Does the ingestion of a 24 hour low glycaemic index Asian mixed meal diet improve glycaemic response and promote fat oxidation? A controlled, randomized cross-over study

The Relationship Between Dietary Patterns and Glycemic Variability in People with Impaired Glucose Tolerance

BACKGROUND

Diurnal glucose fluctuations are increased in prediabetes and might be affected by specific dietary patterns.

OBJECTIVES

The present study assessed the relationship between glycemic variability (GV) and dietary regimen in people with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT).

METHODS

Forty-one NGT (mean age: 45.0 ± 9.0 y, mean BMI: 32.0 ± 7.0 kg/m2) and 53 IGT (mean age: 48.4 ± 11.2 y, mean BMI: 31.3 ± 5.9 kg/m2) subjects were enrolled in this cross-sectional study. The FreeStyleLibre Pro sensor was used for 14 d, and several parameters of GV were calculated. The participants were provided with a diet diary to record all meals. ANOVA analysis, Pearson correlation, and stepwise forward regression were performed.

RESULTS

Despite no difference in diet patterns between the 2 groups, GV parameters were higher in IGT than in NGT. GV worsened with an increase in overall daily carbohydrate and refined grain consumption and improved with the increase in whole grain intake in IGT. GV parameters were positively related [r = 0.14-0.53; all P < 0.02 for SD, continuous overall net glycemic action 1 (CONGA1), J-index, lability index (LI), glycemic risk assessment diabetes equation, M-value, and mean absolute glucose (MAG)], and low blood glucose index (LBGI) inversely (r = -0.37, P = 0.006) related to the total percentage of carbohydrate, but not to the distribution of carbohydrate between the main meals in the IGT group. A negative relationship existed between total protein consumption and GV indices (r = -0.27 to -0.52; P < 0.05 for SD, CONGA1, J-index, LI, M-value, and MAG). The total EI was related to GV parameters (r = 0.27-0.32; P < 0.05 for CONGA1, J-index, LI, and M-value; and r = -0.30, P = 0.028 for LBGI).

CONCLUSIONS

The primary outcome results showed that insulin sensitivity, calories, and carbohydrate content are predictors of GV in individuals with IGT. Overall, the secondary analyses suggested that carbohydrate and daily consumption of refined grains might be associated with higher GV, whereas whole grains and daily protein intake were related to lower GV in people with IGT.

Use of a Continuous Glucose Monitor to Determine the Glycaemic Index of Rice-Based Mixed Meals, Their Effect on a 24 h Glucose Profile and Its Influence on Overweight and Obese Young Adults' Meal Preferences

Postprandial hyperglycaemia is associated with an increased risk of type-2 diabetes. This study aims to determine the glycaemic index (GI) of three varieties of rice-based mixed meals and their effects on glycaemic variability (GV), 24 h mean glucose levels and target ranges, and rice variety preferences among overweight and obese young adults using real-time continuous glucose monitoring (rtCGM). In a randomised controlled crossover design, 14 participants (22.8 ± 4.6 years, 32.9 ± 5.8 kg/m2) were randomly assigned to receive 3 rice-based mixed meals containing 50 g of available carbohydrates (white rice meal = WRM; brown rice meal = BRM; and parboiled basmati rice meal = PBRM) and 50 g of a glucose reference drink on alternate days. GI, GV, 24 h mean glucose levels and target ranges were measured. Rice variety preferences were compared with those of baseline data and determined at the end of the study period. Results: The analysis found that PBRM was low in GI (45.35 ± 2.06), BRM medium in GI (56.44 ± 2.34), and WRM high in GI (83.03 ± 2.19). PBRM had a significantly (p < 0.05) lower 24 h mean glucose level, higher in-target 24 h glucose level percentage and non-significantly (p > 0.05) lower GV compared to WRM. Prior to observing their postprandial glucose levels generated by rtCGM, the participants preferred WRM (64.3%) over other meals, whereas this preference changed significantly (p < 0.05) at the endpoint (PBRM, 71.4%). PBRM reduced 24 h glucose level and GV of overweight and obese young adults. The rtCGM is proven to be reliable in measuring GI, while providing robust continuous glycaemic information. This may serve as an educational tool that motivates eating behaviour changes among overweight and obese young adults.

