Relationship between diabetic retinopathy and diabetic nephropathy; A longitudinal follow-up study from a tertiary care unit of Karachi, Pakistan

Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

Topic

To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.

Clinical relevance

Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.

Methods

The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).

Results

There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.

Conclusion

With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Optical coherence tomography angiography for the differentiation of diabetic nephropathy from non-diabetic renal disease

BACKGROUND

To provide a new non-invasive method for the differentiation of diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) by assessing retinal microstructure using optical coherence tomography angiography (OCTA).

METHODS

OCTA parameters were recorded and their relationship with DN was analysed. A differential diagnosis regression model for DN was established, and the diagnostic efficiency was evaluated.

RESULTS

Based on the pathological results of renal biopsy, 31 DN patients and 35 NDRD patients were included. Multivariate logistic regression analysis showed that DN was independently associated with the following parameters: 15.3 mm-1 ≤ vessel density (VD) full < 17.369 mm-1 (odds ratio [OR]=8.523; 95% confidence interval [CI]=1.387-52.352; P = 0.021), VD full < 15.3 mm-1 (OR=8.202; 95% CI=1.110-60.623; P = 0.039), DM duration > 60 months (OR=7.588; 95% CI=1.569-36.692; P = 0.012), and estimated glomerular filtration rate < 60 mL/min/1.73 m2 (OR=24.484; 95% CI=4.308-139.142; P < 0.001). The area under the receiver operating characteristic curve was 0.911, indicating a high diagnostic efficiency.

CONCLUSIONS

VD full < 17.369 mm-1, DM duration > 60 months, and eGFR < 60 mL/min/1.73 m2 may indicate the presence of DN. OCTA may be an effective non-invasive method for identifying DN and NDRD.

Direct medical cost and medications for patient of diabetes retinopathy in Beijing, China, 2016 to 2018

AIMS

Medications and costs of drug for diabetic retinopathy in outpatient in China have not been evaluated. The purpose of this study was to evaluate the hypoglycemic drugs and medical costs of diabetic retinopathy patients in the Beijing medical insurance system, analyze the characteristics of outpatient treatment, and investigate the changes in the quantity and cost of hypoglycemic drugs from 2016 to 2018 METHODS: This is a retrospective observational study, including diabetic patients with outpatient records in Beijing medical insurance from 2016 to 2018. Data on oral hypoglycemic drugs , insulin and non-hypoglycemic drugs, complications, treatment strategies, and annual medical costs were recorded Results: A total of 2,853,036 diabetic patients in Beijing medical insurance were enrolled in this study. 4.19%-4.67% of patients were diagnosed with retinopathy. Patients with retinopathy have more diabetic complications (1.65±0.71 vs 0.18±0.44. pp<.0001),and use more drugs (5.11±2.60 vs 3.85±2.34, pp <.0001), the annual total drug cost is also higher (¥ 13836±11244 vs ¥ 10030±9375, pp <.0001). The numbers of medication in retinopathy patients increased(5.11±2.60 vs 4.95±2.57, pp <.0001), and the annual total drug cost (¥13836±11244 vs ¥15642±13344, pp <.0001)decreased in 2018 compared with 2016.

CONCLUSIONS

Patients with retinopathy were associated with more complications. Compared with patients without retinopathy, the number of medications and total medical costs were significantly increased. From 2016 to 2018, there was an increase in the number of medication treatments for patients with retinopathy, but a decrease in cost.

Prevalence of Diabetic Retinopathy and Use of Common Oral Hypoglycemic Agents Increase the Risk of Diabetic Nephropathy-A Cross-Sectional Study in Patients with Type 2 Diabetes

OBJECTIVE

This study investigated the effect of amino acid metabolism on the risk of diabetic nephropathy under different conditions of the diabetic retinopathy, and the use of different oral hypoglycemic agents.

METHODS

This study retrieved 1031 patients with type 2 diabetes from the First Affiliated Hospital of Liaoning Medical University in Jinzhou, which is located in Liaoning Province, China. We conducted a spearman correlation study between diabetic retinopathy and amino acids that have an impact on the prevalence of diabetic nephropathy. Logistic regression was used to analyze the changes of amino acid metabolism in different diabetic retinopathy conditions. Finally, the additive interaction between different drugs and diabetic retinopathy was explored.

