Laboratory A1C Research Articles

31-LB: The Relationship between Improvements in Glycemic Control following a Digital Health Intervention and the Food Environment in a Medicare Advantage Population

Diabetes is a complex disease that is influenced by social determinants of health (SDOH). An inadequate food environment, as a function of socioeconomic status and proximity to food retailers, has been associated with both increased prevalence of diabetes and barriers to diabetes self-management. This work examined the relationship between improvements in glycemic control as a result of a digital health intervention and the food environment in Medicare Advantage members at risk for diabetes-related complications (hemoglobin A1c [A1c] ≥ 8%). Participants enrolled in the Vida Health Diabetes Management program as part of their medical insurance benefit and utilized remote sessions with Registered Dietitians and Certified Diabetes Care and Education Specialists as well as content related to goal-setting to account for SDOH factors. The 2010 USDA Food Access Research Atlas was used to identify areas of low income and the absence of a food retailer within 0.5 or 1 urban and 10 rural miles (LILA). Participants were included if they had a laboratory A1c between -365 to -1 and 60 to 365-days of enrollment. Participants with baseline A1c ≥ 8.0 (n=430, 229 F, 68.9 y) demonstrated a decrease of -0.90 points (SD 1.71, p<0.001). Thirty-five percent resided in LILA areas at the 0.5, and twenty percent at 1 mile distance. Participants who resided in LILA areas had higher baseline A1c (0.5 difference: 0.37 (p=0.02), 1 difference: 0.51 (p=0.005)). Those with baseline A1c ≥ 9.0 (n=216, 120 F, 67.8 y) demonstrated a decrease of -1.38 points (SD 1.96, p<0.001). Thirty-nine percent resided in LILA areas at the 0.5, and twenty-three percent at 1 mile distance. Baseline A1c differences were not observed. Differences in improvements in A1c based on LILA were not observed (p’s > 0.05). Irrespective of LILA residence, the Vida Health program demonstrated significant improvements in glycemic control within 1 year in a high-risk Medicare Advantage population. Disclosure C. Edwards: Employee; Vida Health. L. Voelker: Employee; Vida Health. G. Zimmermann: Employee; Vida Health. K. Rawlings: Employee; Vida Health. A. Venkatesan: Employee; Vida Health. Funding Vida Health

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.

670-P: Iron-Deficiency Anemia Case Report Showing the Clinical Relevance of RBC-Lifespan–Adjusted A1C

Laboratory A1C does not precisely reflect glycemic control in those with altered RBC lifespan and our aim was to evaluate an adjusted A1C, a glycemic measure that adjusts A1C to a reference RBC lifespan. By removing the individual RBC lifespan variation, adjusted A1C better reflects intracellular glucose exposure in RBCs and perhaps other organs susceptible to complications. Iron-deficiency anemia and treatment is known to affect RBC lifespan and therefore provides opportunity to evaluate the model. The patient was a 53-year-old woman with iron-deficiency anemia and type 1 diabetes utilizing sensor-augmented pump therapy. Longitudinal A1C and CGM were evaluated with a novel kinetic model to estimate RBC lifespan and glucose uptake rate. During the six months before iron therapy, the RBC lifespan was estimated at 151 days and 93% of a reference glucose uptake. During six months of iron therapy, the RBC lifespan decreased to 122 days, while the RBC glucose uptake increased slightly to 101% of reference value. The RBC lifespan change explains the observed laboratory A1C change (Figure) and can be attribute to the iron therapy and supported by Hb, RDW and Ferritin data. The adjusted A1C based on these two different RBC lifespans agree with the stable CGM mean glucose of this patient. In this case study, we confirmed the clinical relevance of adjusted A1C to account for variations in RBC lifespan. Disclosure Y.Hirota: Speaker's Bureau; Abbott Japan Co., Ltd., Astellas Pharma Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Daiichi Sankyo, Kissei Pharmaceutical Co., Ltd., Kowa Company, Ltd., Kyowa Kirin Co., Ltd., Lilly, Medtronic, Mochida Pharmaceutical Co., Ltd., Novo Nordisk, Ono Pharmaceutical Co., Ltd., Pfizer Japan Inc., Sanofi, Sanwa Kagaku Kenkyusho, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited, Terumo Corporation. Y.Xu: Employee; Abbott Diabetes. A.Yamamoto: None. A.Matsuoka: None. T.Dunn: Employee; Abbott. W.Ogawa: Advisory Panel; Abbott Japan Co., Ltd., Nippon Boehringer Ingelheim Co. Ltd., Novo Nordisk, Other Relationship; Abbott Japan Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Nippon Boehringer Ingelheim Co. Ltd., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Research Support; Abbott, Astellas Pharma Inc., Eli Lilly and Company, Kowa Company, Ltd., Nippon Boehringer Ingelheim Co. Ltd., Novo Nordisk, Ono Pharmaceutical Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd. Funding Abbott Diabetes Care

