Potential Clinical Error Arising From Use of HbA1c in Diabetes: Effects of the Glycation Gap

Association between the haemoglobin glycation index (HGI) and clinical outcomes in patients with acute decompensated heart failure

BACKGROUND

A growing number of studies show that people with similar blood glucose levels have different levels of glycosylated haemoglobin (HbA1c), and relying only on HbA1c may lead to clinical decision-making errors. The haemoglobin glycation index (HGI) quantifies the difference in HbA1c among individuals and is strongly linked to the risk of cardiovascular disease. However, the connection between this phenomenon and the poor outcomes of patients with acute decompensated heart failure (ADHF) is currently unknown.

PATIENTS AND METHODS

This retrospective, single-centre-based cohort study included 1531 hospitalized patients with ADHF from September 2010 to January 2020. The HGI is calculated from the difference between the observed and predicted HbA1c values [predicted HbA1c = 0.024 × fasting plasma glucose (FPG) (mg/dL)+3.1]. The endpoints examined in the study included all-cause death, cardiovascular (CV) death, and major adverse cardiac events (MACE). We fitted multivariable-adjusted Cox proportional hazard models to investigate the association between the HGI and clinical outcomes.

RESULTS

During the five-year follow-up, 427 (27.9%) patients died from all causes, 232 (15.6%) from CV death, and 848 (55.4%) from MACE. The restricted cubic spline analysis also showed that the cumulative risk of all-cause and CV deaths decreased linearly with increasing HGI. According to multivariate Cox proportional hazard models, the highest tertile of the HGI was associated with a lower incidence of all-cause and cardiovascular deaths [all-cause death, adjusted hazard ratio (HR): 0.720, 95% confidence interval (CI): 0.563-0.921, p = 0.009; CV death, adjusted HR: 0.619, 95% CI: 0.445-0.861, p = 0.004]. A 1% increase in the HGI was associated with a 12.5% reduction in the risk of all-cause death and a 20.8% reduction in the risk of CV death.

CONCLUSIONS

A high HGI was directly associated with a reduction in all-cause and CV deaths but was not associated with MACE. These findings may be helpful in the management of patients with ADHF.

Sex-specific associations between haemoglobin glycation index and the risk of cardiovascular and all-cause mortality in individuals with pre-diabetes and diabetes: A large prospective cohort study

AIM

The aim of this study was to investigate the relationship between the haemoglobin glycation index (HGI), and cardiovascular disease (CVD) and all-cause mortality in adults with pre-diabetes and diabetes.

METHODS

This study included 10 267 adults with pre-diabetes and diabetes from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. Sex-differentiated relationships between HGI and mortality were elucidated using multivariate Cox proportional hazards models, restricted cubic splines and a two-piecewise Cox proportional hazards model.

RESULTS

During the median follow-up time of 103.5 months, a total of 535 CVD deaths and 1918 all-cause deaths were recorded. After multivariate adjustment, in males with pre-diabetes and diabetes, there was a U-shaped relationship between HGI and CVD mortality and all-cause mortality, with threshold points of -0.68 and -0.63, respectively. Before the threshold point, HGI was negatively associated with CVD mortality [hazard ratio (HR) 0.60; 95% confidence interval (CI) 0.41, 0.89] and all-cause mortality (HR 0.56; 95% CI 0.43, 0.74), and after the threshold point, HGI was positively associated with CVD mortality (HR 1.46; 95% CI 1.23, 1.73) and all-cause mortality (HR 1.40; 95% CI 1.23, 1.59). In contrast, HGI had an L-shaped relationship with all-cause mortality and no significant association with CVD mortality in females. To the left of the threshold points, the risk of all-cause mortality decreased (HR 0.50; 95% CI 0.35, 0.71) progressively with increasing HGI.

CONCLUSIONS

In the cohort study, HGI in pre-diabetic and diabetic populations was found to have a U-shaped association with CVD mortality and all-cause mortality in males and an L-shaped association with all-cause mortality only in females. Further prospective and mechanistic studies are warranted.

The Association of High and Low Glycation With Incident Diabetic Retinopathy in Adults With Type 1 Diabetes

BACKGROUND

We investigated the risk of incident diabetic retinopathy (DR) among high glycator compared to low glycator patients based on the hemoglobin glycation index (HGI). Visit-to-visit variations in HGI also were assessed.

METHODS

Glycated hemoglobin (HbA1c) and continuous glucose monitoring data were collected up to 7 years prior to the date of eye examination defining incident DR or no retinopathy (control). Hemoglobin glycation index was calculated as difference in measured HbA1c and an estimated A1c from sensor glucose (eA1c) to define high (HbA1c - eA1c >0%) or low (HbA1c - eA1c <0%) glycator. Stable glycators were defined as ≥75% of visits with same HGI category. Logistic regression was used to assess the association between glycation category and incident DR.

RESULTS

Of 119 adults with type 1 diabetes (T1D), 49 (41%) were stable low glycator (HbA1c - eA1c <0%), 36 (30%) were stable high glycator (HbA1c - eA1c >0%), and 34 (29%) were unstable glycator. Using alternate criteria to define high vs low glycator (consistent difference in HbA1c - eA1c of > 0.4% or <0.4%, respectively), 53% of the adults were characterized as unstable glycator. Compared to low glycators, high glycators did not have a significantly higher risk for incident DR over time when adjusted for age, T1D duration and continuous glucose monitoring (CGM) sensor type (odds ratio [OR] = 1.31, 95% confidence interval [CI] = 0.48-3.62, P = .15).

CONCLUSIONS

The risk of diabetic retinopathy was not found to differ significantly comparing high glycators to low glycators in adults with T1D. Moreover, HbA1c - eA1c relationship was not stable in nearly 30% to 50% adults with T1D, suggesting that discordance in HbA1c and eA1c are mostly related either HbA1c measurements or estimation of A1c from sensor glucose rather than physiological reasons.

Advanced Glycation Endproducts: A Marker of Long-term Exposure to Glycemia

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.

Lower Dietary Magnesium Is Associated with a Higher Hemoglobin Glycation Index in the National Health and Nutrition Examination Survey

The data for the effect of dietary magnesium (Mg) on hemoglobin glycation index (HGI) is limited. Thus, this study aimed to examine the relationship between dietary Mg and HGI in the general population. Our research used data from the National Health and Nutrition Examination Survey from 2001 to 2002. The dietary intake of Mg was assessed by two 24-h dietary recalls. The predicted HbA1c was calculated based on fasting plasma glucose. Logistic regression and restricted cubic spline models were applied to assess the relationship between dietary Mg intake and HGI. We found a significant inverse association between dietary Mg intake and HGI (β =  - 0.00016, 95%CI: - 0.0003, - 0.00003, P = 0.019). Dose-response analyses revealed that HGI decreased with increasing intakes of Mg when reached the point above 412 mg/day. There was a linear dose-response relationship between dietary Mg intake and HGI in diabetic subjects, and there was an L-shape dose-response relationship in non-diabetic individuals. Increasing the intake of Mg might help lower the risk associated with high HGI. Further prospective studies are requested before dietary recommendations.

