Hemoglobin glycation index as a useful predictor of therapeutic responses to dipeptidyl peptidase-4 inhibitors in patients with type 2 diabetes

Hemoglobin glycation index predicts renal function deterioration in patients with type 2 diabetes and a low risk of chronic kidney disease

AIMS

Hemoglobin glycation index (HGI) is used to describe the difference between estimated and measured glycated hemoglobin (HbA_1c). We aimed to study whether HGI can predict renal function deterioration in patients with type 2 diabetes and a low risk of chronic kidney disease (CKD).

METHODS

This retrospective cohort study enrolled 780 patients with type 2 diabetes and a low CKD risk according to the criteria of kidney disease: improving global outcomes. Participants were divided into two subgroups according to the baseline HGI calculated by fasting blood glucose and HbA_1c. Multivariate Cox proportional hazard models were used to evaluate the hazard ratios of the study endpoints. Longitudinal data was analyzed using generalized estimating equation (GEE).

RESULTS

The participants were followed for a median of 7.3 years. A high HGI predicted rapid renal function decline without or with a resultant eGFR < 60 ml/min/1.73 m², but not onset of macroalbuminuria. The longitudinal GEE model demonstrated a negative association between HGI and the predicted eGFR changes in both the 1-year and 3-year intervals.

CONCLUSIONS

HGI independently predicted renal function deterioration in patients with type 2 diabetes and a low CKD risk. Further investigations are warranted to elucidate its potential clinical impact.

Hemoglobin glycation index, calculated from a single fasting glucose value, as a prediction tool for severe hypoglycemia and major adverse cardiovascular events in DEVOTE

INTRODUCTION

Hemoglobin glycation index (HGI) is the difference between observed and predicted glycated hemoglobin A1c (HbA1c), derived from mean or fasting plasma glucose (FPG). In this secondary, exploratory analysis of data from DEVOTE, we examined: whether insulin initiation/titration affected the HGI; the relationship between baseline HGI tertile and cardiovascular and hypoglycemia risk; and the relative strengths of HGI and HbA1c in predicting these risks.

RESEARCH DESIGN AND METHODS

In DEVOTE, a randomized, double-blind, cardiovascular outcomes trial, people with type 2 diabetes received once per day insulin degludec or insulin glargine 100 units/mL. The primary outcome was time to first occurrence of a major adverse cardiovascular event (MACE), comprising cardiovascular death, myocardial infarction or stroke; severe hypoglycemia was a secondary outcome. In these analyses, predicted HbA1c was calculated using a linear regression equation based on DEVOTE data (HbA1c=0.01313 FPG (mg/dL) (single value)+6.17514), and the population data were grouped into HGI tertiles based on the calculated HGI values. The distributions of time to first event were compared using Kaplan-Meier curves; HRs and 95% CIs were determined by Cox regression models comparing risk of MACE and severe hypoglycemia between tertiles.

RESULTS

Changes in HGI were observed at 12 months after insulin initiation and stabilized by 24 months for the whole cohort and insulin-naive patients. There were significant differences in MACE risk between baseline HGI tertiles; participants with high HGI were at highest risk (low vs high, HR: 0.73 (0.61 to 0.87)95% CI; moderate vs high, HR: 0.67 (0.56 to 0.81)95% CI; p<0.0001). No significant differences between HGI tertiles were observed in the risk of severe hypoglycemia (p=0.0911). With HbA1c included within the model, HGI no longer significantly predicted MACE.

CONCLUSIONS

High HGI was associated with a higher risk of MACE; this finding is of uncertain significance given the association of HGI with insulin initiation and HbA1c.

TRIAL REGISTRATION NUMBER

NCT01959529.

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.

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.

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.

