The prevalence and risk factors of diabetic retinopathy in selected primary care centers during the 3-year screening intervals

Magliah, Sultan F.; Bardisi, Wedad; Attah, Maha Al; Khorsheed, Manal M.

doi:10.4103/jfmpc.jfmpc_85_18

Cited by 45 publications

(29 citation statements)

References 58 publications

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“…We found a higher prevalence of the disease, even higher than in Asian countries (28.0%; 95% CI: 24.0 to 33.0) [ 9 ]. This difference could be due to the fact that in Latin America and the Caribbean there is a high prevalence of risk factors for the development of diabetic retinopathy such as hypertension, obesity, cardiovascular disease and the intake of foods rich in sugar and fats [ 71 – 74 ], in addition to the lack of implementation of prevention strategies in comparison to more developed countries [ 75 ]. It should be taken into account that the certainty of the evidence of the prevalences in T1DM and T2DM is very low and could be overestimated by the number of studies in primary care centers and hospitals and by the bias effect of small populations, so that future studies with better methodological quality could modify our findings.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and incidence of diabetic retinopathy in patients with diabetes of Latin America and the Caribbean: A systematic review and meta-analysis

Medina-Ramirez,

Soriano-Moreno,

Tuco

et al. 2024

PLoS ONE

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Objectives This systematic review aimed to assess the prevalence and incidence of diabetic retinopathy in patients with diabetes of Latin America and the Caribbean. Methods We searched Web of Science (WoS)/Core Collection, WoS/MEDLINE, WoS/Scielo, Scopus, PubMed/Medline and Embase databases until January 16, 2023. We meta-analyzed prevalences according to type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Results Forty-three prevalence studies (47 585 participants) and one incidence study (436 participants) were included. The overall prevalence of retinopathy in patients with T1DM was 40.6% (95% CI: 34.7 to 46.6; I2: 92.1%) and in T2DM was 37.3% (95% CI: 31.0 to 43.8; I2: 97.7), but the evidence is very uncertain (very low certainty of evidence). In meta-regression, we found that age (T1DM) and time in diabetes (T2DM) were factors associated with the prevalence. On the other hand, one study found a cumulative incidence of diabetic retinopathy of 39.6% at 9 years of follow-up. Conclusions Two out of five patients with T1DM or T2DM may present diabetic retinopathy in Latin America and the Caribbean, but the evidence is very uncertain. This is a major public health problem, and policies and strategies for early detection and opportunely treatment should be proposed.

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Section: Discussionmentioning

confidence: 99%

Prevalence and incidence of diabetic retinopathy in patients with diabetes of Latin America and the Caribbean: A systematic review and meta-analysis

Medina-Ramirez,

Soriano-Moreno,

Tuco

et al. 2024

PLoS ONE

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“…As such, prediction of individual DR risk may become a key element. Currently, to the best of our knowledge, there are two methods of doing this: risk factor based, which is of limited predictive clinical utility,19 20 and that developed by Bora et al, 21 which uses both deep learning and risk factors for optimal results.…”

Section: Discussionmentioning

confidence: 99%

“…While some work has been done on finding risk factors for DR, using patient data such as age, haemoglobin A1c (HbA1c) levels, gender, duration of disease, and the like,19 20 clinicians are traditionally unable to predict the development of DR in patients. However, a previous article published findings of AUC 0.79 using a machine learning algorithm to predict DR development over 2 years using fundus photography 21.…”

Section: Introductionmentioning

confidence: 99%

Predicting the future development of diabetic retinopathy using a deep learning algorithm for the analysis of non-invasive retinal imaging

Rom¹,

Aviv²,

Ianchulev

et al. 2022

BMJ Open Ophth

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AimsDiabetic retinopathy (DR) is the most common cause of vision loss in the working age. This research aimed to develop an artificial intelligence (AI) machine learning model which can predict the development of referable DR from fundus imagery of otherwise healthy eyes.MethodsOur researchers trained a machine learning algorithm on the EyePACS data set, consisting of 156 363 fundus images. Referrable DR was defined as any level above mild on the International Clinical Diabetic Retinopathy scale.ResultsThe algorithm achieved 0.81 area under receiver operating curve (AUC) when averaging scores from multiple images on the task of predicting development of referrable DR, and 0.76 AUC when using a single image.ConclusionOur results suggest that risk of DR may be predicted from fundus photography alone. Prediction of personalised risk of DR may become key in treatment and contribute to patient compliance across the board, particularly when supported by further prospective research.

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“…One of the most harmful and common complications is diabetic retinopathy (DR) [2,3]. This is a leading cause of blindness worldwide [4], with 103.1 million adults affected by DR in 2020 and a predicted 160.5 million affected in 2045 [5]. Moreover, DR individuals often develop a visual impairment, leading to a poor quality of life for the patient and impacting on the health care system in terms of direct and indirect costs.…”

Section: Diabetic Retinopathy (Dr): An Overviewmentioning

confidence: 99%

Inhibition of Galectins and the P2X7 Purinergic Receptor as a Therapeutic Approach in the Neurovascular Inflammation of Diabetic Retinopathy

Lepre

Russo

Trotta

et al. 2023

IJMS

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Diabetic retinopathy (DR) is the most frequent microvascular retinal complication of diabetic patients, contributing to loss of vision. Recently, retinal neuroinflammation and neurodegeneration have emerged as key players in DR progression, and therefore, this review examines the neuroinflammatory molecular basis of DR. We focus on four important aspects of retinal neuroinflammation: (i) the exacerbation of endoplasmic reticulum (ER) stress; (ii) the activation of the NLRP3 inflammasome; (iii) the role of galectins; and (iv) the activation of purinergic 2X7 receptor (P2X7R). Moreover, this review proposes the selective inhibition of galectins and the P2X7R as a potential pharmacological approach to prevent the progression of DR.

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The prevalence and risk factors of diabetic retinopathy in selected primary care centers during the 3-year screening intervals

Cited by 45 publications

References 58 publications

Prevalence and incidence of diabetic retinopathy in patients with diabetes of Latin America and the Caribbean: A systematic review and meta-analysis

Prevalence and incidence of diabetic retinopathy in patients with diabetes of Latin America and the Caribbean: A systematic review and meta-analysis

Predicting the future development of diabetic retinopathy using a deep learning algorithm for the analysis of non-invasive retinal imaging

Inhibition of Galectins and the P2X7 Purinergic Receptor as a Therapeutic Approach in the Neurovascular Inflammation of Diabetic Retinopathy

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