bluuebunny/crossref_metadata_2025_split huggingface dataset:
943-P: Improved Glycemic Outcomes after One Year among Level 2 Members
Authors: BACON SAM , THOMPSON NELS , FERRELL DAVID , FUNG JASON 
Published: June 2023

Background: Level2’s virtual specialty clinic, offered across the United States to people with type 2 diabetes on participating insurance plans, combines providers, coaches, continuous glucose monitors (CGM), and fitness trackers to help members gain insight, and focus on long-term behavior change. We retrospectively assessed differences in glycemic outcomes following one year of participation in Level2.
Methods: People joining Level2 between 01/01/21 and 07/31/21 with 15 months of Level2 participation and 3 prior months of continuous enrollment in a participating commercial health plan were included in the analysis. We used CGM-derived GMI and A1c values available from a database of de-identified administrative claims to determine baseline and 12-month follow-up glycemic outcomes. Wilcoxon signed-rank tests were used to test for pair-wise improvements in glycemic measures between baseline and follow-up overall and stratified by baseline A1c.
Results: Of the 3,440 people identified for the analysis, a subset (N = 200) had both baseline A1c and valid GMIs during follow-up. This subset was 53% (N = 106) male with mean age of 55.4 (SD = 8.5) years and had mean baseline A1c of 7.97% (SD = 1.02). The median difference between baseline A1c and follow-up GMI was -0.83% (p &lt; 0.001) overall, with significant differences of -0.82% (p &lt; 0.001) and -2.41% (p &lt; 0.001) in those with baseline 7% &lt; A1c &lt; 9% and A1c &gt; 9%, respectively. Over 50% of people had a decrease in glycemic outcome of more than 0.5%, with 67% and 95% of people improving by more than 0.5% in the 7-9% and &gt; 9% groups respectively. There were 87 people that also had an A1c at 12-month follow-up; the median difference in A1c was -0.65% (p &lt; 0.001), 50% had a decrease of more than 0.5%, and median difference between GMI/A1c follow up values was 0.13 (p = 0.11).
Conclusion: Level2 members with baseline A1c and follow-up GMI saw significant improvements in glycemic control over one year of engagement. These improvements were observed for measures of both follow-up A1c and GMI.

Disclosure
S.Bacon: Employee; Optum Labs, Research Support; Level2. N.Thompson: Employee; Level2, Stock/Shareholder; UnitedHealth Group. D.Ferrell: Stock/Shareholder; Level2, Medexpress Urgent Care. J.Fung: Employee; Level2.
948-P: Glycemic Monitoring Using the Dexcom G6 in Cardiac Surgery
Authors: INSLER STEVEN R. , WAKEFIELD BRETT J. , BENA JAMES F. , DEBS ANDREA , BRAKE KELLY , NWOSU IKENNA , CECILIA LANSANG M. 
Published: June 2023

Hyperglycemia is associated with adverse post-surgical outcomes. We evaluated Dexcom G6 accuracy following cardiac surgery. A clinical glucose dataset included 1428 readings of 29 patients, Dexcom data included 45,645 measures. Clinical and Dexcom measures were restricted at least 1 hour after prior measure. Matching clinical and Dexcom measures were required within 5 min. Data were included at least 2 hr after Dexcom insertion and ICU admit. A dataset only measuring ≥24 hrs post ICU admit was created to explore device stability. Patients remained on Dexcom until discharge or 10 days postop. The population was 71% male 14% with known diabetes, 66% who went on intravenous insulin infusion. Figure 1a is Clarke error grid plot of all measures post-ICU admission: 53.5% were Zone A, 45.9% Zone B, and 0.6% (n=5) Zones D or E. Figure 1b uses restricted dataset beginning 24 hours post-ICU admission: 55.9% in Zone A, 43.9% in Zone B, and 0.2% in Zone D. MARD between clinical and Dexcom measures was 20.6% and 21.6% in post-ICU admission dataset and dataset excluding first 24 hours after ICU admission. Almost 100% of Dexcom G6 and clinical data matching points fell within areas considered as giving clinically correct decisions (Zone A) and clinically uncritical decisions (Zone B). If CGM readings are used for clinical decision-making, glucose targets or insulin dosing algorithms may need adjustment. As technology evolves, this device may limit glycemic fluctuations.

