A1c Target Research Articles

Optimal hyperglycemia thresholds in patients undergoing chemotherapy: a cross sectional study of oncologists’ practices

PurposeNeither the United States nor the European oncology guidelines include details for appropriate management of hyperglycemia in cancer patients. The aim was to identify fasting and random blood glucose thresholds, and hemoglobin A1c (HbA1c) targets used by oncologists in clinical practice when managing hyperglycemia in patients with cancer undergoing chemotherapy.MethodsThis national, cross sectional study utilized a questionnaire to collect oncologists’ perceptions about optimal blood glucose thresholds and HbA1c targets in patients with cancer undergoing chemotherapy. Descriptive statistics were calculated to summarize glucose thresholds, HbA1c targets, and sample characteristics. Responses to an open-ended question about oncologists’ approach to hyperglycemia management were analyzed via thematic analysis using an inductive approach.ResultsRespondents (n = 229) were on average 52.1 years of age, 67.7% men, and 91.3% White. For patients without diabetes but experiencing hyperglycemia, oncologists targeted lower and upper fasting blood glucose levels between 75-121 mg/dL and 105-135 mg/dL, respectively. For patients with diabetes, the targets for lower and upper fasting blood glucose levels ranged between 100-130 mg/dL and 128-150 mg/dL, respectively. Fasting blood glucose (95.6%) and HbA1c (78.6%) were the most commonly used clinical indicators to consider chemotherapy dose reduction, delay, or discontinuation due to hyperglycemia in patients receiving chemotherapy with curative intent. Among those receiving palliative intent chemotherapy, the preferred clinical parameters were random blood glucose (90.0%), patient-reported blood glucose readings (70.7%), continuous glucose monitoring readings (65.1%), and patient-reported symptoms of hyperglycemia (65.1%). Three main themes emerged about oncologists’ approach to hyperglycemia management: 1) identification of high-risk patients; 2) need for early identification, screening, and diagnosis of hyperglycemia; and 3) multiple hyperglycemia management strategies.ConclusionOncologists reported a wide variation of target blood glucose ranges considered appropriate in patients undergoing chemotherapy. Lack of clear guidance for hyperglycemia management during chemotherapy in the United States may be contributing to a lack of consistency in clinical practice.

982-P: The Dexcom Community Glucose Monitoring Project for People with Type 2 Diabetes—One-Year Outcomes

Background & Objective: Access to CGM for people with type 2 diabetes (T2D) remains limited, especially for those treated in the primary care setting. The objective of this study, a collaboration between a primary care practice and the local public health department, was to evaluate the glycemic impact of CGM access on adults with T2D. Methods: CGM systems (Dexcom G6 and G7) were provided to eligible participants with T2D who were ≥18 years of age, CGM-naïve, and did not have health insurance coverage for CGM. Outcomes included changes in A1C and CGM metrics from baseline to one year. Results: Participants (n=177) were (mean ± SD) 59.1 ± 12.0 years, 41.2% female, BMI 35.5 ± 8.5, duration of diabetes 11.1 ± 9.3 years. After one year, A1C decreased from 9.4% ± 1.6 to 7.1% ± 1.3 (-2.3% ± 1.9, p<0.001). The proportion of participants meeting the ADA target of A1C <7.0% increased from n=1 (0.6%) at baseline to n=91 (51.4%) at one year. Those meeting the HEDIS target of A1C <8.0% increased from 19.2% at baseline to 85.9% at one year. CGM outcomes (n=109) after near-continuous use included an increase in TIR of 9.9% ± 29.4 (p<0.001) and a large increase in TITR (p<0.001, Table). Conclusion: Self-guided use of CGM by primary care patients with T2D for one year was associated with clinically meaningful improvements in A1C and TIR. Disclosure T. Grace: Speaker's Bureau; Sanofi, Novo Nordisk, Eli Lilly and Company, Dexcom, Inc. Employee; Dexcom, Inc. J.E. Layne: Employee; Dexcom, Inc. Stock/Shareholder; Dexcom, Inc. Employee; Verily Life Sciences. C. Hicks: Employee; Dexcom, Inc. C.R. Green: Employee; Dexcom, Inc. T.C. Walker: Employee; Dexcom, Inc. Stock/Shareholder; Dexcom, Inc.

1110-P: Endocrinologists' Perceptions and Implementation of Hemoglobin A1c Targets for Adults with Type 1 Diabetes

1110-P: Endocrinologists' Perceptions and Implementation of Hemoglobin A1c Targets for Adults with Type 1 Diabetes

Transition to self-management among emerging adults with type 1 diabetes: a mixed methods study.

