Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review

Association of Poor Quality of Sleep with Vision-Threatening Diabetic Retinopathy: A Matched Case-Control Study

INTRODUCTION

Vision-threatening diabetic retinopathy, a long-term microvascular complication of diabetes in the eye, is a major cause of blindness worldwide. Sleep is regulated by a special area of the brain, and poor quality of sleep has been implicated in long-term diabetic complications, including diabetic retinopathy. The study was aimed at exploring whether poor sleep quality is associated with the likelihood of developing vision-threatening diabetic retinopathy.

METHODS

A matched case-control study was conducted from 6 May 2022 to 6 September 2022. The study was based on patients with diabetes (both type 1 and type 2), attending a retina clinic of the referral center. Patients with confirmed vision-threatening diabetic retinopathy were defined as cases and matched with control study subjects. Rating of sleep quality was done by Pittsburgh Sleep Quality Index assessment tool. Conditional logistic regression model was applied to assess the effect of overall poor sleep quality on vision-threatening diabetic retinopathy.

RESULTS

The study recruited a total of 126 (63 duration-matched pairs) patients with diabetes. The overall quality of sleep was poor among 71.90% of patients in the case groups, significantly higher compared with those of the control groups (28.10%) (p < 0.00). The mean quality of sleep for cases [mean (M) = 7.10, standard deviation (SD) = 4.30] was higher than the control group (M = 3.60, SD = 2.70). The magnitude of the difference [M difference = 3.50, 95% confidence interval (CI): 2.25-4.75] was significant. Poor sleep quality predicted the chance of developing vision-threatening diabetic retinopathy in a diabetic cohort.

CONCLUSIONS

This study revealed a significant association between the overall poor quality of sleep and vision-threatening diabetic retinopathy. Co-management of sleep disruption and vision-threatening diabetic retinopathy, and better sleep advisory are warranted.

Inhibition of the pyroptosis-associated inflammasome pathway: The important potential mechanism of ginsenosides in ameliorating diabetes and its complications

Diabetes mellitus (DM) and its complications have become an important global public health issue, affecting human health and negatively impacting life and lifespan. Pyroptosis is a recently discovered form of pro-inflammatory programmed cell death (PCD). To date, pyroptosis-associated inflammasome pathways have been identified primarily in the canonical and non-canonical inflammasome pathway, apoptotic caspase-mediated pathway, granzyme-mediated pathway, and streptococcal pyrogenic exotoxin B (SpeB)-mediated pathway. The activation of diabetes-mediated pyroptosis-associated factors play an important role in the pathophysiology of DM and its complications. Studies have shown that ginsenosides exert significant protective effects on DM and its complications. Through inhibiting the activation of pyroptosis-associated inflammasome pathways, and then the DM and its complications are improved. This review summarizes the subtypes of ginsenosides and their chemical characteristics, pharmacokinetics and side effects, the main pyroptosis-associated inflammasome pathways that have been discovered to date, and the potential mechanism of different subtypes of ginsenosides in the treatment of DM and its complications (such as diabetic cardiomyopathy, diabetic nephropathy, diabetic liver injury, diabetic retinopathy, and diabetic ischemic stroke) via anti-pyroptosis-associated inflammasome pathways. These findings may provide ideas for further research to explore ginsenoside mechanism in improving DM and its complications. However, many pyroptosis-associated inflammasome pathways and targets involved in the occurrence and development of DM and its complications are still unknown. In the future, further studies using in vitro cell models, in vivo animal models, and human disease models can be used to further elucidate the mechanism of ginsenosides in the treatment of DM and its complications.

Comparison of the efficacy of intravitreal Anti-VEGF versus intravitreal dexamethasone implant in treatment resistant diabetic Macular Edema

PURPOSE

Comparison of the efficacy of monthly anti-VEGF versus dexamethasone (DEX) implant in patients with diabetic macular edema (DME) whose macular edema persists despite three doses of anti-VEGF therapy.

MATERIALS AND METHODS

This retrospective study included 94 eyes of 94 patients with central macular thickness (CMT) > 300 μm despite previously receiving three doses of anti-VGEF (aflibercept or ranibizumab) injections between January 2014 and January 2019. The patients were divided into four groups. The first and second groups were the patients who received three more doses of initial anti-VGEF treatment after the initial anti-VGEF treatment. The third and fourth groups were patients switched to intravitreal dexamethasone implants. Patients were followed up every month for six months after the injection. The primary outcome measures were best-corrected visual acuity (BCVA), central macular thickness (CMT), and intraocular pressure (IOP) at six months.

RESULTS

The mean age of the patients included in the study was 64.64 ± 7; there were 58 men (61.7%) and 36 women (38.3%). There was no statistically significant difference between the groups regarding age, stage of retinopathy, and lens status. When CMT, BCVA, and IOP were assessed among the four groups at the end of the sixth month, no statistical difference between the groups was found. There was no need for medical intervention despite the statistically significant increase in IOP at the end of the sixth month compared to the third month in the dexamethasone implanted groups. In contrast to the decrease in CMT, which was statistically significant in all four groups at the end of the sixth month compared to the third month, the increase in BCVA was not statistically significant in any of the four groups at the end of the sixth month.

CONCLUSION

According to the results of our study, there is no superiority between continuing with existing anti-VEGF or switching to a dexamethasone implant after three doses of anti-VEGF.

The U-Shape Relationship between Triglyceride-Glucose Index and the Risk of Diabetic Retinopathy among the US Population

Objective: To explore the association of diabetic retinopathy (DR) with TyG index and TyG-related parameters among the United States population. Methods: This cross-sectional study is conducted in adults with diabetes mellitus based on the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Multivariate logistic regression, restricted cubic spline, trend test, receiver operating characteristic curve and subgroup analysis are adopted to uncover the association of DR with TyG index and TyG-related parameter levels in diabetics. Results: An aggregate of 888 eligible participants with diabetes is included, involving 263 (29.6%) patients with DR. The participants are stratified according to the quartile of TyG index and TyG-related parameters (Q1–Q4). Following the adjustments of the confounding factors, a multivariate logistic regression analysis finds that TyG-BMI, TyG index and Q4-TyG index are significant risk factors for DR. The restricted cubic spline shows that TyG index and the DR risk of diabetes patients are proved to be U-shaped related (p for nonlinearity = 0.001). Conclusions: The triglyceride-glucose index has a U-shaped correlation with the risk of diabetic retinopathy, which has potential predictive value.

Persistent diabetic macular edema: Definition, incidence, biomarkers, and treatment methods

Intravitreal antivascular endothelial growth factor (anti-VEGF) treatment has drastically improved the visual and anatomical outcomes in patients with diabetic macular edema (DME); however, success is not always guaranteed, and a proportion of these eyes demonstrate persistent DME (pDME) despite intensive treatment. While standardized criteria to define these treatment-resistant eyes have not yet been established, many studies refer to eyes with no clinical response or an unsatisfactory partial response as having pDME. A patient is considered to have pDME if the retinal thickness improves less than 10-25% after 6 months of treatment. A range of treatment options have been recommended for eyes with pDME, including switching anti-VEGF agents, using corticosteroids and/or antioxidant drugs in adjunct with anti-VEGF therapy, and vitrectomy. In addition, multimodal imaging of DME eyes may be advantageous in predicting the responsiveness to treatment; this is beneficial when initiating alternative therapies. We explore the literature on persistent DME regarding its defining criteria, incidence, the baseline biological markers that may be useful in anticipating the response to treatment, and the available treatment options.

Development and validation of medical record-based logistic regression and machine learning models to diagnose diabetic retinopathy

PURPOSES

Many factors were reported to be associated with diabetic retinopathy (DR); however, their contributions remained unclear. We aimed to evaluate the prognostic and diagnostic accuracy of logistic regression and three machine learning models based on various medical records.

METHODS

This was a cross-sectional study. We investigated the prevalence and associations of DR among 757 participants aged 40 years or older in the 2005-2006 National Health and Nutrition Examination Survey (NHANES). We trained the models to predict if the participants had DR with 15 predictor variables. Area under the receiver operating characteristic (AUROC) and mean squared error (MSE) of each algorithm were compared in the external validation dataset using a replicate cohort from NHANES 2007-2008.

