Mean A1C Research Articles

1008-P: Trends in Improvement in Clinical Outcomes and Health Care Access in Underrepresented Youth with Type 1 Diabetes (T1D)

Background: There is a dearth of research focused on youth living with T1D from publicly insured and ethnically and racially diverse populations. Additionally, there are very few interventions that target social determinants of health (SDH) , despite their documented impact on diabetes outcomes, uptake of diabetes technology, and healthcare utilization. Novel Interventions in Children’s Healthcare (NICH) was developed as a systems and community-based intervention that targets SDH. We describe demographics and outcomes of youth with T1D enrolled in NICH services and related research. Methods: Youth with T1D were eligible if they had >=1 SDH and >=1 of the following in the year prior to enrollment: >=1 DKA hospitalizations after diagnosis, hemoglobin A1c (A1c) >=14%, >=2 emergency room visits or >=2 missed clinic visits. Demographics were collected from chart review and baseline surveys. Outcomes included continuous glucose monitoring (CGM) use, A1c values, hospital costs, and number of hospital days. Results: Participants (n=18) had a mean age of 15.1 (±2.4) years, 67% reported female gender, 89% self-identified as Black and/or Latinx, and 94% were publicly insured. CGM use significantly increased from 33% prior to NICH enrollment to 78% after enrollment (p=0.005) . Noted trends included a 1.0% decrease in mean A1c, 20% reduction in average per member, per month (PMPM) hospital costs, and 33% decrease in mean hospital days per month from the year prior to enrollment to the time since being enrolled in the NICH intervention. Conclusions: Enrollment of publicly insured and ethnically and racially diverse youth living with T1D in research is feasible despite limited existing interventional studies on these populations. Youth with T1D from underrepresented populations experience increased use of technology from participation in the NICH intervention, and they show encouraging trends in improved glycemic control, hospital cost savings, and healthcare utilization. Disclosure L.Yglecias: None. D.V.Wagner: None. J.C.Wong: Consultant; Provention Bio, Inc., Research Support; Dexcom, Inc., Tandem Diabetes Care, Inc. A.Reed: None. C.E.Noya: None. M.T.Mcgrath: None. A.Stone: None. M.Lodish: None. K.B.Spiro: None. I.C.Gomez: None. M.A.Harris: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (G-1911-03543)

283-OR: Neighborhood Disadvantage for Teens with Type 1 Diabetes (T1D) : Associations with Glycemic and Psychosocial Outcomes

Background: For teens with T1D, stress related to neighborhood disadvantage may exacerbate diabetes distress and contribute to disparities in care and outcomes. Objective: We explored associations among neighborhood disadvantage and clinical/psychosocial outcomes to identify patterns to inform future stress-resilience interventions. Methods: At baseline of a 2-site psychosocial intervention trial, 173 teens aged 13-18 years with T1D ≥1 year and elevated diabetes distress (57% Female, 2% American Indian/Alaska Native, 4% Asian, 15% Black, 11% Hispanic White, 6% Hispanic Non-White, 63% Non-Hispanic White, mean A1c= 8.7±2.1, 66% pump use) completed a demographics form and measures of resilience, diabetes distress, diabetes quality of life and family conflict.. We retrieved area deprivation index (ADI) scores for home addresses; this measure of neighborhood disadvantage considers factors related to income, education, employment, and housing quality at the census block group level and provides a state-relative decile score (higher scores indicate more disadvantage) . Fisher’s exact tests and regression analyses were used to examine associations of ADI with A1c and survey data. Results: Participant ADI spanned all deciles, M = 3.7± 2.4. Hispanic White/Non-White and Black teens and those not using pump had higher mean ADI. Higher ADI was associated with higher A1c (β=.18 [.06, 0.29], p=.003) and higher family conflict (β=.63 [.12, 1.1], p=.015) . ADI was not significantly related to resilience, diabetes distress, or quality of life. Conclusions: In a diverse sample of distressed teens with T1D, neighborhood disadvantage was associated with Hispanic ethnicity and Black race, lower pump use, poorer glycemic control and greater family conflict. Considering neighborhood disadvantage may help providers deliver personalized care to target systemic sources of stress, ultimately improving clinical and psychosocial outcomes. Disclosure M.B.O'donnell: None. M.E.Hilliard: None. J.Yi-frazier: None. B.Duran: None. C.Zhou: None. F.Malik: n/a. C.Pihoker: None. S.R.Scott: None. B.M.Ellisor: None. S.Garcia perez: None. D.Desalvo: Consultant; Dexcom, Inc., Insulet Corporation, Research Support; Insulet Corporation. Funding US National Institutes of Health R01DK121224

100-LB: Glycemic Control Using Recommended Settings in Youth and Adults with Type 1 Diabetes (T1D) —Minimed 780G System with the Calibration-Free Guardian 4 Sensor Results

