Hi, everyone. Welcome. Welcome to today's hands-on tips to improve diabetes care webinar. Today our panel will show their expertise on cardio-kidney metabolic essentials, a primer for primary care providers, and we're glad you're here. I'm Dr. Christopher Jones. I'll be moderating today's webinar. To share a little bit about me, I am the chair-elect for the primary care interest group. I'm also an internal medicine physician working with Intermountain Health in Salt Lake City. In that health care system, I have a position as the director of Intermountain Diabetes Committee, and in a fun turn of events, I practiced general medicine for quite a number of years, but seven years ago I switched from delivering general medicine to specialty medicine. So now I treat diabetes patients exclusively and become some of an expert in that area. I love my interactions with all my ADA colleagues, and I'm happy to welcome you to this webinar. We'll spend the next hour together by following the agenda on the screen. First, we'll be using interactive features during today's session. We'll send you important links and information throughout today's session in the chat box. We'll have quiz questions throughout the presentation posted on Zoom. When you see a quiz question pop up on your screen, please take a moment to respond by clicking the answer that you think is correct. We'll get a summary of those answers as we are hearing everybody's input. We'll be using the Zoom Q&A as well. At the end of this entire presentation, we'll use that Q&A for panel questions. If you think of a question as our presenters are speaking, then use the Q&A box on your control panel to type in your question. We'd also like you to join us July 9th, which is just in a few weeks, for the next installment of the hands-on webinar series. Click the link in your chat box to register for that next hands-on webinar series. Now, I'd like to introduce you to the panelists for today's webinar. Dr. Justin Chupo is a physician scientist with a background in endocrinology and epidemiology. His clinical practice focuses on type 2 diabetes, obesity, and lipids. His research focuses on the cardiovascular complications of type 2 diabetes. Dr. Patrick O'Connor is a primary care physician and chronic disease epidemiologist who has served as a co-investigator in the ACCORD trial, co-author for five years of the ADA Standards of Diabetes Care. He's a member of the editorial board for diabetes care and is a principal or co-investigator on 35 large NIH or PCORI-funded grants. Many are designed to improve quality of diabetes care in primary care settings. He's a senior clinical investigator at Health Partners Institute in Minnesota. At this time, I'm going to let our panelists introduce themselves and state their disclosures. Start with Justin Chupo. Yeah, I'm Justin Chupo. As Dr. Jones mentioned, I'm a physician scientist based at Johns Hopkins University and I have no disclosure in relation to this presentation. Hello, I'm Patrick O'Connor at Health Partners in Minnesota. It's a large integrated care system with about one and a half million patients and 2,000 doctors and interest in, like Intermountain, constantly improving care. I've been here for 30 years and enjoy it. I also have no conflicts of interest. Hi, everybody, and as I already mentioned, I'm Justin Ishufo, I'm from Johns Hopkins University. And today, I'm going to briefly share some thoughts with you about some fundamental concepts. And these fundamental concepts are going to, it's going to be an elaboration on the key reasons why, especially in the setting of type 2 diabetes, one should think about cardiac, kidney, and metabolic abnormalities together. So I'm trying to get my slides to move forward, sorry. Okay, so I'm going to start by, you know, talking a little bit about the interrelated part of physiology of cardiac abnormalities, kidney abnormalities, and also metabolic abnormalities. And the framework that I choose to use here, it's a framework that I derived from one of the talk given by Dr. Josh Bakris, sometimes last year during the AHA, where he sort of suggested that when thinking about the cardiac, kidney, and metabolic syndrome, one could conceive of the body as a house, and we have an internal milieu, the milieu being, in this case, the metabolic milieu, which could be, as you can see on this sketch, which could be thought of as, you know, a hyperglycemic milieu, which is the main characteristic of type 2 diabetes. And remember that one of the key drivers of type 2 diabetes is actually excess adiposity. So as you all know, the increase in the incidence and prevalence of type 2 diabetes in the population in the U.S. is mainly driven by excess adiposity, usually measured as an elevated BMI. One thing that you want to pay attention to on this sketch is that excess adiposity does not just drive type 2 diabetes, but it can also directly drive the occurrence of heart disease or vascular disease, but it can also drive the occurrence of kidney disease directly. As you all know, type 2 diabetes is a very well-known risk factor for cardiovascular disease. More than 60% of people with type 2 diabetes mainly die from cardiovascular disease. The same thing is true for kidney disease. With hypertension, type 2 diabetes is one of the key reasons for kidney function decline, ultimately leading to chronic kidney disease and stage 1 disease. These two fundamental facts have been well illustrated in clinical trials of glucose lowering. As you may remember, for type 2 diabetes, the UKPDS showed that the control of glycemia will sort of significantly reduce the incidence of chronic kidney disease and instant urinary disease. The same is true for cardiovascular disease, maybe to a lesser extent, because it's with the long-term follow-up of the DCCT and the UKPDS that one