Thirteen T2DM-related single nucleotide polymorphisms (SNPs) were utilized for constructing genetic risk prediction model. With logistic regression analysis, genetic variations of the FTO (rs8050136) and PSMD6 (rs831571) polymorphisms were independently associated with a higher risk of DR. The area under the curve (AUC) calculated on known nongenetic risk variables was 0.704. Based on the five SNPs with the highest odds ratio (OR), the combined nongenetic and genetic prediction model improved the AUC to 0.722. The discriminative accuracy of our 5-SNP combined risk prediction model increased in patients who had more severe microalbuminuria (AUC = 0.731) or poor glycemic control (AUC = 0.746). In conclusion, we found a novel association for increased risk of DR at two T2DM-associated genetic loci, FTO (rs8050136) and PSMD6 (rs831571). Our predictive risk model presents new insights in DR development, which may assist in enabling timely intervention in reducing blindness in diabetic patients.