Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China

Underlying mechanisms of traditional Chinese medicine in the prevention and treatment of diabetic retinopathy: Evidences from molecular and clinical studies

As one of the most serious microvascular complications of diabetes mellitus (DM), diabetic retinopathy (DR) can cause visual impairment and even blindness. With the rapid increase in the prevalence of DM, the incidence of DR is also rising year by year. Preventing and effectively treating DR has become a major focus in the medical field. Traditional Chinese medicine (TCM) has a wealth of experience in treating DR and has achieved significant results with various herbs and TCM prescriptions. Traditional Chinese Medicine (TCM) provides a comprehensive therapeutic strategy for diabetic retinopathy (DR), encompassing anti-inflammatory and antioxidant actions, anti-neovascularization, neuroprotection, regulation of glucose metabolism, and inhibition of apoptosis. This review provides an overview of the current status of TCM treatment for DR in recent years, including experimental studies and clinical researches, to explore the clinical efficacy and the underlying modern mechanisms of herbs and TCM prescriptions. Besides, we also discussed the challenges TCM faces in treating DR, such as drug-drug interactions among TCM components and the lack of high-quality evidence-based medicine practice, which pose significant obstacles to TCM's application in DR.

Melatonin Attenuates Diabetic Retinopathy by Regulating EndMT of Retinal Vascular Endothelial Cells via Inhibiting the HDAC7/FOXO1/ZEB1 Axis

Diabetic retinopathy (DR) is characterized as a microvascular disease. Nonproliferative diabetic retinopathy (NPDR) presents with alterations in retinal blood flow and vascular permeability, thickening of the basement membrane, loss of pericytes, and formation of acellular capillaries. Endothelial-mesenchymal transition (EndMT) of retinal microvessels may play a critical role in advancing NPDR. Melatonin, a hormone primarily secreted by the pineal gland, is a promising therapeutic for DR. This study explored the EndMT in retinal microvessels of NPDR and its related mechanisms. The effect of melatonin on the retina of diabetic rats was evaluated by electroretinogram (ERG) and histopathologic slide staining. Furthermore, the effect of melatonin on human retinal microvascular endothelial cells (HRMECs) was detected by EdU incorporation assay, scratch assay, transwell assay, and tube formation test. Techniques such as RNA-sequencing, overexpression or knockdown of target genes, extraction of cytoplasmic and nuclear protein, co-immunoprecipitation (co-IP), and multiplex immunofluorescence facilitated the exploration of the mechanisms involved. Our findings reveal, for the first time, that melatonin attenuates diabetic retinopathy by regulating EndMT of retinal vascular endothelial cells via inhibiting the HDAC7/FOXO1/ZEB1 axis. Collectively, these results suggest that melatonin holds potential as a therapeutic strategy to reduce retinal vascular damage and protect vision in NPDR.

Advances in Extracellular-Vesicles-Based Diagnostic and Therapeutic Approaches for Ocular Diseases

Extracellular vesicles (EVs) are nanoscale membrane vesicles of various sizes that can be secreted by most cells. EVs contain a diverse array of cargo, including RNAs, lipids, proteins, and other molecules with functions of intercellular communication, immune modulation, and regulation of physiological and pathological processes. The biofluids in the eye, including tears, aqueous humor, and vitreous humor, are important sources for EV-based diagnosis of ocular disease. Because the molecular cargos may reflect the biology of their parental cells, EVs in these biofluids, as well as in the blood, have been recognized as promising candidates as biomarkers for early diagnosis of ocular disease. Moreover, EVs have also been used as therapeutics and targeted drug delivery nanocarriers in many ocular disorders because of their low immunogenicity and superior biocompatibility in nature. In this review, we provide an overview of the recent advances in the field of EV-based studies on the diagnosis and therapeutics of ocular disease. We summarized the origins of EVs applied in ocular disease, assessed different methods for EV isolation from ocular biofluid samples, highlighted bioengineering strategies of EVs as drug delivery systems, introduced the latest applications in the diagnosis and treatment of ocular disease, and presented their potential in the current clinical trials. Finally, we briefly discussed the challenges of EV-based studies in ocular disease and some issues of concern for better focusing on clinical translational studies of EVs in the future.

