Diabetic Retinopathy and Its Effect on Quality of Life

Mahobia, Ashish; Sahoo, Sujit Ranjan; Maiti, Niladri; Sathyanarayanan.R,; Aravinth, R; Pandraveti, Roopa Reddy; Tiwari, Heena

doi:10.4103/jpbs.jpbs_199_21

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…poor health-related outcomes including reduced quality of life and increased risk of depression, anxiety, and bipolar disorder (Chen et al, 2023;Edalat-Nejad et al, 2014;Khoo et al, 2019;Mahobia et al, 2021;Valluru et al, 2023). In addition to that, this study found diabetic nephropathy and retinopathy were both associated with higher risk of MCI.…”

Section: Discussionsupporting

confidence: 51%

“…Diabetic nephropathy and retinopathy were the most prevalent microvascular complications of T2DM (Seewoodhary, 2021 ). Previous work has linked these two complications with a wide range of poor health‐related outcomes including reduced quality of life and increased risk of depression, anxiety, and bipolar disorder (Chen et al., 2023 ; Edalat‐Nejad et al., 2014 ; Khoo et al., 2019 ; Mahobia et al., 2021 ; Valluru et al., 2023 ). In addition to that, this study found diabetic nephropathy and retinopathy were both associated with higher risk of MCI.…”

Section: Discussionmentioning

confidence: 99%

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Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis

Maimaitituerxun,

Chen,

Xiang

et al. 2024

Brain and Behavior

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BackgroundAs the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self‐care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health‐related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach.MethodsThis hospital‐based case–control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM‐related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi‐square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison.ResultsA total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2 ± 10.3 and 60.4 ± 9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p > .05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC = .75 [95% confidence interval (CI): .71–.78] and .67 [95% CI: .61–.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC = .76 [95% CI: .72–.80] and .69 [95% CI: .62–.75] in the training and validation sets, respectively).ConclusionThe established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice.

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Section: Discussionsupporting

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis

Maimaitituerxun,

Chen,

Xiang

et al. 2024

Brain and Behavior

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“…Diabetes is known as a chronic disease with multiple complications [1]. 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].…”

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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Preserving sight: Managing and preventing diabetic retinopathy

Dar,

Maqbool,

Ara

et al. 2023

Open Health

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Preserving eyesight in the presence of diabetic retinopathy, a subtle complication of diabetes, requires a proactive and comprehensive approach. This condition, characterized by the gradual deterioration of the blood vessels in the retina, can quietly advance and pose a significant threat to vision. Effectively managing this issue involves careful control of blood glucose levels through medications, dietary choices, and physical activity. By preventing prolonged high blood sugar levels, the main culprit behind retinal damage, individuals can slow down the progression of diabetic retinopathy and minimize its impact on vision. Medical interventions, such as laser therapies and anti-vascular endothelial growth factor injections, offer targeted measures to address abnormal blood vessel growth and leakage. Regular eye assessments are crucial, providing early detection opportunities even in the absence of obvious symptoms and allowing for timely intervention before irreversible damage occurs. Prevention is equally important, emphasizing proactive measures to avoid diabetic retinopathy. Monitoring blood pressure and lipid levels, along with adopting a health-conscious lifestyle, strengthens cardiovascular health, indirectly protecting the fragile retinal blood vessels. By following diabetes management protocols, maintaining a balanced diet, engaging in regular physical activity, and avoiding tobacco use, individuals can significantly reduce their vulnerability to diabetic retinopathy. Combined with efforts to increase community awareness and educate about the importance of regular eye check-ups, this comprehensive approach empowers individuals to actively manage their eye health, ultimately preserving the precious gift of sight.

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Diabetic Retinopathy and Its Effect on Quality of Life

Cited by 4 publications

References 29 publications

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis

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

Preserving sight: Managing and preventing diabetic retinopathy

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