Stages Of Diabetic Retinopathy Research Articles

Psychological Domain of Elderly Patients with Diabetic Retinopathy

The continuing increase in the prevalence of diabetic retinopathy among various segments of the population and, especially in older age, combined with a change in the psychological state of such patients. However, the study of the holistic psychological (cognitivedepressive) domain in elderly patients with various stages of diabetic retinopathy, comparable in cardiovascular pathology, which is an independent risk factor for both diabetic retinopathy and cognitive impairment, depression not carried out.Purpose: to assess the psychological domain in patients 60–74 years old suffering from diabetic retinopathy standardized for concomitant cardiovascular pathology. In the Tambov branch of the Tambov branch of S.N. Fedorov NMRC “MNTK “Eye Microsurgery” in 2019–2020, cognitive impairment and depression were studied in 68 patients with non-proliferative, 62 patients with preproliferative and 70 elderly patients with proliferative stage on the Mini-Mental-State-Examination and Center for Epidemiologic Studies — Depression scale, respectively. The diagnosis of diabetic retinopathy established based on the results of a comprehensive ophthalmological examination. The control consisted of 59 patients with the absence of diabetic retinopathy. Patients with non-proliferative stage had mild (21.5 ± 0.3 points), and with preproliferative (17.9 ± 0.4 points) and proliferative stage (16.2 ± 0.3 points) moderate cognitive impairment. The association of cognitive impairment found with preproliferative and proliferative diabetic retinopathy. The level of depression in the non–proliferative stage was 22.4 ± 0.4 points, in the preproliferative stage — 24.8 ± 0.3 points and in the proliferative stage — 26.9 ± 0.5 points versus 19.2 ± 0.3 points in the control with a significant difference in all cases. The values of the relative risk of diabetic retinopathy stages were 1,337, 2,408 and 2,796, respectively. The revealed deterioration of the cognitive-depressive domain in elderly patients with diabetic retinopathy is important for improving compliance, the effectiveness of treatment of diabetic retinopathy and the psychological continuum.

Detection of Diabetic Retinopathy

Biomedical engineering faces a significant challenge in assessing the physiological changes occurring in the human body non-invasively, particularly when detecting abnormalities in the eye due to the complexities involved. Traditional disease identification techniques in retinal images rely on manual intervention, which can be error-prone and have a low success rate. Long-term diabetics are susceptible to the multistage progressive condition known as diabetic retinopathy, which has aberrant retinal imaging characteristics such microaneurysms, haemorrhages, and exudates. It uses the dataset which consists of total 13970 images which includes the images of all stages collected from kaggle APTOS competition. Deep learning, a key method in health informatics, is used in this work to identify diabetic retinopathy and its stages.. Residual Network and Densely connected convolutional network deep learning approaches are employed, and transfer learning is used to achieve good accuracy in identifying the stages of diabetic retinopathy. The accuracy obtained by using the ResNet101 is 92.37% and with the DenseNet121 is 98.37%.

Sensitivity and specificity of optical coherence tomography angiography for diagnosis and classification of diabetic retinopathy; a systematic review and meta-analysis.

Optical coherence tomography angiography (OCTA) is a noninvasive imaging method that can be used for the staging of diabetic retinopathy. In addition, alterations in OCTA parameters can precede the clinical fundus changes. In this review, we aimed to assess the accuracy of OCTA in diagnosis and staging of diabetic retinopathy. Two independent reviewers participated in the literature search using electronic databases (PubMed, Embase, Cochrane Library Central Register of Controlled Trials, ISI, and Scopus) from inception till December 2020. The heterogeneity of data was assessed by Q statistics, Chi-square test and I2 index. Forty-four articles published from 2015 to the end of 2020 were included in this meta-analysis. Of these, 27 were case-control studies, 9 were case series, and 8 were cohort studies. In total, 4284 eyes of 3553 patients were assessed in this study. OCTA could differentiate diabetic retinopathy from diabetes without diabetic retinopathy with a sensitivity of 88% (95% CI: 85% to 92%) and specificity of 88% (95% CI: 85% to 91%). In addition, it could differentiate proliferative diabetic retinopathy from non-proliferative diabetic retinopathy with a sensitivity of 91% (95% CI: 86% to 95%) and specificity of 91% (95% CI:86% to 96%). The sensitivity of OCTA for diagnosing diabetic retinopathy was increased by the size of scan (3 × 3 mm: 85%; 6 × 6 mm: 91%, 12 × 12 mm: 96%). OCTA, as a non-invasive method, has acceptable sensitivity and specificity for diagnosis and classification of diabetic retinopathy. A larger scan size is associated with more sensitivity for discriminating diabetic retinopathy.

