Risk Factors For Diabetic Retinopathy Research Articles

Association of 18bp insertion/deletion polymorphism, at −2549 position of VEGF gene, with diabetic vascular complications in type 2 diabetes mellitus

PurposeDiabetes mellitus type 2 (T2DM) and its vascular complications are a serious world health problem. For this reason it is important to look for new diabetes complication risk factors. The aim of this study was to determine whether 18-bp insertion/deletion (I/D) polymorphism at −2549 position of the vascular endothelial growth factor (VEGF) gene is associated with diabetic vascular complications (DVC). Material and methodsCaucasian subjects (n = 100) with T2DM were recruited for this study. Genotyping of the VEGF gene I/D polymorphism was done by the polymerase chain reaction (PCR) method. The results were correlated with laboratory and clinical data. ResultsIn our population heterozygous of the VEGF gene polymorphism was observed most frequently (57%). DVC were observed in 53 patients. Heterozygous T2DM patients significantly more often suffered from heart failure (HF) and stroke (p = 0.05). Amongst all the DVC, D allele of the VEGF polymorphism had a significantly increased risk of diabetic retinopathy (DR) (OR = 1.31; p = 0.033) irrespective of the duration of diabetes, BMI, the glycemia control expressed by HbA1c, renal function, lipid values or applied treatment. The studied polymorphism did not correlate with coronary heart disease, peripheral vascular disease, cardiovascular death, diabetic kidney disease or applied treatment. ConclusionsThe multivariate logistic regression analysis showed that the D allele in the promoter region of the VEGF gene is an independent risk factor of DR irrespective of other laboratory and clinical variables in T2DM patients. Our study suggests that I/D allele in the studied gene is associated with HF and strokes.

Apnea Hypopnea Index During Rapid Eye Movement Sleep With Diabetic Retinopathy in Patients With Type 2 Diabetes.

Recent studies based on home sleep apnea testing (HSAT) reported the potential association of sleep disordered breathing, such as obstructive sleep apnea (OSA), with diabetic retinopathy (DR). A few studies showed that the apnea-hypopnea index (AHI) during rapid eye movement (REM) sleep (REM-AHI) is associated with glycated hemoglobin and hypertension, two known risk factors for DR. However, there are no studies that have evaluated the association of REM-AHI with DR because previous studies were based on HSAT. To determine the association of REM-AHI with DR. The study subjects were 131 patients with type 2 diabetes mellitus who underwent all-night polysomnography with ≥30 minutes of REM sleep and were free of heart failure or active lung disease and had not yet been treated for OSA. Logistic regression analysis was performed to determine the effect of REM-AHI on the prevalence of DR adjusted by several known risk factors for DR. Quartile of REM-AHI was independently associated with DR (P = 0.024) (Q2: OR, 3.887; 95% CI, 0.737 to 20.495; Q3: OR, 9.467; 95% CI, 1.883 to 47.588; Q4: OR, 12.898; 95% CI, 2.008 to 82.823 relative to Q1), whereas quartile of non-REM (NREM)-AHI was not (P = 0.119). Similarly, continuous REM-AHI (OR, 2.875; 95% CI, 1.224 to 6.752; P = 0.015) was independently associated with DR, whereas NREM-AHI was not (P = 0.107). In addition, AHI was independently associated with DR when controlling for several known risk factors for DR (P = 0.043). REM-AHI was independently associated with DR. REM-AHI could be a potential risk factor for DR.

Age of Onset of Diabetes and Its Comparison with Prevalence and Risk Factors for Diabetic Retinopathy in a Rural Population of India

