Carbohydrate Content Of Meals Research Articles

Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions.

Diabetes mellitus is a prevalent chronic autoimmune disease with a high impact on global health, affecting millions of adults and resulting in significant morbidity and mortality. Achieving optimal blood glucose levels is crucial for diabetes management to prevent acute and long-term complications. Carbohydrate counting (CC) is widely used by patients with type 1 diabetes to adjust prandial insulin bolus doses based on estimated carbohydrate content, contributing to better glycemic control and improved quality of life. However, accurately estimating the carbohydrate content of meals remains challenging for patients, leading to errors in bolus insulin dosing. This review explores the current limitations and challenges in CC accuracy and emphasizes the importance of personalized educational programs to enhance patients' abilities in carbohydrate estimation. Existing tools for assessing patient learning outcomes in CC are discussed, highlighting the need for individualized approaches tailored to each patient's needs. A comprehensive review of the relevant literature was conducted to identify educational programs and assessment tools dedicated to training diabetes patients on carbohydrate counting. The research aims to provide insights into the benefits and limitations of existing tools and identifies future research directions to advance personalized CC training approaches. By adopting a personalized approach to CC education and assessment, healthcare professionals can empower patients to achieve better glycemic control and improve diabetes management. Moreover, this review identifies potential avenues for future research, paving the way for advancements in personalized CC training and assessment approaches and further enhancing diabetes management strategies.

Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions

Diabetes mellitus is a prevalent chronic autoimmune disease with a high impact on global health, affecting millions of adults and resulting in significant morbidity and mortality. Achieving optimal blood glucose levels is crucial for diabetes management to prevent acute and long-term complications. Carbohydrate counting (CC) is widely used by patients with type 1 diabetes to adjust prandial insulin bolus doses based on estimated carbohydrate content, contributing to better glycemic control and improved quality of life. However, accurately estimating the carbohydrate content of meals remains challenging for patients, leading to errors in bolus insulin dosing. This review explores the current limitations and challenges in CC accuracy and emphasizes the importance of personalized educational programs to enhance patients’ abilities in carbohydrate estimation. Existing tools for assessing patient learning outcomes in CC are discussed, highlighting the need for individualized approaches tailored to each patient’s needs. A comprehensive review of the relevant literature was conducted to identify educational programs and assessment tools dedicated to training diabetes patients on carbohydrate counting. The research aims to provide insights into the benefits and limitations of existing tools and identifies future research directions to advance personalized CC training approaches. By adopting a personalized approach to CC education and assessment, healthcare professionals can empower patients to achieve better glycemic control and improve diabetes management. Moreover, this review identifies potential avenues for future research, paving the way for advancements in personalized CC training and assessment approaches and further enhancing diabetes management strategies.

A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model

Healthcare delivery might be completely changed by computer vision technologies. It might be used, in instance, to assess the carbohydrate content of meals for 1 diabetes patients. Based on the bag-of-features (BoF) paradigm, this paper suggests a way for automated food recognition. A thorough technical analysis was carried out to identify and optimize the components that make up the BoF architecture in order to guarantee the best outcomes. Also estimated were the associated parameters. A visual dataset with about 5000 food photos that was divided into 11 groups was developed in order to construct and test the prototype system. Using the scale-invariant 3 feature transform on the HSV colour space, the optimised method calculates dense local features. After that, it utilizes a visual dictionary with 10,000 visual words to create a linear support vector machine classifier is then used to categorize the food photos. The system demonstrated the viability of the suggested strategy in a very difficult picture dataset by achieving a classification accuracy of about 88%. By giving diabetes patients a more precise and effective means to control their carbohydrate consumption, this cutting-edge technology might have a profound effect on their quality of life.

Practical guide in using insulin degludec/insulin aspart: A multidisciplinary approach in Malaysia.

