Dataset Information

Estimation of the Relative Contribution of Postprandial Glucose Exposure to Average Total Glucose Exposure in Subjects with Type 2 Diabetes.

ABSTRACT: We hypothesized that the relative contribution of fasting plasma glucose (FPG) versus postprandial plasma glucose (PPG) to glycated haemoglobin (HbA1c) could be calculated using an algorithm developed by the A1c-Derived Average Glucose (ADAG) study group to make HbA1c values more clinically relevant to patients. The algorithm estimates average glucose (eAG) exposure, which can be used to calculate apparent PPG (aPPG) by subtracting FPG. The hypothesis was tested in a large dataset (comprising 17 studies) from the vildagliptin clinical trial programme. We found that 24 weeks of treatment with vildagliptin monotherapy (n = 2523) reduced the relative contribution of aPPG to eAG from 8.12% to 2.95% (by 64%, p < 0.001). In contrast, when vildagliptin was added to metformin (n = 2752), the relative contribution of aPPG to eAG insignificantly increased from 1.59% to 2.56%. In conclusion, glucose peaks, which are often prominent in patients with type 2 diabetes, provide a small contribution to the total glucose exposure assessed by HbA1c, and the ADAG algorithm is not robust enough to assess this small relative contribution in patients receiving combination therapy.

SUBMITTER: Ahren B

PROVIDER: S-EPMC5007300 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Estimation of the Relative Contribution of Postprandial Glucose Exposure to Average Total Glucose Exposure in Subjects with Type 2 Diabetes.

Ahrén Bo B Foley James E JE

International journal of endocrinology 20160818

We hypothesized that the relative contribution of fasting plasma glucose (FPG) versus postprandial plasma glucose (PPG) to glycated haemoglobin (HbA1c) could be calculated using an algorithm developed by the A1c-Derived Average Glucose (ADAG) study group to make HbA1c values more clinically relevant to patients. The algorithm estimates average glucose (eAG) exposure, which can be used to calculate apparent PPG (aPPG) by subtracting FPG. The hypothesis was tested in a large dataset (comprising 17 ...[more]

PMID: 27635135

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Project description: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.

Project description:BackgroundHigh postprandial glucose excursions may increase risk for disease. Individuals have widely varying glucose responses to different meals, and precision nutrition approaches often seek to personalize diets to minimize postprandial glycemic responses as measured by continuous glucose monitors (CGMs). However, it is unknown whether different CGM devices result in concordant meal rankings according to postprandial glycemic excursions.ObjectiveWe explored whether meal rankings according to postprandial glycemic excursions differ between 2 simultaneously worn CGMs.MethodsWe collected 27,489 simultaneous measurements from Dexcom G4 Platinum and Abbott Freestyle Libre Pro CGMs during 28 inpatient days in 16 adults without diabetes. Simultaneous glucose measurements obtained for 2 h following 760 ad libitum meals were used to compare within-subject meal rankings between the CGM devices according to their incremental glucose response.ResultsPostprandial responses to ad libitum meals were highly variable, with the Abbott and Dexcom systems resulting in within-subject incremental mean ± SD glucose CVs of 91.7 ± 1.9% and 94.2 ± 2.7%, respectively. Within-subject meal rankings for incremental glycemic responses were relatively discordant between CGMs, with a mean Kendall rank correlation coefficient of 0.43 ± 0.05. Meals in the bottom compared with those in the top half of incremental glycemic responses ranked by Abbott resulted in 50 ± 10% (P = 0.0002) less glycemic reduction as measured by Dexcom, and vice versa. The missing glycemic reduction by eating meals ranked according to the discordant CGM was inversely correlated with each subject's Kendall rank correlation coefficient (r = -0.95; P < 0.0001).ConclusionsPrecision nutrition approaches that use CGMs to personalize meal recommendations for minimizing glycemic excursions may be premature given the discordance of within-subject meal rankings between simultaneous CGM devices. More research is needed to clarify the source of this imprecision. This trial was registered at clinicaltrials.gov as NCT03407053.

