Diabetes Technology & TherapeuticsVol. 25, No. S2 ATTD 2023 AbstractsFree AccessThe Official Journal of ATTD Advanced Technologies & Treatments for Diabetes Conference 22‐25 February 2023 I Berlin & OnlinePublished Online:21 Feb 2023https://doi.org/10.1089/dia.2023.2525.abstractsAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail ATTD 2023 Invited Speaker Abstracts A-1ATTD 2023 Oral Abstracts A-38ATTD 2023 E-Poster Abstracts A-85ATTD 2023 Late Breaking Abstracts A-246ATTD 2023 Read By Title A-268ATTD 2023 Abstract Author Index A-270IS001 / #171 OPENING CEREMONYOPENING LECTURE ‐ THE NEW FACE OF DIABETESC. MathieuUniversity Hospitals Leuven ‐ KU Leuven, Endocrinology, Leuven, BelgiumSince the first clinical use of insulin, more than one hundred years ago, the face of diabetes has dramatically changed. Diabetes turns out to be a ‘hydra’ with many faces, with many pathophysiological routes, with many diagnostic paths and more importantly with many therapeutic opportunities. The last 20‐30 years have seen an explosion in our knowledge and in our therapeutic approach of people living with diabetes, ranging from the introduction of novel insulins and novel technologies for measuring glucose and administering insulin, to the availability of direct organ protecting agents and disease modifying therapeutics, in particular in type 2 diabetes, but more recently also in type 1 diabetes. Research is moving on rapidly, with the promise of precision medicine for all just around the corner. In the whirlwind of progress, it will remain important to stay focused on what really matters: the quality of life of the person living with diabetes. For people to live longer and healthier lives, not only tools and techniques are important, but even more so education, motivation, accompaniment of the person living with diabetes. Making the person with diabetes a member of the multidisciplinary team will ultimately determine success. The way we communicate all the novelties and make them matter, really matter for those with diabetes, is crucial and we should never forget that there are as many faces to diabetes as there are people living with this disease. Importantly, we need to strive for an all‐inclusive strategy in diabetes care: access to care should be there for all… independent on age, gender, where you are born in the world, your socio‐economic status…. And probably that is the greatest challenge to be faced in the next years. A challenge however this community can and WILL overcome.IS002 / #172 PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYONDUSE OF CGM WITH PEOPLE WITH DIABETES TYPE 2 NOT TREATED WITH INSULINR. BergenstalInternational Diabetes Center, Healthpartners Institute, Minneapolis, United States of AmericaToday's Headlines for CGM use in T1D:“CGM‐First,” “CGM‐Standard of Care,” “CGM‐ Most significant advance in diabetes management since the discovery of insulin!”Today's Headlines for CGM use in T2D non‐insulin users:Amer. Board Internal Med‐ Choosing Wisely campaign promotes clinician‐patient conversations about avoiding unnecessary care … like this example, “Don't routinely recommend daily home glucose monitoring for patients who have Type 2 diabetes and are not using insulin.”What is the nature of the data we have today on CGM use in T2D non‐insulin users?Intriguing ‐ survey data; “they say it helps…Interesting ‐ pilot data; “I think” it might help…Innovative ‐ technology programs; CGM “seems to” help patients…Incomplete ‐ registry data, hints at populations of PwD who do better, so “maybe it does” help...Informative ‐trialsusing CGM in T2D patients on insulin resulted in glycemic improvement compared to using SMBG, but minimal insulin dose changes were made, with the concluding summary, “It must have been CGM guided lIfestlye changes.”Insistent ‐ powerful anecdotes, and voices of people with diabetes not on insulin, saying “FOR SURE IT HELPS!” “Please ‐ Listen to me.”What do we need to do for CGM to become a standard of care in T2D non‐insulin users?Determine ‐ how A1C and CGM data align/coexist in the management of diabetesDefine ‐ the outcome(s) we are trying to achieve with the help of CGMDecide ‐ if CGM data and profiles can facilitate healthy lifestyle choicesDeliberate ‐ on the role