Follow-up in a point-of-care diabetic retinopathy program in Pittsburgh: a non-concurrent retrospective cohort study.

BACKGROUND: The Point-of-Care Diabetic Retinopathy Examination Program (POCDREP) was initiated in 2015 at the University of Pittsburgh/UPMC in response to low diabetic retinopathy (DR) examination rates, a condition affecting a quarter of people with diabetes mellitus (PwDM) and leading to blindness. Early detection and treatment are critical with DR prevalence projected to triple by 2050. Approximately, half of PwDM in the U.S. undergo yearly examinations, and there are reported varying follow-up rates with eye care professionals, with limited data on the factors influencing these trends. POCDREP aimed to address screening and follow-up gap, partnering with diverse healthcare entities, including primary care sites, free clinics, and federally qualified health centers. METHODS: A non-concurrent retrospective cohort study spanning 2015-2018 examined data using electronic health records of patients who underwent retinal imaging. Imaging was performed using 31 cameras across various settings, with results interpreted by ophthalmologists. Follow-up recommendations were made for cases with vision-threatening DR (VTDR), incidental findings, or indeterminate results. Factors influencing follow-up were analyzed, including demographic, clinical, and imaging-related variables. We assessed the findings at follow-up of patients with indeterminate results. RESULTS: Out of 7,733 examinations (6,242 patients), 32.25% were recommended for follow-up. Among these, 5.57% were classified as having VTDR, 14.34% had other ocular findings such as suspected glaucoma and age-related macular degeneration (AMD), and 12.13% were indeterminate. Of those recommended for follow-up, only 30.87% were assessed by eye care within six months. Older age, marriage, and severe DR were associated with higher odds of following up. Almost two thirds (64.35%) of the patients with indeterminate exams were found with a vision-threatening disease at follow-up. CONCLUSION: The six-month follow-up rate was found to be suboptimal. Influential factors for follow-up included age, marital status, and the severity of diabetic retinopathy (DR). While the program successfully identified a range of ocular conditions, screening initiatives must extend beyond mere disease detection. Ensuring patient follow-up is crucial to DR preventing programs mission. Recommended strategies to improve follow-up adherence include education, incentives, and personalized interventions. Additional research is necessary to pinpoint modifiable factors that impact adherence and to develop targeted interventions.

Retinal ganglion cell complex thickness in subjects with diabetes mellitus and uncontrolled hypertension in China.

PURPOSE: To assess the interactive relationship between blood pressure status and diabetic mellitus (DM) with ganglion cell complex (GCC) thickness in elderly individuals in rural China. METHODS: Participants aged 50 years and older in a rural area of Daxing District, Beijing, were recruited in this study from October 2018 to November 2018. All subjects underwent a comprehensive systemic and ocular examination. Blood pressure status was graded as normotension, controlled hypertension and uncontrolled hypertension according to blood pressure measurements and the use of any medication for hypertension treatment. GCC parameters were measured by spectral-domain optical coherence tomography (SD-OCT). Generalized linear models (GLM) adjusted for related potential confounders were used to assess the interaction between DM and blood pressure status. RESULTS: Among 1415 screened subjects (2830 eyes), a total of 1117 eyes were enrolled in the final analysis. GLM analysis showed a significant interactive relationship between DM with uncontrolled hypertension status (ß = 3.868, p = 0.011). GCC thickness would decrease 0.255 µm per year as the age increased (ß=-0.255, p < 0.001). In a subgroup of 574 subjects with uncontrolled hypertension, DM was associated with an increased average of GCC thickness (ß = 1.929, p = 0.022). CONCLUSIONS: The present results revealed a significant interactive relationship between blood pressure status and DM. The average GCC thickness increased in individuals with DM combined with uncontrolled hypertension, which should be considered in the measurement of GCC. Further studies are warranted to explore ganglion cells changes as a non-invasive method to detect neuron alterations in individuals with DM and uncontrolled hypertension. TRAIL REGISTRATION: The registration number of the present trial in the Chinese Clinical Trial Registry is ChiCTR2000037944.

