Contributions of Basal Glucose and Postprandial Glucose Concentrations to Hemoglobin A1c in the Newly Diagnosed Patients with Type 2 Diabetes--the Preliminary Study

Longitudinal Association of Maternal Pre-Pregnancy BMI and Third-Trimester Glycemia with Early Life Growth of Offspring: A Prospective Study among GDM-Negative Pregnant Women

Intrauterine modifiable maternal metabolic factors are essential to the early growth of offspring. The study sought to evaluate the associations of pre-pregnancy BMI and third-trimester fasting plasma glucose (FPG) with offspring growth outcomes within 24 months among GDM-negative pregnant women. Four hundred eighty-three mother –offspring dyads were included from the Shanghai Maternal-Child Pairs Cohort. The pregnant women were categorized into four mutually exclusive groups according to pre-pregnancy BMI as normal or overweight/obesity and third-trimester FPG as controlled or not controlled. Offspring growth in early life was indicated by the BAZ (BMI Z-score), catch-up growth, and overweight/obesity. Among those with controlled third-trimester FPG, pre-pregnancy overweight/obesity significantly increased offspring birth weight, BAZ, and risks of overweight/obesity (RR 1.83, 95% CI 1.23 to 2.73) within 24 months. Those who had uncontrolled third-trimester FPG had a reduced risk of offspring overweight/obesity within 24 months by 47%. The combination of pre-pregnancy overweight/obesity and maternal uncontrolled third-trimester FPG increased 5.24-fold risk of offspring catch-up growth within 24 months (p < 0.05). Maternal pre-pregnancy overweight/obesity and uncontrolled third-trimester glycemia among GDM-negative women both have adverse effects on offspring growth within 24 months. With the combination of increasing pre-pregnancy BMI and maternal third-trimester FPG, the possibility of offspring catch-up growth increases.

Increased oral processing and a slower eating rate increase glycaemic, insulin and satiety responses to a mixed meal tolerance test

PURPOSE

Variations in specific oral processing behaviours may contribute to differences in glucose, insulin and satiety responses to a standardised test meal. This study tested how natural variations in oral processing between slower and faster eaters contribute to differences in post-prandial glucose (PP glucose), insulin response (PP insulin) and post-meal satiety for a standardised test meal.

METHODS

Thirty-three participants with higher risk for type 2 diabetes consumed a standardised test-meal while being video recorded to derive specific oral processing behaviours. Plasma glucose, insulin and satiety measures were collected at baseline, during and post meal. Participants were split into slower and faster eaters using median split based on their eating rates and individual bolus properties were analysed at the point of swallow.

RESULTS

There were large variations in eating rate (p < 0.001). While there was no significant difference in PP glucose response (p > 0.05), slower eaters showed significantly higher PP insulin between 45 and 60 min (p < 0.001). Slower eaters had longer oro-sensory exposure and increased bolus saliva uptake which was associated with higher PP glucose iAUC. Faster eating rate and larger bolus particle size at swallow correlated with lower PP glucose iAUC. A slower eating rate with greater chews per bite significantly increased insulin iAUC. Faster eaters also consistently rated their hunger and desire to eat higher than slower eaters (p < 0.05).

CONCLUSIONS

Natural variations in eating rate and the associated oral processing contributed to differences in PP glucose, PP insulin and satiety responses. Encouraging increased chewing and longer oral-exposure time during consumption, may promote early glucose absorption and greater insulin and satiety responses, and help support euglycaemia.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04522063.

A new approach for investigating the relative contribution of basal glucose and postprandial glucose to HbA1C

To develop an accurate method for evaluating the relative contributions of basal glucose (BG) and postprandial glucose (PPG) to glycated haemoglobin (HbA1c) in subjects with hyperglycaemia using a Continuous Glucose Monitoring System (CGMS®). The subjects were divided into the normal glucose tolerance (NGT), impaired glucose tolerance (IGT), newly-diagnosed type 2 diabetes (NDDM), and drug-treated type 2 diabetes (T2DM) groups. We evaluated the relative contributions of BG and PPG to HbA1c in patients with hyperglycaemia according to three different baseline values. Subjects (n = 490) were grouped as follows: 92 NGT, 36 IGT, 131 NDDM, and 231 T2DM. The relative contributions of PPG to HbA1c were calculated using baseline values of 6.1 mmol/L, 5.6 mmol/L, and the 24-h glucose curve of the NGT group. The relative contribution of PPG to HbA1c decreased progressively from the IGT group to the T2DM group. Compared with the 24-h glucose curve as the baseline, the relative contribution of PPG was overestimated in 9.04% and 1.76% of the subjects when 6.1 mmol/L and 5.6 mmol/L were used as baselines, respectively (P < 0.01), in T2DM patients. The 24-h glucose curve of NGT is more suitable for studying the relative contributions of BG and PPG to HbA1c and it is more precise, as it considers physiological fluctuations in NGT after meals. However, 5.6 mmol/L can be used when the 24-h glucose curve for NGT is unavailable; using 6.1 mmol/L as a baseline value may overestimate the contribution to the HbA1c. There is no unified standard for assessing the contributions of basal glucose (BG) and postprandial glucose (PPG) to HbA1c. The 24-h glucose curve of NGT is more suitable for studying the relative contributions of BG and PPG to HbA1c, as it considers physiological fluctuations in NGT after meals. However, 5.6 mmol/L can be used when the 24-h glucose curve for NGT is unavailable; using 6.1 mmol/L as a baseline value may overestimate the contribution to the HbA1c.

