Diabetic Retinopathy Disease Research Articles

Assessment of acceptance of Ayurveda Visual Science Medications: Revealing unmet needs

Ayurvedic Visual Science (AVS) has served the nation since Rajashri Nimi, the King of Videha, well documented in Susruta Samhita in 800-600BC. Vision is the most essential sensory function of humans. Loss of vision is considered the highest disability in the general population. Still the unmet needs in ophthalmic research include glaucoma, retinal dystrophies, diabetic retinopathy (DR), retinitis pigmentation (RP), dry eye, progressive myopia, macular degeneration, and corneal diseases. There is no solution of many disorders in spite of the great invention in modern diagnosis and treatment. AVS has progressed tremendously in treating diabetic retinopathy, macular degeneration, retinitis pigmentosa and other incurable diseases. The Ayurvedic ocular medication comprises oral medication, Panchakarma and Kriya kalpa(Ocular procedures) to treat eye diseases holistically after factoring in the entire health profile of patients. A small cross-sectional study was conducted at Sri Sri Ayurveda Medical College, Bangalore, and 885 patients were included, nearly 02% of the total OPD strength of Shalya & Shalakya OPD. Maximum patients are males between the ages of 51-60 years. The most accepted group for Ayurveda Ocular treatment suffered from progressive myopia (26%), followed by 24% of patients with diabetic retinopathy and 13% with chronic conjunctivitis and Age-related macular degeneration. A consensus roadmap from AYUSH should address the unmet need through Ayurveda visual science supported by tangible clinical outcomes.

Interpretable prediction model for assessing diabetes complication risks in Chinese sufferers

AimsWith growing concerns over complications in diabetes sufferers, this study sought to develop an interpretable machine learning model to offer enhanced diagnostic and treatment recommendations. MethodsWe assessed coronary heart disease, diabetic nephropathy, diabetic retinopathy, and fatty liver disease using logistic regression, decision tree, random forest, and CatBoost algorithms. The SHAP algorithm was employed to elucidate the model's predictions, offering a more in-depth understanding of influential features. ResultsThe CatBoost model notably outperformed other algorithms in AUC, achieving an average AUC of 90.47 % for the four complications. Through SHAP analysis and visualization, we provided clear and actionable insights into risk factors, enabling better complication risk assessment. ConclusionsWe introduced an innovative, interpretable complication risk model for people with diabetes. This not only offers a potent tool for healthcare professionals but also empowers sufferers with clearer self-assessment capabilities, encouraging earlier preventive actions. Further studies will underscore the model's clinical applicability.

Diabetic retinopathy disease detection using shapley additive ensembled densenet-121 resnet-50 model

Diabetic retinopathy disease detection using shapley additive ensembled densenet-121 resnet-50 model

Heparanase inhibition as a systemic approach to protect the endothelial glycocalyx and prevent microvascular complications in diabetes

BackgroundDiabetes mellitus is a chronic disease which is detrimental to cardiovascular health, often leading to secondary microvascular complications, with huge global health implications. Therapeutic interventions that can be applied to multiple vascular beds are urgently needed. Diabetic retinopathy (DR) and diabetic kidney disease (DKD) are characterised by early microvascular permeability changes which, if left untreated, lead to visual impairment and renal failure, respectively. The heparan sulphate cleaving enzyme, heparanase, has previously been shown to contribute to diabetic microvascular complications, but the common underlying mechanism which results in microvascular dysfunction in conditions such as DR and DKD has not been determined.MethodsIn this study, two mouse models of heparan sulphate depletion (enzymatic removal and genetic ablation by endothelial specific Exotosin-1 knock down) were utilized to investigate the impact of endothelial cell surface (i.e., endothelial glycocalyx) heparan sulphate loss on microvascular barrier function. Endothelial glycocalyx changes were measured using fluorescence microscopy or transmission electron microscopy. To measure the impact on barrier function, we used sodium fluorescein angiography in the eye and a glomerular albumin permeability assay in the kidney. A type 2 diabetic (T2D, db/db) mouse model was used to determine the therapeutic potential of preventing heparan sulphate damage using treatment with a novel heparanase inhibitor, OVZ/HS-1638. Endothelial glycocalyx changes were measured as above, and microvascular barrier function assessed by albumin extravasation in the eye and a glomerular permeability assay in the kidney.ResultsIn both models of heparan sulphate depletion, endothelial glycocalyx depth was reduced and retinal solute flux and glomerular albumin permeability was increased. T2D mice treated with OVZ/HS-1638 had improved endothelial glycocalyx measurements compared to vehicle treated T2D mice and were simultaneously protected from microvascular permeability changes associated with DR and DKD.ConclusionWe demonstrate that endothelial glycocalyx heparan sulphate plays a common mechanistic role in microvascular barrier function in the eye and kidney. Protecting the endothelial glycocalyx damage in diabetes, using the novel heparanase inhibitor OVZ/HS-1638, effectively prevents microvascular permeability changes associated with DR and DKD, demonstrating a novel systemic approach to address diabetic microvascular complications.