was able to show that lowering glycemia is associated with a reduced incidence of cardiovascular disease. And as you can also see on this sketch, both the heart and the kidney influence each other. And one of the advice from Dr. Batris was to sort of think of the heart and the kidney as a married couple, in the sense that chronic kidney disease or decline of renal function is a well-known risk factor for cardiovascular disease. As you may remember, the main cause of death among people with chronic kidney disease is not end-stage renal disease, it's rather cardiovascular disease. The reverse is true in the sense that heart failure, specifically, tends to sort of lead to a deterioration of kidney function. You may have in mind a well-known cardiovascular syndrome. And so the other thing to mention is that even among people with established heart disease and established kidney disease, type 2 diabetes and obesity also drive poor outcomes. They sort of accelerate the progression of myocardial dysfunction, for example, and they will also accelerate the progression of chronic kidney disease. And so that is, in a nutshell, sort of a conceptual paraphysiological framework that define our sort of approach to thinking of kidney disease, cardiac disease, and metabolic disease as a whole. Next slide. I'm trying… Okay, so another set of reasons to think about kidney disease, cardiac disease, and metabolic disease has to do with the precursors of those conditions. And here I'm talking about risk factors, and a lot of those risk factors are common between the sort of the organs or the abnormalities. And here on this slide, which is not exhaustive, sorry, I mentioned age. Aging is known as a risk factor for all those conditions. Race or ethnicity, it's well-known that certain racial and ethnic subgroups have a higher propensity for having either cardiac disease, kidney disease, or metabolic disease, especially if, in addition to the racial and ethnic background, there's some degree of social disadvantage or, you know, socioeconomic disadvantage. It's also well-known that physical inactivity and unhealthy diet will drive the occurrence of obesity, type 2 diabetes, kidney disease, as well as cardiac disease. Excess weight is also a shared sort of risk factor. In addition to those shared risk factors, there are shared comorbidities, and here I mentioned hypertension in the sense that obesity is a driver of hypertension, and hypertension in itself is a driver of cardiac disease and also a driver of hypertension, and it's usually very frequent among people with type 2 diabetes. The same is true for albuminuria, which could occur as a consequence of type 2 diabetes, but also as a consequence of hypertension, but bear in mind that albuminuria in itself is a well-known independent risk factor for cardiovascular disease, independent of hypertension and independent of diabetes, as well as independent of obesity. Now, the other aspect that is of importance in thinking about, you know, the cardio-kidney metabolic syndrome is the fact that in our clinical practice and also in the community, there's a high degree of occurrence of cardiac, kidney, and metabolic abnormalities, and I'm sharing here with you a recent publication in JAMA where the authors actually look at NHANES data, which is a national representative data from the U.S., and using the stages that were described in a recent American Heart Association statement about cardio-kidney metabolic syndrome that define the degree of overlap, especially for those having the later stages of the syndrome, which is a stage where people have full-blown diseases, be it type 2 diabetes, obesity, or chronic kidney disease, or cardiac dysfunction. And as you can see, the prevalence of co-occurrence of those abnormalities is at least 10% if you look at the graph, and this has been the case over the last, at least the last 10 years or 15 years, and the overall frequency of those abnormalities about, you know, 15% with, you know, variable frequency depending on the racial ethnic group. So another reason to sort of think about the heart, the kidney, and the metabolic media together is mainly because today in routine clinical practice we have powerful shared therapy, and here I did mention the fact that lifestyle modification, which consists of modifying unhealthy diet and also increasing physical activity, has an impact on the outcomes of metabolic abnormalities, obesity, diabetes, but also on the outcome of cardiac and kidney abnormalities. But more importantly, we've now had the advent of pharmacotherapy, which have a combined effect on metabolic abnormality, kidney abnormalities, and also cardiac abnormalities. And here I'm just going to briefly mention three classes of medication or drugs. The first one is the sodium glucose co-transporter, two inhibitors, HFT2 inhibitors, with which a lot of you are familiar, which have been shown in various clinical trials to lower glycemia, but also to reduce the rate of re-hospitalization among people with heart failure, as well as reduce the rate of major adverse cardiovascular events, as well as to decrease the rate of decline in kidney function, as well as end-steroid disease among people with a certain level of EGFR, which is true for almost all the HFT2 inhibitors. The second class of medication that I want to briefly mention are the GLP-1 receptor agonists, which have emerged today as powerful anti-obesity medications, as well as powerful diabetes medication, but they also have a significant cardioprotective effect, especially in preventing atherosclerotic cardiovascular disease, but also are now possibly emerging, especially with the FLOR trial that came out a few weeks ago, as having a possible renal benefit. And I would like to mention that generally, in using those medications, we don't need to