FIG 1 - uploaded by Jennie C Brand-Miller
Content may be subject to copyright.
Source publication
Background:
The Food Insulin Index (FII) is a novel algorithm for ranking foods based on their insulin demand relative to an isoenergetic reference food. We compared the effect of carbohydrate counting (CC) versus the FII algorithm for estimating insulin dosage on glycemic control in type 1 diabetes.
Materials and methods:
In a randomized, contr...
Context in source publication
Context 1
... total of 26 adults responded to recruitment notices and met the inclusion criteria between January and September 2013. Of these, 14 were randomized to FID counting, and 12 were randomized to CC (Fig. 1). Twenty-two subjects com- pleted all testing sessions (10 men, 12 women), with four subjects withdrawing because of work commitments. Two of these subjects (one from each arm) withdrew prior to com- pleting all baseline measures and therefore could not be in- cluded in the intention-to-treat analysis, whereas two subjects (one from ...
Citations
... There are also limited number of intervention studies that have been conducted with food II. Other than the mentioned studies in the introduction of this manuscript, another study by Bell et al., was conducted on Type-1 Diabetics (T1D) over 12 weeks [182]. The main findings of the study were: 1) no hypoglycaemic events among the 26 subjects, 2) reduction of post-breakfast glycaemia, and 3) reduction of overall glucose excursion [182]. ...
... Other than the mentioned studies in the introduction of this manuscript, another study by Bell et al., was conducted on Type-1 Diabetics (T1D) over 12 weeks [182]. The main findings of the study were: 1) no hypoglycaemic events among the 26 subjects, 2) reduction of post-breakfast glycaemia, and 3) reduction of overall glucose excursion [182]. With this new II collectanea, we hope similar findings and other "taming effects" towards hyperinsulinaemia could be elicited and published in interventional studies. ...
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.
... Five studies scored 50-75%. representing average-quality evidence [48,[53][54][55]57], and 19 studies scored 76-100%, representing high-quality evidence [27,29,30,[36][37][38][39][40][41][42][43][44][45][46][47][49][50][51]56]. ...
... Of the twenty-five included articles, eleven were cross-sectional [29,[36][37][38][39]48,50,51,[54][55][56], six were crossover studies [30,41,43,46,47,52], four were randomised controlled trials (RCTs) [27,40,44], three were prospective studies [49,53,57], and one was a regression analysis [45]. Most of the studies were conducted in Australia (n = 11) and Iran (n = 10). ...
... Most of the articles (n = 13) were published between 2019 and 2023. The study populations of the articles included the following: four studies on type 2 diabetic individuals [36][37][38][39], seven studies on type 1 diabetic individuals [40][41][42][43][44]47,52], one study on obese adolescents with insulin resistance [46], eleven on healthy participants [27,29,45,48,49,51,[53][54][55][56][57], one on both healthy and type 2 diabetic participants [30], and one study used healthy participants as well as participants with insulin resistance and T2DM [50]. Characteristics and findings of the included studies are summarised in Table 1. ...
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.
... Several strategies have been proposed to complement carbohydrate counting. For example, some consider not only the amount, but the type of carbohydrates that are consumed (19); others, the macronutrients content of a meal such as fat and protein (20)(21)(22); and others, still simplify the estimation of the carbohydrate content in food (23). ...
Background: Carbohydrate counting is often considered the ideal way to calculate mealrelated insulin doses. Several ways to improve carbohydrate counting have been proposed.
Purpose: We propose that carbohydrate counting can be refined via simulation and, as
such, we present a mobile application for the real-time simulation of postprandial glucose
dynamics: STUDIA.
Methods: We used a phenomenological model of the gastrointestinal tract, coupled with
the minimal glucose model to recreate postprandial glucose challenges in people with
type 1 diabetes (T1DM). A requirements gathering process was implemented to define
the application's functionalities and technical requirements. In addition, a person-based
approach was used to characterize the users. Technological stacks were evaluated under
the UX/UI criteria, learning curve, flexibility, and the possibility of executing mathematical models with a resolution of differential equations. We used data from one patient with T1DM to guide users in how to use the app. Continuous glucose monitor readings were used for comparison.
Results: STUDIA is a mobile app built on Android Studio® with a user interface and a web based administrative module connected to AWS®. The app, allows glucose simulations for day-to-day carbohydrate counting refinement, and patient parameter modification based
on previous glucose readings and data analysis for comparison and clinical research.
