Hypoglycemia Research Articles

A remarkable presentation of a massive Budd-Chiari syndrome.

A 59-year-old female presented to the emergency department with malaise, significant weight loss, abdominal discomfort, dyspnoea and severely swollen peripheral extremities. She had a past medical history of sickle cell anaemia, a latent tuberculosis infection and a chronic hepatitis B that was treated with PEG-interferon alpha-2 in 2008. Since 2018, she was lost to follow-up. The patient was critically ill, sarcopenic and lethargic. Clinical examination revealed icteric sclerae and a markedly distended and diffusely tender abdomen and peripheral oedemas. Her vital signs included a blood pressure of 105/64 mmHg, tachycardia of 130 bpm, hypothermia of 35.6°C and a SpO2 of 100% in ambient air. Laboratory workup demonstrated a total bilirubin of 12.9 mg/dL, AST 500 U/L, ALT 218 U/L, ALP 178 U/L and GGT 126 U/L, a thrombocytopenia of 34 x10E3/ μL, 16,01 x10E3/μL leukocytes, a CRP of 94.8 mg/L and a lactate of 10,12 mmol/L. The serum creatinine was 1.04 mg/dL, eGFR 54.1 ml/min. The INR was 2.77 and there was a severe hypoalbuminemia (22 g/L). D-dimers were >20 000 ng/ml. The patient presented also with spontaneous hypoglycaemia. A multiphasic contrast-enhanced thoracic and abdominal computed tomography (CT) was performed (figure 1). Given the results of the CT, a 2D echocardiogram was urgently requested (figure 2).

Early diagnosing diabetes using data mining algorithms

Diabetes has become a widespread and long lasting condition that continues to impact an increasing number of individuals across the globe. It is crucial to highlight the significance of accurately identifying, predicting, managing and treating diabetes in order to address this growing concern. Utilizing sophisticated data analysis techniques to examine data relating to diabetes can significantly enhance the early detection and prediction of this ailment, along with its associated complications like low or high blood sugar levels. The findings clearly demonstrate that the decision tree algorithm proves to be the most effective approach in promptly diagnosing diabetes patients and ensuring they receive timely access to suitable treatment options.

Glucose variability and mood in people with type 1 diabetes using ecological momentary assessment

ObjectiveMood fluctuations related to blood glucose excursions are a commonly reported source of diabetes-distress, but research is scarce. We aimed to assess the relationship between real-time glucose variability and mood in adults with type 1 diabetes (T1D) using ecological momentary assessments. MethodsIn this prospective observational study, participants wore a masked continuous glucose monitor and received prompts on their smartphone 6 times a day to answer questions about their current mood (Profile Of Mood States (POMS)-SF (dimensions: Anxiety, Depressive symptoms, Anger, Fatigue, Vigor)) for 14 days. Mixed model analyses examined associations over time between daily Coefficient of Variation (CV) of blood glucose and mean and variability (CV) of POMS scores. Further, within-person differences in sleep and nocturnal hypoglycemia were explored. Results18 people with T1D (10 female, mean age 44.3 years) participated. A total of 264 out of 367 days (70.2%) could be included in the analyses. No overall significant associations were found between CV of blood glucose and mean and CV of POMS scores, however, nocturnal hypoglycemia moderated the associations between CV of blood glucose and POMS scales (mean Fatigue Estimate 1.998, p < .006, mean Vigor Estimate −3.308, p < .001; CV Anger Estimate 0.731p = 0.02, CV Vigor Estimate −0.525, p = .006). ConclusionWe found no overall relationship between real-time glycemic variability and mood per day. Further research into within-person differences such as sleep and nocturnal hypoglycemia is warranted.

Evaluation of the safety of inpatient empagliflozin in a real-world setting

Background Empagliflozin is a sodium glucose co-transporter 2 (SGLT2) inhibitor recommended by the American Diabetes Association for outpatient use. Support for its inpatient role is not well established due to possible safety concerns. Methods This was a retrospective study at an academic medical center between January 1, 2021, and December 31, 2021, evaluating the safety and efficacy of empagliflozin compared to other oral antihyperglycemic agents. Patients with established heart failure with or without diabetes were included if they received at least one dose of oral antihyperglycemic agent with 48 hours of fingerstick blood glucose checks during the hospitalization. A total of 227 patients were included. The primary endpoint was a composite of adverse events including urinary tract infection, acute kidney injury, diabetic ketoacidosis, renal replacement therapy, and necrotizing fasciitis. Additional endpoints included daily insulin requirements, hypoglycemia, and hypotension. Results Rates of composite adverse events were similar between the empagliflozin group and other oral antihyperglycemic agents (19.3% vs. 12.6% respectively, P = 0.17). There were no instances of renal replacement therapy, diabetic ketoacidosis, or necrotizing fasciitis. The secondary endpoint of basal insulin requirements showed no differences between the two groups. In the empagliflozin cohort, more patients experienced hypotension (23.4% vs. 7.8%; P < 0.01). Conclusion This real-world study of empagliflozin use in the inpatient setting found no significant differences in safety endpoints between empagliflozin and other oral antihyperglycemic agents. Larger-scale studies need to be performed before the use of empagliflozin can be routinely recommended in the inpatient setting.