of CGM in helping the selection of high value diabetes drugsDecipher ‐ CGM user registry data by separating out and evaluating T2D non‐insulin usersDesign ‐ RCT's and robust real‐word evaluations to demonstrate the value of CMG in non‐insulin usersMy prediction is that after we Investigate and Discuss the “Is” and “Ds” above we will want to rewrite a headline for people with T2D not using insulin that reads something like: “Of Course CGM Should Be Part of Diabetes Education, Management and Support for All People Living with Diabetes.”IS003 / #173 PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYONDUSE OF CGM WITH PEOPLE WITH DIABETES TYPE 2 TREATED WITH BASAL INSULIN ONLYL. LeelarathnaManchester University NHS Foundation Trust, Manchester Diabetes Center, Manchester, United KingdomGlucose monitoring is central to safe and effective management for individuals with type 2 diabetes using insulin. It is estimated that approximately 30% of people living with type 2 diabetes in the USA are treated with insulin, with about two‐thirds using basal insulin without prandial insulin. However, only about one‐third of those individuals using insulin achieved HbA1c of less than 7.0%. Recent data also suggest there had not been much improvement in glycaemic outcomes in the USA between 2005 and 2016. Real‐time (rtCGM) and intermittently scanned continuous glucose monitoring (isCGM), by providing frequent glucose measurements, low and high glucose alerts, and glucose trend information can better inform diabetes management decisions compared with episodic self‐monitoring with fingerstick glucose. Studies have demonstrated that CGM improved glycaemic control in individuals with type 1 diabetes and with type 2 diabetes using insulin regimens with basal plus prandial insulin. However, the role of CGM in individuals with type 2 diabetes using less‐intensive insulin regimens is not well defined. Key Objectives of this lecture include: Understand the status of current glycaemic control in people with type 2 diabetes Glycaemic profiles of patients with type 2 diabetes using basal insulin HbA1c, sensor‐based and other outcomes from studies investigating the efficacy and safety of continuous glucose monitoring in people with T2DM only on basal insulin Impact of CGM on patient‐reported outcomes and quality of life Use of SGLT2 inhibitors and GLP‐1 in studies investigating CGM Mechanisms underpinning the improved outcomes Cost‐effectiveness Gaps in evidence‐based and future studiesIS004 / #174 PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYONDUSE OF CGM IN THE CYSTIC FIBROSIS POPULATIONM. PutmanMassachusetts General Hospital, Endocrinology, Boston, United States of AmericaCystic fibrosis related diabetes (CFRD) affects up to 20% of adolescents and 50% of adults with cystic fibrosis (CF). Although CFRD shares some characteristics of type 1 and type 2 diabetes, it is a unique form of diabetes caused primarily by insulin deficiency from progressive islet cell dysfunction and destruction related to underlying pancreatic exocrine disease and fibrosis. At present, the oral glucose tolerance test (OGTT) is recommended annually in adolescents and adults with CF to screen for CFRD, but screening rates have historically been suboptimal, particularly among adults. Insulin is the only recommended treatment for CFRD, but this can add substantial treatment burden to an already medically complex patient population. Continuous glucose monitoring (CGM) has been validated in people with CF, and CGM measures have been correlated with important clinical outcomes such as pulmonary function and nutritional status. Emerging data suggest that CGM may identify people at risk for the future development of CFRD and may be a promising approach for the diagnosis of CFRD, but prospective longitudinal studies investigating this as a tool for CFRD screening are greatly needed. Although data are very limited, CGM may also have a beneficial effect on the management of CFRD, including in combination with hybrid closed loop insulin pumps, offering the potential for improved glycemic control and decreased diabetes treatment burden. In summary, CGM technology may be particularly useful for addressing current challenges unique to CF, but further