Food as medicine? Exploring the impact of providing healthy foods on adherence and clinical and economic outcomes

Background

Methods

Results

Conclusion

The impact of nutrition and lifestyle modification on health

The main recommendations from public health entities include healthy diets and physical activity as the main lifestyle factors impacting the development of chronic diseases such as cardiovascular and metabolic diseases, cancer, and even neurological diseases. Randomized clinical trials have been designed to demonstrate those lifestyle modifications can change the pattern of chronic diseases development and progression. Among these, nutrition is one of the most impacting factors. Therefore, nutrition and diets were also included in different randomized clinical trials, and most of them showed a favorable impact of nutrition modification on the participant's health. Nevertheless, study designs were considerably different, and future studies are needed to support nutrition modifications further. The choice of a healthy considered diet, like the Mediterranean diet, was shown to impact chronic diseases, cardiovascular risk, and adult life expectancy mainly due to its anti-inflammatory and antioxidant properties. Furthermore, a high intake of fibers, fruits, and vegetables together with a low intake of fat and energy-dense, processed foods contribute to an inflammation reduction and a more robust immune system leading. Besides these well-known properties, all lifestyle modifications must be personalized according to the availability of foods, geographic localizations, and the healthy status of the patient.

The energy balance hypothesis of obesity: do the laws of thermodynamics explain excessive adiposity?

In this work, we reflect upon the energy balance hypothesis of obesity. International organizations, the general population and many scientists hold the belief that obesity is indisputably caused by an imbalance between energy intake and energy expenditure. Most of them argue that the laws of thermodynamics support this view. We identify and review the main arguments used to support this belief, and we explain the reasoning mistakes those arguments harbor. We show that the laws of thermodynamics do not support the idea that obesity is an energy problem nor an energy balance problem more than they do in the growth of any other tissue in the human body. We argue that the validity of the energy balance paradigm for obesity must be questioned. Although correction of a wrong belief is laudable per se, in this particular case harm may arise by influencing the way in which obesity prevention is tackled and obese patients are treated.

The carbohydrate-insulin model: a physiological perspective on the obesity pandemic

According to a commonly held view, the obesity pandemic is caused by overconsumption of modern, highly palatable, energy-dense processed foods, exacerbated by a sedentary lifestyle. However, obesity rates remain at historic highs, despite a persistent focus on eating less and moving more, as guided by the energy balance model (EBM). This public health failure may arise from a fundamental limitation of the EBM itself. Conceptualizing obesity as a disorder of energy balance restates a principle of physics without considering the biological mechanisms that promote weight gain. An alternative paradigm, the carbohydrate-insulin model (CIM), proposes a reversal of causal direction. According to the CIM, increasing fat deposition in the body-resulting from the hormonal responses to a high-glycemic-load diet-drives positive energy balance. The CIM provides a conceptual framework with testable hypotheses for how various modifiable factors influence energy balance and fat storage. Rigorous research is needed to compare the validity of these 2 models, which have substantially different implications for obesity management, and to generate new models that best encompass the evidence.

Race/ethnicity and challenges for optimal insulin therapy

AIMS

We aimed to review insulin dosing recommendations, insulin regulation and its determinants, glycaemic response to carbohydrates, and the efficacy and safety of insulin therapy in different races/ethnicities.

METHODS

We searched for articles in PubMed and Google Scholar databases up to 31 March 2021, with the following keywords: "ethnicity", "diabetes", "insulin", "history of insulin", "insulin therapy", "food/rice", "carbohydrate intake", "insulin resistance", "BMI", "insulin dosing", "insulin sensitivity", "insulin response", "glycaemic index", "glycaemic response", "efficacy and safety", with interposition of the Boolean operator "AND".In addition, we reviewed the reference lists of the articles found.