Background: Safety and effectiveness outcomes of individuals using the MiniMed™ 780G system with the no-calibration Guardian™ 4 sensor during the first three months of use were analyzed. Methods: Participants (N=176, aged 7-75 years) with T1D used the Conformitè Europëenne-marked MiniMed™ 780G system. Safety and effectiveness endpoints (mean A1C, sensor glucose [SG], percentage of time spent at SG ranges and glucose variability) were collected over three months and summarized. Endpoints were also assessed when recommended settings (target of 100mg/dL and active insulin time of 2-3 hours) were used. Results: There was no DKA or severe hypoglycemia. At 3 months, A1C was 7.1% (N=164) , 7.2% (N=101) and 6.8% (N=63) for overall, pediatric and adult participants, respectively. Additional glycemic metrics are shown (Table) . With recommended settings, time spent in target range (TIR, 70-180 mg/dL) was 74.7% (N=119) , 72.7% (N=65) and 77.0% (N=54) , respectively, and time spent below range (TBR<70mg/dL) was 2.1%, 2.4% and 1.7%, respectively. Conclusion: This analysis demonstrates that individuals safely exceeded international consensus-recommended TIR and TBR goals when using the MiniMed™ 780G system with the Guardian™ 4 sensor. These results mirror those of the pivotal trial during use of the calibration-requiring Guardian™ sensor 3. Disclosure R. A. Vigersky: Employee; Medtronic. C. Pihoker: None. R. Pop-busui: Advisory Panel; Averitas Pharma, Inc., Boehringer Ingelheim International GmbH, Nevro Corp., Novo Nordisk, Reata Pharmaceuticals, Inc., Regenacy Pharmaceuticals, Inc. J. Reed: None. J. Sherr: Advisory Panel; Bigfoot Biomedical, Inc., Cecelia Health, Insulet Corporation, Medtronic, Vertex Pharmaceuticals Incorporated, Consultant; Insulet Corporation, Lexicon Pharmaceuticals, Inc., Research Support; Dexcom, Inc., Insulet Corporation, Jaeb Center for Health Research, JDRF, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Speaker’s Bureau; Lilly Diabetes. D. I. Shulman: Advisory Panel; Medtronic. R. H. Slover: None. J. Thrasher: Advisory Panel; Eli Lilly and Company, Medtronic, Board Member; Medtronic, Consultant; Eli Lilly and Company, Medtronic, Research Support; Eli Lilly and Company, Medtronic, Novo Nordisk, Speaker’s Bureau; Bayer AG, Eli Lilly and Company, Medtronic, Sanofi. X. Chen: Employee; Medtronic, Medtronic. M. Liu: None. T. L. Cordero: Employee; Medtronic, Medtronic. B. W. Bode: Advisory Panel; CeQur SA, MannKind Corporation, Medtronic, Novo Nordisk, Zealand Pharma A/S, Consultant; Bigfoot Biomedical, Inc., Research Support; Abbott, Beta Bionics, Inc., Dexcom, Inc., Diasome, Dompé, Eli Lilly and Company, Insulet Corporation, IQVIA Inc., Jaeb Center for Health Research, Medtronic, Novo Nordisk, Provention Bio, Inc., REMD Biotherapeutics, Sanvita Medical, Senseonics, ViaCyte, Inc., Speaker’s Bureau; Abbott, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Insulet Corporation, MannKind Corporation, Novo Nordisk, Sanofi, Xeris Pharmaceuticals, Inc., Stock/Shareholder; AgaMatrix, Glytec, LLC. M. Vella: Employee; Medtronic. A. S. Rhinehart: Employee; Medtronic, Stock/Shareholder; Medtronic. J. Shin: Employee; Medtronic. R. L. Brazg: Research Support; Abbott Diabetes, Lilly Diabetes, Medtronic, Novo Nordisk, Roche Diagnostics, Senseonics. B. A. Buckingham: Advisory Panel; Arecor, Lilly Diabetes, Medtronic, Other Relationship; Insulet Corporation, Research Support; Insulet Corporation, Lilly Diabetes, Medtronic. A. L. Carlson: Advisory Panel; MannKind Corporation, Employee; Bright Health Group, Other Relationship; Medtronic, Research Support; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Novo Nordisk, Sanofi, UnitedHealth Group. K. B. Kaiserman: Advisory Panel; Medtronic, Consultant; Medtronic, Employee; MannKind Corporation, Research Support; Medtronic, Speaker’s Bureau; Medtronic, Stock/Shareholder; MannKind Corporation. M. Kipnes: None. D. R. Liljenquist: None. A. Philis-tsimikas: Advisory Panel; Bayer AG, Novo Nordisk, Research Support; Lilly Diabetes, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Viking Therapeutics.

954-P: Barriers to Enrollment in a Continuous Glucose Monitoring Pilot Program for Medicaid Patients

Background: CGM usage is associated with lower A1c and decreased diabetes-related hospitalizations and emergency room visits. However, there are still many barriers to accessing CGM, with the most prominent being cost and cumbersome insurance eligibility criteria. We aimed to explore and describe barriers to enrollment in a pilot CGM program for Ohio Medicaid patients. Methods: Patients seen at a large academic medical center in the past year, a diabetes diagnosis and A1c >8.5% were identified by Medicaid. The pilot CGM program removed the need for the common CGM coverage requirements of prior authorizations, multiple daily injections, and glucose logs. Patients had to use a mobile CGM application to participate. Patients were contacted via phone and agreeable participants saw a diabetes care and education specialist for CGM education and initiation and an ambulatory care pharmacist for follow-up. Baseline characteristics were obtained via chart review and data were analyzed using descriptive analyses. Results: N=43 were contacted for enrollment (mean age 43.4±10.8 years; 88.4% female; 93.0% African American; mean A1c 10.2±2.0%; 58.1% on multiple daily injections) . In total, n=16 (37.2%) agreed to participate, n=17 (39.5%) were unable to be contacted, n=8 (18.6%) were already actively using CGM and n=2 (4.7%) declined to participate. Older age predicted agreeance to participate (β=0.350, p=.022) . Those who agreed to participate were more likely to see primary care over endocrinology for diabetes care X 2 (1, N=43) = .62, p=0.018. At one-month post-program enrollment, only n=2 (4.7%) patients successfully started CGM. Discussion: Although over one-third of participants agreed to enroll in the CGM pilot, there was low CGM initiation. Removal of cost and insurance coverage requirements alone did not result in high CGM adoption. Patients were difficult to reach via phone and offering CGM initiation and education during primary care appointments may improve CGM access and utilization. Disclosure W.Chen: None. J.E.Blanchette: Advisory Panel; Cardinal Health, Provention Bio, Inc., Board Member; JDRF, Consultant; WellDoc, Other Relationship; Insulet Corporation, Tandem Diabetes Care, Inc., Research Support; Association of Diabetes Care & Education Specialists. J.Macwilliams: None. B.Hatipoglu: None.

