Diabetic Retinal Disease Research Articles

The relationship between ON-OFF function and OCT structural and angiographic parameters in early diabetic retinal disease.

This study measured associations between ON and OFF functional indicators and structural optical coherence tomography (OCT) and OCT angiography (OCTA) markers in diabetic retinal disease. Fifty-four participants with type 1 or type 2 diabetes (mean age = 34.1 years; range 18-60) and 48 age-matched controls (mean age = 35.4 years, range 18-59) underwent visual psychophysical testing, OCT and OCTA retinal imaging. Psychophysical tasks measuring (A) contrast increment and decrement sensitivity and (B) response times to increment and decrement targets were assessed as surrogate measures of ON and OFF retinal ganglion cell function. The group with diabetes had worse foveal contrast increment and decrement thresholds (p = 0.04) and were slower to search for increment and decrement targets relative to controls (p = 0.009). Individuals with diabetes had a less circular foveal avascular zone (FAZ) (p < 0.001) but did not differ from controls in foveal vessel density and FAZ area. Functional and structural outcome measures related to the peripheral retina were also comparable between those with and without diabetes. Functional responses to increments and decrements were not significantly correlated with FAZ circularity or vessel density in individuals with diabetes. Diabetic retinal disease results in impaired performance on measures of inferred ON and OFF pathway function in addition to vascular deficits measurable with OCTA. Future longitudinal studies may determine the temporal relationship between these deficits, and whether they predict future diabetic retinopathy.

Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data.

The 2024 Mary Tyler Moore Vision Initiative (MTM Vision) Workshop on Data convened to discuss best practices and specific considerations for building a comprehensive, shareable MTM Vision data lake. The workshop aimed to accelerate the development of new indications, therapies, and regulatory pathways for diabetic retinal disease (DRD) by standardizing and harmonizing clinical data and ocular 'omics analyses. Standardization of data collection, the use of common data elements, and data interoperability were emphasized, alongside federated learning approaches to promote data sharing and collaboration while maintaining data privacy and security. The integration of molecular data with other multimodal data types was recognized as a promising strategy for leveraging machine learning and AI approaches to advancing therapeutics development and improving treatment outcomes for DRD patients. Partnerships with entities such as the National Eye Institute, part of the National Institutes of Health, foundations, and industry were deemed vital for the successful implementation of these initiatives.

Characterization and Automatic Discrimination between Predominant Hypoperfusion and Hyperperfusion Stages of NPDR.

Diabetic retinopathy (DR) is a common diabetes complication that can lead to blindness through vision-threatening complications like clinically significant macular edema and proliferative retinopathy. Identifying eyes at risk of progression using non-invasive methods could help develop targeted therapies to halt diabetic retinal disease progression. A set of 82 imaging and systemic features was used to characterize the progression of nonproliferative diabetic retinopathy (NPDR). These features include baseline measurements (static features) and those capturing the temporal dynamic behavior of these static features within one year (dynamic features). Interpretable models were trained to distinguish between eyes with Early Treatment Diabetic Retinopathy Study (ETDRS) level 35 and eyes with ETDRS levels 43-47. The data used in this research were collected from 109 diabetic type 2 patients (67.26 ± 2.70 years; diabetes duration 19.6 ± 7.26 years) and acquired over 2 years. The characterization of the data indicates that NPDR progresses from an initial stage of hypoperfusion to a hyperperfusion response. The performance of the classification model using static features achieved an area under the curve (AUC) of the receiver operating characteristics equal to 0.84 ± 0.07, while the model using both static and dynamic features achieved an AUC of 0.91 ± 0.05. NPDR progresses through an initial hypoperfusion stage followed by a hyperperfusion response. Characterizing and automatically identifying this disease progression stage is valuable and necessary. The results indicate that achieving this goal is feasible, paving the way for the improved evaluation of progression risk and the development of better-targeted therapies to prevent vision-threatening complications.

An Intelligent System to Predict Diabetic Retinal Diseases Based on Diabetic Attributes

An Intelligent System to Predict Diabetic Retinal Diseases Based on Diabetic Attributes

Treatment of Anemia Associated with Chronic Kidney Disease: Plea for Considering Physiological Erythropoiesis.