The Correlation Between Glycation Gap and Renal Complications in Patients with Type 2 Diabetes Mellitus

Purpose

The aim of this study was to investigate the correlations between the glycation gap (GG) and renal complications such as urinary albumin-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) in type 2 diabetes mellitus patients.

Materials and Methods

A cross-sectional study was conducted on 104 individuals (52 males and 52 females), aged 36-93 years old. Fasting blood glucose (FBG), HbA1c, and serum fructosamine were measured simultaneously. GG was calculated as the difference between the measured and fructosamine-based predicted HbA1c levels (FHbA1c).

Results

There was a moderately positive correlation between HbA1c and fructosamine concentration (r = 0.488; p < 0.001). GG was positively correlated with UACR (r = 0.3275; p = 0.0007), negatively correlated with eGFR (r = -0.3400; p = 0.0004). HbA1c was positively correlated with UACR (r = 0.2437; p = 0.0127) but not correlated with eGFR (r = -0.444; p = 0.6542). Fructosamine has a positive correlation with eGFR (r = 0.2426; p = 0.0131) but not with UACR (r = -0.1021; p = 0.3025).

Conclusion

GG was positively correlated with UACR and inversely correlated with eGFR in type 2 Diabetes mellitus patients. This suggests that GG is a valuable index for predicting kidney complications due to diabetes.

Clinical Risk Assessment and Comparison of Bias between Laboratory Methods for Estimation of HbA1c for Glycated Hemoglobin in Hyperglycemic Patients

INTRODUCTION

Hemoglobin A1c (HbA1c), also known as glycated hemoglobin, is a blood test used to evaluate and track a patient's blood sugar levels over the previous 2-3 months. We have compared the analytical performance of the D10 hemoglobin (HPLC) testing system to that of the immunoturbidimetric technique, which is a light-scattering immunoassay.

OBJECTIVES

To assess the clinical risk assessment between two methods (Compare the two Immunoturbidometric methods (AU680) vs. HPLC method (D10)) in hyperglycemic patients and assess the acceptability of the respective methods in the Clinical biochemistry laboratory.

METHODS

The charge of the globins in Hb was used as the basis for the HPLC method used to measure HbA1c. HPLC detects and quantifies even the tiniest Hb fractions and the full spectrum of Hb variants. HbA1c was measured using the immunoturbidimetric (AU 680 Beckmann coulter analyzer) and high-performance liquid chromatography (HPLC) techniques. Experiments also made use of immunoturbidimetric techniques (using an AU 680 Beckmann coulter analyzer equipment).

RESULTS

There is no statistically significant difference in HbA1c readings between male and female patients, as measured by either the Immunoturbidimetric or HPLC techniques.

CONCLUSION

The immunoturbidimetric and high-performance liquid chromatography techniques for estimating HbA1c yielded identical results. From the results of this study, we may deduce that both techniques are valid for estimating HbA1c. As a result, it may be suggested that both approaches can be used to estimate HbA1c in diabetic individuals.

1-Amino-1-deoxy-d-fructose ("fructosamine") and its derivatives: An update

1-Amino-1-deoxy-d-fructose (fructosamine, FN) derivatives are omnipresent in all living organisms, as a result of non-enzymatic condensation and Amadori rearrangement reactions between free glucose and biogenic amines such as amino acids, polypeptides, or aminophospholipids. Over decades, steady interest in fructosamine was largely sustained by its role as a key intermediate structure in the Maillard reaction that is responsible for the organoleptic and nutritional value of thermally processed foods, and for pathophysiological effects of hyperglycemia in diabetes. New trends in fructosamine research include the discovery and engineering of FN-processing enzymes, development of advanced tools for hyperglycemia monitoring, and evaluation of the therapeutic potential of both fructosamines and FN-recognizing proteins. This article covers developments in the field of fructosamine and its derivatives since 2010 and attempts to ascertain challenges in future research.

The Association Between Hemoglobin Glycation Index and Carotid Artery Plaque in Patients With Coronary Heart Disease

This study aimed to examine the association between the hemoglobin glycation index (HGI) and carotid artery plaque (CAP) in patients with coronary heart disease (CHD). We conducted a cross-sectional analysis of 10,778 patients with CHD. The participants were divided into three groups by HGI tertiles (T1 HGI<-0.44, T2 -0.44 ≤ HGI ≤ 0.15, T3 HGI>0.15). The presence of CAP was used to diagnose by carotid ultrasonography. Logistic regression analysis was used to analyze the association between the HGI and CAP. The association between HGI and CAP was also assessed according to sex, age, smoking status, and drinking status. We further assessed the association between HGI and the ultrasound characteristics of CAP. The baseline analysis showed substantial differences in relevant parameters between the three groups of patients with CHD according to the tertiles of the HGI. Multivariate logistic regression analysis showed that HGI was significantly associated with CAP (odds ratio [OR] 1.32; 95% confidence interval [CI] 1.26-1.39). The association between HGI and CAP exists among different sex, age, smoking, and drinking status. Furthermore, there was a significant and positive association between HGI and all four different echogenicities of the CAP.

Michaelis-Menten kinetic modeling of hemoglobin A1c status facilitates personalized glycemic control

INTRODUCTION

Discrepancy between measured HbA1c and HbA1c calculated from plasma glucose is associated with higher risk for diabetic complications. However, quantification of this difference is inaccurate due to the imperfect linear conversion models. We propose to introduce a mathematical formula that correlates with the observational data and supports individualized glycemic control.

METHODS

We analysed 175,437 simultaneous plasma glucose and HbA1c records stored in our laboratory database. Employing the Michaelis-Menten (MM) equation, we compared the calculated HbA1c levels to the measured HbA1c levels. Data from patients with multiple records were used to establish the patients' glycemic status and to assess the predictive power of our MM model.

RESULTS

HbA1c levels calculated with the MM equation closely matched the population's average HbA1c levels. The Michaelis constant (Km) had a negative correlation with HbA1c (r2 = 0.403). Using personalized Km values in the MM equation, 85.1% of HbA1c predictions were within 20% error (ADAG calculation: 78.4%). MM prediction also performed better in predicting pathologic HbA1c levels (0.904 AUC vs. 0.849 AUC for ADAG).

CONCLUSION

MM equation is an improvement over linear models and could be readily employed in routine diabetes management. Km is a reliable and quantifiable marker to characterize variations in glucose tolerance.

Interaction of sex and diabetes on the association between hemoglobin glycation index, hemoglobin A1c and serum uric acid

BACKGROUND

Hemoglobin glycation index (HGI), which is calculated by blood glucose and hemoglobin A1c(HbA1c), reflects the individual discrepancy in HbA1c. This study aimed to investigate the association between HGI/HbA1c and serum uric acid(SUA) stratified by sex and diabetes.

METHODS

The study recruited 33772 participants who underwent physical examinations between April 2016 and August 2021 in Beijing Chao-Yang Hospital. A random subsample of 3000 subjects was utilized to calculate the formula of HGI and data of the remaining 30772 participants were used for analysis. HGI and HbA1c were categorized according to quartiles (Q1, Q2, Q3, Q4), using Q1 as the reference. We used multiple linear regression and restricted cubic splines for data analysis.