148-OR: Usefulness of CGM Metrics to Assess Glycemic Control in Non-White Subjects with Diabetes

Introduction: Glucose management indicator (GMI), a CGM-based metric, of mean glucose is thought to give more information on glycemic excursion than laboratory measured A1c. We have shown differences in relationship between A1c and GMI in different age groups, however it is not known whether racial differences affect the relationship between A1c and GMI. Methods: We analyzed data from patients with diabetes undergoing professional CGM (proCGM) at a tertiary care diabetes center between year 2017 and 2020, who had at least 10 days of CGM data. Medical records were queried to obtain demographic information and laboratory A1c results from within 1 month of CGM wear. GMI was calculated as Formula =3.31 + (0.02392 × mean glucose in mg/dL). Results: We evaluated 316 patients (mean age 60 ±18, 51% female, duration of diabetes 24±15 years, T1D 45% and T2D 55%, 78% white and 22% non-white). Subjects were identified by race as non-white (T1D 22%; T2D 78%) or white (T1D 45%; T2D 55%). CGM metrics in the non-white and white cohort were similar; time in range (728 ± 342 vs. 722 ± 307 min/day; p=ns), time <70 mg/dL (90 ± 98 vs. 97 ± 108 min/day; p=ns ), and time >180 mg/dL (568 ± 324 vs. 569 ± 346 min/day; p=ns). While GMI was not different in the nonwhite vs. white cohort (7.6 ± 1.3 vs. 7.7 ± 1.5; p=ns), A1c was higher in non-whites (8.7 ± 1.6 vs. 8.1 ±1.3; p=0.004). An absolute difference between A1c and GMI was higher in nonwhite vs. white (1.5 ± 0.88 vs. 1.1 ± 0.82 vs; p=0.006) and the absolute difference of 0.5% was observed in higher proportion of nonwhite patients (50 [75%] vs. 165 [68%]). Conclusions: In our study cohort, the relationship between GMI and A1c is different in non-white versus white population despite similar CGM metrics. This difference might be important to consider when assessing glycemic control and goals in diverse patient population. Disclosure E. Toschi: Consultant; Self; Medtronic. C. Slyne: None. A. Michals: None. K. Sifre: None. R. Dewar: None. A. Atakov-castillo: None. D. J. Davis: None. M. Munshi: Consultant; Self; Sanofi. Funding National Institutes of Health (1DP3DK112214-01)

1402-P: Impact of Diabetes Duration on Functional and Clinical Status in Older Adults with Type 1 Diabetes

