Unleashing the Potential of Blood Glucose Monitoring Data With the Ambulatory Glucose Profile Report

A1C remains the gold standard for assessing glucose management and is strongly correlated with the incidence of diabetes complications in landmark clinical studies (22,23). The information an A1C provides helps to determine risk and predict outcomes. However, there are clear limitations to using A1C to guide treatment decisions in the absence of the contextual information provided by BGM or CGM. A1C lacks the detail needed to provide real-time clinical insights or guide real-time decision-making and may even provide misleading information. Factors that influence the glycation of hemoglobin, such as anemia, hemoglobinopathies, hemodialysis, HIV treatment, race/ethnicity, and pregnancy, may affect the accuracy of A1C measurements (24). Additionally, A1C is heavily influenced by recent blood glucose levels, does not segregate fasting from postprandial glucose levels, provides no indication of glucose variability, and does not reveal information on the presence or timing of hyper- or hypoglycemia. Therefore, using A1C in isolation does not enable HCPs to make the best-informed decisions about diabetes therapies or glucose monitoring plans. Instead, the clinical insights derived from routine A1C measurements should be augmented by contextual data from daily or aggregated BGM or CGM glucose profiles such as those presented by the AGP Report: BGM or the AGP Report: CGM.

Structured monitoring is a recommended schedule of blood glucose readings taken at specific times of day or over certain days and can be prescribed by HCPs to maximize the amount of actionable glucose data that can be gleaned from BGM. Structured monitoring has been reported to yield clear benefits for insulin-treated people with type 1 or type 2 diabetes and its effective implementation is thought to aid clinicians in helping people with diabetes achieve their desired outcome of safely reducing A1C to goal while simultaneously bolstering diabetes knowledge and empowering self-management behaviors (25). A meta-analysis of studies in people with type 2 diabetes who were not on insulin therapy found an additional 0.23% A1C reduction in those performing structured monitoring compared with routine monitoring (i.e., BGM performed without specific guidance on a schedule for glucose readings) (26). The importance of clinicians and people with diabetes acting on the structured monitoring data were highlighted in that meta-analysis (A1C lowering by an additional 0.28% compared with not adjusting lifestyle based on the data) and in a meta-analysis by Mannucci et al. (27), who detected an additional 0.27% A1C improvement when structured monitoring data were used to adjust diabetes medications compared with unstructured monitoring.

A more significant reduction in A1C of 0.8% was observed in people with diabetes not taking insulin who performed structured monitoring with four-point profiles (checking glucose four times daily at, before, and 2 hours after breakfast and the main meal) twice per week and seven-point profiles (checking glucose seven times daily at, before, and 2 hours after each meal and at bedtime) on 3 days before site visits. These individuals experienced comparable glycemic outcomes to a second intervention group who additionally received monthly telephone support (28). A follow-up analysis of this study (29) confirmed that structured monitoring using paired BGM (before and after meals) to identify patterns of glycemia was associated with significant improvements in glucose management and glycemic variability.

A recent review observed that the utility of different structured monitoring protocols was dictated by individuals’ therapeutic management plan and provided a prescription for structured monitoring in various settings, including with intensive insulin therapy, nonintensive (basal only) insulin therapy, and noninsulin treatment plans. The article reiterated that evidence-based prescriptions of structured BGM with five- to seven-point profiles provide essential information for productive clinician- and individual-directed therapeutic interventions (25). A variety of suggested structured monitoring regimens tailored to diabetes type and treatment that can be recommended by HCPs for people with diabetes can be found in this review. These management plans should be intensified for people with diabetes who have not reached their glycemic goals, with the frequency of BGM depending on individuals’ risk for acute glycemic events and willingness to perform BGM, keeping in mind that five- to seven-point profiles may be necessary during active therapeutic interventions.

One of the barriers to structured monitoring may be the requirement to accurately and consistently record BGM readings. The use of a Bluetooth-connected or downloadable BGM meter and/or a mobile diabetes management app can help overcome this barrier by eliminating the requirement to manually record glucose values and the dates and times of readings. Many state-of-the-art BGM meters and mobile apps also allow users to tag glucose readings with additional information (e.g., to note pre- or post-meal readings, exercise, or illness) to provide context. Bluetooth-connected BGM meters also enable real-time transmission of data to an app that can automatically generate glucose profile reports such as the AGP Report: BGM.

