Search

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.

Introduction: Hybrid closed-loop systems increase time-in-range (TIR) and reduce glycemic variability. Person-reported outcomes (PROs) are essential to assess the utility of new devices and their impact on quality of life. This article focuses on the PROs for pediatric participants (ages 6-13 years) with type 1 diabetes (T1D) and their parents during a trial using the Tandem Control-IQ system, which was shown to increase TIR and improve other glycemic metrics. Research Design and Methods: One hundred and one children 6 to 13 years old with T1D were randomly assigned to closed-loop control (CLC) or sensor-augmented pump (SAP) in a 16-week randomized clinical trial with extension to 28 weeks during which the SAP group crossed over to CLC. Health-related quality of life and treatment satisfaction measures were obtained from children and their parents at baseline, 16 weeks, and 28 weeks. Results: Neither the children in the CLC group nor their parents had statistically significant changes in PRO outcomes compared with the SAP group at the end of the 16-week randomized controlled trial and the 28-week extension. Parents in the CLC group reported nonsignificant improvements in some PRO scores when compared with the SAP group at 16 weeks, which were sustained at 28 weeks. Sleep scores for parents improved from "poor sleep quality" to "adequate sleep quality" between baseline and 16 weeks, however, the change in scores was not statistically different between groups. Conclusions: Children with T1D who used the Control-IQ system did not experience increased burden compared with those using SAP based on person-reported outcomes from the children and their parents. Clinical Trials Registration: NCT03844789.

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. ispartof: PEDIATRIC DIABETES vol:21 issue:8 pages:1375-1393 ispartof: location:Denmark status: published

The advent of insulin pump therapy marked an important milestone in diabetes treatment in the past few decades and has become the tipping point for the development of automated insulin delivery systems (AID). Standalone insulin pump systems have evolved over the course of years and have been replaced by modern high-technology insulin pumps with continuous glucose monitor interface allowing real-time insulin dose adjustment to optimize treatment. This review summarizes evidence from AID studies conducted in children with type 1 diabetes and discusses the outlook for future generation AID systems from a pediatric treatment perspective.

Context Cardiovascular disease (CVD) is a major cause of mortality in adults with type 1 diabetes. Objective We prospectively evaluated CVD risk factors in a large, contemporary cohort of adults with type 1 diabetes living in the United States. Design Observational study of CVD and CVD risk factors over a median of 5.3 years. Setting The T1D Exchange clinic network. Patients Adults (age ≥ 18 years) with type 1 diabetes and without known CVD diagnosed before or at enrollment. Main Outcome Measure Associations between CVD risk factors and incident CVD were assessed by multivariable logistic regression. Results The study included 8,727 participants (53% female, 88% non-Hispanic white, median age 33 years [interquartile ratio {IQR} = 21, 48], type 1 diabetes duration 16 years [IQR = 9, 26]). At enrollment, median HbA1c was 7.6% (66 mmol/mol) (IQR = 6.9 [52], 8.6 [70]), 33% used a statin, and 37% used blood pressure medication. Over a mean follow-up of 4.6 years, 325 (3.7%) participants developed incident CVD. Ischemic heart disease was the most common CVD event. Increasing age, body mass index, HbA1c, presence of hypertension and dyslipidemia, increasing duration of diabetes, and diabetic nephropathy were associated with increased risk for CVD. There were no significant gender differences in CVD risk. Conclusion HbA1c, hypertension, dyslipidemia and diabetic nephropathy are important risk factors for CVD in adults with type 1 diabetes. A longer follow-up is likely required to assess the impact of other traditional CVD risk factors on incident CVD in the current era.

Ketone bodies are an energy substrate produced by the liver and used during states of low carbohydrate availability, such as fasting or prolonged exercise. High ketone concentrations can be present with insulin insufficiency and are a key finding in diabetic ketoacidosis (DKA). During states of insulin deficiency, lipolysis increases and a flood of circulating free fatty acids is converted in the liver into ketone bodies—mainly beta-hydroxybutyrate and acetoacetate. During DKA, beta-hydroxybutyrate is the predominant ketone in blood. As DKA resolves, beta-hydroxybutyrate is oxidized to acetoacetate, which is the predominant ketone in the urine. Because of this lag, a urine ketone test might be increasing even as DKA is resolving. Point-of-care tests are available for self-testing of blood ketones and urine ketones through measurement of beta-hydroxybutyrate and acetoacetate and are cleared by the US Food and Drug Administration (FDA). Acetone forms through spontaneous decarboxylation of acetoacetate and can be measured in exhaled breath, but currently no device is FDA-cleared for this purpose. Recently, technology has been announced for measuring beta-hydroxybutyrate in interstitial fluid. Measurement of ketones can be helpful to assess compliance with low carbohydrate diets; assessment of acidosis associated with alcohol use, in conjunction with SGLT2 inhibitors and immune checkpoint inhibitor therapy, both of which can increase the risk of DKA; and to identify DKA due to insulin deficiency. This article reviews the challenges and shortcomings of ketone testing in diabetes treatment and summarizes emerging trends in the measurement of ketones in the blood, urine, breath, and interstitial fluid.

Numerous studies have demonstrated the clinical value of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) and type 2 diabetes (T2D) populations. However, the eligibility criteria for CGM coverage required by the Centers for Medicare & Medicaid Services (CMS) ignore the conclusive evidence that supports CGM use in various diabetes populations that are currently deemed ineligible. In an earlier article, we discussed the limitations and inconsistencies of the agency’s CGM eligibility criteria relative to current scientific evidence and proposed practice solutions to address this issue and improve the safety and care of Medicare beneficiaries with diabetes. Although Medicaid is administered through CMS, there is no consistent Medicaid policy for CGM coverage in the United States. This article presents a rationale for modifying and standardizing Medicaid CGM coverage eligibility across the United States.