Disclosure
S.R.Insler: None. B.J.Wakefield: None. J.F.Bena: None. A.Debs: None. K.Brake: None. I.Nwosu: None. M.Lansang: Research Support; Dexcom, Inc., Abbott, Alertgy Inc.
955-P: A Novel Pathway to Equitably Promote Diabetes Technology Education and Awareness
Authors: WONG JESSIE J. , ADDALA ANANTA , ALAMARIE SELMA A. , HANES SARAH , KRUGMAN SARA , NARANJO DIANA , NELMES SIERRA , JACQUES ROSE KYLE , SCHNEIDER-UTAKA AIKA , TANENBAUM MOLLY L. , HOOD KOREY K. 
Published: June 2023

Aim: To examine engagement with DiabetesWise.org, a free, unbranded online platform for diabetes technology education and resources.
Methods: A sample of 458 participants (Mage=37.1, SD=9.73; 66% female; 81% type 1) with minimal diabetes device use were enrolled, invited to use DiabetesWise.org, had their website activity tracked, and completed surveys. Chi-square and t-tests evaluated correlates of DiabetesWise.org engagement.
Results: Most participants (69%) logged into DiabetesWise.org at least once, which was related to being female, public/no insurance, lower income, and receiving care in private practice/community health settings. Nearly 38% logged in multiple times, which was more likely for those receiving diabetes care through primary care. Most who logged in used the CheckUp tool (85%), of whom 74% used meter and injections. Most CheckUps recommended meter and pump (63%), followed by sensor and pump (35%), then sensors with Smart pump (2%). Cost was the most frequent concern (51%). Most participants who completed the CheckUp reported diabetes distress (76%).
Conclusions: DiabetesWise.org is an innovative, unbiased approach to promote diabetes device education and awareness. Initial engagement is highest among people with fewer resources; repeat use is more likely when diabetes care is received in primary care. DiabetesWise.org may help offset disparities in diabetes technology awareness and access.

Disclosure
J.J.Wong: None. M.L.Tanenbaum: None. K.K.Hood: Consultant; Cecelia Health. A.Addala: None. S.A.Alamarie: None. S.Hanes: None. S.Krugman: Consultant; Tandem Diabetes Care, Inc., Medtronic. D.Naranjo: None. S.Nelmes: None. K.Rose: Consultant; Diabeloop SA. A.Schneider-utaka: None.

Funding
The Leona M. and Harry B. Helmsley Charitable Trust; National Institutes of Health (K23DK121771)
967-P: Evaluation of CV and SD as Glucose Variability Metrics Based on Data from the GOLD and SILVER Trials
Authors: FATULLA PAVEL J. , IMBERG HENRIK , HIRSCH IRL B. , HEISE TIM , LIND MARCUS 
Published: June 2023

Objective: Coefficient of variation (CV) and standard deviation (SD) are key metrics of glucose variability (GV) in clinical care and research. We evaluated the adequacy of CV and SD as GV metrics over a wide range of mean blood glucose (MBG) levels in people with (T1D).
Methods: Analyses were performed on data from the randomized clinical GOLD and SILVER trials (n=160). Mixed effects models were used to evaluate the SD in relation to MBG during stable therapy, accounting for subject-specific trends and correlations in repeated measures data.
Results: The SD of blood glucose levels did not increase linearly with MBG level during CGM and blood glucose monitoring (BGM) treatments (both p&lt;0.0001). The lack-of-fit of the constant CV model was most distinct at high glucose levels &gt;12 mmol/L (Fig.). During BGM, a 33% reduction in MBG from 12 to 8 mmol/L was associated with a 20% (95% CI 14.9 to 24.9%) reduction in SD. The treatment effect on MBG-adjusted GV measured by CV was underestimated by 27%, compared to an adjusted analysis accounting for a quadratic relation between SD and MBG.
Conclusion: CV is not an optimal GV measure since SD changes less than the MBG during stable glucose-lowering therapy. A quadratic model adjusting SD to MBG is instead suggested when evaluating glucose-lowering therapies in people with T1D.
Fig. - Association between SD and MBG in the GOLD and SILVER trials during stable glucose-monitoring method CGM/SMBG.