Conclusions: We present the first-of-a-kind postprandial simulation app based on a
phenomenological model of the GI tract for patients with T1DM and its subsequent clinical
research use. STUDIA will be tested in silico with data from multiple meals from patients
with T1DM, and in a clinical trial.
... Therefore, several strategies have been proposed to complement CCHO. For example, some take into account not only the amount, but the type of carbohydrates that are consumed (20), others the amount of other macronutrients such as fat and protein (21)(22)(23), and others simplify the estimation of the carbohydrate content in food (24). ...
... Twenty-eight patients, 14 per group, will be required to analyze time in range using a mixed-effects model assuming a standard deviation of 4%, the statistical power of 90%, a type I error rate of 0.05%, and intra-subject variability of 5%, and inter-subject variability of 9% (53). Experiments evaluating carbohydrate counting have reported withdrawal rates of between 0 to 46%, higher as follow-up time increases (20,21,23,(54)(55)(56)(57). The sample size was adjusted to 15 patients per group to admit a withdrawal rate of 10% in the intervention group, assuming that no patients in the control group could access the intervention. ...
... Electronic copy available at: https://ssrn.com/abstract=4223440 P r e p r i n t n o t p e e r r e v i e w e d 23 The informed consent instrument is available in the supplementary material. ...
Abstract
Background and aims: Insulin remains the only approved treatment for type 1 diabetes mellitus patients. Carbohydrate counting is the most recommended way to prescribe prandial insulin. As an iterative process, patients perfect carbohydrate counting by practice and repetition. Simulations allow individuals to practice in a safe environment and help build confidence in their ability to perform a task. We believe that using simulations can improve carbohydrate counting. However, simulation has never been used to enhance carbohydrate counting.
Methods: Thirty patients will be included in a randomized, parallel-group, open-label clinical trial with a change in time in range at four weeks as the primary outcome with block randomization and 1: 1 allocation. In this clinical trial, carbohydrate counting aided by a simulation of blood glucose changes after each meal will be compared to traditional advanced carbohydrate counting. Patients assigned to the intervention group will have installed the STUDIA app, an automatic carbohydrate counter coupled to a mathematical model that simulates glucose excursions at the individual level using the patients' parameters on their smartphones. Time in range will be measured using a continuous glucose monitor.
Discussion: To the best of the authors' knowledge, this is the first clinical experiment designed to probe the efficacy and safety of a simulation generated using a mathematical model of the glucose changes after a meal at the individual level. Simulation may be a valuable tool for patients' training in an iterative process such as carbohydrate counting.
Note:
Clinical Trial Registration Details: ClinicalTrials.gov Identifier: NCT05181917. Registered January 10, 2022.
Funding Information: The development of STUDIA was financed in part by a research grant from Asociación Colombiana de Endocrinología, Diabetes y Metabolismo – ACE.
Declaration of Interests: The authors certify that they have no affiliation or are involved with any organization or entity with any financial interest (such as fees, financial aid for education, shares, employment contracts, work as consultants, or any other type of interest) or non-financial interest (such as personal, professional relationships, affiliations, or beliefs) in the topic of interest or any material discussed in this manuscript. Carlos E. Builes-Montaño has received consulting or speaker fees from Sanofi, Novo Nordisk, Novartis, and Boehringer Ingelheim. Jose Garcia-Tirado reports receiving industry research support and royalties from Dexcom through his institution.
Ethics Approval Statement: The ethical considerations of this protocol are based on the amendment made in 2013 during the general assembly in Fortaleza, Brazil, of the World Medical Association to the Declaration of Helsinki, Ethical Principles for Medical Research on Human Beings, and on the Fourth Version of the Ethical Guidelines for Health-Related Research with Human Beings prepared by the Council for International Organizations of Medical Sciences (CIOMS) in collaboration with the World Health Organization (WHO). The ethical considerations of this protocol comply with Resolution 8430 from the Ministry of Health of Colombia. Furthermore, this protocol was approved by Pablo Tobon Uribe Hospital's ethics committee on 28/January/2021. All significant protocol modifications such as changes to eligibility criteria, outcomes, or analyses will be communicated to the Pablo Tobon Uribes Hospital ethics committee and relevant parties.