RESULTS

Among the 757 participants, 53 (7.00%) subjects had DR, the mean (standard deviation, SD) age was 57.7 (13.04), and 78.0% were male (n = 42). Logistic regression revealed that female gender (OR = 4.130, 95% CI: 1.820-9.380; P < 0.05), HbA1c (OR = 1.665, 95% CI: 1.197-2.317; P < 0.05), serum creatine level (OR = 2.952, 95% CI: 1.274-6.851; P < 0.05), and eGFR level (OR = 1.009, 95% CI: 1.000-1.014, P < 0.05) increased the risk of DR. The average performance obtained from internal validation was similar in all models (AUROC ≥ 0.945), and k-nearest neighbors (KNN) had the highest value with an AUROC of 0.984. In external validation, they remained robust or with modest reductions in discrimination with AUROC still ≥ 0.902, and KNN also performed the best with an AUROC of 0.982. Both logistic regression and machine learning models had good performance in the clinical diagnosis of DR.

CONCLUSIONS

This study highlights the utility of comparing traditional logistic regression to machine learning models. We found that logistic regression performed as well as optimized machine learning methods when classifying DR patients.

Frequency of Retinopathy in Patients Newly Diagnosed With Type 2 Diabetes Mellitus

BACKGROUND

The most prevalent microvascular consequence of diabetes mellitus on the eye is diabetic retinopathy, which is also one of the major reasons for poor vision in the working-age population. The objective of this study was to estimate the prevalence of type 2 diabetic retinopathy in participants.

METHODS

The Department of Medicine at the Hayatabad Medical Complex in Peshawar performed a six-month cross-sectional study from May to November 2022. A total of 196 patients with type 2 diabetes mellitus were included in the research.

RESULTS

Ages ranged from 18 to 60 years with a mean age of 37.59 ± 10.21 years, with the majority of the patients (n=16) belonging to the fourth decade. Thirty-one individuals (15.81%) with clinically diagnosed type 2 diabetes mellitus had diabetic retinopathy, of which 12 (6.12%) were females and 19 (9.69%) were males. Among patients with diabetic retinopathy, glycated haemoglobin (HbA1C) was determined to be 9.4 ± 1.5, and among those with other types of retinopathy, background retinopathy was detected in 11 (5.61%) men and seven (3.57%) female patients.

CONCLUSION

The majority of diabetic retinopathy patients in the current study were older than 40 years and were primarily males. In newly diagnosed type 2 diabetes mellitus participants, retinopathy occurred in 15.81% of cases (n=31), with background retinopathy accounting for the majority of cases (n=18, 9.18%).

Profile of tirzepatide in the management of type 2 diabetes mellitus: design, development, and place in therapy

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is one of the leading causes of morbidity and mortality. Peptide-based multi-targeting agonists represent a new paradigm in metabolic pharmacology as they manifest multiplexed pharmacological actions over mono-agonists. Tirzepatide is a novel dual glucose-dependent insulinotropic polypeptide receptor (GIPR) and glucagon-like peptide-1 receptor (GLP-1R) agonist that has been recently approved by the FDA. This review aims to summarize the available evidence on the discovery, pharmacology, pharmacokinetic, pharmacodynamic, efficacy, and safety of tirzepatide in the pharmacotherapy of T2DM.

AREAS COVERED

We searched PubMed, Embase, and International Pharmaceutical Abstracts to identify relevant papers on tirzepatide use in T2DM. Clinical trial registries were also searched.

EXPERT OPINION

Tirzepatide improves glycemic control compared to baseline, placebo, and active comparators. It is also associated with weight reduction and an improvement in some, but not all, dyslipidemia, cardiovascular risk, and nonalcoholic steatohepatitis (NASH) biomarkers. Tirzepatide has a favorable safety profile with a low risk of hypoglycemia; however, adverse events such as gastrointestinal reactions were frequently reported and sometimes even led to therapy discontinuation. Future research should focus on investigating the role of tirzepatide in obesity, NASH, and cardio-renal benefits. Real-world observational studies are also needed to assess rare and long-term adverse events.

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.

Sex-Related Effects of Gut Microbiota in Metabolic Syndrome-Related Diabetic Retinopathy

The metabolic syndrome (MetS) is a complex disease of metabolic abnormalities, including obesity, insulin resistance, hypertension and dyslipidaemia, and it is associated with an increased risk of cardiovascular disease (CVD). Diabetic retinopathy (DR) is the leading cause of vision loss among working-aged adults around the world and is the most frequent complication in type 2 diabetic (T2D) patients. The gut microbiota are a complex ecosystem made up of more than 100 trillion of microbial cells and their composition and diversity have been identified as potential risk factors for the development of several metabolic disorders, including MetS, T2D, DR and CVD. Biomarkers are used to monitor or analyse biological processes, therapeutic responses, as well as for the early detection of pathogenic disorders. Here, we discuss molecular mechanisms underlying MetS, the effects of biological sex in MetS-related DR and gut microbiota, as well as the latest advances in biomarker research in the field. We conclude that sex may play an important role in gut microbiota influencing MetS-related DR.

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification

AIM

To evaluate the MONA.health artificial intelligence screening software for detecting referable diabetic retinopathy (DR) and diabetic macular edema (DME), including subgroup analysis.

METHODS

The algorithm's threshold value was fixed at the 90% sensitivity operating point on the receiver operating curve to perform the disease classification. Diagnostic performance was appraised on a private test set and publicly available datasets. Stratification analysis was executed on the private test set considering age, ethnicity, sex, insulin dependency, year of examination, camera type, image quality, and dilatation status.

RESULTS

The software displayed an area under the curve (AUC) of 97.28% for DR and 98.08% for DME on the private test set. The specificity and sensitivity for combined DR and DME predictions were 94.24 and 90.91%, respectively. The AUC ranged from 96.91 to 97.99% on the publicly available datasets for DR. AUC values were above 95% in all subgroups, with lower predictive values found for individuals above the age of 65 (82.51% sensitivity) and Caucasians (84.03% sensitivity).

CONCLUSION

We report good overall performance of the MONA.health screening software for DR and DME. The software performance remains stable with no significant deterioration of the deep learning models in any studied strata.

Determination of Factors Influencing the Health Belief Model (HBM) and Adherence to Intravitreal Anti-vascular Endothelial Growth Factor (VEGF) Among Patients With Diabetic Macular Edema (DME)

Background Diabetic macular edema (DME) is becoming one of the leading causes of blindness worldwide with a significant impact on quality of life. The effectiveness of intravitreal (IVT) anti-vascular endothelial growth factor (VEGF) therapy has been established by clinical trials and has become the treatment of choice in the majority of DME patients in reducing macular edema and improving visual acuity. Frequent drop-out and discontinuation of treatment are major issues. Lack of compliance can lead to worsening outcomes and be a burden to patients and the healthcare system. Purpose The purpose of this study is to assess multiple factors that affect adherence to IVT anti-VEGF treatment among patients with DME, including socioeconomic causes and the Health Belief Model (HBM) domains, in addition to exploring the relationship between them. Methods This cross-sectional study was conducted among DME patients in Hospital Canselor Tuanku Muhriz, Kuala Lumpur, Malaysia, from December 2020 to June 2021. We identified eligible patients using a retrospective chart review of clinical findings and optical coherence tomography (OCT) findings. Included subjects were of Malaysian nationality, aged 18 years and above, who were initiated or re-initiated IVT anti-VEGF treatment regime and on follow-up for at least six months from initial injection from January 2019 onwards. A translated and validated self-administered questionnaire was given to the respondents. Data were analyzed using IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States). Demographics of the patient were summarized using descriptive statistics, independent sample t-test was used to compare the difference in components of the HBM questionnaire. Linear regression was further used to explore the relationship between patients' demographics and the HBM component. Results A total of 141 patients participated in this study, of whom 56.2% patients were adherent to treatment. The majority were aged 60 years and above (56.7%), male (52.5%), Malay (38.9%), and married (71.6%). There was a significant statistical difference in patients who were adherent to treatment, in terms of life entourage (p=0.004, Fisher Exact test). HBM domains that influenced adherence to treatment included perceived severity, perceived barriers, perceived benefits, cues to action, and self-efficacy (p<0.05, independent sample t-test). Further, multiple logistic regression tests on sociodemographic factors and HBM domains after eliminating confounding factors narrowed down the significant variables to perceived susceptibility (p= 0.023), perceived benefits (p< 0.001), and self-efficacy (p< 0.001). Conclusion Patients' adherence to IVT anti-VEGF is influenced by perceived susceptibility to complications from DME, perceived benefits to the treatment, and self-efficacy.

