Carbohydrate Counting Research Articles

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.

Effect of adherence to carbohydrate counting on metabolic control in children and adolescents with type 1 diabetes mellitus.

PurposeCarbohydrate counting provides better glycemic control and flexibility than other food planning methods. Consistent adherence to such a complex method is difficult, especially for youth. However, studies that determine adherence to this method and whether it alters metabolic control are limited. The aim of the current study was to determine adherence to this method and investigate its effect on metabolic control, anthropometric measurements, insulin dose, and energy intake.MethodsIn this prospective cross-sectional study, 53 children and adolescents with type 1 diabetes mellitus aged 2 to18 years and receiving intensive insulin therapy were trained and followed for 6 months. Demographics, anthropometrics, insulin requirements, hemoglobin A1c (HbA1c), fasting lipids, and food records at baseline and study conclusion were evaluated. At the end of the study patients were divided into adherer and nonadherer groups according to carbohydrate estimate deviations from standardized daily sample menus and calculations for accurate insulin doses. More than 10-g variation in daily consumed carbohydrate amount or failure to decide bolus insulin dose was defined as a nonadherer.ResultsThe mean HbA1c, low-density lipoprotein cholesterol, and body mass index standard deviation score changed after the carbohydrate counting training while the mean HbA1c between groups was significant (P<0.05). Total daily insulin doses increased, and the mean high-density lipoprotein cholesterol levels decreased in both groups. There were significant correlations between HbA1c and carbohydrate deviation scores as well as HbA1c and caregiver's education level.ConclusionsSince adherence to carbohydrate counting may affect metabolic control, health professionals should evaluate and monitor carbohydrate counting skills of caregivers and patients in order to improve efficiency.

Kistik Fibrozis ile İlişkili Diyabet: Bir Olgu Sunumu

Kistik fibrozis ile ilişkili diyabet (KFİD), kistik fibrozisin (KF) yaygın bir komplikasyonudur. Kistik fibrozis ile ilişkili diyabetin temel nedeni hastalarda yıllar içerisinde pankreas hasarının artması ile beraber pankreas adacık hücrelerinin kaybı ve insülin eksikliğinin gelişmesidir. Tedavideki amaç çocuk ve adolesanlarda normal büyüme ve gelişmenin ve kan glukoz regülasyonunun sağlanmasıdır. Tedavinin temelini insülin tedavisi ve tıbbi beslenme tedavisi oluşturur. İnsülin tedavisi, KFİD hastalarında akciğer fonksiyonunu stabilize eder ve beslenme durumunu iyileştirir. Kistik fibrozis ile ilişkili diyabetli hastalara yüksek enerji, yüksek yağ ve yüksek tuz içeren bir beslenme planı önerilmektedir. Karbonhidrat sayımı, insülin tedavisi alan hastalarda öğün öncesi insülin dozunun belirlenmesinde yararlıdır. Bu yazıda kliniğimizde izlenen KFİD’li bir çocuk olgunun medikal ve tıbbi beslenme tedavisi literatür derlemesi eşliğinde sunulmuştur.

FÁRMACOS NO INSULÍNICOS EN DIABETES MELLITUS TIPO 1 (METFORMINA, IDPP-4, PRAMLINTIDA, AGLP-1, ISGLT-2)

Existen aproximadamente 25 millones de pacientes con diabetes mellitus tipo 1 (DM1) en el mundo, cifra que se triplicaría en el año 2050. Si bien lo óptimo es alcanzar niveles de A1c &lt;7% para evitar y/o enlentecer la aparición/evolución de las complicaciones crónicas, sólo el 30% de los pacientes lo logra. Más aún, el conteo de hidratos de carbono (HD) sin una educación alimentaria general adecuada, asociado a la insulinoterapia intensi cada (IT), se vincula hoy en día a una mayor incidencia de hipoglucemias, obesidad y otros factores de riesgo como dislipidemia (DSL), hipertensión (HTA), obesidad (O) y obesidad abdominal (OA) en esta población, lo que genera un fenotipo muy frecuente de pacientes con DM1 asociado al síndrome metabólico (SM).