Background: Adults with type 1 diabetes (T1D) are aging more successfully than ever. However, in this older, heterogeneous population, diabetes duration can vary widely, as people may have been diagnosed many decades ago in early childhood or as recently as late adulthood. There is little information about the impact of diabetes duration on psychosocial factors and functional status as patients with T1D age. Methods: We evaluated a cohort of older adults (age≥65 years) with T1D. All participants completed surveys regarding demographics, medical history, overall health (Short Form 36; SF-36), hypoglycemia awareness (Clarke Score), and hypoglycemia fear (HFSII). Laboratory A1C was measured, and Montreal Cognitive Assessment (MoCA) was performed to assess cognitive function. Results: We evaluated 148 older adults, mean age 71 ± 5 years, 55% female, 97% white, mean A1C 7.4 ± 0.9%, mean diabetes duration 39 ± 17 years, with 20% of participants living alone. The participants were divided into tertiles of diabetes duration; ≤30 years (n=48), 31-49 years (n=54) years, and ≥50 years (n=46). Comparison of the shortest and longest tertiles of diabetes duration showed that participants of the group with the shortest duration had significantly less depression (35% vs. 59%; p=0.02), less hypoglycemia unawareness (31% vs. 61%; p=0.008), fewer recent falls in the prior 6 months (5 vs. 12; p=0.05), fewer daily medications (9 vs. 11; p=0.02), and fewer comorbidities (8 vs. 9; p=0.05). Additionally, the overall health survey (SF-36) showed that the group with longer duration reported more difficulty and limitation in daily activities due to physical health. The two groups did not differ in age, A1C, cognitive dysfunction (MoCA score <26), or hypoglycemia fear. Conclusion: In older adults with T1D, duration of diabetes impacts clinical and functional status, and should be kept in consideration when developing management strategies for maximum safety and success. Disclosure M. Munshi: Consultant; Self; Eli Lilly and Company, Sanofi. C. Slyne: None. K. Sifre: None. R. O’Donnell: None. A. Atakov-Castillo: None. E. Toschi: None. Funding National Institutes of Health (1DP3DK112214-01)

300-OR: Individualized Hemoglobin A1C: A New Glycemic Marker Determined Using Continuous Glucose Monitoring

Glycation of hemoglobin (Hb) measured by A1C depends on red blood cell (RBC) lifespan, thus may misrepresent intracellular glucose exposure in other tissues, and may explain observed dissociations between A1C and microvascular complications. A kinetic model that accounts for rates of RBC turnover and intracellular glucose transport was evaluated for A1C individualization using continuous glucose monitoring data (CGM) and laboratory A1C from 58 individuals with diabetes (type 1/type 2 = 5/53, mean age 58 years (range 27 to 77, M/F=38/20). A1C was performed at 0, 3, and 6 months with concurrent CGM for 6 months, having a minimum of 80% coverage and no gaps >36 hours. The adjustment required to calculate individualized A1C is expressed as follows: A1C/(A1C+(1-A1C)*KTR/KT), where KT is the Hb and RBC turnover rate, and KTR is the reference Hb and RBC turnover rate. The mean (±SD) rate of Hb and RBC turnover was 1.18±0.20%/day as depicted in the Figure, showing the individualized A1C calculation. These data suggest that laboratory A1C does not only underestimate cellular glucose exposure in some patients but can also overestimate average glucose levels making the individual prone to hypoglycemia through unnecessary escalation of antidiabetic therapies. Quantitative individualization of A1C derived from A1C and CGM readings has the potential to more accurately assess intracellular glycemic exposure. Disclosure R.M. Bergenstal: Research Support; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Glooko, Inc., Hygieia, JDRF, Johnson & Johnson Medical Devices Companies, Lilly Diabetes, Medtronic MiniMed, Inc., Merck & Co., Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk Inc., Roche Diabetes Care, Sanofi. Other Relationship; Self; Abbott, Helmsley Charitable Trust, Hygieia, Lilly Diabetes, Novo Nordisk Inc., Onduo LLC, Sanofi, UnitedHealth Group Inc. T. Dunn: Employee; Self; Abbott Laboratories. Y. Xu: Employee; Self; Abbott Laboratories. R. Ajjan: Consultant; Self; Abbott. Research Support; Self; Abbott, LifeScan, Inc. Funding Abbott Diabetes Care

Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring.