"A disease that god has given me" patients and caregivers' perspectives on diabetes in southeastern Tanzania

BACKGROUND

Prompt diagnosis and appropriate management of diabetes has the potential of improving survival and patient health outcomes. Yet many diabetes patients present themselves to health facilities at an advanced stage of the disease which complicates its management. Individual perceptions about diseases are known to play a critical role in informing responses and actions including seeking health care and self-care practices. However, little is documented in Tanzania regarding the perspectives of diabetes patients and their caregivers about the disease especially in rural settings.

METHODS

We conducted 26 in-depth interviews involving 19 diabetes patients and 7 diabetes patient caregivers to explore in detail their perspectives on diabetes as a disease. Data was analyzed using thematic analysis with the help of NVivo9.

RESULTS

Both patients and caregivers expressed mixed perceptions on diabetes causes. In addition to heredity, and the failure of the pancreas to function well, lifestyle factors including lack of physical activity and eating too many sugary and oily foods were common reported causes. However, none of the participants were clear on the mechanisms between the perceived causes and the actual occurrence of the disease. Perception on susceptibility to diabetes was low even among participants with the disease as they reported not having ever thought of getting the condition before they were diagnosed. Some caregivers expressed worry and fear on their susceptibility to inheriting diabetes from their relatives who had the condition. Diabetes was perceived as a severe and life-threatening condition that can easily cause death if not well managed. Participants indicated uncertainty on its prevention.

CONCLUSION

This study shows mixed perspectives on the causes, susceptibility, severity and prevention of diabetes which were informed by the participants' limited knowledge and awareness about the disease. Interventions to strengthen responses to diabetes, which include buy-in from the patients and their caregiver's perspectives are essential to improve prevention, early diagnosis and appropriate management in rural settings.

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions.

Identification of key ferroptosis genes in diabetic retinopathy based on bioinformatics analysis

OBJECTIVES

Diabetic retinopathy (DR) is a retinal microvascular disease associated with diabetes. Ferroptosis is a new type of programmed cell death that may participate in the occurrence and development of DR. Therefore, this study aimed to identify the DR ferroptosis-related genes by bioinformatics methods.

METHODS

The RNAseq data of DR and healthy control retinas were downloaded from the gene expression synthesis (GEO) database and analyzed using the R package DESeq2. The key modules were obtained using the WGCNA algorithm, and their genes were intersected with ferroptosis-related genes in the FerrDb database to obtain differentially expressed ferroptosis-related genes (DE-FRGs). Enrichment analysis was conducted to understand the function and enrichment pathways of ferroptosis genes in DR, and hub genes were identified by protein-protein interaction (PPI) analysis. The diagnostic accuracy of hub genes for DR was evaluated according to the area under the ROC curve. The TRRUST database was then used to predict the regulatory relationship between transcription factors and target genes, with the mirDIP, ENCORI, RNAnter, RNA22, miRWalk and miRDB databases used to predict the regulatory relationship between miRNAs and target genes. Finally, another data set was used to verify the hub genes.

RESULTS

In total, 52 ferroptosis-related DEGs (43 up-regulated and 9 down-regulated) were identified using 15 DR samples and 3 control samples and were shown to be significantly enriched in the intrinsic apoptotic signaling pathway, autophagosome, iron ion binding and p53 signaling pathway. Seven hub genes of DR ferroptosis were identified through PPI network analysis, but only HMOX1 and PTGS2 were differentially expressed in another data set. The miRNAs prediction showed that hsa-miR-873-5p was the key miRNA regulating HMOX1, while hsa-miR-624-5p and hsa-miR-542-3p were the key miRNAs regulating PTGS2. Furthermore, HMOX1 and PTGS2 were regulated by 13 and 20 transcription factors, respectively.

CONCLUSION

The hub genes HMOX1 and PTGS2, and their associated transcription factors and miRNAs, may be involved in ferroptosis in diabetic retinopathy. Therefore, the specific mechanism is worthy of further investigation.

[Retinal alterations detected by non-mydriatic retinal camera screening and referral to ophthalmology in a population with high cardiovascular risk]

OBJECTIVE

To describe the main retinal alterations detected by non-mydriatic retinal camera screening and to evaluate factors related to referral to ophthalmology in a population at high cardiovascular risk in Palmira, Colombia.

MATERIALS AND METHODS

Cross-sectional observational study, which included 11,983 photographic imaging records of patients with hypertension and diabetes mellitus from Gesencro's S.A.S. comprehensive chronic disease care program between 2018 and 2020. Risk factors associated to referral to ophthalmology were evaluated with logistic regression, and crude and adjusted ORs (odds ratios) were obtained.

RESULTS

A total of 11,880 records were analyzed; 67.7±12years old, and 69.5% were women. Among the retinal alterations were patients with diabetic retinopathy classified as more than mild in 10% and gradeI hypertensive retinopathy in 54.9% right eye, 51.9% left eye. Macular edema was also identified. Only 2069 patients (17.4%) required referral to ophthalmology, and for imaging control 82.6%. In the multivariate analysis, the risk factors associated with the probability of being referred were male gender, age 60years and older, glycosylated hemoglobin out-of-target, advanced chronic kidney disease and the microalbumin-to-creatinine ratio moderate to severely elevated.

CONCLUSION

This study makes it possible to determine the importance of screening with a non-mydriatic retinal camera in patients at high cardiovascular risk to detect retinal abnormalities and assess risk factors associated with referral to ophthalmology. Early documentation of ocular compromise in these patients could prevent and avoid visual impairment and blindness.

OCT and OCT Angiography Update: Clinical Application to Age-Related Macular Degeneration, Central Serous Chorioretinopathy, Macular Telangiectasia, and Diabetic Retinopathy

Similar to ultrasound adapting soundwaves to depict the inner structures and tissues, optical coherence tomography (OCT) utilizes low coherence light waves to assess characteristics in the eye. Compared to the previous gold standard diagnostic imaging fluorescein angiography, OCT is a noninvasive imaging modality that generates images of ocular tissues at a rapid speed. Two commonly used iterations of OCT include spectral-domain (SD) and swept-source (SS). Each comes with different wavelengths and tissue penetration capacities. OCT angiography (OCTA) is a functional extension of the OCT. It generates a large number of pixels to capture the tissue and underlying blood flow. This allows OCTA to measure ischemia and demarcation of the vasculature in a wide range of conditions. This review focused on the study of four commonly encountered diseases involving the retina including age-related macular degeneration (AMD), diabetic retinopathy (DR), central serous chorioretinopathy (CSC), and macular telangiectasia (MacTel). Modern imaging techniques including SD-OCT, TD-OCT, SS-OCT, and OCTA assist with understanding the disease pathogenesis and natural history of disease progression, in addition to routine diagnosis and management in the clinical setting. Finally, this review compares each imaging technique's limitations and potential refinements.

Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention

Diabetic retinopathy (DR) is a common retinal vascular disease, which can cause severe visual impairment. It is of great clinical significance to use fundus images for intelligent diagnosis of DR. In this paper, an intelligent DR classification model of fundus images is proposed. This method can detect all the five stages of DR, including of no DR, mild, moderate, severe, and proliferative. This model is composed of two key modules. FEB, feature extraction block, is mainly used for feature extraction of fundus images, and GPB, grading prediction block, is used to classify the five stages of DR. The transformer in the FEB has more fine-grained attention that can pay more attention to retinal hemorrhage and exudate areas. The residual attention in the GPB can effectively capture different spatial regions occupied by different classes of objects. Comprehensive experiments on DDR datasets well demonstrate the superiority of our method, and compared with the benchmark method, our method has achieved competitive performance.

Segmentation-Assisted Fully Convolutional Neural Network Enhances Deep Learning Performance to Identify Proliferative Diabetic Retinopathy

With the progression of diabetic retinopathy (DR) from the non-proliferative (NPDR) to proliferative (PDR) stage, the possibility of vision impairment increases significantly. Therefore, it is clinically important to detect the progression to PDR stage for proper intervention. We propose a segmentation-assisted DR classification methodology, that builds on (and improves) current methods by using a fully convolutional network (FCN) to segment retinal neovascularizations (NV) in retinal images prior to image classification. This study utilizes the Kaggle EyePacs dataset, containing retinal photographs from patients with varying degrees of DR (mild, moderate, severe NPDR and PDR. Two graders annotated the NV (a board-certified ophthalmologist and a trained medical student). Segmentation was performed by training an FCN to locate neovascularization on 669 retinal fundus photographs labeled with PDR status according to NV presence. The trained segmentation model was used to locate probable NV in images from the classification dataset. Finally, a CNN was trained to classify the combined images and probability maps into categories of PDR. The mean accuracy of segmentation-assisted classification was 87.71% on the test set (SD = 7.71%). Segmentation-assisted classification of PDR achieved accuracy that was 7.74% better than classification alone. Our study shows that segmentation assistance improves identification of the most severe stage of diabetic retinopathy and has the potential to improve deep learning performance in other imaging problems with limited data availability.

