My name is Savita Subramanian. I'm from the University of Washington, and I'll be one of your moderators for today. We'd like to ensure that you keep your presentations to about 10 minutes, with maybe five minutes for questions. So the first speaker is Dr. Fernando Brito. Title of the talk is Differences in HDL Particle Composition in Patients. Thank you. Thank you for the introduction. Can you hear me? There we go. Thank you for the introduction, and thank you to the Endocrine Society for the opportunity of sharing my research. So the title, or what we will be talking about, is how the presence of advanced liver fibrosis in patients with non-alcoholic fatty liver disease, how the proteomic composition of HDL, as I was saying, may be altered, and how that may translate into a higher cardiovascular risk. Let's see if this works. There we go. I don't have any personal conflict of interests, and this was done in collaboration with Quest Diagnostics, so some of the co-authors work for, or have stocks from Quest Diagnostics. So non-alcoholic fatty liver disease is a highly prevalent condition, as we are all aware. So around 1 1⁄4 of the overall population have non-alcoholic fatty liver disease. And it's not only highly prevalent, but it's also a progressive disease. So 1 1⁄4 of those with non-alcoholic fatty liver disease will develop inflammation, NASH, and some will eventually develop liver advanced fibrosis. As endocrinologists, we are at center stage of this condition, because although patients with type 2 diabetes represent 9, 10% of the overall population, once our patients have type 2 diabetes, they are at high risk of developing non-alcoholic fatty liver disease. So around 2⁄3, or 55%, based on that meta-analysis, have non-alcoholic fatty liver disease. And not only it's highly prevalent in patients with type 2 diabetes, also liver disease progresses faster. So they have more NASH, 70% of those with non-alcoholic fatty liver disease develop NASH, and more progress to liver fibrosis. So the important concept is that of all the patients with advanced fibrosis, around 60% have type 2 diabetes. So that means that we are at center stage of this condition. The other important concept is, if we divide patients based on the amount of liver fibrosis, you see stage zero, absence of fibrosis, to stage four, which is cirrhosis, you can see that mortality goes up with more severe liver disease. And that happens pretty early on, once the stage of fibrosis goes from one to two. And you can see in that graph, in the yellow line, how the overall mortality goes up. And some of that is explained by liver-related mortality, that it's cirrhosis or a part of cellular carcinoma. But at the end of the day, these patients die of cardiovascular disease. So it's very important that we target that. As our patients become obese and they have dysfunctional adipose tissue, we know they are at increased risk of non-alcoholic fatty liver disease, and they are at increased risk of cardiovascular disease. However, the crosstalk of how the liver talks with the heart is not well understood. Some years ago, we show, looking at ApoB to ApoA1 ratio, and looking at LDL particle size, that these were altered in patients with non-alcoholic fatty liver disease, and this was independent of obesity and independent of type two diabetes. However, when we look at histology, we didn't see any difference in ApoB to ApoA1 ratio or LDL particle size. So ApoB particles was not explaining increased cardiovascular risk in patients with liver fibrosis. So that brought us to what I will be presenting now. We wanted to look at HDL particles. And the other important concept is that if the liver is the main site that produces all the proteins that are bound to HDL, we wanted to look at the proteomic composition of this HDL as these patients develop advanced liver fibrosis. This was a cross-sectional study, adults with or without diabetes, and we did a pretty in-depth characterization from a liver standpoint with an MRI spectroscopy to look at liver fat and liver biopsy if they had a diagnosis of non-alcoholic fatty liver disease to look at the amount of fibrosis. We did advanced lipid testing by ion mobility, and we had this technique where we added labeled ApoA1 to the samples, and then by immobilized metal affinity chromatography, we were able to isolate and question this technique. So they isolated HDL particles, and then they did targeted proteomics on those isolated HDL particles, looking at 28 proteins that are bound to HDL. And they also have this PCAT score, which is a score that correlates with cholesterol influx capacity, and they also showed that it's associated with increased coronary artery disease, and in those patients with coronary artery disease, it is also associated with higher cardiovascular mortality. Again, a surrogate marker that we will be using along the way of this study. Moving on to results, we divided patients based on the presence or absence of advanced fibrosis, and these are all patients with non-alcoholic fatty liver disease. First, I want you to focus on just basic clinical characteristics, no significant differences in age, gender, ethnicity, weight, or presence of diabetes. And this is important because it give us similar populations to compare, except for the presence of advanced fibrosis. I want you then to focus on the lipid profile of these patients. The use of statins was similar between groups, and that's important because then we can say that probably the use of statins did not affect our results. And we did observe a very small, although statistically significant, reduced HDL cholesterol in patients with advanced fibrosis, and we will bear that in mind as we will need to adjust for that small, yet statistically significant difference. Liver fat was similar between the groups, a little bit of higher A1C. As we know, diabetes is associated with more progression to liver fibrosis, so from a clinical standpoint, that makes sense. We also have 45 patients without non-alcoholic fatty liver disease that we will be using as controls, and you will see in the figures, we will use that as a kind of normal or controlled result, although these are patients with obesity still, but no fatty liver. This is the advanced lipid testing results, and you can see that HDL particle number was also slightly reduced. From a clinical standpoint, maybe not a huge difference, but very small difference in the HDL particle number, but no difference in any of the other lipoprotein particles. So no differences in LDL, VLDL, or IDL, or LDL size, which is similar to what we already showed in the prior data that I showed to you. So no differences in ApoB particles in these groups. This is the targeted proteomic results, and those are 28 proteins that we measured. There were five that, after adjusting for multiple comparison using a false discovery rate of 10%, that were significantly different between the two groups, ApoC1, ApoC4, ApoM, lecithin cholesterol acetyltransferase, and serum amyloid A4. So even after adjusting for presence of diabetes, use of statins, and even after adjusting for HDL particle number, those five proteins remain statistically different between patients with advanced fibrosis and those without advanced fibrosis. When we look at those five proteins, and we divided patients based on the stage of fibrosis from zero absence of fibrosis to F3 to four, which is advanced fibrosis, you can see that the main drop occurs at late stages of liver fibrosis, which from a clinical standpoint makes sense because the synthetic function of the liver only gets affected when we are at very late stages of fibrosis. So you can see that at earlier stages, F1 and F2, we did not in many points see a very robust decrease in those proteins that I mentioned, but that drop happened at later stages of fibrosis. When we look at this surrogate marker of the cholesterol influx capacity and associated with coronary artery disease, we also saw a stepwise increase in the PCAT index when we divided patients based on their liver fibrosis stage, suggesting that this may, at least in part, explain the increased cardiovascular risk in patients with liver fibrosis. So to conclude, we showed in patients with nonalcoholic fatty liver disease and advanced fibrosis, they have a different proteomic composition of HDL particles and this may, at least in part, explain their higher cardiovascular risk. Those are the five specific proteins that we showed to be significantly different between patients with advanced fibrosis and those without advanced fibrosis. If you look at the