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.

Integrated image-based deep learning and language models for primary diabetes care

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Diabetic retinopathy identification based on multi-source-free domain adaptation

AIM

To address the challenges of data labeling difficulties, data privacy, and necessary large amount of labeled data for deep learning methods in diabetic retinopathy (DR) identification, the aim of this study is to develop a source-free domain adaptation (SFDA) method for efficient and effective DR identification from unlabeled data.

METHODS

A multi-SFDA method was proposed for DR identification. This method integrates multiple source models, which are trained from the same source domain, to generate synthetic pseudo labels for the unlabeled target domain. Besides, a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances. Validation is performed using three color fundus photograph datasets (APTOS2019, DDR, and EyePACS).

RESULTS

The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks. It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.

CONCLUSION

The multi-SFDA method provides an effective approach to overcome the challenges in DR identification. The method not only addresses difficulties in data labeling and privacy issues, but also reduces the need for large amounts of labeled data required by deep learning methods, making it a practical tool for early detection and preservation of vision in diabetic patients.

Comprehensive insights into a decade-long journey: The evolution, impact, and human factors of an asynchronous telemedicine program for diabetic retinopathy screening in Pennsylvania, United States

Diabetic Retinopathy stands as a leading cause of irreversible blindness, necessitating frequent examinations, especially in the early stages where effective treatments are available. However, current examination rates vary widely, ranging from 25-60%. This study scrutinizes the Point-of-Care Diabetic Retinopathy Examination Program at the University of Pittsburgh/UPMC, delving into its composition, evolution, challenges, solutions, and improvement opportunities. Employing a narrative approach, insights are gathered from key stakeholders, including ophthalmologists and staff from primary care clinics. A quantitative analysis from 2008 to 2020 provides a comprehensive overview of program outcomes, covering 94 primary care offices with 51 retinal cameras. Program components feature automated non-mydriatic 45° retinal cameras, a dedicated coordinator, rigorous training, and standardized workflows. Over this period, the program conducted 21,960 exams in 16,458 unique individuals, revealing a diverse population with an average age of 58.5 and a balanced gender distribution. Average body mass index (33.96±8.02 kg/m2) and hemoglobin A1c (7.58%±1.88%) surpassed normal ranges, indicating prevalent risk factors for diabetes-related complications. Notably, 24.2% of patients underwent more than one exam, emphasizing program engagement. Findings indicated that 86.3% of exams were gradable, with 59.0% within normal limits, 12.1% showing some evidence of diabetic retinopathy, and 6.4% exhibiting vision-threatening diabetic retinopathy. Follow-up appointments with ophthalmologists were recommended in 31.5% of exams due to indeterminate results, positive diabetic retinopathy (≥moderate or macular exudate), or other findings like age-related macular degeneration or suspected glaucoma. The program demonstrated high reproducibility across diverse healthcare settings, featuring a sustainable model with minimal camera downtime, standardized workflows, and financial support from grants, health systems, and clinical revenues. Despite COVID-19 pandemic challenges, this research emphasizes the program's reproducibility, user-friendly evolution, and promising outcomes. Beyond technical contributions, it highlights human factors influencing program success. Future research could explore adherence to follow-up ophthalmological recommendations and its associated factors.

Association between neutrophil-to-lymphocyte ratio and diabetic retinopathy in patients with type 2 diabetes: a cohort study

Background

Chronic inflammation is implicated in the development of diabetic retinopathy (DR). The neutrophil-to-lymphocyte ratio (NLR) is a marker of systemic inflammation that has been linked to cardiovascular and diabetic kidney diseases. However, the link between NLR and DR remains unclear. As such, this study investigated the association between NLR and DR in Chinese patients.

Method

A total of 857 adults diagnosed with type 2 diabetes mellitus (T2DM) without DR at baseline between 2018 and 2021, from a single center in Ningbo, China, were included. Baseline clinical data, including age, sex, T2DM duration, hypertension, smoking, drinking, glycated hemoglobin level, lipid profile, renal function, and NLR, were recorded and analyzed. Cox proportional hazard regression analysis was used to assess the association between NLR and the risk for incident DR.