make adjustments for the renal function. Those medications can be used across levels of EGFR. And the last group of medications that I'm going to mention, which have, to a lesser extent, as compared to the GLP-1 and SRT2 inhibitors, renal and cardioprotective effect. Here, I'm thinking about non-steroidal immunoparticle receptor antagonists, especially phenylalanine, which in several publications has been shown to be renal protective and also to be cardioprotective. Now, moving forward, so given all the things that I mentioned in terms of paraphysiology, in terms of prevalence of the comorbidities, as well as in terms of the risk factors and the shared therapies, what are the implications for our sort of practice in the setting of diabetes? It has implications for estimating cardiovascular risk among patients with diabetes. And here, I'm briefly mentioning the PreventRisk score because we're going to have to talk about it during this webinar as a tool for estimating cardiovascular risk. It also has implications for addressing the renal risk, which consists of, you know, screening for kidney disease, as well as addressing it using the relevant therapy. But also, you know, paying attention to the microvascular complication in the sense that these may have implications for cardiovascular disease. And here, I'm thinking about retinopathy. But also, implications for addressing obesity, either through lifestyle management or therapies. So, those are sort of the practical implications. But more generally, the fact that it's a common soil, there are common risk factors, there are common therapies between, you know, cardiac abnormalities, kidney abnormalities, and metabolic abnormalities, it just points to the fact that our practice has, or our approach to patients has to be a multidisciplinary approach, which is the only way we can be successful in sort of curbing those abnormalities in our clinical practice or in the community. So, I'm just going to sort of end up by saying thank you. Yeah, there is a question, a quiz question, and I'm going to read the question briefly for the audience, which is, GLP-1 receptor agonists require dose adjustment in the setting of chronic kidney disease. So I would invite you to make a choice as to whether this statement is true or false. Maybe just a quick comment before the poll is closed, just to mention that currently with the GOP1 receptor available on the market, which are mainly injectables, and also one oral GOP1 receptor agonist, one does not need to adjust their dose in the setting of chronic kidney disease or end-stage coronary disease. It used to be the case with Xenotide, but nobody uses Xenotide anymore these days, so the answer is false. Okay. Thank you, Dr. Atyubo, for a terrific overview of the CKM territory. Kind of a new territory to many of us in primary care, and exciting times that we live in, because the old paradigm of focusing exclusively on cardiovascular risk is yielding to this new paradigm of joint consideration of cardio-kidney metabolic risk. And we'll be talking in the next few minutes about some of the tools that are now available to those of us in primary care and elsewhere that allow us to sort of operationalize this concept in practice and quantify CKM risk for our patients that we see in the office and clinic and some of the potential clinical implications of this approach, especially given new therapeutic agents that have recently become available and have shown a multiplicity of beneficial effects, not just on the cardio, but also on the kidney and metabolic aspects of health. Let's look at the next slide and the next one. Yeah. So there's no doubt that in all adults, but particularly in adults with diabetes, cardiovascular risk estimation is a really important kind of a guiding beacon in some ways to the clinical management of a given patient. For one thing, cardiovascular events, including heart attacks, strokes, and others, are the leading cause of excess morbidity and mortality in adults with diabetes, and Dr. Trullo talked about that. There's a very interesting paper that's cited on this slide published some years ago, but an entire population of Denmark over many, many years, 20 or 30 years, and they looked at all the causes of death of people with diabetes in Denmark over a long period of time. There were about 40 cardiovascular deaths for every one microvascular death among these adults who have diabetes. So the importance of cardiovascular risk factor control is very important, and as we just reviewed, renal status contributes a great deal to cardiovascular risk if renal status is impaired. So high cardiovascular risk, and I should probably now say we should probably think high CKM risk, cardio-kidney metabolic risk, can be very useful to identify patients who should have aggressive CKM management. And this whole idea of CKM management hasn't really been explicated in guidelines yet, but there are these drugs, Ankretin, Mimbedix, and SCLT2 inhibitors, that actually have benefits across the range of CKM pathophysiology, and therefore it's, I think, going to be the wave of the future that we estimate CKM risk and then apply CKM responsive intervention that could include lifestyle things and pharmacologic things and some would argue surgical things like metabolic surgery in response to our assessments of an individual patient's CKM risk. So this is really important stuff. And not only that, but if you use these risk equations correctly, and this extends all the way back to the Framingham equation and applies to the pooled cohort equation, which was released in 2013, as well as to the new prevent equations that we'll talk about in detail here in a minute, all those equations can be used to prioritize care options. So you can use those equations, and I don't mean you personally with your slide