A study of diet planning in diabetes using carbohydrate count

Compared to fats and proteins, carbohydrates have the greatest impact on blood sugar except for dietary fibre, which is not digestible. According to the recent data of WHO there will be 333 million people with DM in the year 2030. To reduce the risk of diabetes micro vascular complications the effect of intensive insulin treatment of patients aiming to reduce A1C levels has shown effective results. Too much insulin or inadequate food intake lead to hypoglycaemia and potentially loss of consciousness and seizures. Considering these facts American Diabetes Association [ADA] emphasizes the importance of good glycaemic control to prevent chronic diabetic complications and suggest that changes in diet compositions of patients with DM are a relevant strategy to achieve an appropriate metabolic control. Carbohydrate Counting offers variability of food choices with potential for improving glycaemic control. This study puts in an effort to evaluate the influence of nutrition intervention on metabolic parameters in patients with type 2 DM taking insulin according to carbohydrate counting method

Type 1 diabetes and disordered eating (T1DE): the ComPASSION Project – Wessex

AbstractThe combination of type 1 diabetes and an eating disorder is ill understood with no clear nomenclature, definition or evidence base defined for management and support. It is well recognised that diabetes and mental health concerns often co‐exist and type 1 diabetes per se is associated with specific self‐management requirements which may predispose an individual to challenging cognitions and behaviours around food due to the nature of carbohydrate counting and insulin management.While most diabetes teams recognise the mental health concerns around diabetes, there are few areas where multidisciplinary teams work cohesively combining diabetes care and mental health care. This is particularly relevant in the case of eating disorders where neither team are likely to be sufficiently skilled to manage the combination of type 1 diabetes and an eating disorder individually, and a strong multidisciplinary team approach with shared learning and a holistic ethos is paramount.We describe here an NHS England funded pilot whereby the diabetes teams and eating disorders teams in Wessex work side by side developing a service specifically for people with this incredibly complex and challenging combination. Copyright © 2020 John Wiley &amp; Sons.

1119-P: Alleviating Carbohydrate Counting Burden in Type 1 Diabetes (T1D) with the Artificial Pancreas and Empagliflozin (EMPA)

We aimed to assess whether adding Empa to the artificial pancreas can alleviate the burden of carbohydrate counting without degrading glucose control. We conducted a randomized, open-label, crossover, non-inferiority trial in 30 adults with T1D (age 40±15 years, A1c 7.6±0.7%). Each participant used the artificial pancreas on 5 non-consecutive days at home (9-14 hours/day) with: Empa 25mg and (i) carbohydrate counting, (ii) simple meal announcements (pressing a button, no carbohydrate counting), and (iii) no meal announcements (fully automated), and with: no Empa and (iv) carbohydrate counting (control arm) and (v) simple meal announcements. The fully automated artificial pancreas with Empa was inferior to the artificial pancreas with carbohydrate counting without Empa (mean glucose 10.0±1.6 vs. 8.5±1.5 mmol/L, p&amp;lt;0.001), but the artificial pancreas with simple meal announcement and Empa was non-inferior (8.5±1.4 mmol/L, non-inferiority p-value with a pre-specified margin of 0.75 mmol/L = 0.003). Time spent below 3.9 mmol/L was minimal in all interventions. No diabetic ketoacidosis was observed; mean ketones levels were 0.22±0.18 and 0.13±0.11 mmol/L, with and without Empa, respectively (p&amp;lt;0.001). We conclude that SGLT2 inhibition added to the artificial pancreas may alleviate the need for carbohydrate counting but does not allow a fully closed-loop system. Disclosure A. Haidar: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., Eli Lilly and Company. J. Yale: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck &amp; Co., Inc., Novo Nordisk A/S, Sanofi. Board Member; Self; Diagnos. Research Support; Self; Bayer Inc., Novo Nordisk A/S, Sanofi. Speaker’s Bureau; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Medtronic, Novo Nordisk A/S, Sanofi. L. Lovblom: None. N. Cardinez: None. A. Orszag: None. C.M. Falappa: None. N. Gouchie-Provencher: None. A. El Fathi: None. D. Eldelekli: None. D. Scarr: None. B.A. Perkins: Consultant; Self; Abbott, Boehringer Ingelheim International GmbH, Insulet Corporation. Research Support; Self; Boehringer Ingelheim International GmbH, Novo Nordisk A/S. Other Relationship; Self; Medtronic. Funding Diabetes Canada