Analysis of independent risk factors for progression of different degrees of diabetic retinopathy as well as non-diabetic retinopathy among type 2 diabetic patients

Purpose

To study the independent risk factors for development of different degrees of diabetic retinopathy (DR) as well as non-DR (NDR) among type 2 diabetic patients.

Methods

This cross-sectional study included 218 patients with type 2 diabetes between January 2022 and June 2022. All the patients were divided into two groups: the DR group and the NDR group. The DR group was subdivided into the mild, moderate and severe non-proliferative DR (NPDR) group and the proliferative DR (PDR) group. Data recorded for all patients included age, gender, duration of diabetes, blood pressure, glycated hemoglobin (HbA1c), fasting blood glucose (FBG), blood lipids, best corrected visual acuity (BCVA), intraocular pressure (IOP), axial length (AL), anterior chamber depth (ACD), and renal function. Logistic regression methods were used to analyze the risk factors for DR.

Results

The prevalence of DR in type 2 diabetes was 28.44%. The duration of diabetes, age, mean arterial pressure (MAP), HbA1c, FBG, urinary albumin/creatinine ratio (UACR), BCVA, AL, and ACD were significantly different between the DR and the NDR groups (p < 0.05). Multivariate logistic regression analysis identified age, FBG, UACR, and AL as the independent risk factors for DR (OR = 0.843, 2.376, 1.049, 0.005; p = 0.034, 0.014, 0.016, p < 0.001).

Conclusion

Young age, short AL, higher levels of FBG and UACR were the independent risk factors for the progression of DR in type 2 diabetes.

A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management

Objective

This study aimed to establish a risk prediction model for diabetic retinopathy (DR) in the Chinese type 2 diabetes mellitus (T2DM) population using few inspection indicators and to propose suggestions for chronic disease management.

Methods

This multi-centered retrospective cross-sectional study was conducted among 2,385 patients with T2DM. The predictors of the training set were, respectively, screened by extreme gradient boosting (XGBoost), a random forest recursive feature elimination (RF-RFE) algorithm, a backpropagation neural network (BPNN), and a least absolute shrinkage selection operator (LASSO) model. Model I, a prediction model, was established through multivariable logistic regression analysis based on the predictors repeated ≥3 times in the four screening methods. Logistic regression Model II built on the predictive factors in the previously released DR risk study was introduced into our current study to evaluate the model's effectiveness. Nine evaluation indicators were used to compare the performance of the two prediction models, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, balanced accuracy, calibration curve, Hosmer-Lemeshow test, and Net Reclassification Index (NRI).

Results

When including predictors, such as glycosylated hemoglobin A1c, disease course, postprandial blood glucose, age, systolic blood pressure, and albumin/urine creatinine ratio, multivariable logistic regression Model I demonstrated a better prediction ability than Model II. Model I revealed the highest AUROC (0.703), accuracy (0.796), precision (0.571), recall (0.035), F1 score (0.066), Hosmer-Lemeshow test (0.887), NRI (0.004), and balanced accuracy (0.514).

Conclusion

We have built an accurate DR risk prediction model with fewer indicators for patients with T2DM. It can be used to predict the individualized risk of DR in China effectively. In addition, the model can provide powerful auxiliary technical support for the clinical and health management of patients with diabetes comorbidities.

MicroRNA Profiling from Tears as a Potential Non-invasive Method for Early Detection of Diabetic Retinopathy

Diabetic retinopathy (DR) is a vascular complication of diabetes that can lead to partial or complete loss of vision. Early detection and treatment of DR can prevent blindness. Regular clinical examination is recommended for DR diagnosis; however, it is not always possible or feasible due to limited resources, expertise, time, and infrastructure. Several clinical and molecular biomarkers are proposed for the prediction of DR including microRNAs. MicroRNAs are a class of small non-coding RNAs that are found in biofluids and can be measured using reliable and sensitive methods. The most commonly used biofluid for microRNA profiling is plasma or serum; however, tear fluid (tears) is also demonstrated to contain microRNAs. MicroRNAs isolated from tears present a non-invasive source for DR detection. Different methods of microRNA profiling are available including digital PCR-based methods that can detect up to a single copy of microRNA in the biofluids. Here, we describe microRNA isolation from tears using manual method as well as using a high-throughput automated platform followed by microRNA profiling using digital PCR system.

Cognitive Performance and Diabetic Retinopathy: What Your Eyes Can Reveal About Your Brain

BACKGROUND

Diabetic retinopathy (DR) is a chronic diabetes complication. People with Type 2 Diabetes Mellitus (T2DM) have two times the risk for dementia, suggesting it is a new chronic diabetes complication.

OBJECTIVE

Evaluate the association of DR with cognitive performance in a T2DM population.

METHODS

Cross-sectional study with 400 T2DM adults from whom socio-demographic, clinical, laboratory data were collected, and screening test for depression symptoms (Patient Health Questionaire- 9 (PHQ-9)), Mini-Mental State Examination (MMSE), Semantic Verbal Fluency Test, Trail Making Test A and B, Word Memory test were performed. All cognitive test scores were converted into Global Cognition z-Score (GCS(z)). The association between GCS(z) < 0 with DR was performed using a multivariate binary logistic regression model adjusted for age ≥ 65 years, school years ≤ 6 years, DM duration ≥ 10 years, depression symptoms score > 9 at PHQ-9, arterial hypertension, physical activity, diabetic retinopathy, macular edema, and cardiovascular disease.

RESULTS

After exclusions, the 251 eligible patients were 56.6% female, with a mean age of 61.1 (±9.8) years, DM duration of 12.6 (±8.9) years, and 7.6 (±4.2) years of school education. DR prevalence was 46.5%. Multivariate Logistic Regression Model showed an association between DR and GCS(z) < 0, with odds ratio (CI95%) of 2.50 (1.18-5.34), adjusted for age, low education level, arterial hypertension and depression symptoms (OD and CI95% respectively: 5.46(2.42-12.34); 12.19 (5.62-26.46); 2.55 (0.88-7.39); 3.53 (1.55-8.07)).

CONCLUSION

In this T2DM population, having DR increased the chance for worse cognitive performance even when adjusted for age, low education level, presence of arterial hypertension, and depression symptoms.

Prevalence of diabetic retinopathy in patients with newly diagnosed type 2 diabetes: A systematic review and meta-analysis

AIMS

Type 2 diabetes mellitus (T2DM) can remain undiagnosed for many years, during which micro- and macro-vascular complications may develop. This study aimed to assess the worldwide prevalence of diabetic retinopathy (DR) in patients with newly diagnosed T2DM.

MATERIALS AND METHODS

We systematically searched electronic databases for relevant studies published from inception to 01 January 2022. Selected studies reported the prevalence of DR among patients with newly diagnosed T2DM, specifying the case definition used. Random-effects meta-analysis was used to derive the pooled prevalence. Subgroup and meta-regression analyses were used to investigate variations in the prevalence estimates in terms of available variables.

RESULTS

Data from 77 studies including 99,847 patients with newly diagnosed T2DM were included from 26 countries. The pooled prevalence of DR among patients with newly diagnosed T2DM was 13.1% (95% CI, 11.1%-15.1%; I²  = 97.0%). DR was higher in clinic-based samples compared with community-based samples (15.0%, 95% CI = 12.4%-17.8% vs. 11.5%, 95% CI = 8.9%-14.5%; p = 0.05; I²  = 97.0%) and was higher in countries in the WHO African 19.2% (95% CI, 14.6%-24.3%; I²  = 76.0%), South-East Asia 15.4% (95% CI, 10.0%-21.6%; I²  = 79.1%), and European 15.0% (95% CI, 11.2%-19.2%; I²  = 82.0%) regions. A higher proportion of female patients was significantly associated with a lower prevalence of DR in patients with newly diagnosed T2DM. We observed that the prevalence of DR in patients with newly diagnosed T2DM has remained unchanged over time.

