Carbohydrate counting improves coping ability and metabolic control in patients with Type 1 diabetes managed by Group Care

Carbohydrate counting knowledge and ambulatory glucose profile in persons living with type 1 diabetes

CONTEXT

The amount of consumed carbohydrates is the strongest factor influencing glucose levels during the four hours following a meal. Our aim was to evaluate the association between carbohydrate counting knowledge and continuous glucose monitoring (CGM) parameters in patients with type 1 diabetes (T1D) using different insulin regimens.

METHOD

In this multicenter prospective study, the GluciQuizz questionnaire was used to evaluate carbohydrate knowledge. CGM data for the 14 days preceding completion of the questionnaire were analyzed. The primary endpoint was evaluation of the correlation between the GluciQuizz total score and time in range (TIR) in the study population.

RESULTS

The mean age of the 170 participants was 40.7 ± 14.8 years and duration of T1D 18.8 ± 12.1 years. The mean GluciQuizz total score for all participants was 66 ± 13 %. Mean TIR was 58.6 ± 18.7 %. GluciQuizz total score positively correlated with TIR (r = 0.3001; p < 0.0001). This correlation was observed in CSII users (r = 0.2526; p < 0.05) but not in MDI (r = 0.2510; p = 0.1134) and HCL users (r = -0.1065; p = 0.4914). TIR was also negatively correlated with the mean carb count error in all study participants (r = -0.2317; p < 0.01).

CONCLUSION

In conclusion, as the Gluciquizz score was associated with metabolic control, this easy-to-use self-administered questionnaire could be used widely on a routine basis to assess the carbohydrate knowledge of T1D patients and to offer them targeted education tailored to their needs.

Basic carbohydrate counting and glycemia in young people with type 1 diabetes in India: A randomized controlled trial

OBJECTIVES

The aim of this study was to evaluate the effect on glycemic control and acceptability of basic carbohydrate counting (BCC) in children and young adults with type 1 diabetes (T1DM).

METHODS

Ninety-two children and young adults (6-25 y of age) with T1DM were randomized to receive either routine nutrition education (RNE), which addressed food groups, glycemic index, and effects of food and exercise on glycemia, or learn BCC with personalized portion size education. A continuous glucose monitoring study and glycosylated hemoglobin (HbA1c) were performed at baseline and after 12 wk. The primary outcome was a change in time-in-range from baseline through 12 wk. A questionnaire on the acceptability of BCC was administered.

RESULTS

At 12 wk, there was no significant difference in change in time-in-range between the two groups (BCC group: 1.2 ± 12.2; RNE group: 1.9 ± 12.3; P = 0.786). No significant changes were observed in the percentage of time that blood glucose was >180 or >250 mg/dL; <70 or <54 mg/dL; glycemic variability, percentage of nights with hypoglycemia and HbA1c. In subgroup analysis, there was a significant decrease in HbA1c in the BCC group among participants with higher maternal education (-0.5 versus 0.2, P = 0.042). The total score on the acceptability questionnaire was higher in the BCC group (P = 0.022).

CONCLUSION

Among children and young adults in our region with T1DM, BCC provided flexibility in food choices and perception of greater ease of insulin adjustment. Although BCC was equivalent to RNE in terms of glycemic control, larger studies may reveal benefit in outcomes in certain subgroups.

Carbohydrate Counting vs. Fixed Meal Plan in Indian Children with Type 1 Diabetes Mellitus: A Randomized Controlled Trial

OBJECTIVES

To study the impact of carbohydrate counting vs. fixed-meal plan on glycemic control, quality of life (QoL) and diabetes-related emotional distress in children with Type 1 diabetes mellitus (T1DM).

METHODS

Children aged 6-18 y with T1DM of duration >1 y were eligible for the study if they were on multiple daily injections of insulin and regularly monitoring blood glucose. Those with celiac disease, hypothyroidism, any underlying chronic renal/liver/systemic disease or HbA1c >13% were excluded. Both groups received education on diabetes management and healthy diet. In the intervention arm, parents were taught to quantify carbohydrate content and modify insulin doses according to insulin-carbohydrate ratio. The control arm had dietary prescription according to recommended dietary allowance and food exchange list. Standard validated questionnaires were used to assess the QoL and emotional distress related to diabetes.