722-P: Pictorial Carbohydrate Counting: Engaging Youth with Diabetes in Nutrition Education

Background: Carbohydrate counting is used to determine prandial insulin doses in pediatric diabetes management. Benefits to carbohydrate counting include improved glycemic control, improved diabetes-specific quality of life, and improved coping ability in daily life. Patients self-report knowledge and comfort in carbohydrate counting, but we have no quantifiable way to determine the accuracy of this self-report. Thus, we implemented nutrition quizzes with photos of popular foods to assess fundamental knowledge of carbohydrate counting. Methods: Pictorial nutrition quizzes were developed to review basic, intermediate and advanced fundamentals of carbohydrate counting. The nutritionist administered the quizzes to children &amp;gt;8 years of age, as part of regular outpatient diabetes follow-up visits. Children completed the quizzes independently and were allowed to use their insulin pump and phone to calculate insulin doses. Quiz responses and scores were recorded, in addition to visit hemoglobin A1c (HbA1c). Demographic and clinical data were extracted from the electronic medical record. Results: Nutrition quizzes were completed by 103 youth (99 with T1D, 4 with T2). Mean age was 14.2 (SD 2.6), 44% female, 70% White, mean duration of diabetes 6 y (SD 4). 65% used insulin pumps, 68% used CGMs. All patients (n=103) completed 1 quiz, and 31 completed more than 1 quiz (range 2-5 quizzes). Patients with better glycemic control scored higher on nutrition quizzes. Mean A1c at initial quiz administration was 9%, and at follow-up was 8.6% (p=0.4). Conclusion: Pictorial nutrition quizzes engage children and adolescents with diabetes in reviewing the fundamentals of carbohydrate counting and provide opportunity for nutrition intervention during routine diabetes clinical care. Disclosure M.R. Thivierge: None. M. Waheed: None. M. Seel: None. R. Wolf: None.

158-LB: iSpy: A Pilot Study of a Novel Carbohydrate Counting Smartphone App for Youth with Type 1 Diabetes

Introduction: Carbohydrate counting is an important part of diabetes management, but it is difficult and can negatively impact quality of life. To address this challenge, a novel mobile application (app) called iSpy that uses computer vision and artificial intelligence to identify foods, estimate their carbohydrate content, and provide real-time feedback to users, was developed. Objective: To evaluate iSpy’s functionality among youth aged 10 to 17 years (mean age = 14 years) with type 1 diabetes mellitus (T1DM). Design/Methods: A pilot study among 44 youth (22 each randomized to intervention (iSpy) or control) was conducted to assess: (1) whether iSpy leads to improved carbohydrate counting ability (accuracy and speed) measured using standardized meal plates at baseline and at the end of the 12-week study; and (2) implementation outcomes such as app acceptability and technical problems. Results: Compared to the control group, the intervention group improved its carbohydrate counting accuracy (total grams per meal) over the 12-week study period (p=0.007). Moreover, the percentage of times the carbohydrate estimate of a particular item was off by more than 10g from the true value was reduced by 9.9% (p=0.047). There was no statistically significant difference in total time to count carbohydrates. Qualitative interviews and app acceptability scale scores across 7 domains were primarily positive. 43% of iSpy participants were still using the app and logged at least once per 2 weeks at the end of the study. No major technical challenges were identified. Conclusion(s): Participants using iSpy demonstrated improved carbohydrate counting accuracy and positive acceptability. iSpy has the potential to improve diabetes management, which should be further tested in a formal randomized controlled trial. Disclosure E. Choi: Other Relationship; Self; Inner Analytics Inc. J. Alfonsi: Other Relationship; Self; Inner Analytics Inc. T. Arshad: None. S. Sammott: None. V. Pais: None. J. Stinson: None. M. Palmert: None. Funding Physician Services Incorporated Foundation

148-LB: Timely Administration of Rapid-Acting Insulin in Hospitalized Children with Diabetes: A Quality Improvement Project