Evaluating the potential of retinal photography in chronic kidney disease detection: a review.

Background: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives: The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology: A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results: Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion: In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

The effect of long-term hemodialysis on diabetic retinopathy observed by swept-source optical coherence tomography angiography.

BACKGROUND: Diabetes can cause chronic microvascular complications such as diabetic retinopathy (DR) and diabetic nephropathy (DN). DR and DN can lead to or exacerbate diabetic macular edema (DME). Hemodialysis (HD) is the main treatment method for patients with end-stage kidney disease (ESKD) secondary to DN. PURPOSE: The aim of this prospective cohort study was to determine the immediate effect of single HD session on retinal and choroidal thickness in DR patients with ESKD and the features of DR and the prevalence of DME in these patients who have received long-term HD. METHODS: Eighty-five eyes of 44 DR patients with ESKD who underwent long-term HD were examined by swept-source optical coherence tomography angiography (SS-OCTA). Based on OCTA images, the characteristics of DR and the prevalence of DME in these patients were analyzed. Changes in central retinal thickness (CRT), central retinal volume (CRV), subfoveal choroidal thickness (SFCT) and subfoveal choroidal volume (SFCV) within 30 min before and after single HD session were compared. CRT, CRV, SFCT and SFCV were compared before single HD session and before the next single HD session. RESULTS: There was no significant difference in the average CRT (251.69 ± 39.21 µm vs. 251.46 ± 39.38 µm, P = 0.286) or CRV (0.15 ± 0.62 µm vs. 0.15 ± 0.63 µm, P = 0.324) between before and after single HD session. After single HD session, SFCT (243.11 ± 77.15 µm vs. 219.20 ± 72.84 µm, P < 0.001) and SFCV (0.15 ± 0.10 µm vs. 0.13 ± 0.90 µm, P < 0.001) significantly decreased. There was no statistically significant difference in CRT (251.69 ± 39.21 µm vs. 251.11 ± 38.47 µm, P = 0.206), CRV (0.15 ± 0.62 µm vs. 0.15 ± 0.61 µm, P = 0.154), SFCT (243.11 ± 77.15 µm vs. 245.41 ± 76.23 µm, P = 0.108), or SFCV (0.15 ± 0.10 µm vs. 0.16 ± 0.10 µm, P = 0.174) before HD and before the next single HD session. On en face OCTA images, eighty-five eyes (100%) had retinal nonperfusion areas, foveal avascular zone (FAZ) enlargement, and abnormal retinal microvasculature. Based on cross-sectional OCTA images, retinal neovascularization (RNV) was confirmed in 42 eyes (49.41%), and intraretinal microvascular abnormalities (IRMAs) were detected in 85 eyes (100%). Seventeen eyes (20%) still had DME, all of which were cystoid macular edema (CME). Among eyes with DME, the epiretinal membrane (ERM) was present in 7 eyes (8.24%). CONCLUSIONS: For DR patients with ESKD who have undergone long-term HD, the choroidal thickness still changes significantly before and after single HD session, which may be related to short-term effects such as reduced blood volume and plasma osmotic pressure caused by single HD session. Although macular features seem to have stabilized in DR patients undergoing long-term dialysis, the DR of patients with ESKD should still be given attention.

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.

BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown. METHODS: This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set. RESULTS: In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74-0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86-1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73-1.00). CONCLUSIONS: AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk. TRIAL REGISTRATION: This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).

One-Year Functional and Morphological Prognosis After Intravitreal Injection Treatments According to Different Morphological Patterns of Diabetic Macular Edema in Real-Life: MARMASIA Study Group Report No.13.