Aim: To compare the age of onset of diabetes with the prevalence and risk factors of diabetic retinopathy (DR) in a rural population of India. Methods: This is a population-based study for which 1,190 people with diabetes were recruited from rural areas of the districts of Kanchipuram and Thiruvallur, Tamil Nadu, India. The fundi of all patients were photographed using a 45°, four-field stereoscopic digital fundus camera. The diagnosis of DR was based on the Klein classification of the Early Treatment Diabetic Retinopathy Study scale. Results: The prevalence of DR was 11.5% in individuals with known onset of diabetes (aged ≤40 years) compared to 10.0% in those with late onset. The risk factors for any DR in both groups included longer known duration of diabetes, use of insulin, and the presence of high systolic blood pressure and hypertension. In the group with known onset of diabetes at ≤40 years, older age (p < 0.0001) and male gender (p = 0.018) were the significant factors for DR. In the group with age of onset of diabetes at > 40 years, high diastolic pressure (p = 0.001), higher HbA1c (p < 0.0001), high microalbuminuria (p < 0.0001), and high macroalbuminuria (p < 0.008) were the significant risk factors for DR. Conclusion: Control of risk factors in people with younger age of onset of diabetes may reduce both the prevalence and impact of retinopathy in rural populations.

A Prospective Observational Study on assessment of risk factor associated with diabetic retinopathy in patients diagnosed with type 2 Diabetes Mellitus in south Indian population

Objective: Diabetic Retinopathy (DR) appears to be the most common cause of blindness worldwide. The objective of the study was to assess the risk factors that would increase the onset or progression of the microvascular complication caused due to type 2 Diabetes Mellitus. Methods: A prospective observational study was conducted in a tertiary care hospital, Chennai. The enrolled patients based on their inclusion and exclusion criteria were followed and the information collected includes their demographic data and lab investigations such as, Hypertension, HbA1c, and Dyslipedimia. The data observed was analyzed with SPSS V20.0 to find significance among the risk factors of Diabetic Retinopathy. Results: Among the study population of 230, risk factor was analysed for occurrence of Diabetic Retinopathy, Female patient was more affected with Diabetic Retinopathy compared to male 112 and it showed the P - value of 0.001. Most of the people who have Diabetic Retinopathy fall under the age category of 40 to 60 years. The descriptive statistics for age showed the following results Mean ± SD (48.16±10.29) for patients without Diabetic Retinopathy and Mean ± SD (56.24±7.35) for patients with Diabetic Retinopathy. It showed a P-value of 0.004. A significant correlation was seen between the duration of Diabetes Mellitus and the occurrence of Diabetic Retinopathy, showing a P- value of 0.001. Presence of Hypertension also seemed to increase the occurrence of Diabetic Retinopathy, showing a highly significant P-value of 0.001. Presence of Hyperlipidemia had no correlation with the occurrence of Diabetic Retinopathy. A significant level of correlation was seen between the level of glycated haemoglobin and the occurrence of Diabetic Retinopathy showing a P- value of 0.001. Conclusion: Our study concluded that duration of diabetes, presence of hypertension, presence of dyslipedimia and high level of HbA1c has a significant relationship as a risk factor for the onset and progression of Diabetic Retinopathy (DR).

Diabetic Retinopathy in the Asia-Pacific.

Diabetic retinopathy (DR), the most common complication of diabetes mellitus, is the leading cause of new cases of blindness in middle-aged and elderly in the Asia-Pacific. It has been estimated that 51% of all those with blindness due to DR globally (n = 424,400) and 56% of those with visual impairment due to DR (2.1 million) come from the Asia-Pacific. Prevalence of DR among those with diabetes ranged from 10% in India to 43% in Indonesia within the Asia-Pacific. Awareness of DR among persons with diabetes ranged from 28% to 84%. Most common modifiable risk factors for DR in the Asia-Pacific were hyperglycemia, blood pressure, dyslipidemia, and obesity. Implementation of systematic screening programs for DR and advancement in telemedicine screening methods have increased patient coverage and cost-effectiveness, though there are still numerous factors impeding screening uptake in the low-middle income regions of the Asia-Pacific. Management and treatment of DR in the Asia-Pacific is mainly limited to traditional laser retinopexy, but it is suboptimal despite new clinical approaches such as use of intravitreal anti.vascular endothelial growth factor and steroids due to limited resources. Further research and data are required to structure a more cost-effective public healthcare program and more awareness-building initiatives to increase the effectiveness of DR screening programs.