literature, and we won't have time to discuss that during the presentation, but there have been already reports of ApoC1, of lecithin cholesterol acyl transference, and ApoM to be reduced in patients with cirrhosis for other causes other than nonalcoholic fatty liver disease. So it makes us wonder whether once you develop fibrosis for any reason, either hepatitis C, hepatitis B, or any reason that leads to liver fibrosis, those proteins may be reduced. But that remains to be elucidated. And finally, we didn't measure specifically cholesterol efflux capacity, so it also remains to be determined whether these changes in the proteomic composition of HDL, if they translate into a different function of that HDL particle. And with that, I will leave you, and I'm open to questions. Thank you very much for your attention. We're now open for questions. I think, yeah, over there. I have a question. How do you think that data and these findings can be circulated to the community? So since we see the difference only on waste, but it's a weight for the patient, how do you think this can be translated? Can you repeat the question? Yeah, so the question is, how can we translate these findings into clinical practice? So I think we are many steps behind on how we can actually translate this into clinical practice. It's a great question. But the concept that we need to move into action in clinical practice is consider patients with liver disease to be at increased risk of cardiovascular disease. So remember, using statins and addressing all cardiovascular risk factors are the main issue. We don't have great medications to target HDL. And again, this is just one of the first findings of how HDL composition can affect cardiovascular disease. But the main concept you have to take home is liver disease is associated with increased cardiovascular risk, and we need to do a great job at treating it. So I know I didn't specifically answer your question, because probably we are very behind on this. But remember, using your statins, they are safe in patients with liver disease. Okay, let me ask, I have a question. So you're looking at patients who have advanced fibrosis, and these are people who may progress to cirrhosis. So there's that divergence. So people with fatty liver disease, yes, you mentioned that there's increased cardiovascular risk. But then these are also people who have potential for end-stage liver disease. And this could be representative of their synthetic function going down, as you mentioned yourself. So are we gonna target the heart disease, or are we gonna prevent the cirrhosis from happening? I'm just trying to tease that out in my head. Yeah, so we need to do both things. So I don't know if you had the opportunity, Dr. Cusi spoke a little bit earlier before, and in his final slide, he showed two things that we should be targeting in patients with non-alcoholic fatty liver disease. One is preventing cardiovascular disease, because these are patients that mainly die of that. But the ones that don't die of cardiovascular disease, if we let them be, they will progress to cirrhosis. So we need to be focusing on both things, preventing liver disease and targeting cardiovascular disease. So it's not that we need to just focus on one. We need to target both. Thank you. I also have a question. Do you know if novel therapy for cholesterol have better results or promising results in terms of improving NAFLD and NASH? Yeah, so unfortunately, we don't have huge amounts of data on newer cholesterol medications for non-alcoholic fatty liver disease. I think, for example, Pemafibrate was a newer fibrate, and we were all very eager to see those results, and there were some issues with histology. And then they saw that at baseline, those findings were already there, but we will see what happens with that medication. But many of those have not targeted specifically, so we don't have any histological data. Statins, although they didn't show much of an improvement from a histological standpoint, liver enzymes in some studies do come down, and they are safe. So not a huge amount of data on the newer medications. We can move to the next presentation. Thank you. Chrysandra Schuffert. So the title of the talk is Functional Hypothalamic Amenorrhea and Preclinical Vascular Disease. Is our presenter not here? Chrysandra here. Okay, we should probably, yeah, it's not there. This is really pain in the ass. Can we have the next presentation up to the admin? Yeah. Okay. So, next talk is Shripa Amatya. Title of the talk is LAMPF1 Mediates Hepatocyte Death in Non-Alcoholic Fatty Liver Disease and its Plasma Values Correlate with the Severity of the Disease in Humans. Please. Can I change slides? Okay, let's start. Go ahead. Good morning, everyone. I'm Shripa Amatya and I'm a graduate student at Louisiana State University Health Sciences Center at Shreveport. And today I'm going to present our project on LAMPF1 in Hepatocyte Death in Non-Alcoholic Fatty Liver Disease. I would like to thank all our collaborators who made this work possible, and especially to Dr. Torres for the in vitro work. Non-alcoholic fatty liver disease is one of the most prevalent forms of chronic liver diseases in the United States and worldwide. Non-alcoholic steatohepatitis, or NASH, is an advanced form of non-alcoholic fatty liver disease that can progress to fibrosis and hepatocellular carcinoma. Previous research have shown that triglyceride accumulation, inflammation, and apoptosis are critical processes in the progression of disease from non-alcoholic fatty liver disease to NASH. And the current treatment is limited to adoption of lifestyle changes that is aimed at increasing physical activity, sustained weight loss, or even bariatric surgery. So, in liver diseases, accumulation of fat leads to hepatic steatosis, and this steatosis can trigger an innate immune response involving the Kapha cells and the stellate cells. The Kapha cells can secrete various pro-inflammatory signals that can trigger inflammation and lead to worsening of the fibrotic processes. Eventually, these pathologies lead to hepatocyte death followed by fibrosis. One of the mediators of the immune cell function is the SLAM molecule. SLAM stands for Signaling Lymphocytic Activation Molecule, and this belongs to the family of receptors that has nine family members. SLAMF1 is also known as CD150, and it is a type one glycoprotein present on the plasma membrane of B cells, T cells, and dendritic cells that can modulate the behavior of the immune cells. In case of activated T cells, the SLAMF1 can form a receptor complex with the T cell receptor complex in the immunological synapse and lead to T cell proliferation, antibody production, cytotoxic responses, and cytokine production. Additionally, SLAMF1 is a self-ligand-binding protein, and it has an important role in the modulation of immune responses, as well as in the entry of other viruses such as measles virus. However, although we know that SLAMF1 has a very important role in the immune response, its role in liver in both disease and health condition has not been, is understudied. So first we did immunofluorescent staining to look at the protein levels of SLAMF1, and in this case we used human liver sections from the NASH samples, and we found that the liver sections of NASH samples had more prominent SLAMF1 staining compared to controls. Similarly, in case of mice that were fed on high-fat diet, we found that SLAMF1 protein levels were more prominently stained compared to the mice on normal diet. Then we wanted to find out more of this phenotype, so the mice on high-fat diet, we observed their HNA staining, and we found increased deposition of lipids. This was further, it was further confirmed by the deep redness seen in the oil reddow staining, which confirmed the increased deposition of lipids in the mice that were fed high-fat diet. Additionally, the deposition of collagen was also increased in these mice as shown by the serious red stain. So these phenotypes suggested that the mice on high-fat diet had developed the NASH phenotype. Then we looked at the plasma SLAMF1 levels, and we observed that the mice on high, the NASH mice had 3.14 increase in plasma SLAMF1 levels compared to the control mice. Additionally, we found that this plasma SLAMF1 level was also positively and significantly correlated with the NASH activity score, that is the non-alcoholic fatty liver disease activity score, which is based on the sum of steatosis, hepatocyte ballooning, and inflammation. So this showed that these mice had a worsened liver condition. Similarly, in humans, we