The Association Between HbA1c and Other Biomarkers With the Prevalence and Severity of Diabetic Retinopathy

Dietary Inflammatory Index and diabetic retinopathy risk in US adults: findings from NHANES (2005-2008)

BACKGROUND

Inflammation is associated with the pathophysiology of diabetic retinopathy (DR). Within the framework of complete dietary patterns, the Dietary Inflammatory Index (DII) was formulated to evaluate the inflammatory properties inherent in a diet. The main purpose of the current study was to assess the relationship between DII and DR using National Health and Nutrition Examination Survey (NHANES).

METHODS

The original sample size included 1,148 diabetes patients out of 2005-2008 NHANES surveys. Twenty-four-hour dietary consumptions were used to calculate the DII scores. Demographic characteristics and retina examinations were collected for the comparison between DR and non-DR groups in diabetes patients. The relationship between DII and DR was analyzed by a logistic regression model.

RESULTS

227 subjects (110 non-DR and 117 DR) were selected in the analyses by using undersampling method to balance the sample size. Compared with non-DR group, DR group had higher DII values (1.14 ± 0.29 vs. 1.49 ± 0.21, p = 0.32), higher levels of HbA1c (6.8 ± 1.1% vs. 7.7 ± 2.6%, p < 0.001), longer duration of diabetes (6.52 ± 12 years vs. 14 ± 11 years, p < 0.001). The odds rate (OR) of DII for DR from the logistic regression was 1.38 (95%CI 1.06-1.81, p < 0.001). HbA1c, diabetes duration and obesity were important influencing factors, and their ORs were 1.81 (95% CI:1.31-2.50), 1.12 (95%CI:1.04-1.20), 4.01 (95%CI:1.12-14.32), respectively. In addition, the most important dietary indices for DR were different across males and females.

CONCLUSIONS

The current study demonstrates that a higher DII is associated with an increased risk of DR in US adults. Considering diet as a modifiable factor, limiting pro-inflammatory diets or encouraging an anti-inflammatory diet may be a promising and cost-effective method in the management of DR.

Optimizing treatment for diabetic macular edema during cataract surgery

Diabetic macular edema (DME) causes visual impairment in diabetic retinopathy (DR). Diabetes mellitus is a global epidemic and diabetic individuals are at risk of developing DR. Approximately 1 in 10 diabetic patients suffers from DME, which is the commonest cause of vision-threatening DR at primary-care screening. Furthermore, diabetes predisposes to a higher frequency and a younger onset of cataract, which further threatens vision in DME patients. Although cataract extraction is an effective cure, vision may still deteriorate following cataract surgery due to DME progression or recurrence, of which the risks are significantly higher than for patients without concurrent or previous history of DME at the time of operation. The management of pre-existing DME with visually significant cataract is a clinical conundrum. Deferring cataract surgery until DME is adequately treated is not ideal because of prolonged visual impairment and maturation of cataract jeopardizing surgical safety and monitoring of DR. On the other hand, the progression or recurrence of DME following prompt cataract surgery is a profound disappointment for patients and ophthalmic surgeons who had high expectations for postoperative visual improvement. Prescription of perioperative anti-inflammatory eye drops is effective in lowering the risk of new-onset DME after cataract surgery. However, management of concurrent DME at the time of cataract surgery is much more challenging because DME is unlikely to resolve spontaneously even with the aid of anti-inflammatory non-steroidal or steroid eye drops. A number of clinical trials using intravitreal injection of corticosteroids and anti-vascular endothelial growth factor (anti-VEGF) as first-line therapy have demonstrated safety and efficacy to treat DME. These drugs have also been administered perioperatively for the prevention of DME worsening in patients undergoing cataract surgery. This article reviews the scientific evidence to guide ophthalmologists on the efficacy and safety of various therapies for managing patients with DME who are particularly vulnerable to cataract surgery-induced inflammation, which disintegrates the blood-retinal barrier and egression of fluid in macular edema.

Risk factor analysis and clinical decision tree model construction for diabetic retinopathy in Western China

BACKGROUND

Diabetic retinopathy (DR) is the driving force of blindness in patients with type 2 diabetes mellitus (T2DM). DR has a high prevalence and lacks effective therapeutic strategies, underscoring the need for early prevention and treatment. Yunnan province, located in the southwest plateau of China, has a high pre-valence of DR and an underdeveloped economy.

AIM

To build a clinical prediction model that will enable early prevention and treatment of DR.

METHODS

In this cross-sectional study, 1654 Han population with T2DM were divided into groups without (n = 826) and with DR (n = 828) based on fundus photography. The DR group was further subdivided into non-proliferative DR (n = 403) and proliferative DR (n = 425) groups. A univariate analysis and logistic regression analysis were conducted and a clinical decision tree model was constructed.

RESULTS

Diabetes duration ≥ 10 years, female sex, standing- or supine systolic blood pressure (SBP) ≥ 140 mmHg, and cholesterol ≥ 6.22 mmol/L were risk factors for DR in logistic regression analysis (odds ratio = 2.118, 1.520, 1.417, 1.881, and 1.591, respectively). A greater severity of chronic kidney disease (CKD) or hemoglobin A 1c increased the risk of DR in patients with T2DM. In the decision tree model, diabetes duration was the primary risk factor affecting the occurrence of DR in patients with T2DM, followed by CKD stage, supine SBP, standing SBP, and body mass index (BMI). DR classification outcomes were obtained by evaluating standing SBP or BMI according to the CKD stage for diabetes duration < 10 years and by evaluating CKD stage according to the supine SBP for diabetes duration ≥ 10 years.

CONCLUSION

Based on the simple and intuitive decision tree model constructed in this study, DR classification outcomes were easily obtained by evaluating diabetes duration, CKD stage, supine or standing SBP, and BMI.

Diabetic retinopathy risk prediction in patients with type 2 diabetes mellitus using a nomogram model

BACKGROUND

This study aims to develop a diabetic retinopathy (DR) hazard nomogram for a Chinese population of patients with type 2 diabetes mellitus (T2DM).

METHODS

We constructed a nomogram model by including data from 213 patients with T2DM between January 2019 and May 2021 in the Affiliated Hospital of Zunyi Medical University. We used basic statistics and biochemical indicator tests to assess the risk of DR in patients with T2DM. The patient data were used to evaluate the DR risk using R software and a least absolute shrinkage and selection operator (LASSO) predictive model. Using multivariable Cox regression, we examined the risk factors of DR to reduce the LASSO penalty. The validation model, decision curve analysis, and C-index were tested on the calibration plot. The bootstrapping methodology was used to internally validate the accuracy of the nomogram.

RESULTS

The LASSO algorithm identified the following eight predictive variables from the 16 independent variables: disease duration, body mass index (BMI), fasting blood glucose (FPG), glycated hemoglobin (HbA1c), homeostatic model assessment-insulin resistance (HOMA-IR), triglyceride (TG), total cholesterol (TC), and vitamin D (VitD)-T3. The C-index was 0.848 (95% CI: 0.798-0.898), indicating the accuracy of the model. In the interval validation, high scores (0.816) are possible from an analysis of a DR nomogram's decision curve to predict DR.

CONCLUSION

We developed a non-parametric technique to predict the risk of DR based on disease duration, BMI, FPG, HbA1c, HOMA-IR, TG, TC, and VitD.