Thank you, Doctor Harris. I'm Tom Martins. I'm a medical director at the International Diabetes Center in Minneapolis, Minnesota. I'm also a general internal medicine physician in Minneapolis. And I'm here today to talk with you about applying advances in CGM for improving glycemic management. Before insulin initiation. I do have some relevant disclosures that you can review. So let's think about where we're at this slide in front of you is from the ad A standards of medical care 2024. And this is their grading of evidence for who benefits from using CGM. What we know without question from multiple randomized control trials is that individuals using insulin to manage diabetes benefit that applies if it's type one diabetes, type two diabetes applies if it's basal bolus insulin or basal insulin. All of those folks get a grade A or grade B of evidence for using CGM in managing their diabetes. What's a little less clear is what to do with the larger group of individuals managing type two diabetes without insulin. What the ad A gives them currently is a somewhat more muted statement. Periodic use can be helpful. They give that a grade C based on the level of evidence. So what's the level of evidence grade C? Well, to be honest, it's not very good. It's the support of evidence is from poorly controlled or uncontrolled studies from RCTS with major methodological flaws, observational data with potential for bias. So bottom line, the evidence isn't that strong at this point. One thing we know for sure is that lack of evidence is a different thing from lack of benefit And the honest truth is the evidence is improving. Expect to see movement in this grade of evidence from the ad A as future studies become available as the current studies are further reviewed. Here's some evidence I can point you to. This is the immediate study. So this is a study done by, by Doctor Aronson and his colleagues in Canada. What they did is they took individuals with type two diabetes and at least one non insulin therapy who are not meeting their glycemic goals. They had an A one C of 7.5 or greater and they randomized them either to diabetes, self management education or or to diabetes, self management education with intermittently scanned CGM. And they followed these folks over 16 weeks, they had a little bit of structured education. The education was the same in both arms but the one group had CGM, the other group did not have CGM. And they looked at their primary outcome measure was change in time and range over these 16 weeks. So what did they see? Here is a table with their outcomes time and range in both groups started at 56 or 57%. They were not meeting time and range targets. In the CGM group, it improved to 76.3% in the diabetes self management education alone group, it improved to 65%. So the the group that was randomized to have both diabetes, self management education and CGM did 9% predator. What does that mean? It means that the CGM plus diabetes, self management education group actually was meeting their time and range targets. At the end of the 16 weeks, diabetes, self management education alone was not meeting their greater than 70% time and range target. Here's another one that I can point you to. This is one that's presented at the ad a meeting here today as an abstract. This is a randomized control trial looking at using continuous glucose monitoring to guide food choices and diabetes self care and people with type two diabetes not using insulin. What this group did was take 72 adults, not on insulin or self familiar therapy. They had an A one C between 7.5 and 12. So not meeting goals. They randomized them to either manage their diabetes with CGM alone or CGM with a connected food app. And they then followed these people over three months, they did not have changes in their medications. This was purely AC GM based intervention and they looked at reduction in time above range reduction in hyperglycemia as a primary outcome measure. And so what did they see at the end of three months? Significant reduction in both groups. Actually, both groups improved about the same. The time above range improves them from 54%. So significant time above range to 29%. The hemoglobin A one C improved from 8.4% across both groups to 7.3%. So in a 1.1% improvement in hemoglobin A one C essentially using CGM to help moderate food choices and help inform food choices. Basically impressive results. This again being presented as an abstract at the 2024 ad a scientific meeting. And so there's more to come, I think and I, as we follow presentations throughout the meeting, I do suspect we are gonna see much, much more about the use of CGM in people not using insulin to manage type two diabetes. We do have meta analysis data. So this is a meta analysis published in April of 2024 2 months ago. This is by Rafael Ferrera and his group. What they did is they looked at six randomized control trials that had evaluated people on noninsulin treated diabetes with type two diabetes. This these were randomized control trials that looked at CGM versus finger stick blood glucose monitoring was a mix of of different devices. Some were real time CG MS, some were intermittently scanned CG MS. So it's nice that this is a pretty broad spectrum of technology. And what did they find? So meta analysis data from a hemoglobin A one C standpoint, the CGM group had a significant reduction in hemoglobin A one C relative to people using BGM. So a negative 0.3% greater improvement in the CGM group, they looked at time and range. There was a significantly improved time and range in people using CGM versus BGM