Glucose management indicator (GMI) may not perform equally well for different continuous glucose monitoring (CGM) systems. Thus, we aimed to develop device-specific GMI for the Guardian 3 and 4 sensors, compare them to the original GMI, and investigate the association between the glycaemic gap (=HbA1c-GMI) and HbA1c.

In a single-centre, observational study of adult type 1 diabetes patients using Guardian Sensor 3 (G3) and 4 (G4) CGM devices, we estimated HbA1c using CGM-derived mean glucose for both CGMs using linear mixed models. We compared the estimates and their residuals (G3-gap and G4-gap) to the original GMI and its residuals (Bergenstal-gap) using regression and Bland-Altman plots.

We included 120 adult type 1 diabetes patients (90 with G3 and 30 with G4) and 194 measurement points. We found that for G3 and G4 sensors, GMI significantly underestimated glycaemia in high HbA1c ranges, reaching the clinically significant 0.5 %[4.4 mmol/mol] difference at 7.6 % [60 mmol/mol] for G3 and 8.3 % [67 mmol/mol] for G4 sensors. For G4, GMI significantly overestimated glycaemia in the lower HbA1c range. We found a strong relationship between all 3 gaps and HbA1c, and the slope was steeper for the Bergenstal-gap versus the sensor-specific G3 and G4 gaps. The G3-gap was approximately half as large as the Bergenstal-gap for HbA1c > 7 % [53 mmol/mol], and the G4-gap is approximately half of the Bergenstal-gap for the whole HbA1c range.

Device-specific GMI equations could reduce the risk of clinically significant under- and overestimation of HbA1c, improving clinical decision-making.

The hemoglobin glycation index (HGI) represents the discrepancy between the glucose management indicator (GMI) based on mean blood glucose levels and laboratory values of glycated hemoglobin (HbA1c). The HGI is a promising indicator for identifying individuals with excessive glycosylation, facilitating personalized evaluation and prediction of diabetic complications. However, the factors influencing the HGI in patients with type 1 diabetes (T1D) remain unclear. Autoimmune destruction of pancreatic β cells is central in T1D pathogenesis, yet insulin resistance can also be a feature of patients with T1D and their coexistence is called "double diabetes" (DD). However, knowledge regarding the relationship between DD features and the HGI in T1D is limited.

To assess the association between the HGI and DD features in adults with T1D.

A total of 83 patients with T1D were recruited for this cross-sectional study. Laboratory HbA1c and GMI from continuous glucose monitoring data were collected to calculate the HGI. DD features included a family history of type 2 diabetes, overweight/obesity/central adiposity, hypertension, atherogenic dyslipidemia, an abnormal percentage of body fat (PBF) and/or visceral fat area (VFA) and decreased estimated insulin sensitivity. Skin autofluorescence of advanced glycation end products (SAF-AGEs), diabetic complications, and DD features were assessed, and their association with the HGI was analyzed.

A discrepancy was observed between HbA1c and GMI among patients with T1D and DD. A higher HGI was associated with an increased number of SAF-AGEs and a higher prevalence of diabetic microangiopathy (P = 0.030), particularly retinopathy (P = 0.031). Patients with three or more DD features exhibited an eight-fold increased risk of having a high HGI, compared with those without DD features (adjusted odds ratio = 8.12; 95% confidence interval: 1.52-43.47). Specifically, an elevated PBF and/or VFA and decreased estimated insulin sensitivity were associated with high HGI. Regression analysis identified estimated insulin sensitivity and VFA as factors independently associated with HGI.

In patients with T1D, DD features are associated with a higher HGI, which represents a trend toward excessive glycosylation and is associated with a higher prevalence of chronic diabetic complications.

Since the arrival of continuous glucose monitoring (CGM), the relationship between the glucose management indicator (GMI) and HbA1c has been a topic of considerable interest in diabetes research. This study aims to explore the association between the GMI/HbA1c ratio and the presence of preclinical carotid atherosclerosis in type 1 diabetes (T1D).

Individuals with T1D and no prior history of cardiovascular disease were recruited from two centers. Carotid ultrasonography was performed using a standardized protocol and carotid plaques were defined as intima-media thickness ≥ 1.5 mm. CGM-derived data were collected from a 14-day report. A GMI/HbA1c ratio < 0.90 was selected to identify "fast-glycator" phenotype.

A total of 584 participants were included (319 women, 54.6%), with a mean age of 48.8 ± 10.7 years and a mean diabetes duration of 27.5 ± 11.4 years. Carotid plaques were present in 231 subjects (39.6%). Approximately 43.7% and 13.4% of participants showed absolute differences of ≥ 0.5 and ≥ 1.0 between 14-day GMI and HbA1c, respectively. Among patients ≥ 48 years, the fast-glycator phenotype was independently associated with presence of plaques (OR 2.27, 95%CI: 1.06-4.87), even after adjusting for non-specific and T1D-specific risk factors and statin treatment. No significant association was observed in younger subjects (p for interaction < 0.05).