Emerging adulthood is challenging for young people with type 1 diabetes (T1D). This study evaluated transition to diabetes self-management and perceptions of care transfer using mixed methods. An online survey queried demographics, management characteristics, diabetes knowledge, self-care readiness, adherence, and diabetes distress. T-tests compared survey scores between those with self-reported target A1c <7.0% versus ≥7.0%. Pearson correlations assessed associations between A1c and diabetes distress, stratified by A1c <7.0% versus ≥7.0%. Qualitative semi-structured interviews elicited perceptions of young adults; transcripts were analyzed using directed qualitative content analysis. Of 141 participants (30% male, 84% non-Hispanic white) completing the survey, 41% self-reported target A1c <7.0%. Diabetes knowledge and self-care readiness scores did not differ between those with A1c <7.0% versus ≥7.0%, while diabetes distress was lower (45 ± 20 vs 52 ± 20, p=0.01) and adherence higher (77 ± 12 vs 71 ± 14, p=0.02) in those with A1c <7.0% versus ≥7.0%. Diabetes distress was significantly associated with glycemic outcomes in those reporting A1c ≥7.0% (R=0.36, p<0.01). Qualitative analysis (24 participants) revealed five themes and two sub-themes, notable for need for more mental health support, support from others with T1D, benefits of technology for care autonomy, and challenges of obtaining diabetes supplies. Emerging adults with self-reported target A1c endorsed lower diabetes distress and higher adherence than those with elevated A1c. Mental health access, support from others with T1D, technology use, and guidance for supply acquisition may improve transition to self-management and care transfer for emerging adults with T1D.

Recent Improvements in Attainment of the Hemoglobin A1c Target of ≤7.0% Among Adults with Type 1 Diabetes in Ontario: A Retrospective Cohort Study.

Aims: We evaluated attainment of the hemoglobin A1c (HbA1c) target of ≤7.0%, its temporal trends, and associated factors among adults with type 1 diabetes in Ontario, Canada, using administrative data. Methods: We conducted a retrospective cohort study, including Ontarians with type 1 diabetes ≥18 years old with ≥1 HbA1c test between April 1, 2012 (fiscal year 2013), and March 31, 2023. Generalized estimating equations were used to determine probabilities of meeting the HbA1c target, as well as associations between fiscal year and individual-, physician-, and system-level factors on odds of meeting the target. Results: Among 28,827 adults with type 1 diabetes [14,385 (49.9%) female, 17,998 (62.4%) pump users], with median age at index of 25 years [interquartile range (IQR) 18-37] and median diabetes duration of 12 years [6-18], there were 474,714 HbA1c tests [median 2/individual/year (IQR: 1-3)]. The model-estimated probability of meeting the HbA1c target of ≤7.0% was 22.1% (95% confidence interval, CI: 21.6 to 22.5) in 2013, remained stable until 2020, and increased to 34.7% (95% CI: 34.3 to 35.2) in 2023. The age- and sex-adjusted odds ratio for meeting the target in 2023 versus 2013 was 1.87 (95% CI: 1.79 to 1.96). Young adults (18-25 years), diabetic ketoacidosis, greater comorbidity, and receiving diabetes care from a nonspecialist physician were associated with reduced odds of meeting the HbA1c target. Conclusions: One-third of adults with type 1 diabetes in Ontario met the recommended HbA1c target of ≤7.0% in 2023, with improvement noted since 2021, which may be due to advanced technologies or effects of the COVID-19 pandemic.

Basal Insulin Initiation in Adults With Type 2 Diabetes Mellitus: A Retrospective Cohort Study Using Administrative Health Data in Alberta, Canada

ObjectivesPharmacologic treatment of type 2 diabetes mellitus (T2DM) follows a stepwise approach. Typically, metformin monotherapy is first-line treatment, followed by other noninsulin antihyperglycemic agents (NIAHAs) or progression to insulin if glycated hemoglobin (A1C) targets are not achieved. We aimed to describe real-world patterns of basal insulin initiation in people with T2DM and A1C not at target despite treatment with at least 2 NIAHAs. MethodsA retrospective cohort study was conducted using administrative health data from Alberta, Canada, among adults with T2DM, indexed on the first test with 7.0% < A1C < 9.5% (April 1, 2011, to March 31, 2019), with at least 2 previous NIAHAs but no insulin. Kaplan–Meier (KM) methodology was used to analyze time to basal insulin initiation, with stratification by index A1C. Annual patient status was categorized into 5 groups: basal insulin initiation, death, NIAHA intensification, no change in therapy (subgroups of A1C <7.1% and A1C ≥7.1% [clinical inertia]), or discontinuance. ResultsThe cohort included 14,083 individuals. The KM cumulative probability of initiating basal insulin was 7.7% (95% confidence interval [CI] 7.3% to 8.2%) at 1 year, increasing to 43.1% (95% CI 42.1% to 44.1%) at 8 years of follow-up. Higher A1C levels were associated with greater proportions of basal insulin initiation. By year 8, proportions with NIAHA intensification and clinical inertia were 12.1% and 19.3%, respectively, relative to year 7. ConclusionsDespite current clinical practice guidelines recommending achieving A1C targets within 6 months, less than half of the individuals with T2DM and clear indications for basal insulin initiated treatment within 8 years. Efforts to reduce delays in basal insulin initiation are needed.

Examining Health Inequities in A1C Control over Time across Individual, Geospatial, and Geopolitical Factors among Adults with Type 2 Diabetes: Analyses of a Sample from One Commercial Insurer in a Southern State.