Background: The preferred management of insulin-dependent diabetes (IDDM) for pediatric patients includes rapid-acting insulin analogs administered at mealtimes in conjunction with carbohydrate counting. Hospitalized children with IDDM are dependent on hospital personnel to administer rapid-acting insulin, which should occur within 15-30 minutes of the onset of eating. Delays in insulin administration can result in hyperglycemia, poor patient satisfaction, and inappropriate insulin regimen adjustments. At the Children’s Hospital at Montefiore (CHAM), we found that rapid-acting insulin is administered within 15 minutes of glucose check at breakfast time in only 18% of patients, and within 30 minutes of glucose check in only 40% of patients. Methods: We formed a multi-disciplinary team and used process mapping to identify barriers to timely insulin administration and implement interventions. We studied the time to rapid-acting insulin administration at breakfast for children with IDDM admitted to the 6th floor of CHAM from November 2018 to present before and after PDSA cycles, which have included nurse and resident education in carbohydrate counting, standardization of diabetes menus to include carbohydrate content, and standardization of communication method between nurses and residents. Results: As of December 2019, median time from blood sugar check to rapid-acting insulin administration at breakfast improved from 32.5 minutes at baseline to 26 minutes. The percentage of children with IDDM who receive rapid-acting insulin within 15 minutes of blood sugar check has not changed, but the percentage of children who receive rapid-acting insulin within 30 minutes of blood sugar check improved from 40% at baseline to 60%. Conclusions: Timely administration of rapid-acting insulin to hospitalized children with IDDM is a complex process involving coordination of care by multiple providers, and can be improved upon with a multidisciplinary team approach. Disclosure L. Eng: None. D. Duhame: None. L. Underland: None.

628-P: Educational Sessions at ADA Camp Midicha Improve Carbohydrate Counting and Insulin Calculation in Children with Type 1 Diabetes Mellitus

Introduction: Type 1 diabetes mellitus (DM) is one of the most common chronic diseases in children. The ability of affected children to calculate carbohydrate consumption per meal, apply a correction factor based on their blood glucose and administer an accurate dose of insulin is a key component for good diabetes control. Multiple studies, however, have shown that insulin dosage calculations, particularly carbohydrate assessment, are inaccurate in &amp;gt; 50% of children with DM. Surveys of attendees at diabetes camps show that campers and camp personnel have reported increased confidence and ability in counting carbohydrates and administering insulin, but no quantitative tools have been used in these reports. The aim of this study was to determine whether educational sessions provided during a one-week diabetes camp would improve carbohydrate counting and insulin dosage calculation in children with type 1 DM. Methods and Materials: We consented 144 parents/campers with type 1 DM to participate in the study. The Pediatric Carbohydrate Quiz (PCQ) was administered to the children before or on the first day of camp. All subjects participated in camp activities, which included dietary education. A post-intervention PCQ was administered on the last day. Pre- and post-camp test scores were compared using the paired t-test. Results: Over 2 sessions, 79.2% (114/144) of campers completed both questionnaires. Their mean age was 13.8 ± 1.5 years, 61.4% female, 84.2% White and 16.4% first-time campers., The highest possible score on the PCQ administered is 72. The mean pre-camp PCQ score was 59.0 ± 7.1 vs. a mean post-camp score of 61.2± 5.9 (p&amp;lt;0.0001). The degree of improvement for female campers was greater than for males. Conclusion: Attendance at a one-week session at Camp Midicha improved the ability of adolescent campers to assess carbohydrate counting and calculate insulin doses. Disclosure M. Joseph: None. B.M. Degnan: None. S. Szpunar: None. Funding Ascension St. John Hospital

1000-P: A Feasibility Study Assessing the Accuracy of a Simplified Meal-Time Bolus Calculation Option