RESULTS

30772 participants with a mean age of 44.4 years old were included in the analysis, 48.6% (N = 14944) of which were female and 7.7% (N = 2363) with diabetes. Associations of HGI, HbA1c and SUA were modified by sex and diabetes. The relationship between SUA levels and HGI was positive in women without diabetes, with one unit increase in HGI associating with an 11.3 μmol/L increase in SUA (P < 0.001) after adjusting for other confounders. On average, each one-unit increase in HbA1c was associated with a 14.3 μmol/L decrease in SUA in women with diabetes, a 14.9 μmol/L decrease in SUA in men with diabetes, and a 16.5 μmol/L increase in SUA in women without diabetes (all P < 0.001). The SUA levels in men without diabetes showed a bell-shaped relation with HbA1c, increasing as the HbA1c rose to around 5.7% and then falling with a further increase of HbA1c (P < 0.001).

CONCLUSIONS

SUA levels were inversely correlated with HbA1c in diabetic patients, also in men with prediabetes (HbA1c ≥ 5.7%), but positively correlated with HbA1c and HGI in women without diabetes. Glycemic control may help to reduce the risk of hyperuricemia in non-diabetes women.

Dietary patterns, oxidative Stress, inflammation and biological variation in hemoglobin A1c: Association and Mediation analysis in a rural community in north China

OBJECTIVE

The aim is to assess the relationship between the hemoglobin glycation index(HGI) and dietary patterns, and investigates whether inflammation and oxidative stress mediate the relationship.

METHODS

Cross-sectional data were collected from 453 dwellers in a Chinese rural community. Diet was assessed using 24 h food recalls. Based on the energy intake ratio from three macronutrients, dietary patterns were identified by cluster analysis. The HGI was defined as the observed HbA1c minus predicted HbA1c. Indicators of inflammation and oxidative stress were assessed.

RESULT

3 dietary patterns were clustered, namely "fat(n = 100)", "balance(n = 186)" and "carbohydrate(n = 167)". The fat dietary patterns had lower HGI than the other two dietary patterns. TNFα was higher in the carbohydrate dietary pattern. Linear regression analysis suggested that the carbohydrate dietary pattern was correlated with higher HGI levels(β = 0.204,95 %CI(0.071,0.338)), compared with the fat dietary pattern. The relationship disappeared after accounting for biomarkers of inflammation and oxidative stress. Mediation analyses indicated that TNFα might explain for 19.15 % effects of the carbohydrate dietary pattern on HGI, compared with the fat dietary pattern.

CONCLUSION

The carbohydrate dietary pattern had positive associations with HGI and TNFα. TNFα partly mediated the relationship between dietary patterns and HGI.

The Fast-Glycator Phenotype, Skin Advanced Glycation End Products, and Complication Burden Among People With Type 1 Diabetes

OBJECTIVE

Existence of a fast-glycator phenotype among people with type 1 diabetes (T1D) is debated. Routine use of glucose sensors allows the comparison of long-term average glucose levels with laboratory HbA1c values. We herein evaluated whether participants with T1D and HbA1c values higher than their glucose management indicator (GMI) had greater accumulation of advanced glycation end products (AGEs) and chronic complications.

RESEARCH DESIGN AND METHODS

We included participants with T1D using the intermittently scanned continuous glucose monitoring system consecutively for at least 90 days and having a laboratory-determined HbA1c at the end of observation. Skin AGEs were estimated using the skin autofluorescence (SAF) method. The complication burden was assessed by a standardized screening. The fast-glycator phenotype was defined as having a GMI to HbA1c ratio <0.9.

RESULTS

We included 135 individuals with T1D (58% men; mean age, 44.4 years) with a mean diabetes duration of 21 years and a mean HbA1c value of 7.7%. Thirty (22.2%) were defined as having the fast-glycator phenotype. As expected, fast glycators had higher HbA1c (8.6% vs. 7.5%; P < 0.001) with similar 90-day mean glucose level (172 vs. 168 mg/dL; P = 0.52). Fast glycators had higher SAF than did other participants (2.5 vs. 2.1 arbitrary units; P = 0.005) and had a significantly higher prevalence of dyslipidemia (73% vs. 44%; P = 0.005), macroangiopathy (38% vs. 9%; P = 0.001), albuminuria (25% vs. 7%; P = 0.038), and retinopathy (61% vs. 38%; P = 0.022). After adjusting for age and dyslipidemia, the fast-glycator phenotype remained significantly associated with macroangiopathy (odds ratio 3.72; 95% CI 1.22-11.4).

CONCLUSIONS

In T1D, a fast-glycator phenotype defined by the GMI to HbA1c ratio is characterized by elevated skin AGEs and is associated with the complication burden.

Variation of glucose time in range in type 1 diabetes

INTRODUCTION

The aim of the study was to assess the variation of glucose time in range (TIR) for persons with type 1 diabetes who perform intermittently scanned continuous glucose monitoring (isCGM).

METHODS

Glucose data for 8 weeks were analysed for 166 persons. TIR was calculated over four consecutive 2 weeks periods. Sixty-one of the persons had two downloads with an interval of >3 months.

RESULTS

A total of 140 individuals (84%) used multiple daily injection, and 26 (16%) used continuous insulin infusion. The within-individual standard deviation (SD) for TIR was 6.3% corresponding to 95% limits of agreement for the difference between two TIR values of ±17.6%. Mean TIR calculated from the first and last 2 weeks was 52.2 ± 17.1% and 53.7 ± 16.4%, respectively (difference 1.5%, SD of the difference 10.4%, p = .07). For persons with two downloads separated by months, the SD of the difference in TIR was 12.6%.

CONCLUSIONS

The 95% limit of agreement for TIR is vast for persons using isCGM. It is difficult to draw firm conclusions regarding systematic differences when individual TIR from 2 weeks are compared. This may not be valid for users of insulin pumps with closed-loop insulin delivery.

Interindividual variability in average glucose-glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice

AIM

Glycated haemoglobin (HbA1c) can fail to reflect average glucose levels, potentially compromising management decisions. We analysed variability in the relationship between mean glucose and HbA1c in individuals with diabetes.

MATERIALS AND METHODS

Three months of continuous glucose monitoring and HbA1c data were obtained from 216 individuals with type 1 diabetes. Universal red blood cell glucose transporter-1 Michaelis constant K_M and individualized apparent glycation ratio (AGR) were calculated and compared across age, racial and gender groups.

RESULTS

The mean age (range) was 30 years (8-72) with 94 younger than 19 years, 78 between 19 and 50 years, and 44 were >50 years. The group contained 120 women and 96 men with 106 white and 110 black individuals. The determined K_M value was 464 mg/dl and AGR was (mean ± SD) 72.1 ± 7 ml/g. AGR, which correlated with red blood cell lifespan marker, was highest in those aged >50 years at 75.4 ± 6.9 ml/g, decreasing to 73.2 ± 7.8 ml/g in 19-50 years, with a further drop to 71.0 ± 5.8 ml/g in the youngest group (p <0 .05). AGR differed between white and black groups (69.9 ± 5.8 and 74.2 ± 7.1 ml/g, respectively; p < .001). In contrast, AGR values were similar in men and women (71.5 ± 7.5 and 72.5 ± 6.6 ml/g, respectively; p = .27). Interestingly, interindividual AGR variation within each group was at least four-fold higher than average for between-group variation.