Modified capsule network for diabetic retinopathy detection and classification using fundus images

Diabetic retinopathy becomes an increasingly popular cause of vision loss in diabetic patients. Deep learning has recently received attention as one of the most popular methods for boosting performance in a range of sectors, including medical image analysis and classification. The proposed system comprises three steps; they are image preprocessing, image segmentation, and classification. In preprocessing, the image will be resized, denoising the image and enhancing the contrast of the image which is used for further processing. The lesion region of diabetic retinopathy fundus image is segmented by using Feature Fusion-based U-Net architecture. A blood vessel of a retinal image is extracted by using the spatial fuzzy c means clustering (SFCM) algorithm. Finally, the diabetic retinopathy images are classified using a modified capsule network. The convolution and primary capsule layers collect features from fundus images, while the class capsule and softmax layers decide whether the image belongs to a certain class. Using the Messidor dataset, the proposed system’s network efficiency is evaluated in terms of four performance indicators. The modified contrast limited adaptive histogram equalization technique enhanced the Peak Signal to Noise Ratio (PSNR), mean square error, and Structural Similarity Index Measure (SSIM) have average values of 36.18, 6.15, and 0.95, respectively. After enhancing the image, segmentation is performed to segment the vessel and lesion region. The segmentation accuracy is measured for the proposed segmentation algorithm by using two metrics namely intersection over union (IoU) and Dice similarity coefficient. Then modified capsule network is constructed for classifying the stages of diabetic retinopathy. The experimental result shows that the proposed modified capsule network got 98.57% of classification accuracy.

Correlation of Serum Erythropoietin Levels with Different Stages of Diabetic Retinopathy.

To determine the correlation of serum erythropoietin concentration with diabetic retinopathy in patients with type 2 diabetes mellitus. Cross-sectional study. Place and Duration of the Study: Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology (AFIP), Rawalpindi, from July to December 2021. A total of 180 individuals were enrolled in the study and placed in 2 groups as group 1 have 90 cases of type 2 diabetes mellitus and group 2 having 90 age-matched healthy controls. Group 1 was further subclassified into proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR) subgroups by an expert ophthalmologist. Serum erythropoietin, creatinine, blood HbA1c, and haemoglobin were analysed. Correlation between stages of proliferation and serum erythropoietin, creatinine, blood HbA1c, and haemoglobin were analysed. An independent-sample student t-test was applied to compare mean Serum erythropoietin between PDR and NPDR groups. Pearson's correlation was applied among disease severity, and type of retinopathy. A p-value of ≤0.05 was considered significant. The average age of participants in groups 1 and 2 was 45.88±8.6 and 56.6±10.23 years, respectively. More males (n=60, 66.7%) were noted in cases compared to controls (n=42, 46.7%). serum erythropoietin concentration observed in cases (8.4±1.87 IU/L) was higher than controls (6.50±0.9). The mean serum erythropoietin concentration in PDR (9.35±1.74 IU/L) was significantly greater than that in NPDR (7.3±1.38 IU/L, p <0.001). The serum concentration of erythropoietin in group 1 increased linearly with the severity of the disease (r=0.103). Serum erythropoietin concentrations increased in uncontrolled type 2 diabetics more so in proliferative retinopathy cases, and increased with disease severity. Erythropoietin, Diabetic retinopathy, Proliferative diabetic retinopathy.

An efficient machine learning approach for classification of diabetic retinopathy stages

Diabetic retinopathy (DR) the prime cause of blindness, develops when glucose levels rise, causing retinal damage. DR can be prevented if the illness is detected early. As a result, early grading, categorization, and diagnosis of DR can help diabetic patients avoid visual loss. Several system methods assist in the classification of DR using high-performance criteria. This work proposes an efficient system-based DR classification. The purpose of efficient machine learning dabetic retinopatyy grading classification (EML-DRGC) design is to recognize DR impulsively with highest accuracy. The proposed technique employs preprocessing methods such as employing the Gaussian filtering approach for removing noise present in retinal fundus images. The segmentation process is followed using K-means segmentation algorithm which is used for segmenting the region of interest (ROI) from background. Moreover, Feature extraction process is done by using gray level co-occurrence matrix (GLCM) in which features are extracted bycapturing the image's visual content and features from acceerated segment test (FAST) design is used as extractor of features. Finally, multi support vector machine is utilized as classifier for detecting severity levels of DR. Performance metrics such as accuracy of 98.38% and specificity of 98.34% are obtained which are superior to existing designs.