Disclosure
P.J.Fatulla: None. H.Imberg: None. I.B.Hirsch: Consultant; Abbott Diabetes, Lifecare, Inc., Hagar, Research Support; Beta Bionics, Inc., Insulet Corporation, Dexcom, Inc. T.Heise: Advisory Panel; Novo Nordisk, Consultant; Gan & Lee Pharmaceuticals, Research Support; Adocia, AstraZeneca, Biocon, Crinetics Pharmaceuticals, Inc., Eli Lilly and Company, Genova, Novo Nordisk, Sanofi, Zealand Pharma A/S, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk. M.Lind: Consultant; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Research Support; Eli Lilly and Company.
969-P: Utilization of Continuous Glucose Monitoring (CGM) among Older Adults with Type 1 Diabetes (T1D)—An Analysis of Real-World Clinical Data
Authors: KAHKOSKA ANNA R. , WEINSTEIN JOSHUA M. , MUTHUKKUMAR RASHMI , YOUNG LAURA A. 
Published: June 2023

There is a dearth of data to quantify the utilization of diabetes technology among the growing population of older adults with T1D. We assessed differences in the use of CGM between those receiving endocrine care versus those not. We analyzed electronic health record (EHR) data from a large, public health care system in the southeast US, including 12 affiliated hospitals and over 200 academic and community-based practices. We included data from adults ages 65+ years with T1D in 04/01/2019-2022. We used logistic regression to model the likelihood of using CGM among those who had a visit with endocrinology in the past year versus those who did not. We used entropy balancing weights to create an analytic sample that was balanced on covariates. Our sample included 560 older adults with T1D, comprised of 32% using CGM. Of CGM users, 43% had an endocrinology appointment and 22% did not. Those with endocrine care had higher odds of using CGM compared to those who did not (aOR: 2.38, 95% CI: 1.65, 3.45). In probability terms, those with an endocrinology appointment in the previous year had a 17.8 percentage point (95% CI: 9.9, 25.6) higher probability of using CGM than those who did not (Fig). In a regional sample, we found relatively low CGM utilization and disparities therein across the receipt of endocrine care among older adults with T1D. Limitations include data from one healthcare system only.

Disclosure
A.R.Kahkoska: None. J.M.Weinstein: Research Support; Dexcom, Inc. R.Muthukkumar: None. L.A.Young: Research Support; Novo Nordisk, Rhythm Pharmaceuticals, Inc., Eli Lilly and Company, Jaeb Center for Health Research, Sanofi, Boehringer-Ingelheim.

Funding
National Institutes of Health (KL2TR002490, UL1TR002489)
97-OR: Antidiabetic TIX100 Improves NAFLD/NASH in Mice With and Without Diabetes and Obesity
Authors: JO SEONGHO , CHEN JUNQIN , JING GU , HABEGGER KIRK M. , SHALEV ANATH 
Published: June 2023