RESULTS

The differential effect of race/ethnicity has not yet been considered in current insulin therapy guidelines. Nevertheless, body size and composition, body mass index, fat distribution, diet, storage, and energy expenditure vary significantly across populations. Further, insulin sensitivity, insulin response, and glycaemicresponse to carbohydrates differ by ethnicity. These disparities may lead to different insulin requirements, adversely impacting the efficacy and safety of insulin therapy among ethnic groups.

CONCLUSIONS

Race/ethnicity affects glucose metabolism and insulin regulation.Until now, international guidelines addressing racial/ethnic-specific clinical recommendations are limited. Comprehensive updated insulin therapy guidelines by ethnicity are urgently needed.

The Glycaemic and Insulinaemic Response of Pasta in Chinese and Indians Compared to Asian Carbohydrate Staples: Taking Spaghetti Back to Asia

In this study, we compared the metabolic properties of the Asian staples rice and noodles, which are typically high in glycaemic index (GI), to two types of spaghetti. It is hypothesised that pasta can be a healthy replacement, particularly amongst the Asian population. Thirty Chinese and Indian subjects (17 men, 13 women; BMI: 18.5-25 kg/m2) participated in this randomised crossover trial. On seven occasions, they consumed a glucose reference drink (3 times), white rice, wheat-based mee pok noodles, semolina spaghetti and wholegrain spaghetti. Blood samples were taken to measure glucose and insulin response over a period of 3 h. The current evaluation showed that semolina spaghetti and wholegrain spaghetti can be classified as low GI products, with a GI of 53 and 54, respectively, significantly lower than wheat based mee pok noodles (74) and rice (80) (p < 0.005). In addition, both spaghettis had a lower insulin response compared to rice (p < 0.05). Furthermore, there was no difference in glucose or insulin response between semolina and wholegrain spaghetti. After controlling for gender, ethnicity, fat and fat free mass (kg), the glucose and insulin results did not change. In conclusion, wheat-based pasta can be helpful to modify the carbohydrate-rich Asian diet. Notably, there was no effect of gender, ethnicity and body composition on the glycaemic and insulinaemic response. We speculate that the starch-protein structure as a result of the spaghetti production process is a major driver of its favourable metabolic properties.

The effect of a low glycaemic index diet on reducing day-long glycaemia in healthy young adults: A randomized crossover trial

AIM

To compare the effect of a low glycaemic index (LGI) diet on reducing day-long glycaemia with a macronutrient-matched high glycaemic index (HGI) diet, using customized meal delivery to ensure compliance.

MATERIALS AND METHODS

We conducted a single-blinded, randomized crossover trial in 14 healthy adults (57% female) with a mean ± SD age of 21.6 ± 1.7 years. A flash glucose monitoring sensor was installed on the subjects on day 1 to capture the interstitial glucose level every 15 minutes for 14 days. Subjects were randomized to receive an LGI (dietary GI = 40) or HGI (dietary GI = 60) diet (three meals and two snacks) from day 2 for 5 consecutive days, followed by a 2-day washout, then switched to the alternative diet for another 5 days. A paired t-test was used to test the differences in the incremental area under the curve (iAUC) of glucose, postprandial glucose (PPG) concentration and maximum postprandial glucose rise (MPGR) between the LGI and HGI periods.

RESULTS

Subjects had lower iAUC for average day-long glycaemia during the LGI intervention period compared with the HGI period (mean ± SD, 865 ± 297 vs. 1024 ± 267 mmol x min/L; P = .047). PPG for breakfast and snack 2, and MPGR for breakfast, snack 2 and dinner, were lower in the LGI period.

CONCLUSIONS

In young healthy adults, following an LGI diet resulted in lower average day-long glycaemia compared with a macronutrient-matched HGI diet. Our results support the use of LGI diets to reduce the risk of developing glucose intolerance.

The effect of a split portion of flaxseed on 24-h blood glucose response

Purpose

Flaxseed can be effective at lowering and stabilising blood glucose responses. The aim of this study was to determine whether flaxseed could lower blood glucose response more effectively when consumed as a single portion of 30 g, or a split portion consumed three times per day (10 g flaxseed per portion).