Global and Regional Prevalence of Diabetic Retinopathy; a Comprehensive Systematic Review and Meta-Analysis

Background: Despite the increasing prevalence of diabetes and its complications in the world, no comprehensive studies have investigated the prevalence of diabetic retinopathy (DR) as the most important ocular complication. In this study, we conducted a systematic search to estimate the global and regional prevalence of DR and its prevalence in different age and sex groups, and to evaluate the determinants of heterogeneity in its prevalence. Methods: A systematic and comprehensive search was done in international databases, including Scopus, PubMed, Web of Science, Embase, and other data sources to find studies related to the prevalence of DR. Prevalence and 95% CI were calculated using binomial distribution. The Cochrane's Q-test and I2 index were applied to investigate heterogeneity, a random-effects model was used to estimate the pooled prevalence, and a meta-regression method was administered to investigate the determinants of heterogeneity among studies. Findings: Of 5648 studies, 87 articles with a sample size of 541256 individuals and 212497 diabetic patients were analyzed. The majority of the studies were from the WHO-Regional Office for Europe (n=24). The estimated pooled prevalence of DR in the diabetic population was 28.41% (95% CI: 25.99 to 30.84). The pooled prevalence of DR in the diabetic population was 26.10 (95% CI: 3.34 to 28.86) in female and 28.44 (95% CI: 25.56 to 31.32) in male patients. The heterogeneity of the pooled prevalence according to I2 was 99% (p<0.001). According to the meta-regression results, the variables of WHO region (b: 3.681; p<0.001), age (b: 8.01; p: 0.003), type of DR (b: 18.51; p<0.003), and publication year (b:-0.58; p: 0.001) had a significant correlation with heterogeneity. Interpretation: One third of diabetic patients suffer from DR, mostly NPDR. Age, WHO region, type of DR, and publication year affected the heterogeneity in the prevalence of DR. Our knowledge of the global prevalence of DR can be helpful for effective screening of DR, planning eye care services, management of DR risk factors, and also for prioritization of resource allocation. Funding Statement: Noor Research Center for Ophthalmic Epidemiology. Declaration of Interests: The authors declare that: No conflicting relationship exists for any author. Ethics Approval Statement: All parts of this meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines).

Epidemiology of Diabetic Retinopathy at Eye Clinic Svjetlost Sarajevo: Two Years Retrospective Single Center Study.

Introduction:Diabetic retinopathy (DR) is an important cause of blindness, and occurs as a result of long-term accumulated damage to the small blood vessels in the retina. 2.6% of global blindness can be attributed to diabetes. Disease severity was most often classified by the Early Treatment Diabetic Retinopathy Study (ETDRS) classification for DR severity. Patients are usually categorized based on the severity of DR as having mild nonproliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative diabetic retinopathy (PDR).Aim:To evaluate DR status among patients at Eye Clinic Svjetlost Sarajevo , both, type 1 and type 2 DM patients who presented in our clinic at 2 years period – from June 2016 to June 2018. This is single center study.Methods:Retrospective analysis of 753 diabetic patients that came for the first check up in our institution during those two years, 363 patients were male and 390 were female. Patients were divided in 3 groups (based on DR changes): a) No changes, b) Nonproliferative DR (with and without Diabetic macular edema–DME), c) Prolipherative DR (with and without DME + Advanced PDR).Results:There were 35% of patients with no ocular changes, 41.2% had NPDR and 24% had PDR. Prevalence of DR in our study was 65.32%. Distribution of NPDR was 66.27%, and PDR was 33.73%. DME was present in 33.70% cases. In NPDR, DME was presented in 51% of the cases, while in PDR was presented in 49% of the cases. In state of advanced PDR, PDR was presented in 30.52% cases, tractional detachment and haemophtalmus in 50.20% of cases and neovascular glaucoma in 19.28%. Sixty-three patients ended up with vitroretinal surgery (8.4%) while in other studies that number is up to 3%. Out of that number 9 patients were patient with virgin eyes (14.28%). Neovascular glaucoma occurred in 19.28% of diabetics with proliferative retinopathy and 4.60% in all of diabetics.Conclusion:Diabetic retinopathy status of patients presenting at Eye clinic Svjetlost Sarajevo, Bosnia and Herzegovina is quite poor. There is a big need for early DR screening measures, good prevention and management of DR risk factors. Adequate and ON TIME management of DM and its vision threatening complications is of major importance.