Structure-preserving Contrast Enhancement of Fundus Images using Dualistic Sub-Image Bi-histogram Bezier Curve

Diabetic retinopathy is a common eye disease among diabetic patients which is caused by excessive sugar in the blood vessels that damage the retina. Fundus images are retina images that are captured and diagnosed by ophthalmologists. Ophthalmologists diagnose the progressive stages of diabetic retinopathy so that early detection of pre-diabetic retinopathy can be carried out. However, the quality of the fundus image can be associated with the brightness of the background and the indistinctive vessel contrast. This paper presents a novel extension of Bi-histogram Bezier curve contrast enhancement (BBCCE) based on the mean partition of its histogram. The disadvantage of having mean as the threshold partition is that the histogram distribution can be skewed due to an outlier. The proposed Dualistic Sub-Image Bi-histogram Bezier Curve Contrast Enhancement (DSI-BBCCE) method partitions the original histogram into two, using the median of the active dynamic intensity range of the input image and process two Bezier transform curves separately to replace the original cumulative density function curve as the median is not affected by the outlier. This DSI-BBCCE has the advantage of preserving the structure, median brightness and preventing over enhancement. The result shows that DSI-BBCCE has achieved a power signal to noise ratio (PSNR) of 20.08±0.94 dB, absolute mean brightness error (AMBE) of 20.15±1.89, structural similarity index model (SSIM) of 0.8096±0.0185, structure measure operator (SMO) of 3.2±1.10 and lightness measure order (LMO) of 200.90±44.19.

RIP3-mediated microglial necroptosis promotes neuroinflammation and neurodegeneration in the early stages of diabetic retinopathy

Diabetic retinopathy (DR) is a leading cause of blindness that poses significant public health concerns worldwide. Increasing evidence suggests that neuroinflammation plays a key role in the early stages of DR. Microglia, long-lived immune cells in the central nervous system, can become activated in response to pathological insults and contribute to retinal neuroinflammation. However, the molecular mechanisms of microglial activation during the early stages of DR are not fully understood. In this study, we used in vivo and in vitro assays to investigate the role of microglial activation in the early pathogenesis of DR. We found that activated microglia triggered an inflammatory cascade through a process called necroptosis, a newly discovered pathway of regulated cell death. In the diabetic retina, key components of the necroptotic machinery, including RIP1, RIP3, and MLKL, were highly expressed and mainly localized in activated microglia. Knockdown of RIP3 in DR mice reduced microglial necroptosis and decreased pro-inflammatory cytokines. Additionally, blocking necroptosis with the specific inhibitor GSK-872 improved retinal neuroinflammation and neurodegeneration, as well as visual function in diabetic mice. RIP3-mediated necroptosis was activated and contributed to inflammation in BV2 microglia under hyperglycaemic conditions. Our data demonstrate the importance of microglial necroptosis in retinal neuroinflammation related to diabetes and suggest that targeting necroptosis in microglia may be a promising therapeutic strategy for the early stages of DR.

Оценка микроциркуляции по данным ОКТ-А при диабетической ретинопатии

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM) and the leading cause of blindness worldwide. The purpose of the study was to determine the characteristics of microcirculation in patients with type 2 diabetes without clinical signs of retinopathy in the fundus and at different stages of DR using the OCT-A method. As a result of the studies, an increase in the area of the FAZ with the progression of the severity of the disease, as well as a decrease in the density of parafoveal superficial and deep vessels at different stages of DR, was revealed. In the deep plexus, the decrease in vascular density occurred earlier, manifesting in patients with type 2 diabetes without DR, compared with the superficial plexus, where it began with moderate to severe NPDR and was more pronounced in PDR. Thus, a decrease in the density of the capillary network of the deep vascular plexus, which are detected even at the preclinical stages of the development of DR and increase as the disease progresses. can serve as a marker of disease severity in the early stages of diabetic retinopathy. Keywords: diabetic retinopathy, microcirculation, optical coherence tomography-angiography

The relationship between levels of apolipoprotein A1 and B in aqueous and serum with stage of diabetic retinopathy