Evaluating Ease of Use and Patient Safety of Dasiglucagon Hypo Pal Autoinjector for the Management of Hypoglycemia.

Severe hypoglycemia is a medical emergency that must be treated promptly in order to prevent complications or death in a patient. Patients who have been diagnosed with Type 1 diabetes and treated with insulin have the potential to experience a severe hypoglycemia episode, or low blood sugar. When a patient is experiencing severe hypoglycemia, it is imperative for this patient to receive glucose quickly. However, patients who are experiencing low blood sugar may not be able to manage for themselves, take food or liquid orally or may be unconscious. Glucagon is a medication that reverses low blood sugar. Up until 2018, glucagon had to be reconstituted immediately before injection because of the medication's lack of stability after reconstitution. A medication recently approved for patients 6 years through adult is Dasiglucagon. This form of glucagon comes in a ready-to-administer, pre-filled syringe, making it easier for caretakers and bystanders to administer in an emergency situation due to the readiness of the medication. The purpose of the paper is to evaluate literature that pertains to the ease of use and patient safety of dasiglucagon HypoPal autoinjector for the management of severe hypoglycemia.

Chitosan Derivative-Based Microspheres Loaded with Fibroblast Growth Factor for the Treatment of Diabetes.

Diabetes mellitus type 2 (T2DM) is a disease caused by genetic and environmental factors, and the main clinical manifestation is hyperglycemia. Currently, insulin injections are still the first-line treatment for diabetes. However, repeated injections may cause insulin resistance, hypoglycemia, and other serious side effects. Thus, it is imperative to develop new diabetes treatments. Protein-based diabetes drugs, such as fibroblast growth factor-21 (FGF-21), have a longer-lasting glycemic modulating effect with high biosafety. However, the instability of these protein drugs limits their applications. In this study, we extract protein hypoglycemic drugs with oral and injectable functions. The FGF-21 analog (NA-FGF) was loaded into the chitosan derivative-based nanomaterials, N-2-Hydroxypropyl trimethyl ammonium chloride chitosan/carboxymethyl chitosan (N-2-HACC/CMCS), to prepare NA-FGF-loaded N-2-HACC/CMCS microspheres (NA-FGF-N-2-HACC/CMCS MPs). It was well demonstrated that NA-FGF-N-2-HACC/CMCS MPs have great biocompatibility, biostability, and durable drug-release ability. In addition to injectable drug delivery, our prepared microspheres were highly advantageous for oral administration. The in vitro and in vivo experimental results suggested that NA-FGF-N-2-HACC/CMCS MPs could be used as a promising candidate and universal nano-delivery system for both oral and injectable hypoglycemic regulation.

The Correlation between Breakfast Consumption and the Probability of Developing Alzheimer’s Disease in Bangkok Population

Alzheimer’s disease, which is the most common form of dementia, is known to be widely prevalent in the worldwide population, including Thailand. The incidence of this illness in Thailand is increasing rapidly and is expected to reach a million by 2030 (Doungkaew &amp; Taneepanichskul, 2014). We are concerned that this rise is caused by the trend during these modern days, where skipping breakfast is becoming more popular due to time constraints. Hence, we conducted a survey research by providing questionnaires to 180 individuals of all ages across Bangkok, asking for their frequency in consuming breakfast per week and their likelihood in having Alzheimer’s. This was done through an online platform, Google Form. To test whether the two aforementioned variables are correlated with one another, Statistical Product and Service Solution (SPSS) program version 29 was used for data analysis. One-Way ANOVA (F-test) was used to determine the significance between age group and the risk of getting Alzheimer’s. The result showed that age is a significant factor which can promote the illness. Age 41 and above tend to have a stronger probability in developing the disease because of the easier spread of a protein called tau-protein, which is involved in causing Alzheimer’s disease (Wegmann et al., 2019). Pearson’s correlation test has shown that there is a negative correlation between the frequency of breakfast consumption and the risk of developing Alzheimer’s disease. This is likely due to low blood sugar levels and lack of beneficial nutrients contained in breakfast . In the future, we hope that this research could be beneficial to other people by raising awareness on this particular topic regarding the importance of frequency of breakfast consumption in reducing the risk of developing Alzheimer’s disease.

Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes.

Nocturnal hypoglycemia (NH) remains a major burden for people with type 1 diabetes (T1D). Daytime physical activity (PA) increases the risk of NH. This pilot study tested whether cumulative daytime PA measured using a smartphone-based step tracker was associated with NH. Adults with T1D for ≥ 5 years (y) on multiple daily insulin or continuous insulin infusion, not using continuous glucose monitoring and HbA1c 6 to 10% wore blinded Freestyle Libre Pro sensors and recorded total daily carbohydrate (TDC) and total daily dose (TDD) of insulin. During this time, daily step count (DSC) was tracked using the smartphone-based Fitbit MobileTrack application. Mixed effects logistic regression was used to estimate the effect of DSC on NH (sensor glucose <70, <54 mg/dl for ≥15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect. Twenty-six adults, with 65.4% females, median age 27 years (interquartile range: 26-32) mean body mass index 23.9 kg/m2, median HbA1c 7.6% (7.1-8.1) and mean Gold Score 2.1 (standard deviation 1.0) formed the study population. The median DSC for the whole group was 2867 (1820-4807). There was a significant effect of DSC on NH episodes <70 mg/dl. (odds ratio 1.11 [95% CI: 1.01-1.23, P = .04]. There was no significant effect on NH <54 mg/dl. Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.

Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes.

One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH. We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each. At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%). Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.

Evaluation of effect of somatostatin analogs on pregnancy and lactation

Relevance. Somatostatin analogues (SSA) are a group of drugs that suppress the secretion of growth hormone, both pathologically increased and caused by arginine, exercise and insulin hypoglycemia. In addition, the drug can suppress the secretion of insulin, glucagon, gastrin, serotonin, thyrotropin. Currently, there is limited evidence that the use of SSA affects fertility. In this regard, the doctor may be faced with the issue of the patient’s reproductive function against the background of systemic use of SSA.Target. The solution to this issue involves a thorough study of the effect of this group of drugs on the health of the mother and fetus. It is also extremely important to understand whether there is the safest ‘therapeutic window’ for the introduction of SSA. The article systematizes information on the use of ASS in pregnant women and against the background of lactation.Materials and methods. The article analyzes the works of N. S. Baksheeva, L. K. Kuritsyna, E. G. Ivashkin, studies on the evaluation of reproductive function in animals, as well as clinical observations of patients are presented.Results. The article concludes that an increase in serotonin during pregnancy, which may be associated with a break in therapy with somatostatin analogues, may lead to an increased risk of developing «carcinoid heart», and may also cause a decrease in anxiety reactions in offspring. Based on clinical observations of patients diagnosed with a neuroendocrine tumor of the stomach, it is suggested that the introduction of somatostatin analogues at an early stage of pregnancy can provoke spontaneous abortion.Conclusions. The question of the use of SSA during pregnancy remains open and requires further observation, but based on the studies already known to us, it can be assumed that there is enough data (on a limited number of patients) indicating the absence of undesirable effects of SSA on the course of pregnancy or the health of the fetus /newborn.

Mixed Meal Tolerance Test Versus Continuous Glucose Monitoring for an Effective Diagnosis of Persistent Post-Bariatric Hypoglycemia.

Gastric bypass determines an increase in incretin secretion and glucose excursions throughout the day and may sometimes entail the development of severe post-bariatric hypoglycemia (PBH). However, there is no consensus on the gold standard method for its diagnosis. In this study, we evaluated the usefulness of a mixed meal tolerance test (MMTT) and continuous glucose monitoring (CGM) for the diagnosis of PBH, defined as glucose levels <54 mg/dL (3.0 mmol/L). We found that hypoglycemia occurred in 60% of patients after the MMTT and in 75% during CGM, and it was predominantly asymptomatic. The MMTT confirmed the diagnosis of PBH in 88.9%of patients in whom surgery had been performed more than three years ago, in comparison to 36.4% in cases with a shorter postsurgical duration. CGM diagnosed nocturnal asymptomatic hypoglycemia in 70% of patients, and daytime postprandial hypoglycemia in 25% of cases. The mean duration of asymptomatic hypoglycemia was more than 30 min a day. Patients with ≥2% of their CGM readings with hypoglycemia exhibited a higher degree of glucose variability than those with <1% of the time in hypoglycemia. Our results show that the MMTT may be a useful dynamic test to confirm the occurrence of hypoglycemia in a large number of patients with persistent and recurrent PBH during long-term follow-up after gastric bypass. CGM, on its part, helps identify hypoglycemia in the real-world setting, especially nocturnal asymptomatic hypoglycemia, bringing to light that PBH is not always postprandial.