RESULTS

One hundred twenty five patients (61 intervention, 64 controls) were enrolled and 91.8% and 84.3%, respectively, completed 6-mo follow-up. There was a reduction in HbA1c in both the groups, but was not statistically significant within or between groups {Intervention: 8.9 (1.4) to 8.6 (1.5) vs. control: 9.1 (1.6) to 8.8 (1.9), [95% CI 8.3-9.3 vs. 8.3-9.0, intention to treat (ITT), p = 0.63]}. There was a significant reduction in diabetes distress in the intervention group; DAWN Problem Areas in Diabetes Questionnaire (PAID) score with a median (interquartile range) of 21 (11-33) vs. control: 27 (20-40), (p = 0.04).

CONCLUSIONS

Patients in the carbohydrate-counting group demonstrated lower diabetes distress scores and less emotional burnout compared to fixed-meal plan over a 6 mo period though overall glycemic control was comparable between groups.

Carbohydrate counting implementation on pediatric type 1 diabetes mellitus: systematic review and meta-analysis

PURPOSE

Type 1 diabetes mellitus, which is the most common type of diabetes among children, is not curable but can be managed well without a negative effect on quality of life. One of the treatments of type 1 diabetes mellitus is carbohydrate counting. This systematic review and meta-analysis sought to evaluate the efficacy of carbohydrate counting with regard to hemoglobin A1c (HbA1c) reduction in children with type 1 diabetes mellitus.

METHODS

Nine studies were assessed, with the primary outcome being glycemic control (HbA1c changes). We searched the following electronic databases: ProQuest, PubMed, Scopus, and ScienceDirect. The quality of studies included was assessed using the risk of bias for randomized control trials and the JBI Critical Appraisal Checklist for observational and cross-sectional studies. Quantitative analyses were made and extrapolated into a forest plot.

RESULTS

A total of 1,693 articles were identified. Four reviewers independently screened titles and abstracts. Of the 36 articles screened, 34 articles were found to be eligible. Of these, 25 studies were excluded because of unsuitable outcomes and study designs. Nine articles were included in the final analysis. Meta-analysis showed that there was a reduction in HbA1c in the carbohydrate counting group as compared to the control group. The cumulative effect of carbohydrate counting on HbA1c was a mean difference of -0.55 (95% confidence interval, -0.81 to -0.28, P<0.001). All of the studies exhibited similar results with the mean difference reduction favoring the interventional group. However, the heterogeneity analysis revealed an I2 value of 88%, implying high heterogeneity in the meta-analysis.

CONCLUSION

The meta-analysis showed evidence favoring the use of carbohydrate counting in the management of type 1 diabetes mellitus.

Manual and Application-Based Carbohydrate Counting and Glycemic Control in Type 1 Diabetes Subjects: A Narrative Review

Type 1 diabetes (T1DM) is the most common chronic disease in young adults and children, which is treated with insulin, usually given as basal and boluses. Carbohydrate counting (CHOC) helps patients to determine the correct meal doses. The aim of this review is to study the effect of CHOC on glucose control, body weight, insulin dose and quality of life (QoL). The literature search was conducted using PubMed from January 2010 to October 2022. Studies included in this review are limited to randomized controlled studies involving an intervention group undergoing CHOC and a control group following the usual practice, measuring glycosylated hemoglobin (HbA1c) as a parameter of glucose control and involving only T1DM subjects. A total of ten articles were found to fulfill the criteria involving 1034 patients. Most of the studies showed a positive impact of CHOC on glucose control, especially in adults, where five out of six studies were statistically positive. However, in pediatrics, only two out of four showed a positive outcome. In all four studies using mobile applications, CHOC was better at controlling glucose. No difference was seen between the CHOC group and the control regarding the risk of severe hypoglycemia. In fact, two studies have shown lower hypoglycemia rates. No change in weight was observed in most of the studies (six out of eight). In subjects with T1DM, CHOC might provide better glucose control than traditional care without a significant increment in severe hypoglycemia or weight gain. Mobile application-based models showed promising results in glucose control.