AIMS To explore the auxiliary psychosocial effects of a monetary reinforcement intervention targeting self-monitoring of blood glucose among young people with Type 1 diabetes. METHODS Sixty young people with Type 1 diabetes, HbA1c concentrations between 58 and 119 mmol/mol (7.5-13.0%), and average self-monitoring of blood glucose

Context Type 1 diabetes in adolescence is characterized by insulin deficiency and insulin resistance (IR), both thought to increase cardiovascular disease risk. We previously demonstrated that adolescents with type 1 diabetes have adipose, hepatic, and muscle IR, and that metformin lowers daily insulin dose, suggesting improved IR. However, whether metformin improves IR in muscle, hepatic, or adipose tissues in type 1 diabetes was unknown. Objective Measure peripheral, hepatic, and adipose insulin sensitivity before and after metformin or placebo therapy in youth with obesity with type 1 diabetes. Design Double-blind, placebo-controlled clinical trial. Setting Multi-center at eight sites of the T1D Exchange Clinic Network. Participants A subset of 12- to 19-year-olds with type 1 diabetes (inclusion criteria: body mass index ≥85th percentile, HbA1c 7.5% to 9.9%, insulin dosing ≥0.8 U/kg/d) from a larger trial (NCT02045290) were enrolled. Intervention Participants were randomized to 3 months of metformin (N = 19) or placebo (N = 18) and underwent a three-phase hyperinsulinemic euglycemic clamp with glucose and glycerol isotope tracers to assess tissue-specific IR before and after treatment. Main outcome measures Peripheral insulin sensitivity, endogenous glucose release, rate of lipolysis. Results Between-group differences in change in insulin sensitivity favored metformin regarding whole-body IR [change in glucose infusion rate 1.3 (0.1, 2.4) mg/kg/min, P = 0.03] and peripheral IR [change in metabolic clearance rate 0.923 (-0.002, 1.867) dL/kg/min, P = 0.05]. Metformin did not impact insulin suppression of endogenous glucose release (P = 0.12). Adipose IR was not assessable with traditional methods in this highly IR population. Conclusions Metformin appears to improve whole-body and peripheral IR in youth who are overweight/obese with type 1 diabetes.

Digital health and telehealth connectivity have become important aspects of clinical care. Connected devices, including continuous glucose monitors and automated insulin delivery systems for diabetes, are being used increasingly to support personalized clinical decisions based on automatically collected data. Furthermore, the development, demand, and coverage for telehealth have all recently expanded, as a result of the COVID-19 pandemic. Medical care, and especially diabetes care, are therefore becoming more digital through the use of both connected digital health devices and telehealth communication. It has therefore become necessary to integrate digital data into the electronic health record and maintain personal data confidentiality, integrity, and availability. Connected digital monitoring combined with telehealth communication is known as virtual health. For this virtual care paradigm to be successful, patients must have proper skills, training, and equipment. We propose that along with the five current vital signs of blood pressure, pulse, respiratory rate, temperature, and pain, at this time, digital connectivity should be considered as the sixth vital sign. In this article, we present a scale to assess digital connectivity.

Background: Studies of closed-loop control (CLC) in patients with type 1 diabetes (T1D) consistently demonstrate improvements in glycemic control as measured by increased time-in-range (TIR) 70–180 mg/dL. However, clinical predictors of TIR in users of CLC systems are needed. Materials and Methods: We analyzed data from 100 children aged 6–13 years with T1D using the Tandem Control-IQ CLC system during a randomized trial or subsequent extension phase. Continuous glucose monitor data were collected at baseline and during 12–16 weeks of CLC use. Participants were stratified into quartiles of TIR on CLC to compare clinical characteristics. Results: TIR for those in the first, second, third, and fourth quartiles was 54%, 65%, 71%, and 78%, respectively. Lower baseline TIR was associated with lower TIR on CLC (r = 0.69, P

An 11-and-a-half-year-old boy of Kurdish descent born to consanguineous parents presented to our clinic with polydypsia, polyuria, and weight loss. His prenatal and natal medical history was unremarkable. His prior medical history was significant for hearing loss at 2 years of age and surgical repair of omphalocele. He was reported to have low school performance and was enrolled in a special education class. There was a family history of type 2 diabetes diagnosed in his father, maternal grandmother, and paternal grandmother in their late 40s.

A 28-month-old girl with a history of type 1 diabetes presented to the emergency room with fever and vomiting. She was diagnosed with type 1 diabetes at 2 months of age and had been being administered multiple daily injections of insulin. Past history was otherwise unremarkable. Family history was unknown, including history of consanguinity. Her vital signs revealed a temperature of 102.2°F, heart rate of 140 beats/min, and respiratory rate of 32/min. Her height was 2.4 ft (−4.54 standard deviation score [SDS]) and weight was 17.2 lb (−3.58 SDS). Physical examination was significant for decreased skin turgor, hyperemic pharynx, and hepatosplenomegaly (3 cm below the right costal margin).

It can be argued that little has changed in treatment of type 1 diabetes in children and adolescents for more than 80 years: there have been new ways to produce insulin, better ways to monitor insulin, and new devices to administer insulin. However, the treatment of type 1 diabetes in pediatrics is still solely based on administration of insulin. In this chapter, we will review the limitations of insulin extracted from animal pancreases and the impact of the ability to produce biosynthetic human insulin. This has led to the production of rapid- and long-acting insulin analogs and the further development of ultra-rapid and long-acting insulin preparations. Adjunctive therapies for type 1 diabetes are discussed in Chap. 13.