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

Comparing point-of-care A1C and random plasma glucose for screening diabetes in migrant farm workers

ObjectiveTo compare point-of-care (POC) glycosylated hemoglobin (A1C) and random plasma glucose (RPG) as a POC screening tool for prediabetes and diabetes in migrant farm workers of eastern North Carolina. DesignProspective, observational, single-center study. SettingFederally qualified community health center in eastern North Carolina, from August to October 2011. ParticipantsMigrant farm workers 18 years or older who resided in a migrant camp in eastern North Carolina. InterventionDiabetes screening using POC A1C and RPG via fingerstick followed by venipuncture A1C and basic metabolic panel in individuals with a positive screening. Main outcome measuresPositive predictive value (PPV) of POC A1C and RPG, incidence of positive screening, incidence of confirmed diagnosis, concordance rate of the screening tools, and correlation between POC A1C and laboratory A1C. Results206 workers participated in the screenings; screening identified 39 individuals with a POC A1C greater than 5.7% and 1 individual with both an RPG of 200 mg/dL or more and a POC A1C greater than 5.7%. Of the 39 individuals found to have a positive screening, 24 presented to Carolina Family Health Centers, Inc., for follow-up venipuncture; however, 1 participant did not have a venipuncture A1C, leaving 23 individuals with complete data. Two participants were diagnosed with diabetes and 17 with prediabetes. POC A1C had a PPV of 82.6%; however, the PPV of RPG could not be calculated due to the number of participants lost to follow-up. POC A1C correlated well with laboratory A1C regardless of time to follow-up. ConclusionPOC A1C should be considered for diabetes screening in high-risk populations. If the screening had been performed with RPG alone, 38 individuals would have gone undetected. Early identification of individuals with elevated blood glucose will likely decrease the risk of long-term complications.

Relationship Between Glycosylated Hemoglobin Assessment and Glucose Therapy Intensification in Patients With Diabetes Hospitalized for Acute Myocardial Infarction

OBJECTIVETo evaluate the relationship between A1C and glucose therapy intensification (GTI) in patients with diabetes mellitus (DM) hospitalized for acute myocardial infarction (AMI).RESEARCH DESIGN AND METHODSA1C was measured as part of routine care (clinical A1C) or in the core laboratory (laboratory A1C, results unavailable to clinicians). GTI predictors were identified using hierarchical Poisson regression.RESULTSOf 1,274 patients, 886 (70%) had clinical A1C and an additional 263 had laboratory A1C measured. Overall, A1C was <7% in 419 (37%), 7–9% in 415 (36%), and >9% in 315 patients (27%). GTI occurred in 31% of patients and was more frequent in those with clinical A1C both before (34 vs. 24%, P < 0.001) and after multivariable adjustment (relative risk 1.34 [95% CI 1.12–1.62] vs. no clinical A1C).CONCLUSIONSLong-term glucose control is poor in most AMI patients with DM, but only a minority of patients undergo GTI at discharge. Inpatient A1C assessment is strongly associated with intensification of glucose-lowering therapy.

Utility of a point-of-care device in recruiting ethnic minorities for diabetes research with community partners.

Recruitment of hard-to-reach ethnic minorities such as Korean Americans (KAs) requires substantial time, cost, and strategic effort. A point-of-care (POC) A1c test could facilitate the recruitment of such populations for diabetes research in community settings. A two-step approach for participant screening was employed: Potential participants were first screened using the POC A1c test at a community location. Only those with POC A1c levels ≥7.5% were referred for a confirmatory lab test within two weeks. In total, 237 KAs were screened using the POC A1c test; 92 were referred for confirmatory testing and 83 who got the laboratory A1c measurement were confirmed eligible (A1c ≥7.5%). There was a strong positive correlation between the POC and reference laboratory measurements (ρ=0.83, p≤.001). Using a POC A1c method as a front-line screening test can facilitate the recruitment of KAs with type 2 diabetes, while saving cost, time, and effort.

Impact of Active Versus Usual Algorithmic Titration of Basal Insulin and Point-of-Care Versus Laboratory Measurement of HbA1c on Glycemic Control in Patients With Type 2 Diabetes

