Improved Glycemic Control and Variability: Application of Healthy Ingredients in Asian Staples

Macromolecular, thermal, and nonthermal technologies for reduction of glycemic index in food-A review

Consumers rely on product labels to make healthy choices, especially with regard to the glycemic index (GI) and glycemic load (GL), which identify foods that stabilize blood sugar. Employing both thermal and nonthermal processing techniques can potentially reduce the GI, contributing to improved blood sugar regulation and overall metabolic health. This study concentrates on the most current advances in GI-reduction food processing technologies. Food structure combines fiber, healthy fats, and proteins to slow digestion, reducing GI. The influence of thermal approaches on the physical and chemical modification of starch led to decreased GI. The duration of heating and the availability of moisture also determine the degree of hydrolysis of starch and the glycemic effects on food. At a lower temperature, the parboiling revealed less gelatinization and increased moisture. The internal temperature of the product is raised during thermal and nonthermal treatment, speeds up retrogradation, and reduces the rate of starch breakdown.

Effects of Dietary Carbohydrate Concentration and Glycemic Index on Blood Glucose Variability and Free Fatty Acids in Individuals with Type 1 Diabetes

Monitoring glycemic control status is the cornerstone of diabetes management. This study aimed to reveal whether moderate-carbohydrate (CHO) diets increase the risk of free fatty acid (FFA) levels, and it presents the short-term effects of four different diet models on blood sugar, glycemic variability (GV), and FFA levels. This crossover study included 17 patients with type 1 diabetes mellitus to identify the effects of four diets with different CHO contents and glycemic index (GI) on GV and plasma FFA levels. Diet 1 (D1) contained 40% CHO with a low GI, diet 2 (D2) contained 40% CHO with a high GI, diet 3 (D3) contained 60% CHO with a low GI, and diet 4 (D4) contained 60% CHO with a high GI. Interventions were performed with sensor monitoring in four-day periods and completed in four weeks. No statistical difference was observed among the groups in terms of blood glucose area under the curve (p = 0.78), mean blood glucose levels (p = 0.28), GV (p = 0.59), and time in range (p = 0.567). FFA and total triglyceride levels were higher in the D1 group (p < 0.014 and p = 0.002, respectively). Different diets may increase the risk of cardiovascular diseases by affecting GI, FFA, and blood glucose levels.

Dietary composition and time in range in population with type 2 diabetes mellitus-exploring the association using continuous glucose monitoring device

AIM

To understand the association between macronutrient composition of a diet with Time in Range (TIR), Time above Range (TAR) and Time below Range (TBR) derived using a Continuous Glucose Monitoring (CGM) device for 14 days.

METHODOLOGY

An exploratory analysis on the baseline data of 50 Type 2 Diabetes Mellitus participants with age 25-55 years, HbA1c upto 8% and on Metformin only) enrolled for an interventional clinical trial was performed.

RESULTS

Participants consuming adequate carbohydrates (CHO) of 55 to 60% of total calories had better Average Blood Glucose of 142.0 ± 24.0 mg/dL with a significance of p = 0.03 and Glucose Management Indicator (GMI) of 6.6 ± 0.7% significant at p = 0.01, than those with high CHO intake >60% of the total calories, with Average Blood Glucose - 155.0 ± 13.4 mg/dL and GMI - 7.06 ± 0.4%. Similarly, TIR - 68.2 ± 5.1% and TAR - 23.0 ± 10.8% was significantly better (p = 0.00) among those consuming adequate protein (12-15%) as compared to low protein (≤ 10%) with TIR- 61.0 ± 5.1% & TAR- 32.9 ± 10.3%. A correlation (r = -0.482 & p = 0.00) and simple linear regression analysis (R² = 0.33, F = 7.72, p = 0.000) revealed that when CHO intake increases the TIR decreases whereas TAR increases (r = 0.380 & p = 0.006). We did not find any significant relation between fat intake and TIR, TAR or TBR.

CONCLUSION

Our results suggest that lowering CHO, while increasing protein in the diet may help improve TIR. Further in-depth studies are needed to confirm these findings.

The Effect of Meal Glycemic Index and Meal Frequency on Glycemic Control and Variability in Female Nurses Working Night Shifts: A Two-Arm Randomized Cross-Over Trial

BACKGROUND

Night shift workers are exposed to circadian disruption, which contributes to impaired glucose tolerance. Although fasting during the night shift improves glucose homeostasis, adhering to this dietary strategy may be challenging.

OBJECTIVES

This study evaluated the effect of fasting compared with the consumption of meals with different combinations of glycemic index (GI, low or high) and frequency (1 or 3 times) during the night shift on continuous glucose monitoring metrics.

METHODS

A 2-arm randomized cross-over trial was conducted on female nurses working night shifts. In each of those arms, the participants were either provided with no meal (fasted), low GI, or high-GI meal during the night shift with a meal frequency according to which arm they were randomly allocated to, either 1-MEAL or 3-MEAL. Outcome variables were glycemic control and variability (GC and GV) metrics during the night shift (21:30-7:00), in the morning after the night shift (07:00-13:00), and in the 24 h period (18:00-18:00).