Bergenstal et al. (30) confirmed the value of structured monitoring combined with the AGP Report: BGM compared with CGM combined with the AGP Report: CGM in people with type 2 diabetes who were new to CGM. They found that both technologies delivered similar improvements (with the use of blinded CGM) in glycemic time in range and glucose variability and similar A1C reductions of 0.8%, further reinforcing the value of BGM when the resulting data are used consistently and effectively. The authors concluded that, in people with type 2 diabetes, it is the consistent use of structured glucose data, regardless of device (i.e., structured monitoring with BGM or CGM) that leads to improvements in A1C.

Paired with structured monitoring, the quality and utility of the AGP Report: BGM is amplified not only by the number, but also, more importantly, by the timing of glucose readings. For example, a higher frequency of random blood glucose checking may be less informative than a similar (or even lower) frequency of structured monitoring readings tailored to an individual’s therapeutic management plan.

Even though most clinicians today associate AGP reports with the interpretation of CGM data, the original AGP report was developed and validated by Mazze et al. (31) as a standardized method to organize fingerstick BGM data, with graphics and metrics to reveal glycemic patterns and inform HCP management plans. That original AGP report was then enhanced and adapted for CGM data by Bergenstal et al. (32). The AGP Report: CGM has been widely cited as the recommended way to display CGM data and is described in detail in ADA and European Association for the Study of Diabetes (EASD) guidelines and consensus reports (33,34). Because BGM continues to be a viable option for tracking diabetes management, the IDC created an updated AGP Report: BGM based on the AGP Report: CGM more than 5 years ago to display BGM data in a clinically meaningful way that will be familiar to clinicians already using the AGP Report: CGM. Currently, the AGP Report: BGM is not described in any of the diabetes guidelines produced by the major diabetes professional associations. However, it has been integrated into the LifeScan OneTouch Reveal online app and the Tidepool online data management platform. We anticipate that it will become even more widely available in the future.

The AGP Report: BGM has been continuously improved since its original release. Examples of the latest iteration, version 5.0, can be seen in some of the figures associated with the cases studies below. Important updates have been made to ensure that the language of the AGP Report: BGM is consistent with recent ADA and EASD recommendations. Online surveys with HCPs representing the United States and European Union countries have helped to determine preferred terminology in the report (data on file). For example, using the term BGM “readings” is preferred instead of “tests” or “checks,” which may have more judgmental connotations. The term “readings in range” (RIR) was also chosen as preferred terminology. The AGP Report: BGM consists of three sections organized to provide essential information on the BGM readings collected during the time period selected. The first section contains the percentage of BGM readings in different ranges and various BGM statistics. The RIR bar graph on the AGP Report: BGM is divided into five glucose ranges, including:

• Very high >250 mg/dL

• High 181–250 mg/dL

• Target 70–180 mg/dL

• Low 54–69 mg/dL

• Very low <54 mg/dL

For consistency, the colors and glucose ranges are identical to those in the AGP Report: CGM. The BGM statistics section contains the time period selected, number of BGM readings, average readings per day, and the average glucose derived from all of the readings. The statistics also present glycemic variability, defined as the percentage coefficient of variation (%CV), with a goal of ≤36%. The default time period for the report is 2 weeks, but this can be adjusted.

The second section of the AGP Report: BGM contains the AGP curve. The AGP curve is generated based on BGM readings from the selected time period aggregated and plotted as if they occurred on a single day, with the median (50th percentile), and 25th and 75th percentiles indicated if there are sufficient data. The third section contains daily glucose profiles for the most recent 2 weeks. This section can be used to see more easily the sequence of blood glucose readings over time and compare specific days with one another.

Throughout the report, BGM readings are color-coded as red to demonstrate hypoglycemia (<70 mg/dL), green to denote readings in range (70–180 mg/dL), and gold to highlight hyperglycemia (>180 mg/dL). This color-coding provides HCPs a way to quickly identify readings below, within, or above the target range.