Fast-glycator phenotype is independently associated with atherosclerosis in T1D individuals aged ≥ 48 years, suggesting an age-related increase in the glycation risk. These findings highlight the potential of the GMI/HbA1c ratio for cardiovascular risk stratification in this population.

To explore the relationship between glucose management indicator (GMI) and HbA1c and find the affecting factors in adult T2D patients with good glycemic control.

Adult T2D patients with both HbA1c < 7% and time in range (TIR) > 70% were retrospectively analyzed. A significant difference between GMI and HbA1c was defined as an absolute value of hemoglobin glycation index (|HGI|, HbA1c minus GMI) ≥ 0.5%. Factors associated with high |HGI| were determined by logistic regression analysis. The performance of possible factors in predicting high |HGI| was verified by ROC curve analysis. And the linear relationship between GMI and HbA1c was also investigated.

Of all the 94 patients (median HbA1c 6.18%, mean GMI 6.34%) included, 28.72% had an |HGI | ≥ 0.5% and only 15.96% had an |HGI | < 0.1%. Standard deviation of blood glucose (SDBG), a glycemic variability index, affected |HGI| (OR = 3.980, P = 0.001), and showed the best performance in predicting high |HGI| (AUC = 0.712, cutoff value = 1.63 mmol/L, P = 0.001). HbA1c was linearly correlated with GMI (β = 0.295, P = 0.004). Their correlation weakened after further adjusting for SDBG (β = 0.232, P = 0.012). Linear correlation between them was closer in patients with smaller SDBG ( < 1.63 mmol/L) than those with larger SDBG (P = 0.004).

Even in adult T2D patients with good glycemic control, the discrepancy between GMI and HbA1c existed. Their relationship was affected by glycemic variability. SDBG mainly accounted for this consequence.

Chinese clinical trial registry ( www.chictr.org.cn ), ChiCTR2000034884, 2020-07-23.

The UK national pediatric diabetes audit reports higher HbA1c for children and young people (CYP) with type 1 diabetes (T1D) of Black ethnicity compared with White counterparts. This is presumably related to higher mean blood glucose (MBG) due to lower socioeconomic status (SES) and less access to technology. We aimed to determine if HbA1c ethnic disparity persists after accounting for the above variables.

A retrospective analysis of participants who received structured education in continuous glucose monitoring (CGM) use was conducted at a tertiary center. HbA1c was paired with glucose metrics from 90-day CGM data. The influence of ethnicity, SES determined by Index of Multiple Deprivation (IMD), MBG and other covariates on HbA1c was evaluated using multiple variable regression analysis. Occurrence of hypoglycemia was evaluated.

A total of 168 (79 White, 61 South Asian, 28 Black) CYP with T1D were included. There were no differences between groups for age, MBG, time in range (3.9-10.0 mmol/L), diabetes duration, gender, insulin delivery method (multiple daily injections vs continuous subcutaneous insulin infusion), or percent sensor use (PSU). In multiple variable analysis, MBG (p<0.0001), ethnicity (p<0.0001), age (p<0.001), duration of diabetes (p<0.01) and PSU (p<0.05) accounted for 81% of the variability in HbA1c. Adjusted HbA1c in the Black group (67 mmol/mol) was higher than both South Asian (63 mmol/mol) and White groups (62 mmol/mol) (p<0.001). Despite significant IMD differences between groups, it did not influence HbA1c. Multiple variable analysis showed that the Black group experienced more hypoglycemia than South Asian and White groups (<3.9 and <3.0 mmol/L, p<0.05).

CYP from Black ethnic backgrounds have a higher HbA1c compared with their South Asian and White counterparts which is clinically significant and independent of MBG, potentially contributing to increased complications risk. Additionally, the Black group experienced a higher incidence of hypoglycemia, possibly due to a treat-to-HbA1c target approach.

In this study, we assessed whether the ratio of glucose management index (GMI) to glycated albumin (GA) was linked to microvascular complications in patients with type 2 diabetes mellitus (T2DM) who also possessed a shortened erythrocyte lifespan.

This study encompassed individuals from the Tianjin Diabetic Retinopathy Screening Cohort who completed continuous glucose monitoring and had an erythrocyte lifespan of under 90 days. Differences in GMI/GA were compared between the T2DM patients with or without microvascular complications, including diabetic kidney disease (DKD) and diabetic retinopathy (DR). The relationship between GMI/GA and microvascular complications (DKD and/or DR) was assessed by dividing GMI/GA into three groups based on tertiles.