Project description:IntroductionFew prospective clinical trials have investigated the role of fasting blood glucose (FBG) and/or postprandial glucose (PPG) in assessing overall glycemic control by using different insulin regimens. In the present post hoc analysis, we assessed the contribution of FBG and/or PPG in overall glycemic control in Chinese patients under insulin treatment.MethodsCLASSIFY is a phase IV, randomized, open-label, 26-week, parallel-arm, treat-to-target, multinational, controlled study in patients with type 2 diabetes mellitus to compare the efficacy and safety of insulin lispro mix 25 (LM25) and insulin lispro mix 50 (LM50) as starter insulins. Insulin was titrated with an aim to target pre-meal blood glucose (BG) levels at > 3.9 and ≤ 6.1 mmol/L before breakfast and dinner. The primary outcome assessed was the change in HbA1c from baseline.ResultsChinese patients contributed 38.7% (N = 156) of the total population. The majority of patients were male (52.6%). The mean (SD) body mass index was 24.54 (3.04) kg/m2 and mean (SD) HbA1c was 8.54 (1.10) % at baseline. At week 26, LM50 showed a significantly greater reduction from baseline in HbA1c (- 2.03% vs - 1.55%, P < 0.001), average daily BG (- 3.21 vs - 2.34 mmol/L, P < 0.001), average post-meal BG (- 3.58 vs - 2.39 mmol/L, P < 0.001), and average prandial BG excursion (- 1.01 vs - 0.22 mmol/L, P = 0.006) than the LM25 group. The reductions in average pre-meal BG (- 2.59 vs - 2.28 mmol/L, P = 0.137) were not significantly different between the groups. The proportion of patients achieving HbA1c targets (< 7% or ≤ 6.5%) without nocturnal hypoglycemia or weight gain was greater (P < 0.05) with LM50 compared with LM25.ConclusionLM50 achieved better overall glycemic control than LM25 as a starter insulin in Chinese patients, which may be due to greater improvement in PPG levels.Trial registrationClinicaltrials.gov identification number: NCT01773473.FundingEli Lilly and Company, Shanghai, China.

Project description:IntroductionTime in range (TIR) as assessed by continuous glucose monitoring (CGM) measures an individual's glucose fluctuations within set limits in a time period and is increasingly used together with HbA1c in patients with diabetes. HbA1c indicates the average glucose concentration but provides no information on glucose fluctuation. However, before CGM becomes available for patients with type 2 diabetes (T2D) worldwide, especially in developing nations, fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) are still the common biomarkers used for monitoring diabetes conditions. We investigated the importance of FPG and PPG to glucose fluctuation in patients with T2D. We used machine learning to provide a new estimate of TIR based on the HbA1c, together with FPG and PPG.MethodsThis study included 399 patients with T2D. (1) Univariate and (2) multivariate linear regression models and (3) random forest regression models were developed to predict the TIR. Subgroup analysis was performed in the newly diagnosed T2D population to explore and optimize the prediction model for patients with different disease history.ResultsRegression analysis suggests that FPG was strongly linked to minimum glucose, while PPG was strongly correlated with maximum glucose. After FPG and PPG were incorporated into the multivariate linear regression model, the prediction performance of TIR was improved compared with the univariate correlation between HbA1c and TIR, and the correlation coefficient (95% CI) increased from 0.62 (0.59, 0.65) to 0.73 (0.72, 0.75) (p < 0.001). The random forest model significantly outperformed the linear model (p < 0.001) in predicting TIR through FPG, PPG and HbA1c, with a stronger correlation coefficient 0.79 (0.79, 0.80).ConclusionsThe results offered a comprehensive understanding of glucose fluctuations through FPG and PPG compared to HbA1c alone. Our novel TIR prediction model based on random forest regression with FPG, PPG, and HbA1c provides a better prediction performance than the univariate model with solely HbA1c. The results indicate a nonlinear relationship between TIR and glycaemic parameters. Our results suggest that machine learning may have the potential to be used in developing better models for understanding patients' disease status and providing necessary interventions for glycaemic control.