studies are needed to investigate the use of this tool in the screening, diagnosis, and management of CFRD.IS005 / #175 PLENARY (1) – CGM USE IN TYPE 2 DIABETES AND BEYONDTHE VISION OF THE FUTURE OF CGM IN TYPE 2 DIABETESS. GargBDC, Pediatrics And Internal Medicine, aurora, United States of AmericaWith the increasing number of people diagnosed with both type 1 and type 2 diabetes and related healthcare costs, it is imperative that we find easier ways to manage diabetes remotely and empower self‐diabetes management. Recently, the JDRF launched Type 1 Diabetes (T1D) Index where they revealed stark disparities in T1D life expectancy by countries. They also project a 66‐116% increase in the prevalence of T1D by 2040. Over the last three years, many new continuous glucose monitors (CGMs) have been approved in Western Europe and the USA. We have come a long way in the past 28 years from the first CGM being iPro, developed and launched by MiniMed (now Medtronic MiniMed, Northridge, CA, USA). Many CGM terminologies have been used, such as retrospective vs real‐time, real‐time vs isCGM, and adjunctive vs non‐adjunctive. Now most CGMs are standalone factory‐calibrated devices lasting for 10‐14 days. At the time of this writing, about 8 million people are using a CGM for their diabetes management, and this number is likely to exponentially grow to more than 15‐20 million in the next 5‐10 years. Also, in the near future, we might see another electrolyte or a ketone measurement being measured continuously through the same device (CGM + CKM, etc.). The majority of the available CGMs have a MARD of <10%; and thus, are pretty accurate for their interoperability with other devices like insulin pumps. Just like many years ago, Louis Monnier, et al. had shown that fasting blood glucose (FBG) values relate better in individuals with higher A1c and post‐prandial blood glucose (PPBG) values correlate better with individuals with lower A1c values. Similarly now, Time In Range (TIR) correlates to the contributions by FBG and PPBG. The research data has clearly documented that use of CGM improves glucose control, TIR, reduces hypo‐ and hyperglycemia, and a higher TIR reduces the risk of micro‐ and macrovascular complications. Since the insulin need in patients with T2D has continued to increase, it is likely that the use of CGM will become the standard of care for people with both T1D and T2D. It is also likely that many people with pre‐diabetes (T1D and T2D) could be detected before overt deterioration of glucose control and the risk of diabetic ketoacidosis (DKA). One wonders if glucose could be considered a vital sign just like blood pressure and heart rate.IS006 / #180 PARALLEL SESSION ‐ DECISION SUPPORT SYSTEMSINCORPORATING EXPLAINABILITY AND INTERPRETABILITY INTO AI‐ENABLED DECISION SUPPORT SYSTEMSP. Jacobs1, A. Espinoza1, R. Dodier1, G. Young1, D. Branigan2, J. Eom2, D. Chen2, C. Mosquera‐Lopez1, J. El Youssef2, J. Pinsonault1, J. Leitschuh1, L. Wilson2, J. Castle21Oregon Health & Science University, Department Of Biomedical Engineering, Portland, United States of America, 2Oregon Health & Science University, Harold Schnitzer Diabetes Health Center, SW Pavilion Loop, United States of AmericaArtificial intelligence (AI) and the sub‐field of machine learning (ML) are yielding powerful tools that are beginning to impact the field of diabetes in a number of ways. ML algorithms are being trained to forecast glucose, to predict meal and exercise events, and utilized in decision support systems to make insulin dose recommendations. Larger data sets are now becoming available because of the ubiquity of commercial sensors and these data sets are being used to train new ML algorithms. A challenge in the use of ML algorithms in healthcare, is that the algorithms are oftentimes not interpretable or explainable. An algorithm with high interpretability means that the algorithm is adept at indicating the cause and effect relationship between an input and an output of the algorithm. An algorithm with high explainability is designed in such a way that it is possible to easily understand how an algorithm works and therefore why it provides a specific forecast or recommendation. In this talk, I will discuss how we are incorporating