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From A1c to Time in Range — New Glycemic Management Metrics
By Hope Warshaw, MMSc, RD, CDCES, BC-ADM, FADCES
The popular saying “perspective is everything” may hold true for diabetes management. Let’s glimpse back in time to understand where we are today when it comes to glycemic management metrics.
A1c, or more formally, hemoglobin A1c, was isolated in 1958 and characterized as a glycoprotein a decade later.1 Around 1969, research showed that people with diabetes tend to have elevated A1c levels. By 1976, A1c was proposed as a biomarker of glycemic management and slowly integrated into diabetes care. Interestingly, A1c wasn’t added as a diagnostic indicator of diabetes until 2010.2
The 1980s ushered in the era of self-blood glucose monitoring (BGM), still used today by millions to manage their glucose levels—though today, the meters are smaller and faster, require less blood for a check, and often integrate with an app that offers insights and trends.3
Fast forward to the mid-2010s, research began to poke holes in the accuracy of the A1c test for all people with diabetes.4 Around this time, there was a call for glucose management metrics that went beyond A1c, and the Beyond A1c Movement was born.5 Concomitantly, continuous glucose monitors (CGMs) were becoming available to provide near constant data on glucose levels and associated details throughout the day.6 CGMs measure interstitial glucose found in the fluid between the cells, not blood glucose.
During the last decade, CGM technology has become more available, accurate, useable, and reimbursable.6 Thus, increasing numbers of people with diabetes, especially those who require insulin, have adopted its use. People who use a CGM to monitor and manage glucose levels produce voluminous data. They and their clinicians needed a logical way to access, analyze, and act on the data to make data-driven care decisions. Enter time in range (TIR) and its related metrics.
With this abbreviated history, Today’s Dietitian will define A1c and explore its potential pitfalls, as well as define the terms and parameters of TIR and offer strategies to apply it whether a person with diabetes uses BGM or CGM.
A1c and Its Use
The A1c test reflects a person’s average glucose level over the last two to three months. It remains, at this point, a primary tool for assessing glycemic management.7 A1c has a strong predictive value for diabetes complications, and elevated A1c is considered an independent risk factor for coronary heart disease and stroke in people with diabetes. The American Diabetes Association’s Standards continue to recommend that all people with diabetes get an A1c check at diagnosis and approximately every three to six months to determine whether glycemic targets have been reached and are being maintained.7
A1c Limitations and Pitfalls
A1c shouldn’t be used as the sole measure to assess glycemic management, particularly if clinicians are making care plan changes. The A1c result offers no insight into a person’s glucose variability over the timeframe; it’s simply an average. The illustration, “The Many Faces of a 7% A1c,” clearly paints this picture. A person can have an A1c of 7% with extreme glycemic variability (the graph and pie chart on the left) or with almost no variability (the graph and pie chart on the right).
Source: Reprinted with permission from The diaTribe Foundation and diaTribe’s Time in Range Coalition https://diatribe.org/time-range-coalition
Clinicians should pair the A1c result with BGM data that includes an adequate number of results taken at various times during a day, from overnight, fasting, and pre and post food intake. Clinicians should, using shared-decision making, counsel people with diabetes who use BGM about implementing a structured pattern of glucose checks that over time will offer meaningful information about glycemic variability.3 They can accomplish this with paired checks, staggered checks, or use of a seven-point profile a couple of days each week. People shouldn’t be encouraged, as they often are, to do only fasting blood glucose checks.
With regard to pitfalls, the A1c test has been found to result in discrepancies compared with actual mean glycemia in people with diabetes who have certain medical conditions, such as hemolytic and other anemias, including sickle cell anemia, thalassemia, and iron deficiency anemia; pregnancy; a recent blood transfusion; and end-stage renal disease.7 Clinicians shouldn’t rely on A1c for glycemic management in these individuals.