also. So meta analysis data really looks very favorable and I think the time probably is very close to moving this forward. As far as ad a grade of evidence recommendation, we'll see what they do with it. That's great. Can we do even better? So how do we really optimize the benefits of CGM in people with type two diabetes not using insulin? Here's a piece of evidence that I think actually is really, really important as we think about this group of people who are relatively early on. They're typically recently diagnosed with type two diabetes. The United Kingdom prospective diabetes study UK P DS. This is a landmark study in type two diabetes was looking at almost exactly this population. They were looking at relatively recently diagnosed folks. A one C is above goal. And what they did is they randomized these folks to an intensively treated group. A one C of seven or a conventional group where the A one C was less aggressively managed. And that group averaged an A one C of 7.9 and they looked at these people, they followed them over 10 years. What they saw was the intensively managed group. Again, a one C target was 7.0% had a 25% risk reduction in microvascular disease. A 12% reduction in any diabetes related endpoint, they just did better so aggressive management in people recently diagnosed with diabetes made a difference in UK P DS. What really was even more impressive is that they did a look back at after 10 years. And so the study ended, people were no longer in their study arms, the A one CS equilibrate between the two arms, people were managed essentially the same in the real world. They looked at the group again after 10 years and so they looked at them back in 2008. What they saw surprisingly was the intensively managed group maintained their reduction in diabetes related risk reduction and they had less microvascular disease. They just overall were doing better most recently, just last week, 24 year follow up of the UK P DS. So they, they have now followed these people. The study ended, we're 24 years out from the end of the study and these people being managed just in the real world as, as you would typically manage an individual with type two diabetes, the early intensive glycemic control arm, who were managed with sulfone urea therapy, insulin therapy. That's those are the therapies available, reduced their risk of death from any cause. 25 years out by 10% reduced their risk of heart attack by 17% risk of microvascular disease by 26%. And if you look at the the much smaller group that was managed with Metformin, it looked even better honestly. And so here we are 25 years out from 10 years of more aggressive glycemic management and people are still benefiting. It really is impressive, quoting the authors of this most recent analysis, achieving near normal glycemia immediately following diagnosis may be essential to minimize the lifetime risk of diabetes related complications to the greatest extent possible. So we need to go after these people early, manage them aggressively and they benefit from that many, many, many years out. So what is that, that's the legacy effect, right. It's real, it's powerful and we need to be seeing if we can help people early on to optimize their glycemia to really allow them to get the benefit from the legacy effect. One way more way to think about this is there are some questions whether hemoglobin A one C is really the right marker to follow in this group of folks. So we're talking about people who are not managed with insulin, they have type two diabetes, typically the way they're managed in the real world. A lot of times in primary care settings is based on their hemoglobin A one C values. And the issue with that is we know that there's a lot of variability and gloc location of hemoglobin based on racial factors based on red blood cell lifespan factors. And here's another group that has taken a look at that. This is data presented again as an oral presentation here at the ad a meeting. Doctor Ramsey Aja and his colleagues noted that black individuals with diabetes have greater hypoglycemia hospitalizations. Why is that? Well, perhaps they're overt treated because of elevated A One CS. What we know is that people who are black have a relatively higher A one C relative to Caucasian cohorts, typically it's 0.3% higher. And we know that we is because of differences in red red blood cell biology. But that means that if you're managing people based on A One CS, you're managing them more aggressively. So this group looked to evaluate the scope of this problem and they also looked at a new way of sort of looking at A One CS based on a new glycemic marker to see if they could improve that. So what they did is over 26 weeks, they collected CGM throughout they did bi monthly A one CS, they had a group of 245 individuals, different races, type one diabetes, type two diabetes. Basically a pretty broad collection of folks with diabetes. What they did was they used R BC personal GLC location ratio, which is a mathematical formula to calculate a personalized A one C. Again, another mathematically calculated value that can be used to calibrate A one C versus CGM based average glucose data. GM. I essentially what did they find of 811 A one CS versus average glucose derived A one C which is GM I, they found a 34 4% of those values had a greater than 0.5% disagreement. So really very different values based on a one C than they were seeing based on average glucose drive from CGM data using their calculation, they were able to reduce that close that gap to about 13% which is wonderful, largest improvement was in black