A1C remains an established risk marker for population health and is used extensively in clinical research and regulatory trials. However, factors such as hemoglobinopathies and heritable differences in glycation dynamics can render A1C less useful as a guide to glycemic control for some patients (1). The health care improvement goal of excellent quality and patient experience at reasonable cost is further shifting emphasis away from A1C as the reigning standard of care toward minimizing the daily burdens of living with diabetes. Indeed, many experts contend that it is time to formalize a definition of optimal control that includes A1C being at target (personalized for each individual, but usually ∼7% for most adults) without occurrence of severe hypoglycemia and with only a minimal number of very low or clinically significant low glucose values (2).
Yet A1C, even in combination with the rate of hypoglycemia, still has some inherent barriers to being an ideal personal management guide. First, A1C represents an average glucose level over 2–3 months and, as such, is unable to reveal potentially dangerous episodes of hypoglycemia or hyperglycemia. Second, individuals with the same mean glucose (derived through continuous glucose monitoring [CGM]) may have a clinically significant variation in their laboratory-measured A1C. In practice, this means that a laboratory-measured A1C of 8% may have a CGM-derived mean glucose ranging from 155 to 218 mg/dL, obviously with different clinical management implications. Variation in the relationship between A1C and mean glucose has been observed between races and to an even greater extent between individuals of the same race (3,4). Although the mechanisms for individual variation in the relationship of A1C to mean glucose are still being investigated, inherent differences in the rate of hemoglobin glycation and red blood cell life span remain the leading hypothesis (5).
With several excellent approved CGM systems available, including many that are factory-calibrated, and given the fact that current CGM metrics and glucose profile visualizations are mostly standardized (see the article on p. 20 of this compendium), it is now feasible to define glucose control and management decisions based on CGM data and reports. A key patient-centered metric is to have as many glucose values as possible fall within the individualized target range, referred to as time in target range or simply time in range (TIR), with the common default range of 70–180 mg/dL. The more TIR the better the A1C is likely to be because these two variables are highly correlated. For optimal management, patients should have a TIR level as high as possible with a very low level of time in hypoglycemia (TIHypo). Maximal TIR with minimal TIHypo is a reasonable overarching glycemic target (6).
Below are two ways to assess the correlation of CGMderived TIR data and A1C laboratory data.
Consider the mean TIR, achieved using the most advanced currently approved technology (hybrid closedloop therapy), of 124 individuals with type 1 diabetes who had a mean A1C of 6.9% (secondary analysis of data from Bergenstal et al. ).
TIR (70–180 mg/dL) ∼72% or 17.3 hours/day
TIHypo (<70 mg/dL) ∼3% or 43 min/day (<1% or ∼14 min/day of this <54 mg/dL)
TIHyper (time in hyperglycemia; >180 mg/dL) ∼25% or 6 hours/day (<6% or ∼86 min/day of this >250 mg/dL)
Consider the correlations of TIR and A1C achieved from an analysis of several hundred people with type 1 or type 2 diabetes in a series of clinical trials (Table 1) (secondary analysis of data from Beck et al. ; R. Beck, personal communication).
|Measured TIR (70–180 mg/dL) .||A1C .||95%CI .|
|Measured TIR (70–180 mg/dL) .||A1C .||95%CI .|
In summary, laboratory-derived A1C is a measure of population health and of long-term risk for diabetes complications but is not an individualized management tool. An elevated A1C implies that action is needed but does not help tailor treatment because neither hypoglycemia, glucose variability, nor timing of hyperglycemia are revealed by this average glucose measure. In contrast, a standardized Ambulatory Glucose Profile (AGP) report clearly shows dangerous high or low patterns that need immediate attention. The timing and magnitude of hyperglycemia, hypoglycemia, and glucose variability are clearly visualized in the AGP and quantitated by CGM metrics (TIR, TIHypo, TIHyper, and coefficient of variation/standard deviation). As more fully explained in the article below, with the AGP in front of them, patients and clinicians can agree on a personalized treatment plan aimed at improving the glucose profile while avoiding significant hypoglycemia.
The opinions expressed are those of the authors and do not necessarily reflect those of Abbott Diabetes Care or the American Diabetes Association. The content was developed by the authors and does not represent the policy or position of the American Diabetes Association, any of its boards or committees, or any of its journals or their editors or editorial boards.
Dualities of Interest
I.B.H. has served as a consultant to Abbott Diabetes Care, Adocia, Bigfoot, and Roche. His institution has received research grant support from Medtronic.
T.B. has served on advisory boards of Bayer Health Care, Boehringer Ingelheim, DreaMed Diabetes, Eli Lilly, Medtronic, Novo Nordisk, and Sanofi. His institution has received research grant support, with receipt of travel and accommodation expenses in some cases, from Abbott Diabetes Care, Diamyd, GluSense, Medtronic, Novo Nordisk, Sandoz, and Sanofi. He has received honoraria for participating on the speakers bureaus of Bayer Health Care, Eli Lilly, Medtronic, Novo Nordisk, Roche, and Sanofi. He owns stock in DreaMed Diabetes.
A.L.P. has served on advisory boards for Abbott Diabetes Care, Becton Dickinson, Bigfoot, Boehringer Ingelheim, Eli Lilly, Lexicon, Livongo, Medscape, Merck, Novo Nordisk, OptumHealth, Sanofi, and Science 37. She has received research grant support from Dexcom and Mannkind. She participates on a speakers bureau for Novo Nordisk.J.J.C. participates in speakers bureaus for Janssen, Merck, Novo Nordisk, and Sanofi.
G.A. has served as a consultant and on a steering committee for Dexcom and on an advisory board for Novo Nordisk, and her institution has received research grant support from AstraZeneca and Novo Nordisk.
R.M.B.’s institution has received payment for his services as a research investigator, consultant, or advisory board member for Abbott Diabetes Care, Becton Dickinson, Boehringer Ingelheim, Bristol-Myers Squibb/AstraZeneca, Dexcom, Eli Lilly, Hygieia, Johnson & Johnson, Medtronic, Merck, Novo Nordisk, Roche, Sanofi, and Takeda. R.M.B. has inherited Merck stock, volunteers for the American Diabetes Association and JDRF, and receives funding from the National Institutes of Health for diabetes technology research.
Writing support services for this compendium were provided by Carol Verderese of The Diabetes Education Group in Lakeville, CT. Editorial and project management services were provided by Debbie Kendall of Kendall Editorial in Richmond, VA.
All authors researched and wrote their respective section(s). Lead author I.B.H. reviewed all content and is the guarantor of this work.