Insulin-to-carbohydrate Ratio Research Articles

What Really Matters?: How Insulin Dose, Timing, and Distribution Relate to Meal Composition in Free-Living People with Type 1 Diabetes.

Optimizing postprandial glucose control in persons with type 1 diabetes (T1D) is challenging. We hypothesized that in free-living individuals, meal composition (high and low glycemic index [HGI and LGI], high and low fat [HF and LF]) may impact insulin requirements. Adults (N = 25) with T1D using open-loop insulin and continuous glucose monitoring were provided a meal-tagging app and prepackaged meals with defined macronutrient content. Data from 463 meals were analyzed. LGI meals required significantly more insulin than HGI meals (P = 0.01). Furthermore, the mean (±standard deviation) carbohydrate-to-insulin ratio (CIR) was significantly different overall among the LGI-LF (5.5 ± 3.4), LGI-HF (4.5 ± 3.8), HGI-LF (7.6 ± 5.1), and HGI-HF (8.7 ± 5.8) meals (P = 0.001). The risk of nocturnal hypoglycemia is associated with daytime hypoglycemia and amount of insulin administered prior to the evening and exercise. This exploratory study designed to examine the impact of different meal types on insulin dosing requirements in free-living adults with T1D emphasizes the need for individualized adjustment of the CIR depending on meal composition.

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70–180 mg/dL was 71.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$71.2\\%$$\\end{document} and 76.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$76.2\\%$$\\end{document}, <70\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<70$$\\end{document} mg/dL was 0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.9\\%$$\\end{document} and 0.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.1\\%$$\\end{document}, and >180\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>180$$\\end{document} mg/dL was 26.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$26.7\\%$$\\end{document} and 21.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$21.1\\%$$\\end{document}, respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future.

Expert Panel Recommendations for a Standardized Ambulatory Glucose Profile Report for Connected Insulin Pens.

Connected insulin pens capture data on insulin dosing/timing and can integrate with continuous glucose monitoring (CGM) devices with essential insulin and glucose metrics into a single platform. Standardization of connected insulin pen reports is desirable to enhance clinical utility with a single report. An international expert panel was convened to develop a standardized connected insulin pen report incorporating insulin and glucose metrics into a single, clinically useful report. An extensive literature review and identification of examples of current connected insulin pen reports was performed serving as the basis for creation of a draft of a standardized connected insulin pen report. The expert panel participated in three virtual standardization meetings and online surveys. The AGP Report: Connected Insulin Pen brings all clinically relevant CGM-derived glucose and connected insulin pen metrics into a single simplified two-page report. The first page contains the time in ranges bar, summary of key insulin and glucose metrics, the ambulatory glucose profile (AGP) curve, and detailed basal (long-acting) insulin assessment. The second page contains the bolus (mealtime and correction) insulin assessment periods with information on meal timing, insulin-to-carbohydrate ratio (ICR), average bolus insulin dose and number of days with bolus doses recorded. The report's second page contains daily glucose profiles with an overlay of the timing and amount of basal and bolus insulin administered. The AGP Report: Connected Insulin Pen is a standardized clinically useful report that should be considered by companies developing connected pen technology as part of their system reporting/output.

Mitigating iftar-related glycemic excursions in adolescents and young adults with type 1 diabetes on MiniMed™ 780G advanced hybrid closed loop system: a randomized clinical trial for adjunctive oral vildagliptin therapy during Ramadan fasting