604-P: Self-Efficacy: Relationships with Diabetes Self-Care, Medication Adherence, and Diabetes Distress among Diverse Adults with Suboptimally Controlled Type 2 Diabetes (T2D)

Background: Self-efficacy has been inconsistently operationalized in diabetes, with measures often failing to focus on confidence in one’s capacity to perform self-care. Measures that focus on confidence have been shown to be closely associated with self-care behaviors, but received limited psychometric evaluation and have not been examined in relation to diabetes distress. Methods: We examined the reliability and factor structure of a previously published 8-item scale evaluating self-efficacy for diabetes self-care activities adapted by Sarkar and colleagues in 2006. We assessed cross-sectional relationships in baseline data with overall diabetes self-care, medication adherence, and diabetes distress among 812 predominantly Latino adults with T2D. They participated in a self-management support trial (Female = 56.9%; Latino = 86.5%) ; Mean (SD) Age and A1C = 59.2 (10.8) , 9.3 (1.8) , respectively. Results: The scale demonstrated good internal reliability in the overall sample (α = .80) and among English (n = 179, α = .77) and Spanish (n = 633, α = .80) speakers. Exploratory factor analysis yielded a single factor, explaining 42% of the variance in self-efficacy scores. After adjusting for covariates, higher self-efficacy was independently associated with better diabetes self-care (β = .56, p < .001) , better medication adherence (β = .30, p < .001) , and lower diabetes distress (β = -.42, p < .001) . Moderation analyses suggested consistency in these relationships across age, gender, SES, language, health literacy and numeracy. Conclusions: To our knowledge, the factor structure of this diabetes self-efficacy scale has not been previously evaluated. Findings suggest self-efficacy is a robust correlate of T2D self-care, medication adherence, and distress. The scale is appropriate for predominantly disadvantaged English and Spanish speakers with varying health literacy and numeracy levels. Disclosure R.Fang: None. C.J.Hoogendoorn: None. L.D.Pappalardo: None. H.Mendez-rodriguez: None. E.A.Walker: None. J.S.Gonzalez: Consultant; Virta Health Corp. Funding National Institutes of Health (R18 DK098742) National Institutes of Health (RDK104845) National Institutes of Health (RDK121298) National Institutes of Health (RDK121896) Einstein-Mount Sinai Diabetes Research Center (P30 DK020541) New York Regional Center for Diabetes Translation Research (P30 DK111022)

1246-P: Risk Factors Associated with In-Hospital Mortality In Patients with COVID-and Type 2 Diabetes in Chile

Background: Risk factors associated with in-hospital mortality (IHM) have been identified among patients with T2D hospitalized for COVID-19, including male sex, elderly, impaired renal function and poor metabolic control. At a local setting, data regarding the impact of these variables are lacking. Aim: To assess the association between risk factors and IHM in patients with T2D admitted for COVID-19. Methodology: This is a descriptive observational study. We used electronic health records data provided by a tertiary-level hospital in Chile, between April to July 2020. Included demographic, clinical, and laboratory variables of inpatients with T2D and a positive PCR for SARS-CoV-2. Multiple logistic regression analysis was used to identify predictors of IHM. Results: We identified 200 individuals. Mean age was 63.0±13.6 years, 59.5% (n=119) were adults aged ≥60 years and 58% were male (n=116) . Mean admission glycemia and A1C were 216±120 mg/dl and 8.8±2.4%, respectively. Median admission serum creatinine was 0.8 mg/dL (p25-p75: 0.6-1.1 mg/dL) . A total of 38 patients (19%) died during hospitalization, of these 26.3% (n=10) died within the first week, 63.2% (n=24) between 2-4 weeks, and 10.5% (n=4) after the first month. Age ≥60 years (OR 2.9, 95%CI [1.3-6.7], p=0.01) and elevated admission serum creatinine (OR 1.28, 95%CI [1.04-1.58], p=0.01) were identified as independent predictors of IHM in multiple logistic regression analysis. No association was identified for sex, admission glycemia or A1C, however 37% of our sample lacked an A1C measurement, which could imply a bias. Conclusion: Our study confirms that older age and elevated admission serum creatinine are risk factors for IHM in patients with T2D hospitalized for COVID-19. These results reinforce the importance of identifying risk factors to improve outcomes related to COVID-during in-hospital stay of patients with T2D. We must promote A1C measurement at admission for all patients with diabetes or hyperglycemia. Disclosure O.Vargas: None. P.Poblete: None. E.Vega: None. L.Toro: None. P.C.Gomez: None. A.Villarroel: None. M.G.Sanzana gonzalez: None. C.Vargas: None.