The question arises as to the minimum number of BGM readings and number of days with readings necessary to create an AGP Report: BGM. The IDC has set data sufficiency standards based on clinical experience, combined with studies showing that the more readings that are available, the more effective BGM is in improving glycemic management. In general, ∼100 or more blood glucose readings are needed for a robust AGP Report: BGM. The IDC recommends that the AGP curve only be generated when there are ≥7 days of BGM data available and ≥30 BGM readings. The median line for the hourly bin (a 1-hour block of time such as 9:00–10:00 a.m.) is calculated if there are three or more readings during that time period. The 25th to 75th percentile, or interquartile range, is displayed if there are five or more readings in the hourly bin. When there are <30 readings, the BGM statistics section will still be shown, but clinicians should be wary that there may be an insufficient number of readings to adequately inform clinical decisions. Additionally, glycemic variability, or %CV, is not displayed when there are fewer than 30 readings, and a modal day graph is displayed instead of the AGP curve, with the available readings aggregated and plotted as if they had occurred on a single day.

Although the method for interpreting any AGP report is similar, when comparing the AGP Report: BGM to the AGP Report: CGM, an important difference is the use of the percentage of BGM readings in the various ranges instead of CGM-based percentage of time in those ranges. Given that people with diabetes who perform BGM may have long gaps between readings or may only check at specific times in the day, it is not appropriate to summarize these readings as percentages of time in various ranges because actual blood glucose levels between readings are unknown. For example, if only fasting blood glucose (FBG) readings are performed and they are mostly in the target range, an individual may still have significant postprandial hyperglycemia that would be undetected without additional BGM readings at different times throughout the day and night. This is not the case with CGM, through which glucose measurements are made every 1–5 minutes throughout the day, allowing the calculation of percentage of time in each range. In contrast to the AGP Report: CGM, the AGP Report: BGM does not include specific goals for percentages of readings below, in, and above the target range. Unlike with CGM, at this time, no specific goals or recommendations have been established by professional organizations for readings within the various ranges. HCPs should view these percentages of readings in context with the AGP curve and daily glucose profiles. In lieu of specific goals, HCPs should encourage people with diabetes to strive for “more green and less red” on the RIR bar graph.

Another difference is that the AGP Report: BGM does not display the glucose management indicator (GMI) (35). The GMI is an estimated A1C calculated from a CGM-derived average glucose value. The average glucose derived from BGM and included on the AGP Report: BGM likely would not provide an accurate measure of glucose throughout the entire 24-hour day, and thus GMI is not included in the BGM statistics section of the report.

On the AGP curve in the middle of the report, the actual BGM readings are indicated, along with the median and interquartile range (25th to 75th percentiles) if there are sufficient readings during that time of the day. Unlike the AGP Report: CGM, the 5th and 95th percentiles are not shown because there usually is an insufficient number of readings to calculate them.

Finally, the daily glucose profiles are very different between the two reports. The AGP Report: CGM shows a continuous glucose trend line throughout the day that cannot be determined using BGM. The red, green, and gold dots, representing below-, in-, and above-range BGM readings, respectively, cannot be connected with a solid line because the blood glucose levels between readings are unknown.

Despite these differences, the AGP reports actually have much in common and have been designed especially to make it easier for HCPs to transition between report types and to use either report effectively with people with diabetes.

When unmeasured glucose fluctuations are suspected, such as when there are times of day on the AGP curve with insufficient data to generate a median line and interquartile range or gaps are seen on daily glucose profiles, then a structured monitoring plan should be recommended for individuals to better assess their glycemic patterns.

The interpretation and effective use of the AGP Report: BGM follows a process similar to one the IDC developed for the AGP Report: CGM. The quick, three-step process is called “determine where to act” (Figure 1).