CONCLUSIONS

In this type 1 diabetes cohort, ethnicity and age, but not gender, alter the HbA1c-glucose relationship with even larger interindividual variations found within each group than between groups. Clinical application of personalized HbA1c-glucose relationships has the potential to optimize glycaemic care in the population with diabetes.

Association of hemoglobin glycation index and glycation gap with cardiovascular disease among US adults

AIMS

To investigate the association of hemoglobin glycation index (HGI) and glycation gap (GGap), reflecting mismatches between HbA1c and other measures of glycemia, with cardiovascular disease (CVD) in the general population.

METHODS

5966 US adult (age ≥ 20 years) participants were included from the National Health and Nutrition Examination Survey (NHANES) (1999-2004). In this cross-sectional study, predicted HbA1c was calculated based on fasting plasma glucose (FPG) and glycated albumin (GA), respectively. Multivariable binary logistic regression analysis was performed to explore the association of HGI and GGap with CVD prevalence.

RESULTS

Compared to the lowest tertile, the ORs with 95% CIs for CVD across the tertiles were 1.41 (1.01, 1.96) and 0.87 (0.58, 1.31) for HGI (P for trend = 0.535) and 1.06 (0.77, 1.47) and 1.60 (1.18, 2.17) for GGap (P for trend = 0.002) in the fully-adjusted model. Besides, the discordantly high GGap/low HbA1c group was associated with higher CVD prevalence compared with the low GGap/high HbA1c group (OR = 1.50, 95% CI, 1.04-2.16, P = 0.030).

CONCLUSIONS

GGap derived from GA is associated with CVD independent of traditional risk factors, even HbA1c, in US general adults. Considering the potential limitations of HbA1c, the introduction of GGap is warranted.

1,5-Anhydroglucitol as a Marker of Acute Hyperglycemia in Cardiovascular Events

1,5-anhydroglucitol (1,5-AG) is a biomarker of acute hyperglycemia in diabetology and also in cardiodiabetology. It is used to monitor fluctuating glucose levels. 1,5-AG is a monosaccharide that is biochemically similar to D-glucose and originates from the nutrition. The presence of 1,5-AG in blood and tissue is nearly constant due to reabsorption in the renal proximal tubule. In acute hyperglycemia, renal reabsorption is inhibited by glucose and 1,5- AG is excreted in the urine, while its serum level decreases rapidly. 1,5-AG reflects glucose excursions over 1-3 days to 2 weeks. In this regard, low levels of serum 1,5-AG can be a clinical marker of short- term glycemic derangements such as postprandial hyperglycemia, which is an important risk factor for the pathogenesis of coronary artery disease (CAD) as low levels of 1,5-AG reflect severe plaque calcification in CAD and correlate with high-density lipoprotein cholesterol (HDL-C) levels. For these reasons, 1,5-AG may also be a marker for atherosclerosis; in fact an even better marker than HbA1c or fructosamine which are normally used. 1,5-AG may also be a predictor of cardiovascular disease, left ventricular dysfunction after acute coronary syndrome (ACS), and mortality after ACS. This articles reviews the current knowledge on 1,5-AG related to its use as predictor for cardiovascular events.

Hemoglobin A1c, hemoglobin glycation index, and triglyceride and glucose index: Useful tools to predict low feed intake associated with glucose intolerance in lactating sows

The aim of the present study was to evaluated hemoglobin A1c (HbA1c), the hemoglobin glycation index (HGI), and triglyceride and glucose (TG) index as predictive indicators for low feed intake in lactating sows due to glucose intolerance. Cactus (Opuntia ficus-indica) was included in sow diets as a modulating factor of glucose. Thirty-six sows were separated into three groups (Gs). Although the three groups received a conventional diet during gestation and lactation, 2.0 kg per sow per day of steam-cooked cactus (G1) and fresh cactus (G2) were added to the lactation diet as a glycemic modulating factor, with G3 serving as the control group. Glycemia was assessed via glucometer (blood glucose concentrations), HbA1c and HGI. For each indicator of glycemia the triglycerides and glucose (TG) index was evaluated. The highest blood glucose concentration was observed on day 3 of lactation (88.2 mg/dL). The average glycemic concentrations obtained from HbA1c on farrowing day (61.6 mg/dL) and day 21 of lactation (65.6 mg/dL) were lower (p<0.05) than those measured by a glucometer on the same days (71.8 and 77.7 mg/dL for farrowing day and day 21 of lactation, respectively). At farrowing, the TG index obtained from the HGI indicated that 83.0% of sows were glucose intolerant, compared to 100% according to the TG index obtained from a glucometer. At weaning, 50% of G2 did not show glucose intolerance when the TG index was calculated using the HGI, compared to 54% when it was calculated with blood glucose concentrations measured by a glucometer. All G3 sows presented glucose intolerance, regardless of the test used. The HbA1c, HGI, and TG index tests are viable alternatives to predict low feed intake due to glucose intolerance in lactating sows.

Level of Agreement and Correlation Between the Estimated Hemoglobin A1c Results Derived by Continuous or Conventional Glucose Monitoring Systems Compared with the Point-of-Care or Laboratory-Based Measurements: An Observational Study

INTRODUCTION

Hemoglobin A1C (HbA1c) is an important marker for diabetes care management. With the increasing use of new technologies such as continuous glucose monitoring (CGM) and point-of-care testing (POCT), patients and their physicians have been able to monitor and continuously check their blood glucose levels in an efficient and timely manner. This study aimed to investigate the level of agreement between the standard laboratory test for HbA1c (Lab-HbA1c) with point-of-care testing (POCT-HbA1c) and glucose monitoring index (GMI) derived by intermittently scanned CGM (isCGM) or estimated average glucose (eAG) derived by conventional self-monitored blood glucose (SMBG) devices.

METHODS

A cross-sectional study was conducted at the Diabetes Treatment Center, Prince Sultan Military Medical City, Saudi Arabia, between May and December 2020 with 81 patients with diabetes who used the isCGM system (n = 30) or conventional finger-pricking SMBG system (n = 51). At the same visit, venous and capillary blood samples were taken for routine HbA1c analysis by the standard laboratory and POCT methods, respectively. Also, for isCGM users, the GMI data for 28 days (GMI-28) and 90 days (GMI-90) were obtained, while for SMBG users, eAG data for 30 days (eAG-30) and 90 days (eAG-90) were calculated. The limits of agreement in different HbA1c measurements were evaluated using a Bland-Altman analysis. Pearson correlation and multivariate linear regression analyses were also performed.

RESULTS

Based on the Bland-Altman analysis, HbA1c levels for 96.7% and 96.1% of the patients analyzed by the POCT and the standard laboratory methods were within the range of the 95% limit of agreement in both isCGM and conventional SMBG users, respectively. About 93.3% of the GMI measurements were within the 95% limit of agreement. Also, about 94.12% of the eAG-30 and 90.2% of the eAG-90 measurements were within the 95% limit of agreement. Moreover, the correlation analysis revealed a statistically significant positive correlation and linear regression among Lab-HbA1c, POCT-HbA1c, GMI, and eAG in both conventional SMBG and isCGM users (all p < 0.001). These positive results persisted significantly after adjusting for different factors (all p < 0.001).