Efficacy and tolerability of sitagliptin and metformin compared with insulin as an initial therapy for newly diagnosed diabetic patients with severe hyperglycaemia

The safety and efficacy of dipeptidyl peptidase-4 inhibitors in patients newly diagnosed type 2 diabetes mellitus (T2DM) with severe hyperglycaemia have remained to be sufficiently demonstrated. The aim of the present study was to determine whether sitagliptin combined with metformin as an initial treatment had non-inferior outcomes with regards to glycaemic remission and β-cell function recovery to those of standard insulin therapy in this patient group. A prospective observational study was performed comparing the effects of sitagliptin combined with metformin and insulin therapy in a real-world clinical setting. A total of 168 participants were enrolled and received sitagliptin combined with metformin (Sig) or insulin (Ins) for almost 4 weeks. In addition, each group was further stratified into three subgroups, according to glycosylated haemoglobin (HbA1c) levels (<10, 10-12 and >12%). The primary outcomes were β-cell function and changes in fasting plasma glucose (FPG) and HbA1c at the 3-month follow-up. Both insulin and sitagliptin combined with metformin reduced hyperglycaemia and achieved similar glycaemic outcomes, and no significant differences in FPG and HbA1c levels were obtained. No significant changes were observed in β-cell function concomitant with the glucose-lowering effects of the treatments. Of note, participants in the Ins group exhibited weight gain, whereas those in the Sig group had weight loss, with significant differences becoming evident after 1 month, particularly in the HbA1c <10% subgroup. As compared with insulin injection, early treatment with sitagliptin combined with metformin in newly diagnosed patients with T2DM and severe hyperglycaemia produced non-inferior outcomes with regards to glycaemic remission. Therefore, combination of sitagliptin and metformin may be a viable initial treatment option for patients who prefer an alternative to insulin injection. This study was registered with ClinicalTrials.gov under no. NCT03180281.

Making sense of glucose metrics in diabetes: linkage between postprandial glucose (PPG), time in range (TIR) & hemoglobin A1c (A1C)

While A1C is the standard diagnostic test for evaluating long-term glucose management, additional glucose data, either from fingerstick blood glucose testing, or more recently, continuous glucose monitoring (CGM), is necessary for safe and effective management of diabetes, especially for individuals treated with insulin. CGM technology and retrospective pattern-based management using various CGM reports have the potential to improve glycemic management beyond what is possible with fingerstick blood glucose monitoring. CGM software can provide valuable retrospective data on Time-in-Ranges (above, below, within) metrics, the Ambulatory Glucose Profile (AGP), overlay reports, and daily views for persons with diabetes and their healthcare providers. This data can aid in glycemic pattern identification and evaluation of the impact of lifestyle factors on these patterns. Time-in-Ranges data provide an easy-to-define metric that can facilitate goal setting discussions between clinicians and persons with diabetes to improve glycemic management and can empower persons with diabetes in self-management between clinic consultation visits. Here we discuss multiple real-life scenarios from a primary care clinic for the application of CGM in persons with diabetes. Optimizing the use of the reports generated by CGM software, with attention to time in range, time below range, and postprandial glucose-induced time above range, can improve the safety and efficacy of ongoing glucose management.

Contributions of Fasting and Postprandial Glucose Concentrations to Haemoglobin A1c in Drug-Naïve Mal-Glucose Metabolism in Chinese Population Using Continuous Glucose Monitoring System

AIM

To clarify the contributions of fasting glucose (FG) and postprandial glucose (PG) to HbA1c in drug-naïve patients with type 2 diabetes (T2D) and impaired glucose tolerate (IGT)/impaired fasting glucose (IFG).

METHODS

Continuous glucose monitoring (CGM) was performed in 305 drug-naïve Chinese patients with T2D or IGT/IFG. The incremental area under the curve (AUC) above a glucose value of 6.1 mmol/L or FG glucose levels were calculated to evaluate the contributions of PG or FG to HbA1c values.