PURPOSE: To evaluate the responses of different optical coherence tomography (OCT) patterns of diabetic macular edema (DME) to intravitreal injection therapy. METHODS: In this retrospective, comparative, and multicenter study, patients who had previously untreated DME, who received intravitreal ranibizumab (IVR) or aflibercept (IVA) and/or steroid treatment with the pro re nata (PRN) treatment regimen after a 3-month loading dose, and had a 12-month follow-up in the MARMASIA Study Group were included. Morphological patterns of DME were divided into four groups based on OCT features diffuse/spongious edema (Group 1), cystoid edema (Group 2), diffuse/spongious edema+subretinal fluid (SRF) (Group 3), and cystoid edema+SRF (Group 4). Changes in central macular thickness (CMT) and best-corrected visual acuity (BCVA) at months 3, 6, and 12, and the number of injections at month 12 were compared between the DME groups. RESULTS: 455 eyes of 299 patients were included in the study. The mean baseline BCVAs [Logarithm of the Minimum Angle of Resolution (logMAR)] in groups 1, 2, 3, and 4 were 0.54 ± 0.24, 0.52 ± 0.25, 0.55 ± 0.23, and 0.57 ± 0.27, respectively. There was no significant difference between the baseline mean BCVAs between the groups (p = .35). The mean BCVAs were significantly improved to 0,47 ± 0,33 in group 1, 0,42 ± 0,33 in group 2, 0,47 ± 0,31 in group 3, and 0,45 ± 0,43 at month 12. There was no significant difference between the groups in terms of BCVA change at month 12 (p = .71). The mean baseline CMTs in groups 1, 2, 3, and 4 were 387,19 ± 128,19, 447,02 ± 132,39, 449,12 ± 109,24, and 544,19 ± 178,61, respectively. At baseline, the mean CMT was significantly higher in Group 4 than in the other groups (p = .000). The mean CMTs were significantly decreased to 325,16 ± 97,55, 334,94 ± 115,99, 324,33 ± 79,20, and 332,08 ± 150,40 in four groups at month 12 respectively (p > .05). The groups had no significant difference in mean CMT at month 12 (p = .835). The change in CMT was significantly higher in Group 4 than in the other groups at month 12 (p = .000). The mean number of intravitreal anti-VEGF injections at month 12 was 4.51 ± 1.57 in Group 1, 4.63 ± 1.54 in Group 2, 4.88 ± 1.38 in Group 3, and 5.07 ± 1.49 in Group 4. The mean number of anti-VEGF injections in Group 1 and Group 2 was significantly lower than in Group 4 (p = 0,014 and p = 0,017). CONCLUSIONS: In real life, there was no significant difference between the DME groups in terms of visual improvement at month 12. However, better anatomical improvement was achieved in Group 4 than in the other DME groups.

Association of serum lipid profile and other systemic risk factors with retinal hard exudates in diabetic retinopathy.

PURPOSE: Diabetic macular edema is one of the leading causes of vision loss across the world. Hard exudates at the macula can lead to structural abnormalities in the retina leading to irreversible vision loss. Systemic dyslipidemia and other modifiable risk factors when identified and treated early may help prevent substantial vision loss. The purpose of this study was to study the association between serum lipid levels and other systemic risk factors like hemoglobin, HbA1c, and serum creatinine with hard exudates and macular edema in patients with diabetic retinopathy. METHODS: It is a prospective cross-sectional study conducted in a tertiary health care center in South India. 96 patients having diabetic retinopathy with hard exudates were included. Modified Airlie house classification was used to grade the hard exudates. Blood investigations including serum lipid profile, hemoglobin, HbA1c, and serum creatinine were carried out. Central subfield macular thickness was measured using optical coherence tomography. RESULTS: 96 patients of type II DM with diabetic retinopathy were divided into three groups of hard exudates. A statistically significant correlation was observed between the severity of hard exudates and total cholesterol (p = 0.00), triglycerides (p = 0.00), LDL (p = 0.00), and VLDL (p = 0.00). HbA1c levels showed a statistically significant correlation with the severity of hard exudates (p = 0.09), no significant correlation was noted between hard exudates and hemoglobin levels (p = 0.27) and with serum creatinine (p = 0.612). A statistically significant association between CSMT and hard exudates (p = 0.00) was noted. CONCLUSION: In our study, we concluded that the severity of hard exudates is significantly associated with increasing levels of serum total cholesterol, triglycerides, LDL, VLDL, and HbA1c levels in type II DM patients presenting with diabetic retinopathy. The increasing duration of diabetes is significantly associated with increasing severity of hard exudates. Central subfield macular thickness increases with increasing severity of hard exudates in diabetic retinopathy.