Our study comprised 140 participants with T2DM (62 men and 78 women, with a median age of 67 years) with a median DM duration of 9.68 years, a mean glycated hemoglobin A1c (HbA1c) value of 7.10%, and a median GA value of 16.10%. As expected, the lower GMI/GA group exhibited higher HbA1c and GA (P < 0.001) with similar mean glucose levels (P = 0.099). GMI/GA values were significantly higher in participants without microvascular complications than in those with microvascular complications, including DKD and/or DR (P < 0.05). After adjusting for confounders, the lowest GMI/GA group (T1) had a 3.601-fold increased risk of microvascular complications (95% CI, 1.364-9.508, P = 0.010) and a 3.830-fold increased risk of DKD, specifically (95% CI, 1.364-12.222, P = 0.023) relative to the highest group (T3).

GMI/GA serves as a novel risk indicator for microvascular complications in T2DM, independent of HbA1c.

Since the pionneer work of Meerwaldt and the Groningen team, who related skin autofluorescence (SAF) to the dermal concentrations of advanced glycation end-products (AGEs), hundreds of articles have been devoted to its application in diabetes. Due to the slow turnover of the AGEs formed on collagen of the skin, the SAF can reflect the progressive accumulation of AGEs and hence be a marker of long-term glucose exposure. Accordingly, relations with HbA1c from the previous 3-10 years have been established in both type 1 and type 2 diabetes, and even in gestational diabetes mellitus. Other important determinants of SAF exist however, notably age, renal function, diet, and genetics. SAF is also related to current and future micro- and macrovascular complications of diabetes, as expected for a marker of glycemic memory. It is also related to some important emerging diabetes complications and comorbidities such as cancer, cognitive decline and liver disease. Quantitative information on glucose exposure during the previous years may be pertinent to personnalize care for patients with diabetes: priority for glucose control when SAF is low, and for screening for complications once SAF is high.

The proposed glucosylamine oxidation pathway (GOP) is a two-step, intraerythrocyte, thermodynamically favorable nonenzymatic reaction that first binds glucose to the N-terminal valine of beta globin (βVal1) to form a closed-chain glucosylamine that can spontaneously reduce oxidized vitamin C to its antioxidant form. This review summarizes analytical, biochemical and clinical research supporting the existence of the GOP and the surprising hypothesis that βVal1 glucosylamine is a reducing agent that works cooperatively with reduced glutathione to dynamically regulate vitamin C recycling during naturally occurring periods of transiently or chronically elevated blood glucose and oxidant production. Rationale for the existence of the GOP is presented from the perspective of the hemoglobin glycation index, a clinically practical biomarker of risk for chronic vascular disease that we propose is mechanistically explained by person-to-person variation in GOP activity.

This article examines the importance of advanced glycation endproducts (AGEs) and summarizes the structure of AGEs, pathological changes associated with AGEs, the contribution of AGEs to metabolic memory, and the value of AGEs as a predictor of diabetic complications and cardiovascular disease in people with and without diabetes. As a practical focus, skin autofluorescence (SAF) is examined as an attractive approach for estimating AGE burden. The measurement of AGEs may be of significant value to specific individuals and groups, including Black and Hispanic/Latino Americans, as they appear to have higher concentrations of hemoglobin A1c (HbA1c) than would be predicted by other metrics of mean glycemia. We hypothesize that if the amount of glycation of HbA1c is greater than expected from measured glucose levels, and if AGEs are accumulating, then this accumulation of AGEs might account for the increased rate of complications of diabetes in populations with high rates of vascular disease and other complications. Thus, identifying and modifying the burden of AGEs based on measurement of AGEs by SAF may turn out to be a worthwhile metric to determine individuals who are at high risk for the complications of diabetes as well as others without diabetes at risk of vascular disease. We conclude that available evidence supports SAF as both a clinical measurement and as a means of evaluating interventions aimed at reducing the risks of vascular disease and diabetic complications.

There is an urgent need for a mass population screening tool for diabetes. Skin tissue contains a large number of endogenous fluorophores and physiological parameter markers related to diabetes. We built an excitation-emission spectrum measurement system with the excited light sources of 365, 395, 415, 430, and 455 nm to extract skin characteristics. The modeling experiment was carried out to design and verify the accuracy of the recovery of tissue intrinsic discrete three-dimensional fluorescence spectrum. Blood oxygen modeling experiment results indicated the accuracy of the physiological parameter extraction algorithm based on the diffuse reflectance spectrum. A community population cohort study was carried out. The tissue-reduced scattering coefficient and scattering power of the diabetes were significantly higher than normal control groups. The Gaussian multi-peak fitting was performed on each excitation-emission spectrum of the subject. A total of 63 fluorescence features containing information such as Gaussian spectral curve intensity, central wavelength position, and variance were obtained from each person. Logistic regression was used to construct the diabetes screening model. The results showed that the area under the receiver operating characteristic curve of the model for predicting diabetes was 0.816, indicating a high diagnostic value. As a rapid and non-invasive detection method, it is expected to have high clinical value.