In addition, some studies show that Africans and African Americans, compared with non-Hispanic whites, can have a higher A1c result at a given mean glucose level.7
Lastly, the A1c test sometimes can fail to accurately reflect mean glucose even when the person has no medical condition.4
TIR and Its Use
TIR has been defined by the International Consensus on Time in Range as the percentage of time over a 14-day period that a person’s glucose levels, based on CGM, are within a defined range.8 An aim of these consensus recommendations was to give clinicians and people with diabetes clear targets to achieve the diabetes care trifecta—lower A1c, less hypoglycemia, and staying within goal target TIR.
The table below provides the standardized TIR metrics for use with CGM data.8 A 14-day assessment that represents comprehensive data from at least 70% of the time can substitute for an A1c result to adjust a person with diabetes’ care plan. It’s conjectured that the use of these metrics, instead of A1c, may allow for more timely care plan adjustments, a decrease in therapeutic inertia, and more rapid optimization of care.
“Pairing CGM data with TIR metrics has opened my eyes and those of my clients to see in real-time the fluctuations that occur in glucose levels,” says Dawn Noe, RDN, CDCES, owner of Dawn Noe Nutrition and Consulting in Cleveland, Ohio. “When analyzing metrics, do reinforce that you’re not looking for a TIR of 100%, a TIR of 70% is quite good,” Noe says.
Glucose Management Indicator
The glucose management indicator (GMI), as a glycemic metric, made its debut in 2018 and is integrated into the standardized CGM metrics (see table below).8,9 GMI offers an indication of current glucose management and communicates a person’s approximate A1c based on an average glucose level from CGM readings. GMI was introduced to replace estimated A1c, a term that never gained traction.
Access, Analyze, and Act on CGM Data
Given the volume of CGM data, there was a need to simplify the data and assist people with diabetes and their clinicians to access, analyze, and act on the data. Enter the ambulatory glucose profile (AGP), developed and refined over 30 years at the International Diabetes Center (IDC) in Minneapolis.10 AGP has become the most familiar single-page report for presenting a summary of CGM data. View the IDC CGM AGP v5.0 report at www.agpreport.org/agp/agpreports.
The AGP report, or a somewhat modified format, has been adopted by CGM device manufacturers to use in their downloadable software and product apps. Currently, there are insulin delivery devices, such as advanced hybrid closed loop systems, that use CGM data in tandem with an algorithm to automatically adjust insulin delivery in response to CGM readings. (Read “Insulin Delivery Device Technology” in the November/December 2022 issue of Today’s Dietitian.)
TIR With BGM
TIR was developed with CGM concepts in mind. TIR doesn’t translate to use with BGM data due to the number of hours that may pass between readings, during which blood glucose levels may fluctuate dramatically. “BGM will remain a viable and useful technology for many people with diabetes and clinicians to guide lifestyle and therapy changes,” says Gregg Simonson, PhD, director of diabetes care translation and training at the IDC.
Clinicians and people with diabetes can use BGM readings to calculate average glucose, glucose ranges, and glucose variability, and they can be aggregated to provide percent BGM readings in range. People can use this data to track progress, identify challenges, and strategize for improvement. “The minimum data needed to calculate BGM statistics is at least 30 BGM readings regardless of the number of days represented,” Simonson says. Instead of TIR, a concept called “Readings [number of BGM results] in Range (RIR),” developed by IDC, can be applied to analyze BGM data (see example in SMBG AGP v5.0 Report [Self Monitoring Blood Glucose]).11 “IDC continues this work, and there will be updates,” Simonson says.
Time in Range Coalition Drives Awareness
To get the word out globally about TIR, especially outside of the diabetes ecosystem, The diaTribe Foundation formed the Time in Range Coalition, consisting of researchers and leaders from diabetes organizations and pharmaceutical companies seeking to establish TIR as an essential metric of diabetes management.12 “It’s an effort to drive awareness and adoption of TIR as the primary tool for daily care, which can be complemented by A1c,” says Julie Keller Heverly, senior director of the Time in Range Coalition. “With a focus on TIR metrics,” Heverly says, “clinicians can better assist people with diabetes to make more informed treatment and lifestyle decisions.”
Evolving Diabetes Care
How people with diabetes and their clinicians monitor and manage glucose levels will continue to evolve. A1c remains a viable metric for many when viewed in conjunction with ample BGM results. TIR and its related metrics, with a single-page standardized report, can assist people with diabetes who use CGMs and their clinicians fine tune their care. Either way, having insights into average glucose levels (A1c or TIR) along with day-to-day glucose variability is critical to improving diabetes care.