Diabetic Retinopathy and Cardiovascular Disease: A Literature Review [Response to Letter

Diabetic Retinopathy and Cardiovascular Disease: A Literature Review [Response to Letter

Comparison of quantification of intraretinal hard exudates between optical coherence tomography en face image versus fundus photography.

To compare the quantification of intraretinal hard exudate (HE) using en face optical coherence tomography (OCT) and fundus photography. Consecutive en face images and corresponding fundus photographs from 13 eyes of 10 patients with macular edema associated with diabetic retinopathy or Coats' disease were analyzed using the machine-learning-based image analysis tool, "ilastik." The overall measured HE area was greater with en face images than with fundus photos (en face: 0.49 ± 0.35 mm2 vs. fundus photo: 0.34 ± 0.34 mm2, P < 0.001). However, there was an excellent correlation between the two measurements (intraclass correlation coefficient [ICC] = 0.844). There was a negative correlation between HE area and central macular thickness (CMT) (r = -0.292, P = 0.001). However, HE area showed a positive correlation with CMT in the previous several months, especially in eyes treated with anti-vascular endothelial growth factor (VEGF) therapy (CMT 3 months before: r = 0.349, P = 0.001; CMT 4 months before: r = 0.287, P = 0.012). Intraretinal HE can be reliably quantified from either en face OCT images or fundus photography with the aid of an interactive machine learning-based image analysis tool. HE area changes lagged several months behind CMT changes, especially in eyes treated with anti-VEGF injections.

The role of the gut microbiota in the pathogenesis and therapy of type 2 diabetes mellitus

INTRODUCTION: Diabetes mellitus type II (DM2) is one of the most frequently diagnosed diseases worldwide with a constantly increasing prevalence. It represents an important clinical challenge. The studies are concentrating on improving our knowledge of both pathogenesis and therapeutic opportunities.&#x0D; The influence of intestinal flora on many health conditions is being investigated. It was found that the gut microbiota of the healthy and diabetic individuals are different. This provides a highly important focus for the consideration of whether and how, based on the content of the intestinal flora, the development and treatment of this disease and its complications can be affected.&#x0D; PURPOSE: The aim of the study is to present the current state of knowledge about the The role of the gut microbiota in the pathogenesis and therapy of type 2 diabetes.&#x0D; MATERIALS AND METHOD: The available literature in PubMed was reviewed to write the article, using the keywords ,,gut microbiota diabetes”, ,,diabetes mellitus”, ,,intestinal microbiome diabetes”, ,,dysbiosis diabetes” and ,,gut microbiota diabetes”.&#x0D; CONCLUSION: The intestinal microbiome is a diverse community of microorganisms that inhabit the gut and has been shown to be essential for the health of the body. Modifications in the diversity and frequency of microorganisms residing in the gut, and the accumulation of metabolites they generate, have been correlated with DM2. The study of intestinal microflora could lead to the discovery of new methods of diagnosis and therapies of DM2 and its complications, such as diabetic kidney disease, neuropathy, retinopathy and cardiovascular disease. In conclusion, further studies are needed to understand the role of the gut microbiome in the pathogenesis and therapy of DM2.

DIABETIC RETINOPATHY DETECTION USING RESNET101 AND DENSENET121.

