ATTD 2021 Invited Speaker Abstracts.

Abstract

Diabetes Technology & TherapeuticsVol. 23, No. S2 The Official Journal of ATTD Advanced Technologies & Treatments for Diabetes Conference 2–5 June, 2021–VIRTUALFree AccessATTD 2021 Invited Speaker AbstractsPublished Online:31 May 2021https://doi.org/10.1089/dia.2021.2525.abstractsAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail 004/#828 PLENARY SESSION 01: IMPACT OF COVID‐19 PANDEMIC ON DIABETESHOW TO MANAGE TYPE 2 DIABETES AND OBESITY DURING THE COVID‐19 PANDEMICA. CerielloIRCCS MultiMedica, Diabetes Research, Milan, ItalyPeople with diabetes compared with people without exhibit worse prognosis if affected by COVID‐19 induced by the SARS‐CoV2, particularly when compromising metabolic control and concomitant cardiovascular disorders are present. This perspective article seeks to explore newly occurring cardio‐renal‐pulmonary organ damage induced or aggravated by the disease process of COVID‐19 and its implications for the cardiovascular risk management of people with diabetes, especially also taking into account potential interactions with mechanisms of cellular intrusion of SARS‐CoV2. Severe infection with SARS‐CoV2 can precipitate myocardial infarction, myocarditis, heart failure, and arrhythmias as well as an acute‐respiratory‐distress‐syndrome and renal failure. They may evolve along with multi‐organ failure due to directly SARS‐CoV2 infected endothelial cells and resulting endotheliitis. This complex pathology may bear challenges for the use of most diabetes medications in terms of emerging contraindications that need close monitoring of all people with diabetes diagnosed with SARS‐CoV2‐infection. Whenever possible, continuous‐glucose‐monitoring should be implemented to ensure stable metabolic compensation. Patients in intensive‐care‐unit requiring therapy for glycemic control should solely be handled by intravenous insulin using exact dosing with a perfusion device. Although not only ACE‐inhibitors and angiotensin‐2‐receptor‐blockers, but also SGLT2‐inhibitors, GLP1‐receptor‐agonists, pioglitazone, and probably insulin seem to increase the number of ACE2‐receptors on the cells utilized by SARS‐CoV2 for penetration, no evidence presently exists that this might be harmful in terms of acquiring or worsening COVID‐19 with unequivocal proof urgently awaited. In conclusion, COVID‐19 and related cardio‐renal‐pulmonary damage can profoundly affect cardiovascular risk management of people with diabetes.010 / #842 PARALLEL SESSION 01: FUTURE OF DIGITAL CLINICS FOR THE TREATMENT OF DIABETESDEBATE: IN THE ERA OF REMOTE VISITS – DO WE STILL NEED A1C MEASURES ‐ PROF. CameronRoyal Children's Hospital, Department Of Endocrinology And Diabetes, Melbourne, AustraliaDebate: In the Era of Remote Visits, Do We Still Need A1c Measures? Fergus Cameron and Stuart A Weinzimer Hemoglobin A1c has been the gold standard metric to assess glycemic control in people with type 1 diabetes since the Diabetes Control and Complications Trial, and subsequently has been adopted to other forms of diabetes, as well as for diagnosing diabetes. However, the rapidly increasing use of continuous glucose monitoring (CGM) has enabled clinicians to assess metrics of glycemia more directly; and with the growing popularity of telemedicine, driven most urgently in the past year by the COVID pandemic, the role of A1c as a clinically important measure has been questioned. In this debate, we will discuss the relative merits and limitations of A1c and CGM, and whether continuing use of A1c measures in clinical care is still warranted.011 / #843 PARALLEL SESSION 01: FUTURE OF DIGITAL CLINICS FOR THE TREATMENT OF DIABETESDEBATE: IN THE ERA OF REMOTE VISITS – DO WE STILL NEED A1C MEASURES ‐ CONS. WeinzimerYale University, Pediatrics, New Haven, United States of AmericaHemoglobin A1c has been the gold standard metric to assess glycemic control in people with type 1 diabetes since the Diabetes Control and Complications Trial and has subsequently been adopted to other forms of diabetes, as well as for the diagnosis of diabetes. However, the rapidly increasing use of continuous glucose monitoring (CGM) has enabled clinicians to assess metrics of glycemia more directly; and with the growing