Efficacy and safety of carbohydrate counting versus other forms of dietary advice in patients with type 1 diabetes mellitus: A systematic review and meta-analysis of randomized clinical trials

BACKGROUND AND AIMS

Diabetes mellitus (DM) is one of the most prevalent chronic non-communicable diseases globally, and the only way to reduce its complications is good glycemic control. Insulin remains the only approved treatment for type 1 DM (T1DM) and is used by many with Type 2 DM (T2DM). Carbohydrate counting is considered the ideal way to calculate meal-related insulin doses since it allows greater flexibility in diet and could, in some people, reduce the burden of the disease. This systematic review's primary objective was to assess carbohydrate counting efficacy in reducing glycated hemoglobin (HbA_1c ) and safety by not increasing hypoglycemia risk, inducing an increase in body weight or blood lipids, and reducing the quality of life of people with T1DM.

METHODS

We included randomized controlled clinical trials with a parallel-group design comparing any carbohydrate counting forms with standard care or other forms of dietary advice or insulin dose calculation in people with T1DM with a follow up period of at least three months and with no restrictions in language, age, or settings. As a primary outcome, we consider the change of HbA_1c within at least three months. Secondary outcomes were hypoglycemia events, body weight changes, blood lipids levels, and the total daily insulin dose. We also evaluated health-related quality of life changes and diabetes treatment satisfaction questionnaires.

RESULTS

Data from 11 studies with 899 patients were retrieved with a mean follow of 52 ± 35.5 weeks. Carbohydrate counting is not better in reducing HbA_1c , SMD - 0.24% (95%CI -0.68 to 0.21) than all dietary advice forms. However, this finding was highly heterogeneous. We identified three studies that account for most of the heterogeneity using clustering algorithms. A second analysis excluding these studies shows a meaningful HbA_1c reduction, SMD - 0.52% (95%CI -0.82 to -0.23) with low heterogeneity. In the subgroup analysis, carbohydrate counting significantly reduces HbA_1c compared to usual diabetes education. Carbohydrate counting doesn't relate to any substantial change in blood lipids, body weight, hypoglycemia risk, or daily insulin dose. Finally, we analyzed the effect of trial duration with HbA_1c reduction and found no significant change related to time.

CONCLUSIONS

Carbohydrate counting is an efficacious technique to safely reduce HbA_1c in adults and children compared to standard diabetes education. And its effect doesn't seem to change with prolonged time. Standardization in reporting important outcomes like hypoglycemia and quality of life is vital to produce comparable evidence in carbohydrate counting clinical trials. This SR was registered in PROSPERO under code: CRD42020218499. This article is protected by copyright. All rights reserved.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

ACC may be associated with small improvements in metabolic control and QOL scores in children.

Influence of Flexible Insulin Dosing with Carbohydrate Counting Method on Metabolic and Clinical Parameters in Type 1 Diabetes Patients

OBJECTIVE:

The purpose of providing and maintaining a proper metabolic control is to prevent the development of chronic complications. In this study, we aimed to determine the influence of flexible insulin dosing with carbohydrate counting method on metabolic and clinical parameters in type 1 diabetes patients.

MATERIAL AND METHODS:

This study was conducted with patients following up at the Endocrinology Clinic with a diagnosis of type 1 diabetes mellitus between 2012 and 2015. Metabolic and clinical parameters before and after carbohydrate counting were compared.

RESULTS:

Forty patients were included in the study. Of the patients, 40% (n = 16) were female, and 60% (n = 24) were male, and mean age was 21.5 ± 7 year at the time of diagnosis. Statistically significant differences were not detected when haemoglobin A1c, fasting plasma glucose, post-prandial glucose, LDL-cholesterol, and HDL-cholesterol levels were compared at standard dose insulin use and after carbohydrate counting (P < 0.005). Among the parameters measured when the patients received standard dose of insulin without counting carbohydrate and flexible insulin dosing by counting carbohydrate, statistically, significant differences were not detected for baseline insulin dose, bolus insulin dose, triglyceride level, body mass index, or monthly hypoglycemia episodes (P > 0.05).