found that the liver sections of humans with NASH had a more prominent positive brown staining in the immunohistochemistry section for SLAMF1. Similarly, the plasma SLAMF1 levels measured by ELISA also showed a significantly increased levels of SLAMF1 in the NASH patients compared to the controls. Now, our next step was to understand the mechanism of NASH development in vitro. So we used a human hepatoma cell line, which is the hep G2 cells, and treated it with 0.4 millimolars of palmitic acid, which is a known saturated fatty acid. Our first experiment was to look at the cell viability by MTT assay. MTT assay, in this experiment, is dependent on the ability of metabolically active cells to convert the MTT to insoluble form as in crystals. And here, we observed that treatment of hep G2 cells with palmitic acid significantly decreased the cell viability. Then, the next experiment was to look at the cytotoxicity. So we measured the lactate dehydrogenase levels, which is a cytosolic enzyme, and they are secreted into the cells when the cell undergoes cell lysis. So we observed that treatment of hep G2 cells with palmitic acid significantly increased the LDH activity levels. The next experiment was to find out if this decrease in cell viability and increase in cytotoxicity was associated with changes in apoptotic cells. So we performed the NXN57AAD assay by flow cytometry, and we observed that treatment of hep G2 cells with palmitic acid significantly increased the percentage of cells that were NXN5 positive, suggesting there were more apoptotic cells. This was also confirmed by Western blot, where we observed increased protein expression of cleaved caspase-3P17, suggesting that apoptosis of hep G2 cells was promoted by palmitic acid. Our next experiment was to perform the immunofluorescent staining, where we observed an increased staining for more prominent staining for STAMF1 in cells that were treated with palmitic acid compared to the controlled cells. At the gene expression levels, we observed that the STAMF1 was undetectable in untreated cells. However, their levels significantly increased upon palmitic acid treatment. These results were also observed in protein levels, as we observed significant increase in the levels of STAMF1 proteins after palmitic acid treatment. Previous studies had shown that activated T cells are capable of secreting STAMF1 into the circulation, so we wanted to find out if something similar was happening to the hep G2 cells as well. So hep G2 cells, after being treated with palmitic acid, we took the supernatant, centrifuged it to remove the debris, and then used protein agarose beads and the STAMF1 antibody to precipitate the STAMF1 protein and perform the Western blot. We observed that upon treatment with palmitic acid, there was, we didn't observe it in untreated cells. However, up to 24-hour time point, we saw that palmitic treatment significantly induced the STAMF1 release from hep G2 cells. This release was highest at 12-hour time point. Since all these experiments were done in the hepatoma cell line, which is a cancer cell line, our next experiment was to find out if the primary murine hepatocytes are also capable of increasing STAMF1 levels in response to palmitic acid treatment. So for this, we isolated the primary murine hepatocytes and then treated it with palmitic acid for 24 hours, and we observed more prominent staining for STAMF1 in the cells that were treated with palmitic acid compared to the control cells. Additionally, STAMF1 mRNA levels were also significantly increased in the treated primary hepatocytes compared to the untreated group. So based on these results, we hypothesized that the STAMF1 levels increased on the cell surface, and then probably they were secreted into the circulation. And this secreted STAMF1 may be mediating the cytotoxicity associated with palmitic acid. So our next experiment was to perform SIRNA to knock down the STAMF1, and then look into its association with cell viability and cytotoxicity. So the first experiment was to find out the efficiency of STAMF1 knocked down by both protein and mRNA levels. And we observed that the SIRNA was able to significantly decrease the STAMF1 levels by both mRNA and protein levels. And then the next experiment was to look into the association with the cell viability and cytotoxicity. We observed that inhibition of STAMF1 synthesis significantly increased the cell viability and decreased this by MTT assay, and it decreased this cytotoxicity by the LDH assay levels. Then following this, we wanted to find out if the protective effects of STAMF1 knocked down was due to alterations in triglyceride levels by palmitic acid treatment. So we measured the triglyceride levels in all our treatment groups, and we observed that palmitic acid treatment significantly increased the levels of triglycerides compared to the control group. But the levels of triglycerides was not different in all the treated group. Here we observed that knocking down STAMF1 did not decrease the triglyceride levels, suggesting that the protective effects of STAMF1 knocked down was not due to decreasing the triglyceride levels, but perhaps there is an alternative mechanism by which the protective effects were seen. So our final experiments was to find out if the increased cell viability and decreased cytotoxicity by inhibiting the STAMF1 synthesis was associated with changes in apoptotic cells. So here we found out that inhibiting the STAMF1 synthesis decreased the percentage of NXN5 positive cells, suggesting that STAMF1 downregulation decreased the apoptotic cells. So to recap our conclusion, our results showed that STAMF1 levels were increased in NASH in both humans and mice, and that increased STAMF1 levels positively and significantly correlated with the NASH score in mice livers. The palmitic acid treatment increased the levels of STAMF1 in hepatocytes, and inhibition of STAMF1 by siRNA protected hepatocytes cells from palmitic acid-induced cytotoxicity. Our data also showed that hepatocytes are able to secrete STAMF1, and perhaps in the long run it can have a potential use as a biomarker for NASH diagnosis. With this, I would like to end up my presentation, and I'm grateful to my mentor, Dr. Cruz, to Dr. Oscar Gomez-Torres, who did most of this work with the in vitro work, and all our lab members and collaborators. Thank you. Is there no microphone? I have one very quick question. My understanding of the structure of slammer chloride is that it is a transmembrane protein with both intracellular and extracellular extensions. The measurements you've shown, at least some of them, were in conditioned media or were in plasma. And I was curious as to whether or not you clarified the structure of what you're actually measuring, whether it's a clipped version that's soluble or whether it represents shedded, intact molecules in these compartments. Thank you for your question. Although we have not clarified... At this moment, we don't know exactly what is being shed here, but what we have measured is the shedded SLAM-F1 levels. Although we don't have a structure of what that exact is, but it's the shed SLAM-F1 levels. I have a short question. Oh, sorry. Go ahead. I understand it's a speculative question, but since this appears to be an immune mechanism, do you feel that in the future we might see immune therapy for this disease process? Yes. So far, most of the work was based on immune cells, but our study was able to show that the hepatocytes can also secrete the SLAM-F1. So perhaps it might have not just the immune therapy, but perhaps targeting the hepatocytes as well. I have a short question. This factor is expressed in hepatocytes and also in stellate cells in the liver? Our study was showing just the hepatocytes. I'm not really sure. You don't know about LX1 cells, if they do express stellate cells in the liver, express this factor? At this moment, I'm not really sure about stellate cells, but hepatocytes definitely. Thank you very much. Yeah, very nice, elegant set of experiments. So it's time to move to the next presentation that is by Kirtana Pillai. Is Kirtana Pillai here? Could it be somebody? The title of the presentation is Could it be somebody? The title of the talk is Prediabetes is a Risk Factor for Myocardial Infarction, a National Inpatient Sample Study. Yes, I'm presenting the talk here. I'm Geetika. Geetika Thotta will give the talk. Yes, just introduce yourself. Sure. Hello, everyone. Good afternoon, I'm Geetika Thotta. I'm a PGY-2 internal medicine resident at St. Peter's University Hospital. Today, I'm going to present