Advanced Carbohydrate Counting Research Articles

Carbohydrate Counting, Empowerment and Glycemic Outcomes in Adolescents and Young Adults with Long Duration of Type 1 Diabetes.

The complex treatment for diabetes type 1 (T1D) includes insulin dosing for every meal, which requires education and experience to achieve optimal outcomes. Advanced carbohydrate counting (ACC) is the recommended method. We studied ACC as part of a standard treatment with the aim to explore its associations with glycemic control and empowerment in adolescents and young adults. We used national registry data on glycemic outcomes, a study-specific questionnaire regarding the use of ACC and the Gothenburg Young Persons Empowerment Scale (GYPES) to measure empowerment. A total of 111 participants (10-28 years of age, diabetes duration >9 years, mean HbA1c of 55.4 mmol/mol) answered the questionnaire. We found that most participants (79.3%) who learn ACC, at onset or later, continue to use the method. A higher level of empowerment was associated with lower HbA1c (p = 0.021), making patient empowerment an important factor in achieving optimal glycemic outcomes. No associations were found between ACC and empowerment or glycemic outcomes. A mixed strategy, only using ACC sometimes when insulin dosing for meals, was associated with the lowest empowerment score and highest HbA1c and should warrant extra education and support from the diabetes team to reinforce a dosing strategy.

SAT131 Acceptability And Impact Of Basic Carbohydrate Counting On Glycemic Control In Children And Young People With Type 1 Diabetes

Abstract Disclosure: A. Tandon: None. E. Bhowmik: None. Z. Ali: None. S. Tripathi: None. A. B k: None. V. Bhatia: None. P. Dabadghao: None. S. Sudhanshu: None. Background- Basic carbohydrate counting (BCC) represents a meal-planning approach for people with T1DM, emphasizing on consistency in the timing and amount of carbohydrate intake at each meal. In contrast to advanced carbohydrate counting, it is simpler to learn for people with varied levels of education or numeracy skills. The effect of BCC on glycemic control in children with T1DM using MDI is largely unknown. We aimed to determine whether BCC, compared to routine nutritional education (RNE) of our clinic, would improve glycemic control in these children with typical Indian diets. Methods- This randomized, controlled trial included children, adolescents and young adults with T1DM using MDI. In addition to RNE, the intervention group received BCC education including a personalised diet plan with suggestions for carbohydrate exchanges at each meal based on a 2-week food log prepared by them. The control group received the RNE of our clinic including instructions on healthy food choices, food groups, calorie-free foods, glycemic index, and instructions for special occasions. A retrospective CGM study and HbA1c were performed at baseline and at 12 weeks after the dietary education. The primary outcome was the difference in change in TIR (Time in range; 70-180 mg/dl) from baseline through 12 weeks between the two groups. A questionnaire on acceptability of BCC was administered. Results- Ninety two patients [age 15.7 ± 5.0 years, median (IQR) duration of diabetes 5.9 (5.9) years, baseline HbA1c 8.0 ± 1.5 %] wore the FreeStyle Libre Pro sensor for a median of 14 days. Median (IQR) TIR was 41 (18) %, TBR (Time below range; <70 mg/dl) 14 (13) % and TAR (Time above range; >180 mg/dl) 40.7 ± 20.4 %. Coefficient of variation (% CV) was 49.1±10.3% and 92% had high (>36 %) CV. Multivariate regression showed that older age at onset of diabetes was associated with a higher TIR, TBR and a lower TAR, whereas these glycemic metrics were not influenced by current age, urban vs rural residence, education of mother or socioeconomic status. The use of NPH vs glargine for basal insulin was also associated with higher TBR and %CV but lower TAR. Significant improvement was seen in HbA1c in the BCC group, when compared with their own values during the prior 24 months (baseline [8.2 ± 1.3%], 12 weeks [7.9 ± 1.2%], p = 0.042), but not so in the RNE group (baseline [8.0 ± 1.0%], 12 weeks [8.1 ± 1.0%], p = 0.881). However, no significant differences were observed in the changes in TIR, TBR, TAR, %CV and HbA1c between the two groups after 12 weeks of dietary intervention. Although, acceptability of BCC was high, only half of the participants correctly practiced it at more than 50 percent of their meals. Conclusion- Among our families with T1DM treated with MDI, although BCC had high acceptability, it was not frequently practiced and did not lead to greater improvement in glycemic control as compared to routine nutritional education. Presentation: Saturday, June 17, 2023

Structured nutrition and advanced carbohydrate counting questionnaire for adult type 1 diabetes.