— Hope Warshaw, MMSc, RD, CDCES, BC-ADM, FADCES, is owner of Hope Warshaw Associates, LLC, a diabetes- and nutrition-focused consultancy based in Asheville, North Carolina. She’s a book author and freelance writer who specialized for many years in diabetes care. Warshaw currently serves as the 2022–2023 chair of the Academy of Nutrition and Dietetics Foundation.
Standardized CGM Metrics for Clinical Care
| Metric | Recommendation or Target for Type 1 and Type 2 Diabetes* |
| Number of days CGM worn | 14 days |
| % of time CGM is active | 70% of data from 14 days |
Mean glucose |
none provided |
| Glucose management indicator9 | none provided |
| Glycemic variability (% coefficient of variation) | <36% |
| Time above range (TAR): % of readings and [amount of] time >250 mg/dL (defined as level 2 [hyperglycemia]) | <5% |
| TAR: % of readings and [amount of] time >181–250 mg/dL (level 1 [hyperglycemia]) | <25% |
| Time in range: % of readings and [amount of] time >70–180 mg/dL (defined as in range) | >70% |
| Time below range (TBR): % of readings and [amount of] time <54–69 mg/dL (defined as level 1 [hypoglycemia])13 | <4% |
| TBR: % of readings and [amount of] time <54 mg/dL (defined as level 2 [hypoglycemia])13 | <1% |
*Recommendations and targets are different for older/high-risk persons with type 1 and type 2 diabetes, pregnant women with type 1 diabetes, as well as gestational and type 2 diabetes.8
References
1. Saudek CD, Brick JC. The clinical use of hemoglobin A1. J Diabetes Sci Technol. 2009;3(4):629-634.
2. Cohen RM, Haggerty S, Herman WH. HbA1c for the diagnosis of diabetes and prediabetes. Is it time for a mid-course correction? J Clin Endocrinol Metab. 2010;95(12):5203-5206.
3. Weinstock RS, Aleppo G, Bailey TS, et al. The role of blood glucose monitoring in diabetes management. https://professional.diabetes.org/sites/professional.diabetes.org/files/media/ada_bgm_compendium_fin_rev-web.pdf. Published 2020. Accessed October 29, 2022.
4. Beck RW, Connor CG, Mullen DM, Wesley DM, Bergenstal RM. The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading. Diabetes Care. 2017;40(8):994-999.
5. Outcomes beyond A1c. The diaTribe Foundation website. https://diatribe.org/foundation/beyonda1c. Accessed October 29, 2022.
6. Didyuk O, Econom N, Guardia A, Livingston K, Klueh U. Continuous glucose monitoring devices: past, present, and future focus on the history and evolution of technological innovation. J Diabetes Sci Technol. 2021;15(3):676-683.
7. American Diabetes Association Professional Practice Committe. 6. Glycemic targets: standards of medical care in diabetes—2022. Diabetes Care. 2022;45(Supp 1):S83-S96.
8. Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on Time in Range. Diabetes Care. 2019;42(8):1593-1603.
9. Bergenstal RM, Beck RW, Close KL, et al. Glucose management indicator (GMI): a new term for estimating A1c from continuous glucose monitoring. Diabetes Care. 2018;41(11):2275-2280.
10. About AGP — the single page report for everyone. International Diabetes Center website. http://www.agpreport.org/agp/about. Accessed October 29, 2022.
11. AGP reports — CGM AGP report (continuous glucose monitor) - v5.0. International Diabetes Center website. http://www.agpreport.org/agp/agpreports. Accessed October 31, 2022.
12. Time in Range Coalition. diaTribe website. https://diatribe.org/time-range-coalition. Accessed October 31, 2022.
13. Agiostratidou G, Anhalt H, Ball D, et al. Standardizing clinically meaningful outcome measures beyond HbA1c for type 1 diabetes: a consensus report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange. Diabetes Care. 2017;40(12):1622-1630.