Diagnosis Of Diabetic Macular Edema Research Articles

Effect of Intravitreal Aflibercept on Corneal Endothelial Cells.

To determine the effect of repeated intravitreal injections of aflibercept on the corneal endothelium in patients with diabetic macular edema (DME) and macular edema due to retinal vein occlusion (RVO). In a prospective study conducted between January 2021 and November 2023, a total of 87 treatment-naive eyes with DME and RVO were evaluated. The exclusion criteria were surgery or laser intervention during the follow-up period, contact lens wear, cataract surgery in the last 6 months, dystrophy, or other corneal condition that may cause endothelial damage. In addition to routine examinations on the day of application, we also measured the corneal endothelium using specular microscopy on the 1st, 4th and 8th day of injection. We evaluated 4 parameters: endothelial cell density (CD), hexagonality (HEX), coefficient of variability (CV) and central corneal thickness (CCT). First of all, we evaluated the entire cohort of eyes, and then divided it according to 2 criteria; the diagnosis into DME/RVO and according to the lens status into phakic/pseudophakic eyes. A total of 87 eyes of 68 patients were evaluated. The average age of the patients at the time of diagnosis was 66.8 ±9.3 years. Within the cohort 51 (59%) eyes were phakic and 36 (41%) pseudophakic. A total of 61 (70%) eyes with a diagnosis of DME were treated, and 26 (30%) with RVO. During the follow-up, there were no significant changes in the average values of CD, HEX, CV, CCT due to aflibercept treatment, either in the whole group or in subgroups according to diagnosis or lens condition. The results of this study suggest that intravitreal administration of aflibercept in patients with DME and RVO did not have an impact on corneal endothelial parameters, including CCT, HEX, CD and CV. These parameters were measured using endothelial microscopy during an 8-injection observation period.

Explore the possibilities of the artificial intelligence program in the diagnosis of diabetic macular edema, age-related macular degeneration, central serous choriopathy and vitreomacular interface anomalies on the structural optical coherence tomography scans.

Background: macular diseases present a large group of pathological conditions leading to vision loss and poor vision. Early diagnosis of such changes plays an important role in the treatment tactics and comes one of the crucial factors in predicting results.
 Aims: study the possibilities of the artificial intelligence program in the diagnosis of the macular diseases based on the scans of structural OCT analysis.
 Materials and methods: the study included patients undergoing examination and treatment at the Federal Medico-Biological Agency Federal Research Clinical Center and the Moscow Regional Research Clinical Institute named after M.F. Vladimirsky. 200 eyes with the macular diseases and also eyes without macular pathology were examined. A comparative clinical analysis of structural OCT scans performed on an RTVue XR 110-2 tomograph was carried out. Retina AI software was used to analyze OCT scans.
 Results: during the OCT scans analysis various pathological structures of the macular were identified, and then a probable diagnosis was defined. The obtained results were compared with the diagnosis of the ophthalmologists. The sensitivity of the method was 95.16%; specificity - 97.76%; accuracy - 97.38%.
 Conclusions: Retina.AI allows ophthalmologists to successfully perform automated analysis of the OCT scans and identify various pathological conditions of the eye fundus.

Impact of COVID-19-related lifestyle changes on diabetic macular edema.