676-P: Difference in CGM Estimated Hemoglobin A1c in Adults with Type 1 and Type 2 Diabetes

The relationship between mean glucose measured using a continuous glucose monitor (CGM) and hemoglobin A1c (A1C) was evaluated in adults with type 1 diabetes (T1D) or type 2 diabetes (T2D) . Data from participants enrolled in the MOBILE, DIAMOND, and WISDM trials and randomized to the CGM arm were used in the analysis. Follow-up visits collected A1C measured by a central laboratory at 8 months post-randomization in the MOBILE study and 6 months post-randomization in DIAMOND and WISDM studies. CGM data collected in the 91 days prior to the follow-up visit, when glucose control was thought to be relatively stable, was used to compute mean glucose. A least squares linear regression model estimated A1C using mean glucose as a predictor, and models were fit separately by diabetes type. 199 participants with T1D and 175 participants with T2D were included in the analysis. Mean age was 57±15 years among those with T1D and 58±years among those with T2D. Estimated A1C was similar but slightly higher for participants with T2D vs. T1D for the same mean glucose (Table) . Analyses restricting to White non-Hispanic cohort yielded similar results. Spearman partial correlation coefficients for mean glucose vs. A1C were 0.76 for T1D and 0.84 for T2D. Estimates of A1C based on CGM measured mean glucose were slightly higher for those with T2D compared with T1D. Disclosure M.Salam: Advisory Panel; Lilly Diabetes, Consultant; Neurocrine Biosciences, Inc., Research Support; Mylan N.V., Neurocrine Biosciences, Inc. R.Bailey: None. P.Calhoun: None. J.B.Mcgill: Advisory Panel; Gilead Sciences, Inc., Lilly Diabetes, MannKind Corporation, Novo Nordisk A/S, Provention Bio, Inc., Salix Pharmaceuticals, Consultant; Bayer AG, Boehringer Ingelheim International GmbH, Research Support; Dexcom, Inc., Novo Nordisk. R.Beck: Consultant; Diasome, Eli Lilly and Company, Insulet Corporation, Novo Nordisk, Research Support; Ascensia Diabetes Care, Beta Bionics, Inc., Bigfoot Biomedical, Inc., Dexcom, Inc., Eli Lilly and Company, Medtronic, Novo Nordisk, Roche Diabetes Care, Tandem Diabetes Care, Inc. D.A.Price: Employee; Dexcom, Inc.

4-LB: Remission of Type 2 Diabetes and Improvement in Metabolic Markers with the Twin Precision Treatment Technology (TPT) —A Multicenter, Randomized, Controlled Trial

The study was designed to determine the effect of TPT vs. standard care (SC) on change in A1C and T2DM remission at 90 day intervals, in addition to multiple secondary endpoints. The TPT intervention uses the Whole-Body Digital Twin Platform, with AI and Internet of Things, to integrate multi-dimensional data to give precision nutrition and health recommendations via the TPT app and by coaches. Baseline mean age, diabetes duration and A1C obtained in 319 pts were 45y (±9.7y) , 3.9y (±2.9y) and 9.0% (±1.9%) , respectively. Interim analysis of 262 pts (TPT n=199; SC n=63) who reached 180d showed 94.9% (189/199) of TPT pts had A1C < 6.5%, on no medications or metformin only; 83.9% (167/199) achieved diabetes remission based on ADA criteria. All 9 insulin-using pts stopped insulin before 90d. The Table shows significant improvement in secondary endpoints in TPT pts at baseline and 180d, including A1C (9.0% to 5.7%, P<0.001) . TPT pts had -3.3% change in A1C (95% CI -3.6 to -3%, P=<0.001) compared to SC with -0.39% (95% CI -0.73 to -0.043%, P=0.028) , with a significant between-group difference (P=<0.0001) . The TPT intervention in pts with T2DM allowed for significant reduction in A1C, diabetes remission (∼84%) and improvement in multiple metabolic parameters at 6 months. Future long-term studies are needed to support these initial findings. Disclosure P. Shamanna: None. M. Thajudeen: None. A. Keshavamurthy: None. F. R. Kaufman: Consultant; Twin Health, Employee; Senseonics. S. R. Joshi: Advisory Panel; AstraZeneca, Consultant; Biocon, Franco India, Glenmark Pharmaceuticals, Marico, Sanofi, Twinhealth, Zydus Cadila, Speaker’s Bureau; Abbott, Serdia Pharmaceuticals (India) Pvt. Ltd. L. Shah: None. J. Mohammed: None. M. Mohamed: None. T. Poon: None. M. Dharmalingam: None. B. Saboo: None. S. Damodharan: None. A. Vadavi: None.

127-LB: Real-World A1C Changes and Prescribing Provider Types among T2DM Patients Initiating Treatment with Oral Semaglutide

Purpose: To describe A1c changes and characteristics of patients with type 2 diabetes mellitus (T2DM) initiating oral semaglutide (sema) , by baseline A1c ≥9% and <9%. Methods: Retrospective analysis using medical and pharmacy claims data from 11/01/2018-12/31/2020 from commercial and Medicare Advantage with Part D enrollees, aged 18+. Patients had ≥1 claim for oral sema (first claim=index date) , ≥1 T2DM diagnosis, continuous enrollment for 12 months before and including index (baseline) , and 6 months post-index (follow-up) , pregnant females excluded. Results were stratified by patient’s last baseline A1c (< or ≥9%) . Results: Patients initiating oral sema who had ≥1 baseline A1c (n=994) had a mean age of 59 years, 50.4% were male, and 63.1% had commercial coverage. Among patients with baseline and follow-up A1c values (n=652) , mean A1c change was -0.8% (1.49) . Among a subset with ≥90 days of continuous oral sema treatment and ≥1 A1c after ≥90 days of treatment (n=200) , the A1c ≥9% group had a mean A1c change of -2.7% (2.1) . While half of index treatments were prescribed by primary care and internal medicine providers (50.6% overall) , a higher percentage of patients with A1c <9% had oral sema prescribed by an endocrinologist (24.4% vs. 16.1%; p=0.006) . Conclusions: Results suggest that oral sema is an effective T2DM treatment in the real-world, especially among patients with A1c values ≥9% prior to treatment initiation. Patients with T2DM in this population are more often prescribed oral sema by primary care physicians than endocrinologists. Disclosure M. S. Frazer: None. C. Swift: Employee; Novo Nordisk. M. Leszko: None. A. Sargent: None. E. Buysman: None. S. B. Alvarez: Employee; Novo Nordisk, UnitedHealth Group. J. Noone: Employee; Novo Nordisk. M. Guevarra: None.