Results

During a median follow-up of 3.0 years, 140 patients developed DR. The multivariable-adjusted hazard ratio (HR) for incident DR across ascending NLR quartiles (≤1.46 [reference], 1.47-1.90, 1.91-2.45 and > 2.45) were 1.000, 1.327 (95% confidence interval [CI] 0.754-2.334), 1.555 (95% CI 0.913-2.648) and 2.217 (95% CI 1.348-3.649), respectively. For each 1-standard deviation increase in NLR, the risk for DR increased by 29.2% (HR 1.292 [95% CI 1.112-1.501) after adjusting for confounding factors.

Conclusion

Results revealed that a higher NLR at baseline was associated with an increased risk for incident DR. NLR has the potential to be an inexpensive, reliable, and valuable clinical measure that merits further exploration in future studies.

Upregulation of HMOX1 associated with M2 macrophage infiltration and ferroptosis in proliferative diabetic retinopathy

The roles of immune cell infiltration and ferroptosis in the progression of proliferative diabetic retinopathy (PDR) remain unclear. To identify upregulated molecules associated with immune infiltration and ferroptosis in PDR, GSE60436 and GSE102485 datasets were downloaded from the Gene Expression Omnibus (GEO). Genes associated with immune cell infiltration were examined through Weighted Gene Co-expression Network Analysis (WGCNA) and CIBERSORT algorithm. Common differentially expressed genes (DEGs) were intersected with ferroptosis-associated and immune cell infiltration-related genes. Localization of cellular expression was confirmed by single-cell analysis of GSE165784 dataset. Findings were validated by qRT-PCR, ELISA, Western blotting, and immunofluorescence staining. As a result, the infiltration of M2 macrophages was significantly elevated in fibrovascular membrane samples from PDR patients than the retinas of control subjects. Analysis of DEGs, M2 macrophage-related genes and ferroptosis-related genes identified three hub intersecting genes, TP53, HMOX1 and PPARA. qRT-PCR showed that HMOX1 was significantly higher in the oxygen-induced retinopathy (OIR) mouse model retinas than in controls. Single-cell analysis confirmed that HMOX1 was located in M2 macrophages. ELISA and western blotting revealed elevated levels of HMOX1 in the vitreous humor of PDR patients and OIR retinas, and immunofluorescence staining showed that HMOX1 co-localized with M2 macrophages in the retinas of OIR mice. This study offers novel insights into the mechanisms associated with immune cell infiltration and ferroptosis in PDR. HMOX1 expression correlated with M2 macrophage infiltration and ferroptosis, which may play a crucial role in PDR pathogenesis.

Exploring histone deacetylases in type 2 diabetes mellitus: pathophysiological insights and therapeutic avenues

Diabetes mellitus is a chronic disease that impairs metabolism, and its prevalence has reached an epidemic proportion globally. Most people affected are with type 2 diabetes mellitus (T2DM), which is caused by a decline in the numbers or functioning of pancreatic endocrine islet cells, specifically the β-cells that release insulin in sufficient quantity to overcome any insulin resistance of the metabolic tissues. Genetic and epigenetic factors have been implicated as the main contributors to the T2DM. Epigenetic modifiers, histone deacetylases (HDACs), are enzymes that remove acetyl groups from histones and play an important role in a variety of molecular processes, including pancreatic cell destiny, insulin release, insulin production, insulin signalling, and glucose metabolism. HDACs also govern other regulatory processes related to diabetes, such as oxidative stress, inflammation, apoptosis, and fibrosis, revealed by network and functional analysis. This review explains the current understanding of the function of HDACs in diabetic pathophysiology, the inhibitory role of various HDAC inhibitors (HDACi), and their functional importance as biomarkers and possible therapeutic targets for T2DM. While their role in T2DM is still emerging, a better understanding of the role of HDACi may be relevant in improving insulin sensitivity, protecting β-cells and reducing T2DM-associated complications, among others.