The first step is to review the percentages of BGM readings in the various ranges on the bar graph in the upper left of the AGP Report: BGM to “determine” whether action is needed. A high percentage of BGM readings in the low and very low ranges and/or a high percentage of readings in the high and very high ranges would indicate that action is needed. An easy way to think about this is to focus on more green and less red in the bar graph showing these percentages of readings. A brief review of the BGM statistics will also provide information on whether there are sufficient readings to make clinical decisions (see the earlier discussion of data sufficiency above) along with the average glucose and level of glucose variability.

The second step is to identify “where” action is needed by reviewing the AGP curve located in the center of the report. Start by identifying any times of day with patterns of low or high glucose. The easy way to think about this is to achieve an AGP that is flat, narrow, and in range. Tracking the median line and interquartile range can be helpful to see this. Next, look for gaps in the day when the individual is not routinely monitoring (often at night), where additional glucose readings would be helpful to provide a more complete picture of glucose management throughout the 24-hour day. Use the daily glucose profiles located at the bottom of the report to confirm patterns of low and high glucose. These can also be helpful to determine whether the individual is overtreating low and high glucose readings (e.g., when a dramatic increase in glucose is observed 1–2 hours after a low glucose reading).

The third step is to “act” on the patterns of low and/or high glucose readings by changing the individual’s medication management plan along with making lifestyle changes that will improve glycemic management. The “act” step needs to be conducted in partnership with the individual using the principles of shared decision-making. During this step, it is often helpful to first ask what the person notices in the AGP Report: BGM because the intuitive nature of the report often allows the individual to determine where to act (e.g., “I need to take my medication every day,” “The days I walk seem to keep my glucose readings in range,” or “I need to cut down my carbohydrate intake at breakfast”). The concept here is to follow the mantra “adjust, adjust, adjust” until glycemic goals are achieved.

The two mini case reports discussed below show how the AGP Report: BGM visually presents glucose data in formats that allow HCPs to quickly determine where to act. Diabetes care experts recommend that hypoglycemic readings be addressed first (i.e., the “where” in “determine where to act”). HCPs should review the AGP Report: BGM at regular intervals and “adjust, adjust, adjust” the diabetes care management plan until hypoglycemic patterns are resolved.

After hypoglycemia has been adequately addressed, the next “determine where to act” review should focus on identifying patterns of hyperglycemia. If a pattern of high FBG is identified, this may be targeted next to see if improvement at this time of day carries through to the rest of the day. Otherwise, identification of patterns of post-meal hyperglycemia may be targeted with lifestyle interventions and changes to the diabetes therapeutic regimen.

A visual assessment of the AGP curve should also be made. If blood glucose readings are not seen throughout the day or at specific times that need to be assessed, HCPs should recommend structured monitoring, clearly specifying the times of day when the blood glucose readings should be taken and the frequency of BGM (25).

When using the AGP Report: BGM for decision-making, HCPs should remain cognizant of certain limitations. As mentioned, inadequate data or unstructured monitoring can result in reports that are not representative of individuals’ actual glycemia. Other limitations include that only blood glucose readings from the report’s specified date range are included, that incorrectly set times and dates in the glucose meter could result in incorrect pattern assessment, and that the summary statistics include averages, which do not address the outliers. Importantly, outlier blood glucose readings can be the ones that result in symptoms or morbidity and thus are crucial to address. The AGP curve and daily glucose profiles on the AGP Report: BGM do indicate all blood glucose readings, including the outliers, in the specified date range.

Figures 2 through 5 present two mini cases and their associated AGP Report: BGMs.

The case of Ms. Rodriguez is presented in Figures 2 and 3 (an example of the latest AGP Report: BGM). On review of the AGP Report: BGM, her HCP determines that she has an average glucose of 152 mg/dL, glucose variability of 29.6%, and 76% RIR with no hypoglycemia. Her FBG readings are within her target range of 70–180 mg/dL, although there are only a few bedtime readings. Looking at the AGP curve, the HCP sees a consistent postprandial glucose increase after breakfast and the midday meal. There do not appear to be postprandial readings after the evening meal, which Ms. Rodriguez confirms.