1168-P: Complications in the Second Year of LANDMARC—Longitudinal Nationwide Study on Real-World Outcomes of Type 2 Diabetes in India

Macrovascular and microvascular complications were evaluated during the first 2 years of LANDMARC, a 3-year prospective observational study (CTRI/2017/05/008452) that included participants with T2D on ≥2 antihyperglycemic medications. Out of 6234 evaluable participants (mean baseline values - age: 52.1 years, T2D duration: 8.59 years and A1C: 8.05%) , 5318 participants completed the 2-year follow-up. The mean A1C decreased by 0.58% (baseline: 8.05%) in 2 years. The microvascular complications were frequent in 17.6% participants (1096/6234) ; while the incidence of macrovascular complications was 1.1% participants (66/6234) . Neuropathy was the most commonly reported complication (baseline: 11.8% and 2-years: 14.4%) . Overall, complications were more common in participants with BMI ≥23 kg/m2, A1C ≥7% or having CV risk factors (Table) . A total of 41 deaths were reported; of which 30 deaths were attributed to CV causes (sudden death [n=19], myocardial infarction [n=9], stroke [n=1], and coronary artery procedure [n=1]) . The 2-year results indicate more complications among those who were overweight, or with suboptimal glycemic control or having CV risk factors. Neuropathy was the predominant T2D complication. These results offer insights into disease progression and suggest the need for controlling risk factors and timely treatment adjustment in participants with T2D. Disclosure N. Rais: None. A.K. Das: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Roche Diagnostics, Sanofi. Speaker's Bureau; Dr. Reddy’s Laboratories Ltd., Lupin Pharmaceuticals, Inc., USV Private Limited. S. Joshi: Advisory Panel; Abbott, Boehringer Ingelheim International GmbH, Dr. Reddy’s Laboratories Ltd., Eli Lilly and Company, Novo Nordisk, Roche Diabetes Care. Consultant; Biocon, Glenmark Pharmaceuticals, Sanofi, USV Private Limited. A. Mithal: Advisory Panel; Eris Lifesciences Ltd. Consultant; Glenmark Pharmaceuticals, Lupin Pharmaceuticals, Inc., USV Private Limited. Speaker's Bureau; Abbott Diabetes, AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Dr. Reddy’s Laboratories Ltd., Novartis AG, Novo Nordisk, Sanofi. S. Kalra: Speaker's Bureau; Boehringer Ingelheim International GmbH, Novo Nordisk A/S, Sanofi. A. Unnikrishnan: Advisory Panel; Intas Pharmaceuticals Ltd. Speaker's Bureau; Abbott, AstraZeneca, Boehringer-Ingelheim, Sanofi. Other Relationship; Novo Nordisk, Serdia Pharmaceuticals (India) Pvt. Ltd., Torrent Pharmaceuticals Ltd. H. Thacker: None. B. Sethi: None. S. Chowdhury: None. A. Nair: None. S. Mohanasundaram: Employee; Sanofi. V. Salvi: Employee; Sanofi. D. Chodankar: Employee; Sanofi. C. Trivedi: None. S. Wangnoo: None. A.H. Zargar: Advisory Panel; Sanofi. Speaker's Bureau; AstraZeneca, Biocon, Boehringer Ingelheim International GmbH, Intas Pharmaceuticals Ltd., Janssen Pharmaceuticals, Inc., Lupin Pharmaceuticals, Inc., Novo Nordisk, USV Private Limited. K. Kumar: None. Funding Sanofi, India

88-OR: Discordance between Glucose Management Indicator (GMI) and A1C in Well-Controlled Type 1 Diabetes (T1D) and Nondiabetic Population

Glucose management indicator (GMI) was developed using a linear regression formula to estimate A1C from mean glucose. The study population that was used to develop and validate GMI had a mean A1C of 7.3 ± 0.8%. Therefore, we hypothesize that GMI may overestimate A1C in those with A1C <6.5%. Non-pregnant adults (≥18 years) with T1D for ≥2 years and using Dexcom G6 for ≥6 months from Barbara Davis Center for Diabetes (n=93, 40.6 ± 12.6 years, female 58.4%, A1C 5.9 ± 0.4%) and subjects from “Glucose Sensor Profile in Healthy Non-diabetic Subjects” study (N=153, 31.2± 21.0 years, female 66.0%, A1C 5.1 ± 0.3%) were included. All participants used Dexcom G6 continuous glucose monitor (CGM) . Up to days (healthy participants) and 15 days (T1D patients) of CGM data was compared with laboratory A1C. Discordance in GMI and A1C is presented by A1C <5.7% (healthy participants) and A1C between 5.7-6.4% (T1D patients) . The degree of discordance between GMI and A1C was higher in those with A1C<6.5% compared to original GMI development cohort [Figure 1A]. The differences between A1C and GMI were largely negative in both A1C categories (A1C <5.7% and A1C of 5.7-6.4%) . On average, GMI was 0.59% higher in the A1C < 5.7% group and 0.49% higher in the A1C 5.7 - 6.4% group (both p < 0.0001) [Figure 1B and 1C]. The current GMI formula may overestimate A1C and may need to be re-assessed for those with A1C <6.5%. Disclosure V.Shah: Advisory Panel; Medscape, Sanofi, Consultant; Dexcom, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Novo Nordisk. T.B.Vigers: None. L.Pyle: None. S.Dubose: None. P.Calhoun: None. R.M.Bergenstal: Advisory Panel; Hygieia, Medtronic, Roche Diabetes Care, Zealand Pharma A/S, Consultant; Abbott Diabetes, Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur SA, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, Onduo LLC, Sanofi, United HealthCare Services, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi.