BackgroundRamadan Iftar meal typically causes glucose excursions. Dipeptidyl peptidase-4 inhibitors increase glucagon-like peptide-1 and thus, decrease blood glucose levels with low risk of hypoglycemia.AimTo investigate the efficacy and safety of vildagliptin as an add-on therapy on glucose excursions of Iftar Ramadan meals among adolescents and young adults with type 1 diabetes mellitus (T1DM) using advanced hybrid closed-loop (AHCL) treatment.MethodsFifty T1DM patients on MiniMed™ 780G AHCL were randomly assigned either to receive vildagliptin (50 mg tablet) with iftar meal during Ramadan month or not. All participants received pre-meal insulin bolus based on insulin-to-carbohydrate ratio (ICR) for each meal constitution.ResultsVildagliptin offered blunting of post-meal glucose surges (mean difference − 30.3 mg/dL [− 1.7 mmol/L] versus − 2.9 mg/dL [− 0.2 mmol/L] in control group; p < 0.001) together with concomitant exceptional euglycemia with time in range (TIR) significantly increased at end of Ramadan in intervention group from 77.8 ± 9.6% to 84.7 ± 8.3% (p = 0.016) and time above range (180–250 mg/dL) decreased from 13.6 ± 5.1% to 9.7 ± 3.6% (p = 0.003) without increasing hypoglycemia. A significant reduction was observed in automated daily correction boluses and total bolus dose by 23.9% and 16.3% (p = 0.015 and p < 0.023, respectively) with less aggressive ICR settings within intervention group at end of Ramadan. Coefficient of variation was improved from 37.0 ± 9.4% to 31.8 ± 7.1%; p = 0.035). No severe hypoglycemia or diabetic ketoacidosis were reported.ConclusionAdjunctive vildagliptin treatment mitigated postprandial hyperglycemia compared with pre-meal bolus alone. Vildagliptin significantly increased TIR while reducing glycemic variability without compromising safety.Trial registration This trial was registered under ClinicalTrials.gov Identifier no. NCT06021119.

Splitting Mealtime Insulin Doses for Mixed Fat and Protein Meals in Children and Adolescents with Type 1 Diabetes Using Multiple Daily Injection Regimen: A Randomized Cross-Over Trial

Aims. Assessment of the glycemic outcomes of increasing and splitting mealtime insulin doses for mixed fat and protein meals in pediatric patients with type 1 diabetes mellitus (T1DM) using multiple daily injection regimen and comparing the effects of regular insulin and fast-acting insulin on glycemic outcomes following those meals. Methods. This single-center, randomized, cross-over trial included 43 children and adolescents with T1DM randomly assigned to receive three interventional insulin doses for lunch meals over 3 consecutive days; Intervention A (100% insulin-to-carbohydrate ratio (ICR) dose given as premeal insulin lispro with an additional insulin sensitivity factor-calculated correction dose after 3 hr), Intervention B (130% ICR dose split into 60% premeal insulin lispro and 40% postmeal insulin lispro after 30 min), and Intervention C (130% ICR dose split into 60% premeal insulin lispro and 40% postmeal regular insulin after 30 min). The test meal consisted of two slices of pizza (weight: 150 g, carbohydrates: 40 g, fat: 15 g, protein: 20 g, and calories: 380 kcal). Postprandial blood glucose levels were monitored for 6 hr. Results. There were no significant differences in postprandial blood glucose excursions following the three interventions. However, Intervention C had a significantly lower late (3–6 hr) blood glucose area under the curve ( p = 0.01 ). Postprandial hypoglycemia developed in 12 participants (27.9%) following Interventions A and B and in 17 participants (39.5%) following Intervention C ( p = 0.32 ). Conclusions. Using regular insulin as a postmeal portion of increased and split insulin doses provided better late postprandial glycemic outcomes following mixed fat and protein meals. However, the amount of additional insulin used needs optimization to reduce the frequency of postprandial hypoglycemia. This trial is registered with NCT04783376.

1492-PUB: In Silico Modeling of the Performance of a Novel Closed-Loop Algorithm Using the UVA/PADOVA Type 1 Diabetes Simulator