A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application.

Background: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients.Method: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR).Results: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria.Discussion: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS.Conclusions: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

Prevalence and risk factors for diabetic retinopathy in north-central Nigeria

Background: To determine the prevalence, pattern and risk factors of Diabetic Retinopathy (DR) among patients with Diabetes Mellitus (DM) in a tertiary hospital in north-central Nigeria.Settings and Design: This was a hospital-based cross-sectional study conducted in Jos, north-central Nigeria. Materials and Methods: Consecutive adult patients with DM attending the endocrinology clinic who consented to the study were examined over a six-month period. Demographic data, duration of diabetes and history of any systemic disorder were obtained for each patient. A detailed ocular examination and fundus photography were performed and results of blood investigations such as Fasting Blood Glucose (FBG), Glycosylated Haemoglobin (HbA1c) and serum lipid profile were analysed. Data analysis was done with Statistical Package for Social Sciences (SPSS) version 16.0 software.Results: Three hundred and fifty-six patients were examined comprising of 120 (33.7%) males and 236 (66.3%) females giving a male to female ratio of 1:2. The mean age of the study population was 56.6 ± 12.3 years. Diabetic retinopathy and macular oedema were present in 66 (18.5%) and 51 (14.3%) patients respectively. Diabetes diagnosis of 10 years and above, FBG and HbA1c all had a statistically significant association with DR with p values &lt;0.001.Conclusion: The prevalence of DR was 18.5% in a hospital cohort of diabetic patients in north-central Nigeria with long duration of diabetes and poor glycaemic control being the major risk factors for retinopathy. These findings highlight the need for regular eye screening and good glycaemic control in individuals with diabetes in our environment. Funding: None declared Keywords: Diabetic retinopathy, diabetes mellitus, macular oedema, prevalence, blindness

A multicentre study evaluating the risk and prevalence of diabetic retinopathy in children and young people with type 1 diabetes mellitus

IntroductionThere is currently limited data published on the prognostic factors and prevalence of diabetic eye disease in children and young people (CYP) with Type 1 diabetes mellitus (T1DM), yet diabetic retinopathy remains one of the commonest causes of blindness in young adults. The aim of this study was to determine the risk and prevalence of diabetic retinopathy (DR) and to evaluate the risk factors associated with diabetic retinopathy in CYP with T1DM. MethodsAll CYP with Type 1 diabetes between 12 and 18 years of age, registered with the regional diabetic retinopathy screening programme were evaluated from 2012 to 2013 in four diabetes units within the north west region of the United Kingdom. Patients who had evidence of diabetic retinopathy were reviewed to identify risk factors for presence or absence of diabetic retinopathy. Results237 patients between the ages 12–18 years were included in the. The prevalence of diabetic retinopathy was 11%. Out of 27 patients with evidence of diabetic retinopathy, 44% were reported as background changes and 56% had pre-proliferative DR. Significant risk factors for diabetic retinopathy disease in the population using univariate analyses were duration of diabetes, puberty, age at diagnosis and mean HbA1c in the preceding 12 months. Multivariate logistic regression analysis found age of diagnosis (p = 0.04) and mean HbA1c as significant independent risk factors for presence of diabetic retinopathy (p = 0.02). ConclusionsThe prevalence of diabetic retinopathy in this paediatric population was 11%. Early age at diagnosis and poor metabolic control are independent risk factors for diabetic retinopathy.

Glycemic variability assessed by continuous glucose monitoring and the risk of diabetic retinopathy in latent autoimmune diabetes of the adult and type2 diabetes.