Type 2 diabetes impacts millions and poor maintenance of diabetes can lead to preventable complications, which is why achieving and maintaining target A1C levels is critical. Thus, we aimed to examine inequities in A1C over time, place, and individual characteristics, given known inequities across these indicators and the need to provide continued surveillance. Secondary de-identified data from medical claims from a single payer in Texas was merged with population health data. Generalized Estimating Equations were utilized to assess multiple years of data examining the likelihood of having non-target (>7% and ≥7%, two slightly different cut points based on different sources) and separately uncontrolled (>9%) A1C. Adults in Texas, with a Type 2 Diabetes (T2D) flag and with A1C reported in first quarter of the year using data from 2016 and 2019 were included in analyses. Approximately 50% had A1Cs within target ranges (<7% and ≤7%), with 50% considered having non-target (>7% and ≥7%) A1Cs; with 83% within the controlled ranges (≤9%) as compared to approximately 17% having uncontrolled (>9%) A1Cs. The likelihood of non-target A1C was higher among those individuals residing in rural (vs urban) areas (P < .0001); similar for the likelihood of reporting uncontrolled A1C, where those in rural areas were more likely to report uncontrolled A1C (P < .0001). In adjusted analysis, ACA enrollees in 2016 were approx. 5% more likely (OR = 1.049, 95% CI = 1.002-1.099) to have non-target A1C (≥7%) compared to 2019; in contrast non-ACA enrollees were approx. 4% more likely to have non-target A1C (≥7%) in 2019 compared to 2016 (OR = 1.039, 95% CI = 1.001-1.079). In adjusted analysis, ACA enrollees in 2016 were 9% more likely (OR = 1.093, 95% CI = 1.025-1.164) to have uncontrolled A1C compared to 2019; whereas there was no significant change among non-ACA enrollees. This study can inform health care interactions in diabetes care settings and help health policy makers explore strategies to reduce health inequities among patients with diabetes. Key partners should consider interventions to aid those enrolled in ACA plans, those in rural and border areas, and who may have coexisting health inequities.

Association Between Adherence, A1C Improvement, and Type of Continuous Glucose Monitoring System in People with Type1 Diabetes or Type2 Diabetes Treated with Intensive Insulin Therapy.

Use of continuous glucose monitoring (CGM) systems by people with diabetes is associated with improved glycemic outcomes, including lower glycated hemoglobin (A1C). Less is known about adherence to CGM systems, whether glycemic outcomes are impacted by levels of adherence, or whether adherence rates differ between types of CGM systems-intermittently scanned CGM (isCGM) or real-time CGM (rtCGM). A retrospective analysis of de-identified US administrative health claims and linked laboratory data was conducted using the Merative™ MarketScan® Research Database. The cohort included CGM-naïve people with type1 diabetes (T1D) or type2 diabetes treated with intensive insulin therapy (T2D-IIT) who initiated rtCGM or isCGM between August1, 2019 and March31, 2021 (defined as the index date). Adherence was calculated over a 12-month period using the proportion of days covered (PDC) with PDC ≥ 0.8 defined as adherent. A1C values were obtained within 6months of the index date. A total of 7669 individuals were identified. Subgroups included T1D using isCGM (n = 1578), T1D using rtCGM (n = 1244), T2D-IIT using isCGM (n = 3567), and T2D-IIT using rtCGM (n = 1280). After 12months, PDC was 0.71 (0.30)-0.72 (0.31) (mean(SD)) for T1D and T2D-IIT rtCGM users and 0.55 (0.34)-0.56 (0.34) for T1D and T2D-IIT isCGM users. The proportion of adherent users (PDC ≥ 0.8) was 56.8-59.7% for rtCGM users and 36.3-37.6% for isCGM users. Overall, regardless of diabetes type, the odds of adherence were over two times higher for rtCGM users compared to isCGM users. For those with available A1C information (T1D n = 213; T2D-IIT n = 346), independent of CGM type, adherence to CGM was associated with a greater reduction in A1C and more people reaching A1C targets of < 7.0% or < 8.0%. For people with T1D or T2D-IIT, higher adherence to CGM is associated with greater reductions in A1C, and higher adherence rates were observed with rtCGM systems than with isCGM systems.

Impact of glycated hemoglobin A1c on cardiovascular risk in diabetes patients with and without coronary artery disease