RESULTS

Compared to no meal, the consumption of 1 high-GI meal increased all GV metrics not only during the night shifts but also in the morning, for instance, as observed in the coefficient of variation (β = 0.03 mmol/L; 95% CI: 0.01, 0.05), and GV percentage (β = 4.13; 95% CI: 2.07, 6.18). The consumption of 1 or 3 low GI meals did not raise GC or GV metrics except for continuous overall net glycemic action during the night shifts after consuming 3 low GI meals. When controlling for GI, night shift meal frequency did not affect any metrics in any timeframe.

CONCLUSIONS

High meal GI but not higher meal frequency during the night shift increased GC and GV in female night shift workers. Results for 1 low-GI meal during the night shift were not different from a glucose profile after no meal. This trial was registered at trialsearch.who.int as NL8715.

Determination of glycaemic response to the consumption of two specialised formulas for glycaemic control

To assess the glycaemic response after ingestion of two specialised oral and enteral nutrition formulas for glycaemic control. The participants were sixteen healthy volunteers, aged 21-49 years, with normal glucose tolerance. The volunteers attended the tests fasting for 10 h, for 5 weeks, and consumed the reference food - glucose solution - for 3 weeks, and the two formulas DiamaxO and DiamaxIG in the following weeks, in amounts equivalent to 25 g of available carbohydrates. During the period of 120 min, seven blood samples were taken through capillary blood sampling to determine the glycaemic response. The glycaemic index (GI) was calculated according to the trapezoidal rule, ignoring areas below the fasting line. The glycaemic load (GL) was determined by the formula GL = ((GI(glucose = reference) × 'g' of available carbohydrate per serving]/100. The formulas showed low GI and GL. GI = 37·8 and GL = 6·6 for DiamaxO and GI = 21·5 and GL = 3·5 for DiamaxIG. The peak of the glycaemic response occurred 30 min after ingestion, with a marked difference in blood glucose between the Diamax products in relation to glucose. Differences were also significant at times 15, 45, 60 and 90 min in relation to glucose (ANOVA with post hoc Bonferroni, P < 0·005), but not between the two products. However, the AUC and the GI of DiamaxIG are significantly smaller than that of the DiamaxO second t test (P = 0·0059). The glycaemic response to the products is quite reduced, presenting a curve with a little accentuated shape, without high peak, especially in the modified product.

Analytical Performance of the Factory-Calibrated Flash Glucose Monitoring System FreeStyle Libre2TM in Healthy Women

Continuous glucose monitoring (CGM) is used clinically and for research purposes to capture glycaemic profiles. The accuracy of CGM among healthy populations has not been widely assessed. This study assessed agreement between glucose concentrations obtained from venous plasma and from CGM (FreeStyle Libre2TM, Abbott Diabetes Care, Witney, UK) in healthy women. Glucose concentrations were assessed after fasting and every 15 min after a standardized breakfast over a 4-h lab period. Accuracy of CGM was determined by Bland-Altman plot, 15/15% sensor agreement analysis, Clarke error grid analysis (EGA) and mean absolute relative difference (MARD). In all, 429 valid CGM readings with paired venous plasma glucose (VPG) values were obtained from 29 healthy women. Mean CGM readings were 1.14 mmol/L (95% CI: 0.97 to 1.30 mmol/L, p < 0.001) higher than VPG concentrations. Ratio 95% limits of agreement were from 0.68 to 2.20, and a proportional bias (slope: 0.22) was reported. Additionally, 45% of the CGM readings were within ±0.83 mmol/L (±15 mg/dL) or ±15% of VPG, while 85.3% were within EGA Zones A + B (clinically acceptable). MARD was 27.5% (95% CI: 20.8, 34.2%), with higher MARD values in the hypoglycaemia range and when VPG concentrations were falling. The FreeStyle Libre2TM CGM system tends to overestimate glucose concentrations compared to venous plasma samples in healthy women, especially during hypoglycaemia and during glycaemic swings.

Individual Postprandial Glycemic Responses to Meal Types by Different Carbohydrate Levels and Their Associations with Glycemic Variability Using Continuous Glucose Monitoring

This study aimed to investigate individual postprandial glycemic responses (PPGRs) to meal types with varying carbohydrate levels and examine their associations with 14-day glycemic variability using continuous glucose monitoring (CGM) in young adults. In a two-week intervention study with 34 participants connected to CGM, four meal types and glucose 75 g were tested. PPGRs were recorded for up to 2 h with a 15 min interval after meals. Data-driven cluster analysis was used to group individual PPGRs for each meal type, and correlation analysis was performed of 14-day glycemic variability and control with related factors. Participants had a mean age of 22.5 years, with 22.8% being male. Four meal types were chosen according to carbohydrate levels. The mean glucose excursion for all meal types, except the fruit bowl, exhibited a similar curve with attenuation. Individuals classified as high responders for each meal type exhibited sustained peak glucose levels for a longer duration compared to low responders, especially in meals with carbohydrate contents above 50%. A meal with 45% carbohydrate content showed no correlation with either 14-day glycemic variability or control. Understanding the glycemic response to carbohydrate-rich meals and adopting a meal-based approach when planning diets are crucial to improving glycemic variability and control.