AIAPS is a novel smartphone-based closed-loop algorithm in development that can be combined with any insulin pump and continuous glucose monitor. AIAPS strives to reduce burden of diabetes by requiring only limited input and by handling unannounced meals. Prior to launching the clinical research program in-silico modelling of the most recent features was performed. The objective of this study was to evaluate the AIAPS performance using the UVA/PADOVA Type 1 Diabetes (T1D) Simulator in people with T1D. AIAPS was used in the in-silico population of 33 subjects (children, adolescents, and adults) of the UVA/PADOVA T1D Simulator. Upon initialisation, 5 min CGM data, body weight, average daily insulin dose and sex for each person was available. To model a scenario with limited information for each individual upon initialization, no data on basal insulin profiles (BIP) , carbohydrate-to-insulin ratio (CR) and insulin sensitivity factor (ISF) were put into AIAPS. Additionally, no information on meals (time or carbohydrate content) were put into AIAPS. The following scenario was modelled: 84 days of closed-loop therapy without user interaction and with variable routine conditions representing the duration of the planned clinical study. Data on glycemic control for the three age groups over the 84 days are shown in table 1. Modelling included 2-5 unannounced meals with a random schedule and carbohydrate content of 130-250 g per day. In an in-silico model the AIAPS closed-loop system exceeded current glycemic targets with a TIR &amp;gt;70% and a TBR &amp;lt;4% in all age groups without user interaction or input of BIP, CR and ISF upon initialization. The next step includes testing the AIAPS performance in a randomized controlled trial. Table 1 - Glycemic control for the three age groups. Data are mean standard deviation. Disclosure A.J.Dedeshko: Stock/Shareholder; AI APS France. V.Fedotov: Stock/Shareholder; AIAPS France. A.Simic: None. C.Thivolet: Board Member; Insulet Corporation, Medtronic, Research Support; Abbott Diabetes, Lilly Diabetes, Novo Nordisk A/S, Sanofi. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp &amp; Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp &amp; Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH.

Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy.

Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo.Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.

Diurnal Variation of Real-Life Insulin Sensitivity Factor Among Children and Adolescents With Type 1 Diabetes Using Ultra-Long-Acting Basal Insulin Analogs

BackgroundEstimation of insulin sensitivity factor (ISF) is essential for correction insulin doses calculation. This study aimed to assess real-life ISF among children and adolescents with type 1 diabetes using ultra-long-acting basal insulin analogs and to detect factors associated with ISF among those patients.MethodsThis prospective observational study was conducted at Sohag University Hospital, Egypt, and included 93 participants aged 6–18 years, diagnosed with T1DM for at least 1 year and using insulin glargine 300 Units/mL or insulin degludec 100 Units/mL as basal insulin. The ISF, insulin-to-carbohydrate ratio (ICR) and insulin doses were initially assessed then adjusted as required. The participants were regularly contacted throughout the follow-up period. Glycemic control parameters were assessed after 3 months.ResultsThe ISF showed diurnal variation with higher correction dose requirements for the morning than for the rest of the day (p < 0.001). This pattern of diurnal variation was found in participants with different pubertal stages and in participants using either type of ultra-long acting basal insulin analogs. There was no significant difference between the ISF calculated according to the 1800 rule [1800/Total daily insulin dose (TDD)] and the morning ISF (p = 0.25). The 1800 rule-calculated ISF was significantly lower than the actual ISF for the afternoon (p < 0.001) and the evening (p < 0.001). ISF at different times of the day were significantly correlated with age, body mass index, pubertal stage, diabetes duration, TDD, and ICR. Multiple regression analysis revealed that ICR was the most significant factor associated with ISF. Linear regression analysis revealed that the ISF (in mg/dL) for any time of the day could be estimated as 5.14 × ICR for the same time of the day (coefficient = 5.14, 95% confidence interval: 5.10–5.19, R2 = 0.95, p < 0.001).ConclusionDiurnal variation of ISF that had to be considered for proper calculation of correction doses. This diurnal variation was found in children and adolescents with different pubertal stages. The 1800 rule was appropriate for the morning correction doses but not in the afternoon or the evening. The TDD and the ICR could be used for ISF estimation.

Automated Adaptation of Insulin Treatment in Type 1 Diabetes.

Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial periods, respectively, according to three insulin dosing parameters: basal rate (BR), carbohydrate-to-insulin ratio (CR), and correction factor (CF). Suboptimal BR, CR, and CF profiles leading to incorrect insulin dosing may be the cause of undesired glycemic events, which carry dangerous short-term and long-term effects. Therefore, correct tuning of these parameters is of the utmost importance. In this work, we propose a new algorithm to optimize insulin dosing parameters in individuals with T1D who use a continuous glucose monitor and an insulin pump. The algorithm was tested using the University of Virginia/Padova T1D Simulator and led to an improvement in the quality of glycemic control. Future efforts will be devoted to test the algorithm in human clinical trials.

The relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio required to maintain glycaemia is non-linear in young people with type 1 diabetes: A randomized crossover trial.

To determine if the relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio (ICR) required to maintain glycaemia is linear in people with type 1 diabetes. We used an open labelled randomized four-arm cross-over study design. Participants (N=31) aged 12-27years, HbA1c ≤ 64mmol/mol (8.0%) received insulin doses based on the individual's ICR and the study breakfast carbohydrate quantity and then consumed four breakfasts containing 20, 50, 100 and 150g of carbohydrate over four consecutive days in randomized order. The breakfast fat and protein percentages were standardized. Postprandial glycaemia was assessed by 5h continuous glucose monitoring. The primary outcome was percent time in range (TIR) and secondary outcomes included hypoglycaemia, glucose excursion and incremental area under the curve. Statistical analysis included linear mixed modelling and Wilcoxon signed rank tests. The 20g carbohydrate breakfast had the largest proportion of TIR (0.74±0.29 p<0.04). Hypoglycaemia was more frequent in the 50g (n=13, 42%) and 100g (n=15, 50%) breakfasts compared to the 20g (n=6, 20%) and 150g (n=7, 26%) breakfasts (p<0.029). The 150g breakfast glucose excursion pattern was different from the smaller breakfasts with the lowest glucose excursion 0-2h and the highest excursion from 3.5 to 5h. A non-linear relationship between insulin requirement and breakfast carbohydrate content was observed, suggesting that strengthened ICRs are needed for meals with ≤20 and ≥150g of carbohydrate. Meals with ≥150g of carbohydrate may benefit from dual wave bolusing.

Insulin strategies for dietary fat and protein in type 1 diabetes: A systematic review.

To identify and report the efficacy of insulin strategies used to manage glycaemia following fat and/or fat and protein meals in type 1 diabetes. A systematic literature search of medical databases from 1995 to 2021 was undertaken. Inclusion criteria were randomised controlled trials that reported at least one of the following glycaemic outcomes: mean glucose, area under the curve, time in range or hypoglycaemic episodes. Eighteen studies were included. Thirteen studies gave additional insulin. Five studies gave an additional 30%-43% of the insulin-to-carbohydrate ratio (ICR) for 32-50g of fat and 31%-51% ICR for 7-35g of fat with 12-27g of protein added to control meals. A further eight studies gave -28% to +75% ICR using algorithms based on fat and protein for meals with 19-50g of carbohydrate, 2-79g of fat and 10-60g of protein, only one study reported a glycaemic benefit of giving less than an additional 24% ICR. Eight studies evaluated insulin delivery patterns. Four of six studies in pump therapy, and one of two studies in multiple daily injections showed the combination of bolus and split dose, respectively, were superior. Five studies examined the insulin dose split, four demonstrated 60%-125% ICR upfront was necessary. Two studies investigated the timing of insulin delivery, both reported administration 15min before the meal lowered postprandial glycaemia. Findings highlight the glycaemic benefit of an additional 24%-75% ICR for fat and fat and protein meals. For these meals, there is supportive evidence for insulin delivery in a combination bolus with a minimum upfront dose of 60% ICR, 15min before the meal.

218-OR: Revisiting the Rule of 500: Data-Driven Insights from Sensor Augmented Pump Therapy