rule or calculator, but with a website or if you build it into the EMR-linked website or software, you can estimate for a given patient how much the risk will change if, for example, you drop their blood pressure 10 points, systolic 10 points, or if you start a certain dose of statin, or if they stop smoking. So you can use these equations. So, for example, your patient, George, is a smoker. You figure out their risk given all their current data plus they're an active smoker, and then you run it again with them as a non-smoker. You can sort of see what the potential benefit is of smoking cessation. You can do that for treatment of blood pressure, treatment of lipids, and now with the new equation, management of A1C and renal things like proteinuria. So that kind of ability to prioritize care options is something that you'll see more and more of in the future. So we need to keep that in mind, too. And what will be new in these new equations compared to the old ones is the same equation includes not only the conventional major cardiovascular risk factors, but also metrics of glucose control and renal status. Next slide. And as I said, these treatments now are ideally suited to the CKM paradigm because they have benefits across the CKM spectrum. For example, the GLPs reduce overall mortality, which by itself is huge, right? Now it's GLPs, too. Overall mortality these days when smoking rates are low, statin use is relatively high, blood pressure control is better than it's been before for the most part. To introduce a treatment in a randomized trial that actually reduces overall mortality above and beyond those baseline improvements in care over the last 30 years, that's a big benefit. Like the purported benefit of metformin on cardiovascular events occurred in patients where there was huge amounts of smoking, low management, almost none of lipids, lousy blood pressure control, and, you know, pretty much anything you threw in there that had benefit, you have so much risk you could get a reduction. And today in this better treated population, to show a reduction in overall mortality is truly amazing, as well as reductions in CV mortality, heart attacks and strokes, both in high-risk patients that have been studied well in cardiovascular outcome trials, and now in recent studies, one of which is cited on this slide published a month, a couple months ago, diabetes patients with moderate or, you know, average CV risk but have substantive benefits from GLTs and SGLT2s, especially relative to older treatments. This article, which you should take a look at by McCoy, it's in Nature Cardiovascular Research, shows that the protective effect of SGLT2s and GLT1s in relationship not just, you know, other studies have looked at against placebo, these studies look at it against sulfonylureate or DPP4, huge benefits, so, you know, it raises all kinds of issues, including some ethical issues, financial issues, and so on and so forth, but the benefits are pronounced. Anyway, and the number needed to treat to prevent adverse events varies widely across patients, and that's an important feature of why you want to use these risk equations, because these benefits you read about in all these papers are average benefits across a large pool of patients, and what you want to know is what's the potential benefit for your patient, George, who's sitting across the room from you, and you can characterize George's clinical state in different ways, and you could estimate what, you know, it's not completely kosher to go from group estimates of benefit to telling George what his likelihood would be, but you could say to George, and, you know, 100 patients like you or 1,000 patients like you, you could expect to see X, Y, or Z benefits, and, you know, you don't want to leave out the risks either, but you want to personalize it, you want to get it down to the personal level of your patient, and that's one of the things these risk equations can do. So let's look at the next slide. This is what was in the equation that we've used since 2013 until now. It's called the pooled cohort equation, PCE. Its ancestor was the Framingham equation, and the factors that are used in the calculation are the ones listed in black, which is age, sex, race, systolic blood pressure, total cholesterol, HDL cholesterol, and then yes or no, are you a smoke current smoker, do you have diabetes, are you on a hypertension medication, that's it. You'll notice that this equation has nothing in it about BMI, nothing about A1C, nothing about EGFR or UACR. Next slide. So the new equation called PREVENT, and there are several references listed on the last slide, the papers that have presented this back in November, retains most of the variables from the old equation except for race. Race is gone. And it retains all the other things from the old equation, which is age, sex, systolic blood pressure, total and HDL cholesterol, and yes or no, are you a current smoker, have diabetes, or on hypertension medicine. But it adds new terms listed on the right, including BMI and some of the sets of PREVENT equations, not all of them, but importantly, A1C, EGFR, UACR, also an indicator for whether the person is using statin or not, and then there's a neighborhood deprivation index metric that's also included, and you'll notice race is gone. So this is a different scope. This is a substantial revision from the old equation because the old equation was cardiovascular risk factors. The new one is looking at cardio-kidney metabolic stuff. It's looking at the stuff that causes heart attacks, and it's looking at things related to diabetes and metabolic syndrome, and it's looking at things related to renal static, and all those things are used to compute your CV risk, and you can conceptualize this equation as giving you a metric, not just a CV, predicting