Structured nutrition and advanced carbohydrate counting questionnaire for adult type 1 diabetes.

Carbulin: A Comprehensive Mobile Application for Advanced Carbohydrate Counting and Diet- and Insulin-Regimen Planning for Type 1 Diabetic Patients.

Nowadays, the introduction of the so-called ‘diabetes technology’, either hardware/device or software, to different aspects of day-to-day living in patients with diabetes aims to improve blood glucose control and various lifestyle features. The coordination of vast context of diabetes education/training, particularly in the area of medical nutrition therapy, is considered as a great concern. On the other hand, Iranian food culture consists of a set of traditional dietary patterns and food consumption habit. The study was aimed to develop “the Comprehensive Mobile Application of Advanced Carbohydrate Counting and Diet- and Insulin-Regimen Planning” to help type 1 diabetic patients, improving their health status. The programming language of Kotlin, JavaScript, Node JS, and HTML5 was used for the mobile app development. The app was developed with the following abilities: 1) educating users on different aspects of disease control including, updated general treatment guidelines on physical activity, medical nutrition and insulin therapy, stress management, and the patient’s specific goals and dietary needs, 2) performing advanced carbohydrate counting using both picture-represented and kitchen-scale of carbohydrate foods as well as traditional Iranian foods, 3) recommending the patient’s specific insulin dose, either short- or rapid-acting, based on the carbohydrate content of the selected meal or the selected amount of Iranian foods, 4) recommending the personalized insulin dose needed for decreasing the high blood glucose levels, and 5) performing 3 and 4 simultaneously. Developing Carbulin was an effort to increase type 1 diabetes self-management using the traditional Iranian dietary pattern and menu.

Clinical Application of Reduced-Carbohydrate Diets for Type 1 Diabetes Management: A Retrospective Case Series

Background: The aim of this study was to explore the clinical application of reduced-carbohydrate (RC) diets for type 1 diabetes (T1D) management at a community-based diabetes centre. Methods: To be included in this retrospective case series, adults with T1D must have attended at least two appointments with a Credentialled Diabetes Educator and Accredited Practising Dietitian (CDE/APD) for advice regarding: (a) advanced carbohydrate counting, (b) carbohydrate reduction, and/or (b) low-carbohydrate diet support. Data regarding specific dietary recommendations and clinical outcomes was extracted from patient records stored at the center. A semi-structured interview with the CDE/APD was conducted to collect additional information about the design and delivery of the RC diets. Thematic analysis was used to identify core components of the RC diets, and descriptive statistics were used to assess pre-post changes in clinical T1D outcomes. Results: 26 adults with T1D were eligible and included (77% female). The RC diets represented a patient-led approach involving adjustments to energy and macronutrient intakes, glucose self-monitoring, and insulin management. 22/26 participants attended the center seeking low-carbohydrate diet support, and the average carbohydrate prescription was 63g/day (22-253g/day) which translated to a 37% reduction from baseline. HbA1c reduced from 9.0% (75mmol/mol) to 7.0% (53mmol/mol) (-5.7 to -0.1%), with an average follow-up of 55weeks (n=8). Estimated A1c reduced from 7.1% (54mmol/mol) to 6.3% (45mmol/mol) (-2.9 to+0.6%) over 21 weeks (n=19). Mean total daily insulin reduced from 44 to 31 U/day (-46 to+6 U/day), with an average follow-up of 17 weeks (n=15). Conclusions: This study provides real-world insights into the clinical application of RC diets in the management of adults with T1D at a community-based diabetes centre. Prospective clinical trials are needed to conclusively determine the effects of RC diets on clinical T1D outcomes.