Objective: To further evaluate the safety and efficacy of the Control-IQ closed loop control (CLC) system in children with type 1 diabetes. Research Design and Methods: Following a 16-week randomized clinical trial (RCT) comparing CLC with sensor augmented pump (SAP) therapy in 101 children age 6 to 13 years old with type 1 diabetes, 22 participants in the SAP group initiated use of the CLC system (referred to as SAP-CLC cohort), and 78 participants in the CLC group continued use of CLC (CLC-CLC cohort) for 12 weeks. Results: In the SAP-CLC cohort, mean percentage of time in range 70-180 mg/dL (TIR) increased from 55±13% using SAP during the RCT to 65±10% using CLC (P70% plus time Conclusions: This further evaluation of the Control-IQ CLC system supports the findings of the preceding RCT that use of a closed-loop system can safely improve glycemic control in children 6 to 13 years old with type 1 diabetes from the first day of use and demonstrates that these improvements can be sustained through 28 weeks of use.

We thank Dr Yardley and Dr Sigal for their comments on the position statement pertaining to the use of continuous glucose monitoring (CGM) systems around exercise in type 1 diabetes.

Background: Young adults with type 1 diabetes (T1D) tend to have higher A1C than older adults and are at increased risk for diabetic ketoacidosis (DKA). Oral adjuncts to insulin have not been previously studied in this population. Methods: In this phase 2, multicenter, randomized, double-blind, placebo-controlled, parallel-group study, adults aged 18–30 years with T1D and A1C ≥9.0% were randomly assigned to placebo (n = 42) or sotagliflozin 400 mg (n = 43), in addition to insulin for 12 weeks. Insulin doses were adjusted to meet glucose targets (preprandial 80–130 mg/dL, postprandial

Background: Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study. Methods: Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller’s aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller’s aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention. Results: Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] ( P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] ( P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] ( P < .05) for α = 3. Conclusion: These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.

Few data exist on the frequency of hypoglycemia among nursing home residents with diabetes. We recruited patients 65 years or older, with type 1 or type 2 diabetes, on standing insulin or sulfonylureas, who were long-term residents at 10 Connecticut nursing homes. After informed consent, Freestyle Libre CGM devices were placed for 14 days of blinded monitoring. After omitting the first 24 hours of data, we calculated the percentage (%) of 24-hr periods with at least 2 consecutive blood glucose (BG) levels Among 35 patients who completed the study, 33 had at least 7 days of CGM data. Median age was 80 [IQR 71-86], 46% men, 36% nonwhite, HbA1c 7.3 [IQR 6.5-8.5], ADL disability score 26 [IQR 22-27, max possible value=35], 52% had dementia, 39% chronic kidney disease, and 97% were on insulin. More than 1 in 3 (38% (IQR 15-62%)) of the patient-24 hr CGM periods contained at least 2 consecutive BGs Hypoglycemia detected by flash CGM is common but otherwise under-recognized among older, long-term residents of nursing homes treated with insulin or sulfonylureas. Disclosure K.J. Lipska: None. M.M. Doyle: None. E. Cengiz: Advisory Panel; Self; ADOCIA, Arecor, Lexicon Pharmaceuticals, Inc., MannKind Corporation, Novo Nordisk Inc. Speaker’s Bureau; Self; Novo Nordisk Inc. B. Drozdowicz: None. T.M. Gill: None. T.E. Murphy: None. S.E. Inzucchi: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lexicon Pharmaceuticals, Inc., Novo Nordisk A/S, Sanofi. Consultant; Self; Abbott, Merck & Co., Inc., vTv Therapeutics. Funding National Institutes of Health (P30DK045735)

RECOMMENDATIONS/EXECUTIVE SUMMARY Education is the key to successful management of diabetes [E]. To maximize the effectiveness of diabetes treatment and the advances in diabetes management and technology (especially insulin pumps and continuous glucose monitoring) it is advisable that quality assured structured education is available to all young people with diabetes and their carers [E]. The content and delivery of structured education needs regular review to ensure it suits the needs of people with diabetes in that community, matches local practice, and reflects changes in diabetes management and technology [E]. Evaluation of structured educational programs should include measurement of outcomes directly related to diabetes education such as the patient's achievement of self‐selected diabetes‐care goals, improved psychosocial adaptation and enhanced self‐efficacy, in addition to measures of glycemic control [E]. There is evidence that educational interventions in childhood and adolescent diabetes have a beneficial effect on glycemic and psychosocial outcomes [A]. Educational interventions shown to be effective include those: based on clear theoretical psychoeducational principles [E] integrated into routine clinical care (eg, as an essential integral part of intensive insulin management) [A] referred to as an ongoing process of provision of individualized self‐management and psychosocial support [E] involving the continuing responsibility of parents and other carers throughout adolescence [B] making use of cognitive behavioral techniques most often related to problem solving, goal setting, communication skills, motivational interviewing, family conflict resolution, coping skills, and stress management [A] utilizing new technologies in diabetes care as one of the vehicles for educational motivation [A] Health care professionals require appropriate specialized training in the principles and practice of teaching and education to implement successfully behavioral approaches to education designed to empower young people and carers in promoting self‐management [E]. An interdisciplinary education team sharing the same philosophy and goals and speaking “with one voice” has beneficial effects on metabolic and psychosocial outcomes [B]. It is important that goals and targets for blood glucose and HbA1c align with those of ISPAD. A major task during the first 2 weeks after diagnosis of diabetes is to get the family to agree to encompass the same targets. [E] Mobile and web‐based applications can be useful tools for diabetes self‐management education to improve diabetes management. [E] Interactive web‐based educational resources designed by diabetes‐related device manufacturing companies are widely used for device‐specific patient training and education. [E] Telemedicine, if available, offers an alternative method to face‐to‐face diabetes review for people who live in remote areas and do not have access to professional counseling and diabetes education resources locally. [B] Diabetes peers and/or diabetes youth leaders can reinforce the principles of living well with diabetes and support the families learning especially in the resource limited setting. [E]

According to interpersonal neurobiology approach, mind, brain and interpersonal relationships represent three aspects of the flow of the information and energy in a living system and it is suggested that mental health is a related concept with the integration and harmony of mind, brain and interpersonal relationships. In the other words, our relationships and brain shape our mind, our mind and brain shape our relationships and lastly, our relationships and mind shape our brain interactively. The integrative functioning among the three aspects enables the regulation of anxiety and fear; however, the unbalance among these aspects approaches the individuals towards chaos and rigidity. In the current review, it was aimed to introduce interpersonal neurobiology perspective, which has not been a widespread approach in our country yet, and explain the relationship among attachment styles, anxiety and fear regulation, which are among the main topics in clinical psychology, from interpersonal neurobiology perspective.