Aim: To determine the association between serum and aqueous apolipoprotein (Apo) A1 and Apo B levels and Apo B/A1 ratio in diabetic retinopathy.&#x0D; Material and Method: This cross-sectional prospective study included 63 diabetic patients with or without retinopathy and 38 control subjects who underwent cataract surgery. The study groups were as follows; healthy subjects (Group 1), diabetic patients without retinopathy (Group 2), with non-proliferative diabetic retinopathy (Group 3), and with proliferative diabetic retinopathy (Group 4). Serum and aqueous Apo A1 and Apo B levels were determined by using an enzyme-linked immunosorbent assay. &#x0D; Results: The amount of Apo B was determined in aqueous samples of all (100%) patients in Group 4 and 77.7% of patients in Group 3.The mean serum Apo B/A1 ratio was significantly higher in Group 2, Group 3 and Group 4 compared with Group 1 (p=0.002, p=0.037 and p

Functional OCT angiography reveals early retinal neurovascular dysfunction in diabetes with capillary resolution.

Altered retinal neurovascular coupling may contribute to the development and progression of diabetic retinopathy (DR) but remains highly challenging to measure due to limited resolution and field of view of the existing functional hyperemia imaging. Here, we present a novel modality of functional OCT angiography (fOCTA) that allows a 3D imaging of retinal functional hyperemia across the entire vascular tree with single-capillary resolution. In fOCTA, functional hyperemia was evoked by a flicker light stimulation, recorded by a synchronized time-lapse OCTA (i.e., 4D), and extracted precisely from each capillary segment (space) and stimulation period (time) in the OCTA time series. The high-resolution fOCTA revealed that the retinal capillaries, particularly the intermediate capillary plexus, exhibited apparent hyperemic response in normal mice, and significant functional hyperemia loss (P < 0.001) at an early stage of DR with few overt signs of retinopathy and visible restoration after aminoguanidine treatment (P < 0.05). Retinal capillary functional hyperemia has strong potential to provide sensitive biomarkers of early DR, and retinal fOCTA would provide new insights into the pathophysiology, screening and treatment of early DR.

N-cadherin cleavage: A critical function that induces diabetic retinopathy fibrosis via regulation of β-catenin translocation.

Retinal fibrosis is a severe pathological change in the late stage of diabetic retinopathy and is also the leading cause of blindness. We have previously revealed that N-cadherin was significantly increased in type 1 and type 2 diabetic mice retinas and the fibrovascular membranes from proliferative diabetic retinopathy (PDR) patients. However, whether N-cadherin directly induces retinal fibrosis in DR and the related mechanism is unknown. Here, we investigated the pathogenic role of N-cadherin in mediating retinal fibrosis and further explored the relevant therapeutic targets. We found that the level of N-cadherin was significantly increased in PDR patients and STZ-induced diabetic mice and positively correlated with the fibrotic molecules Connective Tissue Growth Factor (CTGF) and fibronectin (FN). Moreover, intravitreal injection of N-cadherin adenovirus significantly increased the expression of FN and CTGF in normal mice retinas. Mechanistically, overexpression of N-cadherin promotes N-cadherin cleavage, and N-cadherin cleavage can further induce translocation of non-p-β-catenin in the nucleus and upregulation of fibrotic molecules. Furthermore, we found a novel N-cadherin cleavage inhibitor, pigment epithelial-derived factor (PEDF), which ameliorated the N-cadherin cleavage and subsequent retinal fibrosis in diabetic mice. Thus, our findings provide novel evidence that elevated N-cadherin level not only acts as a classic EMT maker but also plays a causative role in diabetic retinal fibrosis, and targeting N-cadherin cleavage may provide a strategy to inhibit retinal fibrosis in DR patients.

Automated Diabetic Retinopathy Grading based on the Modified Capsule Network Architecture

The most significant source of eye disease is Diabetic Retinopathy (DR) among individuals who have had diabetes for a long time. Early or timely detection of DR stages is crucial for a better prognosis. The subtle distinction among different DR stages(level 0 to level 4) and many structures of varying shapes and sizes make manual recognition challenging and consume more time. Thus, this work focuses on the automatic grading of the retinal images (L-0 to L-4) based on the DR severity using a modified Capsule Network (Caps Net). The ability of Caps Net to capture spatial information of an object and the probability of some entity's existence in an image makes it more suitable for grading DR stages. This work proposes a hybrid model architecture that contains Caps Net followed by a Support Vector Machine(SVM) at its output layer. The main reason for incorporating an SVM at the output layer of Caps Net is to enhance SVM classification using automatic features extracted significantly but also alleviates the computational complexity of training and testing by the Caps Net model. The experiments were conducted on the APTOS 2019 Blindness Detection Data set. The investigation's outcome substantiates the proposed architecture's efficiency over the existing CNNs and Capsule Networks.