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are often associated with diabetes, but are becoming an even bigger world-wide epidemic with over 1 billion people affected. Nevertheless, while some diabetes medications such as glucagon-like peptide 1 and peroxisome proliferator-activated receptor agonists have shown some beneficial effects in the context of NAFLD with diabetes, no approved therapy is currently available for NAFLD/NASH. Interestingly, the new antidiabetic small molecule thioredoxin-interacting protein (Txnip) inhibitor, TIX100, effectively protected obese and diabetic db/db mice against steatosis. TIX100 also significantly reduced the elevated expression levels of lipogenic genes found in db/db livers, many of which have also been shown to be elevated in human NAFLD and are being pursued as potential therapeutic targets. Importantly, the beneficial effects of TIX100 were not due to any weight loss as the severe obesity of the db/db mice was not affected by the treatment. In contrast, TIX100 did demonstrated strong antidiabetic effects and normalized the blood glucose of db/db mice to the levels observed in lean control animals. Therefore, to address the question of whether TIX100 would still be effective in the absence of diabetes, obesity and any genetic leptin-receptor deficiency, we next used a validated choline-deficient, amino acid-defined, high-fat diet to induce NASH. Indeed, compared to control chow, this diet significantly increased plasma liver enzymes indicating liver injury, led to elevated hepatic expression of inflammatory and fibrogenic markers and resulted in clear histological changes, demonstrating lipid accumulation and steatosis as well as pronounced fibrosis and upregulation of liver macrophages. Excitingly, TIX100 treatment blunted or improved all these pathological changes. Thus, TIX100 may not only be effective in diabetes, but may also provide an attractive novel approach for NAFLD/NASH.

Disclosure
S. Jo: None. J. Chen: None. G. Jing: None. K.M. Habegger: Research Support; Eli Lilly and Company, Novo Nordisk. Consultant; Glyscend Inc. Stock/Shareholder; Glyscend Inc. Research Support; Glyscend Inc. A. Shalev: Other Relationship; TIXiMED.

Funding
National Institutes of Health (R01DK078752); Human Islet Research Network (U01DK120379)
970-P: Effectiveness of Continuous Glucose Monitoring Use for Diabetes Management in an Academic Endocrinology Practice
Authors: GRENNAN KRISTA N. , RIZVI ALI A. , KATHURIA AMITA 
Published: June 2023

The achievement of individualized glycemic targets in patients with diabetes is often a significant challenge. Empowering patients with technological tools in the ambulatory setting can pave the way for improved glucose control. We retrospectively examined the records of 94 patients managed in an academic endocrinology practice who were referred for management of diabetes over a 12-month period. Forty-one patients (18 females and 23 males, average age 57.7 years), who had at least two hemoglobin A1c measurements and two 14-day periods of personal continuous glucose monitoring (CGM) data, were included in the analysis. Nine patients had type 1 and 32 patients had type 2 diabetes. Eight patients were not on any insulin therapy, 8 patients were on basal insulin, 18 were using multiple daily insulin injections, and 7 were managed with insulin pump therapy. A multidisciplinary management approach was utilized. The pharmacologic regimen, including both insulin and other medications, was adjusted every 2-4 weeks via in-person visits and remote data upload as necessary. The average A1c was 8.4% at initial visit and 7.1% at the most recent follow-up, a decrease of 1.3% (p=0.0002). Review of CGM data showed that the average time-in-range (70-180 mg/dl) increased from 63.6% at first CGM download to 74.1% at a mean follow-up of 301 days (p=0.0097), while the amount of time in the hyperglycemic range (time-above-range, &gt;180 mg/dl) decreased from 33.5% to 22.1% (p=0.0079). Time-below-range (&lt;70 mg/dl) remained statistically unchanged, signifying no worsening of hypoglycemia. In conclusion, in ambulatory patients with diabetes and suboptimal glucose control, frequent and tailored adjustments in insulin and noninsulin medications aided by CGM use with close follow-up can lead to significant improvement in glycemic control. The latter, if sustained, can translate to fewer complications and greater patient satisfaction in the long-term.