Clinical and genetic predictors of diabetes drug's response

Diabetes is a major health problem worldwide. Glycemic control is the main goal in the management of type 2 diabetes. While many anti-diabetic drugs and guidelines are available, almost half of diabetic patients do not reach their treatment goal and develop complications. The glucose-lowering response to anti-diabetic drug differs significantly between individuals. Relatively little is known about the factors that might underlie this response. The identification of predictors of response to anti-diabetic drugs is essential for treatment personalization. Unfortunately, the evidence on predictors of drugs response in type 2 diabetes is scarce. Only a few trials were designed for specific groups of patients (e.g. patients with renal impairment or older patients), while subgroup analyses of larger trials are frequently unreported. Physicians need help in picking the drug which provides the maximal benefit, with minimal side effects, in the right dose, for a specific patient, using an omics-based approach besides the phenotypic characteristics.

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

The glycation gap (GGap) and the similar hemoglobin glycation index (HGI) define consistent differences between glycated hemoglobin and actual glycemia derived from fructosamine or mean blood glucose, respectively. Such a disparity may be found in a substantial proportion of people with diabetes, being >1 U of glycated HbA1c% or 7.2 mmol/mol in almost 40% of estimations. In this review we define these indices and explain how they can be calculated and that they are not spurious, being consistent in individuals over time. We evaluate the evidence that GGap and HGI are associated with variation in risk of complications and mortality and demonstrate the potential for clinical error in the unquestioning use of HbA1c. We explore the underlying etiology of the variation of HbA1c from mean glucose in blood plasma, including the potential role of enzymatic deglycation of hemoglobin by fructosamine-3-kinase. We conclude that measurement of GGap and HGI are important to diabetes clinicians and their patients in individualization of therapy and the avoidance of harm arising from consequent inappropriate assessment of glycemia and use of therapies.

Difference between observed and predicted glycated hemoglobin at baseline and treatment response to vildagliptin-based dual oral therapy in patients with type 2 diabetes

AIM

We aimed to investigate the association of difference between observed and predicted glycated hemoglobin (dopHbA1c) and HbA1c reduction after vildagliptin-based oral therapy in patients with type 2 diabetes (T2D).

METHODS

This was a prospective observational study. Adults ≥ 20 years old with T2D and HbA1c ≧7% treated with oral anti-diabetic drugs (OADs) were eligible if their OADs were shifted to vildagliptin-based dual oral therapy. Fasting plasma glucose (FPG) and HbA1c were recorded at baseline, week 12, and week 24. To determine baseline dopHbA1c, a predicted HbA1c was calculated by inserting baseline FPG into a regression equation (HbA1c = FPG ∗ 0.0225 + 4.3806) developed from linear relationship between HbA1c and FPG in an independent cohort of 3239 outpatients with T2D (dopHbA1c = observed HbA1c - predicted HbA1c). Patients were assigned to low (≦0) or high (>0) dopHbA1c group according to their baseline dopHbA1c levels. The study endpoint was changes from baseline to week 24 in HbA1c levels.

RESULTS

A total of 1224 patients were enrolled. Patients with a dopHbA1c >0 had a greater HbA1c reduction after vildagliptin-based dual oral therapy than those with a dopHbA1c ≦0 (-1.5 ± 2.0 vs. -0.4 ± 1.0%, p < 0.001). Baseline dopHbA1c was positively associated with HbA1c reduction from baseline to week 24 (β coefficient 0.883, 95% CI 0.811 to 0.955, p < 0.001), and the association remained significant after adjustment for confounders.

CONCLUSIONS

In T2D patients with an HbA1c ≧7%, a higher baseline dopHbA1c was associated with a greater HbA1c reduction after shifting to vildagliptin-based dual oral therapy.

Evidence That Differences in Fructosamine-3-Kinase Activity May Be Associated With the Glycation Gap in Human Diabetes

The phenomenon of a discrepancy between glycated hemoglobin levels and other indicators of average glycemia may be due to many factors but can be measured as the glycation gap (GGap). This GGap is associated with differences in complications in patients with diabetes and may possibly be explained by dissimilarities in deglycation in turn leading to altered production of advanced glycation end products (AGEs). We hypothesized that variations in the level of the deglycating enzyme fructosamine-3-kinase (FN3K) might be associated with the GGap. We measured erythrocyte FN3K concentrations and enzyme activity in a population dichotomized for a large positive or negative GGap. FN3K protein was higher and we found a striking threefold greater activity (323%) at any given FN3K protein level in the erythrocytes of the negative-GGap group compared with the positive-GGap group. This was associated with lower AGE levels in the negative-GGap group (79%), lower proinflammatory adipokines (leptin-to-adiponectin ratio) (73%), and much lower prothrombotic PAI-1 levels (19%). We conclude that FN3K may play a key role in the GGap and thus diabetes complications such that FN3K may be a potential predictor of the risk of diabetes complications. Pharmacological modifications of its activity may provide a novel approach to their prevention.

