Effect of Statin Therapy on Diabetes Retinopathy in People With Type 2 Diabetes Mellitus: A Meta-Analysis

Drug Exposure As a Predictor in Diabetic Retinopathy Risk Prediction Models-A Systematic Review and Meta-Analysis

PURPOSE

To conduct a systematic review to assess drug exposure handling in diabetic retinopathy (DR) risk prediction models, a network-meta-analysis to identify drugs associated with DR and a meta-analysis to determine which drugs contributed to enhanced model performance.

DESIGN

Systematic review and meta-analysis.

METHODS

We included studies presenting DR models incorporating drug exposure as a predictor. We searched EMBASE, MEDLINE, and SCOPUS from inception to December 2023. We evaluated the quality of studies using the Prediction model Risk of Bias Assessment Tool and certainty using GRADE. We conducted network meta-analysis and meta-analysis to estimate the odds ratio (OR) and pooled C-statistic, respectively, and 95% confidence intervals (CI) (PROSPERO: CRD42022349764).

RESULTS

Of 5,653 records identified, we included 28 studies of 678,837 type 1 or 2 diabetes participants, of which 38,579 (5.7%) had DR. A total of 19, 3, and 7 studies were at high, unclear, and low risk of bias, respectively. Drugs included in models as predictors were: insulin (n = 24), antihypertensives (n = 5), oral antidiabetics (n = 12), lipid-lowering drugs (n = 7), antiplatelets (n = 2). Drug exposure was modelled primarily as a categorical variable (n = 23 studies). Two studies handled drug exposure as time-varying covariates, and one as a time-dependent covariate. Insulin was associated with an increased risk of DR (OR = 2.50; 95% CI: 1.61-3.86). Models that included insulin (n = 9) had a higher pooled C-statistic (C-statistic = 0.84, CI: 0.80-0.88), compared to models (n = 9) that incorporated a combination of drugs alongside insulin (C-statistic = 0.79, CI: 0.74-0.84), as well as models (n = 3) not including insulin (C-statistic = 0.70, CI: 0.64-0.75). Limitations include the high risk of bias and significant heterogeneity in reviewed studies.

CONCLUSION

This is the first review assessing drug exposure handling in DR prediction models. Drug exposure was primarily modelled as a categorical variable, with insulin associated with improved model performance. However, due to suboptimal drug handling, associations between other drugs and model performance may have been overlooked. This review proposes the following for future DR prediction models: (1) evaluation of drug exposure as a variable, (2) use of time-varying methodologies, and (3) consideration of drug regimen details. Improving drug exposure handling could potentially unveil novel variables capable of significantly enhancing the predictive capability of prediction models.

Novel insights into the mechanisms of hard exudate in diabetic retinopathy: Findings of serum lipidomic and metabolomics profiling

Objective

Retinal hard exudates (HEs) result from lipoproteins leaking from capillaries into extracellular retinal space, and are related to decreased visual acuity in diabetic retinopathy (DR). This study aims to identify differential serum lipids and metabolites associated with HEs.

Materials and methods

A cross-sectional study was conducted Jul 2017 ∼ Mar 2021. We assessed the amount of HEs using standard ETDRS photographs for comparison. HEs severity was rated as "no or questionable", "moderate" or "severe". Serum samples were processed via high coverage pseudotargeted lipidomics analysis, and untargeted liquid chromatography coupled with time-of-flight mass spectrometry for metabolomics study, respectively. Weighted gene co-expression network analyses, partial least squares-discriminant analysis, and multi-receiver operating characteristic analysis were applied.

Results

A total of 167 patients were included. Discovery group: 116 eyes (116 patients). Validation group: 51 eyes (51 patients). 888 lipids were detected and divided into 18 modules (MEs), ME1 ∼ ME18. Lipids in ME1 significantly increased in patients with HEs in DR (NPDR and PDR combined), NPDR, and PDR, respectively. ME1 enriched to triglycerides (29%), ceramides (17%), and N-acylethanolamines (15%). A combined model of 20 lipids was the best to discriminate HEs, area under curve = 0.804, 95% confidence interval = 0.674-0.916. For metabolomics analysis, 19 metabolites and 13 pathways associated with HEs were identified. Taurine and hypotaurine metabolism, cysteine and methionine metabolism were closely related to HEs (P < 0.01).

Conclusions

The lipids and metabolites identified may serve as prediction biomarkers in the early stage of HEs in DR.

A prediction model for worsening diabetic retinopathy after panretinal photocoagulation

BACKGROUND

As one of the severe complications of diabetes mellitus, diabetic retinopathy (DR) is the leading cause of blindness in the working age worldwide. Although panretinal photocoagulation (PRP) was standard treatment, PRP-treated DR still has a high risk of progression. Hence, this study aimed to assess the risk factors and establish a model for predicting worsening diabetic retinopathy (DR-worsening) within five years after PRP.

METHODS

Patients who were diagnosed with severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and treated with PRP were included, and those patients were randomly assigned to either a training or validation cohort. The multivariate logistic regression analysis was used to screen potential risk factors for DR-worsening in the training cohort. Then the model was established after including significant independent risk factors and further validated using discrimination and calibration.

RESULTS

A total of 271 patients were included, and 56.46% of patients had an outcome of DR-worsening. In the training cohort (n = 135), age (odds ratio [OR] = 0.94, 95% confidence interval [CI] 0.90-0.98), baseline best corrected visual acuity (logMAR) (OR = 10.74, 95% CI 1.84-62.52), diabetic nephropathy (OR = 9.32, 95% CI 1.49-58.46), and hyperlipidemia (OR = 3.34, 95% CI 1.05-10.66) were screened out as the independent risk factors, which were incorporated into the predictive model. The area under the receiver operating characteristic curve and calibration slope in the training and validation cohort were 0.79, 0.96 (95% CI 0.60-1.31), and 0.79, 1.00 (95% CI 0.66-1.34), respectively. Two risk groups were developed depending on the best cut-off value of the predicted probability, and the actual probability was 34.90% and 82.79% in the low-risk and high-risk groups, respectively (P < 0.001).

CONCLUSIONS

This study developed and internally validated a new model to predict the probability of DR-worsening after PRP treatment within five years. The model can be used as a rapid risk assessment system for clinical prediction of DR-worsening and identify individuals at a high risk of DR-worsening at an early stage and prescribe additional treatment.