Association of sudomotor dysfunction with risk of diabetic retinopathy in patients with type 2 diabetes

PURPOSE

Sudomotor dysfunction is considered as one of the earliest manifestations of diabetic peripheral neuropathy. We aimed to investigate the association between sudomotor dysfunction non-invasively detected by the SUDOSCAN device and diabetic retinopathy (DR) in patients with type 2 diabetes.

METHODS

A total of 2010 patients admitted to a tertiary hospital located in Shanghai were included from March 2020 to September 2023. Sudomotor function was assessed by the SUDOSCAN device, and sudomotor dysfunction was defined as feet electrochemical skin conductance (FESC) <60 μs. Fundus radiography was used for DR assessment, which was graded according to the severity, specifically: (1) non-DR; (2) mild nonproliferative DR (NPDR); (3) moderate NPDR/vision-threatening DR (VTDR).

RESULTS

Among the enrolled 2010 patients, 525 patients had sudomotor dysfunction; 648 were diagnosed with DR, which was equivalent to 32.2% of all patients. Patients with sudomotor dysfunction had a significantly higher prevalence of DR, compared to those with normal sudomotor function (40.8% vs. 29.2%, P < 0.05). After controlling for confounding factors including HbA1c, sudomotor dysfunction was significantly associated with any DR (odd ratio [OR] = 1.57, 95% CI 1.26-1.96). When FESC was considered as a continuous variable, the multivariable-adjusted OR of DR was 1.29 (95% CI 1.17-1.42) for per 1-SD decrease in FESC. Furthermore, multinomial logistic regression revealed significant associations between sudomotor dysfunction and all stages of DR (mild NPDR: OR = 1.40, 95% CI 1.11-1.78; moderate NPDR/VTDR: OR = 2.35, 95% CI 1.60-3.46).

CONCLUSIONS

Sudomotor dysfunction was significantly associated with DR in patients with type 2 diabetes.

Association between long-term exposure to air pollution and diabetic retinopathy: Evidence from the Fujian Eye Study

BACKGROUND

Diabetic retinopathy (DR), one of the most common microvascular complications of diabetes mellitus (DM), is a major contributor of vision impairment and blindness worldwide. Studies have shown that air pollution exposure is adversely associated with DM. However, evidence is scarce regarding how air pollution exposure affects DR. This study aimed to investigate the association between ambient air pollution exposure and DR risk.

METHODS

The study population was based on the Fujian Eye Study (FJES), an ophthalmologic, epidemiologic survey investigating the eye health condition of residents in Fujian Province from 2018 to 2019. Daily average concentrations of ambient air pollutants (PM2.5, PM10, SO2, NO2, and O3) were acquired from a high-resolution air quality dataset in China from 2013 to 2018. We used a logistic regression model to examine the associations between DR risk and long-term air pollution at various exposure windows.

RESULTS

A total of 2405 out of the 8211 participants were diagnosed with diabetes, among whom 183 had DR. Ambient air pollution, especially particulate matter (i.e., PM2.5 and PM10) and NO2 were positively associated with DR prevalence among all the study subjects. Ambient SO2 and O3 concentrations were not associated with DR prevalence. PM2.5 and NO2 seemed to be borderline significantly associated with increased prevalence of DR in subjects with DM, especially under the model adjusted for sex, age, BMI, SBP, and DBP.

CONCLUSIONS

These findings showed that long-term exposure to ambient particulate matter and NO2 was associated with a high DR risk in Fujian province, where ambient air pollution is relatively low.

Gut microbiota induced abnormal amino acids and their correlation with diabetic retinopathy

AIM

To explore the correlation of gut microbiota and the metabolites with the progression of diabetic retinopathy (DR) and provide a novel strategy to elucidate the pathological mechanism of DR.

METHODS

The fecal samples from 32 type 2 diabetes patients with proliferative retinopathy (PDR), 23 with non-proliferative retinopathy (NPDR), 27 without retinopathy (DM), and 29 from the sex-, age- and BMI- matched healthy controls (29 HC) were analyzed by 16S rDNA gene sequencing. Sixty fecal samples from PDR, DM, and HC groups were assayed by untargeted metabolomics. Fecal metabolites were measured using liquid chromatography-mass spectrometry (LC-MS) analysis. Associations between gut microbiota and fecal metabolites were analyzed.