CV events, but also CKM risk. Next slide. In the derivation of this new equation, I want to spend a couple minutes on this, and what we've got on this slide is on the left side is how the old equation, the pooled cohort equation, was developed and validated, and on the right side is how the PREVENT equation was developed and validated, and I want to just contrast them a little bit. The old equation covers people from 40 to 79 years old. The new one goes down to age 30, which is helpful because, you know, cardio-metabolic stuff is becoming increasingly common in younger adults and even adolescents, so it goes a little bit lower on age. Both of these equations do not apply if a person has pre-existing cardiovascular disease, which is because the cohorts that were used to derive all these equations, dating back to the Framingham cohort, excluded anybody in town who had heart disease was out of the study. And that's why they can't be used to predict cardiovascular risk or future events in people that have heart disease. Now, from a practical clinical point of view, that's not too much of a problem because if somebody carries a diagnosis of cardiovascular disease, you're gonna treat them very, very aggressively. You can assume that they have high risk and you should treat them aggressively. But remember that the output of these equations will generally probably underestimate the CV risk of people if they have CBD and you do run the equation, the number you get out is gonna be too low as a general rule. So that's a limitation of the new and the prior equation. They both give you estimates of 10 or 30 year risk of cardiovascular events. Importantly though, the old equation overestimated risk. There were a number of studies that were done using the pooled equation that showed that it overestimated the occurrence of CV events, which is non-fatal heart attack, non-fatal stroke or CV death, overestimated as much as 50%. And the reason for that, or several fold, one is that the outcomes that were assessed in those studies of 25,000 people inside data sets for the old equation, the outcomes all happened before 2007. And as those of you who graduated from medical school in the prior millennium will recall, the care for people with cardiovascular events was not that hot 25, 30 years ago. The case fatality rates were higher, the treatments were more primitive, the prevention measures weren't in place as well on a population basis. So the new equation, third dot point down on the right, is based on cardiovascular outcomes. Mostly they occur between 2008 and 2017. And this is an era where interventional cardiology as well as population management of cardiovascular risk factors was much better than it was in the time span upon whose data the old equation was developed. Also, the old equation was developed strictly on research cohorts, like the Framingham study, Evans County, and others. Patients had to consent and enroll, and it was about 25,000 patients. The new one, over six million patients. And these data sets were pulled, a lot of them from Optum and other care delivery systems. I'm not sure if Intermountain Health was in there, it might have been. But these are like everybody. So these patients weren't filtered by consenting to enroll in a research study. They're like everybody walking into the offices of 46 different care systems that are using currently available data. Data, I think, ended around 2017. So much more recent data, much less selected patient population, which is a good thing. It's gonna be more accurate if you do that. The outcomes are pretty much the same. The old equation overestimated, and I put in the chat a reference that came out yesterday in the JAMA Internal Medicine. I've been waiting for a paper like this, and it showed up yesterday. So it's not in the slides, but it's in the chat. Excuse me. This is a paper that used NHANES data to compare what, for the same patient, NHANES patients, if you computed the old risk and the new risk, what's the difference? And indeed, for all age, sex, and race groups that they looked at, including older people, and blacks especially, but for all groups that I just mentioned, the new equation will give you about half of the risk estimate of the old equation. So if the PCE gave you like a 10% 10-year risk of a heart attack or stroke or CV death, the new one will give you about 5% risk, right? If the old one gave you 20% risk, the new one gives you about anywhere from a 9% to 11% risk. And you can sort of look at this paper for the exact numbers, but it's very close to, you know, half as high. So whatever you're seeing right now in Epic or whatever source you're using, estimate CV risk, 10% 10-year risk, 20% 10-year risk, this new equation, when it goes in, the 10% is gonna become 5%, the 20% is gonna become 10%. Of course, those are average differences, but you'll see changes like that. And as the authors of this paper point out, this has big implications, for example, for the management of lipids, right? So if you maintain the idea from the US Preventive Service Task Force, treat anybody with a CV risk greater than 10%, 10-year risk, treat them with statins, or the ACC AHA equation is greater than 7.5% CV risk, treat them with a statin. The person who had an 8% risk and got put on a statin now has a 4% risk, so they'd still be on a statin. And the authors of this paper point out that there's a lot of people that are under the current equation, the recent equation recommended for statin who may not be under the new equation. However, this remains to be seen. There'll be new blood pressure and lipid guidelines, I think, before the first quarter, in the first quarter of 2025, is what I've heard. It'll be interesting to see how those guidelines take account of this new preventive equation. Next slide. So just to