To assess diabetic macular edema (DME) progression during the early phases of the COVID-19 pandemic, when severe societal restrictions raised the concern of possible deterioration of health in patients with systemic conditions, particularly those requiring frequent office visits. This is a multicenter retrospective chart review of 370 patients (724 eyes) with an established diagnosis of DME seen on 3 separate visits between January 2019 and July 2021. Period 1 was January 2019 to February 2020 (considered pre-COVID-19), period 2 was March 2020 to December 2020 (considered the height of the pandemic; highest level of pandemic-related clinical and societal regulations) and period 3 was January 2021 to July 2021 (re-adjustment to the new "pandemic norms"). Main outcome measures included visual acuity, body mass index (BMI), blood pressure (BP), hemoglobin A1c (HbA1c), macular thickness, patient adherence to scheduled ophthalmology visits, and DME treatment(s) received at each visit. To facilitate measurement of macular thickness, each macula was divided into 9 Early Treatment Diabetic Retinopathy Study (ETDRS)-defined macular sectors as measured by OCT imaging. There was no change of BMI, systolic BP, and diastolic BP between any of the time periods. HbA1c showed a very small increase from period 1 (7.6%) to period 2 (7.8%, P=0.015) and decreased back to 7.6% at period 3 (P=0.12). Macular thickness decreased for 100% of macular regions. The central macular thickness decreased across all 3 periods from 329.5 to 316.6 µm (P=0.0045). After analysis of multiple variables including HbA1c, BMI, adherence to scheduled appointments, different clinic centers, and treatment interventions, there was no easily identifiable subgroup of patients that experienced the increase in DME. DME doesn't worsen during the COVID-19 pandemic, instead sustaining a very small but statistically significant improvement. While identifying a mechanism behind our findings is beyond the scope of this study, potential explanations may include a delay in retinal changes beyond our study period, an unexpected increase in treatment frequency despite pandemic restrictions, and an unanticipated pandemic-related improvement in some lifestyle factors that may have had a positive impact on DME.

Improved predictive diagnosis of diabetic macular edema based on hybrid models: An observational study

Diabetic Macular Edema (DME) is the most common sight-threatening complication of type 2 diabetes. Optical Coherence Tomography (OCT) is the most useful imaging technique to diagnose, follow up, and evaluate treatments for DME. However, OCT exam and devices are expensive and unavailable in all clinics in low- and middle-income countries. Our primary goal was therefore to develop an alternative method to OCT for DME diagnosis by introducing spectral information derived from spontaneous electroretinogram (ERG) signals as a single input or combined with fundus that is much more widespread. Baseline ERGs were recorded in 233 patients and transformed into scalograms and spectrograms via Wavelet and Fourier transforms, respectively. Using transfer learning, distinct Convolutional Neural Networks (CNN) were trained as classifiers for DME using OCT, scalogram, spectrogram, and eye fundus images. Input data were randomly split into training and test sets with a proportion of 80 %–20 %, respectively. The top performers for each input type were selected, OpticNet-71 for OCT, DenseNet-201 for eye fundus, and non-evoked ERG-derived scalograms, to generate a combined model by assigning different weights for each of the selected models. Model validation was performed using a dataset alien to the training phase of the models. None of the models powered by mock ERG-derived input performed well. In contrast, hybrid models showed better results, in particular, the model powered by eye fundus combined with mock ERG-derived information with a 91 % AUC and 86 % F1-score, and the model powered by OCT and mock ERG-derived scalogram images with a 93 % AUC and 89 % F1-score. These data show that the spontaneous ERG-derived input adds predictive value to the fundus- and OCT-based models to diagnose DME, except for the sensitivity of the OCT model which remains the same. The inclusion of mock ERG signals, which have recently been shown to take only 5 min to record in daylight conditions, therefore represents a potential improvement over existing OCT-based models, as well as a reliable and cost-effective alternative when combined with the fundus, especially in underserved areas, to predict DME.

Diabetic Macular Edema Optical Coherence Tomography Biomarkers Detected with EfficientNetV2B1 and ConvNeXt.