popularity of telemedicine, driven most urgently in the past year by the COVID pandemic, the role of A1c as a clinically important measure has been questioned. In this brief debate, we will discuss the relative merits and limitations of A1c and CGM, and whether continuing use of A1c measurement in clinical care is still warranted.014 / #846 PARALLEL SESSION 02: CLOSED‐LOOPARTIFICIAL PANCREAS DEVICE UPGRADES—BEST PRACTICES FOR ONBOARDING AND FOLLOW‐UPL. MesserUniversity of Colorado, Barbara Davis Center, Aurora, United States of AmericaAn increasing number of Automated Insulin Delivery (AID) devices are available for commercial use for persons with diabetes. Typically, industry trainers are responsible for onboarding users to the new technology, and there is no universal clinical follow‐up in the first few months of use. Ideal onboarding to AID systems should include a) thorough pre‐AID education on general diabetes self‐management, carbohydrate counting, insulin pump and continuous glucose monitoring basics, and expectations for AID systems b) Actual device training via face‐to‐face or teleconference based training, and c) clinical follow‐up with diabetes professionals in the first 2‐6 weeks of use for device optimization, troubleshooting, and reinforcing expectations. Clinical centers should consider ways to implement pre‐AID education and post‐AID clinical follow up for new AID device users to mitigate the risk of potential device discontinuation or unsafe practices.PARALLEL SESSION 03: PREGNANCY AND TECHNOLOGYPREGNANCY OUTCOMES OF 17,375 WOMEN WITH DIABETES: NATIONAL POPULATION‐BASED COHORT STUDYLIVE QA: PARALLEL SESSION 03: PREGNANCY AND TECHNOLOGYH. Murphy1, C. Howgate2, J. O'Keefe2, J. Myers3, M. Morgan4, M. Coleman5, M. Jolly6, J. Valabhji7, E. Scott8, P. Knighton2, B. Young9, N. Lewis‐Barned101University of East Anglia, Med, Norwich, United Kingdom, 2Clinical Audit and Registries Management Service (CARMS), Npid, Leeds, United Kingdom, 3St Mary's Hospital, Manchester, UK, Obstetrics, Manchester, United Kingdom, 4Swansea Bay University Health Board, Obstetrics, Swansea, United Kingdom, 5University Hospital Southampton NHS Foundation Trust, Obstetrics, Southampton, United Kingdom, 6NHS England and NHS Improvement,, Obstetrics, London, United Kingdom, 7NHS England and NHS Improvement,, Diabetes, London, United Kingdom, 8Leeds Institute of Cardiovascular and Metabolic Medicine, Department Of Population And Clinical Sciences, Leeds, United Kingdom, 9NHS Digital, Nda, Leeds, United Kingdom, 10Northumbria Healthcare NHS Foundation Trust, Diabetes, Northumberland, United KingdomBackground: Our aim was to compare risk factors associated with adverse pregnancy outcomes in women with type 1 and type 2 diabetes. Methods: We included 17,375 pregnancies in 15,290 women with diabetes in a population‐based cohort study across 172 maternity clinics. Obstetric complications (preterm delivery, large birthweight) and adverse pregnancy outcomes (congenital anomaly, stillbirth, neonatal death) were obtained for pregnancies during 2014‐2018. We assessed associations between modifiable (glycaemia, obesity, clinic) and non‐modifiable risk factors (age, deprivation, ethnicity) with pregnancy outcomes. Results: Of 17,375 pregnancies, 8,690 (50.0%) were in women with type 1 and 8,685 (50.0%) in women with type 2 diabetes. The rates of preterm delivery (42.5% type 1, 23.4% type 2), and large birthweight (52.2% type 1, 26.2% type 2) were higher in type 1 diabetes (p < 0.001). The prevalence of congenital anomaly (44.8/1000 type 1, 40.5/1000 type 2; p = 0.175), and stillbirth (10.4/1000 type 1, 13.5/1000 type 2; p = 0.072) did not differ but neonatal death rates (7.4/1000 type 1, 11.2/1000 type 2; p = 0.013) were higher in type 2 diabetes. Independent risk factors for perinatal death were third trimester HbA1c > 48mmol/mol (OR 3.06, 95% CI 2.16 to 4.33), living in the highest deprivation quintile (OR 2.29 95% CI 1.16 to 4.52) and having type 2 diabetes (OR 1.65 95% CI 1.18 to 2.31). Variations in glycaemia and large birthweight were associated with maternal characteristics (diabetes duration, deprivation, BMI) without substantial differences between clinics. Interpretation: No clinics were achieving appreciably better