Adherence to Periodic Dilated Eye Examinations and Its Determinants Among Nepalese Patients With Diagnosed Diabetes: A Single-Center Hospital-Based Analysis Using Health Belief Model.

Introduction: To find the adherence rate to periodic dilated eye examinations (DEEs) and its determinants among patients with diagnosed diabetes. Research Design and Methods: In this cross-sectional study of 165 participants with diagnosed diabetes (Type 1/2) attending a general hospital with a diabetes clinic, we explored perceptions of barriers and facilitators of DEE at the individual level using a framework adapted from the health belief model (HBM). Patients were compared using t tests for continuous data and chi-square tests for categorical data. Results: The rate of adherence to DEE (as defined by DEE within a year) was 62.4% (95% confidence interval [CI] = 55.0%-69.8%). The mean age of the patients was 56.81 (±13.29) years. We found that the mean benefit score was significantly higher, and the mean barrier score was significantly lower in those adhering to DEE (p < 0.001); but the susceptibility, severity, and self-efficacy scores were not significantly different. Furthermore, those under treatment for diabetes mellitus (DM), those with diabetic retinopathy (DR) in them or their family member, and those with DM duration of 1 year or less were significantly likely to adhere to DEE (p < 0.005). Additionally, those who had received advice for eye screening from their physicians were about 25 times more likely to adhere to DEE (95% CI =6.80-92.05) than those who were not advised. Conclusion: A larger proportion of people with diabetes did not adhere to periodic DEE. Benefits and barriers were found to be determinants in this population. Further exploration in a larger population and the use of HBM to increase adherence to periodic DEE can be tested by targeting behavioral counseling along with other traditional approaches.

Long-Term Impact of Diabetic Retinopathy on Response to Anti-VEGF Treatment in Neovascular AMD.

Purpose: To explore the long-term effect of diabetic retinopathy on response to anti-vascular endothelial growth factor (VEGF) treatment in age-related macular degeneration-associated type 1 macular neovascularization (MNV) using optical coherence tomography angiography (OCTA). Methods: A total of 45 eyes with exudative neovascular age-related macular degeneration (nAMD) with type 1 MNV were included in the analysis. Among them, 24 eyes of 24 patients had no history of diabetes mellitus (DM) in their anamnesis and were assigned to the Not Diabetic group; 21 eyes of 21 patients had mild diabetic retinopathy and were included in the Diabetic group. We considered the following outcome measures: (1) best-corrected visual acuity changes; (2) central macular thickness; (3) MNV lesion area; and (4) MNV flow area. The OCTA acquisitions were performed at the following time points: (1) baseline visit, which corresponded to the day before the first injection; (2) post-loading phase (LP), which was scheduled at 1 month after the last LP injection; and (3) 12-month follow-up visit. Results: All morphofunctional parameters showed a significant improvement after the LP and at the 12-month follow-up visit. Specifically, both the Diabetic group and the Not Diabetic group displayed a significant reduction of MNV lesion areas at both the post-LP assessment (P = 0.026 and P = 0.016, respectively) and the 12-month follow-up (P = 0.039 and P = 0.025, respectively). Similarly, the MNV flow area was significantly decreased in both the Diabetic group and the Not Diabetic group at the post-LP assessment (P < 0.001 and P = 0.012, respectively) and at the 12-month follow-up (P = 0.01 and P = 0.035, respectively) compared to baseline. A smaller reduction in the MNV lesion area was observed in the Diabetic group at both the post-LP evaluation (P = 0.015) and the 12-month follow-up (P = 0.032). No other significant differences were found between the groups for the other parameters (P > 0.05). Conclusions: Our results indicated that the Diabetic group exhibited a smaller reduction in MNV lesion area after 12 months of anti-VEGF treatment. This highlights the importance of considering diabetic retinopathy as a potential modifier of treatment outcomes in nAMD management, with DM serving as a crucial risk factor during anti-angiogenic treatment.

Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.

BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchmark against which the model's predictions are compared. OBJECTIVE: This study aimed to assess the accuracy of a custom deep learning (DL) algorithm on classifying diabetic retinopathy (DR) and further demonstrate how label errors may contribute to this assessment in a nationwide DR-screening program. METHODS: Fundus photographs from the Lifeline Express, a nationwide DR-screening program, were analyzed to identify the presence of referable DR using both (1) manual grading by National Health Service England-certificated graders and (2) a DL-based DR-screening algorithm with validated good lab performance. To assess the accuracy of labels, a random sample of images with disagreement between the DL algorithm and the labels was adjudicated by ophthalmologists who were masked to the previous grading results. The error rates of labels in this sample were then used to correct the number of negative and positive cases in the entire data set, serving as postcorrection labels. The DL algorithm's performance was evaluated against both pre- and postcorrection labels. RESULTS: The analysis included 736,083 images from 237,824 participants. The DL algorithm exhibited a gap between the real-world performance and the lab-reported performance in this nationwide data set, with a sensitivity increase of 12.5% (from 79.6% to 92.5%, P<.001) and a specificity increase of 6.9% (from 91.6% to 98.5%, P<.001). In the random sample, 63.6% (560/880) of negative images and 5.2% (140/2710) of positive images were misclassified in the precorrection human labels. High myopia was the primary reason for misclassifying non-DR images as referable DR images, while laser spots were predominantly responsible for misclassified referable cases. The estimated label error rate for the entire data set was 1.2%. The label correction was estimated to bring about a 12.5% enhancement in the estimated sensitivity of the DL algorithm (P<.001). CONCLUSIONS: Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.

Provision of care for diabetic retinopathy in New Zealand: are there ethnic disparities?

AIMS: Ethnic disparities have been observed in treatment at first specialist appointments across various specialties within New Zealand. This study aimed to examine documentation and treatment decisions for diabetic retinopathy by ethnicity. METHODS: Retrospective audit of first specialist diabetic retinopathy clinic appointments for 388 patients at the Department of Ophthalmology, Te Whatu Ora Te Toka Tumai Auckland. Multiple domains of care were assessed, including comprehensiveness of history taking, examination, investigations and treatment decisions. RESULTS: Europeans comprised 42%, Maori only 9.5%, Pacific peoples 13.19%, Asian 32.7% and Middle Eastern/Latin American/African in 2%. Maori patients were eligible for a significantly greater number of treatments (p=0.001). The comprehensiveness of history taking (p=0.809), examination (p=0.513), investigations (p=0.623) and proportion of eligible treatments provided (p=0.788) was similar but did not reach the gold standard of care across all ethnicities. CONCLUSIONS: The standard of care provided in first specialist appointments for diabetic retinopathy appear to be similar across all ethnic groups, although Maori were underrepresented and had a higher disease burden at presentation. Our data highlights the need to reduce barriers faced by Maori in accessing GP, optometry and retinopathy screening referrals in Auckland, and improving local consultation and treatment guidelines.

Physics-informed deep generative learning for quantitative assessment of the retina.

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.

Prevalence of diabetic retinopathy and its risk factors in rural patients with type 2 diabetes referring to Beijing Huairou Hospital, China.