CONCLUSION:

Flexible insulin dosing with carbohydrate counting provides significant improvements in clinical and metabolic control. We detected improvements in lipid profiles and glycemic control. Additionally, patients generally did not gain weight despite flexible nutrition, and frequency of hypoglycemia remained unchanged despite strict glycemic control.

Effectiveness and safety of carbohydrate counting in the management of adult patients with type 1 diabetes mellitus: a systematic review and meta-analysis

OBJECTIVE

This study aimed to evaluate the effectiveness and safety of carbohydrate counting (CHOC) in the treatment of adult patients with type 1 diabetes mellitus (DM1).

MATERIALS AND METHODS

We performed a systematic review of randomized studies that compared CHOC with general dietary advice in adult patients with DM1. The primary outcomes were changes in glycated hemoglobin (HbA1c), quality of life, and episodes of severe hypoglycemia. We searched the following electronic databases: Embase, PubMed, Lilacs, and the Cochrane Central Register of Controlled Trials. The quality of evidence was analyzed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE).

RESULTS

A total of 3,190 articles were identified, and two reviewers independently screened the titles and abstracts. From the 15 potentially eligible studies, five were included, and 10 were excluded because of the lack of randomization or different control/intervention groups. Meta-analysis showed that the final HbA1c was significantly lower in the CHOC group than in the control group (mean difference, random, 95% CI: -0.49 (-0.85, -0.13), p = 0.006). The meta-analysis of severe hypoglycemia and quality of life did not show any significant differences between the groups. According to the GRADE, the quality of evidence for severe hypoglycemia, quality of life, and change in HbA1c was low, very low, and moderate, respectively.

CONCLUSION

The meta-analysis showed evidence favoring the use of CHOC in the management of DM1. However, this benefit was limited to final HbA1c, which was significantly lower in the CHOC than in the control group.

Carbohydrate Counting in Children and Adolescents with Type 1 Diabetes

Carbohydrate counting (CC) is a meal-planning tool for patients with type 1 diabetes (T1D) treated with a basal bolus insulin regimen by means of multiple daily injections or continuous subcutaneous insulin infusion. It is based on an awareness of foods that contain carbohydrates and their effect on blood glucose. The bolus insulin dose needed is obtained from the total amount of carbohydrates consumed at each meal and the insulin-to-carbohydrate ratio. Evidence suggests that CC may have positive effects on metabolic control and on reducing glycosylated haemoglobin concentration (HbA1c). Moreover, CC might reduce the frequency of hypoglycaemia. In addition, with CC the flexibility of meals and snacks allows children and teenagers to manage their T1D more effectively within their own lifestyles. CC and the bolus calculator can have possible beneficial effects in improving post-meal glucose, with a higher percentage of values within the target. Moreover, CC might be integrated with the counting of fat and protein to more accurately calculate the insulin bolus. In conclusion, in children and adolescents with T1D, CC may have a positive effect on metabolic control, might reduce hypoglycaemia events, improves quality of life, and seems to do so without influencing body mass index; however, more high-quality clinical trials are needed to confirm this positive impact.

Food exchange estimation by children with type 1 diabetes at summer camp

BACKGROUND

As exchange counting poses difficulty for children with type 1 diabetes (T1D) attending diabetes camps, they often guesstimate food amount without performing an exchange calculation. The aim of the study was to compare the accuracy of estimation with exchange counting using the mobile food exchange calculator (MFEC).

METHODS

During a summer camp, 25 children with T1D on pumps estimated the number of carbohydrate (CE) and fat/protein exchanges (FPE) appropriate for main meals. Afterwards, the number of exchanges was counted with MFEC and electronic scales.

RESULTS

There was a difference between CE (p<0.0001) and FPE (p<0.0001) estimations and counting using MFEC. The youth miscalculated the true values of ≥1 CE and ≥1 FPE by 31% and 23%, respectively. They more often underestimated than overestimated CE and FPE (p<0.0001). The estimation error increased with younger age.

CONCLUSIONS

Carbohydrate counting caused significant error in the exchange number. The use of MFEC facilitates correct exchange calculation. Patients should weigh food and calculate exchanges themselves using mobile applications.