interpretability and explainability into an AI‐driven app‐based decision support tool called DailyDose that is used to provide insulin dosing and behavioral suggestions to people with type 1 diabetes using multiple‐daily‐injection therapy. Specifically, I will review several decision support approaches: (1) a rule‐based system, (2) a k‐nearest‐neighbor approach and (3) a digital twin approach. I will discuss the strengths and weaknesses of each of these approaches as they relate to interpretability and explainability. I will show results from a recent clinical study on DailyDose that showed that glucose outcomes could be improved (6.3% increased time in range), but only when participants followed the recommendations provided by the app. A rule‐based and an AI‐based exercise decision support module within DailyDose will also be described with regards to interpretability and explainability. I will finally describe how the recommender engine in DailyDose compares with physician recommendations and how often the two agree.IS007 / #181 PARALLEL SESSION ‐ DECISION SUPPORT SYSTEMSARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMSL. Wilson1, P. Jacobs2, A. Espinoza2, R. Dodier2, G. Young3, D. Branigan3, J. Eom3, D. Chen3, C. Mosquera‐Lopez3, J. El Youssef4, J. Pinsonault2, J. Leitschuh2, J. Castle31Oregon Health & Science University, School Of Medicine, Portland, United States of America, 2Oregon Health and Sciences University, Biomedical Engineering, Portland, United States of America, 3Oregon Health and Sciences University, School Of Medicine, Portland, United States of America, 4Oregon Health & Science University, Department Of Medicine, Division Of Endocrinology, Portland, United States of AmericaDesigning an integrated, scalable decision support and coaching platform for multiple daily injection therapy Artificial Intelligence (AI) based decision support tools offer great promise to improve the care for people with type 1 diabetes who use multiple daily injections. We designed and tested in a clinical study a smartphone app decision support tool, DailyDose. This system makes insulin dose adjustment recommendations once weekly driven by an AI‐based algorithm. In the pilot clinical study, we found some participants did not accept recommendations even when clinically indicated based on glucose patterns. We conducted interviews with participants at the completion of the study which indicated involvement of clinical diabetes care and education specialists and behavioral health experts may improve uptake and interactions with the decision support system. However, this type of care is costly and resource intensive. In order to ensure scalability of the decision support system, we have designed a follow‐up study whereby those participants not achieving glycemic goals with decision support app use alone would receive diabetes education and behavioral health support tailored to their needs. This approach may allow for greater scalability and effectiveness. This presentation will include discussion of (1) qualitative results from post‐study interviews with participants, (2) incorporation of these findings into the decision support‐app to improve usability and explainability, (3) development of a web portal for interaction of diabetes educators, behavioral health and diabetes providers with app users. These system updates are important to ensure people with type 1 diabetes are able to benefit fully from AI‐based decision support systems. Lastly, the design of the next phase multi‐site clinical study with DailyDose will be presented.IS008 / #182 PARALLEL SESSION ‐ DECISION SUPPORT SYSTEMSPATIENT REPORTED OUTCOMES IN CLOSED LOOP STUDIESK. HoodStanford University School of Medicine, Pediatric Endocrinology & Psychiatry Behavioral Sciences, Stanford, United States of AmericaClosed loop (CL) automated insulin delivery leads to glycemic improvements yet there are mixed findings with regard to patient reported outcomes (PROs). PROs refer to the subjective experience of the person using CL and often include topics such as quality of life, satisfaction, and diabetes distress. Common methods for obtaining PROs are validated surveys and structured interviews or focus groups. This presentation covers the results