(1) Background: Diabetes mellitus (DM) is a growing challenge, both for patients and physicians, in order to control the impact on health and prevent complications. Millions of patients with diabetes require medical attention, which generates problems regarding the limited time for screening but also addressability difficulties for consultation and management. As a result, screening programs for vision-threatening complications due to DM have to be more efficient in the future in order to cope with such a great healthcare burden. Diabetic macular edema (DME) is a severe complication of DM that can be prevented if it is timely screened with the help of optical coherence tomography (OCT) devices. Newly developing state-of-the-art artificial intelligence (AI) algorithms can assist physicians in analyzing large datasets and flag potential risks. By using AI algorithms in order to process OCT images of large populations, the screening capacity and speed can be increased so that patients can be timely treated. This quick response gives the physicians a chance to intervene and prevent disability. (2) Methods: This study evaluated ConvNeXt and EfficientNet architectures in correctly identifying DME patterns on real-life OCT images for screening purposes. (3) Results: Firstly, we obtained models that differentiate between diabetic retinopathy (DR) and healthy scans with an accuracy of 0.98. Secondly, we obtained a model that can indicate the presence of edema, detachment of the subfoveolar neurosensory retina, and hyperreflective foci (HF) without using pixel level annotation. Lastly, we analyzed the extent to which the pretrained weights on natural images "understand" OCT scans. (4) Conclusions: Pretrained networks such as ConvNeXt or EfficientNet correctly identify features relevant to the differentiation between healthy retinas and DR, even though they were pretrained on natural images. Another important aspect of our research is that the differentiation between biomarkers and their localization can be obtained even without pixel-level annotation. The "three biomarkers model" is able to identify obvious subfoveal neurosensory detachments, retinal edema, and hyperreflective foci, as well as very small subfoveal detachments. In conclusion, our study points out the possible usefulness of AI-assisted diagnosis of DME for lowering healthcare costs, increasing the quality of life of patients with diabetes, and reducing the waiting time until an appropriate ophthalmological consultation and treatment can be performed.

SGLT2 inhibitors and diabetic retinopathy progression.

To evaluate whether sodium-glucose co-transporter 2 (SGLT2) inhibitors affect progression of non-proliferative diabetic retinopathy (NPDR) compared to standard of care. A retrospective cohort study compared subjects enrolled in a commercial and Medicare Advantage medical claims database who filled a prescription for a SGLT2 inhibitor between 2013 and 2020 to unexposed controls, matched up to a 1:3 ratio. Patients were excluded if they were enrolled for less than 2years in the plan, had no prior ophthalmologic exam, had no diagnosis of NPDR, had a diagnosis of diabetic macular edema (DME) or proliferative diabetic retinopathy (PDR), had received treatment for vision-threatening diabetic retinopathy (VTDR), or were younger than 18years. To balance covariates of interest between the cohorts, an inverse probability treatment weighting (IPTW) propensity score for SGLT2 inhibitor exposure was used. Multivariate Cox proportional hazard regression modeling was employed to assess the hazard ratio (HR) for VTDR, PDR, or DME relative to SGLT2 exposure. A total of 6065 patients who initiated an SGLT2 inhibitor were matched to 12,890 controls. There were 734 (12%), 657 (10.8%), and 72 (1.18%) cases of VTDR, DME, and PDR, respectively, in the SGLT2 inhibitor cohort. Conversely, there were 1479 (11.4%), 1331 (10.3%), and 128 (0.99%) cases of VTDR, DME, and PDR, respectively, among controls. After IPTW, Cox regression analysis showed no difference in hazard for VTDR, PDR, or DME in the SGLT2 inhibitor-exposed cohort relative to the unexposed group [HR = 1.04, 95% CI 0.94 to 1.15 for VTDR; HR = 1.03, 95% CI 0.93 to 1.14 for DME; HR = 1.22, 95% CI 0.89 to 1.67 for PDR]. Exposure to SGLT2 inhibitor therapy was not associated with progression of NPDR compared to patients receiving other diabetic therapies.

Automatic grading of Diabetic macular edema based on end-to-end network

Diabetic macular edema (DME) is one of the main causes of permanent blindness. Automatic grading is helpful to quick diagnosis of DME and reduction of vision loss. Most of the existing methods grade DME according to the shortest distance from the hard exudates(HEs) to the fovea, which is required to detect the fovea and HEs beforehand. The end-to-end network takes the whole fundus image as input to obtain the grading result, but the accuracy is often low because of the complexity of DME. Some end-to-end architectures simplify the grading to two grades instead of three grades or use other disease information to improve the accuracy. In order to improve the efficiency and accuracy of grading without introducing the lesion segmentation or simplifying the grading task, this paper proposes a new end-to-end architecture with ResNet50 combined with channel attention (SENet) to extract features and introduces the disease attention module to supplement the disease-specific information of DME, which can achieve higher grading results and reduce the grading cost without additional acquisition of fovea and HEs. In order to solve the problem of class imbalance and insufficient training samples in grading, this paper adds class weight to the crossentropy function in the design of the loss function and augmentation techniques to expand data. The proposed network is verified on the MESSIDOR dataset and has a good grading result. The experimental results show that the proposed method has high accuracy, specificity, sensitivity and F1 score, which are 97.06%, 98.97%, 88.64% and 0.9177, respectively.