Disclosure
K.N.Grennan: None. A.A.Rizvi: None. A.Kathuria: None.
972-P: Significant Weight Loss among People with Diabetes Despite Using Antidiabetic Medications Associated with Weight Gain after 90-Day Fitterfly Digital Therapeutics Program
Authors: PATANGE SONALI A. , VERMA RITIKA , SELVAN CHITRA , KALRA SANJAY , TIWASKAR MANGESH H. , METHIL VINOD , PATIL BHAGYASHRI V. , MALDE FORUM , NAIR VINEET , JOSHI SHILPA , SINGAL ARBINDER K. 
Published: June 2023

Background and Aim: Majority of people with Diabetes (PWD) use insulin and sulfonylurea (SU) as a necessary part of treatment regimen for glycemic control. Weight gain associated with these medications becomes a major challenge in effectively managing diabetes. The study evaluates the real-world effectiveness of the Fitterfly Diabetes program for weight management in PWD using antidiabetic medications associated with weight gain and weight loss.
Methods: Deidentified data of 79 participants (mean age: 45.4 ± 10.4 years, female: 54.4 %) with T2DM enrolled in the program was analyzed. Out of all participants, 28 people used antidiabetic medications associated with weight gain (thiazolidinediones, meglitinides, SU, and insulin) while 51 participants used antidiabetic medications associated with weight loss (biguanides, sodium-glucose cotransporter-2 inhibitors). Participants using medications from both groups or any other antidiabetic medications were excluded. Data has been shown as the median (IQR).
Results: After 90 days, participants using antidiabetic medications associated with weight loss showed reduction in HbA1c, weight, and waist circumference by -0.5 (-1.0, 1.0) %, -3.5 (-6.0, -1.1) kg, -5.0 (-9.0, -1.5) cm from the pre-program baseline of 7.6 (7.1, 8.4) %, 81.0 (68.0, 92.0) kg, and 105.0 (97.0, 110.0) cm respectively (P&lt;0.001 for all). Participants using antidiabetic medications associated with weight gain showed reduction in HbA1c, weight and waist circumference by -1.3 (-2.8, -0.02) %, -2.0 (-3.6, 0.9) kg, -2.0 (-4.7, 0.0) cm from the baseline of 9.6 (8.1, 10.7) %, 73.0 (66.2, 79.4) kg, and 99.0 (92.0, 105.8) cm respectively (P&lt;0.05 for all).
Conclusion: Significant reduction in weight, HbA1c, and anthropometric parameters was observed after 90 days on the program irrespective of the effect of antidiabetic medications on weight. PWD looking to lose weight are good candidates for such programs.

Disclosure
S.A.Patange: None. S.Joshi: Consultant; Fitterfly Health Tech Pvt. Ltd. A.K.Singal: Employee; Fitterfly Healthtech Pvt Ltd. R.Verma: Employee; Fitterfly Healthtech Pvt Ltd. C.Selvan: None. S.Kalra: Speaker's Bureau; Abbott, Bayer Inc., Novo Nordisk, Sanofi. M.H.Tiwaskar: None. V.Methil: None. B.V.Patil: None. F.Malde: None. V.Nair: None.

Funding
Fitterfly Healthtech Pvt. Ltd.
973-P: Wearing a Long-Term Implanted Continuous Glucose Monitoring (CGM) System vs. Conventional CGM in Adults with Type 1 Diabetes—The Physical Burden
Authors: ROBINSON LESLEY , OBEYESEKERE VARUNI R. , JONES HANNAH , KONG YEE W. , YI YUAN CHENG , BERTSCH KRISTA , ROUTH TIMOTHY L. , SIMS CATRIONA M. , BUTLER ANTHONY , TAN JASON , MARTHA SANDRA L. , O'NEAL DAVID N. 
Published: June 2023

Aim: To compare device tolerance & participant’s subjective experience wearing a surgically implanted long term glucose sensor versus conventional CGM in adults with Type 1 Diabetes.
Method: A prototype CGM (Eclipse 3 sensor, GlySens, San Diego, CA) with masked glucose data transmitted to a study iPhone was surgically inserted superficial to the rectus sheath in 8 Adults with Type 1 Diabetes (4 Female; Mean [SD] Age 56 [7]Y; BMI 25[3]). One daily fingerstick glucose calibration was required. Participants simultaneously wore a conventional TGA approved multi-Day sensor that also transmitted unblinded glucose information to the Study iPhone. The two systems were compared using a standardized 5-point Leichardt scale questionnaire completed by participants at day 1, 7 and 14 post-implantation and monthly thereafter for the duration of study participation.
Results: The investigation and comparator sensors were worn 100% and 98% percent of the time respectively over the study duration. Figure 1 summarizes the participant experience.
Conclusions: Over time there was a general trend by all participants to favour the GlySens sensor and experience over the comparator CGM.
.