BACKGROUND: China has the largest population of diabetic patients worldwide. A diverse population and regional discrepancy in access to health care and diabetes management may lead to unique risk factors for diabetic retinopathy (DR) in different regions of China. This study aimed to evaluate the prevalence and risk factors of DR in rural patients with type 2 diabetes. METHODS: This hospital-based cross-sectional study recruited a sample of 704 type 2 diabetic patients from rural areas referred to Beijing Huairou Hospital, China, from June 1, 2022, to June 1, 2023. The medical history, demographic information, and results of laboratory examinations of patients were collected and analyzed. The diagnosis of DR were performed by experienced ophthalmologists using mydriatic fundus photography. RESULTS: Out of all patients, 53.8% were male and 46.2% were female. The mean age of patients and duration of diabetes were 54.9 ± 13.0 and 6.2 ± 4.5 years, respectively. The DR prevalence was 16.8%. The independent risk factors for DR in multivariate analysis were diabetes duration > 10 years (OR = 9.16, 95%CI = 5.49-15.30), fasting plasma glucose ≥ 7.2 mmol/L (OR = 3.25, 95%CI = 1.42-7.42), glycosylated hemoglobin ≥ 7% (OR = 6.49, 95%CI = 2.59-16.23), hypertension (OR = 1.59, 95%CI = 1.05-2.40), hyperlipidemia (OR = 2.16, 95%CI = 1.30-3.59), diabetic nephropathy (OR = 1.95, 95%CI = 1.17-3.23), high uric acid level (OR = 3.57, 95%CI = 1.56-8.15), high albumin to creatinine ratio (OR = 2.48, 95%CI = 1.06-5.82), and insulin treatment (OR = 1.79, 95%CI = 1.12-2.88). CONCLUSIONS: This study evaluated the DR prevalence and its associated risk factors among type 2 diabetic patients from rural areas in Beijing's Huairou District, China. Paying attention to these risk factors may be useful in screening high-risk diabetic patients for DR and adopting early preventive and therapeutic interventions.

Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.

Purpose: This study aimed to investigate the impact of distinctive capillary-large vessel (CLV) analysis in optical coherence tomography angiography (OCTA) on the classification performance of diabetic retinopathy (DR). Methods: This multicenter study analyzed 212 OCTA images from 146 patients, including 28 controls, 36 diabetic patients without DR (NoDR), 31 with mild non-proliferative DR (NPDR), 28 with moderate NPDR, and 23 with severe NPDR. Quantitative features were derived from the whole image as well as the parafovea and perifovea regions. A support vector machine classifier was employed for DR classification. The accuracy and area under the receiver operating characteristic curve were used to evaluate the classification performance, utilizing features derived from the whole image and specific regions, both before and after CLV analysis. Results: Differential CLV analysis significantly improved OCTA classification of DR. In binary classifications, accuracy improved by 11.81%, rising from 77.45% to 89.26%, when utilizing whole image features. For multiclass classifications, accuracy increased by 7.55%, from 78.68% to 86.23%. Incorporating features from the whole image, parafovea, and perifovea further improved binary classification accuracy from 83.07% to 93.80%, and multiclass accuracy from 82.64% to 87.92%. Conclusions: This study demonstrated that feature changes in capillaries are more sensitive during DR progression, and CLV analysis can significantly improve DR classification performance by extracting features that are specific to large vessels and capillaries in OCTA. Incorporating regional features further improves DR classification accuracy. Differential CLV analysis promises better disease screening, diagnosis, and treatment outcome assessment.

Serum biomarkers for predicting microvascular complications of diabetes mellitus.