Traditionally, the treatment of anemia associated with chronic kidney disease (CKD) involves prescribing erythropoiesis-stimulating agents (ESAs) or iron preparations. The effectiveness and safety of ESAs and iron have been established. However, several clinical issues, such as hyporesponsiveness to ESAs or defective iron utilization for erythropoiesis, have been demonstrated. Recently, a new class of therapeutics for renal anemia known as hypoxia-inducible factor (HIF)/proline hydroxylase (PH) inhibitors has been developed. Several studies have reported that HIF-PH inhibitors have unique characteristics compared with those of ESAs. In particular, the use of HIF-PH inhibitors may maintain target Hb concentration in patients treated with a high dose of ESAs without increasing the dose. Furthermore, several recent studies have demonstrated that patients with CKD with defective iron utilization for erythropoiesis had a high risk of cardiovascular events or premature death. HIF-PH inhibitors increase iron transport and absorption from the gastrointestinal tract; thus, they may ameliorate defective iron utilization for erythropoiesis in patients with CKD. Conversely, several clinical problems, such as aggravation of thrombotic and embolic complications, diabetic retinal disease, and cancer, have been noted at the time of HIF-PH inhibitor administration. Recently, several pooled analyses of phase III trials have reported the non-inferiority of HIF-PH inhibitors regarding these clinical concerns compared with ESAs. The advantages and issues of anemia treatment by ESAs, iron preparations, and HIF-PH inhibitors must be fully understood. Moreover, patients with anemia and CKD should be treated by providing a physiological erythropoiesis environment that is similar to that of healthy individuals.

An OCT-A Analysis of the Importance of Intermediate Capillary Plexus in Diabetic Retinopathy: A Brief Review.

Optical coherence tomography-angiography is a technique that allows us to non-invasively study in vivo the different retinal vascular networks. This allows a deeper understanding of retinal capillary anatomy and function, in addition to the pathophysiologic changes encountered in diverse diseases. The four retinal capillary layers have different anatomies and functions, implying distinct adaptation and roles in the course of the diseases. Diabetic retinopathy is the leading cause of blindness in working-age adults. Several studies have evaluated how each retinal capillary layer is specifically affected according to the stage of the disease. Unfortunately, too few studies have considered the intermediate capillary plexus as a separate layer, as it has often been incorporated in another layer. In this review, we shed light on the potential role the intermediate capillary plexus plays in the physiopathology of diabetic retinal disease as well as its potential use in grading diabetic retinopathy and its clinical added value in estimating the disease prognosis.

Comparative Analysis of Early Diabetic Retinopathy Detection: Enhanced Minimal CNN vs VGG Architecture on CLAHE- Preprocessed Retinal Images

Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images.

Comparative Analysis of Early Diabetic Retinopathy Detection: Enhanced Minimal CNN Vs VGG Architecture on CLAHE- Preprocessed Retinal Images

Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images

Rationale of Basic and Cellular Mechanisms Considered in Updating the Staging System for Diabetic Retinal Disease

Hyperglycemia is a major risk factor for early lesions of diabetic retinal disease (DRD). Updating the DRD staging system to incorporate relevant basic and cellular mechanisms pertinent to DRD is necessary to better address early disease, disease progression, the use of therapeutic interventions, and treatment effectiveness. We sought to reviewpreclinical and clinical evidence on basic and cellular mechanisms potentially pertinent to DRD that might eventually be relevant to update the DRD staging system. Not applicable. The Basic and Cellular Mechanisms Working Group (BCM-WG) of the Mary Tyler Moore Vision Initiative carefully and extensively reviewed available preclinical and clinical evidence through multiple iterations and classified these. Classification was made into evidence grids, level of supporting evidence, and anticipated future relevance to DRD. A total of 40 identified targets based on pathophysiology and other parameters for DRD were grouped into concepts or evaluated as specific candidates. VEGFA, peroxisome proliferator-activated receptor-alpha related pathways, plasma kallikrein, and angiopoietin 2 had strong agreement as promising for use as biomarkers in diagnostic, monitoring, predictive, prognostic, and pharmacodynamic responses as well as for susceptibility/risk biomarkers that could underlie new assessments and eventually be considered within an updated DRD staging system or treatment, based on the evidence and need for research that would fit within a 2-year timeline. The BCM-WG found there was strong reason also to pursue the following important concepts regarding scientific research of DRD acknowledging their regulation by hyperglycemia: inflammatory/cytokines, oxidative signaling, vasoprotection, neuroprotection, mitophagy, and nutrients/microbiome. Promising targets that might eventually be considered within an updated DRD staging system or treatment were identified. Although the BCM-WG recognizes that at this stage little can be incorporated into a new DRD staging system, numerous potential targets and important concepts deserve continued support and research, as they may eventually serve as biomarkers and/or therapeutic targets with measurable benefits to patients with diabetes. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Prediction and Therapeutic Design for Diabetic Retinopathy Using LabVIEW