RESULTS

A cluster of 2 microbiome and 12 metabolites accompanied with the severity of DR, and the close correlation of the disease progression with PDR-related microbiome and metabolites were found. To be specific, the structure of gut microbiota differed in four groups. Diversity and richness of gut microbiota were significantly lower in PDR and NPDR groups, than those in DM and HC groups. A cluster of microbiome enriched in PDR group, including Pseudomonas, Ruminococcaceae-UCG-002, Ruminococcaceae-UCG-005, Christensenellaceae-R-7, was observed. Functional analysis showed that the glucose and nicotinate degradations were significantly higher in PDR group than those in HC group. Arginine, serine, ornithine, and arachidonic acid were significantly enriched in PDR group, while proline was enriched in HC group. Functional analysis illustrated that arginine biosynthesis, lysine degradation, histidine catabolism, central carbon catabolism in cancer, D-arginine and D-ornithine catabolism were elevated in PDR group. Correlation analysis revealed that Ruminococcaceae-UCG-002 and Christensenellaceae-R-7 were positively associated with L-arginine, ornithine levels in fecal samples.

CONCLUSION

This study elaborates the different microbiota structure in the gut from four groups. The relative abundance of Ruminococcaceae-UCG-002 and Parabacteroides are associated with the severity of DR. Amino acid and fatty acid catabolism is especially disordered in PDR group. This may help provide a novel diagnostic parameter for DR, especially PDR.

Association between blood urea nitrogen levels and diabetic retinopathy in diabetic adults in the United States (NHANES 2005-2018)

Objective

To investigate the association between blood urea nitrogen (BUN) levels and diabetic retinopathy (DR) in adults with diabetes mellitus (DM).

Methods

Seven cycles of cross-sectional population information acquired from NHANES(national health and nutrition examination surveys) 2005-2018 were collected, from which a sample of diabetic adults was screened and separated into two groups based on whether or not they had DR, followed by weighted multivariate regression analysis. This study collected a complete set of demographic, biological, and sociological risk factor indicators for DR. Demographic risk factors comprised age, gender, and ethnicity, while biological risk factors included blood count, blood pressure, BMI, waist circumference, and glycated hemoglobin. Sociological risk factors included education level, deprivation index, smoking status, and alcohol consumption.

Results

The multiple regression model revealed a significant connection between BUN levels and DR [odds ratio =1.04, 95% confidence interval (1.03-1.05), p-value <0.0001],accounting for numerous variables. After equating BUN levels into four groups, multiple regression modeling showed the highest quartile (BUN>20 mg/dl) was 2.22 times more likely to develop DR than the lowest quartile [odds ratio =2.22, 95% confidence interval (1.69-2.93), p- value <0.0001]. Subgroup analyses revealed that gender, race, diabetes subtype, and duration of diabetes had a regulating effect on the relationship between BUN and DR.

Conclusion

BUN levels were related with an increased prevalence of DR, particularly in individuals with BUN >20 mg/dl. These findings highlight the significance of BUN level in assessing the risk of DR.

Genetic Association between Lipid-Regulating Drug Targets and Diabetic Retinopathy: A Drug Target Mendelian Randomization Study

Background

Diabetic retinopathy (DR) is a diabetic microvascular complication and a leading cause of vision loss. However, there is a lack of effective strategies to reduce the risk of DR currently. The present study is aimed at assessing the causal effect of lipid-regulating targets on DR risk using a two-sample Mendelian randomization (MR) study.

Method

Genetic variants within or near drug target genes, including eight lipid-regulating targets for LDL-C (HMGCR, PCSK9, and NPC1L1), HDL-C (CETP, SCARB1, and PPARG), and TG (PPARA and LPL), were selected as exposures. The exposure data were obtained from the IEU OpenGWAS project. The outcome dataset related to DR was obtained from the FinnGen research project. Inverse-variance-weighted MR (IVW-MR) was used to calculate the effect estimates by each target. Sensitivity analyses were performed to verify the robustness of the results.