Methods

The study was a randomised, repeated measures, cross-over design. Fifteen healthy participants consumed either (1) three flaxseed muffins containing a total of 30 g of flaxseed once in the morning, (2) three flaxseed muffins consumed at three different timepoints across the day (10 g flaxseed per muffin) or (3) three control muffins consumed at three different timepoints across the day (0 g flaxseed). The 24-h blood glucose response was measured using a continuous glucose monitor.

Results

The results of this study demonstrated that flaxseed muffins given three times a day were effective at lowering and maintaining blood glucose levels over 24 h, compared to the control muffins and that both flaxseed treatments resulting in a lower blood glucose iAUC during the night.

Conclusion

The results of this study indicated that adding flaxseed to a daily diet produced a lower glucose profile over 24 h in a free-living context compared to the control muffins.

Glycemic variability: adverse clinical outcomes and how to improve it?

Glycemic variability (GV), defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice. Although it remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macrovascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes. In this review, we summarize the adverse clinical outcomes of GV and discuss the beneficial measures, including continuous glucose monitoring, drugs, dietary interventions and exercise training, to improve it, aiming at better addressing the challenging aspect of blood glucose management.

A fructose-based meal challenge to assess metabotypes and their metabolic risk profile: A randomized, crossover, controlled trial

OBJECTIVES

The first aim of this study was to determine the metabolic type of individuals based on the postprandial metabolic response after the ingestion of a meal challenge that was high protein and either high glucose (high GI) or fructose (low GI). The second aim was to compare the baseline characteristics between the different metabolic types (metabotypes). The third aim was to assess whether the inclusion of fructose or glucose in a high-protein breakfast modulated the glucose, insulin, and TG response over a 4-h period.

METHODS

The study included 46 Asian women with a body mass index between 17 and 28 kg/m² in a randomized crossover design. Metabolic typing was based on the assessment of the postprandial glycemic, insulin and triacylglycerol (TG) response after the ingestion of two high-protein meal challenges either high in fructose or glucose. Baseline characteristics were compared between the different metabolic types. Baseline and 4-h postprandial blood samples were collected and glucose, insulin, and TG levels were analyzed. Cluster analysis was used to phenotype the participants in distinct groups. Baseline characteristics including anthropometry, glycemic, and lipid profiles and resting metabolic rate were compared among the metabolic types.

RESULTS

Cluster analysis revealed that women could be grouped into three metabolic types based on postprandial glucose, insulin, and TG response after the fructose meal challenge: cluster 1 with an average glucose + high TG response (highTG; n = 12), cluster 2 with a high glucose + average TG response (highGLU; n = 8), and cluster 3 with an average glucose + average TG response (Avg; n = 26). Post hoc analysis revealed significantly greater waist-to-hip ratio and a worse lipid profile for the highTG cluster and a higher fasting blood glucose, body mass index, fat percentage, and hip circumference in the highGLU cluster.

CONCLUSIONS

Three metabolic types with a distinct metabolic response could be distinguished after a high fructose meal. The results suggest a different risk profile and may indicate why some people develop diabetes in an obesogenic environment. Improved metabolic-type assessments will enable us to develop and optimize nutritional and medical interventions for individuals with differing diabetes risk.

A Review of Recent Evidence from Meal-Based Diet Interventions and Clinical Biomarkers for Improvement of Glucose Regulation

In recent decades, the prevalence of diabetes has rapidly increased worldwide. Medical nutrition therapy has been identified as a major therapeutic support for diabetic patients, while preventive strategies in prediabetic or high-risk individuals have mainly focused on supplementation with bioactive compounds. Recently, meal-based interventions have been investigated as novel and safe long-term strategies for improving glucose regulation. However, evaluation of meal-based interventions is difficult since it requires analysis of sensitive markers. Biomarkers can also be used to identify individuals at risk for diabetes, which is important for disease prevention. In this review, we summarize current evidence from meal-based intervention studies conducted with the aim of improving glucose homeostasis in individuals at risk of diabetes using clinical biomarkers currently used to assess diabetic risk. Very low-calorie diets have significantly improved glucose regulation in obese adults and in adults with type 2 diabetes mellitus. In particular, changing the ratios of macronutrients through calorie restriction reduces fasting glucose level and hemoglobin A1c levels in patients with diabetes mellitus. However, this effect is limited in both obese and healthy adults. To date, multiple glucose-related markers have been identified as clinical biomarkers of diabetes. Additional clinical biomarkers include cholesterol levels, hematological markers, and inflammatory markers. Taken together, the evidence presented in this review may help for selection of clinical biomarkers for meal-based preventive approaches for non- or pre-diabetic individuals to prevent onset of diabetes.