Figure 1: Participant experience over 10 months of sensor implantation.

Disclosure
L.Robinson: None. J.Tan: None. S.L.Martha: Employee; GlySens Incorporated. D.N.O'neal: None. V.R.Obeyesekere: None. H.Jones: None. Y.W.Kong: None. C.Yuan: None. K.Bertsch: Employee; GlySens. T.L.Routh: Employee; GlySens Incorporated. C.M.Sims: Stock/Shareholder; Medtronic. A.Butler: Employee; GlySens Incorporated.
978-P: Correlation between Continuous Glucose Monitoring Metrics, Oral Glucose Tolerance Test, and HbA1c in Individuals with Impaired Glucose Tolerance
Authors: CHOW ELAINE , HE JIE , CHU NATURAL , POON EMILY W.M. , CHAN JULIANA C. 
Published: June 2023

Aims: Prediabetes is defined by HbA1c, fasting (FPG) and 2-hour plasma glucose (2h-PG) during 75 gram oral glucose test (OGTT) although these values often show poor concordance. Most diabetes prevention trials involved people with impaired glucose tolerance (IGT) diagnosed as 2h-PG 7.8-11.0 mmol/L. Continuous glucose monitoring (CGM) may detect early dysglycemia especially postprandial glucose excursions. We examined correlations amongst HbA1c, FPG, 1-h and 2h-PG during OGTT and CGM metrics in individuals with IGT.
Methods: 85 Chinese with IGT (mean±SD age 57±8 years, 51 (60%) female, BMI 26.6±4.0 kg/m2) participating in a lifestyle modification study had measurement of HbA1c, PG during 75g OGTT and 14-day CGM metrics (Freestyle Libre, Abbott) during a 3-week period at baseline. We examined their correlations using Spearman coefficients.
Results: The mean HbA1c was 5.8±0.3 %, FPG 5.3±0.4 mmol/L, 1h-PG 11.0±1.6 mmol/L and 2h-PG 8.5±1.3 mmol/L. For CGM, the mean glucose management index (GMI) was 5.9±0.2 % and time in tight glycemic range (TITR 3.9-7.8 mmol/L) was 86.7±6.6 %. There was a tendency for HbA1c (r=0.587, p=0.097) to correlate with GMI and negatively with TITR (r=-0.644, p=0.061). The respective correlates between FPG and GMI was r=0.623 (p=0.073) and that between FPG and TITR was r=-0.559 (p=0.118). There was weak correlation between 1h-PG and CGM time above range &gt;7.8 mmol/L (r=0.301, p=0.43). There was no correlation amongst 1h-PG, 2h-PG with HbA1c and other conventional CGM metrics.
Conclusions: In individuals with IGT, HbA1c and FPG showed better correlations with CGM metrics of average glycemia than PG values during OGTT. Future studies are needed to define the use of CGM and appropriate metrics in detection of IGT.

Disclosure
E.Chow: Research Support; Medtronic, Merck KGaA, Speaker's Bureau; Novartis, Bayer Inc., Sanofi. J.He: None. N.Chu: None. E.W.M.Poon: None. J.C.Chan: Board Member; Asia Diabetes Foundation, Consultant; Bayer Inc., Celltrion, Boehringer Ingelheim and Eli Lilly Alliance, Sanofi, Research Support; AstraZeneca, Servier Laboratories, Viatris Inc., Hua Medicine, Merck KGaA, Applied Therapeutics Inc., Lee Powder, Pfizer Inc., Speaker's Bureau; Novartis, Stock/Shareholder; GemVCare Ltd.

Funding
Health and Medical Research Fund (17180431)