Carbulin: A Comprehensive Mobile Application for Advanced Carbohydrate Counting and Diet- and Insulin-Regimen Planning for Type 1 Diabetic Patients

Nowadays, the introduction of the so-called ‘diabetes technology’, either hardware/device or software, to different aspects of day-to-day living in patients with diabetes aims to improve blood glucose control and various lifestyle features. The coordination of vast context of diabetes education/training, particularly in the area of medical nutrition therapy, is considered as a great concern. On the other hand, Iranian food culture consists of a set of traditional dietary patterns and food consumption habit. The study was aimed to develop “the Comprehensive Mobile Application of Advanced Carbohydrate Counting and Diet- and Insulin-Regimen Planning” to help type 1 diabetic patients, improving their health status. The programming language of Kotlin, JavaScript, Node JS, and HTML5 was used for the mobile app development. The app was developed with the following abilities: 1) educating users on different aspects of disease control including, updated general treatment guidelines on physical activity, medical nutrition and insulin therapy, stress management, and the patient’s specific goals and dietary needs, 2) performing advanced carbohydrate counting using both picture-represented and kitchen-scale of carbohydrate foods as well as traditional Iranian foods, 3) recommending the patient’s specific insulin dose, either short- or rapid-acting, based on the carbohydrate content of the selected meal or the selected amount of Iranian foods, 4) recommending the personalized insulin dose needed for decreasing the high blood glucose levels, and 5) performing 3 and 4 simultaneously. Developing Carbulin was an effort to increase type 1 diabetes self-management using the traditional Iranian dietary pattern and menu.

Efficacy of Bolus Calculation and Advanced Carbohydrate Counting in Type 2 Diabetes: A Randomized Clinical Trial.

Background: Carbohydrate counting and use of automated bolus calculators (ABCs) can help reduce HbA1c in type 1 diabetes but only limited evidence exists in type 2 diabetes. We evaluated the efficacy of advanced carbohydrate counting (ACC) and use of an ABC compared with manual insulin bolus calculation (MC) in persons with type 2 diabetes. Materials and Methods: A 24-week open-label, randomized clinical study was conducted in 79 persons with type 2 diabetes treated with basal-bolus insulin (mean age 62.5 ± 9.6 years, HbA1c 8.7% ± 1.0% [72 ± 11 mmol/mol], diabetes duration 18.7 ± 7.6 years). Participants were randomized 1:1 into two groups: ABC group received training in ACC and use of an ABC; MC group received training in ACC and manual calculation of insulin bolus. Participants wore blinded continuous glucose monitors for 6 days at baseline and at study end. Primary endpoint was change in HbA1c. Results: After 24 weeks, HbA1c decreased 0.8% (8.8 mmol/mol) in ABC group and 0.8% (9.0 mmol/mol) in MC group with no between-group difference (P = 0.96) and without increase in time in hypoglycemic range (sensor glucose <3.9 mmol/L). Glycemic variability decreased significantly in both groups, whereas the total insulin dose and body mass index (BMI) remained unchanged during the study. Treatment satisfaction increased significantly in both groups after 24 weeks. Conclusion: ACC is an effective, low-cost tool to reduce HbA1c and glycemic variability in persons with basal-bolus insulin-treated type 2 diabetes without increase in hypoglycemia or BMI. Similar effects were seen with use of an ABC and with use of manual bolus calculation. Trial registration: ClinicalTrials.gov NCT02887898.

Effects of advanced carbohydrate counting on glucose control and quality of life in children with type 1 diabetes.