Abstract Background Guidelines recommend a haemoglobin A1c (HbA1c) target &amp;lt;53 mmol/mol (7%) to achieve microvascular risk reduction. However, the impact of HbA1c target levels on macrovascular complications is less certain. Moreover, we have previously shown that the risk of macrovascular complications is dependent on presence of coronary artery disease (CAD). Purpose We tested the hypothesis that HbA1c ≥53 mmol/mol is associated with cardiovascular risk in diabetes patients with and without CAD. Methods We conducted a contemporary cohort study including diabetes patients examined by coronary angiography in Western Denmark from 2011-2021. The association between HbA1c and major adverse cardiovascular events (MACE, a composite outcome of myocardial infarction, ischemic stroke, and cardiovascular death) was modeled using restricted cubic splines. Patients were followed for up to 10 years. We estimated adjusted hazards ratios (aHR) using HbA1c of 52 mmol/mol as reference. We also performed stratified analyses by presence of CAD. Results We included 15,825 patients with diabetes, of whom 12,200 (77%) had CAD. Median HbA1c was 52 mmol/mol in patients with CAD and 51 mmol/mol in patients without CAD. Using restricted cubic splines, we observed significantly increased MACE rates with HbA1c ≥53 mmol in the entire cohort (Figure 1A). MACE rates plateaued with increasing HbA1c above 65 mmol/mol. When stratifying by presence of CAD, this association between HbA1c and MACE was sustained in patients with CAD (Figure 1B). In contrast, we did not observe any relation between HbA1c and MACE rates in patients without CAD (Figure 1C). Conclusions The current study suggest two novel findings. Firstly, a HbA1c &amp;lt;53 mmol/mol target is associated with a reduced risk of macrovascular complications in patients with diabetes. Thus, the impact of glucose control on macrovascular complications corresponds to the HbA1c levels recommended for avoidance of microvascular complications. Secondly, the association between HbA1c and MACE is restricted to diabetes patients with CAD while not observed in those without CAD.Figure 1,A-C

Glycemic outcomes of a family-focused intervention for adults with type 2 diabetes: Main, mediated, and subgroup effects from the FAMS 2.0 RCT

AimsFamily/friend Activation to Motivate Self-care (FAMS) is a self-care support intervention delivered via mobile phones. We evaluated FAMS' effects on hemoglobin A1c (HbA1c) and intervention targets among adults with type 2 diabetes in a 15-month RCT. MethodsPersons with diabetes (PWDs) were randomized to FAMS or control with their support person (family/friend, optional). FAMS included monthly phone coaching and text messages for PWDs, and text messages for support persons over a 9-month intervention period. ResultsPWDs (N = 329) were 52 % male, 39 % reported minoritized race or ethnicity, with mean HbA1c 8.6 ± 1.7 %. FAMS improved HbA1c among PWDs with a non-cohabitating support person (-0.64 %; 95 % CI [−1.22 %, −0.05 %]), but overall mean effects were not significant. FAMS improved intervention targets including self-efficacy, dietary behavior, and family/friend involvement during the intervention period; these improvements mediated post-intervention HbA1c improvements (total indirect effect −0.27 %; 95 % CI [−0.49 %, −0.09 %]) and sustained HbA1c improvements at 12 months (total indirect effect −0.19 %; 95 % CI [−0.40 %, −0.01 %]). ConclusionsDespite improvements in most intervention targets, HbA1c improved only among PWDs engaging non-cohabitating support persons suggesting future family interventions should emphasize inclusion of these relationships. Future work should also seek to identify intervention targets that mediate improvements in HbA1c.

Tirzepatide vs Insulin Lispro Added to Basal Insulin in Type 2 Diabetes

Tirzepatide is a glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist used for the treatment of type 2 diabetes. Efficacy and safety of adding tirzepatide vs prandial insulin to treatment in patients with inadequate glycemic control with basal insulin have not been described. To assess the efficacy and safety of tirzepatide vs insulin lispro as an adjunctive therapy to insulin glargine. This open-label, phase 3b clinical trial was conducted at 135 sites in 15 countries (participants enrolled from October 19, 2020, to November 1, 2022) in 1428 adults with type 2 diabetes taking basal insulin. Participants were randomized (in a 1:1:1:3 ratio) to receive once-weekly subcutaneous injections of tirzepatide (5 mg [n = 243], 10 mg [n = 238], or 15 mg [n = 236]) or prandial thrice-daily insulin lispro (n = 708). Outcomes included noninferiority of tirzepatide (pooled cohort) vs insulin lispro, both in addition to insulin glargine, in HbA1c change from baseline at week 52 (noninferiority margin, 0.3%). Key secondary end points included change in body weight and percentage of participants achieving hemoglobin A1c (HbA1c) target of less than 7.0%. Among 1428 randomized participants (824 [57.7%] women; mean [SD] age, 58.8 [9.7] years; mean [SD] HbA1c, 8.8% [1.0%]), 1304 (91.3%) completed the trial. At week 52, estimated mean change from baseline in HbA1c with tirzepatide (pooled cohort) was -2.1% vs -1.1% with insulin lispro, resulting in mean HbA1c levels of 6.7% vs 7.7% (estimated treatment difference, -0.98% [95% CI, -1.17% to -0.79%]; P < .001); results met noninferiority criteria and statistical superiority was achieved. Estimated mean change from baseline in body weight was -9.0 kg with tirzepatide and 3.2 kg with insulin lispro (estimated treatment difference, -12.2 kg [95% CI, -13.4 to -10.9]). The percentage of participants reaching HbA1c less than 7.0% was 68% (483 of 716) with tirzepatide and 36% (256 of 708) with insulin lispro (odds ratio, 4.2 [95% CI, 3.2-5.5]). The most common adverse events with tirzepatide were mild to moderate gastrointestinal symptoms (nausea: 14%-26%; diarrhea: 11%-15%; vomiting: 5%-13%). Hypoglycemia event rates (blood glucose level <54 mg/dL or severe hypoglycemia) were 0.4 events per patient-year with tirzepatide (pooled) and 4.4 events per patient-year with insulin lispro. In people with inadequately controlled type 2 diabetes treated with basal insulin, weekly tirzepatide compared with prandial insulin as an additional treatment with insulin glargine demonstrated reductions in HbA1c and body weight with less hypoglycemia. ClinicalTrials.gov Identifier: NCT04537923.