about our study. The title of our study is Prediabetes as a Risk Factor for Myocardial Infarction based on a National Inpatient Sample Analysis. So it's a great pleasure and honor to present our study today in front of you all at Endo 2022. So let's start. No financial disclosures or conflicts. So let's start with the background of our study. So we all know that diabetes is a well-established risk factor for atherosclerotic cardiovascular disease. And the current guidelines recommend high-intensity statin therapy in addition to lifestyle modifications and management of other cardiovascular risk factors to reduce the mortality and risk of cardiovascular disease in patients with diabetes. However, despite evidence that clinicians are not aware that prediabetes is also a major risk factor for ASCVD. If you see in this picture, prediabetes could be reversed to normal glycemia or it can progress to type 2 diabetes mellitus. Our study or our objective is to see if prediabetes is associated with coronary artery disease. And we wanted to add this to the growing body of evidence, the hazards of prediabetes. So is prediabetes a major health concern? And the answer is yes. The economic burden associated with prediabetes in 2017 is approximately 43 billion. And as per the latest 2020 CDC diabetic statistics report, about 86 million people were diagnosed with prediabetes. And it accounts for about 38% of the adult population. And this number is only gonna climb to 107 million by 2030. And not only just in United States, prediabetes is a global health concern. With this background, let's move into our study. So the methodology. We utilized national inpatient samples. National inpatient sample is a publicly available largest inpatient database. It has more than seven million hospital stays per year. And in our retrospective study, we included adult population of age more than 50 years who were admitted with primary or secondary diagnosis of prediabetes. And we used the sample from 2016 to 2018. And we utilized ICD-10 coding system to extract the variables. And then we performed multivariate logistic regression analysis to see the association between prediabetes and MI. And all the covariance we used in our model, we got that from the prior literature and also from the univariant analysis. And we made sure that the variance inflation factor is less than five to avoid the collinearity. And then we created three separate models for myocardial infarction, percutaneous coronary intervention, and coronary artery bypass graft. So these are the ICD-10 codes we used to extract the variables. And we included them in the final multivariate regression analysis. So the results. We got a total of 1.79 million weighted hospitalizations, patients who have myocardial infarction, and 330,000 patients had prediabetes. And out of the 1.79 million patients with myocardial infarction, one person had prediabetes. On a univariant analysis, prediabetes was greatly associated with increased odds for myocardial infarction with an odds ratio of 1.41 with a 95% confident interval lying between 1.35 to 1.47. And the p-value is statistically significant. And then we adjusted for age, gender, race, family history of MI, dyslipidemia, hypertension, diabetes, nicotine dependence, and obesity, which are all well-established risk factors for myocardial infarction. Despite adjusting all these, prediabetes was still significantly associated with higher odds for myocardial infarction with an odds ratio of 1.25 and 95% confident interval lying between 1.2 to 1.3 and the p-value is statistically significant. And not only just that, prediabetes was associated with higher odds of percutaneous coronary intervention with an odds ratio of 1.45 and also higher odds for coronary artery bypass graft with odds ratio of 1.95, which is even more higher. So this is the model we created for myocardial infarction. So if we look into the details of the variables, age is a positive risk factor and female is a negative risk factor. And when we look into the details about the race, Asian Pacific Islanders have higher odds for MI. Standing next to them are Native Americans with an odds ratio of 1.18. And then the other variables, family history of myocardial infarction, dyslipidemia, diabetes, hypertension, nicotine dependence, obesity, all of them are found to have higher odds for myocardial infarction. Despite adjusting with all these factors, prediabetes is still showing higher odds for myocardial infarction with an odds ratio of 1.25 and the p-value is statistically significant. So, the results of our study, we want to conclude that despite adjusting for well-established risk factors, pre-diabetes stood as an independent risk factor for myocardial infarction, with a statistically significant study result. And not only that, pre-diabetes was highly associated with percutaneous coronary interventions and coronary artery bypass graft, it's suggesting the severity of atherosclerotic burden and also the macrovascular coronary artery disease association with pre-diabetes. We do have some limitations. So, pre-diabetes is often missed during coding because our analysis is based on the ICD-10 coding system. However, we try to address this by including the teaching hospitals who tend to code better. And the second thing is the majority of the pre-diabetic population we see are in the community and the clinic. And our analysis is mostly inpatient sample. Obviously, the retrospective design is one of the limitation as well. So, where does our study result fit in the current literature that we have so far? So, there's a study published in 2015 showing the coronary angiographic study result that atherosclerosis severity and the block vulnerability are more advanced in patients with pre-diabetes compared to the patients with non-diabetes, and it is more comparable to the patients with diabetes. Also, there is a large meta-analysis with 129 prospective studies who followed the patients up to 9.8 years. They showed that the composite cardiovascular disease is higher with the patients with pre-diabetes, and the relative risk is 1.15, which is closer to our study with an odds ratio of 1.25. And there are two other studies with a similar supporting evidence. So, the discussion. So, our study serves as a wake-up call for the clinicians as well as the patients to shift the focus to prevent pre-diabetes and not just diabetes. And also, we want to reinforce the importance of early recognition through screening and early intervention to aggressively manage the coronary cardiovascular risk factors to prevent myocardial infarction. So, here are some recommendations, and these are from the latest ADA Standards of Care 2022. Early recognition through routine screening. So, start screening for all the patients aged more than 35, and in adults with overweight or obesity with BMI more than 25, or more than 23 in the patients with Asian-Americans. With any of the following, we should start screening, and start screening for the patients, women with gestational diabetes and people with HIV. And early intervention is the key as well. So, the recommendations include diet and physical activity, particularly 150 minutes of moderate to vigorous physical activity per week, or minimum of 75 minutes per week of vigorous intensity. And then lifestyle modifications to address the associated coronary cardiovascular risk factor. So, for tobacco cessation, referring to tobacco cessation program. And then medications, weighing the risk versus benefits. So, medications to consider, like metformin and GLP-1 receptor agonist. So, in future, further prospective studies in this direction would help to strengthen the study results, and also considering the role of pre-diabetes, or statin use in pre-diabetes. And then multidisciplinary approach to aggressively manage cardiovascular risk factors. So, these are the references we used. And finally, I would like to highlight the importance that pre-diabetes is real, and it is a call to action. And thank you all for your time and attention. I would like to thank my co-authors for their support, and my mentor, Dr. Liu, for her constant guidance and support. And my special thanks to Endo 2022 for giving me this great opportunity. Thank you. Thank you. Thank you, Dr. Toto. This presentation is open for questions. And if you wouldn't mind using the microphone, there's a microphone in the back there. Yes, thank you. Yes, please. Dr. Gordon Wong, University of Nevada, Reno. In your pre-diabetes patients, did you separate out, did those people have, how many of those people had hypertension and