The rule of 500 is widely used to calculate the insulin to carbohydrate ratio (ICR) for T1D patients, where ICR is calculated as 500/ total daily dose (TDD) with TDD being the average daily sum of basal+bolus insulin. We analyzed data from the Tidepool Big Data Donation Project for 100 participants on sensor-augmented pump (SAP) therapy, to compute a new ICR prediction factor (ICR-PF) defined as the TDD multiplied by the ICR. Glucose level, bolus delivery, basal delivery, ICR, and correction factor were continuously recorded. For each month of data, we calculated the average daily TDD. For reliable results, we selected a subset of patients for further analyses who showed successful post-meal glycemic control, defined as both average rise in glucose and variability of glucose rise (standard deviation) below 30 mg/dL; 66 out of 72 adults (91.7%) and 18 out of 28 pediatrics (64.3%) demonstrated successful control as per these criteria. Baseline demographics for this subset are provided in Table 1. Adults had a slightly lower ICR-PF of 356.0 [261.8,437.7] (median[IQR]), compared to pediatric participants (405.5 [286.4,488.5], p = 0.35 ). The computed ICR-PF varied across the months of pump use for both adult (coefficient of variation 10.6% [6.6,14.6]%) and pediatric participants (10.9% [9.4,19.9]%, p = 0.17). Our results show the promise of a novel data-driven, personalized ICR-PF for optimizing glycemic control in T1D. Disclosure S. Barua: None. A. Zhang: None. A. Sabharwal: None. D. Desalvo: Consultant; Self; Insulet Corporation, Research Support; Self; Insulet Corporation, Speaker’s Bureau; Self; Dexcom, Inc.

Insulin Requirements and Carbohydrate to Insulin Ratio in Normal Weight, Overweight, and Obese Women With Type 1 Diabetes Under Pump Treatment During Pregnancy: A Lesson From Old Technologies.

AimThe primary aim of this study was to assess insulin requirements and carbohydrate to insulin ratio (CHO/IR) in normal weight, overweight, and obese pregnant women with type 1 diabetes across early, middle, and late pregnancy.MethodsIn this multicenter, retrospective, observational study we evaluated 86 of 101 pregnant Caucasian women with type 1 diabetes under pump treatment. The women were trained to calculate CHO/IR daily by dividing CHO grams of every single meal by insulin units injected. Since the purpose of the study was to identify the CHO/IR able to reach the glycemic target, we only selected the CHO/IR obtained when glycemic values were at target. Statistics: SPSS 20.ResultsWe studied 45 normal weight, 31 overweight, and 10 obese women. Insulin requirements increased throughout pregnancy (p < 0.0001 and <0.001 respectively) in the normal and overweight women, while it remained unchanged in the obese women. Insulin requirements were different between groups when expressed as an absolute value, but not when adjusted for body weight. Breakfast CHO/IR decreased progressively throughout pregnancy in the normal weight women, from 13.3 (9.8–6.7) at the first stage of pregnancy to 6.2 (3.8–8.6) (p = 0.01) at the end stage, and in the overweight women from 8.5 (7.1–12.6) to 5.2 (4.0–8.1) (p = 0.001), while in the obese women it remained stable, moving from 6.0 (5.0–7.9) to 5.1 (4.1–7.4) (p = 0.7). Likewise, lunch and dinner CHO/IR decreased in the normal weight and overweight women (p < 0.03) and not in the obese women. The obese women gained less weight than the others, especially in early pregnancy when they even lost a median of 1.25 (−1 −1.1) kg (p = 0.005). In early pregnancy, we found a correlation between pregestational BMI and insulin requirements (IU/day) or CHO/IR at each meal (p < 0.001 and p = 0.001, respectively). In late pregnancy, a relationship between pre-gestational BMI and CHO/IR change was found (P = 0.004), as well as between weight gain and CHO/IR change (p=0.02). The significance was lost when both variables were included in the multiple regression analysis. There was no difference in pregnancy outcomes except for a higher pre-term delivery rate in the obese women.ConclusionPre-gestational BMI and weight gain may play a role in determining CHO/IR during pregnancy in women with type 1 diabetes under pump treatment.