INTRODUCTION: Diabetic microvascular complications such as retinopathy, nephropathy, and neuropathy are primary causes of blindness, terminal renal failure, and neuropathic disorders in type 2 diabetes mellitus patients. Identifying reliable biomarkers promptly is pivotal for early detection and intervention in these severe complications. AREAS COVERED: This review offers a thorough examination of the latest research concerning serum biomarkers for the prediction and assessment of diabetic microvascular complications. It encompasses biomarkers associated with glycation, oxidative stress, inflammation, endothelial dysfunction, basement membrane thickening, angiogenesis, and thrombosis. The review also highlights the potential of emerging biomarkers, such as microRNAs and long non-coding RNAs. EXPERT OPINION: Serum biomarkers are emerging as valuable tools for the early assessment and therapeutic guidance of diabetic microvascular complications. The biomarkers identified not only reflect the underlying pathophysiology but also align with the extent of the disease. However, further validation across diverse populations and improvement of the practicality of these biomarkers in routine clinical practice are necessary. Pursuing these objectives is essential to advance early diagnosis, risk assessment, and individualized treatment regimens for those affected by diabetes.

Application of smartphone-based infrared thermography devices for ocular surface thermal imaging.

Infrared thermography (IRT) is a well-known imaging technique that provides a non-invasive displaying of the ocular surface temperature distribution. Currently, compact smartphone-based IRT devices, as well as special software for processing thermal images, have become available. The study aimed to determine the possible use of smartphone-based IRT devices for real-time ocular surface thermal imaging. This study involved 32 healthy individuals (64 eyes); 10 patients (10 eyes) with proliferative diabetic retinopathy (PDR) and absolute glaucoma; 10 patients (10 eyes) with PDR, who underwent vitreoretinal surgery. In all cases, simultaneous ocular surface IRT of both eyes was performed. In healthy individuals, the ocular surface temperature (OST) averaged 34.6 ± 0.8 °C and did not differ substantially between the paired eyes, in different age groups, and after pupil dilation. In our study, high intraocular pressure was accompanied by a decrease in OST in all cases. After vitreoretinal surgery in cases with confirmed subclinical inflammation, the OST was higher than the baseline and differed from that of the paired eye by more than 1.0 °C. These results highlight that smartphone-based IRT imaging could be useful for the non-invasive detection of OST asymmetry between paired eyes due to increased intraocular pressure or subclinical inflammation.

Potential Diagnostic Biomarkers of tRNA-Derived Small RNAs in PBMCs for Nonproliferative Diabetic Retinopathy in Patients With Type 2 Diabetes Mellitus.

Purpose: This study aimed to reveal the altered expressions of transfer RNA (tRNA)-derived small RNAs (tsRNAs) in peripheral blood mononuclear cells and identify potential diagnostic biomarkers for nonproliferative diabetic retinopathy (NPDR) from patients with type 2 diabetes mellitus. Methods: Fifty-three patients diagnosed with type 2 diabetes mellitus were enrolled, including 25 patients with NPDR and 28 patients without diabetic retinopathy (DR) as the control group. A small RNA microarray was performed to screen the differentially expressed tsRNAs. Reverse transcriptase quantitative polymerase chain reaction was used to validate the significantly altered tsRNAs in a screening cohort and a verification cohort. The target genes, their enriched functions, and signaling pathways were predicted by bioinformatics analyses. Results: In total, 668 upregulated and 485 downregulated tsRNAs were found in the NPDR group by microarray. Eight tsRNAs were validated preliminarily to be altered significantly by reverse transcriptase quantitative polymerase chain reaction, and their target genes were enriched in cellular macromolecule metabolic process and ubiquitin-mediated proteolysis. The verification experiments confirmed the increased levels of 5'tiRNA-35-PheGAA-8, tRF3-28-PheGAA-1, and tRF3b-PheGAA-6, and the decreased levels of mt-tRF3-19-ArgTCG, mt-tRF3-20-ArgTCG, and mt-tRF3-21-ArgTCG in patients with NPDR, which may serve as potential biomarkers with clinical significance. Conclusions: The study recognized the tsRNA expression changes in peripheral blood mononuclear cells from patients with NPDR and discovered potential diagnostic biomarkers that hold clinical significance. Translational Relevance: The significantly altered tsRNAs identified in the study may serve as potential diagnostic biomarkers for patients with NPDR as well as possible molecular targets of the occurrence and development of DR.