dyslipidemia? Because we know that insulin resistance is really the driver of cardiovascular disease. And I know you didn't address that, but did a lot of those patients that were labeled with pre-diabetes had other comorbidities? So, thank you for the great question. So, our analysis is from the national inpatient sample analysis. So, it has a limitation that we can't go to the patient individual data. But we got the sample that overall, how many patients has hypertension or associated dyslipidemia, but we are not sure that the same patient has both. So, individual patient characteristics are limited in this analysis. Yeah, because we know that the vast majority of people that have pre-diabetes have hypertension and dyslipidemia. So. True, yeah. Thank you. I am Amado Brunet from Panama. I would like to know if you tried to find in your study the homeostasis model assessment index or something like that to know the level of insulin resistance. So, we actually did use Alex Hauser comorbidities score to extract the variables such as diabetes, hypertension, and obesity from the sample. Did that answer your question? Sorry. No, I needed to know if you tried to use a QIKI or EC or OMA, try to find a data that tried to relate the increased risk with the resistance to insulin. I'm not really sure at this point, but I'll get back to you after discussing with my team. Do you think that the increased risk is a matter of high glucose level or a high insulin level? So, basically the pathophysiology of pre-diabetes causing MI is similar to that of diabetes because of insulin resistance and advanced glycation end products. So, yeah. Thank you. Are there other questions? Thank you very much. We can. Thank you so much. We can move to the next presentation. The title is The Relationship of Lipoprotein Fraction as Assessed by Iron Mobility and Insulin Resistance as Measured by the Insulin Suppression Test. I don't know if the speaker. Yeah, I think the speaker is Michael. Michael. Michael. Thank you. I'm sorry we missed you. That's all right. That's better to present than not. So, I'm gonna talk today about the relationship between insulin resistance and iron mobility, lipoprotein fractions. I now work with Quest. I was at UT Southwestern for many years and joined Quest about 10 years ago. And since then, I've become kind of a one-trick pony as it pertains to an interest in insulin resistance. And this is a manifestation of this interest. So, I'm an employee of Quest. That's my primary conflict. Insulin resistance increases the risk of type 2 diabetes and atherosclerotic cardiovascular disease. But rarely is the insulin resistance measured in healthy individuals in a clinical setting. And that's primarily due to the fact that the techniques that are used are quite labor-intensive and expensive and really relegated to the research environment. Owing to variations in clinical manifestation, some patients with very clear-cut indications for evaluation for type 2 diabetes risk might have insulin resistance assessed. But there are a whole bunch of other patients that are likely to remain unaware of their elevated insulin resistance measures and the potential for future complications. Now, this last bullet is unrelated to the presentation, but I wanna make sure that you understand where this comes from. We've employed at Quest a mass spectrometry-based technique to measure intact insulin in C-peptide. And we've demonstrated its correlation with formal assessments of whole-body insulin resistance. This was published in the Journal of the Endocrine Society in 2018. Abassi, Fahim Abassi is the first author. This data I'm gonna show you today is derived from the same cohort using lipoprotein fractionation as assessed by ion mobility to look at the relationship. So for those of you who don't know what ion mobility is, it's a gas-phase electrophoretic method that's used to measure the sizes and numbers of the lipoprotein particles of different classes. The association of ion mobility-based lipoprotein fractions, and I'm gonna use this abbreviation LSIM repeatedly, with a direct measure of insulin resistance, such as SSPG measurements has not been previously reported. Finding a strong association may provide additional information to patients and physicians about insulin-risk driven, risk of type 2 diabetes and atherosclerotic cardiovascular disease. And I say that not because I'm making stuff up, but because this is a clinical test that you can order today, and people are ordering it. And the idea here that I'm gonna sort of develop is that lipoprotein particle distribution might also serve to identify those individuals in this testing environment who may be at high risk for the development or have significant insulin resistance. So therefore, we set out to describe the relationship between LSIM and a direct measure of insulin resistance obtained using an insulin suppression test. The study population is 526 individuals derived from a larger group of 1,072 after throwing away the data from individuals where data was incomplete or excluding patients with type 2 diabetes or those on glucose-lowering medications. At the end, we had 526 individuals. Lipid panels were measured at Stanford University. Lipoprotein subfractinations, or LSIM, was measured at Quest Diagnostics. And SSPG measurements were measured in the GCRC at Stanford University Medical Center. The median age population was about 50 years. About 2 3rds were women. The majority were non-Hispanic and white. And nearly half of the patients were obese. And about 38% of the remainder were overweight. As expected, there were positive correlations between SSPG and both BMI and TGHDL cholesterol. And I'll mention this again in a minute. So on this slide, we see the associations of insulin resistance and the results of lipoprotein fractionation using imobility, or LSIM. And this is after adjusting for sex, age, race, and ethnicity. On the x-axis is shown the increase in SSPG per one SD change after adjustment. And on the y-axis is shown all of the values and the relationships of those to SSPG for all of the individual fractions that are actually been measured in this assay. The majority of LSIM measures were associated significantly with SSPG concentration, as demonstrated by the confidence intervals that do not span zero. And the largest changes were observed in LDL peak size, which is shown as being concordant with very small LDL, excuse me, very large amounts of small-sized LDL particles. And also, LDL small fraction, this is sort of the inverse way of looking at it, was also highly associated. Non-LSIM measures that were also associated with changes in SSPG are TGHDL and BMI. So after doing this manipulation, eight of the LSIM measures, as well as BMI, TGHDL, cholesterol, sex, ethnicity, and race, were the final variables that remained in the backwards stepwise regression analysis of SSPG. On this slide, the x-axis represents the change in SSPG per each one standard deviation of the continuous variables or from the reference category for the categorical variables. Now, what I'm gonna be talking about for the remainder of the talk is basically an integration of all of those different measures into something that is termed the LSIM score. This was calculated using the weighted sum of the significant LSIM fractions with the weights determined by the regression coefficients from this stepwise model. And I'm gonna talk about this as we go farther through this talk. Now, on this slide is shown the relationship between the LSIM score and SSPG across BMI tertiles. If you look at this slide, it's first of all composed of three gray columns which represent the three tertiles of BMI. One, low, BMI intermediate, which is two, and high, which is T3. At the bottom is the LSIM score as segregated by tertiles for each of these different groups of BMIs. On the y-axis is the SSPG measures. Again, SSPG high means insulin resistance. And if you look, the tertiles of the LSIM score had a positive relationship across all tertiles of BMI in women, and only in the two lower BMI intervals for men. As you can see on the right-hand panel on your right, the changes that are observed in the lower two panels are not present in men in the top tertile. Now in the next couple slides, I'm gonna talk about the area under the curve performance of the LSIM score as it pertains to other measures of insulin resistance. On this slider depicted the data points that I'm gonna show you. On this slide is depicted the results of the LSIM score and its ability to predict top-tier insulin resistance. The inset there gives you the