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 HbA_1c 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 HbA_1c 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.

The relationship between carbohydrate and the mealtime insulin dose in type 1 diabetes

A primary focus of the nutritional management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. Different methods exist to quantify carbohydrate including counting in one gram increments, 10g portions or 15g exchanges. Clinicians have assumed that counting in one gram increments is necessary to precisely dose insulin and optimize postprandial control. Carbohydrate estimations in portions or exchanges have been thought of as inadequate because they may result in less precise matching of insulin dose to carbohydrate amount. However, studies examining the impact of errors in carbohydrate quantification on postprandial glycemia challenge this commonly held view. In addition it has been found that a single mealtime bolus of insulin can cover a range of carbohydrate intake without deterioration in postprandial control. Furthermore, limitations exist in the accuracy of the nutrition information panel on a food label. This article reviews the relationship between carbohydrate quantity and insulin dose, highlighting limitations in the evidence for a linear association. These insights have significant implications for patient education and mealtime insulin dose calculations.

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

AIM

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Effects of carbohydrate counting method on metabolic control in children with type 1 diabetes mellitus

OBJECTIVE

Medical nutritional therapy is important for glycemic control in children and adolescents with type 1 diabetes mellitus (T1DM). Carbohydrate (carb) counting, which is a more flexible nutritional method, has become popular in recent years. This study aimed to investigate the effects of carb counting on metabolic control, body measurements and serum lipid levels in children and adolescents with T1DM.

METHODS

T1DM patients aged 7-18 years and receiving flexible insulin therapy were divided into carb counting (n=52) and control (n=32) groups and were followed for 2 years in this randomized, controlled study. Demographic characteristics, body measurements, insulin requirements, hemoglobin A1c (HbA1c) and serum lipid levels at baseline and at follow-up were evaluated.

RESULTS

There were no statistically significant differences between the groups in mean HbA1c values in the year preceding the study or in age, gender, duration of diabetes, puberty stage, total daily insulin dose, body mass index (BMI) standard deviation score (SDS) and serum lipid values. While there were no differences in BMI SDS, daily insulin requirement, total cholesterol, low-density lipoprotein and triglyceride values between the two groups (p>0.05) during the follow-up, annual mean HbA1c levels of the 2nd year were significantly lower in the carb counting group (p=0.010). The mean values of high-density lipoprotein were also significantly higher in the first and 2nd years in the carb counting group (p=0.02 and p=0.043, respectively).

CONCLUSION

Carb counting may provide good metabolic control in children and adolescents with T1DM without causing any increase in weight or in insulin requirements.

Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis

BACKGROUND

Although carbohydrate counting is the recommended dietary strategy for achieving glycaemic control in people with type 1 diabetes, the advice is based on narrative review and grading of the available evidence. We aimed to assess by systematic review and meta-analysis the efficacy of carbohydrate counting on glycaemic control in adults and children with type 1 diabetes.

METHODS

We screened and assessed randomised controlled trials of interventions longer than 3 months that compared carbohydrate counting with general or alternate dietary advice in adults and children with type 1 diabetes. Change in glycated haemoglobin (HbA1c) concentration was the primary outcome. The results of clinically and statistically homogenous studies were pooled and meta-analysed using the random-effects model to provide estimates of the efficacy of carbohydrate counting.

FINDINGS

We identified seven eligible trials, of 311 potentially relevant studies, comprising 599 adults and 104 children with type 1 diabetes. Study quality score averaged 7·6 out of 13. Overall there was no significant improvement in HbA1c concentration with carbohydrate counting versus the control or usual care (-0·35% [-3·9 mmol/mol], 95% CI -0·75 to 0·06; p=0·096). We identified significant heterogeneity between studies, which was potentially related to differences in study design. In the five studies in adults with a parallel design, there was a 0·64% point (7·0 mmol/mol) reduction in HbA1c with carbohydrate counting versus control (95% CI -0·91 to -0·37; p<0·0001).

INTERPRETATION

There is some evidence to support the recommendation of carbohydrate counting over alternate advice or usual care in adults with type 1 diabetes. Additional studies are needed to support promotion of carbohydrate counting over other methods of matching insulin dose to food intake.

