Good evening, ladies and gentlemen, and welcome to this 84th session of the American Diabetes Association and to this symposium on the early use of CGM and type two diabetes where we're gonna talk about a number of issues including late breaking trials on CGM directed glycemic management that will include issues of patient safety resource utilization across the entire spectrum of diabetes care. I'm Doctor James Gavin and I have the pleasure of being your program chair and uh we will have an exciting program uh this evening. Uh This uh symposium is CME certified jointly provided by the UMass Chan Medical School and CME education resources. Uh commercial support has been provided by an unrestricted grant from Abbot Diabetes Care. I'm pleased to be joined in this session this evening uh by exciting speakers including Doctor Eden Miller from Bend Oregon, uh Doctor Thomas Martins from Bloomington Park, Nicolet Clinic, and Doctor Stuart Harris of the uh Western University in London, Ontario. Now we're going to focus on a data driven rationale for the early deployment of CGM in this session. And our objectives are going to be to cover a number of issues. We're gonna talk about some background issues. First, just to put things in perspective, these are core guiding principles of diabetes care no matter who has it, no matter what time. Uh In the course of the disease we're talking about. It's clear that multiple risk factor control is essential to minimize CBD risk in diabetes. But glucose control is central. It's always important across the entire spectrum of diabetes. A type or duration. Glucose metrics remain the most commonly used and best known metrics of overall control. Although we must emphasize that in order to really minimize the outcomes that we are most concerned about reducing CBD risk, especially morbidity and mortality, we have to control multiple risk factors. But because glucose control is so important accurate real time glucose measurement is essential for optimal diabetes care and timely informed decision making. And that makes the use of CGM technology really important because it allows patients the opportunity to participate in data analysis and in um fundamentally important de shared decision making. So achieving glucose targets in diabetes has been greatly facilitated by CGM. Some might argue that the only way that you can really reliably reach such targets is with CGM. Now, most of the excess risk CV, risk in type two is attributed to CV risk factors. And I simply point out this somewhat complicated looking slide. But what it shows is for three major areas, mortality, myocardial infarction and stroke going from top to bottom, the number of risk factors. And what you see in these forest plots is the degree to which those risk factors are controlled, whether it's one of them two and simply highlighted one and five to go to the extremes. And what you can see is that if you have few risk factors or if you control all of your risk factors, your risk for any of these outcomes is pretty, pretty close to what it's like for the control population. Those people who essentially have no risk factors. But if you drop to the bottom and look at those persons who have five risk factors that are not in control. Now, you see that across the board, whether it's mortality, myocardial infarction or stroke, you see along that x axis, you see much higher uh morbidity and mortality when you're talking about risk factors that are not at target. It's also true that early glycemic control, if you focus on glycemic targets early matters, and again, I've highlighted with two arrows, uh two different tiers, the people at the top, uh represent those people who have had longer duration of disease and therefore, they've had longer exposure to hyperglycemia. The people at the bottom are those people who had less uh duration of exposure. Uh Therefore, uh they've had earlier intervention and they, and, and what you see here are different starting points, different baseline A one CS from uh 6.5 to less than seven all the way up to uh in the uh blue diamonds, a one CS of 9% or greater. And what you see is that at every level, later intervention, later, glycemic management is associated with um uh more severe uh outcomes at every level of exposure earlier is better across the board. Now, CGM has really changed the management of diabetes. Uh uh because what we have now are tools that allow in a noninvasive way. Uh The generation of uh glucose measurements on a moment to moment basis and the collection and collation of that data in ways that can be printed out and displayed as we see here with an ambulatory glucose profile. And what you can see from this uh demonstration here with an A one C of 7% it's not black, it's, it, it varies but it varies mostly within the normal uh uh range or the desirable range. But using CGM has been very helpful because it allows for um uh adequate and appropriate decision making. In order to avoid variability. You see these data on the right demonstrated first in a control group that was at at risk for hypoglycemia. And what you see is that that control group where you have um uh no intervention, there's no change of the frequency of hypoglycemia is the same in both uh studies. But now with the introduction of CGM, what you see is that the intervention group that a that acquired the use of CGM in the lighter blue now, there are far fewer episodes of hypoglycemia. A reduction in hypoglycemia of over 70% by CGM. The same is true at the opposite extreme patients at high risk for hyperglycemia. This study uh uh was done in, in