1029-P: INNOVATIONS: Improving Care of Patients with Type 1 Diabetes Mellitus through Utilization of Telemedicine and Outreach

Frequent diabetes support and glucose review via telehealth may help decrease hospitalization rates and improve glycemic control for high-risk youth. This support may also help with uptake and durability of diabetes technology. Eligible youth had insulin dependent diabetes, Medicaid insurance, ages 8-18, and willing to use a continuous glucose monitor (CGM) . The quality improvement project provided weekly check-ins with a diabetes educator, visit reminders, and monthly video visits with a nurse practitioner. 10-item Likert questionnaire (1=not at all comfortable; 5=very comfortable) given at baseline assessed diabetes related comprehension. A needs assessment evaluated a family’s interest in learning about certain areas of diabetes management. A1c was done at quarterly clinic visits and ED/hospitalization rates tracked. 3 month data presented. Youth (N=29, 62% female, 79% NHB) , aged 8-18 yr, diabetes duration 6.3±4.3 years, had baseline mean A1c 12.3±1.7%; 28% on CSII. Mean baseline score of diabetes comprehension was 4.0±0.6. Baseline A1c correlated negatively with diabetes comprehension (r=-0.40, p<0.05) . There was no difference between A1c at baseline and at 3 months (p=0.14) and no differences in ED/hospitalization rates compared to 2 years prior when controlled for time. There was an increase in CGM use from 3 months prior to the intervention (36% of participants) to 3 months after baseline (64%) , though this was not statistically significant (p=0.09) . There was no difference in clinic visit adherence at 3 months despite reminders. Preliminary data reveal that those patients with the highest HbA1c often have the lowest comprehension regarding optimal diabetes care, and may benefit from education reinforcement. CGM use increased with greater understanding of this technology, and may become clinically significant over time. Further research is needed to assess the long-term impact of telehealth interventions. Disclosure L.E.Rasbach: None. V.Purrington: None. D.Adkins: None. R.Benjamin: None. Funding Innovation Health (2933476)

631-P: Youth with Type 1 Diabetes (T1D) and High Social Risk: Predictors of Glycemic Control

Objective: A subset of youth with T1D experience ongoing challenges with diabetes self-management, often due to social risk. This 5-site study examined which social factors were associated with hemoglobin A1c (A1c) , specifically in youth with preexisting high A1c. Methods: Youth with T1D for more than 1 year were included if between 12-17 years old and with at least one A1c value ≥10% in the past year. Chart review captured A1c values, and caregivers reported on social risk- with 16 risk variables (e.g., housing security, employment status) in initial analyses. Multivariable linear regression model included A1c from 1 year prior to enrollment as an outcome and backwards stepwise procedure retained covariates with p<0.10. Results: Participants (n=130) had a mean age of 14.4 (±1.6) years and mean A1c of 11.1 (±1.7) ; 49% reported male gender; 51% self-identified as Non-Hispanic White. A1c prior to study enrollment was predicted to be 0.68 lower in individuals whose caregiver had at least a college education and 0.65 higher when caregivers were divorced or separated. Conclusion: Findings suggest that, in youth with preexisting difficulty with glycemic variability, caregiver education level and marital status are associated with A1c - and, while not causal, may reflect unique value when screening for risk. This cohort will be followed to determine which factors predict future health concerns and associated need for intervention. Disclosure D.V.Wagner: None. L.Yglecias: None. J.J.Flores garcia: None. E.Salcedo-rodriguez: None. A.Torres sanchez: None. P.Dasika: None. J.Viana: None. D.D.Williams: None. D.Ferro: None. M.A.Harris: None. M.A.Clements: Consultant; Glooko, Inc., Research Support; Abbott Diabetes, Dexcom, Inc. J.Raymond: None. J.C.Wong: Consultant; Provention Bio, Inc., Research Support; Dexcom, Inc., Tandem Diabetes Care, Inc. A.Reed: None. D.Naranjo: None. C.Jenisch: None. C.Cruz: None. S.Mitchell: None. Funding JDRF

221-OR: Safety and Effectiveness of Insulin Aspart vs. Other Bolus Insulins in Women with Preexisting Type 1 Diabetes during Pregnancy: A Post Hoc Analysis of the EVOLVE Study