RESULTS

According to quintiles of HbA1c, T2D patients were divided into five groups (group 1 to 5), and patients with IGT/IFG were assigned into group 0. PG was the predominant contributor in the lower groups with HbA1c 4.9∼6.0% and 6.1∼7.8%. The relative contributions of FG and PG to HbA1c had no significance in the middle groups of HbA1c (7.9∼8.7% and 8.8∼9.5%). FG contributed significantly more than PG in the higher groups of HbA1c (9.6∼10.9% and 11.0∼14.6%). Regression analyses indicate that the contributions of FG and PG were equal (both 50%) when the level of HbA1c was 8.5%.

CONCLUSIONS

In drug-naïve patients with T2D or IGT/IFG, PG contributed more in patients with HbA1c < 8.5%, whereas FG became the predominant contributor in the poorly controlled patients with HbA1c ≥ 8.5%. These results may help the health-care provider set appropriate plasma glucose testing goals with the expectation of achieving specific HbA1c values.

Relative contributions of preprandial and postprandial glucose exposures, glycemic variability, and non-glycemic factors to HbA 1c in individuals with and without diabetes

BACKGROUND/OBJECTIVE

There is substantial interest in dietary approaches to reducing postprandial glucose (PPG) responses, but the quantitative contribution of PPG to longer-term glycemic control (reflected in glycated hemoglobin, HbA_1c) in the general population is not known. This study quantified the associations of preprandial glucose exposure, PPG exposure, and glycemic variability with HbA_1c and estimated the explained variance in HbA_1c in individuals with and without type 2 diabetes (T2D).

SUBJECTS/METHODS

Participants in the A1c-Derived Average Glucose (ADAG) study without T2D (n = 77) or with non-insulin-treated T2D and HbA_1c<6.5% (T2D_{HbA1c < 6.5%}, n = 63) or HbA_1c ≥ 6.5% (T2D_{HbA1c ≥ 6.5%}, n = 34) were included in this analysis. Indices of preprandial glucose, PPG, and glycemic variability were calculated from continuous glucose monitoring during four periods over 12 weeks prior to HbA_1c measurement. In linear regression models, we estimated the associations of the glycemic exposures with HbA_1c and calculated the proportion of variance in HbA_1c explained by glycemic and non-glycemic factors (age, sex, body mass index, and ethnicity).

RESULTS

The factors in the analysis explained 35% of the variance in HbA_1c in non-diabetic individuals, 49% in T2D_{HbA1c < 6.5%}, and 78% in T2D_{HbA1c ≥ 6.5%}. In non-diabetic individuals PPG exposure was associated with HbA_1c in confounder-adjusted analyses (P < 0.05). In the T2D_{HbA1c < 6.5%} group, all glycemic measures were associated with HbA_1c (P < 0.05); preprandial glucose and PPG accounted for 14 and 18%, respectively, of the explained variation. In T2D_{HbA1c ≥ 6.5%}, these glycemic exposures accounted for more than 50% of the variation in HbA_1c and with equal relative contributions.

CONCLUSIONS

Among the glycemic exposures, PPG exposure was most strongly predictive of HbA_1c in non-diabetic individuals, suggesting that interventions targeting lowering of the PPG response may be beneficial for long-term glycemic maintenance. In T2D, preprandial glucose and PPG exposure contributed equally to HbA_1c.

Factors That Influence Pancreatic Beta Cell Function and Insulin Resistance in Newly Diagnosed Type 2 Diabetes Patients: A Sub-Analysis of the MARCH Trial

INTRODUCTION

The Metformin and Acarbose in Chinese as the initial Hypoglycemic treatment (MARCH) trial has demonstrated a similar efficacy in HbA1c reduction between acarbose and metformin treatments in newly diagnosed type 2 diabetes mellitus (T2DM) patients. The current sub-analysis of the MARCH trail aims to evaluate the baseline characteristics that may influence the improvement of pancreatic β-cell function and insulin resistance after acarbose therapy in Chinese patients with newly diagnosed T2DM.

METHODS

Of the 784 patients who entered the MARCH trail, 391 were assigned to the acarbose therapy group; 304 of these completed 48 weeks of follow-up of acarbose therapy. At 48 weeks, on the basis of the tertiles of change in homeostasis model assessment-beta cell function (∆HOMA-β) and homeostasis model assessment-insulin resistance (∆HOMA-IR), the subjects were divided into lowly, mediumly, and highly improved groups.

