Validation of the food insulin index in lean, young, healthy individuals, and type 2 diabetes in the context of mixed meals: an acute randomized crossover trial

A collectanea of food insulinaemic index: 2023

BACKGROUND AND AIMS

To systematically update and publish the lnsulinaemic Index (II) value compilation of food/beverages.

METHODS

A literature search identified around 400 scholarly articles published between inception and December 2023. II values were pooled according to the selection criteria of at least 10 healthy, non-diabetic subjects with normal BMI. In addition, the II reported should have been derived from incremental area under the curve (iAUC) calculation of the insulin concentration over time. The reference food used from the pooled articles were either glucose or bread.

RESULTS

The II of 629 food/beverage items were found from 80 distinct articles. This is almost a five-fold increase in the number of entries from a previous compilation in 2011. Furthermore, these articles originated from 32 different countries, and were cleaved into 25 food categories. The II values ranged from 1 to 209. The highest overall recorded II was for a soy milk-based infant formula while the lowest was for both acacia fibre and gin. Upon clustering to single food, the infant formula retained the highest II while both acacia fibre and gin maintained the lowest recording. As for mixed meal, a potato dish served with a beverage recorded the highest II while a type of taco served with a sweetener, vegetable and fruit had the lowest II. Our minimum and maximum II data values replace the entries reported by previous compilations.

CONCLUSION

Acknowledging some limitations, these data would facilitate clinical usage of II for various applications in research, clinical nutrition, clinical medicine, diabetology and precision medicine. Future studies concerning II should investigate standardisation of reference food, including glucose and the test food portion. Although this collectanea adds up new food/beverages II values, priority should be given to populate this database.

The Application of the Food Insulin Index in the Prevention and Management of Insulin Resistance and Diabetes: A Scoping Review

The food insulin index (FII) is a novel algorithm used to determine insulin responses of carbohydrates, proteins, and fats. This scoping review aimed to provide an overview of all scientifically relevant information presented on the application of the FII in the prevention and management of insulin resistance and diabetes. The Arksey and O'Malley framework and the PRISMA Extension for Scoping Reviews 22-item checklist were used to ensure that all areas were covered in the scoping review. Our search identified 394 articles, of which 25 articles were included. Three main themes emerged from the included articles: 1. the association of FII with the development of metabolic syndrome, insulin resistance, and diabetes, 2. the comparison of FII with carbohydrate counting (CC) for the prediction of postprandial insulin response, and 3. the effect of metabolic status on the FII. Studies indicated that the FII can predict postprandial insulin response more accurately than CC, and that a high DII and DIL diet is associated with the development of metabolic syndrome, insulin resistance, and diabetes. The FII could be a valuable tool to use in the prevention and management of T1DM, insulin resistance, and T2DM, but more research is needed in this field.

Insulinaemic potential of diet and lifestyle and risk of type 2 diabetes in the Iranian adults: result from Yazd health study

BACKGROUND

Previous studies have shown that insulin directly affects the risk of type 2 diabetes mellitus (T2DM) but the relationship between insulinaemic potential of diet and lifestyle and the T2DM risk is still unknown. Accordingly, we aimed to investigate the relationship between the insulinaemic potential of diet and lifestyle based on indices including empirical dietary index for hyperinsulinaemia (EDIH), empirical lifestyle index for hyperinsulinaemia (ELIH), empirical dietary index for insulin resistance (EDIR) and empirical lifestyle index for insulin resistance (ELIR) and the T2DM risk in the Iranian adults.

METHODS

This study was performed on data of enrollment phase of the Yazd Health Study (YaHS) and TAghzieh Mardom-e-Yazd (Yazd Nutrition Study) (TaMYZ) on 5714 adults aged 20-70 years (mean: 36.29 years). A validated food frequency questionnaire and clinical tests were used to assess food intake and T2DM ascertainment, respectively. We used the Cox regression analysis for determining the relationship between the indices and T2DM risk.

RESULTS

After adjusting for confounding variables, our findings showed that diet with higher ELIH score is 2.28 times more likely for T2DM risk (RR 2.28 [95% CI 1.69-2.56]), but there was no significant relationship between the EDIH, ELIR and EDIR scores and T2DM risk in adults, in the entire study population.

CONCLUSIONS

Our findings suggest that diets with higher ELIH score increases the T2DM risk, but there was no significant relationship between the EDIH, ELIR and EDIR scores and T2DM risk. Further epidemiological studies are needed to confirm our findings.