MON-663 Prandial Insulin Dosing Based on Carbohydrate Content Does Not Significantly Improve Glycemic Control in Hospitalized Patients with Diabetes

Introduction: Diabetes mellitus (DM) is a highly prevalent concern in hospital medicine. In-hospital hyperglycemia and hypoglycemia are common. Hospitalized patients have variable and unpredictable amounts of carbohydrate content in their meals. We hypothesized that order sets that allow for flexible dosing of prandial insulin using an insulin to carbohydrate ratio (ICR) would provide superior blood glucose control in the complex hospital environment. Methods: We performed a retrospective review of electronic medical records from a single university hospital. The hospital implemented new order sets allowing prandial insulin dosing based on an ICR and inpatient nursing staff received necessary training. We compared glucose levels over the first three days after admission in hospitalized patients prescribed prandial insulin either as a fixed dose or based on an ICR. Patients on fixed dose insulin were selected from a time 3 months prior to the implementation of ICR order sets, to avoid any bias in patient selection. Patients on ICR dosing were selected from 3 months after implementation of ICR order sets, to allow adequate time for initial implementation. Our inclusion criteria included patients admitted to both medicine and surgery services, with Type 1 or Type 2 DM, age between 18-80. Exclusion criteria included transplant patients, patients on insulin infusion, pregnancy, steroid use and dialysis. 65 patients were included in each group. Outcome measures included average blood glucose over 72 hours, fasting and postprandial hypoglycemia (<70 mg/dL) and hyperglycemia incidence (>180 mg/dL). Results: Average glucose over 3 days was 167 +/- 39 mg/dL and 162 +/- 33 mg/dL and did not differ between groups. However, a higher percentage of glucose values were in target range (70-180 mg/dL) in the fixed dosing group (67.9%) compared to ICR (62.5%, p=0.018). The incidence of hypoglycemia was low and did not differ between groups (1.2% in both). However, patients had more overall hyperglycemia with ICR dosing (36% vs 31%, p=0.018), particularly pre-lunch hyperglycemia (52% vs. 38%, p=0.007). Fasting glucose was similar between groups. Conclusions: In conclusion, our study demonstrated that prandial insulin administered based on ICR did not improve overall glycemic control or reduce incidence of hypoglycemia in hospitalized patients with diabetes. In fact, overall and post-meal rates of hyperglycemia were generally higher with ICR dosing. This could be related to inaccurate counting of carbohydrates or delayed timing of insulin administration as it was given after the food was consumed. Additional studies are needed to further evaluate the effectiveness of flexible meal dosing and the impact on patient satisfaction and staff workload.

Prospective evaluation of insulin‐to‐carbohydrate ratio in children and adolescents with type 1 diabetes using multiple daily injection therapy

Assessment of insulin-to-carbohydrate ratio (ICR) in children and adolescents with type 1 diabetes mellitus (T1DM) using multiple daily injection (MDI) therapy. This prospective observational study was conducted over a 2-year period at Sohag University Hospital, Egypt. Children and adolescents aged 4 to 17 years, diagnosed with T1DM for at least 1 year, with fasting serum C-peptide levels <0.24 ng/dL and whose parents accepted to shift their management to flexible MDI using carbohydrate counting, were included. Participants were initially hospitalized for estimation of ICR and insulin doses, then followed-up monthly for further adjustments. Insulin doses, ICR, and glycemic control parameters were assessed after 3 months. The study included 201 participants, 110 (54.7%) of them were males. The median age was 9.5 years (interquartile range: 7-12.5 years). Bolus insulin requirements estimated by the 500 rule were significantly lower than the actual doses used by the study participants for all meals (P < .001). Bolus insulin requirement for morning meal was significantly higher compared to other meals (P < .001). Linear regression analyses between ICR for different meals and the reciprocal of total daily dose (TDD) in 96 participants with optimum glycemic control revealed that ICR could be calculated as 301 to 309/TDD for morning meal (R2 = 0.97, P < .001), 317 to 331/TDD for afternoon meal (R2 = 0.96, P < .001), and 362 to 376/TDD for evening meal (R2 = 0.98, P < .001). Bolus insulin requirements showed diurnal variation. Using 301 to 309/TDD, 317 to 331/TDD, and 362 to 376/TDD formulas would be more appropriate than the 500 rule for initial estimation of ICR for morning, afternoon, and evening meals, respectively.