RESULTS

In the highly improved HOMA-β group, patients had higher systolic blood pressure (SBP), 2-h postprandial blood glucose (PBG), hemoglobin A1c (HbA1c), and lower high-density lipoprotein cholesterol (HDL-c), fasting serum insulin (FINS) concentration, and HOMA-IR in comparison to the lowly improved group (p < 0.05). A positive correlation was observed between HbA1c, SBP, and highly improved ∆HOMA-β (p < 0.05), while an inverse correlation was evident between HDL-c and highly improved ∆HOMA-β (p < 0.05). The highly improved HOMA-IR group had a significantly higher body mass index (BMI), fasting blood glucose (FBG), FINS concentration, and HOMA-β in comparison to the lowly improved group (p < 0.05). A positive correlation was observed between FBG, waist circumference, and highly improved HOMA-IR (p < 0.05).

CONCLUSION

Newly diagnosed T2DM Chinese patients with lower baseline HDL-c and higher HbA1c and SBP values are more likely to achieve improvement in beta cell function whereas baseline fasting blood glucose and waist circumference were the significant factors associated with improvement in insulin resistance with acarbose therapy.

TRIAL REGISTRATION

The clinical trial registry number was ChiCTR-TRC-08000231.

Relationship of glycated hemoglobin, and fasting and postprandial hyperglycemia in type 2 diabetes mellitus patients in Malaysia

AIMS/INTRODUCTION

Studies on the relative contributions of fasting and postprandial hyperglycemia (FH and PPH) to glycated hemoglobin (HbA1c ) in patients with type 2 diabetes have yielded inconsistent results. We aimed to assess the relationship by using continuous glucose monitoring in a multi-ethnic cohort.

MATERIALS AND METHODS

A total of 100 adults with type 2 diabetes were assessed with 6-day continuous glucose monitoring and HbA1c . Area under the curve (AUC) ≥5.6 mmol/L was defined as AUCTOTAL . AUC equal to or greater than each preprandial glucose for 4-h duration was defined as AUCPPH . The total PPH (AUCTPPH ) was the sum of the various AUCPPH. The postprandial contribution to overall hyperglycemia was calculated as (AUCTPPH / AUCTOTAL ) × 100%.

RESULTS

The present study comprised of Malay, Indian, and Chinese type 2 diabetes patients at 34, 34 and 28% respectively. Overall, the mean PPH significantly decreased as HbA1c advanced (mixed model repeated measures adjusted, beta-estimate = -3.0, P = 0.009). Age (P = 0.010) and hypoglycemia (P = 0.006) predicted the contribution difference. In oral antidiabetic drug-treated patients (n = 58), FH contribution increased from 54% (HbA1c 6-6.9%) to 67% (HbA1c ≥10%). FH predominance was significant in poorly-controlled groups (P = 0.028 at HbA1c 9-9.9%; P = 0.015 at HbA1c ≥10%). Among insulin users (n = 42), FH predominated when HbA1c was ≥10% before adjustment for hypoglycemia (P = 0.047), whereas PPH was numerically greater when HbA1c was <8%.

CONCLUSIONS

FH and PPH contributions were equal in well-controlled Malaysian type 2 diabetes patients in real-world practice. FH predominated when HbA1c was ≥9 and ≥10% in oral antidiabetic drug- and insulin-treated patients, respectively. A unique observation was the greater PPH contribution when HbA1c was <8% despite the use of basal and mealtime insulin in this multi-ethnic cohort, which required further validation.

Impact of polyphenol-rich sources on acute postprandial glycaemia: a systematic review

Increasingly, evidence suggests a role for polyphenols in blood glucose control. The objective of this systematic review was to evaluate the effect of polyphenol-rich sources in combination with carbohydrate sources on resulting postprandial glycaemic and insulin responses. A literature search was conducted using Medline, CINHAL and Web of Science databases. Selected studies included randomised controlled trials in which the association of polyphenol-containing food or beverage consumption with a carbohydrate source and effect on acute postprandial glycaemia and/or insulin was reported. A total of thirteen full articles were included in the review. Polyphenol sources included coffee, black tea, fruit juice, plant extracts, berries and different rye breads, and carbohydrate sources included bread, pancakes and simple sugars such as sucrose, glucose and fructose. Although glycaemic and insulin responses differed depending on the polyphenol-carbohydrate combination, overall, polyphenol sources were shown to reduce the peak and early-phase glycaemic response and maintain the glycaemic response in the later stages of digestion. To a lesser extent, polyphenol sources were also shown to reduce peak insulin response and sustain the insulin response, especially when consumed with bread. This review supports epidemiological data suggesting that polyphenols in foods and beverages may have a beneficial effect on reducing the risk of type 2 diabetes. However, the extent of this effect is variable depending on the polyphenol and carbohydrate source.