CONCLUSIONS

Globally, DR is a prevalent complication among patients with newly diagnosed T2DM indicating the importance of establishing effective strategies to promote regular screening for the early diagnosis of T2DM alongside routine ophthalmic assessment at the time of T2DM diagnosis to reduce the burden of vision-threatening retinopathy.

The protective effects of flavonoids and carotenoids against diabetic complications-A review of in vivo evidence

Diabetes mellitus is a chronic metabolic disorder caused either by inadequate insulin secretion, impaired insulin function, or both. Uncontrolled diabetes is characterized by hyperglycemia which over time leads to fatal damage to both macro-and microvascular systems, causing complications such as cardiovascular diseases, retinopathy and nephropathy. Diabetes management is conventionally delivered through modifications of diet and lifestyle and pharmacological treatment, using antidiabetic drugs, and ultimately insulin injections. However, the side effects and financial cost of medications often reduce patient compliance to treatment, negatively affecting their health outcomes. Natural phytochemicals from edible plants such as fruits and vegetables (F&V) and medicinal herbs have drawn a growing interest as potential therapeutic agents for treating diabetes and preventing the onset and progression of diabetic complications. Flavonoids, the most abundant polyphenols in the human diet, have shown antidiabetic effects in numerous in vitro and preclinical studies. The underlying mechanisms have been linked to their antioxidant, anti-inflammatory and immunomodulatory activities. Carotenoids, another major group of dietary phytochemicals, have also shown antidiabetic potential in recent in vitro and in vivo experimental models, possibly through a mechanism of action similar to that of flavonoids. However, scientific evidence on the efficacy of these phytochemicals in treating diabetes or preventing the onset and progression of its complications in clinical settings is scarce, which delays the translation of animal study evidence to human applications and also limits the knowledge on their modes of actions in diabetes management. This review is aimed to highlight the potential roles of flavonoids and carotenoids in preventing or ameliorating diabetes-related complications based on in vivo study evidence, i.e., an array of preclinical animal studies and human intervention trials. The current general consensus of the underlying mechanisms of action exerted by both groups of phytochemicals is that their anti-inflammatory action is key. However, other potential mechanisms of action are considered. In total, 50 in vivo studies were selected for a review after a comprehensive database search via PubMed and ScienceDirect from January 2002 to August 2022. The key words used for analysis are type-2 diabetes (T2DM), diabetic complications, flavonoids, carotenoids, antioxidant, anti-inflammatory, mechanisms of prevention and amelioration, animal studies and human interventions.

A Guide to Genome-Wide Association Study Design for Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common microvascular complication related to diabetes. There is evidence that genetics play an important role in DR pathogenesis, but the complexity of the disease makes genetic studies a challenge. This chapter is a practical overview of the basic steps for genome-wide association studies with respect to DR and its associated traits. Also described are approaches that can be adopted in future DR studies. This is intended to serve as a guide for beginners and to provide a framework for further in-depth analysis.

Vitreous humor proteome: unraveling the molecular mechanisms underlying proliferative and neovascular vitreoretinal diseases

Proliferative diabetic retinopathy (PDR), proliferative vitreoretinopathy (PVR), and neovascular age-related macular degeneration (nAMD) are among the leading causes of blindness. Due to the multifactorial nature of these vitreoretinal diseases, omics approaches are essential for a deeper understanding of the pathophysiologic processes underlying the evolution to a proliferative or neovascular etiology, in which patients suffer from an abrupt loss of vision. For many years, it was thought that the function of the vitreous was merely structural, supporting and protecting the surrounding ocular tissues. Proteomics studies proved that vitreous is more complex and biologically active than initially thought, and its changes reflect the physiological and pathological state of the eye. The vitreous is the scenario of a complex interplay between inflammation, fibrosis, oxidative stress, neurodegeneration, and extracellular matrix remodeling. Vitreous proteome not only reflects the pathological events that occur in the retina, but the changes in the vitreous itself play a central role in the onset and progression of vitreoretinal diseases. Therefore, this review offers an overview of the studies on the vitreous proteome that could help to elucidate some of the pathological mechanisms underlying proliferative and/or neovascular vitreoretinal diseases and to find new potential pharmaceutical targets.

Cholesterols, Apolipoproteins, and Their Associations with the Presence and Severity of Diabetic Retinopathy: A Systematic Review

Serum apolipoproteins have been reported as a more significant marker for diabetic retinopathy (DR) compared with serum cholesterols. This article aims to review the associations between serum cholesterols and apolipoproteins, and the presence and severity of DR. The protocol of this systematic review was registered at the PROSPERO registry (CRD42022303331). We conducted a systematic search of literature published between 2011 to 2022 using the search terms "serum cholesterol" AND/OR "lipoprotein" AND/OR "apolipoprotein" AND/OR "diabetic retinopathy". Fifteen studies were included in this review. Six studies assessed the association between serum cholesterols, apolipoproteins, and the presence of DR. Three studies reported lower levels of apolipoprotein A1, and one study reported higher levels of apolipoprotein B in patients with DR. The remaining nine studies compared serum cholesterol and apolipoprotein levels according to DR severity. Patients with more severe grades of DR presented with lower apolipoprotein A1 in six (66.7%) studies, higher apolipoprotein B levels in seven (77.8%) studies, and a higher apolipoprotein B/apolipoprotein A1 ratio in six out of seven (85%) studies. In conclusion, serum apolipoproteins, in particular the apolipoprotein B/apolipoprotein A1 ratio, were a more consistent marker for DR severity compared with serum cholesterols.

Wide-field swept-source OCTA in the assessment of retinal microvasculature in early-stage diabetic retinopathy

BACKGROUND

To perform a quantitative analysis of retinal microvasculature in patients with early-stage diabetic retinopathy (DR) using wide-field swept-source optical coherence tomography angiography (SS-OCTA).  METHODS: One hundred nineteen eyes of 119 patents (67 eyes with no DR and 52 eyes with mild-moderate nonproliferative diabetic retinopathy (NPDR)) were enrolled in this observational and cross-sectional cohort study, and an age-matched group consisting of 39 eyes of 39 non-diabetic subjects were set as the control. Each participant underwent a full ophthalmic examination, including wide-field SS-OCTA imaging. On OCTA scans (12 mm * 12 mm), the mean perfusion area (PA) and vessel density (VD) were independently measured in all 16 Early Treatment Diabetic Retinopathy Study (ETDRS) sectors. Linear regression analyses were conducted to evaluate the influences of PA.

RESULTS

In the central ring, there were no significant differences in the average PA and VD among the groups. In the 3 mm radius, the PA and VD of the no DR and mild-moderate NPDR were significantly decreased compared with the control group in superior and inferior quadrants. In the wide-field scans (9 and 12 mm radius), there was no significant difference in average PA and VD between the groups in each sectors (p > 0.05). Regression analysis found that the effect of VD on PA was statistically different (b = 1.311, p < 0.001).

CONCLUSION

Wide-field OCTA imaging is useful for evaluating peripheral capillary perfusion in eyes with early-stage DR. Decrease in PA and VD was greater in the S3 and I3 sectors, and reductions in PA and VD were uneven in wide-filed sectors (9 and 12 mm radius).

AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

Convolutional neural networks have been widely applied to medical image segmentation and have achieved considerable performance. However, the performance may be significantly affected by the domain gap between training data (source domain) and testing data (target domain). To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG). Our AADG framework can effectively sample data augmentation policies that generate novel domains and diversify the training set from an appropriate search space. Specifically, we introduce a novel proxy task maximizing the diversity among multiple augmented novel domains as measured by the Sinkhorn distance in a unit sphere space, making automated augmentation tractable. Adversarial training and deep reinforcement learning are employed to efficiently search the objectives. Quantitative and qualitative experiments on 11 publicly-accessible fundus image datasets (four for retinal vessel segmentation, four for optic disc and cup (OD/OC) segmentation and three for retinal lesion segmentation) are comprehensively performed. Two OCTA datasets for retinal vasculature segmentation are further involved to validate cross-modality generalization. Our proposed AADG exhibits state-of-the-art generalization performance and outperforms existing approaches by considerable margins on retinal vessel, OD/OC and lesion segmentation tasks. The learned policies are empirically validated to be model-agnostic and can transfer well to other models. The source code is available at https://github.com/CRazorback/AADG.