Background: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. Method: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka’s glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. Results: The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. Conclusions: The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.

Enthusiasm for the benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as an adjunctive treatment in type 1 diabetes (T1D) has been offset by the possible increased risk of diabetic ketoacidosis (DKA). Since pump-treated T1D patients are susceptible to DKA due to infusion site problems, this study was undertaken to assess how treatment with SGLT2i affects patterns of early metabolic decompensation following suspension of basal insulin.Ten T1D participants (age 19-35 years, duration 10 ± 8 years, A1c 7.4% ± 0.8%) underwent overnight pump suspension studies before and after treatment with canagliflozin (CANA). On both nights, basal insulin was suspended at 3 AM and plasma glucose (PG), β-hydroxybutyrate (BHB), free fatty acids (FFA), plasma insulin (PI), and glucagon were measured. Studies were terminated 6 h after suspension or if PG rose to350 mg/dL or BHB2.5 mmol/L.PI levels at the start of suspension were reduced by 30% after CANA treatment (44 ± 11 uU/mL vs. 31 ± 10 uU/mL, P 0.01), but baseline PG, BHB, FFA, and glucagon levels were not significantly different. During the suspension, PG rose from 104 ± 10 to 301 ± 21 mg/dL before treatment, but only from 109 ± 8 to 195 ± 14 mg/dL after treatment (P = 0.002 vs. pretreatment values). On the other hand, CANA treatment did not significantly affect the magnitude of increases in FFA, BHB, and glucagon levels during the suspension study.These data indicate that SGLT2i do not accelerate the rate of ketogenesis following the interruption of basal insulin infusion in T1D. Rather, the failure of patients to promptly recognize early metabolic decompensation relates to the much more gradual rise in PG levels.

In this work the real-time estimation of plasma insulin concentration (PIC) to quantify the insulin in the bloodstream in patients with type 1 diabetes mellitus (T1DM) is presented. To this end, Hovorka’s model, a glucose–insulin dynamics model, is incorporated with various estimation techniques, including continuous-discrete extended Kalman filtering, unscented Kalman filtering, and moving horizon estimation, to provide an estimate of PIC. Furthermore, due to the considerable variability in the temporal dynamics of patients, some uncertain model parameters that have significant effects on PIC estimates are considered as additional states in Hovorka’s model to be simultaneously estimated. Latent variable regression models are developed to individualize the PIC estimators by appropriately initializing the time-varying model parameters for improved convergence. The performance of the proposed methods is evaluated using clinical data sets from subjects with T1DM, and the results demonstrate the accurate esti...

Background: Members of our group previously reported that monetary reinforcement of SMBG related behaviors shows robust increases in frequency of SMBG and modest decreases in A1c among youth with type 1 diabetes (T1D). In these secondary data analyses, we explored effects on psychosocial functioning. Methods: Sixty youth ages 12-21 (Mage=15.58, SD=2.31) with T1D, A1c 7.5-13%, and Results: Generalized linear models showed no significant time x condition effects. In t-tests at discrete follow-ups, compared to the control group, the reinforce group had significantly lower youth-reported diabetes related family conflict at 12 weeks (22.39 vs. 25.11, p=.043) and significantly higher negative affective responses to out-of-range glucose results at 24 weeks (13.62 vs. 11.71, p=.047). Conclusion: Monetary reinforcers targeting SMBG behaviors may produce transitory decreases in youth perceptions of family conflict and transitory iatrogenic increases in youth’s negative affective responses to out-of-range glucose results. The timing of changes in family conflict, negative affect, and previously reported changes in A1c suggest that psychosocial changes may reflect youth responses to A1c as well as to reinforcers per se. For broader effects, the intervention could include psychosocial components that seek to directly maintain decreased family conflict and mitigate negative affect related to increased awareness of glucose control. Disclosure J.J. Wong: None. A. Addala: None. K.K. Hood: Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Johnson & Johnson Diabetes Institute. J. Wagner: None. E. Cengiz: Advisory Panel; Self; Abvance, ADOCIA, MannKind Corporation, Novo Nordisk Inc. Speaker's Bureau; Self; Novo Nordisk Inc. E.M. Tichy: None. K. Weyman: None. D. Naranjo: Advisory Panel; Spouse/Partner; Eli Lilly and Company. Speaker's Bureau; Spouse/Partner; Johnson & Johnson Diabetes Institute. Other Relationship; Self; Abbott. Funding National Institutes of Health