color for the color coding that's below. They're in order, both within the inset and to the data set on the right, where 0.68 was the AAUC for LSIM score, 0.70 for TGHDL cholesterol, 0.73 for the combination of the two, 0.76 for BMI alone, 0.81 for all non-LSIM variables, and 0.84 for the full model. So it's not designed to really try to give you anything other than a flavor of how these things work individually and in combination. On this slide is depicted the same kind of information. The first two columns to your left represent the different scores. The next column to the right is the AUC. Those are the same data I just showed you on the prior slide. The fourth column from the left is the positive predictive value. The positive predictive value here was calculated for identifying subjects in the top tertile of SSPG concentration when considering the highest 5% of values for each of the same group of variables. The positive predictive values range from 59%, which when considering either TGHDL cholesterol or the LSIM score alone, to point to 89% when considering the full model. So the role of insulin resistance in the pathogenesis of type 2 diabetes and atherosclerotic cardiovascular disease and its association with metabolics abnormalities, including elevated TG and low HDL was first formulated many years ago by Reben. And subsequent work by this group has demonstrated the association of increased insulin resistance with a myriad of metabolic abnormalities and clinical manifestations. Thus the identification of insulin resistance and individuals is of clinical importance because it could prompt changes in behavior and clinical management to reduce risk associated with insulin resistance. And as methods for measurement of direct measurement of insulin resistance are not feasible in a clinical setting, there is a need for a robust and validated clinical measure of insulin resistance to identify individuals at risk for adverse consequences of insulin resistance. And in this study, we aim to add to this need by determining the usefulness of LSIM measured by ion mobility to identify individuals with significant levels of insulin resistance. We show that the incorporation of the LSIM improved prediction of insulin resistance as measured by SSPG concentration. Specifically, when predicting individuals in the top tier of SSPG concentration, the AUC and PPV for the LSIM score alone and then TG cholesterol alone were similar. But when used together, they significantly enhanced AUC and positive predictive value. Furthermore, the score obtained from the full stepwise model that included the LSIM score with sex, race, ethnicity, BMI, TG, HDL, cholesterol, all had significantly improved the AUC and PPV compared to the score that excluded the LSIM score and only included sex, race, ethnicity, BMI, and TG, HDL, cholesterol. The strengths of our study include the fact that we validated the usefulness of this score for the prediction of insulin resistance using the insulin suppression test, which is considered to be a gold standard measure of insulin resistance. In addition, we improved insulin resistance risk prediction using available data from the LSIM clinical testing. This is the most important point here that I wanna make. This is not an attempt to make a new tangled contraption that we're gonna try and push out to the world. I think the impact of this is that I hope to be able to add to the existing clinical testing, which you can order today, a flag that's based on this measure that tells you that your patient may have significant insulin resistance. And I think that that is probably one of the most useful things that may come out of this type of analysis. The limitations of our study include, the individuals that are included in it may not be typical of individuals undergoing LSIM testing currently in the clinical environment. As the individual studies in the studies at Stanford were largely healthy volunteers with significant insulin resistance, while those undergoing LSIM testing in the clinical environment currently are those that are primarily referred for testing by clinicians for the evaluation of risk of atherosclerotic cardiovascular disease. And I think that further studies assessing the LSIM score in patients undergoing LSIM testing will be needed to evaluate the contribution of insulin resistance and the associated dyslipidemia to ASCVD risk. So in conclusion, LSIM as well as TGHDL cholesterol BMA may improve the prediction of insulin resistance. LSIM testing may be used to alert clinicians to treating physicians as to the likelihood of significant insulin resistance if not evident clinically. And I think, again, this is the point that I wanna drive home. Patients likely to have insulin resistance may be candidates for further, more definitive testing using other validated measures of insulin resistance such as fasting insulin or the insulin resistance risk score and to identify individuals who otherwise may be unaware of their insulin resistance. And the identification of insulin resistance may help to stimulate the initiation of lifestyle interventions to improve insulin resistance and decrease the associated risk for future diabetes or atherosclerotic cardiovascular disease. And with that, I'd like to conclude. I'd like to acknowledge my collaborators at Quest, Charlie Rowland, Michael Caulfield, and Dov Shiffman, and my collaborators at Stanford University, Fahim Abbasi, and Josh Knowles. And I'll stop there, answer any questions. Thank you. There's a question over there. Hi. Hi. Excellent presentation. I'm curious, did any of these patients have glucose tolerance tests? That's a good question. We have this large data set. And frankly, I'm still working my way through it. I think that there are a subset of individuals who do have OGTTs. And I haven't looked at that subgroup yet. There's also some other interesting. I mean, this is a treasure trove. And I've only sort of scratched the surface. This was something that's been lurking in the background when I presented today. But we've done a bunch of other things within this group. And I think that the subgroups that I'd like to look at are ones that you have mentioned, OGTT testing. But also, there's some interventions in here. We have before and after data for some of the studies that Jerry conducted at Stanford. Yeah, the reason I bring that up is I actually worked with Jerry Riven in the early 1980s. And we went back about 10 years ago and looked at all the data with the people who had. We did glucose tolerance tests, as well as clamps on all those people. It was a painful, very painful. But what we found was that if you did a two-hour glucose tolerance test and the one hour was over 125 or the two hour was over 120 it correlated incredibly well I believe it I believe it the clamp but can you get the ADA or endo to make get people new glucose tolerance test no we use a stupid pre-diabetes which is voodoo well I would actually agree with you in so many levels so there's a study that I presented here about I think was here maybe was the ADA I can't remember but it was using so this tool that I alluded to the fasting insulin resistance risk score which is based on a combination of fasting insulin and C peptide in the fasting state they're actually Jerry's baseline samples for all of the clamps that he did when we analyze that you needed both but they had a very high correlation with the SSPG measurements okay so I've actually applied that to a group of patients who convenient sample really of pre diabetics and looked at that and a number of those individuals in that are individuals who have not do not have high levels of insulin resistance as you would guess they actually have probably relatively normal insulin resistance but they have an impaired secretion of insulin and that is why they end up being identified as pre-diabetes so it's it's there's a fascinating heterogeneity there and I don't that's a great point I'll be definitely moving that up on the priority list to look at who's who in this combination of testing that they have at Stanford okay in the interest of time we'll take one more question and then maybe save your questions for later I'm over in it look from Panama I would like to know if you use the euglena semi clamming some of those patients are the and if you think that every semi clam would be different from the insulin suppression that you use in the results of your test know that they would be very similar there are differences in methodologies but there are studies which have related the use of the insulin suppression test to the more