CONCLUSION

GMI derived by isCGM or eAG derived by conventional SMBG systems, as well as the POCT-HbA1c measurements, showed a high level of agreement; therefore, we recommend them as potential methods for diabetes monitoring, especially when a rapid result is needed or with patients with uncontrolled diabetes or on intensive insulin therapy.

Understanding the clinical implications of differences between glucose management indicator and glycated haemoglobin

Laboratory measured glycated haemoglobin (HbA1c) is the gold standard for assessing glycaemic control in people with diabetes and correlates with their risk of long-term complications. The emergence of continuous glucose monitoring (CGM) has highlighted limitations of HbA1c testing. HbA1c can only be reviewed infrequently and can mask the risk of hypoglycaemia or extreme glucose fluctuations. While CGM provides insights in to the risk of hypoglycaemia as well as daily fluctuations of glucose, it can also be used to calculate an estimated HbA1c that has been used as a substitute for laboratory HbA1c. However, it is evident that estimated HbA1c and HbA1c values can differ widely. The glucose management indicator (GMI), calculated exclusively from CGM data, has been proposed. It uses the same scale (% or mmol/mol) as HbA1c, but is based on short-term average glucose values, rather than long-term glucose exposure. HbA1c and GMI values differ in up to 81% of individuals by more than ±0.1% and by more than ±0.3% in 51% of cases. Here, we review the factors that define these differences, such as the time period being assessed, the variation in glycation rates and factors such as anaemia and haemoglobinopathies. Recognizing and understanding the factors that cause differences between HbA1c and GMI is an important clinical skill. In circumstances when HbA1c is elevated above GMI, further attempts at intensification of therapy based solely on the HbA1c value may increase the risk of hypoglycaemia. The observed difference between GMI and HbA1c also informs the important question about the predictive ability of GMI regarding long-term complications.

A randomised controlled trial of a probiotic and a prebiotic examining metabolic and mental health outcomes in adults with pre-diabetes

AIMS

To evaluate the effect of the probiotic Lactobacillus rhamnosus HN001 and/or cereal enriched with oat-derived beta-glucan (OBG) on metabolic and mental health outcomes when administered to adults with pre-diabetes.

DESIGN

2×2 factorial design randomised, parallel-groups placebo-controlled; double-blinded for probiotic, single-blinded for cereals.

PARTICIPANTS

Community-dwelling adults aged 18-80 years with pre-diabetes: glycated haemoglobin (HbA_1c) 41-49 mmol/mol.

INTERVENTIONS

Capsules containing Lactobacillus rhamnosus (HN001) (6×10⁹ colony-forming units/day), or placebo capsules; and cereal containing 4 g/day OBG or calorie-matched control cereal, taken daily, for 6 months. Study groups were: (A) HN001 capsules+OBG cereal; (B) HN001 capsules+control cereal; (C) placebo capsules+OBG cereal and (D) placebo capsules+control cereal.

OUTCOME MEASURES

Primary outcome: HbA_1c at 6 months.

SECONDARY OUTCOMES

fasting plasma glucose, fasting insulin, homeostatic model assessment of insulin resistance, fasting lipids, blood pressure, body weight, waist circumference, body mass index and mental well-being.

RESULTS

153 participants were randomised. There was complete HbA_1c outcome data available for 129 participants. At 6 months the mean (SD) HbA_1c was 45.9 (4.4) mmol/mol, n=66 for HN001, and 46.7 (4.3) mmol/mol, n=63 for placebo capsules; 46.5 (4.0) mmol/mol, n=67 for OBG and 46.0 (4.6) mmol/mol n=62 for control cereal. The estimated difference between HN001-placebo capsules was -0.83, 95% CI -1.93 to 0.27 mmol/mol, p=0.63, and between OBG-control cereals -0.17, 95% CI -1.28 to 0.94 mmol/mol, p=0.76. There was no significant interaction between treatments p=0.79. There were no differences between groups or significant interactions between treatments for any of the secondary outcomes.

CONCLUSIONS

This study found no evidence of clinical benefit from the supplementation with either HN001 and/or cereal containing 4 g OBG on HbA_1c and all secondary outcomes relevant to adults with pre-diabetes.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clincial Trials Registry number ACTRN12617000990325.

HbA1c Is Disproportionately Higher in Women and Older People With Type 1 Diabetes Compared With Flash Glucose Monitoring Metrics of Glycemic Control

AIMS

Discrepancy between HbA1c and glucose exposure may have significant clinical implications. We sought to assess predictors of disparity between HbA1c and flash monitoring metrics and how these relate to microvascular complications.

METHODS

We conducted a cross-sectional study of adults with type 1 diabetes (n = 518). We assessed the relationship between clinic HbA1c and flash monitoring metrics, predictors of discrepancy between these measurements, and whether discrepancy was associated with microvascular complications.

RESULTS

Actual HbA1c and estimated HbA1c were strongly correlated (r = .779, P < .001). The likelihood of having a higher actual HbA1c than estimated HbA1c was greater with increasing age (OR = 1.055 per year, P < .001) and lower in men (OR = .208, P < .001). HbA1c was significantly lower in men (58 mmol/mol [51-67]) (7.5% [6.8-8.3]) compared to women (61 mmol/mol [54-70], P = .021) (7.7% [7.1-8.6]), despite no significant differences in any flash monitoring metrics. Whereas HbA1c was not different between younger (≤39 years) and older individuals (>39 years) despite significantly higher glucose exposure, in younger people, based on multiple flash monitoring metrics. Having a lower estimated than actual HbA1c was independently associated with a lower prevalence of retinopathy (OR = .55, P = .004).

CONCLUSIONS

HbA1c appears to overestimate glucose exposure in women and older people with type 1 diabetes. This has potentially important clinical implications, as is hinted at by the independent relationship with retinopathy prevalence. It may also be of relevance when considering the use of HbA1c for the diagnosis of diabetes.

Glycemic Metrics Derived From Intermittently Scanned Continuous Glucose Monitoring

BACKGROUND

Glucose data from intermittently scanned continuous glucose monitoring (isCGM) is a combination of scanned and imported glucose values. The present knowledge of glycemic metrics originate mostly from glucose data from real-time CGM sampled every five minutes with a lack of information derived from isCGM.

METHODS

Glucose data obtained with isCGM and hemoglobin A1c (HbA1c) were obtained from 169 patients with type 1 diabetes. Sixty-one patients had two observations with an interval of more than three months.

RESULTS

The best regression line of HbA1c against mean glucose was observed from 60 days prior to HbA_1c measurement as compared to 14, 30, and 90 days. The difference between HbA1c and estimated HbA1c (=glucose management indicator [GMI]) first observed correlated with the second observation (R2 0.61, P < .001). Time in range (TIR, glucose between 3.9 and 10 mmol/L) was significantly related to GMI (R2 0.87, P < .001). A TIR of 70% corresponded to a GMI of 6.8% (95% confidence interval, 6.3-7.4). The fraction of patients with the optimal combination of TIR >70% and time below range (TBR) <4% was 3.6%. The fraction of patients with TBR>4% was four times higher for those with high glycemic variability (coefficient of variation [CV] >36%) than for those with lower CV.