383-P: Intranasal (IN) Naloxone as a Novel Therapy to Preserve Hypoglycemia Counterregulatory (CR) Response after Exercise in Type 1 Diabetes (T1D)

Hypoglycemia (HG) is common after exercise in people with T1D. Antecedent exercise leads to attenuation of CR response during subsequent HG. In healthy humans, IV administration of naloxone during exercise preserved CR response to HG the following day. We tested the hypothesis that IN administration of naloxone during exercise will preserve CR response to HG in people with T1D with intact awareness of HG. We performed a single-blind randomized cross over trial to compare the impact of IN naloxone vs. IN saline administered during exercise on day 1 (D1) on the symptoms and epinephrine (Epi) response to HG on day 2 (D2). On D1, participants underwent two 90-min hyperinsulinemic-euglycemic clamps during exercise at 60% VO2max with IN naloxone (4 mg at start and +45 min each exercise period) or IN saline exposure; on D2 a HG clamp (target 50 mg/dl) was performed. 27 subjects (10F, age 29 ± 8 yrs) participated in the protocol. Outcomes included D2 average symptom scores (SS) and peak Epi level over HG clamp’s last 30 min. A general linear mixed model for each of SS and Epi with effects for naloxone vs. saline, treatment order (naloxone 1st vs. 2nd), and time (Part 1 vs. Part 2) was performed. SS and Epi response on D2 were not different following IN naloxone compared to placebo. In people with T1D, administration of IN naloxone during exercise did not alter symptom or EPI response to subsequent HG. Disclosure A.Moheet: None. E.R.Seaquist: Consultant; Zucara Therapeutics. L.E.Eberly: None. S.Inkollu: None. A.Kumar: None. A.C.Alvear: None. Y.Zhang: None. L.Coles: None. N.Evanoff: None. D.R.Dengel: None. Funding JDRF

251-OR: The Impact of Identifying Sleep with a Fitbit on Calculated Rates of Nocturnal Hypoglycemia—The Hypo-Metrics Study

Consensus defines nocturnal hypoglycemia as occurring between 00:00 and 06:00 hrs. However, this may introduce bias, as for many, these hours only partially match real sleeping hours. Therefore, we compared rates of hypoglycemia while asleep (defined by Fitbit Charge 4) to rates of conventionally-defined nocturnal hypoglycemia. Hypo-METRICS study participants wore a Fitbit charge 4 and blinded FreeStyle Libre glucose monitor for up to 70 days. Every hypoglycemia episode (≤70mg/dL or ≤54mg/dL for ≥15 min) was classified as ‘asleep’ or ‘awake’ based on Fitbit sleep intervals and ‘nocturnal’ (00:00-06:00 hrs) or ‘day-time’. We compared weekly sensor-detected hypoglycemia (SDH) rates whilst asleep to nocturnal SDH rates using a paired sample Wilcoxon test. We included 542 participants (269 with type 1 diabetes, median (IQR) age 56 (44-66) years, HbA1c 7.3%(6.7-7.9), with 69 (65-70) nights with sleep data/participant). Sleep duration was 6.1 (5.2-6.8) hours. Median weekly rates of SDH while asleep were higher than rates of nocturnal SDH both ≤70mg/dL (1.3 (0.6-2.5) vs 0.9 (0.4-1.8), p&amp;lt;0.001) and ≤54mg/dL (0.4 (0.2-0.9) vs 0.3 (0.1-0.7), p&amp;lt;0.001). Stratifying by type of diabetes did not change the direction or magnitude of the differences in SDH rates. Using clock time to estimate nocturnal hypoglycemia underestimates hypoglycemia whilst asleep by up to a third. Using activity trackers such as Fitbit to identify sleep offers a novel way to estimate hypoglycemia whilst asleep to better reflect lived experience of nocturnal hypoglycemia. Disclosure G.Martine-edith: Other Relationship; Novo Nordisk A/S. R.J.Mccrimmon: Advisory Panel; Sanofi, Speaker's Bureau; Novo Nordisk A/S. E.Renard: Consultant; Abbott Diabetes, Dexcom, Inc., AstraZeneca, Boehringer-Ingelheim, Eli Lilly and Company, Insulet Corporation, MannKind Corporation, Novo Nordisk, Sanofi, Roche Diabetes Care. S.R.Heller: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker's Bureau; Novo Nordisk, Medtronic. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. J.K.Mader: Advisory Panel; Novo Nordisk A/S, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Medtronic, Becton, Dickinson and Company, Pharmasense, embecta, Research Support; A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Dexcom, Inc., Profusa, Inc., Speaker's Bureau; Novo Nordisk A/S, A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Boehringer Ingelheim Inc., Becton, Dickinson and Company, Ypsomed AG, Viatris Inc., Servier Laboratories, Medtrust, Stock/Shareholder; Decide Clinical Software GmbH. S.A.Amiel: Advisory Panel; Medtronic, Other Relationship; Sanofi, Novo Nordisk. P.Choudhary: Advisory Panel; Medtronic, Novo Nordisk, Dexcom, Inc., MannKind Corporation, Insulet Corporation, Research Support; Abbott Diabetes, Speaker's Bureau; Sanofi, Lilly. Hypo-resolve consortium: n/a. P.Divilly: None. N.Zaremba: None. U.Soeholm: Employee; Novo Nordisk A/S. A.Kingsnorth: None. Z.Mahmoudi: Employee; Novo Nordisk. M.Gomes: Employee; Novo Nordisk A/S. B.E.De galan: Research Support; Novo Nordisk. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi, Vertex Pharmaceuticals Incorporated. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460)