Diabetic Retinal Disease (DR) is a gradual obstruction in the functioning of the blood vessels in the retina of the eye caused by chronic hyperglycemia, which may be due to diabetes type 1 or type 2. LabVIEW software play as major role in predicting the diabetic retinopathy at different stages. The real time image of the affected eye is acquired using Vision Actuation tool in LabVIEW and then it is processed using Vision assistant tool. Lot technology is I for sending the output result to the corresponding end client. Image processing and lot. Both are implemented using LabVIEW. Additionally, the system extends its functionality beyond diagnostics by incorporating an innovative approach to monitor the temperature of the eye. By integrating LabVIEW's capabilities with lot, the proposed solution checks the temperature the eye in real-time. If the detected temperature is elevated, a Peltier crystal is employed to cool the eye, demonstrating an adaptive and responsive mechanism to address abnormal temperature conditions. Aims to check the patients whether they are suffering from diabetic retinopathy or not and if they are having the diabetic retinopathy in which stage it is. Because most diabetic people are suffering from this and may also lead to loss of vision. Due to this reason, it is highly required to classify the persons in a short time with high accuracy. Automation reduces human error and subjectivity. Can detect early stages of disease. Ability to process and analyze digital fundus images efficiently.

Tandem Mass Tag LC-MS/MS of Aqueous Humor From Individuals With Type 2 Diabetes Without Retinopathy Reveals Early Dysregulation of Synaptic Proteins.

An early neurodegenerative component of diabetic retinal disease (DRD) that precedes the vascular findings of clinically diagnosed diabetic retinopathy (DR) is increasingly being recognized. However, the relevant molecular mechanisms and biomarkers for early DRD are poorly defined. The purpose of this study was to uncover novel potential mediators of early diabetic retinal neuronal dysfunction through analysis of the aqueous fluid proteome in preclinical DR. Aqueous fluid was collected from subjects with type 2 diabetes mellitus (DM) but no clinical DR and from nondiabetic controls undergoing routine cataract surgery. Preoperative spectral-domain optical coherence tomography of the macula was obtained. Tandem mass tag LC-MS/MS was performed to identify proteins differentially present in diabetic and control aqueous fluid, and proteins with >50% change and P < 0.05 were considered significant. Selected results were validated with western blot of human aqueous fluid samples. We identified decreased levels of proteins implicated in neuronal synapse formation and increased levels of inflammatory proteins in the aqueous fluid from patients with type 2 DM but no DR compared with controls. Of the differentially present synaptic proteins that we identified and confirmed with western blot, the majority have not previously been linked with DRD. The proteomic profile of aqueous fluid from individuals with type 2 DM but no DR suggests that retinal neuronal dysfunction and inflammation represent very early events in the pathophysiology of DRD. These findings support the concept that diabetic retinal neurodegeneration precedes vascular pathology and reveal novel potential mediators and/or biomarkers warranting further investigation.

A retrospective big data study using healthcare insurance claims to investigate the role of comorbidities in receiving low vision services.

The aim was to examine the association between physical and mental comorbidity with receiving low vision services (LVS). A retrospective study based on Dutch claims data of health insurers was performed. We retrieved data (2015-2018) of patients (≥18 years) with eye diseases causing severe vision loss who received LVS at Dutch rehabilitation organizations in 2018 (target group) and patients who did not receive LVS, but who received ophthalmic medical specialist care for glaucoma, macular, diabetic retinal and/or retinal diseases in 2018 (reference group). For examining the association between the patients' comorbidities and receiving LVS, multivariable logistic regression was used. The relative quality of five different models was assessed with the Akaike Information Criterion (AIC). The study population consisted of 574,262 patients, of which 8,766 in the target group and 565,496 in the reference group. Physical comorbidity was found in 83% and 14% had mental comorbidity. After adjustment for all assumed confounders, both physical and mental comorbidity remained significantly associated with receiving LVS. In the adjusted model, which also included both comorbidity variables, the best relative quality was found to describe the association between mental and physical comorbidity and receiving LVS. Mental comorbidity seemed to be independently associated with receiving LVS, implying that the odds for receiving a LVS referral are higher in patients who are vulnerable to mental comorbidity. Physical comorbidity was independently associated, however, the association with receiving LVS might not be that meaningful in terms of policy implications. Providing mental healthcare interventions for people with VI seems warranted.