reiterate, this kind of review slide, that the new equation has a lot of things in it that are very congruent with the cardio-kidney metabolic model. And therefore, it's actually pretty exciting stuff. It's gonna create some growing pains because if your patients are engineers, retired accountants, or colonels, something like that, they're gonna say, how did my cardiovascular risk go from 12% to 6% or 20% to 10%? You're gonna have to do some song and dance and explain it to them. Good luck. So there's gonna be some growing pains and transition pains. But I think that overall, this is a big advance. We'll be able to consider and communicate to patients also that all these things, A1C, the renal metrics, the use of statin, and the Neighborhood Deprivation Index are contributing to cardiovascular health and outcomes. And this is gonna be, I think, without much doubt, the new reality. Our clinicians, our 1,900 clinicians are sort of beating down the door and like, did you program it yet? We want it. Did you program it yet? I'm sure Epic will program it and other vendors will program it soon. Next slide. There are some problems with both the new and the old equations, as I said, aren't good for people with established ASCVD. Importantly, and we'll have to see how they interface with soon-to-arrive new lipid and hypertension guidelines. So fasten your seatbelts to that one. Neither one of them includes coronary artery calcium or BMP or genetic risk or family history risk in a formal way. So, you know, there's always, you can find articles about these things. Is it good to add them? Is it isn't good to add them? Did they add much? And their current guidelines suggest BAC is useful sometimes for clarifying lipid benefits, but they don't really, at a population primary care kind of setting, don't push these other things very hard, except, you know, familial hyperlipidemia is an important genetic thing. But, you know, those things aren't included and some experts think they should be. That is something that'll take some more time to settle. And another important limitation is that the equation, either the new one or the old one, really accommodate lifestyle change benefits. So you can tell a person, if you started statin, this is how much you'd see your benefit. If you dropped your blood pressure or your A1C, this is the benefit you'd see. But there's no way using these equations anyway to estimate if you were more active or what would be the benefit if you ate healthier. Actually, there are ways to estimate benefits of BMI, but they're really not incorporated seamlessly into the new equation, except for the sub-equation that looks at congestive heart failure. So that's a little bit of a disappointment too. And then, sad but true, you know, the equations look at whether you're on a hypertension medicine and now whether you're on a lipid medicine. I would like to see an equation that says, are you on an SGLT2 or a GLT1, or other incretin mimetic, because those things are gonna affect your outcome in multiple ways. I think that it's not in the equation because there isn't enough available data yet, but cohorts are being assembled to look at that. I'm involved in some of those studies myself. They're funded by PCORI and other sources. And I think that eventually, it would be very helpful to know not just what will happen if you start a statin or a blood pressure medicine, but what would happen if you started an SGLT2? What would happen to your risk or an incretin mimetic, like some aglutinase at the time? Okay, next slide. Next slide. So let's think about this right now in terms of a clinical case. If you had a patient with type 2 diabetes and they have obesity, maybe their BMI is quite high, some number north of 27, and they have a CVD or high CV risk, you would be able to figure out what to do with that patient based on the prevent equation, right? But, or the old equation, you'd say, okay, here's a person who has big potential benefits from a GLT1 because they have obesity, they have CVD, they have diabetes, there are multiple benefits from a GLT1. But, you know, maybe not just the people with high CVD conditions or scores according to the old equation, but if they have a high prevent score, maybe they should be treated with a GLT1. I mean, maybe there should be a threshold that says if your risk is above such and such, you should be on a statin. Maybe if your TKM risk is above such and such with this prevent equation, you should be on a GLT1 or an FGLT2. The next slide throws in the wrinkle of renal disease, next slide, or a renal or a CHF patient, then maybe your focus would shift from a GLT1 to an FGLT2. So, you know, it's not just the traditional kind of patient, oh, they have heart failure, oh, they have renal disease. What about the patient that just has, on the next slide, a high prevent score? That person also would be a good candidate for a GLT2 inhibitor, FGLT2 inhibitor. So, there are gonna be these clinical nuances, and not just if the patient has heart disease, a CHF or TKD should you use these new medicines, but maybe also if they have a high prevent score, you should use these new medicines too. And I hope there'll be some questions and discussion around that point. But I think for now, next slide, we'll just wrap it up. And these are some papers that you might be interested in. And let's see if there are questions or discussion. Thank you. I don't think we should spend too much time on these questions, but one is, is there a way to assess CKM risk? And we just said, yes, this new pre-done equation does it. So that's a true. Let's see what else comes up in these boxes. Maybe that's enough. Oh, here's another one. Whoops. The pre-done equation includes which of the following factors that are not included in the prior pooled cohort equation? And I'm going to trust