CONCLUSION

The individual difference between HbA1c and GMI was reproducible. High glycemic variability was related to increased TBR. A combination of TIR and TBR is suggested as a new composite quality indicator.

Effects of the hemoglobin glycation index on hyperglycemia diagnosis: Results from the REACTION study

AIMS

This study aimed to assess the effects of the hemoglobin glycation index (HGI) on hyperglycemia diagnosis and summarize the general characteristics of patients with a high-HGI phenotype.

METHODS

The fasting plasma glucose and glycated hemoglobin (HbA1c) levels of participants (n = 47,648) were used to estimate a linear regression equation and determine the baseline HGI. Overall, 42,317 participants without a history of diabetes were included in the final analysis. The participants were divided into three groups according to the tertiles (low, moderate, and high) of baseline HGI. Proportions and variables were compared among the three HGI groups. A multivariate ordered logistic regression model was used to explore associations between related variables and the high-HGI phenotype.

RESULTS

Regression analysis indicated that the high-HGI phenotype was positively associated with female sex, advanced age, obesity, increased low-density lipoprotein and triglyceride levels, decreased high-density lipoprotein cholesterol, and postprandial glycemic excursion levels (all P < 0.05). The prevalence of hyperglycemia increased from the low- to the high-HGI groups when using HbA1c for diagnosis.

CONCLUSIONS

Individuals with high HGI have similar clinical characteristics. Measuring HbA1c alone for diagnosis could lead to inappropriate diabetes management decisions in people with low or high HGI.

Standardizing the haemoglobin glycation index

Aims

A high haemoglobin glycation index (HGI) is associated with greater risk for hypoglycaemia and chronic vascular disease. Standardizing how the HGI is calculated would normalize results between research studies and hospital laboratories and facilitate the clinical use of HGI for assessing risk.

Methods

The HGI is the difference between an observed HbA1c and a predicted HbA1c obtained by inserting fasting plasma glucose (FPG) into a regression equation describing the linear relationship between FPG and HbA1c in a reference population. We used data from the 2005–2016 U.S. National Health and Nutrition Examination Survey (NHANES) to identify a reference population of 18,675 diabetes treatment–naïve adults without self‐reported diabetes. The reference population regression equation (predicted HbA1c = 0.024 FPG + 3.1) was then used to calculate the HGI and divide participants into low (<−0.150), moderate (−0.150 to <0.150) and high (≥0.150) HGI subgroups. Diabetes status was classified by OGTTs.

Results

As previously reported in multiple studies, a high HGI was associated with black race independent of diabetes status, and with older age, higher BMI and higher CRP in normal and prediabetic but not diabetic participants. The mean HGI was 0.6% higher in self‐reported diabetic adults. The HGI was not associated with plasma insulin, HOMA‐IR or 2 h OGTT in participants classified as normal, prediabetic or diabetic.

Conclusions

The regression equation derived from this demographically diverse diabetes treatment–naïve adult NHANES reference population is suitable for standardizing how the HGI is calculated for both clinical use and in research to mechanistically explain population variation in the HGI and why a high HGI is associated with greater risk for chronic vascular disease.

Can Innovative Technologies Overcome HbA1c Disparity for African-American Youth with Type 1 Diabetes?

Achieving normal or near-normal glycemic control as reflected by HbA1c levels in patients with type 1 diabetes (T1D) is important for preventing the development and progression of chronic complications. Despite delineation and dissemination of HbA1c management targets and advances in insulin pharmacology, insulin delivery systems, and glucose monitoring, the majority of children with T1D do not achieve HbA1c goals. In particular, African Americans are more likely not to reach HbA1c goals and have persistently higher HbA1c than Non-Hispanic Whites. Availability of pumps and other technology has not eliminated the disparity in HbA1c. Multiple factors play a role in the persisting racial disparity in HbA1c outcome. The carefully designed application and deployment of new technology to help the patient/family and facilitate the supportive role of the diabetes management team may be able to overcome racial disparity in glycemic outcome and improve patient quality of life.

Modeling conditional reference regions: Application to glycemic markers

Many clinical decisions are taken based on the results of continuous diagnostic tests. Usually, only the results of one single test is taken into consideration, the interpretation of which requires a reference range for the healthy population. However, the use of two different tests, can be necessary in the diagnosis of certain diseases. This obliges a bivariate reference region be available for their interpretation. It should also be remembered that reference regions may depend on patient variables (eg, age and sex) independent of the suspected disease. However, few proposals have been made regarding the statistical modeling of such reference regions, and those put forward have always assumed a Gaussian distribution, which can be rather restrictive. The present work describes a new statistical method that allows such reference regions to be estimated with no insistence on the results being normally distributed. The proposed method is based on a bivariate location-scale model that provides probabilistic regions covering a specific percentage of the bivariate data, dependent on certain covariates. The reference region is estimated nonparametrically and the nonlinear effects of continuous covariates via polynomial kernel smoothers in additive models. The bivariate model is estimated using a backfitting algorithm, and the optimal smoothing parameters of the kernel smoothers selected by cross-validation. The model performed satisfactorily in simulation studies under the assumption of non-Gaussian conditions. Finally, the proposed methodology was found to be useful in estimating a reference region for two continuous diagnostic tests for diabetes (fasting plasma glucose and glycated hemoglobin), taking into account the age of the patient.

Erythrocytes: Central Actors in Multiple Scenes of Atherosclerosis

The development and progression of atherosclerosis (ATH) involves lipid accumulation, oxidative stress and both vascular and blood cell dysfunction. Erythrocytes, the main circulating cells in the body, exert determinant roles in the gas transport between tissues. Erythrocytes have long been considered as simple bystanders in cardiovascular diseases, including ATH. This review highlights recent knowledge concerning the role of erythrocytes being more than just passive gas carriers, as potent contributors to atherosclerotic plaque progression. Erythrocyte physiology and ATH pathology is first described. Then, a specific chapter delineates the numerous links between erythrocytes and atherogenesis. In particular, we discuss the impact of extravasated erythrocytes in plaque iron homeostasis with potential pathological consequences. Hyperglycaemia is recognised as a significant aggravating contributor to the development of ATH. Then, a special focus is made on glycoxidative modifications of erythrocytes and their role in ATH. This chapter includes recent data proposing glycoxidised erythrocytes as putative contributors to enhanced atherothrombosis in diabetic patients.

Improved Estimation of Glycated Hemoglobin from Continuous Glucose Monitoring and Past Glycated Hemoglobin Data

Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination (R²), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R² = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. -0.152%, P < 0.0001], and overall 95% CI = -0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R² = 0.83, bias = 0.026%, 95% CI = -0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients (R² = 0.76, bias = -0.069%, 95% CI = -0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.

