Diabetic retinopathy in Greece: prevalence and risk factors studied in the medical retina clinic of a Greek tertiary hospital

Abstract

Purpose: To determine the prevalence of diabetic retinopathy (DR) and diabetic macular edema (DME) in a cohort of Greek diabetic patients and identify possible risk factors. Methods: Population-based, non-interventional, cross-sectional study of 300 diabetic Greek patients attending the medical retina clinics of a tertiary hospital. Clinical and imaging data were recorded and statistical analysis was performed. Con dence intervals (CI) at 95% and statistically signi cant p values ≤0.05 were set.Results: A total of 300 diabetic patients were included. Of these patients, 21 (7%) were diagnosed with diabetes mellitus (DM) type I and 279 (93%) with DM type II. The average duration of diabetes was 15 ± 9.4 years (95% CI, 13.9-16.1) and the mean level of HbA1c was 7.2 ± 1.3 (95% CI, 7.1-7.4) overall. Prevalence of DR was 38.7% (116 patients), only 15 patients (5%) had proliferative DR and diabetic macular edema (DME) was detected in 19 patients (6.3%). In DM type I patients, 52.4% had DR and 9.5% had DME, while in the DM type II group, 37.6% had DR and 6.1% had DME. Binary logistic regression analysis identi ed duration of diabetes, increased HbA1c and hypertriglyceridemia as potential risk factors.Conclusions: This study is the rst one to present the extent and severity of DR and DME in a Greek cohort of diabetic patients and also identify risk factors associated with these entities. Our ndings highlight the signi cance of a properly organized national screening program for the early detection and management of the vision threatening complications of DR.

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

“…The relationship between elevated blood lipid profiles and DR development of DR was complex. Previous studies on the correlation between conventional serum lipid levels (TG, TC, HDL-C, and LDL-C) and DR have shown conflicting results ( 10 , 13 , 19 , 37 ). Similarly, the current focus on the role of lipoprotein(a) in the pathogenesis of DR remains controversial ( 38 , 39 ).A recent study focused on the relationship between apolipoproteins and DR ( 40 ); however, these novel laboratory parameters have not yet been widely used in clinical practice.…”

“…Previous studies reported that common risk factors for DR included both duration of DM and age at visit ( 10 , 11 ); however, designing a prompt referral for DR screening should take into account the length of DM as a crucial determinant ( 4 , 13 ). A longer duration may represent a longer period of hyperglycemia-induced retinal toxicity, which is believed to be associated with both vascular and neural retinal death.…”

“…Earlier studies revealed that glycated hemoglobin ( 10 , 11 , 13 , 15 – 17 , 19 ) or glycemic variability ( 20 , 21 ) could be predictors for DR, but structured glycemic detection adherence was highly correlated with social support ( 24 ). Local experience-based studies from China, India, and Brazil have indicated that the routine use of SMBG was frequently challenging, mainly because of the out-of-pocket expenditures associated with glucose monitoring ( 35 ).…”

“…Several molecular and biochemical pathways are involved in the incidence and development of DR, but the interactions between various mechanisms remain to be fully elucidated ( 9 ). Clinical studies have identified a number of risk factors for DR, including demographic characteristics such as age ( 10 – 12 ), duration of diabetes ( 10 , 11 , 13 – 17 ), obesity ( 10 ), and pregnancy status in diabetic women of childbearing age ( 18 ), comorbidities or complications such as hypertension ( 10 , 12 , 16 ), dyslipidemia ( 10 , 12 , 13 ), cardiovascular disease(CVD) ( 11 , 12 ) and diabetic nephropathy ( 10 , 12 , 17 , 19 ), and other laboratory parameters such as glycated hemoglobin ( 10 , 11 , 13 , 15 – 17 , 19 ), glycemic variability ( 20 , 21 ), and susceptibility genes ( 22 ). However, the aforementioned risk factors derived from population-based studies can only account for 9% of DR progression ( 23 ).…”

“…Considerable research effort has recently focused on developing predictive models for DR using machine learning algorithms ( 10 , 12 , 13 , 16 , 19 , 21 ), but these models cannot be applied well in low-medical-resource settings. Nearly 510 million Chinese people (36.1% of the total population) live in rural areas ( 25 ), and 72.5% of patients do not comply with structured SMBG ( 24 ), not to mention consecutive follow-up records or regular laboratory tests.…”