Diabetic Retinopathy Telemedicine Outcomes With Artificial Intelligence-Based Image Analysis, Reflex Dilation, and Image Overread

PURPOSE

To examine real-world telemedicine outcomes of diabetic retinopathy (DR) screening with artificial intelligence (AI)-based image analysis, reflex dilation, and secondary image overread in a primary care setting.

DESIGN

Validity and reliability analysis.

METHODS

Single institution review of 1052 consecutive adult patients who received diabetic retinopathy photoscreening in the primary care setting over an 18-month period. Nonmydriatic fundus photographs were acquired and analyzed by the IDx-DR AI-based system. When nonmydriatic images were ungradable, reflex dilation (1% tropicamide) and mydriatic photography were performed for repeat AI-based analysis. Manual overread was performed on all images. Patient demographics, clinical characteristics, and screening outcomes were recorded.

RESULTS

A total of 965 of 1052 patients (91.7%) had AI-gradable fundus photographs: 580 had gradable nonmydriatic imaging (55.1%) and 440 of 472 patients with ungradable nonmydriatic photographs had reflex dilation (93.2%). One hundred thirty-eight of 965 patients (14.3%) were AI-graded as "positive" (greater than mild NPDR) and 827 of 965 were "negative" (85.7%), with 100% sensitivity (95% CI 90.8-100%), 89.2% specificity (95% CI 87.0-91.1%), 27.5% positive predictive value (95% CI 24.0-31.4%), and 100% negative predictive value (95% CI 99.6-100%) compared with manual overread assessment of greater than mild NPDR requiring further evaluation with a comprehensive dilated examination. Image gradeability was inversely related to patient age: 93.5% gradable (61.9% nonmydriatic) for patients aged <70 years vs 85.3% (31.0% nonmydriatic) for patients aged 70+ years (P < .001).

CONCLUSION

Incorporation of AI-based image analysis into real-world primary care diabetic retinopathy screening yielded no false negative results and offered excellent image gradeability within a protocol combining nonmydriatic fundus photography and pharmacologic dilation, as needed. Image gradeability was lower with increasing patient age.

Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages

Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detection method and compares the classification of conventional and deep features. Especially, the method identifies hemorrhage connected with blood vessels or residing at the retinal border and was reported challenging. Initially, adaptive brightness adjustment and contrast enhancement rectify degraded images. Prospective locations of hemorrhages are estimated by a Gaussian matched filter, entropy thresholding, and morphological operation. Hemorrhages are segmented by a novel technique based on the regional variance of intensities. Features are then extracted by conventional methods and deep models for training support vector machines and the results are evaluated. Evaluation metrics for each model are promising, but findings suggest that comparatively, deep models are more effective than conventional features.

DNA methylation in diabetic retinopathy: pathogenetic role and potential therapeutic targets

Background

Diabetic retinopathy (DR), a specific neuron-vascular complication of diabetes, is a major cause of vision loss among middle-aged people worldwide, and the number of DR patients will increase with the increasing incidence of diabetes. At present, it is limited in difficult detection in the early stages, limited treatment and unsatisfactory treatment effects in the advanced stages.

Main body

The pathogenesis of DR is complicated and involves epigenetic modifications, oxidative stress, inflammation and neovascularization. These factors influence each other and jointly promote the development of DR. DNA methylation is the most studied epigenetic modification, which has been a key role in the regulation of gene expression and the occurrence and development of DR. Thus, this review investigates the relationship between DNA methylation and other complex pathological processes in the development of DR. From the perspective of DNA methylation, this review provides basic insights into potential biomarkers for diagnosis, preventable risk factors, and novel targets for treatment.

Conclusion

DNA methylation plays an indispensable role in DR and may serve as a prospective biomarker of this blinding disease in its relatively early stages. In combination with inhibitors of DNA methyltransferases can be a potential approach to delay or even prevent patients from getting advanced stages of DR.

Co-existence of Age-Related Macular Degeneration and Diabetic Retinopathy in a Tertiary Referral Center in Greece

PURPOSE

To investigate the co-existence of diabetic retinopathy (DR) and age-related macular degeneration (AMD), based on five-year data in a University setting.

METHODS

Participants in the study included 1739 patients with diabetes mellitus, who were examined in our setting from 2015 to 2019. The presence of DR and AMD was recorded while the clinical characteristics of patients were evaluated.

RESULTS

In our study sample, 183 out of 1739 patients with diabetes mellitus (10.5%) were diagnosed with AMD, 116 without any sign of DR, and 67 with DR. In the group of patients with DR, dry AMD was noticed mostly in patients with mild non-proliferative DR (NPDR) (11.5% dry AMD) compared to those with moderate NPDR (4.5% dry AMD), severe NPDR (4.2%) and proliferative DR (PDR) (2.4%). Similar results were found for neovascular AMD (3% in mild NPDR, 1.9% in moderate NPDR, 1% in severe NPDR, and 1.8% in PDR). There was a significant correlation between the co-existence of both diseases and the severity of DR, with AMD being less prevalent in patients with more severe DR. In patients with diabetic macular edema, dry AMD was observed in 12 (4.6%) and neovascular AMD in nine (3.4%).

CONCLUSIONS

The five-year prevalence of AMD in DR patients was 9% while in diabetic patients without DR it was found to be 11.5%. Therefore, the co-existence of DR and AMD is not common, suggesting that DR may be protective for AMD development.

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.

Proportion and risk factors of diabetic retinopathy by stage in less-developed rural areas of Hunan province of China: A multi-site cross-sectional study

Aims

To investigate the proportion and risk factors of diabetic retinopathy (DR) by stages in less-developed rural areas in Hunan Province of China.

Background

DR is common among people with diabetes but not well recognized in less-developed rural areas. There is insufficient evidence on the risk factors of DR by stages, making it challenging to develop targeted prevention and intervention programs for DR in primary care settings.

Methods

A multi-site cross-sectional survey was conducted among people with type 2 diabetes mellitus (T2DM) from four less-developed counties in Hunan Province of China. All participants underwent the screening of DR via digital fundus photography and completed self-reported questionnaires on their socio-demographic and clinical characteristics, diabetes self-efficacy, diabetes self-care behaviors, social support, family function, and health service accessibility. The multinomial logistic regression models were employed to explore the risk factors of DR by stage, which were selected based on the socio-ecological model, literature, and clinical experience.

Results

A total of 196 participants were included in this study with an average age of 57.43 ± 10.26. 59.6% (117/196) of the participants were identified as DR, including 37.2% (73/196) non-proliferative DR and 22.4% (44/196) proliferative DR. Compared to the non-DR group, the risk factors of non-proliferative DR and proliferative DR were diabetes duration (OR: 1.10, 95 CI%: 1.04–1.17; OR: 1.14, 95 CI% 1.06–1.22) and self-monitoring of blood glucose (OR: 1.09, 95 CI% 1.01–1.17; OR: 1.11, 95 CI%: 1.02–1.20); the protective factors of non-proliferative DR was accessible complication prevention and management education (OR: 0.37, 95 CI% 0.14–0.94) while the protective factors of proliferative DR were physical activities (OR: 0.89, 95 CI%: 0.80–0.98). Compared to the non-proliferative DR group, the protective factors of proliferative DR were physical activities (OR: 0.89, 95 CI% 0.02–0.89) and family function (OR: 0.84, 95 CI%: 0.04–0.84).

Conclusion

DR was prevalent among people with T2DM in less-developed rural areas, indicating the need of strengthening DR screening. Risk factors of DR varied by stage while sharing some common factors. Future DR prevention and intervention programs may benefit from improving these factors to reduce the risk of DR by stage.

Validation of an autonomous artificial intelligence-based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context

PURPOSE

This study aims to evaluate the ability of an autonomous artificial intelligence (AI) system for detection of the most common central retinal pathologies in fundus photography.

METHODS

Retrospective diagnostic test evaluation on a raw dataset of 5918 images (2839 individuals) evaluated with non-mydriatic cameras during routine occupational health checkups. Three camera models were employed: Optomed Aurora (field of view - FOV 50º, 88% of the dataset), ZEISS VISUSCOUT 100 (FOV 40º, 9%), and Optomed SmartScope M5 (FOV 40º, 3%). Image acquisition took 2 min per patient. Ground truth for each image of the dataset was determined by 2 masked retina specialists, and disagreements were resolved by a 3rd retina specialist. The specific pathologies considered for evaluation were "diabetic retinopathy" (DR), "Age-related macular degeneration" (AMD), "glaucomatous optic neuropathy" (GON), and "Nevus." Images with maculopathy signs that did not match the described taxonomy were classified as "Other."