outcomes, suggesting that healthcare system changes are needed across all clinics.026 / #858 PARALLEL SESSION 04: SPORT AND DIABETESDECISION SUPPORT AND CLOSED LOOP CONTROL DURING EXERCISE: NEW FINDINGS FROM CLINICAL STUDIES AND LARGER DATA SETSP. JacobsOregon Health & Science University, Biomedical Engineering, Portland, United States of AmericaExercise remains a challenge for people with type 1 diabetes. Hypoglycemia during and following exercise is a common problem. People with type 1 diabetes oftentimes have difficulty maintaining normal glucose levels during and following exercise. Automated hormone delivery and decision support systems can provide assistance in adjusting dosing in response to various types of exercise to help people with type 1 diabetes avoid hypoglycemia and maintain glucose within a target range. These automated systems and decision support systems rely on expert knowledge and predictive models that can determine adjustments to hormone dosing, food consumption, or behavior interventions. We show how we used data collected under both free‐living conditions and data collected from highly controlled glucose clamp physiology studies during and following different types of exercise to build glucose forecasting models. We show how aerobic and resistance exercise models of metabolism are designed using ordinary differential equations (ODE) and Markov Chain Monte Carlo system identification methods with data collected from two studies of people with type 1 diabetes undergoing three glucose clamp studies under three different insulin loading conditions (1x, 1.5x, and 3x basal infusion rate) and at moderate and intense exercise. And we show how data‐driven models are trained using larger data sets and machine learning to predict the impact of free‐living exercise on glucose changes during and following exercise, including nocturnal hypoglycemia on nights following exercise. We provide demonstrations of how the ODE and machine learning algorithms are integrated into automated hormone delivery and decision support systems.027 / #859 PARALLEL SESSION 04: SPORT AND DIABETESCLOSED‐LOOP AND PHYSICAL ACTIVITY IN YOUTH WITH TYPE 1 DIABETESK. DovcDepartment for Paediatric Endocrinology, Diabetes and Metabolic Diseases,UMC ‐ University Children's Hospital and University of Ljubljana Faculty of Medicine, Ljubljana, SloveniaClosed‐loop glycemic control, characterized by glucose‐responsive automated insulin, is now a part of regular clinical reality for many individuals living with type 1 diabetes. The management of type 1 diabetes during exercise is complex. At the same time, dosing insulin adequately either in advance of activity or in real‐time can generate positive outcomes and reduce the likelihood of hypoglycemia.The performance of closed‐loop glycemic control in individuals with type 1 diabetes during and after the physical activity has been extensively evaluated, especially in the controlled environment, while there is less data regarding unsupervised physical activity in home settings. Closed‐loop therapy was in the past challenged with different exercise protocols of different durations and intensity, in heterogeneous age groups, with additional devices to detect physical activity, such as activity and heart rate monitoring, and adding glucagon to prevent hypoglycemia.In this presentation, we will present contemporary data on closed‐loop glycemic control challenged by physical activity in children, adolescents and young adults with type 1 diabetes.029 / #861 PLENARY SESSION 02: ADVANCES IN CLOSED‐LOOP SYSTEMS – LESSONS LEARNT FROM CLINICAL STUDIESPERFORMANCE OF STUDIES ON ADVANCE HYBRID CLOSED‐LOOP 780GA.L. CarlsonInternational Diabetes Center. Minneapolis, USAThere have been major advancements in automated insulin delivery systems in the past several years, with hybrid closed‐loop systems offering patients new means to manage their diabetes. Such systems rely on algorithms to modulate the insulin delivery, increasing or decreasing insulin delivery based upon programed target glucose, patient‐entered carbohydrates and correction boluses that can be user or device initiated. The MiniMedTM Advanced Hybrid Closed Loop (AHCL) system, or MiniMedTM 780G, is a new iteration of pump therapy that