patients who had uh uh time in above range uh at, at fairly high uh levels and people were given CGM and then their frequency of scanning was judged over a certain period of time and the frequency differences were extreme. Those people who were low scanners were generally scanning less than eight times a day. High scanners, high frequency scanners more than 20 times a day. Look at what the difference is that when you see people at high risk for hyperglycemia who are high frequency scanners, you see there is a uh uh a significant and substantial drop in the time spent above 240 mg per deciliter and the hours that are spent even above 180 uh milligrams per deciliter much, much more um uh improvement uh in terms of hy hyperglycemia uh in these high frequency scanners. Now, CG MS secret sauce in many ways is the fact that it provides you data and a readout that allows you to detect. Where is the problem you see here that the target range is that range between those two green lines. Uh We would call that tir the time uh in range. And then you see below that you have low and very low uh levels. And then above that are high and very high. You can see at a glance where the problems are when you see first the most uh unnerving part of variability, which is the hypoglycemic uh excursions. And then you can see equally where the hyperglycemic excursions are. And this is what gives both the providers and the patients insights on where's the problem and what needs to be fixed. But the advantages don't stop there. You can reduce acute diabetes events, reduce all cause inpatient hospitalizations. As these data show that pre CGM acquisition versus post CGM acquisition, there is a substantial and clinically significant uh decrease in acute diabetes events or in all cause inpatient hospitalizations after the introduction of CGM use shown by uh the red lines uh over time. Now, given that background, what's new and what's on the horizon in terms of advances in CGM, given the enormous benefits that we know are possible. Let's summarize a few of the things that are generating a great deal of deserved excitement in this particular study that looks at the association of CGM utilization and glycemic outcomes. In type two patients treated with basal insulin and G LP ones. Uh there were type two patients uh treated with basal and G LP ones that were initiated on CGM and they had the GMISG glucose management indicators and time and range calculated both at baseline at six months and 12 months. Post uh index of post beginning of the study. And then those metrics were assessed and what you see, basically the results show that subgroup analysis showed that regardless of their adherent, this was uh uh largely an adherent study. That means that those patients who were not taking their medicines as prescribed less uh uh uh 80% or more of the time were considered non aer and those people that were inconsistent in their use of CGM, that is to say uh uh missing uh uh scans uh doing infrequent scanning. What they showed was that frequent use of CGM played a pivotal role in glucose management among people with type two treated with these injectable agents where there was consistent use, it was associated with sustained control and inconsistent use was associated with worsened glycemic outcomes. And we'll show that in these boxes, what you see uh uh with every level of G LP one adherence, you see overall, you see non adherent in the middle and adherent in the bottom lines in the red box. You see where there is consistent use, where there is consistent adherence. You'll see that there is much improved um uh uh A one C um uh compared to uh baseline. And those are statistically significantly different in every case, consistent versus uh non uh or inconsistent when it comes to the mean differences uh in uh time and range between baseline and the measurement points again driven by uh uh use of, of of freestyle libre uh uh CGM and G LP one adherence to therapy. Again, same dynamics. There was a statistically significant and clinically meaningful difference in those that use. These interventions consistently shown as the bottom line of both these of these pairs of, of, of data. In every instance, whether the patients were adherent or non adherent, use of uh uh CGM uh was uh absolutely uh superior. Another outcome that's of interest was this study that showed reduced rates of hospitalization. So you not only get better A one CS but you get better outcomes in terms of events. This was a retrospective study done with a database from uh in France that identified people with type two diabetes that were on oral seals and they were using CGM initiation. Now, right now, we don't have a lot of those kinds of, of patients. But these are important insights that come from studies like this, almost 5000 patients on oral secretos initiating AC GM of those people. At least 8.7% had at least one hospitalization for some uh acute diabetes event in the year prior to their use of CGM. But after 12 months of use of CGM, there was a reduction of 72% driven by 81% fewer admissions for DK A or hyperglycemia. And that's, this is a study that suggests use of CGM is associated with reduced hospital admissions for AD ES in persons with type two diabetes study in France of course treated with oral secretagogue. And these are the data that show the extent of the difference in in the dark bars before, for the use of CGM on the left DK A or hyperglycemic crises on the right uh uh in the middle hypoglycemia or coma and overall uh the total on the far right? OK. CGM. Made a huge difference by the same