from CL studies and real‐world publications with regard to PROs, why there are mixed findings (e.g., some studies show PROs improvements while others show no change), and how we can improve methods for PROs data collection in clinics and future studies.IS009 / #186 PARALLEL SESSION ‐ TECHNOLOGY USE IN PREGNANCYAUTOMATED INSULIN DELIVERY IN TYPE 1 DIABETES PREGNANCY ‐ ARE WE NEARLY THERE YET?H. Murphy1,2,31University of East Anglia, Department Of Medicine, Norfolk, United Kingdom, 2Cambridge University Hospitals NHS Foundation Trust, Norwich, Cambridge, United Kingdom, 3Norfolk and Norwich University Hospitals NHS Foundation Trust, Diabetes And Antenatal Care, Norwich, United KingdomDespite increasing use of continuous glucose monitoring (CGM) and insulin pumps, the pregnancy glucose targets of >70% time in range (TIR 3.5‐7.8 mmol/L, 63‐140mg/dl) ) and mean glucose 6.0‐6.5mmol/L (108‐117mg/dl) is often only reached in the third trimester, which is too late for optimal neonatal outcomes. Outside pregnancy, hybrid closed‐loop (HCL) insulin delivery systems are associated with improved glucose levels and early data for T1D pregancy suggest feasibility of use at home and in hospital settings, including after corticosteroids, and during labour/birth. The Cambridge adaptive MPC, is the first interoperable HCL android app (CamAPS® Fx) compatible with several insulin pumps (mylife YpsoPump®, DANA Diabecare RS® DANA‐i®) and with Dexcom G6 sensors (G6, G7). It offers customizable personal glucose targets which can be tightened to keep pace with gestational changes in insulin pharmacokinetics and variations in insulin sensitivity and/or resistance. Randomised controlled trials are underway evaluating this and the Tandem t:slim X2®, and Medtronic 780G® HCL systems. Case reports on other systems including Diabeloop® (Dexcom G6 with Kaleido® insulin pump) and do‐it‐yourself artificial pancreas systems (DIY‐APS) are available. This session will summarize the progress of ongoing HCL studies and translation into antenatal care.IS010 / #189 PARALLEL SESSION ‐ EXERCISE IN DIABETESEXERCISE WITH AUTOMATIC INSULIN DELIVERYK. NørgaardSteno Diabetes Center Copenhagen, Clinical Science, Diabetes Technology Research, Herlev, DenmarkPhysical exercise with type 1 diabetes is a challenge, regardless of whether the insulin is given as multiple daily insulin injections or insulin pumps. Current consensus guidelines for avoiding hypo‐ and hyperglycemia during and after exercise are available. Recent advances in diabetes technology have led to the development of automated insulin delivery (AID) systems for glycemic management of people with type 1 diabetes. However, little is known about their safety and efficacy around exercise, which can cause significant and often worrisome disruptions in acute glycemic control. Although consensus recommendations exist for exercise management with AID, the guidance is based on first‐generation AID systems. Therefore, it is unknown how best to use the latest diabetes technologies around exercise.In this presentation, data from new and ongoing studies that have investigated different glucose management strategies for training with the different generations of AID systems will be discussed. In addition, possible future management options for spontaneous exercise during AID treatment will be discussed.IS011 / #190 PARALLEL SESSION ‐ EXERCISE IN DIABETESIS TECHNOLOGY USEFUL FOR BREAKING DOWN BARRIERS TO EXERCISE IN DIABETES?D. Zaharieva1, V. Ritter2, M. Desai2, P. Prahalad1, D. Scheinker3, K. Hood1, F. Bishop1, A. Addala1, M. Riddell4, M. Tanenbaum5, D. Maahs11Stanford University, Pediatrics, Stanford, United States of America, 2Stanford, Quantitative Sciences Unit, Stanford, United States of America, 3Stanford, Management Science And Engineering, Stanford, United States of America, 4York University, School Of Kinesiology And Health Science, Toronto, Canada, 5Stanford, Medicine, Stanford, United States of AmericaClinical exercise guidelines recommend that children should aim to achieve at least 60 minutes of moderate‐to‐vigorous physical activity (MVPA) daily, but many youths with type 1 diabetes (T1D) are falling short of these recommendations. For individuals with T1D, exercise and physical