Postchallenge glucose increment was associated with hemoglobin glycation index in subjects with no history of diabetes

We investigated the association between postchallenge glucose increment and hemoglobin glycation index (HGI), the difference between observed and predicted glycated hemoglobin (HbA1c), in subjects with no history of diabetes. We enrolled 1381 subjects who attended our outpatient clinic for an oral glucose tolerance test (OGTT) to screen for diabetes. HGI was defined as observed HbA1c minus predicted HbA1c. The predicted HbA1c was calculated by entering fasting plasma glucose (FPG) level into an equation [HbA1c(%)=FPG(mg/dL)*0.029+2.9686] determined from an HbA1c versus FPG regression analysis using data from an independent cohort of 2734 subjects with no history of diabetes. The association between 2-hour glucose increment and HGI was analyzed using linear regression analyses with adjustment of relevant parameters. Overall, the proportions of subjects with normal glucose tolerance, pre-diabetes, and newly diagnosed diabetes were 42.3%, 41.3%, and 16.4%, respectively. Compared with subjects who had an HGI≤0, subjects with an HGI>0 had a lower FPG (95.0±13.3 vs 98.5±15.3 mg/dL, p<0.001) but a higher 2-hour plasma glucose (151.1±52.8 vs 144.6±51.4 mg/dL, p=0.027) and 2-hour glucose increment (56.1±46.1 vs 46.1±45.0 mg/dL, p<0.001). The 2-hour glucose increment after an OGTT was independently associated with HGI (β coefficient 0.003, 95% CI 0.002 to 0.003, p<0.001). Our findings suggested that postchallenge glucose increment was independently associated with HGI in subjects with no history of diabetes.

Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel

Diabetes mellitus is a global health concern associated with significant morbidity and mortality. Inadequate control of diabetes leads to chronic complications and higher mortality rates, which emphasizes the importance of achieving glycemic targets. Although glycated hemoglobin (HbA_1c) is the gold standard for measuring glycemic control, it has several limitations. Therefore, in recent years, along with the emergence of continuous glucose monitoring (CGM) technology, glycemic control modalities have moved beyond HbA_1c. They encompass modern glucometrics, such as glycemic variability (GV) and time-in-range (TIR). The key advantage of these newer metrics over HbA_1c is that they allow personalized diabetes management with person-centric glycemic control. Basal insulin analogues, especially second-generation basal insulins with properties such as longer duration of action and low risk of hypoglycemia, have demonstrated clinical benefits by reducing GV and improving TIR. Therefore, for more effective and accurate diabetes management, the development of an integrated approach with second-generation basal insulin and glucometrics involving GV and TIR is the need of the hour. With this objective, a multinational group of endocrinologists and diabetologists reviewed the existing recommendations on TIR, provided their clinical insights into the individualization of TIR targets, and elucidated on the role of the second-generation basal insulin analogues in addressing TIR.

Oral glucose tolerance test does not affect degree of hemoglobin glycation as measured by routine assay

BACKGROUND

Hemoglobin A1c (HbA1c) is an accurate index of fluctuation in glycemia over the 2-3 months prior to quantitative assessment. During this time, hemoglobin (Hb) slowly glycates until it shows the properties of advanced glycation end-products. Glycation kinetics is intensified by prolonged glucose exposure. In subjects undergoing oral glucose tolerance testing (OGTT), immediately after ingestion, glucose is ostensibly transported by the glucose transporter 1 (GLUT1) to erythrocyte corpuscular hemoglobin. The earliest significant measurable level of hemoglobin glycation associated with this transportation is still not clear.

SUBJECTS AND METHODS

We attempted to explore the early impact of short-term glucose load on HbA1c levels, because it is now known that transmembrane GLUT1-mediated glucose transport occurs immediately. A total of 88 participants (46 patients and 42 clinically healthy controls) underwent fasting plasma glucose quantitation during an OGTT. HbA1c, revealed by a monoclonal anti-glycation epitope antibody and adiponectin, was quantitated before (T0) and 2 hours (T120) after 80 g glucose ingestion.

RESULTS

Wilcoxon test revealed that the HbA1c values did not significantly vary (P=0.15) during the OGTT, whereas glucose concentration varied strongly between T0 and T120.

DISCUSSION

It is well known that quantitative estimation of HbA1c is informative for clinical care, independently of glucose level. The molecular mechanisms and dynamics by which glucose enters/exits red blood cells are incompletely known and may differ between individuals. We here show, for the first time, that HbA1c levels do not significantly increase during OGTT, supporting the view that non-enzymatic glycation of hemoglobin occurs slowly and that glycation during the 2 hours of an OGTT is insignificant.

Association of hemoglobin glycation index and its interaction with obesity/family history of hypertension on hypertension risk: a community-based cross-sectional survey

Background

Hemoglobin glycation index (HGI) is considered to be a convenient measurable indicator to assess the inter-individual variation of HbA1c. In the present study, we tested the relationship between HGI and risk of hypertension, and further explored the possible interacting influences of HGI with other such factors on hypertension risk among Chinese individuals.

Methods

The eligible subjects were chosen from a community-based cross-sectional survey in China. We collected relevant data and clinical indicators for each participant. HGI was calculated as “measured HbA1c-predicted HbA1c” and divided into four categories according to quartile. The following indicators were used to assess interactive effects: (1) relative excess risk due to interaction (RERI); (2) attributable proportion due to interaction (AP); and (3) synergy index (SI). Statistical analysis was performed using R software.

Results

Specifically, 1777 eligible participants were selected in this cross-sectional survey. There were 433 subjects who were identified to have hypertension (24.4%). A significant increase in the prevalence of hypertension from Q1 to Q4 of HGI was observed (p < 0.001). Multivariable logistic model demonstrated that subjects at the highest HGI group had a substantially increased risk of being hypertensive than subjects in the first quartile of HGI, as indicated by the OR value of 1.87 (95% CI 1.26–2.78). Moreover, a significant interaction between family history of hypertension and HGI on hypertension risk was detected (RERI: 1.36, 95% CI 0.11–2.63; AP: 0.43, 95% CI 0.17–0.69; and SI:2.68, 95% CI 1.10–6.48). The interactive effect between HGI and abdominal obesity was also found to be significant, as estimated by the value of RERI (1.04, 95% CI 0.24–1.85), AP (0.33, 95% CI 0.11–0.56) and SI (1.96, 95% CI 1.01–3.79). However, in the analysis of the interaction between HGI and general obesity, only the AP value (0.28, 95% CI 0.01–0.54) was observed to be significant.

Conclusion

High HGI was independently associated with the risk of hypertension. Moreover, HGI significantly shared interactions with obesity and family history of hypertension that influenced the risk of hypertension.

The Oral Glucose Tolerance Test: 100 Years Later

For over 100 years, the oral glucose tolerance test (OGTT) has been the cornerstone for detecting prediabetes and type 2 diabetes (T2DM). In recent decades, controversies have arisen identifying internationally acceptable cut points using fasting plasma glucose (FPG), 2-h post-load glucose (2-h PG), and/or HbA1c for defining intermediate hyperglycemia (prediabetes). Despite this, there has been a steadfast global consensus of the 2-h PG for defining dysglycemic states during the OGTT. This article reviews the history of the OGTT and recent advances in its application, including the glucose challenge test and mathematical modeling for determining the shape of the glucose curve. Pitfalls of the FPG, 2-h PG during the OGTT, and HbA1c are considered as well. Finally, the associations between the 30-minute and 1-hour plasma glucose (1-h PG) levels derived from the OGTT and incidence of diabetes and its complications will be reviewed. The considerable evidence base supports modifying current screening and diagnostic recommendations with the use of the 1-h PG. Measurement of the 1-h PG level could increase the likelihood of identifying high-risk individuals when the pancreatic ß-cell function is substantially more intact with the added practical advantage of potentially replacing the conventional 2-h OGTT making it more acceptable in the clinical setting.