token use of CGM and healthcare uh resource utilization. Very important, especially when we think about patients treated with these kinds of, of oral agents. And there still remain a fairly significant number of them. This was a study that looked at CGM use in AD ES all cause hospitalizations, emergency, emergency department visits in people treated with S US or G or GLYNOS. OK. Now, this was a retrospective cohort study looking at claims data and what they found in two subgroups that were analyzed and each of these had thousands of patients mean age in the mid fifties. OK. Um A and a second group uh that had a mean age uh of 73 that was the older group. But for both subgroups, all of these events that were were reviewed uh were significantly lower during the uh CGM use uh period compared to pre CGM. So the conclusion here based on CGM use compared to pre CCGM, those people with type two and on oral agents had lower healthcare utilization overall and we need more study uh uh studies done uh to uh validate this. And, and this basically just basically shows you in this one column here, the percent reduction in mean event rate was, was substantial anywhere from 21 to 50% in both groups under 65 and older 65 showing that and statistically significant for the most part, in all cases, showing that CGM utilization uh leads to a marked reduction in healthcare um resource utilization. Now, there's been this interesting development that that has been uh ongoing and you'll hear more about this during this meeting uh of non uh integrated CG MS uh that have been approved uh that are entering the market. These are, are devices that aren't coupled to uh automated insulin delivery uh devices or not coupled uh to uh other uh elements of automated delivery systems. Now, many of these devices, one has to be very, very cautious about how rigidly uh uh have they been assessed in terms of their accuracy. So you have to uh be careful because uh these devices cannot automatically be perceived as having the same performance or quality standards as those that have been approved for integrated CGM use by the FDA where the standards are a, a good bit more rigorous. OK. So uh these devices tend to cost less they carry uh than those that carry FDA clearance and they can be attractive from the standpoint of payers and cost considerations. But they uh we should be cheerful uh to limit uh the use of these and consider the wider value of the total benefit rather than just the cost. Now here, the, the the comparisons that that are important, what you see are the FDA standards for CGM accuracy studies and versus those uh that uh are characteristic of the non IC GM approved devices. Some don't have accurate studies, accuracy studies that have been published in peer reviewed journals. Some only have uh information in government letters. Uh The study populations may be small uh uh with high proportions of persons with type two diabetes. Uh some are conducted in ways so that the uh uh MS uh will will look better than it would look if you uh analyze real world use because of peculiarities and protocols. There are a lot of limitations that apply to these non IC GM approved uh devices and, and, and therefore many of these are not representative of performances across um target populations that we would like uh to have in order to use them effectively in many of our patient populations. And then finally, there's this issue here of overall accuracy, point accuracy has now been directly compared between uh head to head between two widely used CG MS. And we don't have much data of this sort, but this is something that's important uh to consider. This was a head to head uh study uh between the Dexcom G7 uh and the Freestyle Libre three multi center, single arm prospective study, type one and type two patient uh accuracy was assessed by comparing sensor data to laboratory reference values uh which uh uh allowed for the determination of the mean absolute relative uh uh uh differences. The mars and each participant underwent three frequent sample studies uh days 15 and nine or group 226 and day 10. And what you see here is that the accuracy in the 1st 24 hours showed that in the 1st 12 hours, they're pretty much comparable, but in the subsequent 12 to 24 hours, now you see a, a relatively uh significant reduction uh in uh the MA RD for FS uh for Freestyle Libre as opposed to uh G7. So there was much, much better accuracy compared to the laboratory determined glucose value. The MA RD was much, much closer uh to that absolute standard with uh uh Freestyle Libre. And you see uh overall agreement against the lab standard, the Yellow Springs Instrument reference standard. You see the mark for FSL three. Freestyle Libre three was much, much less than 10% of variance from uh the uh uh compared to the G7 and the percentage achieving that plus or minus 20 mg per deciliter or plus or minus 20%. Again, notice that much, much uh higher uh alignment with that outcome with FSL three Libre +83 than with uh Dexcom seven. And then when you look at the accuracy by day of wear compared to the laboratory standard. Notice that again, a lot more accuracy with the Libre three than the Dexcom G7 in all of the metrics that were evaluated. So we need to have more studies like this. But this is important where accuracy is uh uh essential. And then the final topic here is DK A, you know, insulin insufficiency, which is something that can happen in anybody that has diabetes, whether they're type one or type two depends on the, the duration of disease and their overall clinical circumstance. As ketosis worsens DK A can occur. It's the leading cause of death among Children and adults with diabetes. 