The effect of advanced carbohydrate counting (ACC) on metabolic and quality of life (QOL) outcomes is uncertain in children with type 1 diabetes. Our aim was to determine whether ACC would improve HbA1c and QOL scores as compared with standard nutrition in this population. We randomized 87 patients using pump and rapid-acting analogs in a 1 year randomized multicenter study (age 9.6 ± 3.5 years, diabetes duration 4.6 ± 2.7 years, HbA1c 7.8 ± 0.5% [62 ± 5 mmol/mol]). The ACC group received CC education and the control group received traditional dietary education. HbA1c was measured every 3 months. At 0 and 1 year, general, diabetes-specific, and diet-related QOL were respectively assessed by the KIDSCREEN and WHO-5 questionnaires, the diabetes-specific module of the DISABKIDS, and the diet restriction items of the DSQOLS. Mean HbA1c was lower in the ACC than the control group at 3 months (P < .05) and tended to be lower at 6 months (P = .10), 9 months (P = .10), but not at 12 months. The mean of individual average HbA1c during the one-year study period (from M3 to M12) was 7.63 ± 0.43 in the ACC vs 7.85 ± 0.47% in the control group (60 ± 5 vs 62 ± 5 mmol/mol)(P < .05). ACC was associated with significantly higher scores at 1 year on the KIDSCREEN children's psychological scale and the KIDSCREEN parents' physical scale, the DISABKIDS children's treatment scale, and the children's and parents' dietary restriction scales of the DSQOLS (indicating better QOL or lower perceived diet restriction). ACC may be associated with small improvements in metabolic control and QOL scores in children.

Study protocol for optimising glycaemic control in type 1 diabetes treated with multiple daily insulin injections: intermittently scanned continuous glucose monitoring, carbohydrate counting with automated bolus calculation, or both? A randomised controlled trial

IntroductionThere are beneficial effects of advanced carbohydrate counting with an automatic bolus calculator (ABC) and intermittently scanned continuous glucose monitoring (isCGM) in persons with type 1 diabetes. We aim to...

The dietary education trial in carbohydrate counting (DIET-CARB Study): study protocol for a randomised, parallel, open-label, intervention study comparing different approaches to dietary self-management in patients with type 1 diabetes

IntroductionClinical guidelines recommend that patients with type 1 diabetes (T1D) learn carbohydrate counting or similar methods to improve glycaemic control. Although systematic educating in carbohydrate counting is still not offered...

765-P: Effects of an Advanced Carbohydrate Counting Course on Diet Composition in Insulin-Treated Type 2 Diabetes

Background and Aim: Training in advanced carbohydrate counting improves glycemic control in type 1 diabetes and type 2 diabetes. We aimed to investigate, whether a course in advanced carbohydrate counting reduced intake of carbohydrates and glycemic index of carbohydrates in persons with basal-bolus insulin treated type 2 diabetes. Material and Methods: We conducted a 24-week open-label, randomized controlled study in 79 participants with basal-bolus insulin treated type 2 diabetes. Participants were randomized 1:1 into two groups. ABC group received 6-hour training in advanced carbohydrate counting and use of an automated bolus calculator. MC group also received 6-hour training in advanced carbohydrate counting but was trained in manual calculation of insulin bolus. Diet composition and intake of macronutrients were assessed at baseline, week 12 and week 24 using a validated electronic food frequencies questionnaire. Results: Baseline characteristics were similar between groups (mean age 62.5 ± 9.6 years, mean HbA1C 72 ± 11 mmol/mol, mean diabetes duration 18.7 ± 7.6 years, mean BMI 33 ± 6 kg/m2). Mean energy intake was significantly reduced in the ABC group from baseline to week 24 (P=0.03), primarily due to significantly reduced intake of carbohydrates in form of cereals (P=0.005). No significant changes in energy intake were seen in the MC group (P=0.62). Further, glycemic index of ingested carbohydrates was reduced significantly in the ABC group (P=0.03), but not in the MC group (P=0.09). As previously reported HbA1c had decreased significantly by 9.0 mmol/mol in both groups at week 24. There were no significant changes in BMI throughout the study. Conclusion: Advanced carbohydrate counting with the use of an automated bolus calculator may help to reduce energy intake and glycemic index of ingested carbohydrates in basal-bolus insulin treated type 2 diabetes. Disclosure M.B. Christensen: None. T.I. Halldorsson: None. A. Gotfredsen: None. E. Hommel: None. P. Gæde: None. S.F. Olsen: None. K. Nørgaard: Advisory Panel; Self; Abbott, Medtronic, Novo Nordisk A/S. Speaker's Bureau; Self; Bayer US, Medtronic, Roche Diabetes Care, Rubin Medical, Sanofi, Zealand Pharma A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Funding Roche A/S