Aims/IntroductionThe relationship between glycemic variability (GV) and diabetic complications has gained much interest and remains under debate. Furthermore, the association of GV with diabetic complications has not been examined in latent autoimmune diabetes of the adult (LADA). Therefore, we evaluated the relationships among several metrics of GV with diabetic retinopathy (DR) in patients with LADA and type 2 diabetes mellitus.Materials and MethodsA total of 192 patients with LADA and 2,927 patients with type 2 diabetes mellitus were enrolled. After continuous glucose monitoring for 72 h, three metrics of GV including standard deviation, coefficient of variation and mean amplitude of glycemic excursions were calculated. DR was assessed by fundus photography performed with a digital non‐mydriatic camera.ResultsThe prevalence of DR was 20.3 and 26.4% in LADA and type 2 diabetes mellitus patients (P < 0.001), respectively. Generally, LADA patients had fewer cardiometabolic risk factors than type 2 diabetes mellitus patients, and all GV metrics were significantly higher in LADA than in type 2 diabetes mellitus. In the multivariate logistic regression analysis, no metrics for GV were identified as independent risk factors of DR (standard deviation: P = 0.175; coefficient of variation: P = 0.769; mean amplitude of glycemic excursions: P = 0.388) in LADA. However, the standard deviation was significantly associated with DR (OR 1.15, P = 0.017) in patients with type 2 diabetes mellitus after adjusting for confounders. The independent relationships of coefficient of variation and mean amplitude of glycemic excursions with DR (P = 0.194 and P = 0.251, respectively) did not reach statistical significance in type 2 diabetes mellitus.Conclusions GV is more strongly associated with DR in type 2 diabetes than in LADA, suggesting that different glucose‐lowering strategies should be used for these two types of diabetes.

Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.

Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very less human interference. In effect, convolutional neural networks (a deep learning method) have been taught to recognize pathological lesions from images. Diabetes has high morbidity, with millions of people who need to be screened for diabetic retinopathy (DR). Deep neural networks offer a great advantage of screening for DR from retinal images, in improved identification of DR lesions and risk factors for diseases, with high accuracy and reliability. This review aims to compare the current evidences on various deep learning models for diagnosis of diabetic retinopathy (DR).

Prevalence of and Risk Factors for Diabetic Retinopathy in a Rural Chinese Population: The Yangxi Eye Study.

To investigate the prevalence and determinants of diabetic retinopathy (DR) among older adults in rural Southern China. Using random cluster sampling, persons aged 50 years or older were randomly selected in rural Yangxi County, Guangdong Province, China. All participants underwent a standardized interview, fundus photography, and point of service glycosylated hemoglobin A1c (HbA1c) testing. Diabetes mellitus (DM) was diagnosed based on confirmed medical history or HbA1c ≥6.5%. Fundus photographs were graded for DR and diabetic macular edema (DME) based on the United Kingdom National Diabetic Eye Screening Program guidelines. Prevalence of and risk factors for DR and vision-threatening diabetic retinopathy (VTDR) were evaluated. Among 5825 subjects who participated (90.7% response rate) in the Yangxi Eye Study, 562 (9.6%) were diagnosed with DM, including 79 (14.1%) known and 483 new (85.9%) cases. Among DM cases, 476 (84.7%) had gradable fundus photos. The prevalence of any DR and VTDR were 8.19% (95% confidence interval [CI] 5.9-11.0) and 5.25% (95% CI 3.43-7.66), respectively. These figures were 23.9% and 12.7% for known and 5.43% and 3.95% for new DM cases. Risk factors for any DR were higher HbA1c level (OR [odds ratio] per unit 1.34, P < 0.001), longer duration of DM (OR per year = 2.29, P < 0.001) and having previously undergone cataract surgery (OR 4.11, P < 0.030). Our study found a lower prevalence of DR among adults 50 years and older than in previously reports. Perhaps this difference can be explained by the short duration of most cases.

Polymorphisms of Aldose Reductase (ALR2) Regulatory Gene are Risk Factors for Diabetic Retinopathy in Type-2 Diabetes Mellitus Patients in Bali, Indonesia.