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement.

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.

Study of retinal nerve fiber layer thickness in diabetic patients using optical coherence tomography.

This study aimed to assess and compare the changes in peripapillary retinal nerve fiber layer (RNFL) thickness in nondiabetics and diabetics with various stages of diabetic retinopathy (DR). The study subjects were divided into four groups based on their diabetic status and findings, namely, controls (normal subjects without diabetes [NDM]), diabetics without retinopathy (NDR), nonproliferative DR (NPDR), and proliferative DR (PDR). Peripapillary RNFL thickness was assessed using optical coherence tomography. One-way analysis of variance (ANOVA) with the post-Tukey HSD test was done to compare RNFL thickness in different groups. The Pearson coefficient was used to determine the correlation. There was statistically significant difference in measured average RNFL (F = 14.8000, P < 0.05), superior RNFL (F = 11.7768, P < 0.05), inferior RNFL (F = 12.9639, P < 0.05), nasal RNFL (F = 12.2134, P < 0.05), and temporal RNFL (F = 4.2668, P < 0.05) across the different study groups. Pairwise comparison showed that there was a statistically significant difference in RNFL measured (average and all quadrants) in patients with DR (NPDR and PDR) and the NDM control group (P < 0.05). In diabetics without retinopathy, the RNFL measured was reduced compared to controls, but it was statistically significant only in the superior quadrant (P < 0.05). Average RNFL and RNFL in all quadrants showed a small negative correlation with the severity of DR and it was statistically significant (P < 0.001). In our study, peripapillary RNFL thickness was reduced in diabetic retinopathy compared to normal controls and the thinning increased with the severity of DR. This was evident in the superior quadrant even before the fundus signs of DR set in.

Relationship Between Index of Cardiac Electrophysiological Balance, Frontal QRS-T Angle and Retinopathy in People with Type 2 Diabetes.

Diabetic retinopathy (DR) is strongly associated with cardiovascular disease, which is a risk factor for sudden cardiac death (SCD). The index of cardiac electrophysiological balance (iCEB) and the frontal QRS-T angle are recommended to predict the risk of ventricular arrhythmias more than other ECG parameters. However, the relationships between these two markers and DR have not yet been explored. The aim of this study was to investigate the variation in the iCEB, corrected iCEB (iCEBc) and frontal QRS-T angle in different stages of DR and determine whether there are associations between these markers and DR. The sample comprised 665 patients with Type 2 diabetes mellitus (T2DM) who were classified into three groups: no DR (NDR), mild to moderate non-proliferative DR (NPDR), and vision-threatening DR (VTDR). Twelve-lead ECG was performed and the QT, QTc, QRS duration, iCEB, iCEBc and frontal QRS-T angle were recorded and compared across the groups. The VTDR group had a significantly higher iCEBc and frontal QRS-T angle than the NDR and NPDR groups. After controlling for confounding variables, the correlations between the iCEBc (OR=2.217, 95% CI=1.464-3.358, P<0.001), frontal QRS-T angle (OR=1.017, 95% CI=1.008-1.025, P<0.001) and DR risk remained (P<0.05). Subjects in the fourth iCEBc quartile (adjusted OR=2.612, 95% CI=1.411-4.834, p=0.002) had a much higher chance of developing DR compared to those in the first quartile. In comparison to the first frontal QRS-T angle quartile, subjects in the third (adjusted OR=1.998, 95% CI=1.167-3.422, P=0.012) and fourth (adjusted OR=2.430, 95% CI=1.420-4.160, P=0.001) frontal QRS-T angle quartiles had significantly greater risks of DR. With the progression of DR, the iCEBc and frontal QRS-T angle increase. An increased iCEBc and frontal QRS-T angle are associated with an increased risk of DR.

Quantitative assessment of textural features in the early detection of diabetic retinopathy with optical coherence tomography angiography.