Estimate of insulin sensitivity (SI) and its daily variation are essential for optimizing insulin therapy in type 1 diabetes (T1D). Recently, a SI index based on continuous glucose monitoring (CGM) and insulin pump (CSII) data (SISP) has been validated in adults with T1D. Herein, we validated the SISP index against the oral minimal model (MM) one (SIMM) during two sequential meals in youth with T1D during hybrid closed-loop (HCL) treatment. Ten youths with T1D (4F; age=20.9±3.7 y; BMI=23.6±4.5 kg/m2; TDD= 50.6±16.3 U/day; HbA1c 7.3±0.6%) underwent two consecutive, standardized meal studies (breakfast, B, and lunch, L) while wearing the UVA-DiAS HCL (Dexcom CGM and t-slim CSII) system. Plasma glucose and insulin concentration data (measured every 10 min for 4 h during each meal) were used for SIMM estimation, while the corresponding CGM and CSII data, derived from the HCL system, were used for SISP calculation. SISP well correlated with SIMM (Fig. 1) for both B (18.1±7.6 vs. 21.5±12.1 10-4dL/kg/min per uU/mL, R=0.70) and L (26.5±10.1 vs. 26.0±13.8 10-4dL/kg/min per uU/mL, R=0.77). Between-meal (L-B) variation of SISP was also well correlated with its MM counterpart (R= 0.82). SISPis a reliable proxy of the oral minimal model derived SI and can be used to customize both open- and closed-loop treatments to improve glycemic outcomes in young people with T1D. Disclosure M. Schiavon: None. A. Galderisi: None. K.A. Kraemer: None. C. Cobelli: None. C. Dalla Man: None. E. Cengiz: Advisory Panel; Self; Abvance, ADOCIA, MannKind Corporation, Novo Nordisk Inc. Speaker's Bureau; Self; Novo Nordisk Inc. Funding JDRF (to E.C.); International Society for Pediatric and Adolescent Diabetes; Patterson Foundation (to A.G.); European Medical Information Framework (to A.G.); University of Padova (to M.S., C.D.M.)

Objective: This randomized, controlled trial evaluated a monetary-based reinforcement intervention for increasing self-monitoring blood glucose (SMBG) among youth with poorly controlled type 1 diabetes. SMBG frequency was the primary outcome, A1c was the secondary outcome. Methods: After a 2-week baseline, 60 youth age 12-19 years with 7.5% but ≤13% were randomized to enhanced usual care (EUC) or Reinforcers. The Reinforcers group earned monetary rewards for SMBG and associated behaviors such as uploading glucose meters, completing pattern recognition forms, and reviewing results with clinicians. Reinforcers were withdrawn at 24 weeks. A follow-up evaluation occurred at 36 weeks. Results: Randomization produced groups that did not differ on any demographic or clinical characteristics. Participants in the Reinforcers group increased the proportion of days they completed ≥4 SMBG from 14.6% at baseline to 64.4%, 47.5%, and 37.8% at 6, 12, and 24 weeks, respectively. In contrast EUC participants declined from 22.7% at baseline to 17.5%, 10.5%, and 11.1% (ps Conclusions: Monetary-based reinforcement of adolescents with type 1 diabetes caused durable increases in SMBG. Modification of the reinforcement structure may be needed to sustain improved metabolic control in this challenging age group. Disclosure J. Wagner: None. K. Weyman: None. E.M. Tichy: None. E. Cengiz: Advisory Panel; Self; Abvance, ADOCIA, MannKind Corporation, Novo Nordisk Inc. Speaker's Bureau; Self; Novo Nordisk Inc. K. Zajac: None. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceutical Company Limited.

Background: In type 1 diabetes (T1D), closed-loop systems provide excellent overnight fasting blood glucose control by adjusting the insulin infusion rate based on corresponding changes in sensor glucose levels. In patients on multiple daily insulin (MDI) injections, such control in overnight glucose levels has not been possible due to the inability to alter the absorption rate of long-acting insulin after injection. In this study, we tested the hypothesis that increases/decreases of fasting glucose levels could be achieved by cooling/warming the skin around the injection site, which would result in lower/higher Glargine absorption rates from its subcutaneous depot. Methods: Fourteen subjects with T1D (4 females; age 39.6 ± 16.7 years, HbA1c 7.8 ± 1.1%, BMI 25.4 ± 2.8 kg/m2) on MDI therapy underwent fasting pharmacokinetic and pharmacodynamic studies that started at ~8 am and lasted 240 min on 3 separate days in random order: a control day without warming or cooling of the injection site and two experimental days, one day with injection site warming and the other with cooling. Results: Cooling the skin around the glargine injection site reduced insulin concentrations by >40% ( P < .01 versus the warming study, P = .21 versus the control study), accompanied by a 55 mg/dL increase in serum glucose ( P < .01 versus the control study). Conversely, skin warming prevented the fall in serum insulin ( P = .2 versus the control study; P < .01 versus the cooling study), resulting in a 40 mg/dL reduction in serum glucose ( P < .001 versus the cooling study, P = .11 versus the control study). Conclusions: This proof of concept study has shown that cooling and warming the skin around the injection site provides a means to decrease and increase the rate of absorption and action of insulin glargine from its subcutaneous depot.

BACKGROUND This randomized, controlled trial evaluated a monetary-based reinforcement intervention for increasing self-monitoring of blood glucose (SMBG) among youth with poorly controlled type 1 diabetes. METHODS After a 2-week baseline, 60 participants were randomized to enhanced usual care (EUC) or Reinforcers. The Reinforcers group earned monetary rewards for SMBG and associated behaviors such as uploading glucose meters. Reinforcers were withdrawn at 24 weeks. A follow-up evaluation occurred at 36 weeks. RESULTS Participants in the reinforcers group increased the proportion of days they completed ≥4 SMBG from 14.6% at baseline to 64.4%, 47.5%, and 37.8% at 6, 12, and 24 weeks, respectively. In contrast, EUC participants declined from 22.7% at baseline to 17.5%, 10.5%, and 11.1% (Ps