15 to 75% of youth present with DKA A diagnosis and the annual prevalence of DK A in those with established diseases vary 3 to 10% in adults, 1 to 10% in youth. And you see that over time, uh there's, you know, relatively, it's relatively flat, but there's some variability according to age. And you see that pictured here that generally the time where you see the highest frequency of DK A events over uh a twelvemonth period of observation those times between adolescents and young adulthood. And that actually corresponds with their glycemic variability as you see in the inset over these two time periods that are, are referenced. This is when they tend to have their worst glucose uh control. So there's variability. Now, how do we measure ketones where we can use these uh current ketone uh meters that have a strip. Uh the, the uh they measure beta hydroxybutyric acid, which is really sort of the uh key compound in, in DK A or urine, which measures uh aceto ace uh aceto acetate. And this is uh uh a cheaper way of going about it. Uh But in both instances, there are uh some rather substantial uh limitations uh in the A a uh approaches to um measurements ketones. And when you ask people, how frequently do you monitor? For example, when you're vomiting, this is key, a key uh period of time. When you're sick, you have diabetes, you can't take in uh the fluids, you can't hold things down. You need to be concerned if your blood sugar is going up, you need to be concerned about ketones. And you see over here on the right, those people in the young adult and older age group, over 50% say never they don't check it and many of them only rarely or occasionally generally in the lower subgroups. We, we have somebody that's probably overseeing and therefore they're, they're sort of forced to, to, to check it. If you ask at the time of hyperglycemia, when you have uh glucose excursions that are significantly in the above range, um uh desirable range. How many uh what frequency do you monitor your ketone again? Look at those people in the adult and older populations, many of them say never, this is an area that we really need to focus on sharply. And one of the things that's limiting is that it would be much better if we had easier ways uh of measuring ketones. Well, now in the future, we're gonna have continuous ketone monitors. Uh This was a, a study that was done with 12 healthy participants on low carb diets. They wore three sensors for 14 days. The data was compared to capillary ketone meters. Remember those require AAA drop of blood. Um And this was a single retrospective uh calibration. And what you see in the data on the right is that there was a linear response over the 0 to 8 millimolar range. Really striking data. The first of its kind bow wearable glucose ketone monitoring system will enable people with diabetes to continuously monitor glucose and ketones using one sensor. It'll be the same size as the CGM will connect to a digital ecosystem. It, it's received breakthrough designation from the US FDA. It will have personal and caregiver mobile apps so that the data uh can be uh uh viewed by uh uh caregivers and and close supporters. There's a cloud based data management software for remote monitoring and now we have a continuous system that can really be important for those people at higher risk of developing DK A. And there have been studies that have shown that using the such a AC KM device could help prevent DK A because you can see rising Chee stone levels early. And this is a study um um result that showed in graphic form or, or diagrammatic form that shows rather than during the illness. While somebody is actually in the throes of DK A, this was a study that aimed to determine if routine point of care. Um capillary blood glucose, blood ketone measurements could actually predict future DK A. And they looked at this study population over a 6 to 12 month um period. There were 12 adjudicated DK A events. And what they found was that the maximum capillary blood ketone level of greater than point eight millimoles per liter led to a threefold increase DK A risk. So this is very important because we know that a ketone level of greater than 1.5 millimolar uh uh during illness and hyperglycemia represents an immediate risk of progression to DK A. But now what it looks like is at lower levels up to 0.8 during routine measurements and imagine routine measurements now might be collected with a continuous monitoring device. Those lower levels are you have some uh insight into what can potentially uh happen. So CGM is an easy way to generate real time uh glucose data, interpretable pictures of glycemic control throughout the course of the disease. One second, painless scans, you don't have to stick fingers anymore. User-friendly scans, small, fully disposable sensors that can be uh worn generally. Now up to 14 days in some instances. Now we're going up to 15 days and probably will be more in the future. No calibration, no finger pricks automatically measures, captures stores the data with good digital connectivity. And the ambulatory glucose profile gives you this visual report of several days or weeks making it look like one day, making you making it possible to easily visualize where the problem and what interventions are necessary to fix it. And the clinical benefits are well established and similar strategies are now emerging for ketones. It's an exciting time to see where we are in CGM technology.
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