129-OR: Effect of Tirzepatide on the Counterregulatory Response to Hypoglycemia during a Hypoglycemic Clamp in People with Type 2 Diabetes

We explored the effects of tirzepatide (TZP) on counter-regulatory response during a hypoglycemic clamp. Patients with type 2 diabetes (N=42) were randomized to TZP (15 mg) or placebo (PBO) for 12 wk, in a crossover design, with a wash-out period of 8 to 12 wk. The primary objective was the change in glucagon (GCG) during clamp-induced hypoglycemia (HG) from plasma glucose (PG) 100 mg/dL to nadir PG (45 mg/dL). Secondary measures were changes in other counter-regulatory hormones, insulin, and C-peptide during clamp-induced HG and PG recovery from nadir PG to 72 mg/dL. Time to recovery and HG symptom scores were assessed. At 12 wk, HbA1c change from baseline was -1.5% with TZP vs +0.5% with PBO. Increases in GCG from PG 100 mg/dL to nadir PG and during recovery did not differ between treatments (p=0.756 and p=0.565, respectively) (Table). Growth hormone and adrenaline responses did not differ between treatments, while increases in cortisol and noradrenaline responses were delayed with TZP, which explains a lower HG symptom score with TZP vs PBO at nadir (p=0.007). The proportion of patients aware of HG did not differ between treatments. Mean time to recovery from HG was 47 min with TZP and 43 min with PBO (p=0.002); however, nadir PG was lower with TZP (44 vs 47 mg/dL; p=0.015). In conclusion, the response of glucagon to hypoglycemia is maintained with TZP. Disclosure T.Pieber: Advisory Panel; Arecor, Novo Nordisk A/S, Consultant; Lilly, Research Support; AstraZeneca, Novo Nordisk A/S, Sanofi, Speaker's Bureau; Roche Diagnostics. E.Svehlikova: Other Relationship; Mylan. S.Urva: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. A.Haupt: Employee; Lilly, Stock/Shareholder; Lilly. C.Zhou: Employee; Eli Lilly and Company. T.Coskun: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. Z.Milicevic: Employee; Eli Lilly and Company. E.J.Pratt: Employee; Eli Lilly and Company. Funding Eli Lilly and Company

922-P: Improved Satisfaction while Maintaining Safety and High Time in Range (TIR) with a Medtronic Investigational Enhanced Advanced Hybrid Closed-Loop System (eAHCL)