OBJECTIVE Closed-loop (CL) insulin delivery effectively maintains glucose overnight but struggles when challenged with meals. Use of single-day, 30-μg/meal pramlintide lowers meal excursions during CL. We sought to further elucidate the potential benefits of adjunctive agents after 3–4 weeks of outpatient dose titration. RESEARCH DESIGN AND METHODS Two CL studies were conducted: one evaluating adjunctive pramlintide and the other liraglutide. Ten subjects (age 16–23 years; A1C 7.2 ± 0.6% [55 ± 6.6 mmol/mol]) completed two 24-h sessions: one on CL alone and one on CL plus 60-μg pramlintide (CL + P), after a 3–4-week outpatient dose escalation. Eleven subjects (age 18–27 years; A1C 7.5 ± 0.9% [58 ± 9.8 mmol/mol]) were studied before and after treatment with 1.8 mg liraglutide (CL + L) after a similar 3–4-week dose escalation period. Timing and content of meals during CL were identical within experiments; meals were not announced. RESULTS Pramlintide delayed the time to peak plasma glucose (PG) excursion (CL 1.6 ± 0.5 h vs. CL + P 2.6 ± 0.9 h, P < 0.001) with concomitant blunting of peak postprandial increments in PG (P < 0.0001) and reductions in postmeal incremental PG area under the curve (AUC) (P = 0.0002). CL + L also led to reductions in PG excursions (P = 0.05) and incremental PG AUC (P = 0.004), with a 28% reduction in prandial insulin delivery. Outpatient liraglutide therapy led to a weight loss of 3.2 ± 1.8 kg, with a 26% reduction in total daily insulin dose. CONCLUSIONS Adjunctive pramlintide and liraglutide treatment mitigated postprandial hyperglycemia during CL control; liraglutide demonstrated the additional benefit of weight loss in an insulin-sparing manner. Further investigations of these and other adjunctive agents in long-term outpatient CL studies are needed.

Substance use can have adverse effects on many health conditions, yet little is known about the prevalence of use in individuals with type 1 diabetes (T1D). We evaluated the frequency of use and problem use of psychoactive substances in adults with T1D. Standardized instruments for assessing tobacco, alcohol, and psychoactive substance use were emailed to 4311 adult participants at 69 T1D Exchange Clinic Registry centers. A total of 936 respondents (61% female, 90% non-hispanic white, age 38±16 years) completed the survey. In the sample, tobacco use was reported by 166 (18%) participants in the past year and 51 (5%) daily (Figure 1). Past year alcohol use was reported by 742 (79%) participants, past month use by 592 (63%) and daily or near daily use by 87 (9%). Use of any other psychoactive substance in the past year was reported by 228 respondents (24%), with marijuana being the most commonly used substance (Figure 2). Past year problem use of these substances was noted in 31 (3%) respondents. Adults with T1D in the U.S. use substances at rates that meet or exceed that of the general population; problematic use occurs at rates similar to the general population. These data delineate the need to inquire about regular, intermittent and problematic use of nicotine, alcohol, and other substances in individuals with T1D. A better understanding of the impact of moderate and occasional use of substances on T1D management and clinical outcomes is also needed. Disclosure N. Petry: None. N.C. Foster: None. E. Cengiz: Advisory Panel; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Novo Nordisk Inc.. Advisory Panel; Self; MannKind Corporation, ADOCIA, Arecor. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceuticals U.S.A., Inc.. J. Wagner: None. S. Polsky: Research Support; Self; Dexcom, Inc.. Other Relationship; Self; T1D Exchange. Research Support; Self; National Institute of Diabetes and Digestive and Kidney Diseases.

The T1D Exchange (T1DX) Clinic Registry assessed CVD risk factors in a large, contemporary cohort of adults with T1D living in the U.S. To understand the incidence and CVD risk factors, we evaluated the association of CVD risk factors and 5-year risk of CVD in adults ≥18 years of age, without CVD at enrollment in the T1DX. CVD was defined as a composite outcome of clinic reported fatal or non-fatal events of ischemic heart disease and heart failure. Associations between diabetic specific and traditional CVD risk factors and incident CVD were examined by logistic regression adjusting for potential confounders. The study included 4,463 participants (55% female, 91% non-Hispanic white, mean age 41 years, T1D duration 21 years). At enrollment, mean HbA1c was 7.7%, 43% used statin, and 45% used blood pressure medication. Incident CVD was reported by 419 (9.4%) participants during the 5-year follow-up. Age, diabetes duration, elevated BMI, triglycerides (TG), and diabetic nephropathy were associated with greater odds of CVD [Table]. Sex, mean HbA1c, HbA1c variability, pulse pressure, and hypertension were not associated with CVD. Markers of insulin resistance (BMI and TG) and diabetic nephropathy are important risk factors for CVD. Longer follow-up is required to assess the impact of other CVD risk factors on CVD in adults with T1D. Disclosure S.N. Mehta: None. M. Wu: None. N.C. Foster: None. R. Pop-Busui: Research Support; Self; AstraZeneca. M. Katz: None. J.P. Crandall: None. F. Bacha: Research Support; Self; AstraZeneca, JAEB Center For Health Research, National Institutes of Health, Pediatric Diabetes Consortium. K.J. Nadeau: None. I. Libman: Consultant; Self; Novo Nordisk A/S. P. Hiers: None. K.R. Mizokami-Stout: None. L. DiMeglio: Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., Medtronic, Sanofi, Caladrius Biosciences, Inc., Janssen Research & Development, Xeris Pharmaceuticals, Inc., Sanofi. J. Sherr: Consultant; Self; Medtronic MiniMed, Inc.. Advisory Panel; Self; Insulet Corporation, Eli Lilly and Company, Bigfoot Biomedical. R.E. Pratley: Other Relationship; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Inc., GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., Ligand Pharmaceuticals, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Pfizer Inc., Sanofi-Aventis, Takeda Development Center Americas, Inc.. S. Agarwal: None. J.K. Snell-Bergeon: Stock/Shareholder; Self; Abbott. Research Support; Self; Roche Diagnostics Corporation. E. Cengiz: Advisory Panel; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Novo Nordisk Inc.. Advisory Panel; Self; MannKind Corporation, ADOCIA, Arecor. S. Polsky: Research Support; Self; Dexcom, Inc.. Other Relationship; Self; T1D Exchange. Research Support; Self; National Institute of Diabetes and Digestive and Kidney Diseases. V.N. Shah: None.

