Pediatric Diabetic Retinopathy: Updates in Prevalence, Risk Factors, Screening, and Management

Lin, Tyger; Gubitosi-Klug, Rose; Channa, Roomasa; Wolf, Risa M.

doi:10.1007/s11892-021-01436-x

Cited by 16 publications

(8 citation statements)

References 110 publications

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“…In type 2 diabetes, the incidence with 5 years of evolution is 20%, while with 15 years of evolution, it reaches 80% [4]. The risk of developing DR is thought to be associated with the duration of diabetes, hypertension, hyperlipidemia, and glycated hemoglobin (HbA1c) levels [5]. HbA1c levels have been reported to be significantly associated with the progression of DR [6], and glycemic variability has been found to be associated with DR in type 2 diabetes [7].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Diabetic Nephropathy and Development of Diabetic Macular Edema in Addition to Diabetic Retinopathy

Suzuki

Kiyosawa²

2023

Biomedicines

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This study aimed to examine the relationship between diabetic retinopathy (DR) and systemic factors. We evaluated 261 patients (143 men, 118 women, aged 70.1 ± 10.1 years) with type 2 diabetes. All participants underwent a fundus examination, fundus photography using spectral domain optical coherence tomography (SD-OCT), and blood tests. For glycated hemoglobin (HbA1c) levels, the average and highest values in the past were used. We observed DR in 127 (70 men and 57 women) of 261 patients. Logistic regression analyses revealed a significant correlation between DR development and the duration of diabetes (OR = 2.40; 95% CI: 1.50), average HbA1c level (OR = 5.57; 95% CI: 1.27, 24.4), highest HbA1c level (OR = 2.46; 95% CI: 1.12, 5.38), and grade of diabetic nephropathy (DN) (OR = 6.23; 95% CI: 2.70, 14.4). Regression analyses revealed a significant correlation between the severity of DR and duration of diabetes (t = –6.66; 95% CI: 0.21, 0.39), average HbA1c level (t = 2.59; 95% CI: 0.14, 1.02), and severity of DN (t = 6.10; 95% CI: 0.49, 0.97). Logistic regression analyses revealed a significant correlation between diabetic macular edema (DME) development and DN grade (OR = 2.22; 95% CI: 1.33, 3.69). DN grade correlates with the development of DR and DME, and decreased renal function predicts the onset of DR.

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Section: Introductionmentioning

confidence: 99%

Relationship between Diabetic Nephropathy and Development of Diabetic Macular Edema in Addition to Diabetic Retinopathy

Suzuki

Kiyosawa²

2023

Biomedicines

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“…Diabetes mellitus (DM), a metabolic disease characterized by chronic elevated blood glucose levels, can cause progressive damage to microvasculature, large vessels and nerves throughout the body (1,2). Diabetic retinopathy (DR) is one of the most common and destructive microvascular complications of DM, and has become a major cause of irreversible visual impairment (3)(4)(5). Clinically, DR can be divided into two main stages: non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR).…”

Section: Introductionmentioning

confidence: 99%

Analysis of retinal and choroidal characteristics in patients with early diabetic retinopathy using WSS-OCTA

Feng

et al. 2023

Front. Endocrinol.

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IntroductionDiabetic retinopathy (DR) is one of the most common and destructive microvascular complications of DM, and has become a major cause of irreversible visual impairment. The purpose of this study was to evaluate the changes in fundus microcirculation in non-diabetic retinopathy (NDR) and mild non-proliferative diabetic retinopathy (NPDR) in patients with type 2 diabetic mellitus (T2DM) using widefield swept-source optical coherence tomography angiography (WSSOCTA), and to investigate the correlation with laboratory indices of T2DM.MethodsEighty nine, 58 and 28 eyes were included in the NDR, NPDR and Control groups, respectively, were enrolled in this study. The 12mm×12mm fundus images obtained by WSS-OCTA were divided into 9 regions (supratemporal, ST; temporal, T; inferotemporal, IT; superior, S; central macular area, C; inferior, I; supranasal, SN; nasal, N; inferonasal, IN) to evaluate changes in vessel density (VD) of the superficial capillary plexus (SCP), deep capillary plexus (DCP), choriocapillaris, and mid-large choroidal vessel (MLCV), as well as changes in inner retinal thickness (IRT), outer retinal thickness (ORT), and choroidal thickness (CT). Results: Compared with control group, MLCV VD (I, N, IN) was significantly decreased in NDR group, SCP VD (IT, C, I) and DCP VD (T, IT, I) were significantly decreased in NPDR group. In NPDR group, DCP VD (IT) was significantly decreased compared with that in NDR group. Compared with control group, CT (ST, T, IT, S, SN, IN) was significantly declined in NDR group, and IRT (ST, IT) and ORT (ST, N) were significantly increased in NPDR group. In NPDR group, IRT (ST) and ORT (T, S) were significantly increased compared with NDR group. Correlation analysis showed that age, body mass index, fasting blood glucose, fasting insulin, fasting C-peptide, and estimated glomerular filtration rate in T2DM patients were statistically correlated with retinal and choroidal thickness/VD. DiscussionStructural and blood flow changes in the choroid occur before the onset of DR and precede changes in the retinal microcirculation, and MLCV thickness/VD is a more sensitive imaging biomarker for the clinical detection of DR. WSS-OCTA enables large-scale non-invasive visual screening and follow-up of the retinal and choroidal vasculature in DR patients, providing a new strategy for the prevention and monitoring of DR in patients with T2DM.

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“…10 While the prevalence of DR among youths with well-controlled T1DM is low, adolescents with T2DM have a higher risk of DR progression compared to adults, especially when glycaemic control is poor. 11 , 12 Adolescence is a critical period for the development and management of DM, as hormonal changes can complicate glycaemic control. Adolescents with DM face unique psychological challenges during puberty, including body image concerns, peer pressure and increased responsibility for DM self-management.…”

Section: Introductionmentioning

confidence: 99%

CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review

Curran,

Whitestone,

Zabeen

et al. 2023

Clin Med Insights Endocrinol Diabetes

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Background: Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods: Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International’s Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results: Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions: Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.

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Pediatric Diabetic Retinopathy: Updates in Prevalence, Risk Factors, Screening, and Management

Cited by 16 publications

References 110 publications

Relationship between Diabetic Nephropathy and Development of Diabetic Macular Edema in Addition to Diabetic Retinopathy

Relationship between Diabetic Nephropathy and Development of Diabetic Macular Edema in Addition to Diabetic Retinopathy

Analysis of retinal and choroidal characteristics in patients with early diabetic retinopathy using WSS-OCTA

CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review

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