Identification of Mixed-Meal Effects on Insulin Needs and Glycemic Control

Abstract Patients with type 1 diabetes mellitus (T1DM) usually determine their bolus insulin requirements using methods from advanced carbohydrate counting (ACC). In standard ACC only the carbohydrate content of the meal is used, whereas newer research suggests that also fat and protein should be considered. Especially meals with a high fat content are reported to require additional insulin. However, it remains disputed how to account for mixed meal composition appropriately when calculating bolus insulin needs. The current paper shows how the previously published Adaptive Bolus Calculator (ABC) method can be used to identify the effect of the mixed meal composition on the bolus insulin requirements. Additionally, a pragmatic strategy is proposed how the mixed meal composition can be taken into account when determining the bolus insulin needs with the ABC method. It is shown in Deviation Analysis simulations that taking into account the mixed meal composition for calculating the bolus insulin needs leads to a small, but statistically significant improvement in glycemic control.

Impact of Carbohydrate Counting Errors on Glycemic Control in Type 1 Diabetes

Abstract Patients with type 1 diabetes mellitus (T1DM) typically determine their bolus insulin needs based on methods from Advanced Carbohydrate Counting (ACC). In ACC the amount of required bolus insulin is assumed to be directly proportional to the carbohydrate content of the ingested meal. It is well known that many T1DM patients have difficulties to accurately estimate meal carbohydrates. Though these estimates are used for calculating the meal bolus amount, there is a scarcity of data on how estimation errors affect glycemic control. The current paper analyzes the effect of carb counting errors on glycemic outcomes during basal-bolus-therapy in a simulation study using so-called Deviation Analyses. Furthermore, the effect of inaccurate estimates on the settings of the insulin therapy is studied by means of the previously published Adaptive Bolus Calculator (ABC) algorithm. It is found that whereas random carb counting inaccuracies indeed do lead to an inferior glycemic control, systematic biases in the estimates are expected to hardly affect the results since these are usually implicitly accounted for in the therapy settings.

Exploring factors influencing HbA1c and psychosocial outcomes in people with type 1 diabetes after training in advanced carbohydrate counting.

The purpose of this secondary analysis of the StenoABC Study was to identify determinants of the changes in HbA1c observed after training of people with type 1 diabetes in advanced carbohydrate counting (ACC) and automated bolus calculator (ABC) use, and further to investigate psychosocial effects of these insulin dosing approaches. Validated diabetes-specific questionnaires were used to assess diabetes treatment satisfaction, problem areas in diabetes, fear of hypoglycemia and diabetes dependent quality of life before and one year after the training. In addition, numeracy was tested (using a non-validated test developed specifically for this study) and behavioral measures (number of daily blood glucose measurements and self-reported use of ACC) were obtained. Associations between change in HbA1c and these measures plus sex, age, diabetes duration and BMI were tested. Numeracy was the only baseline predictor of yearly change in HbA1c identified. Higher levels of numeracy were associated with greater reductions in HbA1c (P=0.031). No associations between change in HbA1c and the behavioral measures investigated were found, nor were any clinically relevant associations between changes in HbA1c and questionnaire scores. Treatment satisfaction increased in all users of ACC (P<0.001). People who also used an ABC reported significantly lower levels of fear of hypoglycemia than people who practiced ACC without such device (P=0.005). Improvements in HbA1c after training in ACC were inversely related to numeracy. Use of an ABC did not compensate for poor numeracy skills. However, device use reduced fear of hypoglycemia compared with ACC without ABC use.

Carbohydrate Counting: A Simple Method of Dietary Management for Glycemic Control in Japanese Diabetic Hemodialysis Patients.