Results

There was suggestive evidence that PCSK9-mediated LDL-C levels were positively associated with DR, with OR (95% CI) of 1.34 (1.02-1.77). No significant association was found between the expression of HMGCR- and NPC1L1-mediated LDL-C levels; CETP-, SCARB1-, and PPARG-mediated HDL-C levels; PPARA- and LPL-mediated TG levels; and DR risk.

Conclusions

This is the first study to reveal a genetically causal relationship between lipid-regulating drug targets and DR risk. PCSK9-mediated LDL-C levels maybe positively associated with DR risk at the genetic level. This study provides suggestive evidence that PCSK9 inhibition may reduce the risk of DR.

Early change of retinal nerve fiber layer in children with type 1 diabetes mellitus in northern China

OBJECTIVES

This study aimed to identify discrepancies in the retinal nerve fiber layer (RNFL) between type 1 diabetes mellitus (T1DM) children without retinopathy and healthy subjects in northern China.

METHODS

This was a cross-sectional hospital-based study carried out from Jan 2019 until Jul 2021 at the department of pediatrics in Tianjin medical university general hospital. Children with T1DM but no retinal disease were screened. RNFL thickness was obtained via spectral domain optical coherence tomography. Disease duration, HbA1c, 25-hydroxyvitamin D level, insulin regimen, and diet control status were also collected.

RESULTS

A total of 20 children with T1DM and 20 matched health participants were enrolled. The mean age in the T1DM group was 10.3 ± 2.8 years, and the median duration of diabetes was 1 (range 1-3) year. Children with T1DM had thinner average RNFL than control subjects (105 ± 6 vs. 110 ± 11 μm, p=0.008), especially in temporal and nasal parts. There was a significant negative association between HbA1c levels and the RNFL thickness in the T1DM group (B (95 % confidence interval): -4.313 (-7.055 to -1.571); p=0.005).

CONCLUSIONS

In our study, the decreased thickness of RNFL was negatively associated with elevated HbA1c in children with early stages of T1DM.

Ferric particle-assisted LDI-MS platform for metabolic fingerprinting of diabetic retinopathy

OBJECTIVES

To explore the metabolic fingerprints of diabetic retinopathy (DR) in individuals with type 2 diabetes using a newly-developed laser desorption/ionization mass spectrometry (LDI-MS) platform assisted by ferric particles.

METHODS

Metabolic fingerprinting was performed using a ferric particle-assisted LDI-MS platform. A nested population-based case-control study was performed on 216 DR cases and 216 control individuals with type 2 diabetes.

RESULTS

DR cases and control individuals with type 2 diabetes were comparable for a list of clinical factors. The newly-developed LDI-MS platform allowed us to draw the blueprint of plasma metabolic fingerprints from participants with and without DR. The neural network afforded diagnostic performance with an average area under curve value of 0.928 for discovery cohort and 0.905 for validation cohort (95 % confidence interval: 0.902-0.954 and 0.845-0.965, respectively). Tandem MS and Fourier transform ion cyclotron resonance MS with ultrahigh resolution identified seven specific metabolites that were significantly associated with DR in fully adjusted models. Of these metabolites, dihydrobiopterin, phosphoserine, N-arachidonoylglycine, and 3-methylhistamine levels in plasma were first reported to show the associations.

CONCLUSIONS

This work advances the design of metabolic analysis for DR and holds the potential to promise as an efficient tool for clinical management of DR.

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification

Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.

New concepts drive the development of delivery tools for sustainable treatment of diabetic complications