Aims: This post hoc analysis of the real-world EVOLVE study investigated safety and effectiveness of insulin aspart (IAsp) vs. other bolus insulins in pregnant women with pre-existing type 1 diabetes. Methods: Maternal, pregnancy and infant outcomes were assessed in women receiving bolus insulins on enrolment and during pregnancy in a prospective cohort, by estimating risk differences following adjustment for potential confounders using propensity score matching. Results: Insulin treatment in early pregnancy and maternal, pregnancy and infant outcomes are presented (Table) . In total, 1434 women were treated with IAsp and 4with other bolus insulins on enrolment. Significantly lower mean A1c values in the third trimester were observed with IAsp vs. other bolus insulins (adjusted % point difference -0.16% [95% CI -0.28;-0.05]; p=0.005) . No significant differences were seen with IAsp vs. other bolus insulins in the risk of adverse maternal-fetal outcomes including major maternal hypoglycemia, abortion, pre-term delivery, pre-eclampsia, birthweight large for gestational age, major congenital malformations and perinatal or neonatal death, in the matched dataset. Conclusion: Compared with other bolus insulins, IAsp was associated with significantly lower A1c during the third trimester and a similar risk of adverse pregnancy outcomes. Disclosure E.Mathiesen: Consultant; Novo Nordisk A/S, Speaker's Bureau; Novo Nordisk A/S. On behalf of the evolve study group: n/a. A.Alibegovic: Employee; Novo Nordisk A/S, Novo Nordisk A/S. G.Anil: Employee; Novo Nordisk Global Business Services. F.P.Dunne: None. T.Halasa: Employee; Novo Nordisk A/S. M.Ivanisevic: None. D.R.Mccance: None. R.Baastrup nordsborg: Employee; Novo Nordisk A/S, Stock/Shareholder; Novo Nordisk A/S. P.Damm: Advisory Panel; Novo Nordisk A/S. Funding Novo Nordisk A/S

172-LB: Early-Onset Type 2 Diabetes Is Associated with Higher A1C and Glycemic Variability in a Cohort of Veterans followed Prospectively

Introduction: Early-onset type 2 diabetes in adults (eoT2D; T2D diagnosed between 18 and 39 years) is increasingly prevalent and is associated with high rates of diabetic complications. We hypothesized that eoT2D would be associated with adverse glycemic parameters (e.g., higher A1c and glycemic variability) . Methods: We used data from the Veterans Administration Diabetes Risk Cohort, a dynamic cohort of all U.S. Veterans enrolled in the Veterans Health Administration since 1/1/08 who were free from diabetes at enrollment. Among participants who developed T2D (n=936,596) , we fit linear regression models examining associations of age at detection with A1c-based measures of glycemia. Results: A ten-year younger age at detection was associated with a 0.24% higher A1c at T2D detection, a 0.29% higher mean A1c, a 0.02% higher A1c coefficient of variation (CV) , and a 0.02% higher A1c average real variability (ARV) . Compared to T2D with onset after 40 years, eoT2D was associated with a 0.73% higher A1c at T2D detection, a 0.82% higher mean A1c, a 0.05% higher CV, and a 0.04% higher ARV (all p<0.0001, Table 1) . Conclusions: eoT2D is associated with higher A1c and glycemic variability. Further study of glycemic trajectories, modifiable risk factors, and clinical course in this T2D subgroup are needed. Disclosure S. Avramovic: None. M. D. Schwartz: None. D. Enquobahrie: None. E. J. Boyko: None. P. Wander: None.

121-LB: Prescribing Patterns of Sodium–Glucose Cotransporter 2 Inhibitors in Patients with Heart Failure With or Without Type 2 Diabetes

The goal of heart failure (HF) pharmacotherapy is to improve disease-related morbidity and mortality. Recent clinical trials demonstrated a reduction in cardiovascular death and HF-related hospitalizations with the addition of sodium glucose co-transporter 2 inhibitors (SGLT2i) in patients with HF with reduced ejection fraction (HFrEF) , as well as HF with preserved ejection fraction (HFpEF) with or without type 2 diabetes (T2D) . The objective of this study was to identify SGLT2i prescribing rates in these patients and to identify predictors of drug class utilization. The University of New Mexico (UNM) Health Sciences Center IRB approved this retrospective, single-center, descriptive study. Individuals ≥ 18 years with a diagnosis of HF, an eGFR of ≥ 20 ml/min/1.73m2, and a visit to the UNM Hospital system between August 17, 2020 to August 16, 2021 were included. Patients were excluded if they were diagnosed with type 1 diabetes or had no documented EF in their patient chart. A total of 2,905 patients with HF were identified, of which 1,101 were randomly reviewed. After exclusion, the study sample included: 377 with HFrEF, 335 with HFpEF, and 88 with HF with mid-range ejection fraction. The mean age was 65 years; 60% were female; 76% were white; 47% were Hispanic; and 49% had established atherosclerotic cardiovascular disease. Overall, 42% (n = 339) had T2D with a mean A1C of 8.0%. SGLT2is were prescribed in 6.5% of patients overall, 14.7% of patients with T2D, and 0.4% of patients without T2D. Predictors of SGLT2i prescribing were: T2D (OR = 38.4; 95% CI, 8.9 - 165.58) , HFrEF (OR = 3.7; 95% CI, 1.79 - 7.47) , having a cardiology provider (OR = 2.2; 95% CI, 1.79 - 7.47) , being seen by a pharmacist with prescriptive authority (OR = 4.1; 95% CI, 2.01 - 8.26) , and enrollment in UNM’s patient assistance program (OR = 2.3; 95% CI, 1.08 - 4.97) . Despite guideline recommendations, SGLT2is are underutilized in patients with HF with or without T2D. Future studies are needed to determine the barriers to utilization. Disclosure S. R. Bermudez: None. J. R. Anderson: None. G. Ray: None.

High prevalence of diabetes in elderly of Iran: an urgent public health issue.