Dietary insulin index and load and cardiometabolic risk factors among people with obesity: a cross-sectional study

BACKGROUND

The hypothesis of the effect of the insulinogenic effects of diet on the development of cardiometabolic disorders has been suggested, but limited data are available for adults with obesity. This study aimed to determine the association of dietary insulin index (DII) and dietary insulin load (DIL) with cardiometabolic risk factors among Iranian adults with obesity.

METHODS

The study was conducted with a total of 347 adults aged 20-50 years in Tabriz, Iran. Usual dietary intake was assessed through a validated 147-item food frequency questionnaire (FFQ). DIL was computed using published food insulin index (FII) data. DII was calculated by dividing DIL by the total energy intake of each participant. Multinational logistic regression analysis was performed to evaluate the association between DII and DIL and cardiometabolic risk factors.

RESULTS

Mean age of participants was 40.78 ± 9.23 y, and mean body mass index (BMI) was 32.62 ± 4.80 kg/m2. Mean of DII and DIL was 73.15 ± 37.60 and 196,242 ± 100,181. Participants with higher DII had higher BMI, weight, waist circumference (WC), and blood concentrations of triglyceride (TG) and Homeostasis model assessment insulin resistance index (HOMA-IR) (P < 0.05). After taking potential confounders into account, DIL was positively associated with MetS (OR: 2.58; 95% CI: 1.03-6.46), and high blood pressure (OR: 1.61; 95% CI: 1.13-6.56). Moreover, after adjustment for potential confounders, moderate DII was associated with increased odds of MetS (OR: 1.54, 95% CI: 1.36-4.21), high TG (OR, 1.25; 95% CI, 1.17-5.02), and high blood pressure (OR: 1.88; 95% CI: 1.06-7.86).

CONCLUSION

This population-based study revealed that adults with higher DII and DIL associated with cardiometabolic risk factors and consequently, replacement of high with low DII and DIL may have reduce the risk of cardiometabolic disorders. Further studies with longitudinal design are required to confirm these findings.

The comparison of two mealtime insulin dosing algorithms for high and low glycaemic index meals in adolescents with type 1 diabetes

AIMS

Postprandial glycaemic variability carries on being a clinical challenge in optimizing glucose control in type 1 diabetes. The aim of this study was to compare the postprandial glycaemic effects of carbohydrate counting and food insulin index algorithms following the consumption of protein-rich, high-fat meals with different glycaemic index (GI) in adolescents with type 1 diabetes.

METHODS

A randomized, single-blind and crossover trial included 15 adolescents aged 14-18 years with type 1 diabetes. Participants consumed two different test meals with similar energy, macronutrients and food insulin index but the approximately twofold difference in GI, in random order on four consecutive mornings at their home. Insulin dose for high- and low-GI test meals was determined by using the carbohydrate counting and food insulin index algorithms. Four-hour postprandial glycaemia was assessed by the continuous glucose monitoring system.

RESULTS

Compared with carbohydrate counting, the food insulin index algorithm significantly decreased peak glucose excursion (-57%, p = 0.02), incremental area under the curve (-65%, p = 0.02) and coefficient variation of blood glucose (-37%, p = 0.03) in the high-GI meal, though there was no difference between the two algorithms in the low-GI meal. The occurrence of hypoglycaemia did not significantly differ between insulin dosing algorithms for the high-GI (p = 0.58) and low-GI (p = 0.20) meals.

CONCLUSIONS

The food insulin index algorithm may be beneficial for postprandial glycaemic control after the consumption of high-GI meals in adolescents with type 1 diabetes.

Acute inspiratory muscle exercise effect on glucose levels, glucose variability and autonomic control in patients with type 2 diabetes: A crossover randomized trial

Inspiratory muscle exercise (IME) can be an alternative to conventional exercise. We aimed to evaluate the effect of IME on glucose, glucose variability, and autonomic cardiovascular control in type 2 diabetes. Fourteen diabetic subjects were randomly assigned to IME with 2% maximal inspiratory pressure (PImax) or 60% PImax wearing a continuous glucose monitoring system for three days. Glucose variability [glucose variance (VAR), glucose coefficient of variation (CV%), glucose standard deviation (SD), and mean amplitude of glycemic excursions (MAGE)] were evaluated. Glucose reduction was observed in 5 min (60% of PImax 33.2% and 2% of PImax 32.0%), 60 min (60% of PImax 29.6% and 2% of PImax 31.4%) and 120 min (60% of PImax 21.4% and 2% of PImax 24.0%) after IME (vs.1 h before the exercise), with no difference between loads. This reduction in glucose levels was observed in all moments of the IME protocol. Glucose variability was reduced after 12 h and 18 h of the IME (ΔCV: P < 0.001, ΔSD: P < 0.001 and ΔVAR: P < 0.001) for both loads. No difference was found in MAGE (P = 0.594) after IME. Mean arterial pressure and heart rate rose during the exercise session with 60% of PImax. Although sufficiently strong to induce cardiovascular changes, an inspiratory muscle exercise session with 60% of PImax in subjects with type 2 diabetes has failed to induce any significant improvement in glucose, glucose variability and autonomic control, compared to the 2% Plmax exercise session.