Insulin degludec/insulin aspart (IDegAsp) co-formulation provides both basal and mealtime glycaemic control in a single injection. The glucose level-lowering efficacy of IDegAsp is reported to be superior or non-inferior to that of the currently available insulin therapies with a lower rate of overall hypoglycaemia and nocturnal hypoglycaemia. An expert panel from Malaysia aims to provide insights into the utilisation of IDegAsp across a broad range of patients with type 2 diabetes mellitus (i.e. treatment-naive or insulin-naive patients or patients receiving treatment intensification from basal-only regimens, premixed insulin and basal-bolus insulin therapy). IDegAsp can be initiated as once-daily dosing for the main meal with the largest carbohydrate content with weekly dose adjustments based on patient response. A lower starting dose is recommended for patients with cardiac or renal comorbidities. Dose intensification with IDegAsp may warrant splitting into twice-daily dosing. IDegAsp twice-daily dosing does not need to be split at a 50:50 ratio but should be adjusted to match the carbohydrate content of meals. The treatment of patients choosing to fast during Ramadan should be switched to IDegAsp early before Ramadan, as a longer duration of titration leads to better glycated haemoglobin level reductions. The pre-Ramadan breakfast/lunch insulin dose can be reduced by 30%-50% and taken during sahur, while the preRamadan dinner dose can be taken without any change during iftar. Education on the main meal concept is important, as carbohydrates are present in almost all meals. Patients should not have a misconception of consuming more carbohydrates while taking IDegAsp.

Nutritional Impact on Metabolic Homeostasis and Brain Health.

Aging in modern societies is often associated with various diseases including metabolic and neurodegenerative disorders. In recent years, researchers have shown that both dysfunctions are related to each other. Although the relationship is not fully understood, recent evidence indicate that metabolic control plays a determinant role in neural defects onset. Indeed, energy balance dysregulation affects neuroenergetics by altering energy supply and thus neuronal activity. Consistently, different diets to help control body weight, blood glucose or insulin sensitivity are also effective in improving neurodegenerative disorders, dampening symptoms, or decreasing the risk of disease onset. Moreover, adapted nutritional recommendations improve learning, memory, and mood in healthy subjects as well. Interestingly, adjusted carbohydrate content of meals is the most efficient for both brain function and metabolic regulation improvement. Notably, documented neurological disorders impacted by specific diets suggest that the processes involved are inflammation, mitochondrial function and redox balance as well as ATP production. Interestingly, processes involving inflammation, mitochondrial function and redox balance as well as ATP production are also described in brain regulation of energy homeostasis. Therefore, it is likely that changes in brain function induced by diets can affect brain control of energy homeostasis and other brain functions such as memory, anxiety, social behavior, or motor skills. Moreover, a defect in energy supply could participate to the development of neurodegenerative disorders. Among the possible processes involved, the role of ketone bodies metabolism, neurogenesis and synaptic plasticity, oxidative stress and inflammation or epigenetic regulations as well as gut-brain axis and SCFA have been proposed in the literature. Therefore, the goal of this review is to provide hints about how nutritional studies could help to better understand the tight relationship between metabolic balance, brain activity and aging. Altogether, diets that help maintaining a metabolic balance could be key to both maintain energy homeostasis and prevent neurological disorders, thus contributing to promote healthy aging.

Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data.

Despite technological advances, results from various clinical trials have repeatedly shown that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal that will match the expected postprandial glycemic response (PPGR). The objective of this study was to develop a prediction model for PPGR in individuals with T1D. We recruited individuals with T1D who were using continuous glucose monitoring and continuous subcutaneous insulin infusion devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 900 healthy individuals to 41,371 meals were also integrated into the model. The performance of the models was evaluated with 10-fold cross validation. A total of 121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model with emulation of standard of care (correlation of R = 0.59 compared with R = 0.40 for predicted and observed PPGR respectively; P < 10-10). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 min prior to meal, meal carbohydrate content, and meal's carbohydrate-to-fat ratio were the most influential features for the model. Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D on the basis of meals with expected low glycemic response.