individuals. So what do we make of that? We know that A one C it's a great measure of diabetes care quality on a population basis. But having said that we also know that it can be problematic in managing therapy on an individual basis because of individual differences in gly cation. What this this group asks is, can we use CGM to calibrate the A one CS to see if we can make them more appropriate and accurate? But beyond that is a time to just move past A one C as a tool for managing therapy adjustments in individuals. It just isn't always as good as it could be. So CGM before insulin, how do we optimize the benefits of CGM in this group of people not yet using insulin. What we know is we have wonderful new therapies. This isn't the era of UK P DS anymore where we're relegated to self in a therapy to insulin therapy. We now have therapies that optimize glycemia but they also minimize hypoglycemia risk. They also address the type two diabetes phenotype. They also improve outcomes. And so we're following the AD A EASD algorithm as we manage people G LP one therapy, stlt two therapy has fundamentally changed the way we manage type two diabetes. So do we even need to worry about glycemia so much with these powerful medications? And in fact, we do, we know that microvascular risk is decreased by improving glycemia. We know from emerging evidence that perhaps CGM can benefit people using GOP one therapy. For instance, this is a study by Doctor Eugene Wright and his um coin investigators, what they did is they did an observational study where they looked at people initiating G LP one therapy who are using CG MS and they compared them to a group who are not using CG MS. So GP G LP one therapy alone. And what they saw is the the group initiating G LP one therapy who were using CG MS actually had a greater benefit than the group you who are initiating G LP ones alone. So there really did seem in this observational study to be evidence that adding CGM to G LP one therapy can have an additive benefit can improve glycemia. And so I think there's more data coming on this also, but it's pretty clear that adding CGM to these newer diabetes agents really can be helpful. So CGM before insulin, how do we make it work? We need to optimize both point and time data. The data that people with diabetes are looking at, we need to optimize the way we're using retrospective data. Also. That's the the data that we as clinicians typically are looking at. Here's a picture of point and time data. We know that patients are using this quite successfully to improve glycemia. Why does point and time data help? Because it gives people insights to modify diet to improve hyperglycemia. It gives an indication of what's causing those postprandial spikes and they can correlate the very directly with what they're doing and modified portions, modified diet and really make improvements very quickly and very intuitively. How about retrospective data? This is the data that we as clinicians typically are looking at in a clinic visit, gives us insights into when intensification and advancement of therapy is needed. We have time and range metrics. We have an ambulatory glucose profile in a very quick period of time. We can come to a conclusion on whether people are doing ok with what they're doing based on point in time data. Is it time to intensify medication perhaps? And so we have dual goals when we think about people using CGM who are not using insulin. We need to help people use CGM optimally as they look at their data day to day, make food choices, make exercise choices. We need to teach to that and we need to optimize how we as clinicians are using retrospective data at the time of visits. Obviously, we need to use that in shared decision making to reinforce lifestyle impacts. But we can also take a look at the big picture adequacy of our glycemic management very quickly using CGM. We can know are they doing ok with what they're doing with lifestyle interventions? Or do we need to intensify our medications to give them a little extra help to get to their goals? Because we need to be getting people to their goals. So point in time data, what do we teach to? We all know what, what the glycemic targets are before meals. We all know this 80 to 100 30 is where you'd like to be 1 to 2 hours after meal. We'd like to be less than 100 80. We need to teach to that. We need to let people know where we'd like them to be. What we know is that interventions that involve goal setting are more effective than usual care. And so give people goals to shoot for and then we need to help them, use their, their CGM to meet those goals. Where are you at in the morning? You wanna be 80 to 100 30 after breakfast, you wanna be less than 100 80. We need to encourage them to watch, learn and act on the data. So be curious what's making a difference in your, your gly post brand glycemia? What foods are making a difference? Does activity make a difference? We need to have them turn those insights into action, modify their food choices, modify their portion sizes, know the impact of exercise. Basically, if you can see it, you can change it. And most people really can go a long ways. If we teach to it, that's just brief nutritional guidance. It's powerful and it works. I'm gonna make my pitch to also consider a referral to a diabetes educator with, with dietician knowledge, the RDN CDC ES. Why should we consider it? Because it isn't all about glycemia. You can improve glycemia with a diet that honestly is not that healthy. This is data from the UK ban. They looked at a huge group of individuals and they looked at what