Disorganization of the inner retinal layers in diabetic macular edema: systematic review

The objective of this article was to review the disorganization of inner retinal layers as a biomarker in diabetic macular edema. A systematic search was conducted in PubMed®/MEDLINE®, Cochrane and Embase until August 2021. The keywords used were: “disorganization of inner retinal layers (DRIL)”, “diabetic macular edema (DME)” and “biomarkers”. No restrictions were imposed on the types of study to be included. The studies selected for eligibility were those that included the diagnosis of diabetic macular edema (center involved, [...]

Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study

Diabetic retinopathy is a leading cause of preventable blindness, especially in low-income and middle-income countries (LMICs). Deep-learning systems have the potential to enhance diabetic retinopathy screenings in these settings, yet prospective studies assessing their usability and performance are scarce. We did a prospective interventional cohort study to evaluate the real-world performance and feasibility of deploying a deep-learning system into the health-care system of Thailand. Patients with diabetes and listed on the national diabetes registry, aged 18 years or older, able to have their fundus photograph taken for at least one eye, and due for screening as per the Thai Ministry of Public Health guidelines were eligible for inclusion. Eligible patients were screened with the deep-learning system at nine primary care sites under Thailand's national diabetic retinopathy screening programme. Patients with a previous diagnosis of diabetic macular oedema, severe non-proliferative diabetic retinopathy, or proliferative diabetic retinopathy; previous laser treatment of the retina or retinal surgery; other non-diabetic retinopathy eye disease requiring referral to an ophthalmologist; or inability to have fundus photograph taken of both eyes for any reason were excluded. Deep-learning system-based interpretations of patient fundus images and referral recommendations were provided in real time. As a safety mechanism, regional retina specialists over-read each image. Performance of the deep-learning system (accuracy, sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were measured against an adjudicated reference standard, provided by fellowship-trained retina specialists. This study is registered with the Thai national clinical trials registry, TCRT20190902002. Between Dec 12, 2018, and March 29, 2020, 7940 patients were screened for inclusion. 7651 (96·3%) patients were eligible for study analysis, and 2412 (31·5%) patients were referred for diabetic retinopathy, diabetic macular oedema, ungradable images, or low visual acuity. For vision-threatening diabetic retinopathy, the deep-learning system had an accuracy of 94·7% (95% CI 93·0-96·2), sensitivity of 91·4% (87·1-95·0), and specificity of 95·4% (94·1-96·7). The retina specialist over-readers had an accuracy of 93·5 (91·7-95·0; p=0·17), a sensitivity of 84·8% (79·4-90·0; p=0·024), and specificity of 95·5% (94·1-96·7; p=0·98). The PPV for the deep-learning system was 79·2 (95% CI 73·8-84·3) compared with 75·6 (69·8-81·1) for the over-readers. The NPV for the deep-learning system was 95·5 (92·8-97·9) compared with 92·4 (89·3-95·5) for the over-readers. A deep-learning system can deliver real-time diabetic retinopathy detection capability similar to retina specialists in community-based screening settings. Socioenvironmental factors and workflows must be taken into consideration when implementing a deep-learning system within a large-scale screening programme in LMICs. Google and Rajavithi Hospital, Bangkok, Thailand. For the Thai translation of the abstract see Supplementary Materials section.

Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N = 48) and the control group (N = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group (P < 0.05). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.