Retinal artery to vein ratio is associated with cerebral microbleeds in individuals with type 1 diabetes.

A third of asymptomatic individuals with type 1 diabetes (T1D) show signs of cerebrovascular disease in brain MRI. These signs associate with advanced stages of diabetic retinal disease, but not in mild or moderate retinopathy. We aimed to evaluate a wider spectrum of retinal changes by exploring the relationship between quantitative measures of retinal vessel parameters (RVP) and cerebrovascular changes in T1D. We included 146 neurologically asymptomatic individuals with T1D [51% women, median age 40 (33.0-45.1) years] and 24 healthy, sex-matched and age-matched controls. All individuals underwent a clinical and biochemical work-up and brain MRI, which was evaluated for cerebral microbleeds (CMBs), white matter hyperintensities, and lacunar infarcts. RVPs, including central retinal arteriole (CRAE) and central retinal vein (CRVE) equivalents and the ratio of the two variables (arteriovenous ratio, AVR) were assessed quantitatively by a computer-assisted method (IVAN software, version 3.2.6) from fundus images. Among T1D participants, those with CMBs had a lower arteriovenous ratio (AVR) compared with those without CMBs ( P = 0.023). AVR was inversely associated with the amount of CMBs ( r = -0.063, P = 0.035). CMB prevalence was higher in those with AVR below the median (31%) compared with above the median (16%, P < 0.001), and this difference was significant also after individuals with only no-to-mild retinopathy were included (28 vs. 16%, P = 0.005). A correlation between blood pressure and CRAE ( r = -0.19, P = 0.025) appeared among those with T1D. Regardless of the severity of diabetic retinopathy, AVR is associated with the existence of CMBs in T1D.

Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study.

Diabetic retinopathy (DR) is the leading global cause of vision loss, accounting for 4.8% of global blindness cases as estimated by the World Health Organization (WHO). Fundus photography is crucial in ophthalmology as a diagnostic tool for capturing retinal images. However, resource and infrastructure constraints limit access to traditional tabletop fundus cameras in developing countries. Additionally, these conventional cameras are expensive, bulky, and not easily transportable. In contrast, the newer generation of handheld and smartphone-based fundus cameras offers portability, user-friendliness, and affordability. Despite their potential, there is a lack of comprehensive review studies examining the clinical utilities of these handheld (e.g. Zeiss Visuscout 100, Volk Pictor Plus, Volk Pictor Prestige, Remidio NMFOP, FC161) and smartphone-based (e.g. D-EYE, iExaminer, Peek Retina, Volk iNview, Volk Vistaview, oDocs visoScope, oDocs Nun, oDocs Nun IR) fundus cameras. This review study aims to evaluate the feasibility and practicality of these available handheld and smartphone-based cameras in medical settings, emphasizing their advantages over traditional tabletop fundus cameras. By highlighting various clinical settings and use scenarios, this review aims to fill this gap by evaluating the efficiency, feasibility, cost-effectiveness, and remote capabilities of handheld and smartphone fundus cameras, ultimately enhancing the accessibility of ophthalmic services.

