In conjunction with the American Diabetes Association Scientific Sessions conference held in San Francisco earlier this month (June 2019), the ADA released online an ahead-of-print summary (June 8, 2019) regarding using continuous glucose monitor or CGM data to affect better outcomes for people with diabetes.
Here’s the introduction from the summary.
Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.
I’ve long been a proponent of using diabetes data to guide and inform the tactics I use to rein in the inherent metabolic chaos that is diabetes. I realize that my interest in data is not shared with all people in our community but my experience and success convinces me that it’s a worthwhile resource.
I regularly monitor my Dexcom CGM data and will often examine the 14-day Ambulatory Glucose Profile (AGP) report found within the Dexcom Clarity software. The AGP helps me make adjustments to my insulin to carb ratios (I:C) and insulin sensitivity factors (ISF).
As Sugar Surfing’s Stephen Ponder reminds us, diabetes is a dynamic disease. It changes all the time and this is what makes managing it so difficult. Managing diabetes with a static mindset will test your sanity! Once a person with diabetes accepts this reality, you can develop ways to manage this moving target.
One fundamental personal truth I hold is that merely watching data on a regular basis will subconsciously increase my motivation to improve that data. Not sure if this works for everyone but it certainly does for me.
The whole report is worth reading but there are a few things that caught my eye. The consensus for this group set the glucose limits for the time in range statistic at 70-180 mg/dL (3.9-10 mmol/L) for people with type 1 and type 2 diabetes.
Interestingly, this panel chose lower, more aggressive targets, for diabetes management during pregnancy. For this cohort the consensus set glycemic targets at 63-140 mg/dL (3.5-7.8 mmol/L). I find this of interest since I set my targets at 65-140 mg/dL (3.6-7.8 mmol/L). I do not become hypo symptomatic until I reach 65 mg/dL (3.6), so I use that as my personalized lower limit.
The report noted that personalizing this target range is appropriate.
It was agreed that CGM-based glycemic targets must be personalized to meet the needs of each individual with diabetes.
Many of you know about my recent discovery of the effects on the A1c number of iron deficiency anemia. I puzzled for many years wondering why my A1c number consistently floated about 0.5% above the number predicted by my CGM data. I questioned several endocrinologists about why this was happening and heard no answers.
It wasn’t until recently, when I discovered that the iron levels in my blood were low, that I put 2 and 2 together and realize that that was the reason for my skewed A1c. This report also noted that the A1c did not correspond accurately with glycemic exposure for all people with diabetes. Once I started to supplement with iron, my A1c measured within 0.1% of the CGM predicted number.
Moreover, certain conditions such as anemia (37), hemoglobinopathies (38), iron deficiency (39), and pregnancy (40) can confound A1C measurements. Importantly, as reported by Beck et al. (41), the A1C test can fail at times to accurately reflect mean glucose even when none of those conditions are present.
Even given these A1c/glycemic exposure discordances, the group affirmed the importance of the A1c number and sees CGM data as a strong complement that people with diabetes can use to increase their positive health outcomes.
Finally the consensus report makes this conclusion.
This information allows people with diabetes to optimize dietary intake and exercise, make informed therapy decisions regarding mealtime and correction of insulin dosing, and, importantly, react immediately and appropriately to mitigate or prevent acute glycemic events (87–89).
I wholeheartedly agree with this and hope that clinical practitioners make an effort to understand these metrics and help persuade their patients about the utility of this data.
If you want to take your management of diabetes using a CGM beyond the realtime benefit of out-of-range alarms, you may want to consider the ideas presented in this paper. My attention to this data detail greatly improves my quality of life and keeps metabolic mayhem at a minimum.
What do you think?