Keywords: Diabetes Mellitus, Computer Simulation, Mathematical model, clinical trial
... The glycemic peak is a common consequence of ingesting carbohydrate-rich meals [2]. To achieve the postprandial glycemic target, carbohydrate (CHO) counting can be a crucial factor [3][4][5][6]. A single mealtime insulin dose will cover a range of CHO amounts, with the insulin dose calculated for a meal containing 60 g CHO covering 10 g variations in CHO quantity (50-70 g) [7]. Interestingly, the postprandial glycemic peak rises with increasing CHO intake in a range of 20-80 g of CHOs, but meals containing over 80 g do not cause a greater glycemic peak and instead cause prolonged hyperglycemia [8,9]. ...
... It was also proved that in T1D patients, CHO-based meals caused an increase in the blood glucose level peak within 60-90 min with variations among individuals [10,11,15]. PPH and rapid and large glycemic fluctuations are adverse prognostic factors and are related to the development of cardiovascular complications, enhancement of oxidative stress, retinopathy, and certain types of cancers [5,16]. Furthermore, a correlation between poor glycemic control and negative psychological outcomes, such as depressive symptoms, has been reported in teenagers (10-16 years) [17]. ...
Background
Postprandial hyperglycemia (PPH) is a common clinical problem among patients with type 1 diabetes (T1D), which is related to high glycemic index (h-GI) meals. The main problem is linked to high, sharp glycemic spikes following hypoglycemia after h-GI meal consumption. There is a lack of effective and satisfactory solutions for insulin dose adjustment to cover an h-GI meal. The goal of this research was to determine whether a Super Bolus is an effective strategy to prevent PPH and late hypoglycemia after an h-GI meal compared to a Normal Bolus.
Methods
A total of 72 children aged 10–18 years with T1D for at least 1 year and treated with continuous subcutaneous insulin infusion for more than 3 months will be enrolled in a double-blind, randomized, crossover clinical trial. The participants will eat a h-GI breakfast for the two following days and receive a prandial insulin bolus in the form of a Super Bolus 1 day and a Normal Bolus the next day. The glucose level 90 min after the administration of the prandial bolus will be the primary outcome measure. The secondary endpoints will refer to the glucose levels at 30, 60, 120, 150, and 180 min postprandially, the area under the blood glucose curve within 180 min postprandially, peak glucose level and the time to peak glucose level, glycemic rise, the mean amplitude of glycemic excursions, and the number of hypoglycemia episodes.
Discussion
There are still few known clinical studies on this type of bolus. A Super Bolus is defined as a 50% increase in prandial insulin dose compared to the dose calculated based on the individualized patient’s insulin-carbohydrate ratio and a simultaneous suspension of basal insulin for 2 h. Our patients reported the best experience with such a combination. A comprehensive and effective solution to this frequent clinical difficulty of PPH after an h-GI meal has not yet been found. The problem is known and important, and the presented solution is innovative and easy to apply in everyday life.
Trial registration
ClinicalTrials.gov NCT04019821
... Since food energy is used as the constant, all foods and their metabolic interactions could be included in the algorithm, allowing a broader assessment of insulin demand [41]. Its use has been compared to CC in adult studies, showing a better control in postprandial glycaemia in subjects with T1D using FII [42,43], also specifically for protein-containing food [41]. However, no significant changes in HbA1c levels and relatively high rates of mild hypoglycemia with both methods were described [41,43]. ...
... Its use has been compared to CC in adult studies, showing a better control in postprandial glycaemia in subjects with T1D using FII [42,43], also specifically for protein-containing food [41]. However, no significant changes in HbA1c levels and relatively high rates of mild hypoglycemia with both methods were described [41,43]. The efficacy of novel counting methods in children and adolescents with T1D need further studies to be established, since no clear benefit among one method to another was reported up to now [44]. ...
... Since food is used as the constant, all foods and their metabolic interactions could be include algorithm, allowing a broader assessment of insulin demand [41]. Its use has be pared to CC in adult studies, showing a better control in postprandial glycaemia jects with T1D using FII [42,43], also specifically for protein-containing food [41 ever, no significant changes in HbA1c levels and relatively high rates of mild hyp mia with both methods were described [41,43]. The efficacy of novel counting me children and adolescents with T1D need further studies to be established, since benefit among one method to another was reported up to now [44]. ...