Enhancing diabetic retinopathy detection through preprocessing and feature extraction with MGA-CSG algorithm

Anticipatory monitoring of diabetic retinal (DR) disease is crucial in preventing vision loss and blindness, making it a leading cause of worldwide vision impairment. In this study, we propose a novel technique, the Modified Generative Adversarial-based Crossover Salp Grasshopper (MGA-CSG) approach, for early prediction and accurate classification of diabetic retinal diseases using fundus image datasets. The proposed MGA-CSG approach combines deep learning with an optimization algorithm to achieve superior classification accuracy. We start with pre-processing of data to attenuate image variation, convert intensities, denoise, and enhance contrast in the fundus images. Next, a Convolutional Neural Network (CNN) is used for feature extraction, capturing essential information from the images. To address the limited amount of feature vectors generated by the CNN and improve learning uncertainty, we employ a Generative Adversarial Network (GAN) model to augment the feature vectors. It generates additional images, allowing the classifier to train with fewer input samples, leading to improved classification performance. One of the key novelties of our approach lies in the utilization of both labeled and unlabeled data samples, enhancing the GAN training performance. We replace the binary classifier in the GAN with a multiclass classifier, enabling separate discrimination of retinal disease classes. The MGA-CSG approach leverages the crossover Grasshopper Optimizer Algorithm (GOA) and Salp Swarm Algorithm (SSA) to optimize the model and avoid falling into local optimal solutions. This optimization process further enhances the accuracy of diabetic retinal disease classification. Experimental results demonstrate the superiority of the proposed MGA-CSG approach, achieving high accuracy, precision, recall, specificity, F1-measure, and Kappa coefficient in predicting glaucoma, diabetic retinopathy, cataract, macular edema, and myopia. The classification achieved impressive accuracy, precision, recall, specificity, F1-measure, and Kappa coefficient of 98.8%, 98.7%, 96.5%, 97.1%, 98.2%, and 95%, respectively. Overall, our novel MGA-CSG approach offers an efficient and accurate method for early screening and classification of diabetic retinal diseases. The combination of CNN-based feature extraction, GAN-based augmentation, and MGA-CSG optimization contributes to the model's high performance. This approach holds significant potential in improving healthcare management and providing timely interventions for patients with retinal diseases.

Imaging Modalities for Assessing the Vascular Component of Diabetic Retinal Disease: Review and Consensus for an Updated Staging System

To review the evidence for imaging modalities in assessing the vascular component of diabetic retinal disease (DRD), to inform updates to the DRD staging system. Standardized narrative review of the literature by an international expert workgroup, as part of the DRD Staging System Update Effort, a project of the Mary Tyler Moore Vision Initiative. Overall, there were 6 workgroups: Vascular Retina, Neural Retina, Systemic Health, Basic and Cellular Mechanisms, Visual Function, and Quality of Life. The Vascular Retina workgroup, including 16 participants from 4 countries. Literature review was conducted using standardized evidence grids for 5 modalities: standard color fundus photography (CFP), widefield color photography (WFCP), standard fluorescein angiography (FA), widefield FA (WFFA), and OCT angiography (OCTA). Summary levels of evidence were determined on a validated scale from I (highest) to V (lowest). Five virtual workshops were held for discussion and consensus. Level of evidence for each modality. Levels of evidence for standard CFP, WFCP, standard FA, WFFA, and OCTA were I, II, I, I, and II respectively. Traditional vascular lesions on standard CFP should continue to be included in an updated staging system, but more studies are required before they can be used in posttreatment eyes. Widefield color photographs can be used for severity grading within the area covered by standard CFPs, although these gradings may not be directly interchangeable with each other. Evaluation of the peripheral retina on WFCP can be considered, but the method of grading needs to be clarified and validated. Standard FA and WFFA provide independent prognostic value, but the need for dye administration should be considered. OCT angiography has significant potential for inclusion in the DRD staging system, but various barriers need to be addressed first. This study provides evidence-based recommendations on the utility of various imaging modalities for assessment of the vascular component of DRD, which can inform future updates to the DRD staging system. Although new imaging modalities offer a wealth of information, there are still major gaps and unmet research needs that need to be addressed before this potential can be realized. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Report From the 2022 Mary Tyler Moore Vision Initiative Diabetic Retinal Disease Clinical Endpoints Workshop.

The Mary Tyler Moore Vision Initiative Diabetic Retinal Disease (DRD) Clinical Endpoints Workshop was held on October 22, 2022 to accelerate progress toward establishment of useful clinical and research endpoints and development of new therapeutics that have important relevance across the full spectrum of DRD pathology. More than 90 patient representatives, clinicians, scientists, funding and regulatory agencies, diagnostic, therapeutic and biotech industry representatives discussed the needs for new diagnostic and therapeutic approaches to prevent and restore retinal neurovascular unit integrity. Phase I of the MTM Vision Initiative plans, notably updating the DRD staging system and severity scale, establishing a human ocular biorepository and resource, and clinical endpoints and biomarker development and validation, was emphasized.