Diabetic cheiroarthropathies limit hand mobility due to fibrosis and could be markers of a global profibrotic trajectory. Heterogeneity in definitions and lack of a method to measure it complicate studying associations with organ involvement and treatment outcomes. We measured metacarpophalangeal (MCP) joint extension as a metric and describe magnetic resonance (MR) imaging determinants of MCP restriction.

Adults with type 1 diabetes were screened for hand manifestations using a symptom questionnaire, clinical examination, and function [Duruoz hand index (DHI) and grip strength]. Patients were segregated by mean MCP extension (<20°, 20°-40°, 40°-60°, and >60°) for MR imaging (MRI) scanning. Patients in the four groups were compared using ANOVA for clinical features and MRI tissue measurements (tenosynovial, skin, and fascia thickness). We performed multiple linear regression for determinants of MCP extension.

Of the 237 patients (90 men), 79 (33.8%) with cheiroarthropathy had MCP extension limitation (39° versus 61°, p < 0.01). Groups with limited MCP extension had higher DHI (1.9 vs. 0.2) but few (7%) had pain. Height, systolic blood pressure, and nephropathy were associated with mean MCP extension. Hand MRI (n = 61) showed flexor tenosynovitis in four patients and median neuritis in one patient. Groups with MCP mobility restriction had the thickest palmar skin; tendon thickness or median nerve area did not differ. Only mean palmar skin thickness was associated with MCP extension angle on multiple linear regression.

Joint mobility limitation was quantified by restricted mean MCP extension and had structural correlates on MRI. These can serve as quantitative measures for future associative and interventional studies.

This study described the relationship of HbA1c and continuous glucose monitoring (CGM) derived glucose management indicator (CGM-derived GMI) and developed a model to estimate GMI based on clinical parameters (clinical-parameter GMI) for hospitalized DKD treated with insulin pump.

This observational study collected clinical data of hospitalized DKD treated with insulin pump between February 2022 to February 2023. According to estimated glomerular filtration rate (eGFR), 156 participants were divided into G1-2 (n = 64), G3 (n = 56) and G4-5 (n = 36). Correlation between HbA1c and CGM-derived GMI was tested. Study population was divided into training and validation set based on ratio of 6:4. In training set, a linear model was established to calculate clinical-parameter GMI. In validation set, paired t-test and residual analysis was used to examine the difference between CGM-derived GMI and clinical-parameter GMI.

With renal function reduced, the correlation of HbA1c and CGM-derived GMI decreased. Meanwhile, as renal function reduced, the discordance between HbA1c and CGM-derived GMI decreased as well (P = 0.009). In training set, based on eGFR stages, anemia, albumin, FBG and HbA1c, formula to clinical-parameter GMI was established. In validation set, the differences between clinical-parameter GMI and CGM-derived GMI was around 0, with 95 % confidence interval of -1.8 % to 1.5 %.

HbA1c may be less accurate to reflect glycemic condition for DKD with impaired renal function. A easily accessiable model based on clinical parameter to estimate GMI may help assess glycemia for hospitalized DKD treated with insulin pump.

Background: In recent years, continuous glucose monitor (CGM) use is increasing in people without diabetes to promote healthy lifestyle. CGM metrics such as glucose management indicator (GMI), a statistical formula to estimate glycated hemoglobin (HbA1c) from sensor glucose, is commonly used to approximate HbA1c. This study was aimed to evaluate discordance between GMI and HbA1c in people without diabetes. Methods: Children and nonpregnant adults (age ≥6 years) without diabetes (laboratory HbA1c <5.7% and negative islet antibodies) were invited to participate in a multicenter prospective study aimed to evaluate glycemic profiles in nondiabetic individuals. Each participant wore a blinded Dexcom G6 for up to 10 days. GMI was calculated from mean sensor glucose and discordance between GMI and HbA1c was analyzed. Results: Of 201 screened participants, 153 participants (mean age 31.2 ± 21.0 years, 66.0% female, HbA1c 5.1% ± 0.3%) were included in the analysis. Mean GMI was 0.59% higher than laboratory HbA1c in participants without diabetes. The discordance between GMI and HbA1c of 0.4% or greater was 71% in participants without diabetes compared with 39% in the original GMI development cohort. Conclusion: GMI does not accurately estimate HbA1c in healthy people without diabetes. Clinical trial registration number is: NCT00717977.