Background: Hybrid closed loop (HCL) systems are evolving. We aimed to assess treatment satisfaction and glucose control with an investigational eAHCL system. Methods: Adults with type 1 diabetes (T1D) received sequentially the MiniMed™ 780G /Guardian™ Sensor 3 AHCL system, and a Medtronic investigational eAHCL system each for 12 weeks. The eAHCL system incorporates updated software, a new simple to use, no calibration, all-in-one sensor, and 7 day extended wear infusion set. The primary outcome was difference in treatment satisfaction measured using “current” and “change” versions of the Diabetes Technology Questionnaire (DTQ) following each 12 week period. Secondary outcomes included time spent changing infusion sets and sensors recorded on a phone app and CGM metrics. Results: Twenty-one adults (11 men, mean [SD] age 48 [12.4] years) completed the trial. DTQ “current” score improved on eAHCL, and 86% of participants reported less “Effort to keep low blood sugar from happening” on DTQ “change”. There was less fear of hypoglycemia; more time in Auto Mode; fewer alarms; less time spent changing infusion sets and sensors; reduced CGM hypoglycemia and glycemic variability (Table 1). Conclusions: Transition to eAHCL in T1D adults resulted in a better user experience and reduced fear of and time in hypoglycemia while maintaining safety and &amp;gt;80% TIR. Disclosure C.Yuan: None. R.Macisaac: None. E.Netzer: None. B.Paldus: None. L.Robinson: None. A.Roy: Employee; Medtronic. C.M.Sims: Stock/Shareholder; Medtronic. S.Trawley: Research Support; Insulet Corporation, Juvenile Diabetes Research Foundation (JDRF). S.Vogrin: None. D.N.O'neal: None. Y.W.Kong: None. T.Amoore: None. K.Brown: None. B.Grosman: Employee; Medtronic. A.Jenkins: Advisory Panel; Insulet Corporation, Board Member; Insulin for Life, Research Support; Abbott Diabetes, Medtronic, Hemsley Charitable Trust, Juvenile Diabetes Research Foundation (JDRF), National Institutes of Health. H.Jones: None. N.Kurtz: Employee; Medtronic. M.H.Lee: Speaker's Bureau; Medtronic.

632-P: Glycemic Control in Diabetes Camp Attendees Remotely Monitored and Nonadjunctively Managed with CGM

Remote monitoring of CGM data of children and youth attending diabetes camp has been shown to reduce overall and nocturnal hypoglycemia, compared to treatment decisions based on self-monitored blood glucose (SMBG) values. However, some diabetes camps still require regular SMBG testing. We compared at-home versus at-camp glucose control where CGM data were used as the basis for diabetes treatment decisions. Thirty-four campers with T1D (mean±SD age 12±3 years, A1C 7.5±1.2%) were eligible for inclusion if they were using Control-IQ technology (Tandem Diabetes Care) prior to and during their attendance at Camp Buck, a diabetes-focused facility near Lake Tahoe. At camp, their CGM data (Dexcom G6) were integrated with the CampViews EMR system (Nevada-California Diabetes Association). CGM data from at-home and at-camp intervals were compared. As shown in the Table, mean glucose and overall time in range (TIR, 70-180 mg/dL) improved at camp, with statistical significance in the overnight (10PM-6AM) interval. Although time in Level 1 hypoglycemia increased at camp, there were no significant changes between at-home and at-camp levels of Level 2 hypoglycemia. CGM data can be used nonadjunctively in the camp setting to maintain or improve glycemic control. Centralized remote monitoring of glucose data allows camp attendees and staff to safely reduce or eliminate SMBG testing. Disclosure S.E.Gleich: Research Support; Dexcom, Inc. H.R.Tecca: Employee; Dexcom, Inc. N.D.Gibson-north: None. S.B.Andrade: Employee; Dexcom, Inc. J.Welsh: Employee; Dexcom, Inc. E.Schuster: Employee; Dexcom, Inc. T.C.Walker: Employee; Dexcom, Inc., Stock/Shareholder; Dexcom, Inc.

575-P: Do Overnight Dips Exist following Afternoon Exercise? Factors Associated with Nocturnal Hypoglycemia in the Type 1 Diabetes Exercise Initiative Pediatric (T1DexiP) Study