activity can lead to disturbances in glycemia without proper preparation and implementation of these strategies. Some common strategies include insulin dose adjustments and/or carbohydrate feeding to reduce the risk of hypoglycemia during exercise. In addition to barriers such as lack of motivation to exercise and fear of hypoglycemia, exercise interventions in adults with T1D have been shown to be acceptable and feasible to deliver. Our team at Stanford is currently implementing a structured telehealth exercise education program in newly diagnosed youth with T1D. The exercise pilot study is part of the larger Teamwork, Targets, Technology, and Tight Control 4T Study that started youth with new‐onset T1D on continuous glucose monitoring (CGM) technology, physical activity trackers, and exercise education approximately 1‐month after diagnosis. This study also examined the potential association between physical activity and glycemia on active days in youth with T1D. We present data from focus groups aimed at understanding the parental and youth experiences in exercise education after T1D diagnosis and also benefits and challenges with real‐world use of physical activity trackers.IS012 / #191 PARALLEL SESSION ‐ EXERCISE IN DIABETESPHYSICAL ACTIVITY WITH LONG AND ULTRA‐LONG‐ACTING BASAL INSULINSL. BallyUniversity Hospital Bern and University of Bern, Department Of Diabetes, Endocrinology, Nutritional Medicine And Metabolism, Bern, SwitzerlandThe use of long and ultra‐long acting basal insulins exposes people who are physically active to insulin levels that are entirely different from normal physiology. Consequences involve exercise‐induced hypo‐ and hyperglycaemic excursions, alterations in substrate utilization and post‐exercise metabolism. Large variations in individual clinical needs (e.g. purpose for engagement in exercise) introduces additional complexity. However, several pro‐active strategies, including variation of exercise intensity, use of digital tools, pharmacological agents and nutritional strategies can help people on long and ultra‐long acting basal insulins achieving maximal benefits from being physically active.IS013 / #192 PARALLEL SESSION ‐ EXERCISE IN DIABETESAUTOMATED DETECTION OF MEALS AND EXERCISE EVENTS IN PEOPLE WITH DIABETESA. CinarIllinois Institute of Technology, Chemical And Biological Engineering, Chicago, United States of AmericaBackground and aims The occurrence of several factors that disturb glucose homeostasis must be detected In real time to develop fully‐automated insulin delivery (AID) systems. Meals, physical activities (PA) and acute psychological stress (APS) events should be detected and their characteristics should be determined to compute the optimal insulin doses to be infused by the AID system or suggested to a user in an advisory system. Methods Machine learning techniques ranging from support vector machines and decision trees to qualitative trend analysis and deep neural networks, along with systems engineering and multivariate statistical techniques can detect the occurrence of specific events, discriminate between different types of events and estimate the characteristics of the specific event (carbohydrates in a meal, intensity of PA, type of APS). Processing of signals from CGMs or wearable devices to filter out signal noise and motion artifacts, imputation of missing data, generation of features from measured variables and pruning of features to minimize collinearity in information improve detection and diagnosis accuracy. Results Various techniques can infer events that have occurred from CGM values reported (yielding feedback information) or from wearable device data as the event is occurring, well before it affects CGM readings (feedforward disturbance information that will affect glucose levels). Estimates of carbohydrates in a meal based on CGM data can provide miniboluses of insulin during a meal, Detection of physical activity type and estimates of energy expenditure inform the control algorithm of the AID system to enable adjustments of insulin infusion doses. Discrimination between PA and APS prevent incorrect dosing of insulin based on erroneous assumption that an increase in heart rate would always indicate PA. Conclusions The quest