Diabetic complications, especially diabetic retinopathy, diabetic nephropathy and painful diabetic neuropathy, account for a large portion of patients with diabetes and display rising global prevalence. They are the leading causes of blindness, kidney failure and hypersensitivity to pain caused by diabetes. Current approved therapeutics against the diabetic complications are few and exhibit limited efficacy. The enhanced cell-specificity, stability, biocompatibility, and loading capacity of drugs are essential for the mitigation of diabetic complications. In the article, we have critically discussed the recent studies over the past two years in material sciences and biochemistry. The insightful concepts in these studies drive the development of novel nanoparticles and mesenchymal stem cells-derived extracellular vesicles to meet the need for treatment of diabetic complications. Their underlying biochemical principles, advantages and limitations have been in-depth analyzed. The nanoparticles discussed in the article include double-headed nanodelivery system, nanozyme, ESC-HCM-B system, soft polymer nanostars, tetrahedral DNA nanostructures and hydrogels. They ameliorate the diabetic complication through attenuation of inflammation, apoptosis and restoration of metabolic homeostasis. Moreover, mesenchymal stem cell-derived extracellular vesicles efficiently deliver therapeutic proteins to the retinal cells to suppress the angiogenesis, inflammation, apoptosis and oxidative stress to reverse diabetic retinopathy. Collectively, we provide a critical discussion on the concept, mechanism and therapeutic applicability of new delivery tools to treat these three devastating diabetic complications.

Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions

Diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) pose significant global health challenges, often resulting in vision impairment and blindness. Automatic detection of these conditions is crucial, particularly in underserved rural areas with limited access to ophthalmic services. Despite remarkable advancements in artificial intelligence, especially convolutional neural networks (CNNs), their complexity can make interpretation difficult. In this study, we curated a dataset consisting of 15,089 color fundus photographs (CFPs) obtained from 8110 patients who underwent fundus fluorescein angiography (FFA) examination. The primary objective was to construct integrated models that merge CNNs with an attention mechanism. These models were designed for a hierarchical multilabel classification task, focusing on the detection of DR, RVO, AMD, and other fundus conditions. Furthermore, our approach extended to the detailed classification of DR, RVO, and AMD according to their respective subclasses. We employed a methodology that entails the translation of diagnostic information obtained from FFA results into CFPs. Our investigation focused on evaluating the models' ability to achieve precise diagnoses solely based on CFPs. Remarkably, our models showcased improvements across diverse fundus conditions, with the ConvNeXt-base + attention model standing out for its exceptional performance. The ConvNeXt-base + attention model achieved remarkable metrics, including an area under the receiver operating characteristic curve (AUC) of 0.943, a referable F1 score of 0.870, and a Cohen's kappa of 0.778 for DR detection. For RVO, it attained an AUC of 0.960, a referable F1 score of 0.854, and a Cohen's kappa of 0.819. Furthermore, in AMD detection, the model achieved an AUC of 0.959, an F1 score of 0.727, and a Cohen's kappa of 0.686. Impressively, the model demonstrated proficiency in subclassifying RVO and AMD, showcasing commendable sensitivity and specificity. Moreover, our models enhanced interpretability by visualizing attention weights on fundus images, aiding in the identification of disease findings. These outcomes underscore the substantial impact of our models in advancing the detection of DR, RVO, and AMD, offering the potential for improved patient outcomes and positively influencing the healthcare landscape.

Dissecting causal associations of type 2 diabetes with 111 types of ocular conditions: a Mendelian randomization study

Background

Despite the well-established findings of a higher incidence of retina-related eye diseases in patients with diabetes, there is less investigation into the causal relationship between diabetes and non-retinal eye conditions, such as age-related cataracts and glaucoma.

Methods

We performed Mendelian randomization (MR) analysis to examine the causal relationship between type 2 diabetes mellitus (T2DM) and 111 ocular diseases. We employed a set of 184 single nucleotide polymorphisms (SNPs) that reached genome-wide significance as instrumental variables (IVs). The primary analysis utilized the inverse variance-weighted (IVW) method, with MR-Egger and weighted median (WM) methods serving as supplementary analyses.

Results

The results revealed suggestive positive causal relationships between T2DM and various ocular conditions, including "Senile cataract" (OR= 1.07; 95% CI: 1.03, 1.11; P=7.77×10-4), "Glaucoma" (OR= 1.08; 95% CI: 1.02, 1.13; P=4.81×10-3), and "Disorders of optic nerve and visual pathways" (OR= 1.10; 95% CI: 0.99, 1.23; P=7.01×10-2).

Conclusion

Our evidence supports a causal relationship between T2DM and specific ocular disorders. This provides a basis for further research on the importance of T2DM management and prevention strategies in maintaining ocular health.