This study presents an innovative approach to classifying Diabetic Retinopathy Disease (DRD) levels through the application of Graph Neural Networks (GNNs). By transforming retinal images into graph representations and extracting topological features, the proposed method aims to enhance the precision of DRD classification. GNNs are employed to leverage complex relationships within graph-structured data, offering a more nuanced understanding of retinal structures and disease progression. The results demonstrate that this approach significantly improves classification accuracy compared to traditional methods, providing valuable insights for early diagnosis and personalized treatment strategies. This advancement promises to contribute to more effective management of diabetic retinopathy and improved patient outcomes.

Diabetic Retinopathy and Cardiovascular Disease: A Literature Review [Letter

Diabetic Retinopathy and Cardiovascular Disease: A Literature Review [Letter

Fundus Image Classification for the Early Detection of Issues in the DR for the Effective Disease Diagnosis

Diabetic Retinopathy and fundus images, “DR” could also refer to Digital Retinography, which involves the digital imaging of the retina. Digital retinography allows for the storage, retrieval, and transmission of retinal images for analysis and diagnosis. It plays a crucial role in screening for diabetic retinopathy and other eye diseases. The use of artificial intelligence and machine learning algorithms in the analysis of fundus images has gained traction in recent years. These technologies can assist healthcare professionals in the early detection and monitoring of diabetic retinopathy by analyzing patterns and abnormalities in the retinal images. This paper introduces a novel approach to image analysis, combining Local Semantic Object Feature Extraction (LsOFE) and Gray Level Co-occurrence Matrix (GLCM) metrics for robust object recognition and classification. The LsOFE process captures intricate local features, enhancing the representation of objects in images. The subsequent GLCM-based feature extraction provides detailed insights into texture characteristics. The final classification step, utilizing LsOFE features, demonstrates high precision, recall, and F1-Score for most images, showcasing the effectiveness of the proposed methodology. However, challenges are identified in the classification of specific images, suggesting areas for improvement and refinement. Overall, this research contributes a valuable framework for image processing, with the potential for broader applications in fields such as computer vision and pattern recognition. The results presented underscore the promising performance of the proposed approach, offering a foundation for further exploration and optimization in the realm of image analysis.

Patients with diabetes in regions with population decline and likelihood of receiving diabetes management education and screenings for related complications in Korea

ObjectiveMany municipal districts in Korea face population decline owing to overall population decreases and aging. We investigated the association between geographic rurality and likelihood of receiving diabetes management education and diabetic retinopathy and diabetes-related kidney disease screenings among adults with diabetes. MethodsPatient data were obtained from the 2021 Korea Community Health Survey (27,391 individuals; aged ≥19 years and physician-diagnosed with diabetes mellitus). Geographic rurality was categorized based on population decline as follows: with, at risk of, or without population decline. The association between geographic rurality and likelihood of receiving diabetes management education and diabetic retinopathy and diabetes-related kidney disease screenings was examined using multilevel logistic regression analyses. ResultsAmong 27,391 patients with diabetes, 31.1% received diabetes education; 40.0% and 46.4% were screened for diabetic retinopathy and diabetes-related kidney disease, respectively. Individuals residing in regions with population decline were less likely to receive diabetes education (odds ratio [OR] 0.62, 95% CI 0.50–0.75) and diabetic retinopathy (OR 0.79, 95% CI 0.70–0.90) and diabetes-related kidney disease (OR 0.64, 95% CI 0.55–0.75) screenings, as compared with their counterparts. ConclusionsOur findings highlight the importance of increased monitoring and providing diabetes education and screenings for patients with diabetes living in rural areas.

An improved supervised and attention mechanism-based U-Net algorithm for retinal vessel segmentation

The segmentation results of retinal blood vessels are crucial for automatically diagnosing ophthalmic diseases such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular diseases. To improve the accuracy of vessel segmentation and better extract information about small vessels and edges, we introduce the U-Net algorithm with a supervised attention mechanism for retinal vessel segmentation. We achieve this by introducing a decoder fusion module (DFM) in the encoding part, effectively combining different convolutional blocks to extract features comprehensively. Additionally, in the decoding part of U-Net, we propose the context squeeze and excitation (CSE) decoding module to enhance important contextual feature information and the detection of tiny blood vessels. For the final output, we introduce the supervised fusion mechanism (SFM), which combines multiple branches from shallow to deep layers, effectively fusing multi-scale features and capturing information from different levels, fully integrating low-level and high-level features to improve segmentation performance. Our experimental results on the public datasets of DRIVE, STARE, and CHASED_B1 demonstrate the excellent performance of our proposed network.