Aim: The study aimed to elucidate whether the polymorphisms of the aldose reductase regulatory gene were risk factors for Diabetic Retinopathy (DR) in type-2 diabetes mellitus (T2DM) patients in Bali.Methods:This is a case-control study including 35 cases of T2DM patients with DR paired with 35 cases with non-DR as controls. PCR analysis and DNA-sequencing were carried out to detect the C(-106)T and C(-12)G polymorphisms at the regulatory region of Aldose Reductase (ALR2) gene. Genotype and allele distributions were analyzed by Chi-squared test and independent t-and Mann-Whitney U tests were used to analyze other data.Results:Among all subjects in both groups, the baseline characteristics were homogenous except for systolic blood pressure, fasting blood glucose and 2-hours post-prandial blood glucose. This study found two polymorphisms, C(-104)T and C(-9)G, in the regulatory region of ALR2 gene. The result showed that the C(-104)T polymorphism was a risk factor for DR (OR=36; 95% CI = 4.43-292.85; p=0.001), but not the C(-9)G polymorphism (OR=1.28; 95% CI=0.48-3.38; p=0.621). Other findings in the study revealed that CC/CC haplotype is a protective factor for DR (OR=0.198; p=0.002), whereas CT/CC and CT/CG haplotypes as risk factors for DR with OR=15.58; p=0.002 and OR=2.29; p=0.005 respectively.Conclusion:It can be concluded that C(-104)T polymorphism in the regulatory region of Aldose Reductase (ALR2) gene was the risk factor for DR among T2DM patients in Bali, Indonesia. However, small sample size, systolic blood pressure, fasting blood glucose and 2-hours post-prandial blood glucose could affect our finding.

Association between endoplasmic reticulum stress and risk factors of diabetic retinopathy.

Diabetic retinopathy (DR) is one of the most common and challenging ocular complications of diabetes mellitus. As a chronic, progressive ocular disease that poses a serious threat to vision, DR has gradually become a leading cause of blindness worldwide. Emerging evidence points to an important role of endoplasmic reticulum (ER) stress in not only maintaining the steady-state equilibrium in the body, but also in intracellular synthesis, protein folding, and other essential functions. Recent studies have demonstrated clear associations between ER stress-related physiological functions and the pathogenesis of DR. When cells are stimulated by external stimuli, UPR pathway is activated firstly to protect it. However, long-term harmful factors can induce ER stress. which interferes with the physiological metabolism of retinal cells and participates in the occurrence of DR via the ATF6 pathway, PERK pathway and IRE1 pathway. At present, ER stress blocker is expected to become a new anti-DR therapy. Thus, understanding the relationship between ER stress and DR will help to develop new effective preventative treatments. In this review, we summarize the risk factors of DR pathogenesis induced by ER stress toward revealing potentially new therapeutic targets.

Multilocus genetic risk score for diabetic retinopathy in the Han Chinese population of Taiwan

The aim of this study is to explore the effect of genetic variation on diabetic retinopathy (DR) risk in a Taiwanese population. The logistic regression model was used to evaluate the relationship between DR status and risk factors, including the conventional parameters and genetic risk score (GRS). Candidate single nucleotide polymorphisms (SNPs) in GRS were selected based on previous reports with a combined P < 10−4 (genome-wide association) and P < 0.05 (meta-analysis). In total, 58 SNPs in 44 susceptibility loci were selected, and four were used to calculate GRS. After adjustment for age, systolic blood pressure, diabetes duration, and HbA1c, the DR risk was 4.95 times higher for patients in the top GRS third tile than for those in the bottom third tile (95% CI = 2.99–8.18; P < 0.001). The addition of genetic information improved DR prediction, increasing the area under the curve (AUC) from 0.72 to 0.77 (P = 0.0024) and improving the sensitivity of the model such that 40 more subjects were reclassified into DR status. The developed multivariate logistic regression model combining conventional risk factors and the multilocus GRS can predict DR, thus enabling timely treatment to reduce blindness in T2D patients.

Prevalence and Associated Risk Factors for Diabetic Retinopathy among in-patients Diagnosed with Diabetes Mellitus: A Retrospective Study Conducted in Nobel Medical College and Teaching Hospital, Biratnagar