prototypical you glycemic clamp and they have a very high degree of relationship to one another I don't think the results would be different but unfortunately or fortunately depending how you look on it this was dr. Riven's baby and this is how he performed his studies so I feel fortunate to be able to use samples from his study cohort but I don't have any data related to you glycemic clamps at all thank you very much we move now to the last talk of this session coronary microvascular dysfunction is present among well-treated that symptomatic person with HIV similar to those with diabetes dr. Daniel Huck from Brigham and women's health thank you so much good day everyone happy to be here to present this is actually my first endo meeting I'm a Daniel Huck I'm a cardiovascular imaging specialist in a research fell to Brigham and Women's Hospital I'm presenting this on behalf of the Massachusetts General Metabolism Unit my co-authors listed here the results of coronary microvascular dysfunction among people with HIV in the miracle HIV trial and baseline results I have no financial disclosures so I'll go over the background of cardiovascular disease in HIV coronary microvascular dysfunction talk about the outline of the miracle HIV trial and then discuss this sub study that I'm looking at in the baseline results so people with HIV are an effective antiretroviral therapy in the u.s. and has transformed it to a chronic medical condition many people live longer and more than half of people with HIV will die of non-communicable diseases especially there's increases in cardiometabolic disease like renal impairment diabetes obesity this is contributing to increasing cardiovascular events so in this study here of Medicare and Medicaid patients from 2003 to 2013 there's an almost doubling of cardiovascular events multiple cohort studies have found increased myocardial infarction coronary heart disease risk in these patients compared to people without HIV including our cohort in the 2007 study in Mass General Brigham in the VAX cohort in HIV veterans in a global systemic review by Sean others and other studies found a 1.5 to two fold increased risk of coronary heart disease and then similar coronary artery disease the heart failure risk is also increased among people with HIV in multiple studies with relative risk of heart failure being up to twofold and diastolic dysfunction and echo up to threefold and so there's a lot of mechanisms for heart disease in HIV however one we're particularly interested at Brigham Women's is coronary microvascular dysfunction so coronary microvascular dysfunction can be measured by coronary flow reserve or CFR on cardiac PET stress testing so CFR is basically calculated by the ratio of the maximal myocardial blood flow during vasodilator stress divided by the baseline flow at rest and it's basically a measure of the ability of the coronary circulation to augment coronary flow when necessary it can lead to sub endocardial ischemia and cardiomyocyte injury which can be assessed by troponin BNP imaging findings like diastolic dysfunction on echo then lead to atherosclerosis and heart failure this can set up a cycle with LV wall tension and abnormalities myocardial oxygen supply decrease and supply demand mismatch leading to further ischemia and further symptomatic heart failure so in the non HIV population CFR coronary flow reserve is a powerful marker of cardiovascular risk so here from our cohort at Brigham and Women's in the red and blue as a CFR less than two which is a clinical cutoff we use it was we found three to six fold increase of cardiac death risk compared to the green tertiale of greater than two and then on the right just to hammer the point home the red dotted line is CFR less than two and as you can see as it decreases the annualized cardiac mortality rapidly increases an impaired CFR is a integrated effect of macrovascular and microvascular coronary dysfunctions what I mean by that is here on the left on the left side obstructive epicardial stenosis and diffuse atherosclerosis that's not obstructive is a key obviously in coronary disease but also equally important is the corny microvascular function so the arterials and the capillaries can also be dysfunctional and have endothelial damage but in the key point for our study is that in absence of large vessel disease reduced CFR represents coronary microvascular dysfunction and so to introduce the miracle HIV study which is a really great clinical trial run by the Massachusetts General Metabolism Unit so it's a randomized clinical trial assessing the effect of mineral corticoid mineral corticoid blockade on cardiovascular health and it randomizes a player known and placebo one-to-one in these patients with a baseline visit and then a 12-minute month visit where there's imaging done at both the primary outcome that I'm looking at today and is a cardiac PET derived CFR or coronary flow reserve so that's what I'll be talking about and I'm gonna present at baseline but we have a rich amount of imaging information as well as renin-angiotensin-aldosterone system studies that we hope to present at future meetings so our study objective today is to assess corny microvascular function using cardiac PET among people with HIV comparing to other groups that are high risk so people with HIV were middle-aged with suppressed viral loads on antiretroviral therapy that was stable and no known history of diabetes and in particular importantly there was no significant coronary stenosis this was by history and also on the baseline coronary CTs and the cardiac PETs and so we match these people two-to-one to our clinical database at Brigham Women's Hospital of high-risk clinical referrals and we matched on traditional risk factors like hypertension tobacco use and dyslipidemia we also think obesity and visceral adipose tissue and other things are important so we matched on BMI and then finally we compared to a previous trial we did at Brigham Women's Hospital the MRAD trial looking at coronary flow in diabetics and the effect of mineral corticoid blockade so at baseline people were similar across all the groups and according to age male race and BMI so the population was middle-aged in the 50s predominantly male reflecting people with HIV in Boston and then an obese range BMI 32 comparing cardiometabolic risk factors despite best efforts to match there were some differences so there was less hypertension in people with HIV and more tobacco use among people with HIV and then in terms of lipid profiles there was higher total cholesterol LDL and triglycerides and people with HIV especially compared to the people with diabetes which was somewhat related to how the trial was run with purposeful stata initiation additionally the ASCVD risk or the 10-year risk of heart disease or stroke estimator is was lower in people with HIV this has been found in other studies that this risk or underestimates risk in people with HIV compared to other populations and then the lower statin use all and also in people in HIV which is something I've seen kind of clinically so on on PET imaging at baseline there's a key few a few key points here one we purposely enrolled people without obstructive coronary artery disease so that's reflected in the myocardial perfusion on cardiac PET so the sum stress score was zero in all the groups basically normal perfusion and then the ejection fraction the systolic function was normal in all groups although slightly lower in people with HIV 57% for 61% but normal and then looking at coronary atherosclerosis we can look at the calcification in the arteries and very few people had severe coronary calcified atherosclerosis less than 10% in each group so this is the primary finding of this initial study that is that CFR among people with HIV and pink is lower compared to people without HIV in gray I mean it's similar to people with diabetes in our prior trial and because I mentioned that there's no obstructive stenosis and there's limited diffuse atherosclerosis in this population I attribute it to that I would say the corny microvascular dysfunction is present among well-treated asymptotic people with HIV for a clinical relevance there was a recent meta-analysis where looked at CFR changes and a 0.1 unit reduction was associated with an 8% increase in hazard of cardiovascular events further stratifying just to give some more context people with HIV had CFR impairment and CFR at higher rates so CFR less than 2 as I said is what we use clinically so 31% versus 14% without HIV or diabetes and there may be an interesting sex difference in females