Diabetic Retinopathy and Cardiovascular Disease: A Literature Review.

Diabetic complications can be divided into macrovascular complications such as cardiovascular disease and cerebrovascular disease and microvascular complications such as diabetic retinopathy, diabetic nephropathy and diabetic neuropathy. Among them, cardiovascular disease (CVD) is an important cause of death in diabetic patients. Diabetes retinopathy (DR) is one of the main reasons for the increasing disability rate of diabetes. In recent years, some studies have found that because DR and CVD have a common pathophysiological basis, the occurrence of DR and CVD are inseparable, and to a certain extent, DR can predict the occurrence of CVD. With the development of technology, the fundus parameters of DR can be quantitatively analyzed as an independent risk factor of CVD. In addition, the cytokines related to DR can also be used for early screening of DR. Although many advances have been made in the treatment of CVD, its situation of prevention and treatment is still not optimistic. This review hopes to discuss the feasibility of DR in predicting CVD from the common pathophysiological mechanism of DR and CVD, the new progress of diagnostic techniques for DR, and the biomarkers for early screening of DR.

Investigating the Blink Reflex Abnormality in Dry Eye Patients.

Introduction Dry eye is an ocular surface disorder caused by increased evaporation, decreased tear production, or mixed form. Tears are secreted by the lacrimal gland after lacrimal nerve stimulation connected to the facial nerve.In nerve damage, tear secretion decreases, and dry eye develops. Our aim is to investigate the presence of pathology in the facialtrigeminal nerveand neuronal pathways that provide reflex connections between these nerves by measuring the blink reflex in patients with dry eyes. Methods Schirmer test and tear breakup time were performed. Tear breakup time measurement was repeated three times, and the average of three was accepted. Tear breakup time<10 secondsand Schirmer test <10 mm without local anesthesia were accepted as dry eye. Patients having traumatic corneal pathology, ectatic corneal disease, inflammatory and microbial keratitis, previous ocular surgery, glaucoma, diabetic retinopathy, and chronic neurological diseases were excluded. The control group was randomly formed from 42 eyes of 21 healthy volunteers. Blink reflex was measured in both groups, and the R1 and R2 responses of the two groups were compared. Written consent was obtained from the patient (or legal guardian) so that her medical data could be published. Results There was no significant difference between the two groups in R1 and R2 responses in both eyes. There was no significant difference in terms of gender between the two groups (p=0.100). The mean age in the patient group was significantly higher than in the control group (p<0.000).The mean Schirmer test in the patient group was 8.6±1.1 mm in the right eye and 8.97±1.0 mm in the left eye. Conclusion There was no central pathology observed in terms of reflex blinking in dry eye disease.However, in future studies, brainstem fiesta sequence magnetic resonance imaging (MRI) can be planned to evaluate central pathologies in more detail.

Diabetic Retinopathy and Chronic Kidney Disease: Associations and Comorbidities in a Large Diabetic Population - The Tongren Health Care Study.