FUNDING

None.

Diabetes-specific variables associated with quality of life changes in young diabetic people: the type 1 diabetes Registry of Turin (Italy)

BACKGROUND AND AIMS

Type 1 diabetes (T1DM) affects young people during the most active years of their life. Our aim was to assess quality of life (QoL) and associated variables in a large cohort of adults with childhood-onset and adult-onset T1DM.

METHODS

A cohort of adult patients (18 years and older) from the T1DM Registry of Turin, Italy, was recruited. Clinical characteristics and Diabetes QoL (DQOL) questionnaire were assessed by standardized procedures.

RESULTS

310 adults completed the questionnaire. Age and diabetes duration at assessment (mean ± SD) were 32.8 ± 7.3 years and 17.3 ± 6.3 years, respectively. DQOL and its subscores were in the lower quartiles of their distributions, indicating a good level of QoL. However, scores were significantly higher in females than in males, particularly for the subscale of diabetes-related worries. In multivariate analysis, lower QoL was independently associated with female sex (β = 1.07, 95% CI 1.03-1.11, p = 0.003), higher age at onset (β = 1.03, 1.00-1.05, p = 0.009), lower schooling (β = 1.05, 1.00-1.09, p = 0.02), higher fasting plasma glucose (β = 1.03, 1.01-1.05, p = 0.008), daily SMBG >4 (β = 1.06, 1.01-1.10, p = 0.01), severe hypoglycemia over the last year (β = 1.06, 1.01-1.11, p = 0.02), lower numbers of diabetologic visits (β = 1.07, 1.01-1.13, p = 0.02) and hypertension (β = 1.06, 1.02-1.10, p = 0.005). Autonomic neuropathy was associated with diabetes impact. Female sex (β = 4.36, 2.43-7.83) and daily SMBG >4 (β = 3.77, 1.72-8.30) were independently associated with worst level and CSII with better level (β = 0.22, 0.07-0.68) of diabetes-related worries.

CONCLUSIONS

The impact of T1DM on QoL may depend on demographic, metabolic control-related variables, presence of complications and insulin delivery modality.

Effects of carbohydrate counting on glucose control and quality of life over 24 weeks in adult patients with type 1 diabetes on continuous subcutaneous insulin infusion: a randomized, prospective clinical trial (GIOCAR)

OBJECTIVE

Few studies have assessed the efficacy of carbohydrate counting in type 1 diabetes, and none have validated its efficacy in patients who are treated with continuous subcutaneous insulin infusion (CSII). The aim of our study was to test the effect of carbohydrate counting on glycemic control and quality of life in adult patients with type 1 diabetes who are receiving CSII.

RESEARCH DESIGN AND METHODS

Sixty-one adult patients with type 1 diabetes treated with CSII were randomly assigned to either learning carbohydrate counting (intervention) or estimating pre-meal insulin dose in the usual empirical way (control). At baseline and 12 and 24 weeks, we measured HbA(1c), fasting plasma glucose, BMI, waist circumference, recorded daily insulin dose, and capillary glucose data, and administered the Diabetes-Specific Quality-of-Life Scale (DSQOLS) questionnaire.

RESULTS

Intention-to-treat analysis showed improvement of the DSQOLS score related to diet restrictions (week 24 - baseline difference, P = 0.008) and reduction of BMI (P = 0.003) and waist circumference (P = 0.002) in the intervention group compared with control subjects. No changes in HbA(1c), fasting plasma glucose, daily insulin dose, and hypoglycemic episodes (<2.8 mmol/L) were observed. Per-protocol analysis, including only patients who continuously used carbohydrate counting and CSII during the study, confirmed improvement of the DSQOLS score and reduction of BMI and waist circumference, and showed a significant reduction of HbA(1c) (-0.35% vs. control subjects, P = 0.05).

CONCLUSIONS

Among adult patients with type 1 diabetes treated with CSII, carbohydrate counting is safe and improves quality of life, reduces BMI and waist circumference, and, in per-protocol analysis, reduces HbA(1c).