Background: Our research study group recently evaluated a PLGS system embedded on the Tandem t:slim X2 with Basal-IQ insulin pump. The system was designed to work "in the background" without alarms when suspending and restarting insulin delivery. System usability and effectiveness in decreasing hypoglycemia are both critical to the success of a PLGS device. Methods: The PROLOG study was a randomized crossover trial conducted at 4 U.S. sites. Participants with type 1 diabetes (age ≥6 years, n=102) previously treated with MDI or CSII (with and without CGM) were randomized to the order of treatment: PLGS during one 3-week period and sensor-augmented pump (SAP) during the alternate 3-week period. We recently reported the primary outcome of the PROLOG study-a reduction of mean sensor time Results: The overall SUS score for at-home use of the Basal-IQ system was 88.8 (out of 100). A score above 68 is considered above average, and 88 is exceptional. Subgroup analyses revealed no differences related to age, baseline glycemic control or baseline diabetes therapy (MDI, CGM or pump use, Table 1). Conclusions: The t:slim X2 with Basal-IQ was safe, effective, and easy for participants to use, regardless of previous experience with diabetes technology. Disclosure J.E. Pinsker: Research Support; Self; Insulet Corporation, Dexcom, Inc., Tandem Diabetes Care, Inc.. Z. Li: None. B.A. Buckingham: Advisory Panel; Self; Novo Nordisk Inc., ConvaTec Inc.. Research Support; Self; Medtronic, Insulet Corporation, Dexcom, Inc., Tandem Diabetes Care, Inc.. Consultant; Self; Tandem Diabetes Care, Inc., Becton, Dickinson and Company. G.P. Forlenza: Advisory Panel; Self; Dexcom, Inc.. Research Support; Self; Medtronic, Tandem Diabetes Care, Inc., Insulet Corporation, Dexcom, Inc., Novo Nordisk Inc., Bigfoot Biomedical. E. Cengiz: Advisory Panel; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Novo Nordisk Inc.. Advisory Panel; Self; MannKind Corporation, ADOCIA, Arecor. M. Church: None. L. Ekhlaspour: None. R. Wadwa: Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; MannKind Corporation, Dexcom, Inc., Xeris Pharmaceuticals, Inc., Bigfoot Biomedical. S.A. Weinzimer: Speaker's Bureau; Self; Medtronic MiniMed, Inc., Insulet Corporation. Consultant; Self; Sanofi. Stock/Shareholder; Self; InsuLine Medical Ltd.. C.C. Andre: None. T. Marcal: None. E. Jost: Other Relationship; Self; Medtronic MiniMed, Inc.. L.R. Carria: None. W.J. Woodall: None. B. Dokken: Employee; Self; Tandem Diabetes Care, Inc. V. Swanson: Employee; Self; Tandem Diabetes Care, Inc. J.W. Lum: Other Relationship; Self; Bigfoot Biomedical, Tandem Diabetes Care, Inc., Eli Lilly and Company, Ascensia Diabetes Care. C. Kollman: Research Support; Self; JDRF, Bigfoot Biomedical, Dexcom, Inc., Tandem Diabetes Care, Inc., Medtronic MiniMed, Inc., Helmsley Charitable Trust. R.W. Beck: Consultant; Self; Eli Lilly and Company. Research Support; Self; Abbott. Consultant; Self; Bigfoot Biomedical. Research Support; Self; Dexcom, Inc.. Consultant; Self; Insulet Corporation. Research Support; Self; Roche Diabetes Care Health and Digital Solutions. Consultant; Self; Merck & Co., Inc., Xeris Pharmaceuticals, Inc..

OBJECTIVE This study evaluated a new insulin delivery system designed to reduce insulin delivery when trends in continuous glucose monitoring (CGM) glucose concentrations predict future hypoglycemia. RESEARCH DESIGN AND METHODS Individuals with type 1 diabetes (n = 103, age 6–72 years, mean HbA1c 7.3% [56 mmol/mol]) participated in a 6-week randomized crossover trial to evaluate the efficacy and safety of a Tandem Diabetes Care t:slim X2 pump with Basal-IQ integrated with a Dexcom G5 sensor and a predictive low-glucose suspend algorithm (PLGS) compared with sensor-augmented pump (SAP) therapy. The primary outcome was CGM-measured time RESULTS Both study periods were completed by 99% of participants; median CGM usage exceeded 90% in both arms. Median time 180 mg/dL (32% vs. 33%, P = 0.12). One severe hypoglycemic event occurred in the SAP arm and none in the PLGS arm. Mean pump suspension time was 104 min/day. CONCLUSIONS The Tandem Diabetes Care Basal-IQ PLGS system significantly reduced hypoglycemia without rebound hyperglycemia, indicating that the system can benefit adults and youth with type 1 diabetes in improving glycemic control.

Objective To describe vitamin D levels and prevalence of vitamin D sufficiency, insufficiency and deficiency in a large, ethnically/racially diverse population of youth with type 1 diabetes (T1D) and type 2 diabetes (T2D) in comparison to national data and examine the associations between clinical/demographic factors and vitamin D levels. Methods 25-hydroxy vitamin D (25OHD) levels were measured in 215 youth with T1D and 326 youth with T2D enrolled in the Pediatric Diabetes Consortium (PDC). These levels were compared with those of youth of the same age without diabetes from the 2005–2006 NHANES Survey. Results Vitamin D deficiency (

This study examines the trends in reference evapotranspiration (ETo) in Turkey by analysing data from 77 weather stations for a 32-year period (1975–2006). ETo values were calculated using the Penman–Monteith method using air temperature, wind speed, relative humidity, and sunshine hours data. Trends in annual and monthly ETo were determined using the Mann–Kendall trend test with the trend-free prewhitening procedure. The magnitude of trends was estimated by calculating the Sen's slope. The collective or field significance of the trends was evaluated using Walker test. The possible causes of changes in ETo were discussed by analysing the trends in air temperature, wind speed, relative humidity, and solar radiation data collected at the same stations. The implications of ETo trends for crop water requirements were evaluated. The analyses showed that the majority of stations (88%) in Turkey had annual ETo between 750 and 1200 mm during the 32-year period and ETo decreased gradually from south to north. From 1975 to 2006, 58% of stations had upward trends in annual ETo. Upward trends were statistically significant at the 0.05 level for 32% of stations. The rates of changes in annual ETo were on average 1.20 mm year−2. The trends detected in monthly ETo were mostly upward with an average magnitude between −0.01 and 0.14 mm month year−1. Trends detected at the annual timescale and for the majority of the months provided the field significance at the 0.05 level. Analysis of other climatic data showed that upward trends in air temperatures, downward trends in wind speeds, and downward trends in relative humidity were widespread over Turkey for the same time period. Changes in these three parameters could explain the majority of the changes in ETo rates. The ETo changes affect crop water requirements and increase the demand for irrigation.