New Insight into Diabetes Management: From Glycemic Index to Dietary Insulin Index

BACKGROUND

Despite efforts to control hyperglycemia, diabetes management is still challenging. This may be due to focusing on reducing hyperglycemia and neglecting the importance of hyperinsulinemia; while insulin resistance and resultant hyperinsulinemia preceded diabetes onset and may contribute to disease pathogenesis.

OBJECTIVE

The present narrative review attempts to provide a new insight into the management of diabetes by exploring different aspects of glycemic index and dietary insulin index.

RESULTS

The current data available on this topic is limited and heterogeneous. Conventional diet therapy for diabetes management is based on reducing postprandial glycemia through carbohydrate counting, choosing foods with low-glycemic index and low-glycemic load. Since these indicators are only reliant on the carbohydrate content of foods and do not consider the effects of protein and fat on the stimulation of insulin secretion, they cannot provide a comprehensive approach to determine the insulin requirements.

CONCLUSION

Selecting foods based on carbohydrate counting, glycemic index or glycemic load are common guides to control glycemia in diabetic patients, but neglect the insulin response, thus leading to failure in diabetes management. Therefore, paying attention to insulinemic response along with glycemic response seems to be more effective in managing diabetes.

Does food insulin index in the context of mixed meals affect postprandial metabolic responses and appetite in obese adolescents with insulin resistance? A randomised cross-over trial

The food insulin index (II) is a novel classification to rank foods based on their physiological insulin demand relative to an isoenergetic reference food and may be a valid predictor of postprandial insulin responses and appetite. The present study aimed to compare the postprandial metabolic responses and appetite sensations to two macronutrient- and glycaemic index-matched meals with either high or low II in obese adolescents with insulin resistance (IR). A randomised, single-blind and cross-over trial included fifteen obese adolescents aged 12-18 years with IR. All participants were provided with two different breakfasts: low glycaemic index, low insulin index (LGI-LII) and low glycaemic index, high insulin index (LGI-HII), with a 1-week washout period between meals. At time 0 (just before breakfast), 15, 30, 45, 60, 90, 120, 180 and 240 min after the meal, serum glucose, insulin and C-peptide levels and appetite scores were measured. At the end of 4 h, participants were served ad libitum lunch. Early (0-30 min), late (45-240 min) and total (0-240 min) postprandial insulin responses were lowered by 56·1, 34·6 and 35·6 % after the LGI-LII meal v. LGI-HII meal (P < 0·05). The feeling of hunger was also decreased by 25·8 and 27·5 % after the LGI-LII meal v. LGI-HII meal during the late and total responses (P < 0·05). The calculation II of meals or diets may be a useful dietary approach to reduce postprandial hyperinsulinaemia and the perceived hunger in obese adolescents with IR.

Glycemic Index and Insulinemic Index of Foods: An Interlaboratory Study Using the ISO 2010 Method

An official method for determining food glycemic index (GI) was published by the Organization for International Standardization (ISO) in 2010, but its performance has not been assessed. Therefore, we aimed to determine the intra- and inter-laboratory variation of food GI values measured using the 2010 ISO method. Three laboratories (Australia, Canada and France) determined the GI and insulinemic-index (II) of six foods in groups of 13–15 participants using the 2010 ISO method and intra- and inter-laboratory Standard Deviations (SDs) were calculated. Overall mean food GIs varied from 47 to 86 (p < 0.0001) with no significant difference among labs (p = 0.57) and no food × laboratory interaction (p = 0.20). Within-laboratory SD was similar among foods (range, 17.8–22.5; p = 0.49) but varied among laboratories (range 17.5–23.1; p = 0.047). Between-laboratory SD of mean food GI values ranged from 1.6 to 6.7 (mean, 5.1). Mean glucose and insulin responses varied among foods (p < 0.001) with insulin (p = 0.0037), but not glucose (p = 0.054), varying significantly among labs. Mean II varied among foods (p < 0.001) but not among labs (p = 0.94). In conclusion, we found that using the 2010 ISO method, the mean between-laboratory SD of GI was 5.1. This suggests that the ISO method is sufficiently precise to distinguish a mean GI = 55 from a mean GI ≥ 70 with 97–99% probability.