that you were listening and that you know that it's an A1C is new, the EGFR is new, a flag for whether they're on a statin is new, and the Neighborhood Deprivation Index is also new. So the right answer is all of the above. Back to you, Chris. Excellent. Thank you both for presenting. Love this information, and your presentations have been marvelous. So we wanted to point out the five key tips and takeaways that we have from today's webinar. As you recall, this is a hands-on webinar series. So with our hand, we have five key tips from every webinar that we have. So please look these over. These will be available to you as you look through this. And I'm going to continue to talk, but hope you can digest these. And of course, as you come back and look at the slides in the future, if you do so, you'll have that available. I'd like to thank everyone for communicating with us today. If you have any questions you want to put in the Q&A box, please do that now. Type those in, and we'll get those questions answered. We also have, let's see. All right, yeah, OK. So we'll be watching for those questions and answers. Also in the chat, if you want to throw anything in the chat, that would be great. One of the questions that I'd like to present first to Dr. Ciuffo, if I could, is that you presented beautifully about the shared comorbidities of hypertension and albuminuria. Is there any data that some of the shared treatments that you have that one is better than another or should be approached sooner or is more comprehensive for the CKM approach than another of those treatments? Oh, that's an interesting question. There is no, to my knowledge, there is no data where we have a head-to-head comparison between one class of medication versus the other. For example, SGLT2 inhibitors versus GOP1 receptor antagonists or with things like non-steroidal immunocorticoid receptor antagonists. But based on the available evidence, one could argue that the SGLT2 inhibitors for now are the medications that could basically address each and every aspect of the cardio-kidney metabolic syndrome because we know for a fact that they have an effect on glycemia. They do have an effect on cardiac outcomes. It's been shown to reduce hospitalizations for heart failure, but also to have an effect on major adverse cardiovascular events and, to a certain extent, mortality. But also, depending on the EGFR, if the EGFR is not less than, I think, 20 or 25, if my recollection is correct, above that cut point, they have a beneficial effect in terms of reducing the occurrence of microalbuminuria as well as the decline in EGFR as well as end-stage renal events. So for now, that's what I can say. But with the emerging evidence, it may be that the other class of medication, the GOP1 receptor agonist, may also end up having an effect on all components or all aspects of the cardio-kidney metabolic syndrome because we're having emerging evidence from the FLOW trial. I think it's early to say something about the kidney outcomes, but it looks like GOP1 receptor agonist will also probably be beneficial in terms of their ability to reduce kidney outcomes or adverse kidney outcomes. But we already know that they have an effect on diabetes, obesity, and they also have an effect on cardiovascular outcomes. That's what I can say for now. Fantastic. Yeah, that's so helpful because those are the decisions that we make every day as we're sitting with our patients is now what to do, what next. And so thank you for that direction. Dr. O'Connor. Let me add a comment. I certainly agree with all that. One of the references, the last one on the slide by McCoy in Nature of Cardiovascular, a couple, I think a month ago, she did like, these are trial emulation cohort studies. They're not randomized trials, but she did head-to-head comparisons of GLPs and SGLT2s. McCoy has funded four big cohort analyses of those questions, and she's the first to publish. I'm actually working on one of the other teams. And she found that the GLP1s had some pretty good, looked like they had pretty good renal effects. But the thing that still differentiates in favor of SGLT2s is CHF stuff. But I think you'll see more, as Dr. Etubo said, more head-to-head studies soon. Awesome. And clearly, you've looked at the pharmacy benefits of those two medicines above the others. And we're getting the understanding very clearly over the last number of years that these two medication classes definitely have some distinct cardiokidneal metabolic benefits beyond most of the other classes of medications. So thank you. Another question, probably for Dr. O'Connor, but of course, both of you, if you'd choose. The new PREVENT includes some metrics that are new that we talked about, especially focusing on some CKM parameters. The A1c, the UACR, and the SDI all had kind of a little asterisk with optional on those. How important is it for us to put those in? So for instance, we're having a discussion with our patient. Would it be important to delay that discussion, send them to the lab to get those, because they're rather critical? Or maybe we should continue the discussion anyway without those? What's the optional comment there? Well, for the UACR and the A1c, it's not too big of a problem. There is an adjustment in the equation if they're missing. Because clearly, not everybody is going to have an A1c outside of diabetes patients, right? And the same with the UACR. But the EGFR, it wants the EGFR to run in order to run. So you have a couple of options. Same as if you're missing some of the lipid data. You could either not run it, send the patient to the lab, and then forget about it for another 2 and 1⁄2 years. Or you could put in a placeholder value, say maybe an EGFR of 60 or something like that, and run it and see what ballpark you're in. And then update it later on when