Background: Diabetes mellitus (DM) being disease of modern world occurrence of Diabetic retinopathy (DR) has become more frequent. Knowledge on the prevalence and associated risk factors of diabetic retinopathy helps to detect the disease in its early course. The objective of the study was to establish the prevalence and to analyze the associated risk factors and help to screen the disease as early as possible so as to prevent and /or to delay the onset as well as progression of DR. Materials and Methods: A hospital based retrospective study conducted among 213 in-patients of Nobel Medical College, Biratnagar diagnosed with DM. Result: The prevalence of diabetic retinopathy was 32.39% and prevalence of mild NPDR, moderate NPDR, severe NPDR, very severe NPDR, proliferative diabetic retinopathy and clinically significant macular edema was 12.7%, 8.9%, 6.1%, 5%, 1.9% &amp; 2.3% respectively. There was statistically significant relation of diabetic retinopathy with duration of diabetes (p value 0.004) and the mean duration was 8.704 years. Conclusions: The prevalence of diabetic retinopathy among in-patients was 32.39%. Though there was no significant relation with occurrence of DR with type of diabetes, age, sex, alcoholism, smoking and drug intake history, the duration of diabetes and hyperlipidemia, poor hyperglycemic control were highly significantly associated with DR while high BP showed marginally insignificant relation with the same. Journal of Nobel Medical College Volume 7, Number 1, Issue 12, January-June 2018, Page: 50-55

The association of smoking and risk of diabetic retinopathy in patients with type 1 and type 2 diabetes: a meta-analysis.

To clarify the relevance between smoking and diabetic retinopathy in patients with type 1 and type 2 diabetes mellitus. Published evidence were searched in MEDLINE and EMBASE from the databases began until Feb. 2017. Studies evaluating the association between smoking and diabetic retinopathy or evaluating the risk factors of diabetic retinopathy including smoking were included. Totally 73 studies were identified, among which 19 studies included type 1 diabetes patients and 56 studies included type 2 diabetes patients. In type 1 diabetes, compare with non-smokers, the risk of diabetic retinopathy significantly increased in smokers (risk ratio (RR) = 1.23, 95% CI 1.14, 1.33, P < 0.001), and the risk of proliferative diabetic retinopathy also significantly increased in smokers (RR = 1.48, 95% CI 1.20, 1.81, P < 0.001). In type 2 diabetes, compare with non-smokers, the risk of diabetic retinopathy significantly decreased in smokers (RR = 0.92, 95% CI 0.86, 0.98, P = 0.02) and the risk of proliferative diabetic retinopathy also significantly decreased in smokers (RR = 0.68, 95% CI 0.61, 0.74, P < 0.001). Compare with non-smokers, the risk of diabetic retinopathy significantly increased in smokers with type 1 diabetes while significantly decreased in smokers with type 2 diabetes. However, this result did not change the importance of smoking cessation for public health.

Current epidemiology of diabetic retinopathy in patients with type 1 diabetes: a national multicenter study in Brazil

BackgroundDiabetic retinopathy is the leading cause of blindness in economically active populations. The aims of this study were to estimate the prevalence and to identify risk factors for diabetic retinopathy in patients with type 1 diabetes in Brazil.MethodsThis was a nationwide, cross-sectional study conducted between August 2010 and August 2014. The study included 1760 patients with type 1 diabetes. Patients underwent a standard questionnaire, clinical and laboratory analyses and were screened for diabetic retinopathy. To analyze the risk factors related to diabetic retinopathy, two models of logistic regression models were performed, one considering vision-threatening cases and the other with any diabetic retinopathy cases as dependent variables. The group with vision-threatening included patients with severe non-proliferative diabetic retinopathy, proliferative diabetic retinopathy and macular edema.ResultsIn total, 1644 patients (mean age, 30.1± 12.0 years; duration of diabetes, 15.3 ± 9.3 years; female, 55.8%) were studied. 35.7% presented diabetic retinopathy and 12% presented vision-threatening diabetic retinopathy. Three risk factors associated with diabetic retinopathy were in common to both groups: longer diabetes duration (OR 1.07; 95% CI, 1.05–1.09), higher levels of HbA1c (OR 1.24; CI, 1.17–1.32) and higher levels of serum uric acid (OR 1.22; CI, 1.13–1.31) (p < 0.001 for all comparisons).ConclusionThe higher rate of vision-threatening retinopathy found in our study highlights the need to improve access to eye care and screening programs for diabetic retinopathy in Brazil. In addition to traditional risk factors, we found an association between serum uric acid levels and diabetic retinopathy. Further studies are needed to address this association.

Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms

BackgroundThe risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.ResultsExperimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy.ConclusionsOur method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future.