Association between dietary insulin index and load with obesity in adults

PURPOSE

Hyperinsulinemia is linked to the development of various chronic diseases, especially obesity given to the role of insulin responses in body fat accumulation; hence, the current study aimed to examine the association of insulinemic potential of the diet with general and abdominal obesity among a large population of Iranian adults.

METHODS

This cross-sectional study was carried out among 8691 adult participants aged 18-55 years. Dietary data were collected using a validated dish-based 106-item semi-quantitative food frequency questionnaire. Dietary insulin index (DII) was computed through considering food insulin index values published earlier. Dietary insulin load (DIL) was also calculated using a standard formula. Assessment of anthropometric measures was conducted through a self-administered questionnaire. General obesity was defined as body mass index ≥ 25 kg/m², and abdominal obesity as waist circumference ≥ 94 cm for men and ≥ 80 cm for women.

RESULTS

Mean age of study participants was 36.8 ± 8.1 years; 60.3% were women. Compared with the lowest quintile, women in the highest quintile of DIL were less likely to be abdominally obese (OR 0.73; 95% CI 0.57-0.92). Such significant association was not seen after controlling for potential confounders (OR 0.86; 95% CI 0.44-1.67). Neither in crude nor in adjusted models, we observed a significant association between DIL and general obesity among men and women. In terms of dietary insulin index, men in the top quintile of DII were more likely to be generally overweight or obese compared with those in the bottom quintile (OR 1.27; 95% CI 1.00-1.62). This association became non-significant after controlling for demographic characteristics (OR 1.14; 95% CI 0.84-1.56). Furthermore, in thefully adjusted model, women in the top quintile of DII were more likely to have general obesity compared with those in the bottom quintile (OR 1.40; 95% CI 1.07-1.84).

CONCLUSIONS

We found that adherence to a diet with a high DII was associated with greater odds of general obesity among women, but not in men. Although such information might help to draw conclusions on the practical relevance of the shown findings, further studies, specifically of prospective design, are warranted.

The β-cell burden index of food: A proposal

The quantity and quality of dietary fat and/or carbohydrate may alter one or more of the basic components of the insulin-glucose system, which in turn affect the pathways leading to alterations in glucose homeostasis and, possibly, to cardiovascular disease. This viewpoint article, reviewing some of the currently available tools aiming at quantifying the impact of dietary carbohydrates on the glucose-insulin homeostatic loop, highlights the unmet need of a more thorough assessment of the complex interaction between dietary factors and the glucose-insulin system. A novel index, the "β-cell burden index", may turn out to be a valuable tool to quantify the role played by the diet in shaping the risk of type 2 diabetes, cardiovascular disease and other metabolic and degenerative disorders, ideally orienting their prevention with strategies based on dietary modifications.

Emerging Evidence for the Importance of Dietary Protein Source on Glucoregulatory Markers and Type 2 Diabetes: Different Effects of Dairy, Meat, Fish, Egg, and Plant Protein Foods

Observational studies provide evidence that a higher intake of protein from plant-based foods and certain animal-based foods is associated with a lower risk for type 2 diabetes. However, there are few distinguishable differences between the glucoregulatory qualities of the proteins in plant-based foods, and it is likely their numerous non-protein components (e.g., fibers and phytochemicals) that drive the relationship with type 2 diabetes risk reduction. Conversely, the glucoregulatory qualities of the proteins in animal-based foods are extremely divergent, with a higher intake of certain animal-based protein foods showing negative effects, and others showing neutral or positive effects on type 2 diabetes risk. Among the various types of animal-based protein foods, a higher intake of dairy products (such as milk, yogurt, cheese and whey protein) consistently shows a beneficial relationship with glucose regulation and/or type 2 diabetes risk reduction. Intervention studies provide evidence that dairy proteins have more potent effects on insulin and incretin secretion compared to other commonly consumed animal proteins. In addition to their protein components, such as insulinogenic amino acids and bioactive peptides, dairy products also contain a food matrix rich in calcium, magnesium, potassium, trans-palmitoleic fatty acids, and low-glycemic index sugars-all of which have been shown to have beneficial effects on aspects of glucose control, insulin secretion, insulin sensitivity and/or type 2 diabetes risk. Furthermore, fermentation and fortification of dairy products with probiotics and vitamin D may improve a dairy product's glucoregulatory effects.