Pitfalls of HbA1c in the Diagnosis of Diabetes

Many health care providers screen high-risk individuals exclusively with an HbA1c despite its insensitivity for detecting dysglycemia. The 2 cases presented describe the inherent caveats of interpreting HbA1c without performing an oral glucose tolerance test (OGTT). The first case reflects the risk of overdiagnosing type 2 diabetes (T2D) in an older African American male in whom HbA1c levels, although variable, were primarily in the mid-prediabetes range (5.7-6.4% [39-46 mmol/mol]) for many years although the initial OGTT demonstrated borderline impaired fasting glucose with a fasting plasma glucose of 102 mg/dL [5.7 mmol/L]) without evidence for impaired glucose tolerance (2-hour glucose ≥140-199 mg/dl ([7.8-11.1 mmol/L]). Because subsequent HbA1c levels were diagnostic of T2D (6.5%-6.6% [48-49 mmol/mol]), a second OGTT performed was normal. The second case illustrates the risk of underdiagnosing T2D in a male with HIV having normal HbA1c levels over many years who underwent an OGTT when mild prediabetes (HbA1c = 5.7% [39 mmol/mol]) developed that was diagnostic of T2D. To avoid inadvertent mistreatment, it is therefore essential to perform an OGTT, despite its limitations, in high-risk individuals, particularly when glucose or fructosamine and HbA1c values are discordant. Innate differences in the relationship between fructosamine or fasting glucose to HbA1c are demonstrated by the glycation gap or hemoglobin glycation index.

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.

Consistency of the Glycation Gap with the Hemoglobin Glycation Index Derived from a Continuous Glucose Monitoring System

BACKGROUND

Discordances between glycated hemoglobin (HbA1c) levels and glycemic control are common in clinical practice. We aimed to investigate the consistency of the glycation gap with the hemoglobin glycation index (HGI).

METHODS

From 2016 to 2019, 36 patients with type 2 diabetes were enrolled. HbA1c, glycated albumin (GA), and fasting blood glucose levels were simultaneously measured and 72-hour continuous glucose monitoring (CGM) was performed on the same day. Repeated tests were performed at baseline and 1 month later, without changing patients' diabetes management. The HGI was calculated as the difference between the measured HbA1c and the predicted HbA1c that was derived from CGM. The glycation gap was calculated as the difference between the measured and GA-based predicted HbA1c levels.

RESULTS

Strong correlations were found between the mean blood glucose (MBG)-based HGI and the prebreakfast glucose-based HGI (r=0.867, P&lt;0.001) and between the glycation gap and the MBG-based HGI (r=0.810, P&lt;0.001). A close correlation was found between the MBG-based HGI at baseline and that after 1 month (r=0.729, P&lt;0.001), with a y-intercept of 0 and a positive slope.

CONCLUSION

The HGI and glycation gap were highly reproducible, and the magnitudes of repeated determinations were closely correlated. Patients with similar mean glucose levels may have significantly different HbA1c levels.

Diabetes: an Overview for Clinical Oncologists

Diabetes and cancer are common conditions highly prevalent in the general population. The co-existence of diabetes and cancer in a patient is therefore not unexpected. Diabetes increases the risk of mortality from cancer and morbidity from the treatment of cancer. Furthermore, many cancer chemotherapeutic regimens increase glucose levels, especially those involving glucocorticoids. Many clinical oncologists will deal with patients with diabetes in their clinical work, and some working knowledge of diabetes diagnosis and management is helpful when managing such patients. This overview aims to summarise the clinical diagnosis and management of diabetes, review the potential links between diabetes and cancer, and provide some practical guidance on the management of hyperglycaemia in patients undergoing cancer therapy.

Implications of the Hemoglobin Glycation Index on the Diagnosis of Prediabetes and Diabetes

OBJECTIVE

Fasting plasma glucose (FPG), 2-hour plasma glucose (2hPG) from a 75-g oral glucose tolerance test (OGTT) and glycated hemoglobin (HbA1c) can lead to different results when diagnosing prediabetes and diabetes. The Hemoglobin Glycation Index (HGI) quantifies the interindividual variation in glycation resulting in discrepancies between FPG and HbA1c. We used data from the Vitamin D and Type 2 Diabetes (D2d) study to calculate HGI, to identify HGI-associated variables, and to determine how HGI affects prediabetes and diabetes diagnosis.

MEASUREMENTS

A linear regression equation [HbA1c (%) = 0.0164 × FPG (mg/dL) + 4.2] was derived using the screening cohort (n = 6829) and applied to calculate predicted HbA1c. This was subtracted from the observed HbA1c to determine HGI in the baseline cohort with 2hPG data (n = 3945). Baseline variables plus prediabetes and diabetes diagnosis by FPG, HbA1c, and 2hPG were compared among low, moderate, and high HGI subgroups.

RESULTS

The proportion of women and Black/African American individuals increased from low to high HGI subgroups. Mean FPG decreased and mean HbA1c increased from low to high HGI subgroups, consistent with the HGI calculation; however, mean 2hPG was not significantly different among HGI subgroups.

CONCLUSIONS

High HGI was associated with Black race and female sex as reported previously. The observation that 2hPG was not different across HGI subgroups suggests that variation in postprandial glucose is not a significant source of population variation in HGI. Exclusive use of HbA1c for diagnosis will classify more Black individuals and women as having prediabetes compared with using FPG or 2hPG.

Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

AIMS

Our aim was to investigate the impact of glycemic variability (GV) on the relationship between glucose management indicator (GMI) and laboratory glycated hemoglobin A1c (HbA1c).

METHODS

Adult patients with type 1 diabetes mellitus (T1D) were enrolled from five hospitals in China. All subjects wore the iPro^™2 system for 14 days before HbA1c was measured at baseline, 3 months and 6 months. Data derived from iPro^™2 sensor was used to calculate GMI and GV parameters [standard deviation (SD), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE)]. Differences between GMI and laboratory HbA1c were assessed by the absolute value of the hemoglobin glycation index (HGI).

RESULTS

A total of 91 sensor data and corresponding laboratory HbA1c, as well as demographic and clinical characteristics were analyzed. GMI and HbA1c were 7.20 ± 0.67% and 7.52 ± 0.73%, respectively. The percentage of subjects with absolute HGI 0 to lower than 0.1% was 21%. GMI was significantly associated with laboratory HbA1c after basic adjustment (standardized β = 0.83, p < 0.001). Further adjustment for SD or MAGE reduced the standardized β for laboratory HbA1c from 0.83 to 0.71 and 0.73, respectively (both p < 0.001). In contrast, the β remained relatively constant when further adjusting for CV. Spearman correlation analysis showed that GMI and laboratory HbA1c were correlated for each quartile of SD and MAGE (all p < 0.05), with the corresponding correlation coefficients decreased across ascending quartiles.

CONCLUSIONS

This study validated the GMI formula using the iPro^™2 sensor in adult patients with T1D. GV influenced the relationship between GMI and laboratory HbA1c.