Carbohydrate counting used in dietary therapy for diabetes is based on the concept that the postprandial rise in blood glucose levels is primarily affected by ingested carbohydrates. This method has been widely accepted and used since 1993, when its usefulness was demonstrated in the United States, largely due to the ease with which patients can understand the method. However, in Japan, there is a common misunderstanding that energy intake determines postprandial blood glucose levels. We examined the effectiveness of using basic carbohydrate counting and advanced carbohydrate counting with Japanese diabetic dialysis patients. With both methods, predialysis blood glucose and HbA1c levels were significantly decreased at the final follow-up compared with preinstruction values. There were no significant changes in other parameters. The carbohydrate counting method was able to be applied independently of, but concurrently with, the control of potassium and phosphorus intake, which is the basis of dietary therapy for dialysis patients. Moreover, those patients who completed the basic carbohydrate counting instruction sessions had a mean relative carbohydrate intake (% of total energy) of 51.0 ± 4.7% per meal, indicating they did not consume a low-carbohydrate diet. Key Messages: At present, there is no literature on carbohydrate counting performed by dialysis patients. Carbohydrate counting is a useful method of dietary management for glycemic control that can be applied independently of, but concurrently with, the control of potassium and phosphorus intake in dietary therapy for dialysis patients.

Effectiveness of advanced carbohydrate counting in type 1 diabetes mellitus: a systematic review and meta-analysis.

Potential benefits of carbohydrate counting for glycemic control in patients with type 1 diabetes mellitus (T1DM) remain inconclusive. Our aim is to systematically assess the efficacy of carbohydrate counting in patients with T1DM. We searched PubMed, Embase, Web of Science, Cochrane Library and the Chinese Biology Medicine (CBM) up to December 2015. Randomized controlled trials (RCTs) with at least 3 months follow-up that evaluated carbohydrate counting compared with usual or other diabetes dietary education in patients with T1DM were included. Overall meta-analysis identified a significant decrease in HbA1c concentration with carbohydrate counting versus other diabetes diet method or usual diabetes dietary education (SMD: −0.35, 95%CI: −0.65 to −0.05, P = 0.023). Subgroup analysis restricted to trials which compared carbohydrate counting with usual diabetes dietary found a significant decrease in HbA1c in carbohydrate counting group (SMD: −0.68, 95%CI: −0.98 to −0.38, P = 0.000), and a similar result has emerged from six studies in adults (SMD: −0.40, 95%CI: −0.78 to −0.02, P = 0.037). Carbohydrate counting may confer positive impact on glucose control. Larger clinical trials are warranted to validate this positive impact.

Effects of advanced carbohydrate counting guided by an automated bolus calculator in Type 1 diabetes mellitus (StenoABC): a 12‐month, randomized clinical trial

To test whether concomitant use of an automated bolus calculator for people with Type 1 diabetes carrying out advanced carbohydrate counting would induce further improvements in metabolic control. We conducted a 12-month, randomized, parallel-group, open-label, single-centre, investigator-initiated clinical study. We enrolled advanced carbohydrate counting-naïve adults with Type 1 diabetes and HbA1c levels 64-100 mmol/mol (8.0-11.3%), who were receiving multiple daily insulin injection therapy. In a 1:1-ratio, participants were randomized to receive training in either advanced carbohydrate counting using mental calculations (MC group) or advanced carbohydrate counting using an automated bolus calculator (ABC group) during a 3.5-h group training course. For 12 months after training, participants attended a specialized diabetes centre quarterly. The primary outcome was change in HbA1c from baseline to 12 months. Between August 2012 and September 2013, 168 participants (96 men and 72 women) were recruited and randomly assigned to the MC group (n = 84) and the ABC group (n = 84). Drop-out rates were 23.8 and 21.4%, respectively (P = 0.712); 130 participants completed the study. The baseline HbA1c was 75 ± 9 mmol/mol (9.0 ± 0.8%) in the MC group and 74 ± 8 mmol/mol (8.9 ± 0.7%) in the ABC group. At 12 months, change in HbA1c was significant within both groups: MC group: -2 mmol/mol (95% CI -4 to -1) or -0.2% (95% CI -0.4 to -0.1; P = 0.017) and ABC group: -5 mmol/mol (95% CI -6 to -3) or -0.5% (95% CI -0.6 to -0.3; P < 0.0001), but HbA1c reductions were significantly greater in the ABC group (P = 0.033). No episodes of severe hypoglycaemia were reported. People with Type 1 diabetes initiating advanced carbohydrate counting obtained significantly greater HbA1c reductions when guided by an automated bolus calculator (NCT02084498).