has a target set point of either 100 mg/dL (5.6 mmol/L) or 120 mg/dL (6.7 mmol/L), along with automated corrections that can deliver boluses every 5 minutes as needed. The MiniMedTM 780G system has been studied for safety and efficacy in recent trials. The largest trial was a multi‐site pivotal trial of 39 adolescents (age 14‐21 years) and 118 adults (>22 years) during which patients with type 1 diabetes used either the 100 mg/dL or 120 mg/dL set point for approximately 45 days, and then crossed over to the other set point for another period of about 45 days. For the overall study group, time in range (70‐180 mg/dL/ 3.9 mmol/L‐10 mmol/L) increased from 68.8% to 74.5% (p = 0.001). For those using the set point of 100 mg/dL and an active insulin time of 2 hours, time in range increased to 78.8%. Auto Mode was in use for 95% of the time for both set points during the study. Of the total bolus insulin delivered during the study, approximately 22% of this was administered by the auto‐correction function. Time in hypoglycemia was reduced in both adolescents and adults, and there were no DKA or severe hypoglycemia events. Additional studies have compared the MiniMedTM 780G system against both the first commercially available hybrid closed‐loop system in the United States, the MiniMedTM 670G, and to a system with predictive low glucose threshold suspend MiniMedTM 640G. Both studies concluded the AHCL system provided improved glycemic control without increasing time in hypoglycemia. The MiniMedTM 780G has consistently demonstrated improved time in range without increasing time below range, and has been safe in the trials to date. The system also seemed to have improved user experience with patient‐selected targets and fewer Auto Mode exits.030 / #862 PLENARY SESSION 02: ADVANCES IN CLOSED‐LOOP SYSTEMS – LESSONS LEARNT FROM CLINICAL STUDIESPIVOTAL TRIAL AND REAL‐LIFE DATA OF A CLOSED‐LOOP CONTROL (CLC) SYSTEM ‐ CONTROL IQB. KovatchevUniversity of Virginia, Center For Diabetes Technology, Charlottesville, United States of AmericaIn 2018‐2020, two randomized controlled pivotal trials tested a new CLC system ‐ Control‐IQ® from Tandem Diabetes Care ‐ based on an algorithm developed at the University of Virginia. Both studies were part of the International Diabetes Closed‐Loop (iDCL) Trial sponsored by NIH/NIDDK. NCT03563313 randomized 168 participants ages 14 years or older to Control‐IQ vs. sensor‐augmented pump (SAP) and met all primary and secondary outcomes. The time in range (TIR, 70‐180mg/dL) increased in the Control‐IQ group by 11%, compared to SAP, and the time below range (TBR) was reduced by 0.9%, without severe hypoglycemia. NCT03844789 randomized 101 participants ages 6‐13 and achieved similar outcomes: Control‐IQ compared to SAP resulted in 11% increase in TIR, 0.4% reduction in TBR without severe hypoglycemia, and reduction of HbA1c by 0.4%. Two papers in the New England Journal of Medicine presented the complete results, leading to FDA clearance of this system for use by children and adults ages 6 and up. In February 2021, one‐year real‐life data became available for 9,451 users of Control‐IQ with type 1 and type 2 diabetes. The median percent time in automated control was 94.2%; TIR increased from 63.6% at baseline 73.6% during Control‐IQ use; TBR remained consistent at approximately 1%, and the Glucose Management Indicator (GMI) was reduced from 7.2 at baseline to 6.9. We can therefore conclude that the outcomes of the two pivotal trials of this system were replicated during 1‐year real‐life use by thousands of children and adults with type 1 or type 2 diabetes.034 / #866 PARALLEL SESSION 05: ARTIFICIAL INTELLIGENCE (AI): TANGIBLE APPLICATIONS TO IMPROVE DIABETES CAREAI APPLICATIONS TO SUPPORT DECISION MANAGEMENT IN INSULIN THERAPYS. Bidet1, N. Caleca1, P. Soulé1, L. De La Brosse1, P. Calmels1, T. Camalon1, M. Rehn1, J. Place2, E. Renard31hillo, Data Science, Palaiseau, France, 2University of Montpellier, Institute Of Functional Genomics, MONTPELLIER, France, 3University of Montpellier, Endocrinology, Diabetes & Metabolism, Montpellier, FranceThe advent of big data and artificial intelligence opens new perspectives for diabetes mellitus monitoring and management. Yet, complexity and uniqueness