Factors Associated With Attainment of Glycemic Targets Among Adults With Type 1 and Type 2 Diabetes in Canada: A Cross-sectional Study Using Primary and Specialty Care Electronic Medical Record Data

ObjectiveUsing a new database combining primary and specialty care electronic medical record (EMR) data in Canada, we determined attainment of glycemic targets and associated predictors among adults with diabetes. MethodsWe conducted a cross-sectional observational study combining primary and specialty care EMR data in Canada. Adults with diabetes whose primary care provider contributed to the National Diabetes Repository or who were assessed at a diabetes specialty clinic (LMC Diabetes and Endocrinology) between July 3, 2015, and June 30, 2019, were included. Diabetes type was categorized as type 2 diabetes (T2D) not prescribed insulin, T2D prescribed insulin, and type 1 diabetes (T1D). Covariates were age, sex, income quintile, province, rural/urban location, estimated glomerular filtration rate, medications, and insulin pump use. Associations between predictors and the outcome (glycated hemoglobin [A1C] of ≤7.0%) were assessed by multivariable logistic regressions. ResultsAmong 122,106 adults, consisting of 91,366 with T2D not prescribed insulin, 25,131 with T2D prescribed insulin, and 5,609 with T1D, attainment of an A1C of ≤7.0% was 60%, 25%, and 23%, respectively. Proportions with an A1C of ≤7.5% and ≤8.0% were 75% and 84% for those with T2D not prescribed insulin, 41% and 57% for those with T2D prescribed insulin, and 37% and 53% for those with T1D. Highest vs lowest income quintile was associated with greater odds of meeting the A1C target (adjusted odds ratio [95% confidence interval] for each diabetes category: 1.15 [1.10 to 1.21], 1.21 [1.10 to 1.33], and 1.29 [1.04 to 1.60], respectively). Individuals in Alberta and Manitoba had less antihyperglycemic medication use and attainment of A1C target than other provinces. ConclusionsAttainment of glycemic targets among adults with diabetes was poor and differed by income and geographic location, which must be addressed in national diabetes strategies.

Safety Event Outcomes and Glycemic Control with a Hybrid Closed-Loop System Used by Chinese Adolescents and Adults with Type 1 Diabetes Mellitus.

Background: While evidence supports glycemic control benefits for individuals with type 1 diabetes mellitus (T1DM) using hybrid closed-loop (HCL) systems, HCL automated insulin delivery therapy in China has not been assessed. This study evaluated safety events and effectiveness during HCL system use by Chinese adolescents and adults with T1DM. Methods: Sixty-two participants (n = 12 adolescents with a mean ± standard deviation [SD] of 15.5 ± 1.1 years and n = 50 adults [mean ± SD of 37.6 ± 11.1 years]) with T1DM and baseline A1C of 7.1% ± 1.0% underwent a run-in period (∼2 weeks) using open-loop Manual Mode (sensor-augmented pump) insulin delivery with the MiniMed™ 770G system with the Guardian™ Sensor (3) glucose sensor, followed by a study period (4 weeks) with HCL Auto Mode enabled. Analyses compared continuous glucose monitoring data and insulin delivered during the run-in versus study period (Wilcoxon signed-rank test or t-test). Safety events included rates of severe hypoglycemia and diabetic ketoacidosis (DKA). Results: Compared to baseline run-in, overall Auto Mode use increased time in range (TIR, 70-180 mg/dL) from 75.3% to 80.9% (P < 0.001) and reduced time below range (TBR, <70 mg/dL) from 4.7% to 2.2% (P < 0.001). Subgroup analysis demonstrated that participants (n = 29) with baseline A1C <7.0% had TBR that reduced from 5.6% to 2.0%, while participants (n = 21) with baseline A1C ≥7.5% had time above range (TAR, >180 mg/dL) that reduced from 31.6% to 20.8%. Auto Mode use also increased the percentage achieving combined recommendations for time at sensor glucose ranges (i.e., TIR of >70%, TBR of <4% and TAR of <25%) from 24.2% at baseline to 77.4% at study end. Total daily insulin dose reduced from 42.8 ± 19.8 to 40.7 ± 18.9 U (P = 0.013). There were no severe hypoglycemic, DKA, or serious adverse events. Conclusions: Chinese adolescents and adults, some of whom met target A1C at baseline, safely achieved significantly improved glycemia with 1 month of MiniMed 770G system use when compared to open-loop insulin delivery. ClinicalTrials.gov ID: NCT04663295.

Racial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c.