Carbohydrate counting is a significant source of burden for individuals using insulin to mitigate postprandial glucose excursion. To address this, a meal simplification (MS) algorithm was developed that allows one to choose a ready and user-specific small, medium, large, extra-large (S/M/L/XL) carb-entry size for meal-time boluses. Subjects with T1D (N=12, 15-76 yrs) underwent a 4-week learning phase where traditional carb-counting and entry were carried out via the MinMed™ 670G system. At the end of the learning phase, the MS algorithm used machine-learning techniques and insulin pump data to identify clusters of meals (a set of S/M/L/XL meal sizes along with a corresponding centroid carb value) on which a meal bolus would be calculated. Immediately after, there was a 2-week study phase where subjects announced carbs based on their personalized S/M/L/XL meal sizes. Postprandial SG levels after S/M/L/XL announcements did not differ from those after traditional carb-counting (Table) and suggest that the personalized S/M/L/XL option is a valid alternative for carbohydrate counting. It may alleviate some of the burden associated with meal-time carb-counting, and potentially improve meal-time bolusing compliance. Disclosure A. Roy: Employee; Self; Medtronic. B. Grosman: Employee; Self; Medtronic. L.J. Lintereur: Employee; Self; Medtronic. N. Parikh: None. D. Wu: None. D.I. Shulman: Advisory Panel; Self; Medtronic. M.P. Christiansen: Research Support; Self; Abbott, Ascensia Diabetes Care, Biolinq, Dexcom, Inc., Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Merck &amp; Co., Inc., Novo Nordisk A/S, Sanofi-Aventis, Senseonics, ViroMed Laboratories, Xeris Pharmaceuticals, Inc. R.H. Slover: None. P.E. Weydt: Employee; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic.

879-P: Positive Impact of Use of Continuous Glucose Monitoring on Glycemic Outcomes in Young Adults with Type 1 Diabetes, in Adult Clinical Setting, Independent of Insulin Administration Method

Introduction: Young adults (YA) with type 1 diabetes (T1D) are at risk of poor glycemic control. Use of continuous glucose monitoring (CGM) has been shown to improve glucose control. However, real-world data on the impact of CGM use on glucose control in YA is limited. Methods: We analyzed data from electronic medical records collected across 2018 from a tertiary adult clinic of YA patients age 18- 30 yrs with T1D. A1C and insulin administration methods defined as sliding scale (SS), multiple daily injection while carbohydrate counting (MDI), or via pump therapy (Pump) were collected along with CGM use. Results: Data from 891 patients (mean age 26±3, T1D duration 12±7 years, 52% female, average A1C 7.9%±1.5) were analyzed. Twelve % (n= 110) were on SS; 37% (n=331) on MDI, and 51% (n= 450) on pump therapy; mean age of all 3 groups was 26 ± 3yrs. Use of CGM was recorded in 32% of patients on SS, while use of CGM was recorded in 46% and 66% of patients using MDI and pump, respectively. A1C differed between CGM users and non-users in each subgroup. Conclusion: With increasing complexity of insulin regimen, CGM use rises. However, independent of insulin administration method, CGM use was associated with lower A1C. These findings highlight the potential benefit of CGM use, in YA with T1D in adult clinic settings. Disclosure E. Toschi: None. R.A. Gabbay: Advisory Panel; Self; FormHealth, Health Reveal, Lark, Onduo, Vida Health. A. Clift: None. M. Bennetti: None. A. Atakov-Castillo: None.

912-P: Two Novel Pharmacokinetic Parameters Assess Dietary Glucose Concentration Changes in a Diabetic Patient Using Freestyle Libre Continuous Glucose Monitoring (CGM)