of the human body hardly allow to define a general statistical approach able to accurately predict blood glucose variations in all patients. Our technology, MIND, is a personalized, patient‐specific blood glucose level (BGL) prediction service integrated in a diabetes management platform. For each patient, a machine Learning model is created using his/her historical data, including BGL and insulin inputs collected through wide‐spread devices and meals recorded by the patient when available. The CDDIAB study conducted in 2018 demonstrated that our prediction technology is accurate enough to allow safe therapeutical decisions ; an extension of this study conducted in 2019 showed that accurate BGL predictions drive better decision making on treatment options than patient alone. The positive outcomes of this study triggered new research cases. First, the technology is tested on another patient cohort to assess its robustness; then, the confidence in the prediction provided is studied. The second point is essential for large‐scale industrialization. Hence the study of a confidence index and an envelope curve, to provide visual insights of the accuracy and an additional security on the predictions. Based on our results, the next challenge is to predict accurate bolus doses given the historical data and the predicted BGL. Another challenge is to build a system able to detect and reconstruct meals, thus meal management would be streamlined by avoiding manual inputs.035 / #867 PARALLEL SESSION 05: ARTIFICIAL INTELLIGENCE (AI): TANGIBLE APPLICATIONS TO IMPROVE DIABETES CAREAI UPGRADES AUTOMATED INSULIN DELIVERY TOWARDS A FULLY CLOSED‐LOOPP. Herrero1, R. Armiger1, J. Daniels1, M. Reddy2, N. Oliver2, P. Georgiou11Imperial College London, Centre For Bio‐inspired Technology, Electrical And Electronic Engineering, London, United Kingdom, 2Imperial College London, Department Of Metabolism, Digestion And Reproduction, London, United KingdomThe Bio‐inspired Artificial Pancreas (BiAP) is an advanced hybrid closed‐loop insulin delivery system based on mathematical modelling of the pancreatic beta‐cell physiology. BiAP incorporates an innovative adaptive meal‐insulin bolus calculator which uses artificial intelligence to provide adaptive and individualised mealtime insulin dosing by learning from past post‐prandial glycaemic outcomes, user behaviour, and controller's functioning. The BiAP control algorithm is designed for embedded low‐power solutions. It has been implemented in a dedicated microchip‐based handheld device and, more recently, in an iPhone connected to a Dexcom G6 continuous glucose sensor, a Tandem t:slim AP insulin pump, and a dedicated remote web‐based platform. BiAP has been successfully assessed in‐clinic and an ambulatory crossover randomised controlled trial is planned to evaluate its longer‐term clinical effectiveness. In this talk, we review the latest algorithmic and software developments within BiAP, as well as the results of a realistic in‐silico head‐to‐head comparison of the BiAP controller and an open‐source do‐it‐yourself (DIY) artificial pancreas controller. Finally, the latest developments on enhancing BiAP with a machine learning‐based meal detection algorithm will be introduced.044 / #876 PARALLEL SESSION 07: DIGITAL/VIRTUAL DIABETES CLINICSMANAGING T1D‐NEW‐ONSETS AND DKA THROUGH TELEHEALTHS. GargBarbara Davis Center for Childhood Diabetes, Endocrinology, Aurora, United States of AmericaSince December of 2019, the COVID‐19 outbreak has affected more than 215 countries, translating to more than 125 million cases worldwide of COVID‐19 at the time of this writing. More than 2.8 million people have died from COVID‐19 across the globe; specifically, in the USA and South America, there have been more than 30 million cases reported, with a total of 560,000 deaths due to the virus. The exact prevalence of infection is currently unknown. However, it is commonly believed that ∼70‐80% of the population will need to be infected or vaccinated for herd immunity to be effective. Many new‐onset patients with type 1 diabetes delayed seeking medical advice during COVID‐19 because of the risk of getting infected. Many hospitals noted a higher number of patients presenting with diabetic ketoacidosis