The aim of the study was to investigate associations between diabetic retinopathy (DR) and chronic kidney disease (CKD) in patients with type 2 diabetes (TD2). The participants of the cross-sectional, community-based Tongren Health Care Study underwent a detailed medical and ophthalmological examination. We defined TD2 by a fasting plasma glucose concentration of ≥7.0 mmol/L or a medical history. CKD was classified as either reduced estimated glomerular filtration rate (eGFR) of &lt;60 mL/min/1.73 mm2 or presence of albuminuria. DR was assessed using color fundus photographs. Out of 62,217 participants of the Tongren Health Care Study, 5,103 (8.2%) patients had TD2. The prevalence of DR was 12.8% (95% CI, 11.8%, 13.7%), CKD was 13.3% (95% CI, 12.4%, 14.3%), and the subtypes of CKD including reduced eGFR and albuminuria was 4.6% (95% CI, 4.2%, 5.1%) and 10.1% (95% CI, 9.3%, 10.9%), respectively. DR was detectable in 21.0% of the patients with CKD, while CKD was present in 20.9% of the DR patients. Higher DR prevalence was associated with higher prevalence of albuminuria and reduced eGFR (both p &lt; 0.05). Factors independently associated with the presence of CKD instead of DR were older age (p &lt; 0.001, OR = 1.05), a higher body mass index (p &lt; 0.001, OR = 1.14), a higher serum concentration of triglycerides (p &lt; 0.001, OR = 1.26), and a lower blood glucose (p &lt; 0.001, OR = 0.93). Having hypertension was additionally associated with the presence of reduced eGFR as compared with DR (p = 0.005, OR = 4.47). TD2 patients of older age and with higher body mass index, hypertension, and dyslipidemia had a higher probability of being affected by CKD rather than DR, while those with a higher blood glucose level were more prone to DR than CKD.

Improved ResNet_101 assisted attentional global transformer network for automated detection and classification of diabetic retinopathy disease

An essential complication of the diabetes disease is termed as Diabetic Retinopathy (DR), and it leads visual impairment in long-term vision. In general, DR is a kind of eye disease occurred by diabetes mellitus and also it is a general cause of blindness around the world. In this scenario, early detection of DR is more necessary to prevent people from being afflicted by blindness. Classification is difficult since there are so many different fundus imaging options, especially in Proliferative DR, which includes bleeding and the development of new arteries in the retina. The publicly accessible fundus image datasets from Kaggle (EyePACS) are used in this study together with real-time datasets for training and testing a transformer network on the datasets. The proposed model is used to develop an automated transformer network mechanism for classifying DR disease in earlier stages from the given inputs. The most crucial areas, including blood vessels, hemorrhage, soft exudates, and hard exudates from the pre-processed images are segmented using D2_UNet approach to make accurate classification. Finally, a new SE-ResCA-GTNet model is proposed to classify the varied severity of retinopathy disease from the given input images. Whereas, the Gazelle Optimization (GO) algorithm is used to fine-tune the proposed classifier to attain improved accuracy results. Using the fundus image dataset, the proposed SE-ResCA-GTNet classification model obtains accuracy, recall,and f1-measures of 99.8 %, 99.4 %, and 99.3 %. When compared to state-of-the-art methods, the experimental results demonstrate that the proposed method outperforms them.

Abstract 11425: Association Between Obstructive Coronary Disease and Diabetic Retinopathy: A Cross-Sectional Study of Coronary Angiotomography and Digital Retinal Images

Introduction: Diabetic retinopathy (DR) and obstructive coronary artery disease (OCAD) are the main microvascular and macrovascular complications of diabetes mellitus (DM), respectively. The assessment of DR by means of spectral domain optical coherence tomography (SD-OCT) and angiography (OCTA) conferred a remarkable progress. Objective: investigate if SD-OCT and OCTA can detect DR that is associated with OCAD, determined by coronary angiotomography (CCTA) Hypothesis: DR, assessed by OCTA and OCT-SD, is associated with OCAD, assessed by CCTA Methods: In this single-center, cross-sectional, single-blind study, patients with DM who had undergone CCTA in the previous 3 years (independent indication) were eligible. They were divided according to the presence of OCAD in two groups and underwent SD-OCT and OCTA. The association between OCAD and DR was evaluated through uni and multivariate analysis and another analysis was performed to identify variables independently associated with the presence of DR. Results: We included 171 patients, 87 OCAD and 84 without OCAD. Age (66 vs 64 y,p=0.28), DM duration (12 vs 13 y, p=0.5) and HbA1c (7.5% vs 6,9%,p=0.08) did not differ. Patients with OCAD were more frequently men (74% vs 38%, P&lt;0.01), had a higher prevalence of insulin use (52% vs 38%, p&lt;0.01) and 64% had already undergone some type of revascularization. SD-OCT and OCTA revealed a higher prevalence of DR (48% vs 22%,p=0.01). The main findings were: microaneurysms (25% vs 13%,p=0.04), intraretinal cysts (22% vs 8%, p=0.01) and areas of reduced capillary density in the superficial retinal plexus (46% vs 20%,p&lt;0.01).We also found lower mean vascular density (MVD) (15.7 vs 16.5, p=0.049) and lower circularity of the foveal avascular zone(FAZ) (0.64±0.1 vs 0.69±0.1, p= 0.04).There were significant and negative correlations between Duke coronary score and MVD (r=-0.189; p=0.03) and circularity of the FAZ (r=-0,206; p= 0.02). In an adjusted model, presence of OCAD increased the chance of DR (OR 4.05 [1.4 -11.6], p&lt;0.01). OCAD, DM duration and insulin use were independently associated to DR. Conclusions: OCAD was associated with a greater chance of presenting DR. OCAD, longer DM duration and insulin use were independently associated to the presence of DR.

