Insights & Innovators: A Q&A with Greg Norman and Poorva Nemlekar from Dexcom

Q&A with Greg Norman and Poorva Nemlekar from Dexcom

In part one of this two-part blog series, Greg Norman and Katia Hannah introduced us to their team’s research and discussed one study they conducted in IHD. In this second and final part of the series, we’ll hear more about the team’s exploratory work and insights from the study titled Association Between Change in HbA1c and Professional CGM Use in Adults with Type 2 Diabetes on Non-Insulin Therapies – A Real-World Evidence Study. Read on for Greg and Poorva’s insights.

Q: In part 1, you told us about your team’s work analyzing real-world data on continuous glucose monitors (CGMs), but can you talk a little more about the value of real-world data analytics for exploratory work?

Greg: Like I said, at Dexcom, we use real-world evidence studies to complement our clinical trial work, since clinical trials take significant amounts of time and money. Ever since we started using IHD about a year ago, we have been able to do more exploratory work by having the data and analytics capabilities readily available. For example, we recently conducted a study focused on professional CGMs in patients with T2D not on insulin, which is an area of research that was previously unexplored. With the analysis of real-world data, we were able to explore what the value of CGMs might be in this population. Exploratory studies like this one could help with things like label expansion, identifying unmet need, and finding patients who might need our device in the future.

Q: Poorva, can you tell us a little about this study, Association Between Change in HbA1c and Professional CGM Use in Adults with Type 2 Diabetes on Non-Insulin Therapies – A Real-World Evidence Study?

Poorva: There are two types of CGM devices – personal and professional – and in contrast to the study that Katia discussed in part 1, which focused on personal CGMs, this one analyzed professional CGM use. A personal CGM is the patient’s own device used to manage their diabetes. The professional CGM is given to the patient by their provider and often in a blinded mode to monitor their glucose patterns over a 10-day period.

The effects of professional CGM use in real-world settings have not been studied much in the T2D population. Furthermore, there is very limited information on patients with Type 2 diabetes in poor glycemic control using two or more non-insulin therapies but not using insulin. Given this background, we conducted a retrospective observational database study assessing the glycemic effect of professional CGM use in this particular Type 2 diabetes population.

Q: What did the results show and why are they significant?

Poorva: We first observed statistically significant reductions in HbA1c, an indicator of blood glucose levels, in CGM users after device initiation. This result is significant because it indicates that CGM devices such as the Dexcom G6 can be useful in the Type 2 population not using insulin, something that has been relatively unexplored to this point.

We then looked at subsequent insulin use. This study started with a cohort of all non-insulin users and in the follow-up, we saw that 20% of CGM users were on insulin compared to just 10% in the non-user group. These results indicate that more reliable glucose monitoring can help providers make more informed decisions about care and help them identify when their patients need insulin or a tweak to their medication.

Next, we would like to explore professional CGMs in other diabetic subgroups where there is not yet much real-world evidence.

Q: What is the broader impact of this study?

Poorva: One insight I find particularly interesting is the fact that after device initiation, CGM users were more likely to be on insulin than non-users, but even for patients who did not start insulin there was a greater decrease in HbA1c for the CGM users compared to the non-users. This suggests the insights from the CGM may have helped health care providers to counsel their patients about lifestyle changes and medication adherence. Overall, the findings suggest that more reliable and regular glucose monitoring can help patients realize when they need insulin or certain changes to their care. From our perspective, this provides important evidence to providers to consider recommending CGMs to their patients sooner and more frequently.

Q: How did IHD impact the success of this study and your other research beyond this study?

Greg: This is our first year using IHD and previously, we weren’t doing any internal analyses or licensing any data. We were always outsourcing our analytics work so it was difficult to get exploratory work like this study completed. This is the first time we’ve had the chance to do our own analyses and using IHD let us get up and running much quicker than we would have if we were attempting to do work in SAS or outsourcing the work.

Poorva: On a more general note, I would also mention that we often have to create demo projects for our diabetes research, and it becomes easier to run the analyses when we have these demo projects already set up in the IHD platform.

Greg: Also, we talked about how quickly we onboarded, which allowed us to generate many impactful real-world evidence studies in just one year. But something that I didn’t expect was also how IHD can help with ad hoc questions and analyses. We have been able to run quick queries to provide valuable business insights in real-time to inform decision-making. That has really been impactful for us since that wasn’t easy with outsourcing.

If you haven’t read part 1 of this blog series, click here to read now.


Greg Norman, PhD is Director of Health Economics and Outcomes Research at Dexcom where he leads studies to assess real-world evidence of the clinical and health economic impact of continuous glucose monitors for people with diabetes.

Poorva Nemlekar, MS is the Lead HEOR Specialist within the Global Access team at Dexcom. Poorva works on multiple aspects of outcomes research including the execution of real-world evidence studies, evidence gap analysis, and systematic literature reviews.