Background: Studies have reported significantly higher hemoglobin A1c (A1C) in African American patients than in White patients with the same mean glucose, but less is known about other racial/ethnic groups. We evaluated racial/ethnic differences in the association between mean glucose, based on continuous glucose monitor (CGM) data, and A1C. Methods: Retrospective study among 1788 patients with diabetes from Kaiser Permanente Northern California (KPNC) who used CGM devices during 2016 to 2021. In this study population, there were 5264 A1C results; mean glucose was calculated from 124,388,901 CGM readings captured during the 90 days before each A1C result. Hierarchical mixed models were specified to estimate racial/ethnic differences in the association between mean glucose and A1C. Results: Mean A1C was 0.33 (95% confidence interval: 0.23-0.44; P < 0.0001) percentage points higher among African American patients relative to White patients for a given mean glucose. A1C results for Asians, Latinos, and multiethnic patients were not significantly different from those of White patients. The slope of the association between mean glucose and A1C did not differ significantly across racial/ethnic groups. Variance for the association between mean glucose and A1C was substantially greater within groups than between racial/ethnic groups (65% vs. 9%, respectively). Conclusions: For African American patients, A1C results may overestimate glycemia and could lead to premature diabetes diagnoses, overtreatment, or invalid assessments of health disparities. However, most of the variability in the mean glucose-A1C association was within racial/ethnic groups. Treatment decisions driven by guideline-based A1C targets should be individualized and supported by direct measurement of glycemia.

Lack of Association of A1C With Postoperative Complications in Children With Type 1 or Type 2 Diabetes.

Not meeting recommended A1C targets may be associated with postoperative complications in adults, but there are no studies reporting on the relationship between preoperative A1C and postoperative complications in children with type 1 or type 2 diabetes. The objective of this study was to determine whether elevated A1C levels were associated with an increased incidence of postoperative complications in children with diabetes presenting for elective noncardiac surgery or diagnostic procedures. It found no such association, suggesting no need to delay elective surgery in children with diabetes until A1C is optimized.

1127-P: 4T Study—Early CGM Initiation and Tighter Glucose Targets Improves A1C in Youth with T1D

Methods to optimize care after T1D diagnosis are needed. We hypothesized lowering A1c targets to &amp;lt;7% would further lower A1c in the 4T Study in which CGM with asynchronous remote patient monitoring (RPM) is initiated after T1D diagnosis. All youth with newly diagnosed T1D (June 2020-March 2022) were offered CGM and RPM after diagnosis (Study 1, n=133). We compared A1c at 1-year in Study 1 with the 4T Pilot (2018-20) and Historical cohorts (2014-16). We visualized population-based A1c trajectories using locally estimated scatter plot smoothing (Fig) and % meeting A1c targets. Mean A1c at diagnosis was similar in Pilot (12.2%±2.1%) and Study 1 (12.2±2.4%) and higher than the Historical cohort (10.7±2.5%). In Study 1, the median age of diagnosis was 10.8 years, 55% male, 40% non-Hispanic White, and 38% with public insurance. CGM initiation occurred within 30 days of diagnosis in 98.5%. At 3, 6, 9, and 12 months post-diagnosis, the Study 1 cohort had LOESS-based mean A1c differences of 0.16%, 0.24%, 0.31%, and 0.58% lower than the Pilot and 0.04%, 0.60%, 0.83%, and 1.06% lower than the Historical cohort. A1c target &amp;lt;7% was met by 61% of youth in Study 1, 51% in the Pilot and 28% in the Historical cohort. Time &amp;lt;70mg/dl was ≤2.3%. The 4T program which emphasizes early CGM initiation, RPM, tighter glucose targets, and consistent team messaging was associated with lower A1c. These data support implementation of the 4T program in youth with T1D. Disclosure P.Prahalad: None. D.M.Maahs: Advisory Panel; Medtronic, LifeScan Diabetes Institute, MannKind Corporation, Consultant; Abbott, Research Support; Dexcom, Inc. 4t study group: n/a. V.Ding: None. D.P.Zaharieva: Advisory Panel; Dexcom, Inc., Research Support; Hemsley Charitable Trust, International Society for Pediatric and Adolescent Diabetes, Insulet Corporation, Speaker's Bureau; American Diabetes Association, Ascensia Diabetes Care, Medtronic. A.Addala: None. F.K.Bishop: None. D.Scheinker: None. R.Johari: None. M.Desai: None. K.K.Hood: Consultant; Cecelia Health. Funding National Institutes of Health (P30DK116074), (R18DK122422 to D.M.M.); Dexcom, Inc.; Lucile Packard Children’s Hospital Auxiliaries Endowment; Stanford Maternal and Child Health Research Institute

123-LB: Implementing a Multidisciplinary Model of CGM Care in Real-World Pharmacy Practice—A Clinical Consensus for Canadian Pharmacists