The objective of this study was to assess the impact of active versus usual monitoring of algorithmic insulin titration and point-of-care (POC) versus laboratory HbA1c (A1C) measurement on glycemic control in primary care. The Glycemic Optimization with Algorithms and Labs at Point of Care (GOAL A1C) study was a 24-week, randomized, parallel-group, four-arm, open-label study of 7,893 adults with type 2 diabetes uncontrolled by oral antidiabetic agents and requiring insulin. Patients were randomly assigned by investigators from 2,164 sites in the U.S. to insulin glargine with either 1) usual (no unsolicited contact between visits) insulin titration using a simple algorithm with laboratory A1C testing, 2) usual titration with POC A1C testing, 3) active (weekly monitored) titration with laboratory A1C testing, or 4) active titration with POC A1C testing. Outcome measures included a change in A1C and fasting self-monitoring of blood glucose (SMBG) levels, percentage of patients achieving A1C <7.0%, and hypoglycemia frequency. Significant A1C and SMBG reductions were observed in all arms (P < 0.0001). Compared with usual insulin titration, active titration achieved greater A1C reduction (1.5 vs. 1.3%; P < 0.0001), SMBG reduction (88 vs. 79 mg/dl; P < 0.0001), and proportion of patients achieving A1C <7.0% (38 vs. 30%; P < 0.0001). Among patients receiving active titration, POC A1C testing was associated with an increase in the proportion achieving an A1C <7.0% (41% for POC vs. 36% for laboratory; P < 0.0001). Hypoglycemia rates were low (usual vs. active groups: 3.7 vs. 6.0 all confirmed episodes/patient-year [P < 0.001]; 0.09 vs. 0.14 severe episodes/patient-year [NS]). In a predominantly primary care setting, addition of insulin glargine using a simple algorithm achieved significant improvements in glycemic control in patients with type 2 diabetes in all four study arms. Active titration resulted in significant incremental improvements in glycemic control, and, among patients receiving active titration, POC A1C testing resulted in a greater portion achieving A1C <7.0%.

Glycated Hemoglobin Assessment in Clinical Practice: Comparison of the A1cNow™ Point-of-Care Device with Central Laboratory Testing (GOAL A1C Study)

The Glycemic Optimization with Algorithms and Labs At Po1nt of Care (GOAL A1C) Study assessed the effect of titration monitoring strategies and methods of A1C testing on glycemic control in patients with type 2 diabetes failing oral therapy and beginning basal insulin glargine. The availability of both point-of-care (POC) and central laboratory A1C values provided an opportunity to evaluate correlation and statistical agreement between these methods of testing. This analysis forms the basis of the current report. This is a 24-week, randomized, four-arm, open-label study conducted in 7,758 subjects enrolled at 2,130 sites. At baseline, patients had A1C measurements both by POC testing using the A1cNow device (Metrika, Inc., Sunnyvale, CA), which applies an immunoassay method, and by central laboratory analysis using ion exchange high-performance liquid chromatography. These measures were compared statistically. An r value of 0.72 was calculated for POC and laboratory A1C assessments. Although the mean POC A1C values were in agreement with the central laboratory values, there was a large range in individual POC A1C values. POC testing of A1C in predominantly primary care settings using the A1cNow device was correlated with central laboratory results. The correlation was less than expected based on each method's reproducibility data. Although there was agreement between the average POC A1C values and the corresponding central laboratory values, the dispersion of individual POC A1C values was large. Thus, we conclude that these two methods of A1C testing should not be used interchangeably.

Underdiagnosis of Peripheral Neuropathy in Type 2 Diabetes

Diabetic peripheral neuropathy and its late sequelae cost >$4 billion annually in the U.S. (1). Improved glycemic control, early detection, and preventive care can prevent or delay adverse outcomes (2–4). An annual foot exam, which may include monofilament testing, is recommended by the American Diabetes Association (5). Unfortunately, neuropathy screening is underutilized in primary care practice (6,7). The GOAL A1C (Glycemic Optimization with Algorithms and Labs At Po1nt of Care) study assessed methods of A1C testing and insulin titration monitoring strategies in a large nationwide sample of predominantly primary care patients with type 2 diabetes. At baseline, physician perception of the presence of neuropathy was assessed via a survey before monofilament testing. A comparison between physician perception and monofilament testing is reported. GOAL A1C was a randomized, open-label, parallel, four-arm study that compared the impact of point of care versus laboratory A1C testing and weekly versus less frequent insulin titration monitoring on glycemic control in patients with type 2 diabetes adding insulin glargine to existing oral antidiabetic therapy. Eligible patients were aged ≥18 years, inadequately controlled on oral antidiabetic agents (A1C >7.0%), and candidates for insulin. All signed informed consent …