Project description:Background: Asymmetric ?-carotene, a provitamin A carotenoid, is cleaved to produce retinol (vitamin A) and ?-retinol (with negligible vitamin A activity). The vitamin A activity of ?-carotene-containing foods is likely overestimated because traditional analytic methods do not separate ?-retinol derivatives from active retinol.Objective: This study aimed to accurately characterize intestinal ?-carotene cleavage and its relative contribution to postprandial vitamin A in humans after consumption of raw carrots.Design: Healthy adults (n = 12) consumed a meal containing 300 g raw carrot (providing 27.3 mg ?-carotene and 18.7 mg ?-carotene). Triglyceride-rich lipoprotein fractions of plasma were isolated and extracted, and ?-retinyl palmitate (?RP) and retinyl palmitate were measured over 12 h postprandially via high-performance liquid chromatography-tandem mass spectrometry. The complete profile of all ?-retinyl esters and retinyl esters was measured at 6 h, and total absorption of ?- and ?-carotene was calculated.Results: ?RP was identified and quantified in every subject. No difference in preference for absorption of ?- over ?-carotene was observed (adjusting for dose, 28% higher, P = 0.103). After absorption, ?-carotene trended toward preferential cleavage compared with ?-carotene (22% higher, P = 0.084). A large range of provitamin A carotenoid conversion efficiencies was observed, with ?-carotene contributing 12-35% of newly converted vitamin A (predicted contribution = 25.5%). In all subjects, a majority of ?-retinol was esterified to palmitic acid (as compared with other fatty acids).Conclusions: ?-Retinol is esterified in the enterocyte and transported in the blood analogous to retinol. The percentage of absorption of ?-carotene from raw carrots was not significantly different from ?-carotene when adjusting for dose, although a trend toward higher cleavage of ?-carotene was observed. The results demonstrate large interindividual variability in ?-carotene conversion. The contribution of newly absorbed ?-carotene to postprandial vitamin A should not be estimated but should be measured directly to accurately assess the vitamin A capacity of ?-carotene-containing foods. This trial was registered at clinicaltrials.gov as NCT01432210.

Project description:ObjectiveVarious methods are used to quantify postprandial glycemia or glucose variability, but few have been compared and none are standardized. Our objective was to examine the relationship among common indexes of postprandial glycemia, overall hyperglycemia, glucose variability, and A1C using detailed glucose measures obtained during everyday life and to study which blood glucose values of the day provide the strongest prediction of A1C.Research design and methodsIn the A1C-Derived Average Glucose (ADAG) study, glucose levels were monitored in 507 participants (268 type 1 diabetic, 159 type 2 diabetic, and 80 nondiabetic subjects) with continuous glucose monitoring (CGM) and frequent self-monitoring of blood glucose (SMBG) during 16 weeks. We calculated several indexes of glycemia and analyzed their intercorrelations. The association between glucose measurements at different times of the day (pre- and postprandial) and A1C was examined using multiple linear regression.ResultsIndexes of glucose variability showed strong intercorrelation. Among postprandial indexes, the area under the glucose curve calculated from CGM 2 h after a meal correlated well with the 90-min SMBG postprandial measurements. Fasting blood glucose (FBG) levels were only moderately correlated with indexes of hyperglycemia and average or postprandial glucose levels. Indexes derived with SMBG strongly correlated with those from CGM. Some SMBG time points had a stronger association with A1C than others. Overall, preprandial glucose values had a stronger association with A1C than postprandial values for both diabetes types, particularly for type 2 diabetes.ConclusionsIndexes of glucose variability and average and postprandial glycemia intercorrelate strongly within each category. Variability indexes are weakly correlated with the other categories, indicating that these measures convey different information. FBG is not a clear indicator of general glycemia. Preprandial glucose values have a larger impact on A1C levels than postprandial values.

Dataset Information

Estimation of the Relative Contribution of Postprandial Glucose Exposure to Average Total Glucose Exposure in Subjects with Type 2 Diabetes.

Publications

Estimation of the Relative Contribution of Postprandial Glucose Exposure to Average Total Glucose Exposure in Subjects with Type 2 Diabetes.

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