Trends in Prevalence and Treatment of Diabetic Macular Edema and Vision-Threatening Diabetic Retinopathy Among Medicare Part B Fee-for-Service Beneficiaries

While diabetes prevalence among US adults has increased in recent decades, few studies document trends in diabetes-related eye disease. To examine 10-year trends (2009-2018) in annual prevalence of Medicare beneficiaries with diabetes with a diagnosis of diabetic macular edema (DME) or vision-threatening diabetic retinopathy (VTDR) and trends in treatment. In this cross-sectional study using Centers for Medicare & Medicaid Services research identifiable files, data for patients 65 years and older were analyzed from claims. Beneficiaries were continuously enrolled in Medicare Part B fee-for-service (FFS) insurance for the calendar year and had a diagnosis of diabetes on 1 or more inpatient claims or 2 or more outpatient claims during the calendar year or a 1-year look-back period. Using diagnosis and procedure codes, annual prevalence was determined for beneficiaries with 1 or more claims for (1) any DME, (2) either DME or VTDR, and (3) anti-vascular endothelial growth factor (VEGF) injections, laser photocoagulation, or vitrectomy, stratified by any DME, VTDR with DME, and VTDR without DME. Racial and ethnic disparities in diagnosis and treatment are presented for 2018. In 2018, 6 960 823 beneficiaries (27.4%) had diabetes; half were aged 65 to 74 years (49.7%), half (52.7%) were women, and 75.7% were non-Hispanic White. From 2009 to 2018, there was an increase in the annual prevalence of beneficiaries with diabetes who had 1 or more claims for any DME (1.0% to 3.3%) and DME/VTDR (2.8% to 4.3%). Annual prevalence of anti-VEGF increased, particularly among patients with any DME (15.7% to 35.2%) or VTDR with DME (20.2% to 47.6%). Annual prevalence of laser photocoagulation decreased among those with any DME (45.5% to 12.5%), VTDR with DME (54.0% to 20.3%), and VTDR without DME (22.5% to 5.8%). Among all 3 groups, prevalence of vitrectomy in 2018 was less than half that in 2009. Prevalence of any DME and DME/VTDR was highest among Hispanic beneficiaries (5.0% and 7.0%, respectively) and Black beneficiaries (4.5% and 6.2%, respectively) and lowest among non-Hispanic White beneficiaries (3.0% and 3.8%, respectively). Among those with DME/VTDR, anti-VEGF was most prevalent among non-Hispanic White beneficiaries (30.3%). From 2009 to 2018, prevalence of DME or VTDR increased among Medicare Part B FFS beneficiaries alongside an increase in anti-VEGF treatment and a decline in laser photocoagulation and vitrectomy.

Association of metformin treatment with enhanced effect of anti-VEGF agents in diabetic macular edema patients

To investigate the effect of metformin combined with anti-VEGF agents in patients with diabetic macular edema (DME). This study was a prospective, nonrandomized case-control study. Patients were included in with a diagnosis of DME who received anti-VEGF agents injection. Basic information, medical history, best-corrected visual acuity (BCVA), central macular thickness (CMT), the number of intravitreal injections, panretinal photocoagulation (PRP), and macular grid photocoagulation treatment during the 6-month follow-up, were recorded for each patient. A total of 50 DME patients were collected (24 patients with a history of oral metformin ≥ 6months and 26 patients who had not taken metformin). The BCVA and the CMT were significantly improved after anti-VEGF treatment in two groups (F1 = 19.35, F2 = 26.78; F1 = 65.45, F2 = 76.23; P < 0.05). The BCVA in the metformin group was better than that in non-metformin group at every point after treatment (F = 34.45, P < 0.05). The CMT in metformin group decreased much more than that in non-metformin group during the follow-up period (F = 87.05, P < 0.05). The injection numbers decreased in the metformin group compared with the non-metformin group (t = 5.14, P < 0.05). However, there was no difference in PRP and macular grid photocoagulation therapy between the two groups during the 6-month follow-up. Metformin can enhance the therapeutic effect of anti-VEGF agents on DME patients to improve their visual acuity, improve the structure of the macular area, and reduce the number of intravitreal injections 90.

DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis

Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis of DME is necessary for effective treatment of the disease. Visual detection of DME in retinal screening images by ophthalmologists is a time-consuming process. Recently, many computer-aided diagnosis systems have been developed to assist doctors by detecting DME automatically. In this paper, a new deep feature transfer-based stacked autoencoder neural network system is proposed for the automatic diagnosis of DME in fundus images. The proposed system integrates the power of pretrained convolutional neural networks as automatic feature extractors with the power of stacked autoencoders in feature selection and classification. Moreover, the system enables extracting a large set of features from a small input dataset using four standard pretrained deep networks: ResNet-50, SqueezeNet, Inception-v3, and GoogLeNet. The most informative features are then selected by a stacked autoencoder neural network. The stacked network is trained in a semi-supervised manner and is used for the classification of DME. It is found that the introduced system achieves a maximum classification accuracy of 96.8%, sensitivity of 97.5%, and specificity of 95.5%. The proposed system shows a superior performance over the original pretrained network classifiers and state-of-the-art findings.

Treatment patterns in patients with age-related macular degeneration and diabetic macular edema: A real-world claims analysis in Dubai.

ObjectivesTo characterize the pattern of approved anti-vascular endothelial growth factor (VEGF) treatments among patients with neovascular age-related macular degeneration (nAMD) and diabetic macular edema (DME) in the United Arab Emirates (UAE).MethodThis was a retrospective, nonrandomized, observational cohort analysis of the Dubai Real-world Claims Database with a 360-day follow-up period. Adult patients diagnosed with nAMD or DME treated with ranibizumab or aflibercept for the first time were included. The primary objective was to evaluate anti-VEGF treatment patterns with respect to the proportion of patients receiving ranibizumab and aflibercept for nAMD and DME separately.ResultsOf the 451 patients included in the final study cohort, 83.6% and 16.4% had a diagnosis of DME (ranibizumab: 48.5%; aflibercept: 51.5%) and nAMD (ranibizumab: 40.5%; aflibercept: 59.5%), respectively, at baseline. Treatment frequency of ranibizumab/aflibercept was similar for nAMD (mean: 2.4/2.9 injections; p = 0.2389) with fewer injections in the ranibizumab cohort for DME (mean: 1.9/2.5 injections; p = 0.0002). Most patients received ≤3 anti-VEGF injections during the 360-day follow-up period. The time between consecutive treatments was large (nAMD: 73.6 days/10.5 weeks; DME: 80.5 days/11.5 weeks). Approximately 10%–13.5% of patients switched their anti-VEGF therapy. Most patients (83.8%) had a diabetes diagnosis during the follow-up period.ConclusionsThis real-world study provides an initial understanding of anti-VEGF treatment patterns in patients with nAMD and DME in the UAE. Treatment frequency of the 2 anti-VEGF agents assessed was similar in both patient populations. Both treatments were infrequently administered with large dosing intervals.

The Eye in Internal Medicine: Diabetic retinopathy

Diabetic retinopathy has seen tremendous progress in diagnostic tools and treatment in recent 15 years. Sight threatening stages like proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME) can be treated much more effectively now. The recognition of vascular endothelial growth factor (VEGF) as a driver of proliferation and macular edema has led to the development of VEGF inhibiting drugs such as antibodies (Bevacizumab), fragments of an antibody (Ranibizumab), or a so called VEGF trap (Aflibercept). Laser treatment is no longer a gold standard. Today laser therapy is part of a combined treatment strategy. DME can be monitored quantitatively by ocular coherence tomography (OCT) measurements. For non-responders to VEGF inhibiting drugs, we have secondline corticosteroidal implants. However, screening examinations and early diagnosis of DME and PDR remain crucial. The same accounts for the collaboration of opthalmologists, general practioners, and diabetologists. Good control of diabetes and blood pressure is still important.