RESULTS

It is showed that the protective effect of some amino acids on the risk of developing diabetic nephropathy is masked in diabetic retinopathy. Additionally, the additive effect of the combination of different drugs on the risk of diabetic nephropathy was greater than that of any one drug alone.

CONCLUSIONS

We found that diabetic retinopathy patients have a higher risk of developing diabetic nephropathy than the general type 2 diabetes population. Additionally, the use of oral hypoglycemic agents can also increase the risk of diabetic nephropathy.

The link between diabetic retinal and renal microvasculopathy is associated with dyslipidemia and upregulated circulating level of cytokines

Purpose

To investigate the mechanisms underlying the correlations between diabetic retinopathy (DR) and diabetic nephropathy (DKD) and examine whether circulating cytokines and dyslipidemia contribute to both DR and DKD in patients with 2 diabetes mellitus (T2DM).

Methods

A total of 122 patients with T2DM were enrolled and categorized into the DM group (without no DR and DKD), DR group [non-proliferative DR (NPDR), and proliferative DR (PDR)] with no DKD), DR complicated with DKD groups (DR+DKD group). The biochemical profile, including fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and lipid profile were estimated, and plasma inflammatory and angiogenic cytokines [monocyte chemoattractant protein-1 (MCP-1), interleukin (IL)-6, IL-8, vascular endothelial growth factor (VEGF)-A, C, D, and placental growth factor (PlGF)] were analyzed by protein microarrays. The atherogenic plasma index (API) was defined as low-density lipoprotein cholesterol (LDL-C)/high-density lipoprotein-cholesterol (HDL-C); atherogenic index (AI) was calculated as [(total cholesterol (TC) -HDL-C)/HDL-C], and atherogenic index of plasma (AIP) was defined as log (TG/HDL-C).

Results

By multivariable disordered regression analysis, after controlling for duration of DM and hypertension, LDL-C (p = 0.019) and VEGF-D (p = 0.029) resulted as independent risk factors for DR. Albumin-to-creatinine ratio (uACR) (p = 0.003) was an independent risk factor for DR with DKD. In DR, NPDR, and PDR groups, grades of A1, A2, and A3 of albuminuria increased with the severity of DR. In A1, A2, and A3 grade groups, the severity of DR (DM, NPDR, and PDR) increased with higher albuminuria grades. Kendall's tau-b correlation coefficient analysis revealed that FBG (p = 0.019), circulating level of PlGF (p = 0.002), and VEGF-D (p = 0.008) were significantly positively correlated with the grades of uACR (p < 0.001), and uACR grades were significantly correlated with DR severity (p < 0.001).

Conclusions

The occurrence and severity of DR are closely correlated with kidney dysfunction. Among the three kidney functional parameters, uACR resulted as the better indicator of DR severity and progression than glomerular filtration (eGFR) and serum creatinine (Scr). Impaired FBG was associated with microalbuminuria, emphasizing that well-controlled FBG is important for both DR and DKD. The link between diabetic retinal and renal microvasculopathy was associated with dyslipidemia and upregulated circulating level of angiogenic cytokines.

Predicting diabetic nephropathy in type 2 diabetic patients using machine learning algorithms

Background

Global healthcare centers today are challenged by the dramatic increase in the prevalence of diabetes. Also, complications from diabetes are a major cause of deaths worldwide. One of the most frequent microvascular complications in diabetic patients is diabetic nephropathy (DN) which is the leading cause of death and end-stage renal disease (ESRD). Despite the different risk factors for DN identified in previous research, machine learning (ML) methods can help determine the importance of the predictors and prioritize them.

Objective

The main focus of this investigation is on predicting the incidence of DN in type 2 diabetic mellitus (T2DM) patients using ML algorithms.