A fully artificial pancreas versus a hybrid artificial pancreas for type 1 diabetes: a single-centre, open-label, randomised controlled, crossover, non-inferiority trial

For people with type 1 diabetes, there is currently no automated insulin delivery system that does not require meal input. We aimed to assess the efficacy of a novel faster-acting insulin aspart (Fiasp) plus pramlintide fully closed-loop system that does not require meal input. In this open-label, randomised controlled, crossover, non-inferiority trial we compared the Fiasp (Novo Nordisk, Bagsværd, Denmark) plus pramlintide closed-loop system with no meal input (fully artificial pancreas) and the Fiasp-alone closed-loop system with precise carbohydrate counting (hybrid artificial pancreas). Adults (≥18 years) who had a clinical diagnosis of type 1 diabetes for at least 12 months, had glycated haemoglobin 12% or lower, and had been on insulin pump therapy for at least 6 months were enrolled at McGill University Health Centre, Montreal, QC, Canada. The Fiasp plus pramlintide fully closed-loop system delivered pramlintide in a basal-bolus manner with a fixed ratio of 10 μg:U relative to insulin. A research staff member counted the carbohydrate content of meals to input in the hybrid closed-loop system. Participants completed the two full-day crossover interventions in a random order allocated by a computer-generated code implementing a blocked randomisation (block size of four). The primary outcome was the percentage of time spent within the glucose target range (3·9-10·0 mmol/L), with a 6% non-inferiority margin, assessed in all participants who completed both interventions. This trial is registered with ClinicalTrials.gov, NCT03800875. Between Feb 8, 2019, and Sept 19, 2020, we enrolled 28 adults, of whom 24 completed both interventions and were included in analyses. The percentage of time spent in the target range was 74·3% (IQR 61·5-82·8) with the fully closed-loop system versus 78·1% (66·3-87·5) with the hybrid Fiasp-alone closed-loop system (paired difference 2·6%, 95% CI -2·4 to 12·2; non-inferiority p=0·28). Eight (33%) participants had at least one hypoglycaemia event (<3·3 mmol/L) with the fully closed-loop system compared with 14 (58%) participants with the hybrid closed-loop system (2200-2200 h). Non-mild nausea was reported by three (13%) participants and non-mild bloating by one (4%) participant with the fully closed-loop system compared with zero participants with the hybrid closed-loop system. The Fiasp plus pramlintide fully closed-loop system was not non-inferior to the Fiasp-alone hybrid closed-loop system for the overall percentage of time in the glucose target range. However, participants still spent a high percentage of time within the target range with the fully-closed loop system. Outpatient studies comparing the fully closed-loop hybrid systems with patient-estimated, rather than precise, carbohydrate counting are warranted. Diabetes Canada.

Evaluation of Meal Carbohydrate Counting Errors in Patients with Type 1 Diabetes.

Correct estimation of meal carbohydrate content is a prerequisite for successful intensified insulin therapy in patients with diabetes. In this survey, the counting error in adult patients with type 1 diabetes was investigated. Seventy-four patients with type 1 diabetes estimated the carbohydrate content of 24 standardized test meals. The test meals were categorized into 1 of 3 groups with different carbohydrate content: low, medium, and high. Estimation results were compared with the meals' actual carbohydrate content as determined by calculation based on weighing. A subgroup of the participants estimated the test meals for a second (n=35) and a third time (n=22) with a mean period of 11 months between the estimations. During the first estimation, the carbohydrate content was underestimated by -28% (-50, 0) of the actual carbohydrate content. Particularly meals with high mean carbohydrate content were underestimated by -34% (-56, -13). Median counting error improved significantly when estimations were performed for a second time (p<0.001). Participants generally underestimated the carbohydrate content of the test meals, especially in meals with higher carbohydrate content. Repetition of estimation resulted in significant improvements in estimation accuracy and is important for the maintenance of correct carbohydrate estimations. The ability to estimate the carbohydrate content of a meal should be checked and trained regularly in patients with diabetes.

Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App.

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered.

SUN-LB329: Liquid Carbohydrate Content of Meals is a Major Determinant of Gastric Emptying Rate

SUN-LB329: Liquid Carbohydrate Content of Meals is a Major Determinant of Gastric Emptying Rate

Evaluation of the Effect of Carbohydrate Intake on Postprandial Glucose in Patients With Type 1 Diabetes Treated With Insulin Pumps.

It has been suggested that dietary freedom in functional insulin therapy may be detrimental to glycemic control in type 1 diabetes. This study evaluates the effect of carbohydrate intake on glycemic control and postprandial blood glucose concentrations. Insulin pump data from 148 adults with type 1 diabetes, trained in functional insulin therapy, using pumps for ≥6 months, with ≥2 weeks of consecutive downloaded data, ≥80% use of a bolus calculator, ≥3 capillary blood glucose tests/day, and a concurrent HbA1C, were analyzed. More detailed periprandial data (pre- and postmeal glucose, carbohydrate intake, insulin bolus) were collected from a subset of 105 downloads (3495 meals). Mean (± SD) age of contributors was 43 ± 13 years, HbA1C 7.84% ± 0.93 (62.19 mmol/mol); daily carbohydrate intake 166 ± 71 g. HbA1C reduced with increased meals/day (r = -.370, P < .0005) and increased with mean carbohydrate content/meal (r = .198, P = .043). However, total daily carbohydrate intake had a weak but significant negative association with HbA1C (r = -.181, P = .027). There was no association between standard deviation of carbohydrate intake and HbA1C (r = .021, P = .802) or between meal carbohydrate content and postprandial change in blood glucose (r = -.004, P = .939) for meals with early postprandial (1-3 hours; n = 390) readings. There was a weak positive correlation (r = .184, P = .008) between meal carbohydrate content and late (4-7 hours; n = 390) postprandial readings. With appropriate training, patients using insulin pumps can accommodate a flexible diet with variable carbohydrate intake, without detriment to glycemic control. However, large carbohydrate meals may contribute to poorer outcomes, through impact on late postprandial glycemia.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

BackgroundDiabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference.ObjectiveThe main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires.MethodsThe study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB.ResultsThe mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use.ConclusionsThis study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results.

This paper presents the architecture and initial usability results of an advanced insulin bolus calculator for diabetes (ABC4D), which provides personalized insulin recommendations for people with diabetes by differentiating between various diabetes scenarios and automatically adjusting its parameters over time. The proposed platform comprises two main components: a smartphone-based patient platform allowing manual input of glucose and variables affecting blood glucose levels (e.g., meal carbohydrate content and exercise) and providing real-time insulin bolus recommendations; and a clinical revision platform to supervise the automatic adaptations of the bolus calculator parameters. The system implements a previously in silico validated bolus calculator algorithm based on case-based reasoning, which uses information from similar past events (i.e., cases) to suggest improved personalized insulin bolus recommendations and automatically learns from new events. Usability of ABC4D was assessed by analyzing the system usage at the end of a six-week pilot study (n = 10). Further feedback on the use of ABC4D has been obtained from each participant at the end of the study from a usability questionnaire. On average, each participant requested 115 ± 21 insulin recommendations, of which 103 ± 28 (90%) were accepted. The clinical revision software proposed a total of 754 case revisions, where 723 (96%) adaptations were approved by a clinical expert and updated in the patient platform.