Using shared decision-making, the HCP and Ms. Rodriguez agree to maintain the current medication plan and act on the postprandial hyperglycemia by reducing carbohydrate intake at meals and adding more activity, specifically walking for 5–10 minutes during her work breaks in the mornings and afternoons. A structured monitoring plan is prescribed that includes checking glucose readings when fasting and 1–2 hours after each meal daily (four-point profile) for the next 2 weeks, at which time a telehealth encounter will be scheduled to make further adjustments as needed. Ms. Rodriguez is reminded to send her BGM readings the day before the encounter if her BGM system does not support automatic uploads to the Cloud via an app.

The case of Mr. Johnson is presented in Figures 4 and 5. After reviewing his AGP Report: BGM, his HCP determines that there is an average glucose of 144 mg/dL, glucose variability of 41.9%, and 68% RIR, with 8% of readings below range (RBR). Given the percentage of RBR, examination of the AGP curve and daily glucose profiles is done next to see where to act. The curve shows that RBR are confined to the time period 7:00–10:00 a.m. daily, which Mr. Johnson confirms are all fasting readings. While evaluating the fasting hypoglycemia, the HCP notes that the preceding glucose readings, which are bedtime readings, are often above range, with a large drop in glucose overnight. Called a BeAM value (36,37), this overnight change in blood glucose should be assessed by subtracting the median morning fasting (AM) glucose from the median bedtime (Be) or postprandial supper glucose reading on the AGP Report: BGM. A BeAM value >50 mg/dL may be seen in people taking too much basal insulin. These factors suggest that Mr. Johnson is “overbasalized” with an excessive dose of basal insulin. Occasional hyperglycemic blood glucose readings after the midday meal are also seen.

Adjustments made include decreasing Mr. Johnson’s dose of basal insulin by 10%, from 38 to 34 units/day, to address the overbasalization and fasting hypoglycemia, in addition to moderating his intake of carbohydrates with the midday and evening meals to address postprandial hyperglycemia. Mr. Johnson is referred to a diabetes care and education specialist for updated diabetes education. Given his comorbidities of chronic kidney disease, congestive heart failure, and obesity, the HCP also introduces the possibility of adding a glucagon-like peptide 1 receptor agonist as the next adjustment and suggests they discuss this in more detail at the next appointment. Mr. Johnson is instructed to continue to check his glucose levels as he has been doing and to consider adding occasional BGM at 2:00–3:00 a.m. to assess for nocturnal hypoglycemia. Arrangements are made for uploading of his glucose meter in 1 week so his HCP can review the AGP Report: BGM to see if a telehealth encounter is needed to further address fasting hypoglycemia (i.e., “adjust, adjust, adjust”).

By using the AGP Report: BGM as a routine practice tool, busy HCPs can efficiently and effectively review blood glucose readings, leading to improved decision-making for people with diabetes who are using BGM to monitor their glucose. The AGP Report: BGM supplements the important but limited information that can be gleaned from an A1C value. Importantly, the AGP Report: BGM allows HCPs to “determine where to act” to address dysglycemia. Reassessments as necessary provide opportunities to “adjust, adjust, adjust” medications, lifestyle, and other factors to achieve the mutual goal of maintaining most glucose readings in the target range to improve outcomes for people with diabetes.

G.D.S. has received educational grants from Abbott Diabetes Care and Sanofi. E.H.H., M.G., and G.H. are employed by LifeScan, and L.M.G. was previously employed by LifeScan. R.M.B. has received research support, acted as a consultant, or been on a scientific advisory board for Abbott Diabetes Care, Ascensia, Bigfoot Biomedical, CeQur, Dexcom, Eli Lilly, Embecta, Hygieia, Insulet, Medtronic, Novo Nordisk, Onduo, Sanofi, United Healthcare, and Vertex. G.D.S. and R.M.B. are employed by the nonprofit HealthPartners Institute International Diabetes Center, which contracts for their services, and they receive no personal income for these contracted services.

G.D.S., E.H.H., and M.G. researched data and wrote, reviewed, and edited the manuscript. G.H., L.M.G., and R.M.B. researched data and reviewed and edited the manuscript. G.D.S. is the guarantor of this work and, as such, had full access to all the data presented and takes responsibility for the integrity of the data and the accuracy of the content.