for fully‐automated AID systems benefit from leveraging real‐time data provided by wearable devices such as activity wristbands. Signal processing, systems engineering and machine learning techniques that can work with data generated in free living must be used for generating reliable information for use by the AID system.IS014 / #198 PARALLEL SESSION ‐ RESOLVING HYPOGLYCEMIALEARNINGS FROM THE HYPO‐RESOLVE PROJECTB. De Galan1,2,31Radboud University Medical Center, Internal Medicine, Nijmegen, Netherlands, 2Maastricht University, Carim School For Cardiovascular Diseases, Maastricht, Netherlands, 3Maastricht university medical center+, Internal Medicine, Maastricht, NetherlandsTherapeutic insulin is lifesaving for many people with diabetes. However, despite 100 years of experience and many innovations, its use is still associated with elevated risks of hypoglycaemia, the burden of which impacts considerably on many aspects of daily life with diabetes. Hypoglycaemia remains a major barrier to achieving optimal glucose control, reduces quality of life, increases health care demand and costs, and is associated with cardiovascular events, cognitive decline and death. The Hypoglycaemia REdefining SOLutions for better liVEs (Hypo‐RESOLVE) project is a public‐private partnership that aims to increase our understanding of hypoglycaemia through a comprehensive multilevel approach in order to reduce the burden of hypoglycaemia. One of the activities is the construction of the Hypo‐RESOLVE database, which contains data on hypoglycaemia from 98 clinical trials on insulin treatment among 60,000 participants with type 1 or type 2 diabetes, analysis of which will reveal better insight into the consequences of and risk factors for different levels of hypoglycaemia. In addition, the embedded 10‐week Hypo‐METRICS study will examine the psychological, clinical and health‐economic impact of sensor‐detected low interstitial glucose values and its relevance as compared to patient‐reported hypoglycaemia. A large hypoglycaemic glucose clamp study, conducted among over 100 people with type 1 or type 2 diabetes, aims to reveal potential mechanisms underlying the association between hypoglycaemia and cardiovascular disease, focussing on inflammatory parameters and cardiac function. Qualitative research and a quantitative survey examine the widespread impact of hypoglycaemia on various aspects of quality of life, diabetes distress and related aspects in people with or affected by diabetes Finally, the basic science component of the project will reveal novel pathways of hypoglycaemia sensing, so as to better understand the pathophysiology of impaired awareness of hypoglycaemia. Data from Hypo‐RESOLVE will provide the evidence needed to solidify the current and widely adopted International Hypoglycaemia Study Group (IHSG) 3‐level classification of hypoglycaemia. Collectively, the outcomes of Hypo‐RESOLVE will advance our understanding of hypoglycaemia, so as to alleviate its burden and improve the lives of people with diabetes.IS015 / #230 PARALLEL SESSION ‐ EMERGING TREATMENT OPTIONS FOR OBESITY AND TYPE 2 DIABETESGLP‐1 ANALOGS FOR THE TREATMENT OF OBESITYM. Jensterle Sever1,21University Medical Centre Ljubljana, Department Of Endocrinology, Diabetes And Metabolic Disease, Ljubljana, Slovenia, 2University of Ljubljana, Faculty Of Medicine, Ljubljana, SloveniaThe classic approach to obesity treatment is a “treat to failure” model. If patients fail to lose weight or regain lost weight, they progressively escalate in a stepwise fashion to more intensive therapies, from lifestyle/behavioural therapy to pharmacotherapy and bariatric surgery.Weight loss that is associated with clinically impactful outcomes for most adiposity based chronic disease (ABCD) is 10 to 20%. The marked increment in efficacy of modern anti‐obesity medications (AOMs) permits the weight loss within this range of magnitude as a new treatment target. The first AOM that fully enables such “treat to target” approach is GLP‐1 receptor agonist (RA) semaglutide 2.4 mg.The safety and efficacy of semaglutide was evaluated in The Semaglutide Treatment Effect in People with Obesity (STEP) Phase 3a clinical developmen
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