you get an actual value. So in my world, where we do decision support systems that are web-based, we'll probably, when we put this in and program it right now, we're probably going to just plug in a 60 for the EGFR if it's missing. And then put some decision support that says, hey, why don't you check it? There aren't any guidelines that say every adult should have an EGFR. So there's that to consider as well. Excellent. Thank you. I had an interesting experience I'd like to ask both of you about. It was a well-meaning primary care doctor who had kind of a multiple reasons for this. So it wasn't as simple as this. But making it rather simple, the primary care doctor said, I noticed the endocrinologist was treating the blood pressure and cholesterol. And I'd prefer they just treat the sugars and leave the other stuff to me. It seems like the cardio-kidney metabolic concept involves primary care and specialists working together. I'd love to have each of you share with me, what does it look like for the primary care and the specialist to work together in really absorbing the whole concept of the shared pathophysiology of cardio-kidney metabolic? Well, I do think your case report there was a pretty misguided person. I think that we've been spending a lot of time trying to get our endocrinology clinics to really focus more on blood pressure and lipids. Because they tend to be really good with glucose. And then sometimes they don't pay as much attention to blood pressure and the lipids. And the idea is, hey, what's best for the patient? So if they're going to go to the trouble of going to the endocrinology clinic, I mean, lipids is part of endocrinology anyway. Some people would contend, right? So we'd like them to do the whole bailiwick. I don't see any reason to be possessive. I mean, believe me, there are so many of these patients floating around that no primary care doc is going to be short on patients. And our colleagues who are cardiologists, endocrinologists, nephrologists, milk them for all they're worth. Get them to weigh in on as many issues as possible and take it as information. Believe me, the patient's going to come back to you. And it'll be good to have those inputs from the subspecialists. That's bread and butter, primary care patients first, right? Good if you got subspecialty input, it's terrific. And believe me, the patient is going to come back sooner or later. So don't worry about that. That's what I think. Well, I do agree with that. But as an endocrinologist, I will agree with the fact that most of the time we tend to focus only on glucose, which is to a certain extent counterproductive because if one manages only the glucose, diabetes is also about all those other things, right? There's going to be, almost always going to be high blood pressure. There's going to be some sort of renal complication. Anyway, we screen for that. So if we're screening for kidney disease and if you're sort of calculating or estimating the cardiovascular risk is probably because we want to do something about it. But that being said, I believe the integration of the counter kidney metabolite abnormalities is probably going to be successful if there's a high level of involvement of primary care physicians, because they probably to a certain extent dealing with all those aspects to a certain extent. So the first line will probably be the primary care physician and for specific issues or for specific or complicated issues, probably they will have to refer or collaborate with the endocrinologist. Because I don't, I mean, very competent primary care physician can probably deal with the blood pressure or can deal with the surveillance of kidney function or up to a certain point. And so they have a sort of high ability to sort of coordinate all those things than the individual specialist. So my contention is that primary care practice is key to a successful approach to the counter kidney metabolite wall in which we're getting into. So it requires a primary care physician to be at the center of all these things for the coordination. But also it also requires an endocrinologist to go just beyond, at least I'm speaking for endocrinologist to just go beyond the glucose control because all those other things contribute to preventing adverse events, reducing mobility and reducing mortality. I think those are critical points. I love those. I think it's a culture change that is happening across America where it used to be rather siloed. The cardiologist is doing the cardiology work and the nephrologist, the kidney work. And it seems now with this understanding of the shared pathophysiology that we're all working together, titrating doses, getting labs, getting stuff done when the patient is with you and not being so siloed or territorial, but rather grateful for colleagues that help and assist in the care of the patient, regardless of obtaining labs, getting the PREVENT score, titrating the SGLT-2 or the GLP-1. So love this information that you've given us today. This is critical for us. And everyone who gets to hear this webinar has a chance to change and modify and improve what they do for better patient care. We're thankful for your time today, Dr. O'Connor and Dr. Okwujo. Thank you so much. So in the last one minute, we'll just remind you in our post-webinar tips that there will be a post-test that you'll receive. Please get your post-test to claim your CE credit. And so check your email for the webinar recordings and hopefully that will be coming to you and you'll be able to complete this webinar. And we'll see you for future webinars as well. Have a wonderful day. Thank you.

webinar

diabetes care

cardio-kidney metabolic essentials

primary care providers

specialists

lifestyle modifications

SGLT2 inhibitors

GLP-1 receptor agonists

PREVENT equation

holistic patient care