because of the delay in diagnosing type 1 diabetes. Similar concerns were noted by many of the physicians managing diabetes during pregnancy. However, in many instances, most diabetes care including patients with DKA could be effectively managed remotely by using newer technologies like CGM, insulin pumps, and hybrid closed‐loop systems. In several instances, even the pump and CGM initiation were initiated remotely with no adverse outcomes. The virtual care gave a similar or better Time in Range (TIR) for glucose levels with no increase in Time Below Range during the virtual care period, irrespective of how patients were treated (whether it was remotely or in‐person).047 / #879 PARALLEL SESSION 07: DIGITAL/VIRTUAL DIABETES CLINICSUSING TELE‐EDUCATION WITH THE ECHO MODEL TO REACH PRIMARY CARE PROVIDERS IN RURAL AREAS TO IMPROVE THE LEVEL OF CARE FOR PEOPLE WITH DIABETESM. Haller1, A. Walker1, D. Maahs2, X. Echo Diabetes Study Group11University of Florida, Pediatrics, Gainesville, United States of America, 2Stanford University School of Medicine, Pediatrics, Palo Alto, United States of AmericaIntroduction and Objectives: Project ECHO (Extension for Community Healthcare Outcomes) is a tele‐education outreach model seeking to democratize specialty knowledge, reduce disparities, and improve outcomes. Limited access to endocrinologists forces many primary care providers (PCPs) to care for patients with T1D without specialty support. Accordingly, an ECHO T1D program was developed and piloted in Florida and California. Our goal was to demonstrate feasibility and improve PCPs' abilities to manage patients with T1D. Methods: Health centers (i.e. spokes) were recruited through an innovative approach, focusing on Federally Qualified Health Centers (FQHC) and through identification of high‐need catchment areas using the Neighborhood Deprivation Index (NDI) and provider geocoding. Participating spokes received weekly tele‐education provided by the University of Florida and Stanford University hub team, real‐time support with T1D medical decision making, access to diabetes support coaches, and access to an online repository of resources. Participating PCPs completed pre/post‐tests assessing diabetes knowledge and confidence and exit surveys. Results: In Florida, 12 spoke sites enrolled with 67 clinics serving >1,000 patients with T1D. In California, 11 spoke sites enrolled with 37 clinics serving >900 patients with T1D. During the 6‐month intervention, 27 tele‐education clinics were offered and n = 70 PCPs (22 from Florida, 48 from California) from participating spoke sites completed pre/post‐test surveys assessing knowledge and confidence in diabetes care. There was statistically significant improvement in knowledge (p ≤ 0.01) and diabetes confidence (p ≤ 0.01). Conclusions: ECHO T1D's pilot demonstrated proof of concept for a T1D‐specific ECHO program and represents a viable model to reach medically underserved communities.052 / #954 PARALLEL SESSION 08: JDRFFUTURE OF ADJUNCTIVE THERAPY: SIMPLIFYING THE TREATMENT OF T1D THROUGH GK ACTIVATIONC. Valcarce1, J. Freeman1, I. Dunn1, C. Dvergsten1, K. Klein2, M. Kirkman2, J. Buse21vTv Therapeutics LLC, Translational Medicine, High Point, United States of America, 2UNC Chapel Hill, Endocrinology And Metabolism,, Chapel hill, United States of AmericaDespite more physiologic insulin analogs, continuous glucose monitoring (CGM), and continuous subcutaneous insulin infusion (CSII) therapy, a minority of patients with type 1 diabetes (T1D) achieve adequate glycemic control, and hospitalizations for diabetic ketoacidosis (DKA) and hypoglycemia are increasing globally. Several therapies for the treatment of type 2 diabetes have been evaluated as potential adjunctive treatment for T1D, however they have either had no effect in T1D or improved glycemic control, but at increased risk for life‐threatening complications like DKA or hypoglycemia. Approaches that harness the body's existing glucose regulatory machinery may be the solution to the need for adjunctive therapies providing effective glycemic control while minimizing the frequency and severity of hypoglycemia and DKA. The novel hepato‐selective glucokinase (GK) activator TTP399 was developed to preserve the physiologic