To determine the distribution of blood glucose and prevalence of diabetes in people above 60 years living in Tehran and their relationship with some variables. In this cross-sectional population-based study, multistage cluster sampling was performed in the over 60-year-old population of Tehran. Blood samples were collected from all participants and the data of the history of diabetes and the use of blood glucose lowering agents or other drugs were collected using interviews. Of 3791 selected subjects, 3310 participated in the study (response rate = 87.3%). The mean blood Sugar (BS) and hemoglobin A1c (HbA1c) of the patients was 118.11(95% CI: 115.34 -120.88) and 6.12(95% CI: 6.05-6.2) respectively. The prevalence of diabetes was 29.03%(95% CI: 27.12-30.94) in all subjects, 26.83%(95% CI: 24.58-29.07) in men, and 31.2%(95% CI: 28.24-34.16) in women. Odds of diabetes was significantly worse in women. systolic blood pressure, diasstolic blood pressure, height, weight, waist circumference, wrist circumference, hip circumference, neck circumference and body mass Index were significantly higher in diabetic after adjusting for the effect of sex and age. The odds of blindness was 2.69 (95% CI: 1.10-6.59) times higher in diabetic than in non-diabetics. On average, one in every three persons above 60 years of age was diabetic. Therefore, attention should be paid to this age group, especially women, due the higher prevalence of diabetes. All anthropometric measurements except height had a strong correlation with diabetes. Blindness was significantly more in diabetics.

A Missing Piece of Diabetes Management: A Correctional Health Perspective

A cross-sectional study was conducted in January 2020 to evaluate type 1 diabetes (T1D) management during and between incarcerations at the Los Angeles County Jail. Inclusion criteria were men with a T1D diagnosis who had been incarcerated twice within 5 years with at least two hemoglobin A1c values during each incarceration and a minimum of 3 months in the community between incarcerations. The primary outcome was the percentage change in A1c after release compared with the A1c during incarcerations. Five patients met inclusion criteria. During the first and second incarcerations, mean A1c was reduced by 13% and 9.6%, respectively. While in the community, mean A1c increased by 14%. These results highlight the importance of comprehensive reentry programs for patients with T1D, including linkage to community resources for diabetes management, so health gains made during incarceration are not lost.

Factors associated with poor glycemic control in diabetic patients in Kirkuk

Background Iraq's health-care system has faced tremendous obstacles in its recovery from the Islamic State's conflict. There is no public insurance system in place. Iraq has set targets to prevent and control noncommunicable diseases (NCDs) such as diabetes, but safety confrontation and governmental insecurity have made these objectives difficult to achieve. Better glycemic control is critical in allowing patients to perform at their best in terms of diabetes management and preventing long-term complications. The goal is to identify the roadblocks to better glycemic management. The participants in this cross-sectional study were recruited from a diabetes out-patient clinic at Baba Gurgur diabetic facility. From April to December 2019, K1 hospital – North oil company in Kirkuk city. A validated questionnaire was used to interview those with an uncontrolled glycemic index (AIC7%). Patients were asked to name the primary causes of inadequate glucose control and to select more than one response based on their opinion. The mean A1C was 8.3 2.1 percent, with 256 (22.5 percent) patients having an A1C less than 7% and 880 (77.5 percent) having an A1C equal to or greater than 7. Poor glycemic control is caused by a lack of medication and/or a lack of drug supply from PHC in 51.1 percent of cases. Diet and medication non-compliance, as well as illiteracy, account for 35.1 percent and 19.8 percent of the population, respectively. Glycemic regulation is greatly influenced by one's financial situation. However, security issues and political instability play a significant impact. Conclusion: Diabetic patients confirmed poor glycemic control, with the majority of cases being linked to Iraq's current health situation.

CORRELATION OF DIABETES MELLITUS AND DYSLIPIDEMIA WITH OBESITY IN INFERTILE FEMALES: A CROSS SECTIONAL STUDY

Aims: To study the prevalence of infertility in obese or overweight females and its association with Dyslipidemia and Diabetes mellitus. Materials And Methods: The study was performed on 160 infertile women between age 18-45 years who attended Obstetrics and Gynaecology OPD and Medicine OPD in Swaroop Rani Hospital, Prayagraj.The duration of study was from December 2019 to March 2021.Patients were divided into lean/normal weight females and obese/overweight females based on BMI. Results: Out of total 200 patients 40 were excluded and remaining 160 were divided in two groups, Group 1: lean or normal weight infertile women(N1 =63) and Group 2: obese /overweight infertile women(N2= 97) Mean age of controls and cases were found to be comparable(31.54±2.82 vs 31.22±2.97 years) with p =0.50. Mean BMI of control group was found to be 20.23±1.39 kg/m2 as compared to cases with mean BMI 27.77±2.86 kg/m2 which was signicantly higher with p=<.001. Mean W:H ratio in cases and controls was found to be 0.83±0.05 and o.74±0.04 respectively with p-value= <0.001. Mean A1C levels in cases was signicantly higher than in the control group (5.91±0.91% vs 4.61±0.58%) with p=<.001. Mean S, Triglycerides levels in cases was signicantly higher than in the control group. (160.4±96.9 vs 110.2±15.8) with p=<.001. Mean S.Cholesterol levels in cases was signicantly higher than in the control group. (165.1±41.06 vs 100±22.1) with p=<.001. Mean S.VLDL levels in cases was signicantly higher than in the control group (43.9±18.94 vs 54.9±14.07) with p=<.001.Mean S.LDL levels in cases was signicantly higher than in the control group (64.16±22.4 vs 54.33±16.17) with p=<.002. Mean S.HDL levels in control was higher than in the case group. (78.65±6.86 vs 40.77±12.83) with p=<.001. Conclusion: The prevalence of obesity in infertile females at a tertiary care centre was 60.62 %. Positive association of obesity was found with dyslipidemia and diabetes mellitus(A1C -5.91±0.91% vs 4.61±0.58% with p=<.001)in obese females. Most of the females were in the range of impaired glucose tolerance phase which could be reversed by dietary modication and early intervention