RESULTS

The combination of algorithms to detect any abnormalities had an area under the curve (AUC) of 0.963 with a sensitivity of 92.9% and a specificity of 86.8%. The algorithms individually obtained are as follows: AMD AUC 0.980 (sensitivity 93.8%; specificity 95.7%), DR AUC 0.950 (sensitivity 81.1%; specificity 94.8%), GON AUC 0.889 (sensitivity 53.6% specificity 95.7%), Nevus AUC 0.931 (sensitivity 86.7%; specificity 90.7%).

CONCLUSION

Our holistic AI approach reaches high diagnostic accuracy at simultaneous detection of DR, AMD, and Nevus. The integration of pathology-specific algorithms permits higher sensitivities with minimal impact on its specificity. It also reduces the risk of missing incidental findings. Deep learning may facilitate wider screenings of eye diseases.

Study on the Development of a Conceptual Framework to Identify the Risk Factors of Diabetic Retinopathy among Diabetic Patients: A Concept Paper

The most common complication of diabetes mellitus (DM) is diabetic retinopathy (DR). The control of DR risk factors is essential for the effective prevention of DR. There is currently a lack of research to guide DR-related research in Malaysia. This concept paper aimed to review published studies and propose a conceptual framework (CF) as a guide for future research to determine the prevalence of DR and its risk factors across DM patients. After a review of prior research, this study has presented a CF that takes into account these four key elements: the patient's sociodemographic characteristics, comorbidities, complications, and diabetes conditions, namely, the length of the disease and glycaemic control. In the suggested CF, ethnicity was highlighted as a crucial risk factor for DR across lower- to middle-income countries (LMIC) and multiracial countries. In order to guide future studies, scientific guidance is essential. The proposed CF would help future research to conduct scientific research related to DR. Also, the proposed CF was tailored to suit research across LMIC and multiracial countries.

The association of AGER and ALDH2 gene polymorphisms with diabetic retinopathy

PURPOSE

To evaluate the possible associations between AGER (rs1051993, rs2070600) and ALDH2 (rs671) gene polymorphisms with nonproliferative (NPDR) and proliferative (PDR) diabetic retinopathy, in a well-defined Greek population.

MATERIALS

66 NPDR patients and 57 PDR patients participated in our study, along with 156 age- and gender-matched healthy-control subjects (CL). All the participants underwent a complete ophthalmological examination, while clinical and demographic data were collected. Furthermore, they were genotyped for the studied polymorphisms.

RESULTS

No significant differences were detected among the studied groups regarding the participants' age and gender status. We found that the ALDH2 AA genotype was significantly more frequent in PDR patients than in CL (p = 0.014). Furthermore, between NPDR and PDR groups, the AGER rs1051993 GT and TT genotype frequencies were significantly elevated in PDR patients (p < 0.0001 and 0.04, respectively). Moreover, we demonstrated that the heterozygous GT genotype in DR patients is accompanied by 71.11 times higher risk of developing PDR (OR = 71.11: 95% CI- 4.14-1215.2), while the homozygous TT genotype is associated with 12.71 times elevated risk for PDR development (OR = 12.71: 95% CI- 0.63-254.1).

CONCLUSIONS

We documented that the ALDH2 AA and AGER rs1051993 GT and TT genotypes were observed significantly more frequently in PDR Greek diabetic patients. Our findings also support the genetic theory, suggesting that heritability is significantly implicated in the development of DR, providing additional evidence in the understanding of DR pathogenesis.

Urolithin A ameliorates diabetic retinopathy via activation of the Nrf2/HO-1 pathway

Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes mellitus and is characterised by excessive inflammation and oxidative stress. Urolithin A (UA), a major metabolite of ellagic acid, exerts anti-inflammatory and antioxidant functions in various human diseases. This study, for the first time, uncovered the role of UA in DR pathogenesis. Streptozotocin-induced diabetic rats were used to determine the effects of UA on blood glucose levels, retinal structures, inflammation, and oxidative stress. High glucose (HG)-induced human retinal endothelial cells (HRECs) were used to elucidate the anti-inflammatory and antioxidant mechanisms of UA in DR in vitro. The in vivo experiments demonstrated that UA injection reduced blood glucose levels, decreased albumin and vascular endothelial growth factor concentrations, and ameliorated the injured retinal structures caused by DR. UA administration also inhibited inflammation and oxidative damage in the retinal tissues of diabetic rats. Similar anti-inflammatory and antioxidant effects of UA were observed in HRECs induced by HG. Furthermore, we found that UA elevated the levels of nuclear Nrf2 and HO-1 both in vivo and in vitro. Nrf2 silencing reversed the inhibitory effects of UA on inflammation and oxidative stress during DR progression. Together, our findings indicate that UA can ameliorate DR by repressing inflammation and oxidative stress via the Nrf2/HO-1 pathway, which suggests that UA could be an effective drug for clinical DR treatment.

Risk Identification of Diabetic Macular Edema Using E-Adoption of Emerging Technology

The accumulation of the blood leaks on the retina is known as diabetic macular edema (DME), which can result in irreversible blindness. Early diagnosis and therapy can stop DME. This study presents an e-adoption of emerging technology such as RadioDense model for detecting and classifying DME from retinal fundus images. The proposed model employs a modified version of DenseNet121, radiomics features, and the gradient boosting classifier. The authors evaluated many classifiers on the concatenated features. The efficacy of the classifier is determined by comparing each classifier's accuracy values. According to the evaluation results, the concatenated features extraction using gradient boosting classifier outperforms all other classifiers on the IDRiD dataset. For multi-class classification, the suggested electronic adoption of emerging technology such as RadioDense model outperformed these classifiers and attained an accuracy of 87.4%. It can help to decrease the strain of ophthalmologists diagnosing the DME during locking and unlocking the worldwide lockdown.

Terrestrial health applications of visual assessment technology and machine learning in spaceflight associated neuro-ocular syndrome

The neuro-ocular effects of long-duration spaceflight have been termed Spaceflight Associated Neuro-Ocular Syndrome (SANS) and are a potential challenge for future, human space exploration. The underlying pathogenesis of SANS remains ill-defined, but several emerging translational applications of terrestrial head-mounted, visual assessment technology and machine learning frameworks are being studied for potential use in SANS. To develop such technology requires close consideration of the spaceflight environment which is limited in medical resources and imaging modalities. This austere environment necessitates the utilization of low mass, low footprint technology to build a visual assessment system that is comprehensive, accessible, and efficient. In this paper, we discuss the unique considerations for developing this technology for SANS and translational applications on Earth. Several key limitations observed in the austere spaceflight environment share similarities to barriers to care for underserved areas on Earth. We discuss common terrestrial ophthalmic diseases and how machine learning and visual assessment technology for SANS can help increase screening for early intervention. The foundational developments with this novel system may help protect the visual health of both astronauts and individuals on Earth.

A preliminary observation on rod cell photobiomodulation in treating diabetic macular edema

Purpose

To evaluate the safety and effectiveness of photobiomodulation (PBM) in the treatment of diabetic macular edema (DME).

Methods

It was a single-center, self-controlled prospective study. The clinical records of 12 diabetic retinopathy patients (5 males and 7 females, 20 eyes in total) who were treated with PBM for DME at the Second Affiliated Hospital, Zhejiang University School of Medicine, were analyzed. The mean age was 56 (26-68) years. All the participants received PBM treatment during darkness at night in no less than 5 days per week and no less than 8 ​h per day. In the baseline check and follow-up checks (1, 2, 6, 10, and 12 months after the start of treatment), the best-corrected visual acuity, the thickness of the retina in the macula, and the changes of the fundus lesions were observed. Wilcoxon signed rank test was used to compare the results before and after treatment. P ​< ​0.05 was considered statistically significant.