Background: CGM is used to monitor/assess diet related glucose excursions and suggest treatment changes in types 1 and 2 diabetes. Two CGM data derived parameters, timed glucose clearance (GLCL(T,I) and timed insulin sensitivity index Si(T,I) are developed to quantify treatment responses to food, medication, and activity. Method: Glucose Clearance (GLCL) = Interstitial fluid volume (Vd)/time of the (t1) rise and return to baseline (t2) of the diet related glucose concentration curve. GLCL(T,I) indicates the time = T between insulin administration (t0) to the initial glucose concentration rise from baseline (t1). I = insulin dose. The Si(T,I) = GLCL(T,I)/rapid acting insulin dose administered. Vd = 0.15 * kg body weight * 10 expressed as deciliters (dL). Result: Example #1 - Two similar breakfasts: oatmeal (1 cup), 6 strawberries, ½ cup blueberries and ½ cup cottage cheese (46 g CHO) ingested on two different occasions. Vd = 116 dL. Breakfast (A): T = 0 minutes, glucose curve duration = 180 min, I = 13 units, GLCL(0,13) = 64.4 ml/min and SI(0,13) = 5.0 mL/(min*unit). Breakfast (B): T = 45 min; curve duration = 90/min; I = 12 units, GLCL(45,12) = 129 mL/minute and SI(45,12) = 10.8 mL/(minute*unit). Example #2 - Lunch: madras lentils and hot dog. T = 0 minutes, I = 14 units. Glucose curve duration = 270 min. GLCL(T,I) = 43 mL/min; Si(T,I) = 3.07 mL/(min*unit). Conclusion: Two quantitative parameters assessed similar meals and showed marked differences in duration and height of the glucose concentration curves. They illustrate the importance of insulin injection timing and dosing. The calculations are also applied to a meal without carbohydrate (CHO) counting and can be compared with a comparable subsequent meal. The calculations do not depend on CHO counting and can be applied to Identical mixed meals. Straightforward calculations are applied retrospectively to CGM generated data beyond current more qualitative CGM data evaluations. Disclosure J.S. Melish: None.

1003-P: Estimating an Optimal Meal Bolus for Persons with Diabetes on Multiple Daily Injections Therapy without Carb Counting

Background: MDI therapy requiring carbohydrate (carb) counting can be burdensome for persons with diabetes (PWD). We conducted an in-silico feasibility study assessing the glycemic performance of a post-prandial rather than a carb-based bolus optimizer. Methods: The data were collected from seven adults with T1D (Gender:M2/F5; Age:22-45) on MDI using the Guardian™ Connect continuous glucose monitoring system. All participants had three bolus events matched to three meals for ≥7 days. In the training stage, a glucose-insulin minimal model extracted the rate of glucose appearance in blood (Ra) for each meal. The Ra was taken as an input to adjust in-silico simulated bolus amounts until the greatest time in sensor glucose range (70-180 mg/dL, TIR) within 4hrs was reached. In the testing stage, two bolus strategies were tested: the average of the adjusted bolus from the training set (Uniform Bolus) and the average of the adjusted bolus from the training set associated with the time of day (TOD) of the meal announcement (TOD Bolus). Results: We found increased TIR for TOD Bolus strategy compared to the User Bolus (baseline) strategy (See Table below). Conclusion: The data suggested that the automated bolus optimizer without carb counting may allow for an increased time spent in the target range, without comprising time spent below range while reducing the burden of carb counting for PWD on MDI. Disclosure B. Jiang: Employee; Self; Medtronic. Y. Zhong: Employee; Self; Medtronic. P. Agrawal: None. T.L. Cordero: Employee; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic.

MON-LB117 Differences in Metabolic Control, Treatment and Associated Complications in Patients Living With Type 1 Diabetes Receiving Private or Public Health Care in Mexico

Background. Type 1 Diabetes (T1D) is the most common chronic endocrinological disease diagnosed during childhood. It requires continuous education, monitoring and treatment. An analysis was developed to learn if there were discrepancies in the level of metabolic control and complications between private and public healthcare in Mexico, based on the data obtained in the National Registry of patients with Type 1 Diabetes (RENACED DT1). Objective: Describe the sociodemographic characteristics, metabolic control, treatment and complications in patients receiving private or public health care registered in RENACED DT1.Methodology: Sociodemographic and anthropometric variables, metabolic control, diabetes education, type of insulin and delivery method used for treatment, glucose monitoring and acute and chronic complications were compared between patients receiving private vs public health care. Results Of the 1458 patients registered, significant differences between HbA1c levels were seen (7.8% private health care vs 8.7% public healthcare, p<0.001), achievement of glycemic goal HbA1c <7% (30.1% private healthcare vs 19.6% public healthcare, p<0.001). In private institutions, 47.1% of patients use an insulin pump and 44.7% a basal bolus regimen with insulin analogues (MDI) (p<0.001). Meanwhile in public institutions, 2.6% use an insulin pump and 84.8% use MDI (p<0.001). 30.4% of patients in private institutions check their blood glucose levels 6 to 10 times a day vs 14.3% of patients from public institutions (p<0.001). Continuous glucose monitoring is used in 46.4% of patients from private healthcare vs 5% from public healthcare (p<0.001). A larger number of patients from private healthcare use insulin to carbohydrate ratios to calculate meal insulin compared with patients from public healthcare (89.1% vs31.3%, p<0.001). No significant differences were found related to the incidence of mild to moderate hypoglycemia, but there was a higher incidence of severe hypoglycemia in the public sector compared to the private one (57 vs 43%, p<0.001). Also, a higher prevalence of diabetic nephropathy was found in patients from public vs private healthcare (82.9 vs 17.1%, p=0.034). Conclusions: Significant differences were found in glucose monitoring, carbohydrate counting and insulin delivery methods, between patients from public and private healthcare, which could explain the differences observed in metabolic control and diabetes associated complications. It is imperative that better public policies are implemented in public health, to reduce this health disparity.