1169-P: The Insulin Dosage Advisor Diassist Can Help Optimize Glucose Control

Background: The 500-rule for the insulin to carbohydrate ratio (ICR) and 100-rule for insulin sensitivity factor (ISF) are easy to use but unfortunately do not provide good ratio estimates for many patients. Analysis of downloaded CGM and pump data can give good estimates but require a lot of experience from the user and is time consuming. We have developed an algorithm (Diassist), based on clinical experience and signal processing, which automatically estimates ICR and ISF, and investigated this method in a clinical context. Methods: Pump and CGM downloads over 2 weeks were used for analysis. The physician interpreted these and compared with the Diassist results. Diassist calculated only valid meals and corrections, i.e., when blood glucose and carbohydrates had been entered by the patient. 8 patients with poor glucose control participated with 3-5 visits over 2-8 months. Mean (±SD) age was 12.7 ± 1.8 (range 10.4-16.5) years, diabetes duration 8.0±1.1 years. Insulin dose was 0.78±0.17 (0.6-1.1) units/kg/24 hours (% basal 40.8±13.0 (23-58)). The tool estimated ICR and ISF over 2 weeks and proposed to increase or decrease the settings based on actual glucose excursions. To validate the robustness of the method, cross validation with two data sets was used. Data was divided into 2 sets per patient; running Diassist on the two sets and comparing the results. Results: The physician followed the Diassist recommendations for ICR titration in 73% of the time slots (4-5/day, 58 out of 79), increasing or decreasing by 10-25%. In one occasion the ICR was changed in the opposite direction to the recommendation. In 98%, the ICR was lowered (more insulin for the meal), in 2% it was increased. HbA1c was not changed (61±4.4 vs. 57±6.3 mmol/mol, 7.7±0.4 vs. 7.4±0.6%). The quota of carbohydrate and boluses used in analysis were 67% and 58% of the logged data. Conclusions: Compared to methods used today, Diassist decreased both time and complexity of the estimation task. The proposed method shows promising results for usability, robustness and safety. Disclosure H. Svensson: Stock/Shareholder; Self; Diassist Technology AB. R. Hanas: Advisory Panel; Self; Insulet Corporation. Speaker's Bureau; Self; Novo Nordisk A/S. Other Relationship; Self; AstraZeneca.

2326-PUB: The Correction Factor (CF) and Carbohydrate-to-Insulin Ratio (CIR) Are More Correlated to Each Other than to the Total Daily Dose (TDD)

The accepted bolus dosing factor estimation formulas are CIR = 450/TDD and CF = 1700/TDD. These formulas were not based on controlled studies. We have combined two of our previously published well-controlled studies to reevaluate these relationships. Both studies (30 pump treated T1DM and 11 pump- or multiple daily dose-treated T1 or 2 subjects) established the basal insulin dose by frequent glucose testing while on a controlled diet and meal omissions to assess the post-meal period. Then CIR was determined by the return of the 2-4 h post meal plasma glucose (PG) to ± 20% of the pre-meal and the CF was independently determined by greatest PG reduction during the 2-4th hour after a corrective bolus following hyperglycemia induced by a reduction (about 30%) of the basal insulin. CIR = CF/4.49 (r = 0.940), CIR = 295/TDD (r =0.720) and CIR = 107/TBD (r = 0.7310). The strong relationship between CIR and CF may be explained by CF is a measure of the ∆ PG/U and CIR is a measure of the anticipated ∆ PG/U and both are expressions of the insulin sensitivity of the same tissue bed. If one bolus dosing factor is established, the other could better be estimated using CF = CIR*4.5 than from TDD. The usual order of titration is TBD, then CIR and CF and finally TDD is calculated. After TBD is established and before bolus dosing factors and TDD, CIR = 100/TBD would be a better estimate than 450/TDD. Disclosure J. Socha: None. A.B. King: None.

Optimizing a Hybrid Closed Loop System in Type 1 Diabetes: A Case Report.