The diagnostic power of the textural features and fractal dimension (FD) for early detection of diabetic retinopathy (DR) were quantitatively evaluated with optical coherence tomography angiography (OCTA). 41 normal healthy OD eyes (age: 46.41±13.69), and 10 diabetic OD eyes (age: 60.90±13.46) in the early stages of DR (mild or moderate non-proliferative diabetic retinopathy (NPDR)) were employed. Four retinal vascular plexuses including nerve fiber layer vascular plexus (NFLVP), superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP) were generated by using OCTA. The FD and the five textural features including contrast, correlation, entropy, energy, and homogeneity in four parafoveal sectors were extracted from OCTA images. The factor of aging on textural features and FD was evaluated based on the comparisons among five normal healthy subgroups. Our results showed that FD in superior sector of NFLVP and in nasal sector of ICP had the significant decreases when comparing the older healthy subgroup (age range: 60-69) with the younger healthy subgroup (age range: 20-29). Our results also indicated that the correlation did not show the significant differences in all sectors of the four retinal sublayers among the normal healthy subgroups except in the temporal sector in NFLVP. Furthermore, our results indicated that the correlation in nasal and inferior sectors in SVP can effectively differentiate diabetic patients in early stages of DR from normal healthy subjects with the highest AUROC values. In our study, the specific textural feature - correlation can effectively stage the early DR, which may contribute to the diagnosis of DR in clinic practice.

An Effective Hybrid Convolutional-Modified Extreme Learning Machine in Early Stage Diabetic Retinopathy

An Effective Hybrid Convolutional-Modified Extreme Learning Machine in Early Stage Diabetic Retinopathy

Caffeine and the Risk of Diabetic Retinopathy in Type 2 Diabetes Mellitus: Findings from Clinical and Experimental Studies

The aim of this study was to assess the potential benefits of caffeine intake in protecting against the development of diabetic retinopathy (DR) in subjects with type 2 diabetes (T2D). Furthermore, we tested the effect of topical administration of caffeine on the early stages of DR in an experimental model of DR. In the cross-sectional study, a total of 144 subjects with DR and 147 individuals without DR were assessed. DR was assessed by an experienced ophthalmologist. A validated food frequency questionnaire (FFQ) was administered. In the experimental model, a total of 20 mice were included. One drop (5 μL) of caffeine (5 mg/mL) (n = 10) or vehicle (5 μL PBS, pH 7.4) (n = 10) was randomly administered directly onto the superior corneal surface twice daily for two weeks in each eye. Glial activation and retinal vascular permeability were assessed using standard methods. In the cross-sectional study in humans, the adjusted-multivariable model showed that a moderate and high (Q2 and Q4) caffeine intake had a protective effect of DR (odds ratio (95% confidence interval) = 0.35 (0.16-0.78); p = 0.011 and 0.35 (0.16-0.77); p = 0.010, respectively). In the experimental model, the administration of caffeine did not improve either reactive gliosis or retinal vascular permeability. Our results suggest a dose-dependent protective effect of caffeine in the development of DR, while the potential benefits of antioxidants in coffee and tea should also be considered. Further research is needed to establish the benefits and mechanisms of caffeinated beverages in the development of DR.

Imaging biomarkers and clinical factors associated with the rate of progressive inner and outer retinal thinning in patients with diabetic macular edema

The aim of this study was to assess the relationship of clinical characteristics to the rate of retinal thinning in eyes with diabetic macular edema (DME) treated with anti-vascular endothelial growth factor (VEGF) therapy. We analyzed subjects with a long-term follow-up (≥ 3 years) and evidence of resolved DME after the initiation of anti-VEGF therapy (baseline visit). To measure the long-term rate of retinal thinning during treatment, a second visit (first visit with evidence of resolved DME after 3 years) was also considered. A longitudinal quantitative topographical assessment of the inner and outer retinal thicknesses was provided. Clinical characteristics were associated with the rate of longitudinal retinal thinning. We included 56 eyes (50 patients) in the analysis. A significant longitudinal thinning in the inner and outer retina was detected in all the analyzed regions (p values between 0.027 and < 0.0001). In the multivariable analysis, type of diabetes (type 2 vs. type 1) was associated with increased foveal inner retinal thinning (p = 0.019). A higher number of subfoveal neuroretinal detachment during follow-up (p = 0.006) was associated with faster rates of foveal outer retinal thinning. Type of diabetes (p < 0.0001), higher age (p = 0.033) and cystoid macular edema phenotype (p = 0.040) were associated with increased parafoveal inner retinal thinning. Gender (p = 0.003) and diabetic retinopathy stage (p = 0.013) were associated with faster rates of perifoveal inner retinal thinning, while diabetic retinopathy stage (p = 0.036) was associated with increased perifoveal outer retinal thinning. In conclusion, clinical factors, including DME phenotypes, were associated with the rates of retinal thinning in patients undergoing anti-VEGF treatment.