Use of advanced carbohydrate counting and an automated bolus calculator in clinical practice: the BolusCal®training concept

AbstractBackground: BolusCal® is a newly developed training concept for patients with Type 1 diabetes (T1D) on a basal-bolus regime. The training, which is provided by a diabetes nurse and a dietician, consists of a 4-hour group session followed by a 1-hour follow-up and includes training in advanced carbohydrate counting and the use of an automated bolus calculator.Aims: The aim of this article is to describe the BolusCal training concept and to report changes in HbA1c and body mass index (BMI) as well as resources spent 12 months after implementing the BolusCal training concept in routine clinical practice.Methods: During 14 months in 2012–2013, 86 patients with T1D participated in a BolusCal training course. We retrospectively collected patient data from electronic medical records.Results: From training course participation to 12 months, HbA1c decreased from 66 to 57 mmol/mol (8.2–7.4%) (p < 0.001). BMI did not change. Within the first 6 months the number of follow-up consultations ranged from 0 to 9 (...

Effects of advanced carbohydrate counting in patients with Type 1 diabetes: a systematic review

Advanced carbohydrate counting, a systematic method for insulin bolus calculation, is recommended in the management of type 1 diabetes. The aim of this systematic review was to summarize all available evidence from randomized and observational studies of the effects of advanced carbohydrate counting on glycaemic control (HbA(1c)), psychosocial measures, weight and hypoglycaemic events in patients of all age groups with type 1 diabetes on a basal-bolus insulin regimen. An electronic search of Scopus, MEDLINE and The Cochrane Library conducted in January 2013 identified 27 relevant articles. Six were randomized controlled trials and 21 were observational studies. Large heterogeneity existed across studies with regard to study design and patient populations. Reporting of statistical measures was insufficient to serve as a basis for a meta-analysis. Overall, the studies demonstrated a positive trend in change in HbA(1c) after introduction of advanced carbohydrate counting. Reductions in HbA(1c) ranged from 0.0 to 13 mmol/mol (0.0-1.2%). Most psychosocial measures improved; however, only few improvements were considered clinically relevant. Both weight gain and reduction were registered, but most studies found no significant weight changes. The majority of studies assessing the incidence of hypoglycaemic events found a significant reduction in the event rate and none reported an increase in the incidence. In summary, the currently available literature does not provide sufficient evidence to definitively determine the effects of advanced carbohydrate counting on HbA(1c), psychosocial measures, weight or hypoglycaemic events. Nevertheless, the method still appears preferable to other insulin dosing procedures, which justifies continued use and inclusion of advanced carbohydrate counting in clinical guidelines.

Impact of advanced and basic carbohydrate counting methods on metabolic control in patients with type 1 diabetes

ObjectiveDiets based on carbohydrate counting remain a key strategy for improving glycemic control in patients with type 1 diabetes. However, these diets may promote weight gain because of the flexibility in food choices. The aim of this study was to compare carbohydrate counting methods regarding anthropometric, biochemical, and dietary variables in individuals with type 1 diabetes, as well as to evaluate their knowledge about nutrition. MethodsParticipants were allocated in basic or advanced groups. After 3 mo of the nutritional counseling, dietary intake, anthropometric variables, lipemia, and glycemic control were compared between groups. A questionnaire regarding carbohydrate counting, sucrose intake, nutritional knowledge, and diabetes and nutrition taboos also was administered. ResultsTen (30%) participants had already used advanced carbohydrate counting before the nutritional counseling and these individuals had a higher body mass index (BMI) (P < 0.01) and waist circumference (WC) (P = 0.01) than others (n = 23; 69.7%). After 3 mo of follow-up, although participants in the advanced group (n = 17; 51.52%) presented higher BMI (P < 0.01) and WC (P = 0.03), those in the basic group (n = 16; 48.48%) showed a higher fat intake (P < 0.01). The majority of participants reported no difficulty in following carbohydrate counting (62.5% and 88% for basic and advanced groups, respectively) and a greater flexibility in terms of food choices (>90% with both methods). ConclusionsAdvanced carbohydrate counting did not affect lipemic and glycemic control in individuals with type 1 diabetes, however, it may increase food intake, and consequently the BMI and WC, when compared to basic carbohydrate counting. Furthermore, carbohydrate counting promoted greater food flexibility.