Some controlled studies have associated afternoon exercise with a biphasic pattern of hypoglycemia (hypo) risk: during exercise and 7-11hrs later. We explored factors relating to nocturnal hypo following afternoon exercise (12PM-6PM) among youth in the observational T1DexiP study. Youth with T1D (n=203; [mean±SD] age14±2 yrs; HbA1c=7.0± 1.2%; 42% female; T1D duration 5.4±3.9yrs; 12% on MDI and 58% on AID) wore a continuous glucose monitor, an activity monitor and logged activity using the Bant app for 10 days. Repeated measures logistic regression adjusted for bedtime glucose and % time below range (TBR &amp;lt; 70mg/dL) in the prior 24hrs. Of the 833 afternoon exercise sessions, 14% led to nocturnal hypo (≥15 consecutive minutes &amp;lt;70 mg/dL). Median TBR was higher in the 24hrs before exercise in youth who developed nocturnal hypo vs those who did not (3% vs 1%), while pre-exercise glucose level, heart rate, and insulin on board were similar. Nocturnal hypo risk was higher among teens who exercised more often during the study (Table). A trend for lower risk was noted with AID use. Our model revealed that participants who exercised &amp;gt;90 min/day had increased risk of nocturnal hypoglycemia, while AID use may reduce risk. This suggests algorithmically modulated insulin delivery may help to mitigate the impact of afternoon exercise on nocturnal glycemia. Disclosure J.Sherr: Advisory Panel; Bigfoot Biomedical, Inc., Insulet Corporation, Medtronic, Vertex Pharmaceuticals Incorporated, Cecelia Health, StartUp Health T1D Moonshot, Consultant; Bigfoot Biomedical, Inc., Insulet Corporation, Medtronic, Lilly, Research Support; Insulet Corporation, Medtronic, NIH - National Institutes of Health, Juvenile Diabetes Research Foundation (JDRF), Speaker's Bureau; Insulet Corporation, Zealand Pharma A/S, Lilly, Medtronic. S.Bergford: None. S.R.Patton: None. M.A.Clements: Consultant; Glooko, Inc., Research Support; Dexcom, Inc., Abbott Diabetes. P.Calhoun: None. R.L.Gal: None. M.Riddell: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Indigo Diabetes, Consultant; Lilly Diabetes, Eli Lilly and Company, Jaeb Center for Health Research, Speaker's Bureau; Dexcom, Inc., Novo Nordisk, Sanofi, Stock/Shareholder; Supersapiens, Zucara Therapeutics. T1dexip study group: n/a. Funding The Leona M. and Harry B. Helmsley Charitable Trust; Dexcom, Inc.

1217-P: Willingness to Deintensify in Patients with Type 2 Diabetes—The Diabetes and Aging Study

Background: The ADA recommends treatment de-intensification be considered in older patients with poor health, on complex regimens, or experiencing side effects. We examined factors associated with older patients’ willingness to de-intensify treatment. Methods: Cross-sectional analysis of Diabetes Preferences and Self-Care Survey responses and EHR data in adults with type 2 diabetes from Kaiser Permanente Northern California. We calculated proportions of patients who responded to the survey question “I would be willing to take less medication for my diabetes” as willing, neutral, or unwilling. Willingness was contrasted across demographic, clinical, and health status factors. Results: Analysis included 1,337 adults prescribed glucose-lowering medications (age 74.2 ± 6.0 years, 44% women, 54.4% non-Hispanic white). The proportions of willing, neutral, and unwilling to de-intensify treatment were 51.2%, 27.3%, and 21.5%, respectively. Willingness to de-intensify varied by age (p&amp;lt;0.001) with 54.2% of younger adults (age 65-74) willing to de-intensify compared to 38.5% of older adults (age 85+). Willingness varied by duration of diabetes (p&amp;lt;0.001) with 61.0% of adults with diabetes for 0-4 years willing to de-intensify compared to 44.2% of adults with diabetes for longer duration (&amp;gt;15 years). Willingness among those with good, intermediate, or poor health status (as defined by the ADA) was 54.2% vs. 47.5% vs. 55.1%, respectively. Individuals with an A1c greater than 9% were more willing to de-intensify treatment compared to individuals with A1c of 7%-9%, or &amp;lt;7%, (57% vs 51% vs 50%, p&amp;lt;0.001). Willingness to de-intensify decreased as number of medications increased (p&amp;lt;0.001) and little difference was noted in willingness among patients by insulin use or hypoglycemia history. Discussion: Patterns of patient willingness to de-intensify treatment are misaligned with clinical guideline recommendations and highlight the influence of patient experience living with diabetes over time. Disclosure S.Haider: None. M.M.Parker: Research Support; Dexcom, Inc. E.Huang: Advisory Panel; Twin Health, Stock/Shareholder; AbbVie Inc., Cardinal Health, Amgen Inc. R.W.Grant: None. H.H.Moffet: Research Support; Dexcom, Inc. N.Laiteerapong: None. J.Liu: None. K.J.Lipska: Other Relationship; UpToDate. A.J.Karter: None. Funding National Institute on Aging (5R01AG063391)

218-LB: Younger Veterans with Adult-Onset Diabetes Who Have High Type 1 Diabetes (T1D) Genetic Risk Are Disproportionately of Minority Ancestry and Have Features Typical of T1D but Are Often Unrecognized—A Health Disparity