happened with various diets. And what they saw was that a sustained change from an unhealthy diet pattern to a healthy pattern. At age 40 brought you 8.6 to 8.9 years of life expectancy. If you take that same group and you move them from unhealthy diet to a longevity associated dietary pattern, you got them 10.4 to 10.8 years of increased life expectancy, largest gains, it's what you'd expect more whole grains, nuts, fruits, less sugar, sweetened beverages, less processed meat, honestly. Yes, I did. Look, this is more than you get from quitting smoking, honestly. So optimal nutrition, it's not just about improving the glucose. We need to make sure that people are making healthy choices with their food and we need to teach to that and that's why I lean on RDN CDC ES colleagues about retrospective data. This is what we're looking at, right? And we're all pretty experienced with this. We have glycemic targets. This is from the 2019 consensus guidelines, time and range. We'd like it to be greater than 70%. We'd like to minimize hyperglycemia less than 4%. Basically, we like to push to more green, more time in the target range, less red, less time and ty hypoglycemia. So more time greater than 70% time and range. We'd like to minimize that hypoglycemia. But honestly, we're living in a post glycemic era. We've got more goals than just glycemic. We've got cardiovascular goals, we've got cardiorenal goals, we've got weight related goals and we have glycemic goals. So, should we really be stopping at a time and range of greater than 70? Could we keep going? Could it be time and range greater than 80%? Could it be time and range greater than 90%? Can we really work towards the goal of that legacy effect that they saw so clearly in the UK P DS study. So reasons to keep going. Where did time and range greater than 70% even come from. Well, what we know about time and range greater than 70 is that there are proven microvascular benefits based on a number of studies. It's an achievable goal for people using insulin to manage diabetes. And that the trick with insulin is you're balancing improvement in hyperglycemia against the risk of hypoglycemia. And what we know is that for many people with type one diabetes, especially managing with multiple daily dose insulin, it's tough to go higher than 70% time and range without increasing hypoglycemia risk. And so that is part of where that came from. We're working with a different group. This is type two diabetes. These people are not on insulin. There isn't much hypoglycemia risk, especially if they're following the ad a guidance which leans you away from self auria therapy. So why limit our glycemic benefit? Our weight and cardiorenal benefit with a time and range goal of 70%. I'm gonna argue we should keep going. There isn't much hypoglycemia. We need to maximize our cardiovascular or cardiorenal and weight benefits. We need to maximize our glycemic legacy benefits and honestly, we need to optimize the likelihood that we're going to reach our hemoglobin A one C goals. I like many of you are graded on my A one CS. It's still the measure of diabetes care quality and we know that A one c honestly is pretty variable for a time and range of 70%. And so I'm gonna argue, we should just keep going. And so this is where the new concept of time and tight range comes from. So time and tight range people have proposed a time and range of not 70 to 100 80 mg per deciliter. But how about 70 to 100 40 mg per deciliter, how about we tighten up that time and optimal range for the people who are able to do it. This is not for everybody, but there's a subset of people who can safely do it. And this is the subset. It's the people with type two diabetes who are not using medications predisposing to hypoglycemia. What's the goal for time and tight range hasn't really been clearly defined yet. People are talking about perhaps 50% time and tight range. I think you can think about this simply in time and range terms essentially. If there's no red, if there's no hypoglycemia, why don't we just go for more green? So push your time and target range to 80%. That correlates with the time and tight range around 50%. You don't even need to stop there. If it's, if the medications are helping and not causing problems, maybe it could be 90%. Maybe we keep going. I think the bottom line is this is the group, they are relatively recently diagnosed. They are at relatively low risk for hypoglycemia and they benefit in study after study from aggressive management of glycemia up front. So let's think about it. In the big picture. We need to help people with diabetes know where they're going. When they look at their data on their telephone, we need to help them know what their targets are for postprandial glucose. We need to help them to understand how to improve that with diet and lifestyle. We as clinicians need to look at retrospective data to know when medication advancement is needed to help people achieve goals. Keeping in mind that aggressive time and range goals may make sense for this group with type two diabetes, not on insulin. And we need to think about cycle time. We need to not delay in advancing therapies. We need to keep people moving forward because really we need to move people to optimize therapy and maximize benefit. That's what I have to you today. I am going to turn over the talk to doctor Eden Miller who's going to work with us to work through a few clinical cases and really see how to apply CGM on individuals with type two diabetes, not on insulin therapy. Thank you very much.
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