We present the optimization of a hybrid closed-loop (HCL) insulin pump system in a type 1 diabetes (T1D) patient in the first 3 months of using the Minimed 670G. An 11-year-old female with 7 years of T1D history started using the Minimed 670G HCL system at our center. The patient had been using the Medtronic Veo insulin pump without a sensor for almost 4 years with HbA1c levels between 7.8% and 10.2%. We introduced the Minimed 670G to improve overall glucose control, improve quality of life and lessen the diabetes burden. Insulin pump characteristics of the Minimed Veo pump were used as the initial setting in the HCL system. The optimization of the Minimed 670G in the next 3 months led to the following changes: an insulin-to-carbohydrate ratio (ICHR) from 1 to 10 g in the morning period (6 a.m. to 12 a.m.) and 15–12 g for the rest of the day (12 p.m. to 6 a.m.); an active insulin time from 4 to 3 h and insulin sensitivity factor (ISF) from 120 to 90 mg/dl with no change in target range. The HbA1c level decreased by − 1.3%, and the time in range significantly increased to 77% with SG values of 139 ± 60 mg/dl, sensor wear of 82% and an auto mode period of 84% per week. Health providers should consider immediate adjustment of the bolus wizard settings such as the ICHR, ISF and active insulin time. The HCL system can improve overall glucose control with increased time in the range of sensor glucose values and reduction of HbA1c levels.

Automatic Tuning of CIR and ISF with the Adaptive Bolus Calculator (ABC) Approach Is Safe and Reliable—Results from a Hybrid In Silico Evaluation

Introduction: T1DM patients usually determine their bolus insulin requirements using a patient and daytime specific carbohydrate to insulin ratio (CIR) and insulin sensitivity factor (ISF). These factors are tuned by the patients themselves with assistance of their MDs. Recently, the Adaptive Bolus Calculator (ABC) method has been proposed for the automatic tuning of CIRs and ISFs based on data that is routinely collected by T1DM patients. Methods: Data from a recent clinical trial with 12 patients are used for evaluating the ABC method. CIR and ISF values are identified using the ABC method based on the first 6 days of the data, whereas the 7th day is used for the evaluation. The effect of the estimated CIRs and ISFs on the glycemic outcomes is simulated using a deviation based method. The simulation outcomes are compared to the outcomes obtained with the clinically used settings (in the figure below abbreviated as CUS) that the patients employed on the previous 6 days for calculating their bolus insulin needs. Results: The CIRs and ISFs estimated by the ABC approach lead in average to a 6.8% reduction for time in hypoglycemia (BG&amp;lt;70 mg/dl) as compared to the clinically used CIR/ISF settings (avg. time in hypoglycemia: from 9.8% to 3.0%, p&amp;lt;0.01). Time in hyperglycemia (BG&amp;gt;180 mg/dl) on the other hand increases only slightly (avg. time in hyperglycemia: 10.9% vs. 9.3%, p=0.08). Disclosure F. Reiterer: None. L. del Re: None.

Carbohydrate Counting during Pregnancy in Women with Type 1 Diabetes: Are There Predictable Changes That We Should Know?

Background/Aims: Carbohydrate counting (CC) is a helpful strategy for the treatment of type 1 diabetes mellitus (T1DM) and the main parameters used in this method are the insulin to carbohydrate ratio (ICR) and the sensitivity factor (SF). Throughout pregnancy, a state of insulin resistance develops. Therefore, we hypothesized that ICR and SF change and our aim was to describe the pattern of modification of these parameters in pregnant women with T1DM on CC. Methods: This study followed 21 women with T1DM throughout pregnancy. Starting ICR was 1:15 and SF was calculated using the formula: 1,500/total daily insulin dose (TDID; for regular insulin) or 1,800/TDID (for ultra-rapid analogs). ICR was adjusted every 1-2 weeks according to self-monitoring of blood glucose. SF was recalculated every 1-2 weeks. Results: Throughout gestation there was a mean decrease in the ICR in breakfast, lunch and dinner of 8.2 (p < 0.0001), 7.7 (p = 0.003) and 7 (p = 0.005) grams per international units (g/IU), respectively. Mean SF reduction from first to third trimester was 10 mg/dL per IU (mg/dL/IU; p < 0.0001). Conclusions: Women with T1DM in CC during pregnancy evolve with a progressive reduction in the ICR at every meal (mean of 8.2 g/IU for breakfast, 7.7 g/IU for lunch and 7 g/IU for dinner) and also in the SF (10 mg/dL/IU).