Trends in Diabetic Retinopathy, Visual Acuity, and Treatment Outcomes for Patients Living With Diabetes in a Fundus Photograph–Based Diabetic Retinopathy Screening Program in Bangladesh

Long

et al. 2021

WJD

BACKGROUND Melatonin is reported to be related to diabetes mellitus (DM) risk; however, the effect of melatonin on diabetic retinopathy (DR) risk remains unclear. AIM The aim of this study was to determine the effect of melatonin on DR risk. METHODS A hospital-based case-control study was conducted from January 2020 to June 2020. DR was assessed using the Diabetic Retinopathy preferred practice pattern (PPP)-updated 2019 criteria. The participants were divided into the DM cases without DR (NDR) group, non-proliferative DR (NPDR) group and proliferative DR (PDR) group. Plasma melatonin concentration was detected with the enzyme-linked immunosorbent assay kit. The relationship between plasma melatonin concentration and DR risk as well as severity was assessed. RESULTS It was found that plasma melatonin was 72.83 ± 16.25, 60.38 ± 13.43, 44.48 ± 10.30 and 44.69 ± 8.95 pg/mL in healthy controls, NDR group, NPDR and PDR group, respectively. In addition, it was found that plasma melatonin could be used as a potential diagnostic biomarker for DR (AUC = 0.893, P < 0.001). There was a significant positive relationship between total bilirubin and melatonin content ( P < 0.001) based on the correlation assay. Significant associations between total bilirubin and melatonin content were also detected in the NPDR ( R 2 = 0.360, P < 0.001) and PDR ( R 2 = 0.183, P < 0.001) groups. CONCLUSION The data obtained in this study demonstrated that plasma melatonin concen-tration was decreased in DR cases and could be used as a sensitive and specific marker for the diagnosis of DR. A significant positive relationship between total bilirubin and melatonin was detected. More related studies are required to understand the role of melatonin in DR.

Section: Discussionmentioning

confidence: 99%

Plasma melatonin levels in patients with diabetic retinopathy secondary to type 2 diabetes

Long

et al. 2021

WJD

“…There is adequate evidence that retinal screening and referral for treatment can prevent avoidable blindness [3]. Advances in fundus imaging are expected to decrease preventable visual morbidity by enabling convenient and timely eye disease screening [4,5]. Color fundus camera imaging is an essential and easy-to-master technique for detecting a variety of eye diseases, such as diabetic retinopathy (DR) [6], age-related macular degeneration (AMD) [7], glaucoma [8], cataracts [9], and myopia [10,11].…”

Section: Introductionmentioning

confidence: 99%

Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

Han¹,

Li²,

Liu³

et al. 2021

J Med Internet Res

Background The supervised deep learning approach provides state-of-the-art performance in a variety of fundus image classification tasks, but it is not applicable for screening tasks with numerous or unknown disease types. The unsupervised anomaly detection (AD) approach, which needs only normal samples to develop a model, may be a workable and cost-saving method of screening for ocular diseases. Objective This study aimed to develop and evaluate an AD model for detecting ocular diseases on the basis of color fundus images. Methods A generative adversarial network–based AD method for detecting possible ocular diseases was developed and evaluated using 90,499 retinal fundus images derived from 4 large-scale real-world data sets. Four other independent external test sets were used for external testing and further analysis of the model’s performance in detecting 6 common ocular diseases (diabetic retinopathy [DR], glaucoma, cataract, age-related macular degeneration, hypertensive retinopathy [HR], and myopia), DR of different severity levels, and 36 categories of abnormal fundus images. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the model’s performance were calculated and presented. Results Our model achieved an AUC of 0.896 with 82.69% sensitivity and 82.63% specificity in detecting abnormal fundus images in the internal test set, and it achieved an AUC of 0.900 with 83.25% sensitivity and 85.19% specificity in 1 external proprietary data set. In the detection of 6 common ocular diseases, the AUCs for DR, glaucoma, cataract, AMD, HR, and myopia were 0.891, 0.916, 0.912, 0.867, 0.895, and 0.961, respectively. Moreover, the AD model had an AUC of 0.868 for detecting any DR, 0.908 for detecting referable DR, and 0.926 for detecting vision-threatening DR. Conclusions The AD approach achieved high sensitivity and specificity in detecting ocular diseases on the basis of fundus images, which implies that this model might be an efficient and economical tool for optimizing current clinical pathways for ophthalmologists. Future studies are required to evaluate the practical applicability of the AD approach in ocular disease screening.

Exp Clin Endocrinol Diabetes

“…Moreover, in subgroup analyses, the categories of advanced versus low retinopathy risk were significantly related to visual acuity. In a study from 2019, individuals with DR had significantly worse visual acuity than those with no DR (best-corrected visual acuity, 0.35 vs 0.21 logMAR; p<0.001) 22 . This corresponds to the finding in our cohort with the severity of DR correlating with the worst visual acuity of the patients (r=−0.209; p<0.001).…”

Section: Discussionmentioning

confidence: 96%

Prevalence of Impairment of Visual Acuity and Severity of Retinopathy in Patients with Diabetes Mellitus

Cleemen

Müller

Voigt

et al. 2022

Aims No information exists on the frequency of visual impairment in people with diabetes mellitus (DM) in Germany. In this study, the prevalence of vision impairment in those individuals was investigated. Methods We retrospectively analyzed a cohort of 295 people (14221 consultations) at a university outpatient clinic with any type of DM and an available ETDRS-Score and visual acuity. The primary outcome was the prevalence of visual impairment, the secondary outcome was the correlation of the ETDRS-Score and limitations of visual acuity and the prevalence of higher ETDRS-Score with a visual impairment defined as a decimal-visus</=0.3. Results The prevalence of visual impairment in participants with DM was 11.2%; among these individuals, 81.8% had no or non-proliferative retinopathy. In the DM2 subgroup, 81.5% (n=22) of the visually impaired participants had no DR, in contrast to only 16.7% (n=1) in the DM1 subgroup. Progression in ETDRS-Score led to worse visual acuity (r=−0.209; p<0.001). A significantly related covariates with impairment of the visual acuity for individuals with DM1 was the duration of diabetes (B=−0.007; p=0.001) and for individuals with DM2, the age (B=−0.008; p=0.009). Conclusions The prevalence of impaired vision in people with diabetes in our cohort was 11.2%,<20% of visual impairment in people with diabetes is caused by diabetic retinopathy, and 69.7% of participants with visual impairment had no DR. In our study patients without visual impairment showed a similar distribution of DR severity levels regardless of the type of diabetes.