Glycemic Gap as a Useful Surrogate Marker for Glucose Variability and Progression of Diabetic Retinopathy

Current landscape and comprehensive management of glycemic variability in diabetic retinopathy

Diabetic retinopathy (DR), a well-known microvascular complication of diabetes mellitus, remains the main cause of vision loss in working-age adults worldwide. Up to now, there is a shortage of information in the study regarding the contributing factors of DR in diabetes. Accumulating evidence has identified glycemic variability (GV), referred to fluctuations of blood glucose levels, as a risk factor for diabetes-related complications. Recent reports demonstrate that GV plays an important role in accounting for the susceptibility to DR development. However, its exact role in the pathogenesis of DR is still not fully understood. In this review, we highlight the current landscape and relevant mechanisms of GV in DR, as well as address the mechanism-based therapeutic strategies, aiming at better improving the quality of DR management in clinical practice.

Predictive value of glycemic gap and stress glycemia ratio among critically ill patients with acute kidney injury: a retrospective analysis of the MIMIC-III database

BACKGROUND AND AIMS

Acute hyperglycemia has been identified as a risk factor for acute kidney injury occurrence and mortality in various diseases. The aim of the current study was to investigate the relationship between stress-induced hyperglycemia and adverse outcomes in critically ill patients with AKI.

METHODS

We extracted clinical data from Multiparameter Intelligent Monitoring in Intensive Care III version 1.4. Blood glucose and glycosylated hemoglobin during the first 24 h of ICU admission were used to calculate glycemic gap and stress hyperglycemia ratio (SHR). The outcomes included ICU mortality and need for renal replacement therapy. The association of the glycemic gap and SHR with outcomes were determined via logistic regression model and receiver-operating curves. The subgroup analysis of patients with and without diabetes was performed separately.

RESULTS

Higher glycemic gap and SHR were observed in patients who had increased need of RRT, higher mortality rates and longer ICU stay. Multivariate analysis demonstrated that higher glycemic gap (OR 1.01, 95%CI 1.00-1.02, P = 0.015), as well as SHR (OR 1.32; 95%CI 1.07-1.64, P = 0.009), were independently associated with ICU mortality after adjusting for potential covariates. In subgroup analysis, the association of glycemic gap and SHR were only significant in the non-diabetic population as for the outcome of ICU mortality (OR 2.25, 95%CI 1.64-3.08, P < 0.001 and OR 1.99; 95%CI 1.46-2.72, P < 0.001, respectively).

CONCLUSIONS

The glycemic gap and SHR might serve as a potential prognostic indicator of ICU mortality in critically ill patients with AKI, especially in the non-diabetic population.

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

Background

Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.

Methods

Clinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

Results

The predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram.

Conclusions

The predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy.

Type 2 Diabetes Mellitus with Tight Glucose Control and Poor Pre-Injury Stair Climbing Capacity May Predict Postoperative Delirium: A Secondary Analysis

(1) Background: Previous evidence demonstrates that tight glycemic control and good physical function could reduce the risk of delirium. This study aimed to investigate whether the occurrence of postoperative delirium (POD) in older hip fracture surgery patients is associated with preoperative glycemic control factors or pre-injury physical performance. (2) Methods: Three-hundred and nine individuals aged over 65 years and scheduled for hip fracture surgery were included at a single center. Glycemic control factors and pre-injury physical performance were assessed preoperatively. The presence of delirium was assessed using the Confusion Assessment Method on postoperative hospitalization days. Univariate and multivariable logistic regression models and a risk prediction model of POD were established. (3) Results: Among the 309 patients, 52 (16.83%) experienced POD during the hospital stay. The numbers of pre-injury physical performance and type 2 diabetes mellitus (T2DM) patients were significantly different in the POD and non-POD groups. The multivariable model showed that development of delirium was significantly explained by preoperative fasting blood glucose (FBG) (OR 0.804, p = 0.004), stair climbing (OR 0.709, p = 0.003), T2DM (odds ratio (OR) 3.654, p = 0.001), and age-adjusted Charlson comorbidity index (ACCI) (OR 1.270, p = 0.038). The area under the receiver operating characteristic curve (AUROC) of the risk prediction model including those covariates was 0.770. (4) Conclusions: More older T2DM patients develop POD after hip fracture surgery than patients without T2DM. A simple assessment of preoperative FBG and pre-injury stair climbing capacity may identify those at high risk for the development of POD. Higher preoperative FBG and good pre-injury stair climbing capacity are protective factors for POD.