Nutrition therapy is a cornerstone of type 1 diabetes (T1D) management. Glycemic control is affected by diet composition, which can contribute to the development of diabetes complications. However, the specific role of macronutrients is still debated, particularly fat intake. This review aims at assessing the relationship between fat intake and glycemic control, cardiovascular risk factors, inflammation, and microbiota, in children and adolescents with T1D. High fat meals are followed by delayed and prolonged hyperglycemia and higher glycated hemoglobin A1c levels have been frequently reported in individuals with T1D consuming high amounts of fat. High fat intake has also been associated with increased cardiovascular risk, which is higher in people with diabetes than in healthy subjects. Finally, high fat meals lead to postprandial pro-inflammatory responses through different mechanisms, including gut microbiota modifications. Different fatty acids were proposed to have a specific role in metabolic regulation, however, further investigation is still necessary. In conclusion, available evidence suggests that a high fat intake should be avoided by children and adolescents with T1D, who should be encouraged to adhere to a healthy and balanced diet, as suggested by ISPAD and ADA recommendations. This nutritional choice might be beneficial for reducing cardiovascular risk and inflammation.
... Importantly, recent research has recognized FII as a predictor of the risk of chronic diseases (23). Several studies have suggested that FII leads to increased risk of diabetes, CVD, and cancer (14,(24)(25)(26)(27). All these conditions share common metabolic parameters with NAFLD. ...
Aim we aimed to study the association between food insulin index and biochemical parameters with the risk of developing NAFLD in adult Iranians.
Background: Hyperinsulinemia may play an important role in the development of non-alcoholic fatty liver disease (NAFLD), due to the relationship between insulin response and body fat accumulation.
Methods: A case-control study of 169 NAFLD patients and 200 healthy adults aged 18-55 years was conducted. Dietary data were collected using a validated 168- items quantitative food frequency questionnaire (FFQ). Food insulin index (FII) was calculated by dividing the total insulin load by total energy intake (kcal / day). Total insulin load (ILoverall) was also calculated using a standard formula.
Results: Mean age of study participants was 43.9 ± 5.9 years. Patients with NAFLD were significantly associated with higher body mass index, levels of liver enzymes, triglyceride, low density lipoprotein-cholesterol (LDL), total cholesterol, and fasting blood sugar (FBS) compared to the healthy subjects. (P < 0.05) The highest tertiles of FII was associated with higher risk of NAFLD (OR=1.4, 95% CI:0.88-2.48, P for trend<0.001) and obesity (OR=2.33, 95% CI: 0.97-5.75) compared to the lowest tertiles. The potential confounders for the association were controlled.
Conclusions: We found that adherence to a diet with a high FII was associated with greater risk of NAFLD and overweight or obesity. Additional studies are required to better understand this association.
... American Diabetes Association (ADA) did not differentiate postmeal norms, National Institute for Health and Care Excellence (NICE) established them at the level above 162mg/dl (9mmol/L), whereas International Society for Pediatric and Adolescent Diabetes (ISPAD) above 180mg/dl (10 mmol/L). 1 Glycemic peak is an often consequence after the ingestion of a carbohydrate-rich meal. 2 To achieve the post meal glycemic targets carbohydrates (CHO) counting seems to be a crucial factor. [3][4][5][6] A single mealtime insulin dose will cover a range in carbohydrate amounts and the insulin dose calculated for meal contains 60g CHO covers the 10g variations in CHO quantity (50-70g). 7 Interestingly, the postprandial glycemic peak raises with increasing carbohydrate intake in the range between 20-80 grams of carbohydrates, but meals contain over 80 grams do not cause a greater glycemic peak, but prolonged hyperglycemia. ...
... 10,11,15 PPH as well as rapid and large glycemic fluctuations are adverse prognostic factors and are implicated in the development of cardio-vascular complications, enhancement of oxidative stress, retinopathy and certain type of cancers. 5,16 Furthermore, among teenagers (10-16years) the correlation between poor glycemic control and negative psychological outcomes as depressive symptoms was reported. 17 In the interest of good glycemic control patients with T1D should consume l-GI products, but this recommendation is rarely followed, especially in pediatric population. ...
Background: Postprandial hyperglycemia (PPH) is common clinical problem among patients with type 1 diabetes (T1D), especially in relation to high glycemic index (h-GI) meals. The main problem are high, sharp glycemic spikes with following hypoglycemia after h-GI meal consumption. There is lack of effective and satisfactory solutions concerning insulin dose adjustment to cover the h-GI meal. The goal of this research is to find out whether a Super Bolus is an effective strategy to prevent postprandial hyperglycemia and late hypoglycemia after h-GI meal in comparison to the normal bolus.
