Exploring the Impact of Glycemic Control on Diabetic Retinopathy: Emerging Models and Prognostic Implications.
Nicola TecceGilda CennamoMichele RinaldiCiro CostagliolaAnnamaria ColaoPublished in: Journal of clinical medicine (2024)
This review addresses the complexities of type 1 diabetes (T1D) and its associated complications, with a particular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of this review is devoted to technological advances in diabetes management, particularly the use of hybrid closed-loop systems (HCLSs) and to the potential of open-source HCLSs, which could be easily adapted to different patients' needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.
Keyphrases
- diabetic retinopathy
- big data
- glycemic control
- machine learning
- type diabetes
- optical coherence tomography
- artificial intelligence
- cardiovascular disease
- risk factors
- blood glucose
- weight loss
- end stage renal disease
- deep learning
- newly diagnosed
- ejection fraction
- metabolic syndrome
- physical activity
- risk assessment
- peritoneal dialysis
- case report
- insulin resistance
- human health
- skeletal muscle