SURGICAL OUTCOMES IN CONCURRENT SICKLE CELL AND DIABETIC RETINOPATHY.

PURPOSE: To report the clinical features and outcomes in eyes that underwent vitreoretinal surgery for complications of concurrent sickle cell retinopathy and diabetic retinopathy. METHODS: Retrospective, consecutive case series of all eyes that underwent vitreoretinal surgery for complications secondary to concurrent sickle cell retinopathy and diabetic retinopathy between January 01, 2014, and December 31, 2021. RESULTS: The study included 20 eyes of 14 patients. Indications for surgery included tractional retinal detachment in 12 eyes (60%), combined tractional retinal detachment/rhegmatogenous retinal detachment in 6 eyes (30%), and vitreous hemorrhage in 2 eyes (10%). Pars plana vitrectomy was performed in all eyes. One eye received a scleral buckle at the same time as pars plana vitrectomy. There was no change in mean best-corrected visual acuity at the last follow-up examination (1.5 [20/678]) when compared with mean preoperative best-corrected visual acuity (1.6 [20/762], P = 0.83). Preoperative best-corrected visual acuity correlated with postoperative best-corrected visual acuity at the last follow-up examination in eyes with retinal detachment (r = 0.49, P = 0.04). Single operation anatomic success was achieved in 11 of 17 eyes (65%) with retinal detachment. CONCLUSION: Functional and anatomic outcomes after surgery in eyes with concurrent sickle cell retinopathy and diabetic retinopathy are relatively poor.

MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy.

Reliable automatic diagnosis of Diabetic Retinopathy (DR) and Macular Edema (ME) is an invaluable asset in improving the rate of monitored patients among at-risk populations and in enabling earlier treatments before the pathology progresses and threatens vision. However, the explainability of screening models is still an open question, and specifically designed datasets are required to support the research. We present MAPLES-DR (MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy), which contains, for 198 images of the MESSIDOR public fundus dataset, new diagnoses for DR and ME as well as new pixel-wise segmentation maps for 10 anatomical and pathological biomarkers related to DR. This paper documents the design choices and the annotation procedure that produced MAPLES-DR, discusses the interobserver variability and the overall quality of the annotations, and provides guidelines on using the dataset in a machine learning context.

Nutritional Risk Factors in Albuminuria and Retinopathy in Patients Newly Diagnosed With Type 2 Diabetes: A Cross-sectional Case Series Study.

BACKGROUND/AIM: Although nutritional risk factors for developing complications in type 2 diabetes mellitus (T2DM) have been examined, the effect of protein intake on nephropathy is debated, and there is little research on retinopathy. This cross-sectional case-series study aimed to examine the risk factors, including nutritional status, for complications in patients newly diagnosed with T2DM. PATIENTS AND METHODS: Fifty-four patients were recruited, based on the results of examinations of blood glucose and/or glycated hemoglobin level for T2DM. To evaluate nutritional status, blood and urine examinations were performed and the Food Frequency Questionnaire was administered. Two-way analysis of variance, Fisher's exact test and logistic regression analyses were performed. RESULTS: The patients were categorized into four groups: 24 without albuminuria and without retinopathy, four without albuminuria with retinopathy, 21 with albuminuria without retinopathy, and five with albuminuria with retinopathy. Logistic analysis of albuminuria revealed that estimated sodium intake was significantly independent as the explanatory factors of age, sex, and body mass index. Patients with retinopathy had significantly higher blood urea nitrogen, and significantly lower plasma total protein levels than patients without retinopathy, suggesting that retinopathy is related to a higher catabolic state. Through a questionnaire on food intake, patients with retinopathy had a significantly lower intake of fat and monounsaturated fatty acids and a significantly higher intake of iodine based on intake of seaweed, corrected for energy intake, than patients without retinopathy. CONCLUSION: The present study may lead to planning a large cohort study for examining nutritional risk factors related to complications in patients newly diagnosed with T2DM in Japan.