Comparative Study on Adding Pioglitazone or Sitagliptin to Patients with Type 2 Diabetes Mellitus Insufficiently Controlled With Metformin

BACKGROUND

Diabetes mellitus is a progressive disorder that often requires combination therapy.

AIM

This study aimed to compare and study of add-on sitagliptin versus pioglitazone in patients with type 2 diabetes inadequately controlled with metformin.

METHODS

This 12-week, randomised, open-label and single centre study compared sitagliptin (100 mg daily, n = 80) and pioglitazone (30 mg daily, n = 80) in type 2 diabetic patients whose disease was not adequately controlled with metformin.

RESULTS

The mean change in HbA1c from baseline was -1.001 ± 0.83 with sitagliptin and -0.75 ± 1.20 with pioglitazone, and there were no significant difference between groups (P = 0.132). The mean change in fasting blood sugar (FBS) was -18.48 ± 33.32 mg/dl with sitagliptin and -20.53 ± 53.97 mg/dl with pioglitazone, and there were no significant difference between groups (P = 0.773). Sitagliptin caused 1.08 ± 2.39 kg decrease in weight, whereas pioglitazone caused 0.27 ± 2.42 kg increase in weight, with a between-group difference of 0.81 kg (P < 0.001). On the other hand, in sitagliptin group, there was greater improvement in lipid profile than pioglitazone group.

CONCLUSION

Sitagliptin and Pioglitazone demonstrated similar improvements in glycemic control in type 2 diabetes mellitus patients whose diabetes had been inadequately controlled with metformin. Nevertheless, sitagliptin was more effective than pioglitazone regarding lipid and body weight change.

Interplay between innate immunity and iron metabolism after acute pancreatitis

Emerging evidence shows that chronic low-grade inflammation and changes in markers of innate immunity are implicated in a range of metabolic abnormalities following an episode of acute pancreatitis. Also, deranged iron metabolism has been linked to type 2 diabetes mellitus, gestational diabetes, and new-onset diabetes after pancreatitis - the conditions characterized by high haemoglobin glycation index (HGI). This study aimed to investigate the associations between markers of innate immunity and iron metabolism in individuals after acute pancreatitis. Fasting blood samples were collected to analyse lipopolysaccharide binding protein (LBP), interleukin (IL)-6, tumor necrosis factor-α, hepcidin, ferritin, soluble transferrin receptor, HbA1c, and glucose. Participants were categorized into two groups: low HGI and high HGI. Linear regression analyses were conducted, and potential confounders (age, sex, ethnicity, body mass index, diabetes mellitus status, smoking status, aetiology of pancreatitis, duration, recurrence, and severity of pancreatitis) were adjusted for in 5 statistical models. A total of 93 patients following an episode of acute pancreatitis were included, of who 40 (43%) had high HGI. In the overall cohort, LBP was significantly associated with hepcidin and ferritin, and IL-6 was significantly associated with hepcidin, consistently in all the models. Further, LBP contributed to 7.7% and 9.5% of variance in hepcidin and ferritin levels, respectively, whereas IL-6 contributed to 5.3% of hepcidin variance. Upon subgroup analysis, the observed LBP associations were maintained in the high HGI subgroup only and the IL-6 association in the low HGI subgroup only. No consistently significant associations were found between any of the other markers. The interplay between LBP, IL-6, hepcidin, and ferritin characterizes metabolic derangements after acute pancreatitis and may play a role in the pathogenesis of new-onset diabetes after pancreatitis.