Plasma amino acids and oxylipins as potential multi-biomarkers for predicting diabetic macular edema

To investigate the pathophysiologic characteristics of diabetic complications, we identified differences in plasma metabolites in subjects with type 2 diabetes (T2DM) with or without diabetic macular edema (DME) and a disease duration > 15 years. An cohort of older T2DM patients with prolonged disease duration was established, and clinical information and biospecimens were collected following the guidelines of the National Biobank of Korea. DME phenotypes were identified by ophthalmologic specialists. For metabolomics studies, propensity matched case and control samples were selected. To discover multi-biomarkers in plasma, non-targeted metabolite profiling and oxylipin profiling in the discovery cohort were validated in an extended cohort. From metabolomic studies, 5 amino acids (asparagine, aspartic acid, glutamic acid, cysteine, and lysine), 2 organic compounds (citric acid and uric acid) and 4 oxylipins (12-oxoETE, 15-oxoETE, 9-oxoODE, 20-carboxy leukotriene B4) were identified as candidate multi-biomarkers which can guide DME diagnosis among non-DME subjects. Receiver operating characteristic curves revealed high diagnostic value of the combined 5 amino acids and 2 organic compounds (AUC = 0.918), and of the 4 combined oxylipins (AUC = 0.957). Our study suggests that multi-biomarkers may be useful for predicting DME in older T2DM patients.

Outcomes of Patients With Active Diabetic Macular Edema at the Time of Cataract Surgery Managed With Intravitreal Anti–Vascular Endothelial Growth Factor Injections

To investigate the outcomes of cataract surgery in patients with active diabetic macular edema (DME) who are receiving active treatment with intravitreal anti-vascular endothelial growth factor (VEGF) injections in the perioperative period. Retrospective clinical cohort study. We reviewed all patients who underwent cataract surgery and were receiving intravitreal anti-VEGF injections from January 1, 2012 through December 31, 2017. Thirty-seven eyes underwent cataract surgery and received ≥1 intravitreal anti-VEGF injection for a diagnosis of DME within 6 months before surgery. Outcome measures included the development of subretinal or intraretinal fluid in the 6 months after surgery, timing of injections, number of injections, best-corrected visual acuity, and central subfield thickness. There was a significant improvement between pre- and postoperative best-corrected visual acuity when comparing all eyes (Ps < .0001) and no significant difference in central subfield thickness before and after surgery (P > .05). There were 30 eyes (81.1%) that had fluid on the preoperative optical coherence tomography scan. Seventeen eyes (45.9%) developed new or worsening postoperative DME. Comparing the eyes that did or did not develop worsening DME, there were no differences in postoperative visual acuities (P > .05). Eyes that did develop new fluid had significant increases in postoperative central subfield thickness at both 1 month (350 vs 320 μm, P=.036) and 6 months (342 vs 305 μm, P=.013). In a real-world setting, patients with cataracts and actively treated DME may undergo cataract surgery but may see a worsening of DME not immediately affecting the best-corrected visual acuity.

Higher levels of circulating ANGPTL2 are associated with macular edema in patients with type 2 diabetes.

Macular edema (ME) is an inflammatory disease characterized by increased microvascular permeability. Here, we proposed that plasma angiopoietin-like protein 2 (ANGPTL2) level may be related to the severity of ME patients with type 2 diabetes mellitus (T2DM). In this cross-sectional study, 172 T2DM patients were recruited and divided into clinically significant macular edema (CSME), non-CSME (nCSME), and control groups. Serum ANGPTL2 level was quantified by ELISA and best corrected vision acuity (BCVA) was detected. After adjust age, sex, body mass index (BMI), and duration of diabetes variables, ANGPTL2 performed statistics difference among CSME-, nCSME-groups, and control group (4.46 [3.97, 4.96, 95%CI] ng/mL in CSME group, 3.80 [3.42, 4.18, 95%CI] ng/mL in nCSME-group, 3.33 [3.03, 3.63, 95%CI] ng/mL in control, P < .01). After adjustment of confounding factors, high levels of circulating ANGPTL2 were related with the diagnosis of ME, BCVA, and C reactive protein (CRP) through univariate regression analysis (P < .05). Meanwhile, in the multiple regression model, ANGPTL2 took the mainly effect proportion for the diagnosis of diabetic macular edema (DME), with a LogWorth value 3.559 (P < .001). Our study suggested that elevated circulating ANGPTL2 may be associated with the development of DME and the severity of visual impairment in patients with type 2 diabetes.