Objective Type 2 diabetes (T2D) in youth is recognized as a pediatric disease, but few reports describe the characteristics during diagnosis. We describe the clinical presentation of 503 youth with T2D. Methods The Pediatric Diabetes Consortium (PDC) T2D Clinic Registry enrolled T2D participants from eight pediatric diabetes centers in the USA. Clinical and laboratory characteristics at the time of diagnosis were analyzed. Results In total 67% presented with symptoms of diabetes and confirming laboratory data, but 33% were identified by testing at risk children, 11% presented with diabetic ketoacidosis (DKA), and 2% with hyperglycemic hyperosmolar state (HHS). The mean age was 13.1 ± 2.3 yr (range, 4.6–19.8 yr) with 38 (8%) less than 10 yr of age at diagnosis. The majority was female (65%), Hispanic (54%) and had a family history of T2D (92%). The median body mass index (BMI) z-score was 2.3 (interquartile range 2.0–2.6). Fewer than half (46%) lived with both parents, only 30% had parents with education beyond high school, and 43% lived in a household with an income of

Objective To describe C-peptide levels in a large cohort of children with type 2 diabetes T2D and examine associations with demographic and clinical factors. Methods The Pediatric Diabetes Consortium (PDC) T2D Registry has collected clinical and biologic data from youth with T2D cared for at eight US Pediatric Diabetes Centers. In this study, we assessed C-peptide levels in 331 youth with T2D (mean age, 16.1 ± 2.5 yr; median T2D duration, 2.4 yr). Results Median (interquartile range) for 90 fasted C-peptide measurements was 3.5 ng/mL (2.3–4.8 ng/mL) [1.2 nmol/L (0.8–1.6 nmol/L)] and for 241 random non-fasted C-peptide measurements were 4.2 ng/mL (2.6–7.0 ng/mL) [1.4 nmol/L (0.9–2.3 nmol/L)]. C-peptide levels were lower with insulin therapy (p

CONTEXT: Postprandial hyperglycemia remains a challenge in type 1 diabetes (T1D) due, in part, to dysregulated increases in plasma glucagon levels after meals. OBJECTIVE: This study was undertaken to examine whether 3 to 4 weeks of therapy with pramlintide or liraglutide might help to blunt postprandial hyperglycemia in T1D by suppressing plasma glucagon responses to mixed-meal feedings. DESIGN: Two parallel studies were conducted in which participants underwent mixed-meal tolerance tests (MMTTs) without premeal bolus insulin administration before and after 3 to 4 weeks of treatment with either pramlintide (8 participants aged 20 ± 3 years, hemoglobin A(1c) 6.9 ± 0.5%) or liraglutide (10 participants aged 22 ± 3 years, hemoglobin A(1c) 7.6 ± 0.9%). RESULTS: Compared with pretreatment responses to the MMTT, treatment with pramlintide reduced the peak increment in glucagon from 32 ± 16 to 23 ± 12 pg/mL (P < 0.02). In addition, the incremental area under the plasma glucagon curve from 0 to 120 minutes dropped from 1988 ± 590 to 737 ± 577 pg/mL/min (P < 0.001), which was accompanied by a similar reduction in the meal-stimulated increase in the plasma glucose curve from 11,963 ± 1424 mg/dL/min pretreatment vs 2493 ± 1854 mg/dL/min after treatment (P < 0.01). In contrast, treatment with liraglutide had no effect on plasma glucagon and glucose responses during the MMTT. CONCLUSIONS: Adjunctive treatment with pramlintide may provide an effective means to blunt postmeal hyperglycemia in T1D by suppressing dysregulated plasma glucagon responses. In contrast, plasma glucose and glucagon responses were unchanged after 3 to 4 weeks of treatment with liraglutide.

Decades after the invention of insulin pump, diabetes management has encountered a technology revolution with the introduction of continuous glucose monitoring, sensor-augmented insulin pump therapy and closed-loop/artificial pancreas systems. In this review, we discuss the significance of the 2016 Endocrine Society Guidelines for insulin pump therapy and continuous glucose monitoring and summarize findings from relevant diabetes technology studies that were conducted after the publication of the 2016 Endocrine Society Guidelines. The 2016 Endocrine Society Guidelines have been a great resource for clinicians managing diabetes in this new era of diabetes technology. There is good body of evidence indicating that using diabetes technology systems safely tightens glycemic control while managing both type 1 and type 2 diabetes. The first-generation diabetes technology systems will evolve as we gain more experience and collaboratively work to improve them with an ultimate goal of keeping people with diabetes complication and burden-free until the cure for diabetes becomes a reality.

In artificial pancreas (AP) systems, continuous glucose monitoring (CGM) data are used to compute the required insulin amount to be infused with an insulin pump to regulate blood glucose concentration of people with type 1 diabetes (T1D). Real-time plasma insulin concentration (PIC) estimations will facilitate calculation of more realistic insulin infusion rates and prevent hypoglycemia caused by overdosing of insulin. Our objective is to develop a method to estimate PIC in real time from CGM and infused insulin data in real-time by using a mathematical model.