Salmon in Combination with High Glycemic Index Carbohydrates Increases Diet-Induced Thermogenesis Compared with Salmon with Low Glycemic Index Carbohydrates⁻An Acute Randomized Cross-Over Meal Test Study

The study investigated the acute effects of meals containing either salmon or veal in combination with carbohydrates with high or low glycemic index (GI) on diet-induced thermogenesis (DIT) (primary endpoint), appetite sensations, and energy intake (EI). Twenty-five overweight men and women ingested four iso-caloric test meals: salmon with mashed potatoes (high GI) (SM), salmon with wholegrain pasta (low GI) (SP), veal with mashed potatoes (VM) and veal with wholegrain pasta (VP). Energy expenditure was measured in the fasting state and six times postprandially for 25 min with 5-min breaks between each measurement. Appetite sensations were measured every 30 min. Blood samples, from arterialized venous blood, were drawn every 20 min until an ad libitum buffet-style lunch was served 3.5 h later. DIT was 40% higher after the SM meal compared to the SP meal (p = 0.002). Prospective food consumption was lower after the SM meal compared with the VP meal (p = 0.01). There were no differences in satiety, hunger, fullness, or ad libitum EI between the test meals (all p > 0.05). In conclusion, salmon with high GI carbohydrates increased DIT compared to salmon with low GI carbohydrates. This indicates that DIT is sensitive to the GI of the carbohydrates after intake of salmon but not veal.

Effect of nutrient composition in a mixed meal on the postprandial glycemic response in healthy people: a preliminary study

BACKGROUND/OBJECTIVES

The glycemic index (GI) is a measure of the postprandial glucose response (PPGR) to food items, and glycemic load (GL) is a measure of the PPGR to the diet. For those who need to maintain a healthy diet, it is beneficial to regulate appropriate levels of blood glucose. In reality, what influences the meal GI or GL depends on the macronutrient composition and the physical chemistry reactions in vivo. Thus, we investigated whether different macronutrients in a meal significantly affect the PPGR and the validity of calculated GI and GL values for mixed meals.

SUBJECTS/METHODS

12 healthy subjects (6 male, 6 female) were recruited at a campus setting, and subjects consumed a total of 6 test meals one by one, each morning between 8:00 and 8:30 am after 12 h of fasting. PPGR was measured after each consumed meal and serial finger pricks were performed at indicated times. Test meals included 1) 68 g oral glucose, 2) 210 g rice, 3) rice plus 170 g egg white (RE), 4) rice plus 200 g bean sprouts (RS), 5) rice plus 10 g oil (RO), and 6) rice plus, egg white, bean sprouts, and oil (RESO). The incremental area under the curve (iAUC) was calculated to assess the PPGR. Mixed meal GI and GL values were calculated based on the nutrients the subjects consumed in each of the test meals.

RESULTS

The iAUC for all meals containing two macronutrients (RS, RO, or RE) were not significantly different from the rice iAUC, whereas, the RESO iAUC (2,237.5 ± 264.9) was significantly lower (P < 0.05). The RESO meal's calculated GI and GL values were different from the actual GI and GL values measured from the study subjects (P < 0.05).

CONCLUSIONS

The mixed meal containing three macronutrients (RESO) decreased the PPGR in healthy individuals, leading to significantly lower actual GI and GL values than those derived by nutrient-based calculations. Thus, consuming various macronutrient containing meals is beneficial in regulating PPGR.