Background: Continuous glucose monitoring (CGM) devices have transformed diabetes care, but there is a lack of guidance for pharmacists to follow when onboarding patients to begin using CGM systems. Our study aimed to develop a blueprint for best practices for pharmacists to support people living with diabetes (PWD) using CGM systems. Methods: We used a modified DELPHI process to gather insights from 11 Key Opinion Leaders (KOLs) across Canada, including two endocrinologists, one family physician, and eight pharmacists. Consensus was reached for each criterion when 75% agreement was achieved. Criteria for CGM device initiation, short-term or episodic CGM use, and best practices for CGM onboarding and monitoring were developed based on the results. Results: Amongst the ideal candidates for CGM use are PWD using insulin, those with type 1 diabetes, individuals not reaching their A1C target, those experiencing frequent hypoglycemia, pregnant individuals, and others at risk of hypoglycemia. Best practices for onboarding and monitoring included shared decision-making conversations, a comprehensive overview of cost and coverage options, customized alerts and alarms, and timely follow-up sessions. Discussion: The KOL group ensured a comprehensive and diverse perspective. The developed blueprint provides a valuable resource for pharmacists to improve the use of CGM devices and enhance diabetes management for their patients. Incorporating these best practices into clinical practice has the potential to transform diabetes care and improve outcomes. Disclosure A. S. Sihota: Consultant; Novo Nordisk Canada Inc., Other Relationship; Becton, Dickinson and Company. S. Sivapalan: Advisory Panel; Novo Nordisk, Pfizer Inc., AstraZeneca, Consultant; Pear Healthcare Solutions Inc., Other Relationship; Boehringer Ingelheim (Canada) Ltd., GlaxoSmithKline plc., Novo Nordisk, Bristol-Myers Squibb Company, Pfizer Inc., AbbVie Inc., Ferring Pharmaceuticals, AstraZeneca. T. Smith: Advisory Panel; Novo Nordisk Canada Inc., Dexcom, Inc., Emergent Biosolutions. I. Halperin: Advisory Panel; Sanofi, Speaker's Bureau; 3Boehringer Ingelheim Canada Ltd. /Ltée, Abbott Diabetes, Dexcom, Inc., Novo Nordisk. A. B. Jain: Advisory Panel; Abbott, Amgen Canada, Dexcom, Inc., AstraZeneca, Novo Nordisk, Bayer Inc., Insulet Corporation, Takeda Canada, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Research Support; Abbott, Amgen Canada, Novo Nordisk, Speaker's Bureau; Abbott, Amgen Canada, Dexcom, Inc., AstraZeneca, Novo Nordisk, Bausch Health, Canada, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Pfizer Inc. A. Chin: Other Relationship; Abbott Diabetes, BMO Bank of Montreal, Diabetes Canada, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care, Inc., Research Support; Novavax, Centricity Research. W. Chow: Advisory Panel; GlaxoSmithKline plc., Pfizer Inc., Speaker's Bureau; Abbott Diagnostics, AstraZeneca, Bayer Inc., Bausch Health, Canada, Boehringer Ingelheim (Canada) Ltd., Eisai Inc., Novo Nordisk. S. Jin: Advisory Panel; Dexcom, Inc., Eisai Inc., Novo Nordisk Canada Inc., Board Member; Wounds Canada, Consultant; Abbott Diabetes, Boehringer Ingelheim Pharmaceuticals Inc., Canadian Collaborative Research Network, Diabetes Canada, GlaxoSmithKline plc., HLS Therapeutics Inc., MDBriefcase, Novo Nordisk Canada Inc., AstraZeneca, Speaker's Bureau; Eisai Inc., Dexcom, Inc., Novo Nordisk Canada Inc. T. Molberg: None. S. Patil: None. R. Siemens: Consultant; Canadian Collaborative Research Network, Novartis Canada, AstraZeneca, Montmed, Sanofi, MDBriefcase, Other Relationship; Abbott Diabetes, Amgen Canada, Boehringer Ingelheim and Eli Lilly Alliance, GlaxoSmithKline plc., HLS Therapeutics, Novo Nordisk, Dexcom Canada, Diabetes Canada. Funding Dexcom Canada

186-OR: Genetic Variants That Lower Hemoglobin A1c Independently of Glycemia Are Associated with Higher Diabetic Retinopathy Risk in Diverse Ancestries

Introduction: People with type 2 diabetes (T2D) who carry genetic variants that lower hemoglobin A1c (A1c) independently of glycemia may have higher real, but undetected, hyperglycemia compared to those who do not despite achieving similar A1c targets, placing them at greater risk for complications. Methods: Using data from 39,199 T2D cases of European ancestry (EA) and African ancestry (AA) in UK Biobank and All of Us, we calculated a nonglycemic A1c polygenic score (ngA1cPS) composed of 123 variants previously associated with A1c at genome-wide significance, but not with glucose. We tested the association of the ngA1cPS with DR (n = 3,859), adjusting for A1c and known DR risk factors (T2D duration, kidney disease, hypertension, lipids, smoking). Results: Participants at the bottom ngA1cPS quintile showed 25-50% higher DR prevalence vs. the rest despite having lower measured A1c (6.6 vs 6.7%). Odds ratios were 1.28 in EA and 1.37 in AA (Fig. 1). Among carriers of the African-specific G6PD variant rs1050828 known to lower A1c, DR prevalence was 27% higher among those with ngA1cPS below the median vs. above the median. Conclusions: The aggregate effect of variants that lower A1c independently of glycemia is associated with a higher DR risk in both EA and AA. Genetic effects need to be considered to define personalized A1c targets to improve the outcomes of patients with T2D. Disclosure P.H.Schroeder: None. R.Mandla: None. J.C.Florez: Consultant; AstraZeneca, Novo Nordisk, Other Relationship; AstraZeneca, Merck &amp; Co., Inc. J.M.Mercader: None. A.Leong: Other Relationship; Merck &amp; Co., Inc. Funding Doris Duke Charitable Foundation (2020096)