Methods

Demographic information, laboratory results, and examinations on 6235 patients with T2DM covering a period of 10 years (2011-2020) were extracted from the electronic database of the Diabetes Clinic of the Imam Khomeini Hospital Complex (IKHC) in Iran. Recursive feature elimination using the cross-validation (RFECV) technique was then used with the three classification algorithms to select the important risk factors. Next, five ML algorithms were used to construct a predictive model for DN in T2DM patients. Finally, the results of the algorithms were evaluated according to the AUC criteria and the one with the best performance in terms of prediction and classification was selected.

Results

The 18 DN risk factors selected by RFECV were age, diabetes duration, BMI, SBP, hypertension, retinopathy, ALT, CVD, 2HPP, uric acid, HbA1c, waist-to-hip ratio, cholesterol, LDL, HDL, FBS, triglyceride, and serum insulin. Based on a 10-fold cross-validation, the best performance among the five classification algorithms was that of the random forest with 85% AUC.

Conclusions

This investigation validates the known risk factors for DN and emphasizes the importance of controlling the blood pressure, weight, cholesterol, and blood sugar of T2DM patients. In addition, as an example of the application of ML approaches in medical predictions, the findings of this study demonstrate the advantages of using these techniques.

Status and Trends of the Association Between Diabetic Nephropathy and Diabetic Retinopathy From 2000 to 2021: Bibliometric and Visual Analysis

Background: Diabetic nephropathy (DN) and diabetic retinopathy (DR) are microvascular complications of diabetes that share a similar pathogenesis and clinical relevance. The study aimed to visually analyze the research status and development trend of the relationship between DN and DR by means of bibliometrics and knowledge mapping.

Methods: Publications were collected from the Science Citation Index-Expanded of the Web of Science Core Collection between 2000 and 2021. CiteSpace, Alluvial Generator, and Microsoft Excel were used to analyze and present the data.

Results: A total of 3,348 publications were retrieved and 3,285 were included in the analysis after deduplication. The publications demonstrated an annually increasing trend. The results of the collaborative network analysis showed that the United States, Steno Diabetes Center, and Tien Y. Wong were the most influential country, institution and author, in this field of research, respectively. The analysis of references and keywords showed that the pathogenesis of DN and DR and their relationship with cardiovascular disease are research hotspots. The clinical relevance and drug therapy for DN and DR will become frontiers of future research in this field.

Conclusion: This study is the first to visualize the correlation between DN and DR using a bibliometric approach. This study provides a reference of research trends for scholars.

Evaluation of Functional Magnetic Resonance Imaging under Artificial Intelligence Algorithm on Plan-Do-Check-Action Home Nursing for Patients with Diabetic Nephropathy

This study aimed to evaluate the effect of functional magnetic resonance imaging (fMRI) under the fuzzy C-means (FCM) clustering algorithm on plan-do-check-action (PDCA) home nursing for patients with diabetic nephropathy (DN). As the characteristics of fMRI image data were combined, the FCM algorithm was improved and applied into the clustering processing of fMRI activation regions of patients. 64 patients with DN were chosen as the research objects and were divided into the research group with PDCA home nursing and the control group with routine home nursing. The patients were randomly divided into the research group (n = 32) and the control group (n = 32). The curative effect, nursing satisfaction, and quality of life of patients after nursing were compared. The results showed that the coverage of fMRI activation points was significantly higher as being detected by the FCM algorithm, and the running time was shortened by 33.6 min. After nursing, the total effective rates in the research group and the control group were 87.5% vs. 34.4% in 3 months, 93.8% vs. 68.8% in 6 months, and 96.9% vs. 75.0% in 12 months, respectively; those in the research group were significantly higher than those in the control group (P < 0.05). The nursing satisfaction score (91.3 ± 4.5 vs. 80.9 ± 5.2) and nursing service quality score (89.7 ± 6.6 vs. 80.3 ± 7.1) in the research group were also significantly higher than those in the control group (P < 0.05). Meanwhile, the scores of each item after nursing in the research group were significantly higher than those in the control group (P < 0.05). The improved FCM algorithm detected the activation regions in the fMRI images more effectively, which could provide help for diagnosis and reduce error and misdiagnosis. At the same time, the PDCA home nursing also offered great help to the recovery of patients with DN, which was more superior for the curative effect of hospitalization, the promotion of recovery, and the improvement of patients' quality of life.