Methods: A total of 72 children aged 10-18 years with T1D for at least 1 year, treated with continuous subcutaneous insulin infusion for more than 3 months will be enrolled in a double-blind, randomized, cross-over clinical trial. Participants will receive the prandial insulin bolus for h-GI breakfast in the form of Super Bolus and as Normal Bolus another day. The primary outcome measure will be the glucose level 90 minutes after administration the prandial bolus. The secondary endpoints will refer to glucose level 30, 60, 120, 150, 180 minutes postprandially; the area under the blood glucose curve within 180 min postprandially; the peak glucose and time to peak glucose level; the glycemic rise, mean amplitude of glycemic excursion, time in postprandial glucose range and the number of hypoglycemia episodes.
Discussion: There is a lack of clinical studies concerning this kind of bolus. Available literature refers only to in-silico studies and case reports. The Super Bolus was defined as a prandial insulin dose increased by 50% in comparison to the dose calculated based on individualized patient’s Insulin-Carbohydrate Ratio (ICR) and simultaneous suspension of the basal insulin for 2 hours. The comprehensive and effective solution to this frequent clinical difficulty of PPH after h-GI meals has not been found yet. The problem is known and important but the presented solution innovatory and easy to apply in every-day life.
Trial registration: The trial was registered at the ClinicalTrials.gov prior to the inclusion of the first patient, 15 July 2019 on registration number: NCT04019821.
... The Pankowska Equation and Food Insulin Index Insulin-Dosing Algorithms(39,40) Pankowska Equation. With this algorithm, an individual's ICR is used to calculate the insulin dose for the carbohydrate component of the meal, which is administered before the start of the meal. ...
For many years, carbohydrate counting has been a popular strategy for determining mealtime insulin doses for people with diabetes who are on a multiple daily injection regimen or continuous subcutaneous insulin infusion. This approach assumes that only carbohydrate-containing foods and beverages affect postprandial glucose levels. However, many studies have indicated that the fat and protein content of a meal can play an important role in delaying postprandial hyperglycemia and should be considered when trying to optimize postprandial glucose levels. This article reviews research on making insulin dose adjustments for high-fat and high-protein meals, as well as the timing of mealtime insulin doses.
... The used SD and dropout rate were based on previous BCC courses at SDCC where mean changes and SD of HbA1c after 6 months were calculated based on completers with T2D. MAGE has only been used as an outcome measure of glucose variability in a few randomised controlled dietary intervention studies of people with diabetes, 37 38 showing differences in changes in MAGE up to 4.8 mmol/L (SD 1.0) after a 12-week carbohydrate counting intervention, 37 but is regularly used in other clinical studies evaluating glucose variability. By including 113 participants in each study group, we will have a power of 80% (alpha level of 0.05) in a two-sided test to detect a difference in the change in MAGE during the intervention period (6 months) of ≥0.30 mmol/L (SD 0.7 mmol/L) between the two study groups. ...
Introduction
Recommendations on energy intake are key in body weight management to improve glycaemic control in people with type 2 diabetes (T2D). International clinical guidelines recommend a variety of eating patterns to promote energy restriction as the primary dietetic approach to body weight control in managing T2D. In addition, individualised guidance on self-monitoring carbohydrate intake to optimise meal timing and food choices (eg, basic carbohydrate counting (BCC)) is recommended to achieve glycaemic control. However, the evidence for this approach in T2D is limited. The objective of this study was to compare the effect of an educational programme in BCC as add-on to the usual dietary care on glycaemic control in people with T2D.
Methods and analyses
The study is designed as a randomised, controlled trial with a parallel-group design. The study duration is 12 months with data collection at baseline, and after 6 and 12 months. We plan to include 226 adults with T2D. Participants will be randomised to one of two interventions: (1) BCC as add-on to usual dietary care or (2) usual dietary care. The primary outcome is changes in glycated haemoglobin A1c or mean amplitude of glycaemic excursions from baseline and after 6-month intervention between and within study groups. Further outcome measures include changes in time in range, body weight and composition, lipid profile, blood pressure, mathematical literacy skills, carbohydrate estimation accuracy, dietary intake, diet-related quality of life, perceived competencies in diet and diabetes and perceptions of an autonomy supportive dietician-led climate, physical activity and urinary biomarkers.
Ethics and dissemination
The protocol has been approved by the Ethics Committee of the Capital Region, Copenhagen, Denmark. Study findings will be disseminated widely through peer-reviewed publications and conference presentations.
Trial registration number
NCT03623139 .