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation.

In the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction. Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant. Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation. We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori. This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.

Increasing Energetic Demands on Photoreceptors in Diabetes Corrects Retinal Lipid Dysmetabolism and Reduces Subsequent Microvascular Damage

Mechanisms responsible for the pathogenesis of diabetic retinal disease remain incompletely understood, but they likely involve multiple cellular targets, including photoreceptors. Evidence suggests that dysregulated de novo lipogenesis in photoreceptors is a critical early target of diabetes. Following on this observation, the present study aimed to determine whether two interventions shown to improve diabetic retinopathy in mice-pharmacologic visual cycle inhibition and prolonged dark adaptation-reduce photoreceptor anabolic lipid metabolism. Elevated retinal lipid biosynthetic signaling was observed in two mouse models of diabetes, with both models showing reduced retinal AMP-activated kinase (AMPK) signaling, elevated acetyl CoA carboxylase (ACC) signaling, and increased activity of fatty acid synthase, which promotes lipotoxicity in photoreceptors. Although retinal AMPK-ACC axis signaling was dependent on systemic glucose fluctuations in healthy animals, mice with diabetes lacked such regulation. Visual cycle inhibition and prolonged dark adaptation reversed abnormal retinal AMPK-ACC signaling in mice with diabetes. Although visual cycle inhibition reduced the severity of diabetic retinopathy in control mice, as assessed by retinal capillary atrophy, this intervention was ineffective in fatty acid synthase gain-of-function mice. These results suggest that early diabetic retinopathy is characterized by glucose-driven elevations in retinal lipid biosynthetic activity, and that two interventions known to increase photoreceptor glucose demands alleviate disease by reversing these signals.

Assessment of Parafoveal Diabetic Macular Ischemia on Optical Coherence Tomography Angiography Images to Predict Diabetic Retinal Disease Progression and Visual Acuity Deterioration

The presence of diabetic macular ischemia (DMI) on optical coherence tomography angiography (OCTA) images predicts diabetic retinal disease progression and visual acuity (VA) deterioration, suggesting an OCTA-based DMI evaluation can further enhance diabetic retinopathy (DR) management. To investigate whether an automated binary DMI algorithm using OCTA images provides prognostic value on DR progression, diabetic macular edema (DME) development, and VA deterioration in a cohort of patients with diabetes. In this cohort study, DMI assessment of superficial capillary plexus and deep capillary plexus OCTA images was performed by a previously developed deep learning algorithm. The presence of DMI was defined as images exhibiting disruption of fovea avascular zone with or without additional areas of capillary loss, while absence of DMI was defined as images presented with intact fovea avascular zone outline and normal distribution of vasculature. Patients with diabetes were recruited starting in July 2015 and were followed up for at least 4 years. Cox proportional hazards models were used to evaluate the association of the presence of DMI with DR progression, DME development, and VA deterioration. Analysis took place between June and December 2022. DR progression, DME development, and VA deterioration. A total of 321 eyes from 178 patients were included for analysis (85 [47.75%] female; mean [SD] age, 63.39 [11.04] years). Over a median (IQR) follow-up of 50.41 (48.16-56.48) months, 105 eyes (32.71%) had DR progression, 33 eyes (10.28%) developed DME, and 68 eyes (21.18%) had VA deterioration. Presence of superficial capillary plexus-DMI (hazard ratio [HR], 2.69; 95% CI, 1.64-4.43; P < .001) and deep capillary plexus-DMI (HR, 3.21; 95% CI, 1.94-5.30; P < .001) at baseline were significantly associated with DR progression, whereas presence of deep capillary plexus-DMI was also associated with DME development (HR, 4.60; 95% CI, 1.15-8.20; P = .003) and VA deterioration (HR, 2.12; 95% CI, 1.01-5.22; P = .04) after adjusting for age, duration of diabetes, fasting glucose, glycated hemoglobin, mean arterial blood pressure, DR severity, ganglion cell-inner plexiform layer thickness, axial length, and smoking at baseline. In this study, the presence of DMI on OCTA images demonstrates prognostic value for DR progression, DME development, and VA deterioration.