Dataset Information

A pilot study on the effect of D-allulose on postprandial glucose levels in patients with type 2 diabetes mellitus during Ramadan fasting.

ABSTRACT:

Background

During Ramadan fasting, postprandial hyperglycemia is commonly observed after iftar (break of fast at sunset) meal. D-allulose is a rare sugar and is reported to have several health benefits, including the suppression of increase in postprandial glucose levels. This study investigates whether D-allulose (a C-3 epimer of D-fructose) improves the postprandial glucose in patients with type 2 diabetes mellitus (T2DM) during Ramadan.

Methods

This was a pilot, prospective single-arm study design that was conducted for 10 consecutive days; 5 days of control and 5 days of consumption. The primary outcome was postprandial peak glucose levels. During the consumption period, 8.5 g of D-allulose was consumed by the participants before iftar meal. Postprandial glucose was measured using a continuous glucose monitoring system.

Results

A total of 12 participants completed the study. Significant lower (p < 0.01) postprandial glucose values and the glucose incremental area under the curve (iAUC) were observed from 0 to 180 min during the consumption period compared to the control period. The consumption period demonstrated significantly higher percentages of time in which glucose values were found in the target range (p = 0.0032), and when the glucose levels above the target range were reduced (p = 0.0015).

Conclusions

The supplementation with D-allulose has the potential to improve postprandial hyperglycemia in patients with T2DM after iftar during Ramadan. Further studies are needed to confirm these findings. Trial registration ClinicalTrials.gov NCT05071950. Retrospectively registered, 8 October 2021.

SUBMITTER: Japar S

PROVIDER: S-EPMC9209837 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

A pilot study on the effect of D-allulose on postprandial glucose levels in patients with type 2 diabetes mellitus during Ramadan fasting.

Japar Salimah S Fukunaga Kensaku K Kobayashi Toshihiro T Imachi Hitomi H Sato Seisuke S Saheki Takanobu T Ibata Tomohiro T Yoshimura Takafumi T Soh Kim Lam KL Ong Swee Leong SL Muhamed Zamri Z Murao Koji K

Diabetology & metabolic syndrome 20220621 1

<h4>Background</h4>During Ramadan fasting, postprandial hyperglycemia is commonly observed after iftar (break of fast at sunset) meal. D-allulose is a rare sugar and is reported to have several health benefits, including the suppression of increase in postprandial glucose levels. This study investigates whether D-allulose (a C-3 epimer of D-fructose) improves the postprandial glucose in patients with type 2 diabetes mellitus (T2DM) during Ramadan.<h4>Methods</h4>This was a pilot, prospective sin ...[more]

PMID: 35729673

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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.

Dataset Information

A pilot study on the effect of D-allulose on postprandial glucose levels in patients with type 2 diabetes mellitus during Ramadan fasting.

Background

Methods

Results

Conclusions

Publications

A pilot study on the effect of D-allulose on postprandial glucose levels in patients with type 2 diabetes mellitus during Ramadan fasting.

Similar Datasets

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