Results

No fundus complication was observed during follow-up checks. In baseline and 12-month follow-up checks, the best-corrected visual acuity was 71.75 ​± ​12.47 and 79.50 ​± ​10.85, maximal retinal thickness in macular area was 390.95 ​± ​77.12 ​μm and 354.13 ​± ​55.03 ​μm, average retinal thickness in macular area was 334.25 ​± ​36.45 ​μm and 314.31 ​± ​33.28 ​μm, foveal thickness was 287.00 ​± ​46.79 ​μm and 265.63 ​± ​67.14 ​μm. The best-corrected visual acuity, average retinal thickness in macular area in consecutive follow-up results except that in the 1st month showed significant difference compared with baseline results. There were significant difference between every follow-up result and baseline result of maximal retinal thickness in macular area (P ​< ​0.05). All follow-up results of foveal thickness were not significantly different (P ​> ​0.05) from the baseline result, except that in the 6th month (P ​= ​0.049). Obvious improvement could be observed in retinal fundus fluorescein angiography images.

Conclusions

PBM is a safe and effective treatment of DME, which deserves further investigation.

Prevalence, characteristics and risk factors of diabetic retinopathy in type 2 diabetes mellitus patients in Jordan: a cross-sectional study

Objectives

To measure the prevalence of diabetic retinopathy in patients with type 2 diabetes, to define their characteristics, and identify the associated risk factors.

Methods

We performed a cross sectional study of 1316 adult patients with type 2 diabetes mellitus who attended an ophthalmology clinic. Demographic, clinical, and laboratory data were analyzed. Diabetic retinopathy (DR) was diagnosed using a complete ophthalmic evaluation, including a fundic examination. Two regression models were constructed to identify the risk factors associated with DR and the parameters associated with the stage of retinopathy.

Results

Men accounted for 774 (58.8%) of the participants. The prevalence of DR was 28.2% (371 participants). DR was significantly more common in participants who were ≥60 years old, were women, had had diabetes for >10 years, were taking insulin, were not taking metformin, had a body mass index >30 kg/m², were current smokers, or had a history of hypertension. Advanced stages of DR were more common in participants in the later stages of nephropathy and with albuminuria.

Conclusions

Poor glycemic control, smoking, and advanced diabetic kidney disease are most closely associated with retinopathy. Further longitudinal studies are necessary to identify the mechanisms underlying these relationships and to guide community-based interventions.

Diabetic retinopathy: long-term follow-up of Ecuadorian patients with type 2 diabetes in primary care

Objectives

The aim of this study is to evaluate the prevalence, associated factors, and time for diabetic retinopathy (DR) development in Ecuadorian patients with type 2 diabetes (DM2) followed for 10 years.

Methods

A retrospective cohort study between 2007 and 2017 included 487 patients with DM2 who had at least one dilated fundus eye examination in Diabetes Center in a primary-health-care level followed up for ten years. Data was collected from clinical records. Sociodemographic and laboratory variables were analysed, determining their association (mean difference and bivariate logistic regression) with DR. Survival time was calculated through life tables and Kaplan-Meier analysis.

Results

The prevalence of DR was 19.95% during the 10-year follow-up period. The median time for developing DR was 28.53 (95% CI: 26.89–27.05), showing that 50% of patients have the risk (HR: 4.57) to develop DR in the third decade of DM disease diagnosis. The significant risk factors for progression of DR were duration of DM2, high glycosylated hemoglobin level > 7% (HbA1c Expβ: 1.709, 95% CI: 1.00–2.89), systemic hypertension (HNT Expβ: 2.348 Expβ: 2.348 95% CI: 1.17–4.70%) 95% CI: 1.17–4.70%), low glomerular filtration rate (Expβ: 1.805, GFR, < 60 ml/min/sc1.73) (95% CI: 1.10–2.94) and albuminuria (Expβ: 2.48 Expβ: 2.48 95% CI: 1.49–4.13).

Conclusions

Half of the patients with DM2 treated in a primary level of care will develop DR in the third decade of the disease. There are risk factors related to development of DR, such as low GFR, high HbA1c, albuminuria and HTN. Low GFR, high HbA1c, albuminuria and high blood pressure are related with the presence of retinopathy in diabetes patients.

Risk Factors of Microvascular Complications Among Type 2 Diabetic Patients Using Cox Proportional Hazards Models: A Cohort Study in Tabuk Saudi Arabia

Purpose

The burden of type 2 diabetes (T2D) is high in Saudi Arabia, but data related to its complications are limited. This study aimed to determine the incidence of microvascular complications caused by T2D and evaluate the impact of the associated risk factors.

Patients and Methods

This retrospective cohort study was conducted at two military hospitals in Tabuk, Saudi Arabia. Data on the socio-demographics, glycaemic profile, blood lipid indices, duration of T2D, and associated microvascular complications were collected from electronic health records and medical files. Descriptive statistics and Cox proportional hazards models were used for data analysis.

Results

This study included 1563 T2D patients. The incidence of microvascular complications was 34.3% (95% confidence interval [CI], 32.0-36.6). Retinopathy was the most common complication (incidence=20.0%; 95% CI, 18.0-22.0%), while nephropathy was the least common complication (incidence=12.2%; 95% CI, 10.6-13.8%). Advanced age (≥65 years) showed the highest risk of retinopathy (Hazard ratios [HR], 2.86; 95% CI, 2.56-3.21), neuropathy (HR, 2.70; 95% CI, 2.40-3.05), and nephropathy (HR, 2.37; 95% CI, 2.12-2.64) compared with their counterparts. After adjusting for potential confounders, the study found that the significant risk factors for microvascular complications were longer duration (≥10 years) of T2D (HR, 5.3; 95% CI, 5.1-5.6), uncontrolled hypertension (HR, 3.9; 95% CI, 3.3-4.2), poor glycaemic control (HR, 4.6; 95% CI, 4.3-5.1), obesity (HR, 2.3; 95% CI, 2.2-2.6), and dyslipidaemia (HR, 1.6; 95% CI, 1.2-2.0).

Conclusion

Given the high burden of microvascular complications in military healthcare facilities in Tabuk, Saudi Arabia, a context-specific accessible public health program focusing on the promotion of a healthy lifestyle, physical activity, and consumption of a healthy diet, as well as the early diagnosis and management of diabetes, needs to be developed and implemented.

Risk factors for cognitive decline in type 2 diabetes mellitus patients in Brazil: a prospective observational study

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) patients are twice as likely to develop dementia. The study's goal was to evaluate cognitive performance and risk factors for cognitive decline in this population.

METHODS

Prospective observational study was conducted with 400 T2DM adults, of whom, during routine baseline and follow-up appointments, had socio-demographic, clinical, and laboratory data collected, and underwent physical examination, screening for depression symptoms (Patient Health Questionaire-9-PHQ-9), and cognitive tests: Mini-Mental State Examination (MMSE), Semantic Verbal Fluency Test, Trail Making Test A/B, and Word Memory Tests. Each cognitive test score was converted to a z-score and its average resulted in a new variable called Global Cognitive z-Score [GCS(z)]. Averages of the cognitive test scores and GCS(z) at both moments were compared by the Student's T-Test for paired samples. Multivariate binary logistic regression models were built to assess the association of GCS(z) < zero with risk factors for cognitive decline at the baseline and follow-up.

RESULTS

After exclusions, 251 patients were eligible, being 56.6% female, mean age of 61.1 (± 9.8) years, 12.6 (± 8.9) years of DM duration, and 7.6 (± 4.2) years of school education. Follow-up had 134 patients reevaluated and took place after a mean of 18.4(± 5.0) months. Eleven (14%) patients with a GCS(z) ≥ 0 at baseline turned into a GCS(z) < 0 at follow-up. There were no significant differences between the means of cognitive test scores and GCS(z) at the two evaluation moments. At the baseline, the multivariate logistic regression model identified five risk factors associated with GCS(z) < zero: age ≥ 65 years, schooling ≤ 6 years, arterial hypertension, depression symptoms, and diabetic retinopathy (DR), with odds ratio (OR) and 95% confidence interval (CI95%) respectively: 5.46 (2.42-12.34); 12.19 (5.62-26.46); 2.55 (0.88-7.39); 3.53 (1.55-8.07) e 2.50 (1.18-5.34). At follow-up, the risk factors for GCS(z) < zero were: schooling ≤ 6 years, DM duration ≥ 10 years, depression symptoms, arterial hypertension, and cardiovascular disease (CVD), OR and CI95% respectively: 10.15 (3.68-28.01); 2.68 (0.96-7.48); 4.92 (1.77-13.70); 7.21 (1.38-35.71) e 5.76 (1.93-17.18).

CONCLUSIONS

Based on our results, cognitive evaluation and follow-up should be incorporated on the routine of T2DM patients, especially for those with advanced age, low education level, prolonged DM duration, arterial hypertension, depression symptoms, CVD, and DR.