719-P: Feasibility and Acceptability of the Quality of Life, Burden of Treatment, Safety, and Avoidance of Future Events Agenda Setting Kit (QBSAFE ASK)

Objectives: Diabetes care has traditionally focused on target A1c, leaving other patient goals and needs under-addressed. We assessed the feasibility and acceptability of conversation cards (QBSAFE agenda setting kit) during routine clinic visits. Methods: We recruited clinicians and their patients with diabetes into a single-arm intervention study. The intervention consisted of a set of 14 conversation cards (QBSAFE ASK) describing diabetes-related topics. The cards were presented to patients and asked to select 0-3 cards for discussion. Clinicians and patients completed post-encounter acceptability surveys. Feasibility was assessed based on participant enrollment, time to enrollment, and percent who completed study procedures. Encounters were recorded and analyzed to describe the cards selected and clinician responses to the cards. Results: A total of 85 patients (63yo ± 14.7, 43.5% female, and 84.5% white) and 7 clinicians were recruited over 5 months. Among 85 patients, 68.2% completed all study procedures. Most patients (63.8%) agreed the cards helped discuss their situation, 77.6% agreed other patients could benefit from them, but only 37.9% would use the cards again. Clinicians felt confident responding to issues raised by the cards during 89.9% of encounters and would recommend the cards to their colleagues (82.4%). Based on video analysis, 83.3% of patients selected at least 1 card, with the common being “I have difficulties with diet and exercise” (34.5%). Clinicians most often responded by asking elaborating questions (81.8% of encounters) followed by change in treatment (34.5%). The tool often elicited collaborative conversations between clinicians and patients (58% of encounters). Conclusions: QBSAFE ASK can be feasibly implemented into clinical care, is generally acceptable, and holds promise in helping patients discuss their situation with clinicians and in eliciting collaborative problem-solving. Disclosure S.Haider: None. K.J.Lipska: Other Relationship; UpToDate. C.Gonzalez lopez: None. J.Clark: None. D.L.Gravholt: None. M.Breslin: None. K.Boehmer: None. S.A.Hartasanchez: None. B.B.M.Sanchez: None. V.M.Montori: None. Funding National Institute on Aging (5R21AG061427)

1456-P: 2022 State of Type 1 Diabetes in the U.S.—Real World T1D Exchange Multicenter Data from over 60,000 People

Background: In response to sub-optimal type 1 diabetes (T1D) care data in the US, intensive quality improvement (QI) programs were implemented in the T1D Exchange QI collaborative (T1DX-QI) to improve outcomes for individuals with T1D. The aim of this analyses is to characterize the T1DX-QI patient population consisting of over 60,000 individuals with T1D Method: This cross-sectional analysis included 2022 electronic medical record (EMR) data of 60,915 individuals with T1D from 26 diabetes centers. Population attributes analyzed included demographics, duration of diabetes, glycemic outcomes and diabetes technology use. Result: In this population described in table 1, more adults ≥ 26 years of age were at A1c target goal (&amp;lt;7%) (34%), compared to children and adolescents (22%). Diabetes technology use was higher in the 6-13y age group (CGM: 64%, insulin pump: 53%) relative to adults 26-50y (CGM: 46%, insulin pump: 48%); whereas Hybrid Closed Loop System (HCLS) use was higher in adults 26-65y (13%) relative to children and younger adults 6-26y (6%). Self-reported Diabetes Ketoacidosis events were also higher in the younger age groups. Conclusion: This study reports characteristics of persons with T1D from a multi-center database and demonstrates that efforts are still needed to improve optimal management of diabetes. Table 1: T1DX-QI EMR T1D Data Distribution 2022 (N=60915) Disclosure O.Ebekozien: Advisory Panel; Medtronic, Research Support; Eli Lilly and Company, Dexcom, Inc. D.M.Maahs: Advisory Panel; Medtronic, LifeScan Diabetes Institute, MannKind Corporation, Consultant; Abbott, Research Support; Dexcom, Inc. .T1d exchange quality improvement collaborative (t1: n/a. N.Noor: None. L.Dimeglio: Advisory Panel; Merck &amp; Co., Inc., Consultant; Abata Therapeutics, Vertex Pharmaceuticals Incorporated. M.Yau: None. R.Rapaport: None. J.Weissberg-benchell: Advisory Panel; Beta Bionics, Inc., Research Support; Beta Bionics, Inc. T.S.Hannon: Advisory Panel; Eli Lilly and Company. K.Mcnamara: None. K.Miller: Consultant; Dexcom, Inc. Funding The Leona M. and Harry B. Helmsley Charitable Trust