Corneal endothelial alterations in patients with diabetic macular edema

Background/Aim: Diabetic macular edema (DME) is the main cause of visual loss in diabetic patients. Although it is known that diabetes mellitus could affect all corneal layers, there is no data about the morphological and quantitative changes of corneal endothelium in patients with DME. The aim of this study is to evaluate the corneal endothelial cell density (CD), morphology and central corneal thickness (CCT) in patients with DME. Methods: This retrospective study included 47 diabetic patients (79 eyes) with DME, 48 diabetic patients (93 eyes) without DME, and 46 nondiabetic subjects (74 eyes). Diagnosis of DME was based on fundoscopy and optical coherence tomography imaging. The corneal endothelial structure and CCT were evaluated using non-contact specular microscopy. The endothelial CD (cells/mm2), coefficient variation of cell area (CV), percentage of hexagonality (HEX) and CCT of the three subgroups were compared. Results: The mean age of participants was 59.8 (8.3) years. There was no significant difference in terms of age between diabetic patients and control subjects (P=0.761). In the diabetic subgroups, HbA1c levels and the number of patients receiving insulin were similar (P=0.962, P=0.082, respectively), but the mean duration of diabetes was significantly longer in the DME subgroup than in the no-DME subgroup (P=0.015). Patients in the DME subgroup did not differ from the patients in the no-DME and control subgroups with regards to endothelial CD and CCT. However, there was a statistically significant decrease in HEX and increase in CV in patients with DME (P=0.012, P=0.012, respectively). Conclusion: Patients with DME were found to have higher rates of polymegathism and polymorphism although there were no significant changes in corneal endothelial CD and CCT. These alterations may be the first signs of early corneal damage in patients with DME.

Interdigitation and Ellipsoid Zones Disruption Correlate with Visual Outcomes among Treatment-Naive Patients with Diabetic Macular Edema

Introduction: We have recently shown that defects in interdigitation and ellipsoid zones (IZ and EZ) can predict response to anti-VEGF therapy in a small group of treatment-naive diabetic macular edema (DME) patients. The aim of the current study is to further evaluate this association in a larger study group of patients over a longer follow-up time. Methods: Thirty eyes of 30 treatment-naive DME patients were analyzed in this retrospective study. The integrity of foveal IZ and EZ was evaluated using optical coherence tomography at the diagnosis of DME and following anti-VEGF injections. The defect size was correlated with best-corrected visual acuity (BCVA) and central macular thickness (CMT). Results: The mean patients’ age at baseline was 63.0 ± 10.0 years. Patients underwent 3.9 ± 2.9 anti-VEGF injections for a mean of 9.1 ± 4.8 months. Following treatment, the mean Snellen visual acuity (VA) improved from 20/52 to 20/44 (p = 0.05), CMT decreased from 432.5 ± 141.4 μm to 375.2 ± 121.4 µm (p = 0.05) and IZ/EZ defect size decreased from 259.83 ± 375.94 µm to 65.34 ± 143.97 µm (p = 0.001). In patients with no IZ/EZ defects at baseline, the mean Snellen VA was better when compared to those with IZ/EZ defects (20/36 vs. 20/70, p = 0.031). The number of eyes with IZ/EZ defects decreased from 17 (57%) at baseline to 6 (20%) at end of follow-up (p < 0.01). BCVA gain correlated with IZ/EZ defect size reduction (r = 0.41, p = 0.02) but not with improvement in CMT (r = 0.28, p = 0.121). Conclusions: IZ/EZ defect size correlated not only with baseline BCVA but also predicted the change in BCVA after anti-VEGF treatment. Possible future automatic measurement of IZ/EZ defect size might prove helpful for the evaluation of treatment response.