Efficacy of dual-hormone artificial pancreas to alleviate the carbohydrate-counting burden of type 1 diabetes: A randomized crossover trial

AimCarbohydrate-counting is a complex task for many patients with type 1 diabetes. This study examined whether an artificial pancreas, delivering insulin and glucagon based on glucose sensor readings, could alleviate the burden of carbohydrate-counting without degrading glucose control. MethodsTwelve adults were recruited into a randomized, three-way, crossover trial (ClinicalTrials.gov identifier No. NCT01930097). Participants were admitted on three occasions from 7AM to 9PM and consumed a low-carbohydrate breakfast (women: 30g; men: 50g), a medium-carbohydrate dinner (women: 50g; men: 70g) and a high-carbohydrate lunch (women: 90g; men: 120g). At each visit, glucose levels were randomly regulated by: (1) conventional pump therapy; (2) an artificial pancreas (AP) accompanied by prandial boluses, matching the meal's carbohydrate content based on insulin-to-carbohydrate ratios (AP with carbohydrate-counting); or (3) an AP accompanied by prandial boluses based on qualitative categorization (regular or large) of meal size (AP without carbohydrate-counting). ResultsThe AP without carbohydrate-counting achieved similar incremental AUC values compared with carbohydrate-counting after the low- (P=0.54) and medium- (P=0.38) carbohydrate meals, but yielded higher post-meal excursions after the high-carbohydrate meal (P=0.004). The AP with and without carbohydrate-counting yielded similar mean glucose levels (8.2±2.1mmol/L vs. 8.4±1.7mmol/L; P=0.52), and both strategies resulted in lower mean glucose compared with conventional pump therapy (9.6±2.0mmol/L; P=0.02 and P=0.03, respectively). ConclusionThe AP with qualitative categorization of meal size could alleviate the burden of carbohydrate-counting without compromising glucose control, although more categories of meal sizes are probably needed to effectively control higher-carbohydrate meals.

GoCARB in the Context of an Artificial Pancreas.

In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework. The combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics' skills in carbohydrate estimation. For bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB. The use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.

The factors affecting on estimation of carbohydrate content of meals in carbohydrate counting.

.The objective of this study was to identify factors affecting on errors in carbohydrate (CHO) content estimation during CHO counting. Thirty-seven type 1 diabetes patients and 22 of their parents and 28 physicians/dieticians were enrolled in this study. CHO counting was counted in “Carb”, with 1 Carb defined as 10 g of CHO. To evaluate the accuracy of CHO counting, 80 real-size photographs of cooked meals were presented to the subjects for Carb estimation. Carbs tended to be overestimated for foods containing relatively small amounts of Carbs. On the other hands, Carbs tended to be underestimated for foods with higher than 6 Carbs. Accurate estimation of the Carbs in food containing a large amount of rice was particularly difficult even in the subjects having the CHO counting experience. The Carb contents of high-calorie foods such as meats, fried foods, and desserts tended to be overestimated. This error was smaller in subjects having the CHO counting experience. In conclusion, misunderstanding of high-calorie dishes containing high amounts of CHO was observed in inexperienced subjects, indicating the efficacy of the current methodology of CHO counting. On the other hand it was difficult even for experienced subjects to assess the amount of seasoned rice, suggesting the need for a new methodology for accurate estimation.

A food recognition system for diabetic patients based on an optimized bag-of-features model.

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Return of hunger following a relatively high carbohydrate breakfast is associated with earlier recorded glucose peak and nadir

The aim of this study is to test the hypothesis that a breakfast meal with high carbohydrate/low fat results in an earlier increase in postprandial glucose and insulin, a greater decrease below baseline in postprandial glucose, and an earlier return of appetite, compared with a low carbohydrate/high fat meal. Overweight but otherwise healthy adults (n = 64) were maintained on one of two eucaloric diets: high carbohydrate/low fat (HC/LF; 55:27:18% kcals from carbohydrate:fat:protein) versus low carbohydrate/high fat (LC/HF; 43:39:18% kcals from carbohydrate:fat:protein). After 4 weeks of acclimation to the diets, participants underwent a meal test during which circulating glucose and insulin and self-reported hunger and fullness, were measured before and after consumption of breakfast from their assigned diets. The LC/HF meal resulted in a later time at the highest and lowest recorded glucose, higher glucose concentrations at 3 and 4 hours post meal, and lower insulin incremental area under the curve. Participants consuming the LC/HF meal reported lower appetite 3 and 4 hours following the meal, a response that was associated with the timing of the highest and lowest recorded glucose. Modest increases in meal carbohydrate content at the expense of fat content may facilitate weight gain over the long-term by contributing to an earlier rise and fall of postprandial glucose concentrations and an earlier return of appetite.