Diabetes in Veterans is usually presumed to be T2D because T1D precludes enlistment, but T1D may present in adults; how T1D genetic risk impacts different racial/ethnic groups is unknown. To assess the contributions of T1D genetic risk, we examined 109,954 Veterans in the VA's Million Veteran Program (MVP), using a 30 SNP T1D genetic risk score (GRS) for those with European, Hispanic, and Asian ancestry, and a separate 7-SNP GRS for those of African ancestry. High T1D genetic risk (GRS ≥90th percentile) was characterized according to age at diabetes onset (20-39, 40-59, and 60+ years). High T1D genetic risk was more common with the earliest onset group (13.2%, 10.0% and 9.0%, respectively), and that group had the highest proportion of minority ancestry (47%, 37%, and 25%), all p&amp;lt;0.001. The three high T1D risk groups had similar follow-up (13, 12, and 11 years), GAD antibodies if tested (39%, 41%, and 40%), and low C-peptide levels if measured (36%, 23%, and 33%). However, typical T1D features were more common with earlier onset: use of insulin (80%, 63%, and 43%), time to insulin (0.2, 2.4, and 1.4 yr), diabetic ketoacidosis (11%, 3%, and 0.8%), and severe hypoglycemia prompting an ED visit (10%, 5%, and 3%), all p&amp;lt;0.001. While earlier age of onset groups had more use of T1D ICD codes (50%, 25% and 13%), testing for GAD antibodies (18%, 4%, and 2%), and testing for C-peptide (24%, 9%, and 3%), respectively, all p&amp;lt;0.001, less than half of any group had diagnostic testing for T1D. Conclusions: Increased T1D genetic risk is associated with earlier age of diabetes onset in adults and increased prevalence of typical T1D characteristics, but similar GAD antibodies and C-peptide levels. Since individuals with high T1D risk and early diabetes onset are disproportionately of minority ancestry, health policies aimed to improve T1D recognition and management are needed to remedy this health disparity. Disclosure P. K. Yang: None. L. S. Phillips: Other Relationship; Diasyst Inc., Research Support; Abbott Diabetes, Janssen Pharmaceuticals, Inc., Pfizer Inc., Kowa Pharmaceuticals America, Inc., GlaxoSmithKline plc. Y. Sun: None. S. Raghavan: None. E. M. Litkowski: None. B. T. Legvold: None. M. K. Rhee: Research Support; Kowa Research Institute, Inc. M. Vujkovic: None. P. Reaven: Research Support; Dexcom, Inc. A. Leong: Other Relationship; Merck &amp; Co., Inc. Funding National Institutes of Health (U01DK098245, P30DK111024, AI156161, DK127083); U.S. Department of Veterans Affairs (I01-CX001025, I01-BX005831, I01-BX003340, CX001899, I01CX001737)

1156-P: Diabetes Technology in the Pediatric Inpatient Setting

Introduction: Continuous subcutaneous insulin infusion (CSII) pumps and continuous glucose monitors (CGMs) improve glycemic control and quality of life for children with diabetes. This study assessed the existence and content of pediatric inpatient diabetes technology policies in U.S. hospitals. Methods: A cross-sectional survey was distributed to the Pediatric Pharmacy Association listserv. The survey had multiple-choice and open-ended items related to hospital practices on insulin pump and CGM use in the pediatric inpatient setting. Results: Only 17 of the 31 responding hospitals (54.8%) allowed CGM use in the pediatric inpatient setting with 13 (76.5%) of those having written CGM policies. Primary barriers to allowing CGM use were lack of policy (n=11, 78.6%), limited policy knowledge (n=9, 64.3%), and lack of EMR integration (n=6, 42.9%). Most hospitals reported not utilizing CGM high and low blood sugar alarms (n=10, 58.8%). Hospitals typically used finger stick blood glucose testing while patients used a CGM (n=13, 76.5%). More hospitals (n=29, 93.5%) reported an option for insulin pump use in the pediatric inpatient setting with 96.6% (n=28) having a written policy. Overall, hospitals’ reported policies did not differ for pump use in children admitted for diabetes complications versus nondiabetes diagnoses (n=24, 85.7%). Less than half had specific policies for suspected pump site failure (n=13, 46.4%). Verification of pump doses varied greatly with hospitals reporting nursing verification (n=17, 58.6%), patient report (n=10, 34.5%) and no verification policy (n=2, 6.9%). Conclusion: Most hospital respondents had policies for inpatient insulin pump use while fewer had policies allowing CGM use. Best practice standards support having written guidelines to ensure patient safety and to guide the pediatric care team. Policy repositories may be a helpful resource for hospitals that lack written guidelines. Disclosure N. Rajagopal: None. A. Bailey: None. C. Ngo: None. A.D. Hendrix-Dicken: Stock/Shareholder; Catalyst Pharmaceuticals, Johnson &amp; Johnson, AstraZeneca. S. Mercer: None. M. Condren: Consultant; Wolters Kluwer Health.