Factors Beyond Carbohydrate to Consider When Determining Meantime Insulin Doses: Protein, Fat, Timing, and Technology.

For many years, carbohydrate counting has been a popular strategy for determining mealtime insulin doses for people with diabetes who are on a multiple daily injection regimen or continuous subcutaneous insulin infusion. This approach assumes that only carbohydrate-containing foods and beverages affect postprandial glucose levels. However, many studies have indicated that the fat and protein content of a meal can play an important role in delaying postprandial hyperglycemia and should be considered when trying to optimize postprandial glucose levels. This article reviews research on making insulin dose adjustments for high-fat and high-protein meals, as well as the timing of mealtime insulin doses.

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

Despite efforts to control hyperglycemia, diabetes management is still challenging. This may be due to focusing on reducing hyperglycemia and neglecting the importance of hyperinsulinemia; while insulin resistance and resultant hyperinsulinemia preceded diabetes onset and may contribute to disease pathogenesis. The present narrative review attempts to provide a new insight into the management of diabetes by exploring different aspects of glycemic index and dietary insulin index. The current data available on this topic is limited and heterogeneous. Conventional diet therapy for diabetes management is based on reducing postprandial glycemia through carbohydrate counting, choosing foods with low-glycemic index and low-glycemic load. Since these indicators are only reliant on the carbohydrate content of foods and do not consider the effects of protein and fat on the stimulation of insulin secretion, they cannot provide a comprehensive approach to determine the insulin requirements. Selecting foods based on carbohydrate counting, glycemic index or glycemic load are common guides to control glycemia in diabetic patients, but neglect the insulin response, thus leading to failure in diabetes management. Therefore, paying attention to insulinemic response along with glycemic response seems to be more effective in managing diabetes.

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 role of carbohydrate counting in glycemic control and oxidative stress in patients with type 1 diabetes mellitus (T1DM).

Oxidative stress is closely related to type 1 diabetes mellitus (T1DM), playing a key role in the pathogenesis of the disease and progression of complications. It is characterized by loss of equilibrium between oxidative factors and antioxidant protective mechanisms. Several markers have been used to assess both components of oxidative status; two of which are malondialdehyde (MDA) and ferric reducing antioxidant power (FRAP). We investigated glycated hemoglobin (HbA1c), lipid profile, MDA, and FRAP in 35 patients with T1DM, aged 2-23years, at the end of two 4-month observational periods: period A: standard insulin dosing per meal, and period B: proper prandial insulin dosing based on the amount of carbohydrates contained in each meal. At the end of period B, (i) glucose control (HbA1c) was improved; (ii) oxidative stress, estimated by MDA, showed a tendency to decrease; and (iii) antioxidant capacity, estimated by FRAP, was significantly increased compared with that of period A. No significant differences were observed in the lipid profile of the patients between the two periods. Proper insulin dosing based on carbohydrate counting (CC) may have an impact on the antioxidant defensive mechanisms of patients with T1DM through the attainment of a better glycemic profile. There are also indications that it may reduce MDA, an important biomarker of oxidative stress and a significant mediator of complications in T1DM. Therefore, prompt dietetic intervention using CC as early as possible after the diagnosis of T1DM is important for achieving optimal glycemic control and improved oxidative status.