Développement d’un nouvel outil d’évaluation de comptage des glucides pour faciliter la mise en oeuvre d’une insulinothérapie fonctionnelle

Carbohydrate counting is the most difficult component of functional insulin therapy. We thus designed a tool to facilitate carbohydrate counting of meals. The tool consists of an illustrated directory (16 x 10 cm, 119 pages) presenting 389 pictures of food, classified in 12 categories (breads, fruits, vegetables, etc.). For each photo, the name and mode of preparation of the foods are given, with the weight of the illustrated portion and its carbohydrate content as a multiple of 5 g. During the first phase of the study (3 days a week for 12 weeks), twelve patients with type 1 diabetes completed a precise food diary using a list and weight of all consumed foods. We were thus able to determine, for each of three meals (breakfast, lunch and dinner), the variability of their carbohydrate content. During the second phase of the study (2 weeks), the patients were given the possibility of using the illustrated food directory. We asked them first to estimate, from the photos, the global carbohydrate content of their meals, and then to weigh each food. This allowed us to calculate the true carbohydrate content of the meals from nutritional tables. During the first phase, the carbohydrate contents of breakfast, lunch and dinner were 67 +/- 29, 72 +/- 30 and 74 +/- 30 g, respectively (mean +/- SD, n = 12). For a given patient, the variability in the carbohydrate content of each meal was expressed by its standard deviation (SD). For the 12 patients, this variability was 18+/-+ 8, 25+/-+ 8 and 27+/-+ 11 g, respectively, for breakfast, lunch and dinner, and represented on the average about one-quarter of the total carbohydrate content. During the second phase, carbohydrate content, estimated by the patients using the illustrated food directory, correlated well with the retrospective evaluation based on nutritional tables (y = 0.95 x + 5 g, r2 = 0.8 ; n = 12, 235 meals). This new illustrated food repertory allows accurate evaluation of the highly variable carbohydrate content of meals, and could thus facilitate functional insulin therapy.

Can children with Type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks?

Carbohydrate (CHO) counting allows children with Type 1 diabetes to adjust mealtime insulin dose to carbohydrate intake. Little is known about the ability of children to count CHO and whether a particular method for assessing CHO quantity is better than others. We investigated how accurately children and their caregivers estimate carbohydrate, and whether counting in gram increments improves accuracy compared with CHO portions or exchanges. One hundred and two children and adolescents (age range 8.3-18.1 years) on intensive insulin therapy and 110 caregivers independently estimated the CHO content of 17 standardized meals (containing 8-90 g CHO), using whichever method of carbohydrate quantification they had been taught (gram increments, 10-g portions or 15-g exchanges). Seventy-three per cent (n = 2530) of all estimates were within 10-15 g of actual CHO content. There was no relationship between the mean percentage error and method of carbohydrate counting or glycated haemoglobin (HbA(1c)) (P > 0.05). Mean gram error and meal size were negatively correlated (r = -0.70, P < 0.0001). The longer children had been CHO counting the greater the mean percentage error (r = 0.173, P = 0.014). Core foods in non-standard quantities were most frequently inaccurately estimated, while individually labelled foods were most often accurately estimated. Children with Type 1 diabetes and their caregivers can estimate the carbohydrate content of meals with reasonable accuracy. Teaching CHO counting in gram increments did not improve accuracy compared with CHO portions or exchanges. Large meals tended to be underestimated and snacks overestimated. Repeated age-appropriate education appears necessary to maintain accuracy in carbohydrate estimations.