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

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

Guideline-level monitoring, biomarker levels and pharmacological treatment in migrants and native Danes with type 2 diabetes: Population-wide analyses.

The prevalence of type 2 diabetes (T2D) is higher in migrants compared to native populations in many countries, but the evidence on disparities in T2D care in migrants is inconsistent. Therefore, this study aimed to examine this in Denmark. In a cross-sectional, register-based study on 254,097 individuals with T2D, 11 indicators of guideline-level care were analysed: a) monitoring: hemoglobin-A1c (HbA1c), low-density lipoprotein cholesterol (LDL-C), screening for diabetic nephropathy, retinopathy, and foot disease, b) biomarker control: HbA1c and LDL-C levels, and c) pharmacological treatment: glucose-lowering drugs (GLD), lipid-lowering drugs, angiotensin-converting enzyme-inhibitors/angiotensin receptor blockers, and antiplatelet therapy. Migrants were grouped by countries of origin: Middle East, Europe, Turkey, Former Yugoslavia, Pakistan, Sri Lanka, Somalia, Vietnam. In all migrant groups except the Europe-group, T2D was more prevalent than in native Danes (crude relative risk (RR) from 0.62 [0.61-0.64] (Europe) to 3.98 [3.82-4.14] (Sri Lanka)). In eight indicators, non-fulfillment was common (>25% among native Danes). Apart from monitoring in the Sri Lanka-group, migrants were at similar or higher risk of non-fulfillment than native Danes across all indicators of monitoring and biomarker control (RR from 0.64 [0.51-0.80] (HbA1c monitoring, Sri Lanka) to 1.78 [1.67-1.90] (LDL-C control, Somalia)), while no overall pattern was observed for pharmacological treatment (RR from 0.61 [0.46-0.80] (GLD, Sri Lanka) to 1.67 [1.34-2.09] (GLD, Somalia)). Care was poorest in migrants from Somalia, who had increased risk in all eleven indicators, and the highest risk in nine. Adjusted risks were elevated in some migrant groups, particularly in indicators of biomarker control (fully-adjusted RR from 0.84 [0.75-0.94] (LDL-C levels, Vietnam) to 1.44 [1.35-1.54] (LDL-C levels, Somalia)). In most migrant groups, T2D was more prevalent, and monitoring and biomarker control was inferior compared to native Danes. Migrants from Somalia received the poorest care overall, and had exceedingly high lipid levels.

UTILIZATION OF THE CERTAINTY FACTOR METHOD TO DIAGNOSE EYE DISEASES

In Indonesia, the number of people with eye disease increases every year. The blindness rate of the population in Indonesia is around 1.2% of the total population. People with this eye disease have problems ranging from mild to blind spots. The main causes of blindness are cataracts, corneal disorders, glaucoma, refractive errors, dry eyes, retinal disorders and nutritional disorders. Conjunctivitis, macular degeneration, diabetic retinopathy and other diseases that affect the eyes. As vision, it is necessary to keep the function of the eye from decreasing. As a person ages, the accommodation ability of the eye also decreases. The causes include sitting too long in front of a computer or staring at a cellphone screen for too long, reading a book at a distance that is not within a normal healthy distance, or the presence of dirty air and solar radiation.