relationship between GK and GK Regulatory Protein. Therefore, TTP399 only enhances GK activity during periods of hyperglycemia, thus limiting risk of hypoglycemia. The data from the SimpliciT1 study, showed the potential of TTP399 to significantly lower HbA1c, improve time in range, and reduce hypoglycemia in the absence of an increase in blood ketones. If confirmed in phase 3, adding TTP399 to insulin would represent a substantial improvement over insulin administration alone for the treatment of T1D. While a cure for T1D remains the long‐term objective, novel adjunctive therapies such as TTP399 may have an important role to play by enabling patients with T1D to achieve and maintain glycemic control without the fear and negative health effects of hypoglycemia and DKA.054 / #948 PARALLEL SESSION 09: UPDATES ON NAFLD/NASH AND DIABETESNAFLD/NASH IN TYPE 1 DIABETES: OVERRATED OR UNDERAPPRECIATEDC. De Block, J. MertensAntwerp university hospital, Endocrinology‐diabetology, Edegem, BelgiumNAFLD is the most common chronic liver disease in western countries, affecting 25‐30% of the general population and up to 65% in those with obesity and/or type 2 diabetes. Accumulation of visceral fat and insulin resistance (IR) are pivotal factors contributing to NAFLD. NAFLD is not an innocent entity as it not only may cause liver‐associated disease but also contributes to cardiovascular morbidity and mortality. More and more people with type 1 diabetes (T1D) are becoming overweight and present with features of IR, but the prevalence and impact of NAFLD in this population is still unclear. The utility of non‐invasive risk scores to screen for NAFLD in T1D is being explored. Based upon ultrasonographic criteria NAFLD is present in ∼22% in adults with T1D. MRI based data show a prevalence rate of ∼8.6%. However multiple factors affect these data, ranging from study design and referral bias to discrepancies in diagnostic accuracy. Subjects with T1D have a 7‐fold higher risk of cardiovascular disease (CVD) and CV mortality is the most prominent cause of death in T1D. IR may contribute to NAFLD and to CV complications in T1D. The independent contribution of NAFLD to CV events has to be determined in this population. Furthermore, preliminary data in T1D point towards a 2‐3x higher risk for microvascular complications in those with NAFLD. We will discuss epidemiological and diagnostic challenges of NAFLD in T1D, explore the role of IR in NAFLD and NAFLD‐associated complications, and examine the contribution of NAFLD to the presence of macro‐ and microvascular complications.055 / #949 PARALLEL SESSION 09: UPDATES ON NAFLD/NASH AND DIABETESNAFLD/NASH IN METABOLIC SYNDROME AND EARLY TYPE 2 DIABETESC. ByrneUniversity of Southampton, Nutrition & Metabolism, Southampton, United KingdomAbstract Non‐alcoholic fatty liver disease (NAFLD) is a metabolic liver disease that is strongly associated with obesity, type 2 diabetes (T2DM) and other metabolic and vascular risk factors. It is now established that NAFLD is a multisystem disease with consequences beyond the liver. NAFLD increases risk of many extra‐hepatic diseases such as cardiovascular disease (CVD), chronic kidney disease (CKD) and certain cancers. NAFLD encompasses a spectrum of lipid‐associated liver disease and in affected individuals NAFLD may progress from simple steatosis to steatohepatitis, liver fibrosis and cirrhosis. This presentation will discuss the relationships between NAFLD and type 2 diabetes that form part of a vicious cycle of spiralling and worsening metabolic disease. Not only does NAFLD increase risk of developing diabetes with insulin resistance and poor glycaemic control, but development of diabetes further increases risk of worsening liver disease, liver fibrosis and hepatocellular carcinoma. Relationships between NAFLD and cardiovascular disease and the modifying influence on cardiovascular disease of certain genotypes known to increase severity of liver disease will also be discussed.059 / #885 PARALLEL SESSION 10: COVID‐19 AND MANAGEMENT OF PATIENTS WITH DIABETES: HOW TO IMPLEMENT SCIENTIFIC KNOWLEDGE TO CLINICAL PRACTICE?GLUCOSE CONTROL DURING COVID‐19 INFECTION: TARGETS AND ACUTE DERANGEMENTSO. MustafaKing's College Hospital, Department Of Diab