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

Abstract

(1) Background: Recent studies have reported that the glucose variability (GV), irrespective of glycosylated hemoglobin (HbA1c), could be an additional risk factor for the development of diabetic retinopathy (DR). However, measurements for GV, such as continuous glucose monitoring (CGM) and fasting plasma glucose (FPG) variability, are expensive and time consuming. (2) Methods: This present study aims to explore the correlation between the glycemic gap as a measurement of GV, and DR. In total, 2565 patients were included in this study. We evaluated the effect of the different types of glycemic gaps on DR progression. (3) Results: We found that the area under the curve (AUC) values of both the glycemic gap and negative glycemic gap showed an association with DR progression. (4) Conclusions: On eliminating the possible influences of chronic blood glucose controls, the results show that GV has deleterious effects that are associated with the progression of DR. The glycemic gap is a simple measurement of GV, and the predictive value of the negative glycemic gap in DR progression shows that GV and treatment-related hypoglycemia may cause the development of DR. Individual treatment goals with a reasonable HbA1c and minimal glucose fluctuations may help in preventing DR.

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

“…Earlier studies revealed that glycated hemoglobin ( 10 , 11 , 13 , 15 – 17 , 19 ) or glycemic variability ( 20 , 21 ) could be predictors for DR, but structured glycemic detection adherence was highly correlated with social support ( 24 ). Local experience-based studies from China, India, and Brazil have indicated that the routine use of SMBG was frequently challenging, mainly because of the out-of-pocket expenditures associated with glucose monitoring ( 35 ).…”

“…Several molecular and biochemical pathways are involved in the incidence and development of DR, but the interactions between various mechanisms remain to be fully elucidated ( 9 ). Clinical studies have identified a number of risk factors for DR, including demographic characteristics such as age ( 10 – 12 ), duration of diabetes ( 10 , 11 , 13 – 17 ), obesity ( 10 ), and pregnancy status in diabetic women of childbearing age ( 18 ), comorbidities or complications such as hypertension ( 10 , 12 , 16 ), dyslipidemia ( 10 , 12 , 13 ), cardiovascular disease(CVD) ( 11 , 12 ) and diabetic nephropathy ( 10 , 12 , 17 , 19 ), and other laboratory parameters such as glycated hemoglobin ( 10 , 11 , 13 , 15 – 17 , 19 ), glycemic variability ( 20 , 21 ), and susceptibility genes ( 22 ). However, the aforementioned risk factors derived from population-based studies can only account for 9% of DR progression ( 23 ).…”

“…Considerable research effort has recently focused on developing predictive models for DR using machine learning algorithms ( 10 , 12 , 13 , 16 , 19 , 21 ), but these models cannot be applied well in low-medical-resource settings. Nearly 510 million Chinese people (36.1% of the total population) live in rural areas ( 25 ), and 72.5% of patients do not comply with structured SMBG ( 24 ), not to mention consecutive follow-up records or regular laboratory tests.…”

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

“…The diagnostic criteria for SIH were as follows: (1) no previous history of diabetes, (2) admission FBG ≥ 7 mmol/L, and (3) normal HbA1c values [ 28 ]. The effect of long-term glycemic control on hyperglycemia can be excluded from the calculation of GG in diabetic patients, and GG could be a simple measurement for glucose variability [ 28 , 29 , 30 ]. GG was calculated as preoperative FBG minus the HbA1c-derived average glucose level [ 31 ].…”

Age-Related Macular Degeneration and Diabetic Retinopathy

“…Hsing et al investigated the possibility of using the glycemic gap as a surrogate for glucose variability. Findings suggest that a negative glycemic gap is associated with progression and show the importance of minimizing glucose fluctuations [10].…”