so relatively lower CFR may be more prevalent among women with HIV 40% of women with HIV had impaired CFR versus only 6% in the women without HIV or diabetes which was a bigger difference than like and then the difference in men and then finally it's an independent predictor of reduced coronary flow reserve so as I mentioned there was a few differences in the groups and we looked at some of them controlling for those including tobacco use cholesterol triglycerides statin use and renal function and remained significant predictor that is HIV so in summary subclinical coronary microvascular dysfunction is present among asymptomatic infected people with HIV on ART and therefore our understanding of the spectrum of subclinical cardiovascular disease and HIV has been expanded beyond macrovascular disease plaques stenosis to include my coronary microvascular disease and it further highlights further other studies tiling well-treated HIV infection as a cardiovascular risk enhancing factor similar to diabetes so you know previous studies we've found that renin angiotensin aldosterone system activation leads to metabolic and inflammatory dysregulation in people with HIV and so we're really interested in seeing the follow-up results of the trial the miracle HIV trial after you know corticoid blockade and whether this is a physiologic target strategy to improve CFR in HIV but that would be a future meeting so thank you so much to end of 2022 everyone for listening the participants and my colleagues including at Mass General Metabolism Unit especially a Suman Srinivas who is the first author and what wasn't able to be here to present so I'm presenting on her behalf and then Steve Grinspoon who's just had a remarkable research career studying cardiovascular disease and HIV as an endocrinologist and really multidisciplinary clinician and then thank you to the imaging department's cardiology endocrinology and our collaborator Inova and Temple and I'd be happy to take any questions thank you dr. huck the floor is open for questions as people come in I have a question for you yes so what do you think is the ideology of this microvascular dysfunction is it the virus itself is it the medications and how do you treat this yeah that's all great really great questions and certainly active areas of research so I think you know be I think it's both the virus specifically there's a lot of research ongoing in some immuno inflammatory dysregulation metabolic dysregulation we found increased visceral adipose tissue in people with HIV that may the systemic adipose tissue could affect the local coronary circulation so there's a lot of different avenues to explore there but I certainly think it's partially the virus we've also done other studies looking at the antiretroviral therapy and certain antiretroviral therapies like protease inhibitors and are associated with an increased cardiovascular risk and there's been some basic and translational science looking at some of the mechanisms so I think it's both of those and then in terms of treatment of this one I think there's as you saw in the only 15% were on statins in this group because the risk scores don't really predict the risk very well so people don't put these people on statins you know and like I think we need better ways to screen people with HIV so that we can get them on the effective therapies earlier so that they don't develop cardiovascular disease later on so that's answers your question so great talk Sunil Koliwad from UC San Francisco yes I'm wondering if you've actually looked at much leaner people with HIV you matched on BMI but as you sort of alluded to the visceral adiposity at a given BMI might be much higher in the people with HIV versus the BMI matched people without HIV and so I'm wondering whether you could either stratify by increasing visceral adiposity by DEXA and see if the relationship you know correlates with a mediator effect of the increased visceral adiposity or whether this is independent of BMI that you see an increase by looking at only lean people with BMI less than 25 or something with and without HIV I just wondered whether you've thought about like how to delve deeper into that oh yes that's a really great question I think and definitely an active interest of the group and especially Suman and Steve Grinspoon so I would say one we actually did MRIs of all these patients at the L2 level to look at visceral adipose tissue and so we're still analyzing that but looking at the amount of visceral adipose tissue and it's definitely a great idea to look at stratified by BMI and some of the lower versus whether there's a different kind of phenotype of fat in those individuals and whether it relates to CFR and their imaging findings I think that's a really great suggestion hopefully we can you know talk more about that as we start analyzing the kind of the rich data in the miracle HIV trial and you you may need to on that front also we just factor specifically for integrate strand inhibitor presence or absence because it has a direct effect on BMI and a direct effect on fat distribution specifically as opposed to any other HIV med so that's another part that might relate to that yeah yeah I don't have offhand how many people were on those but yeah that's a great point yeah most studies of decreased coronary flow reserve suggests that the stress induced flow is normal and the real change is in the resting flow was that typical of your studies also actually in people with HIV it seems like the stress flows are reduced and that's kind of the product the rest flows were actually similar to the the MRAD 8 diabetes trial and lower than the clinical patients which isn't too surprising to me because the clinical patients are referred for clinical symptoms and they have like hypertension their blood pressures are on cold controlled where these people are trial patients that are more like activated in care and have better controlled hypertension and rest flows have correlation with blood pressure blood pressure at the time so actually yeah the the mean stress flow was like one around 1.8 milliliters per minute per gram in the HIV population whereas in the the non the non HIV controls it was over it was over two but then the rest flows in that group were higher too so that's very interesting that's really very different than most other studies it's actually the opposite that's very interesting you also mentioned a linear correlation and you with decrease in flow reserve but then in your graph that you presented it was a sigmoidal risk unless I missed so it have you looked at maybe part of the problem why people don't look at it is that this linear risk factor doesn't really correlate very well with the risk you've got a little better model that correlated the sigmoidal risk that you showed maybe we could get people more interested yeah I agree it's certainly not a linear I kind of simplified in one of the meta-analyses to say that it was like linear just to simplify things but it's certainly not limerent linear and the cutoff of CFR less than two is really kind of the the clinical cutoff we use for that reason that it rapidly increases risk at that point but I think that's why we kind of label subclinical corneal microvascular dysfunction it's kind of like pre-diabetes you want to identify things early so you can treat before they get like if their CFR is less than two then they've got very high risk it's unclear whether you can reverse it as much or you know or prevent it I'm Mike McFaul I'm with quest and the question I have I'm sort of a hammer and insulin resistance is a nail at this point so I just wonder given the similarities in the patterns that you saw for your flow measurements between type 2 diabetes and individuals with who are HIV who are HIV positive and given the metabolic parameters that you presented have you looked at groups of individuals who are affected with insulin resistance at similar levels to what is implied by your TGH DL cholesterol ratios that you showed in your studies I just wonder if you know one of the common than I find the metabolic abnormality to be more attractive hypothesis than the viral burden I would assume that these people are pretty well controlled so that's the reason I would make that assumption yeah that's a really great point and suggestion I believe you know they have some things like homo IR and baseline and there's a lot of like endocrine studies that were done in these patients at baseline that other people are analyzing and I certainly in terms of CFR in general and insulin resistance that's really an interest of mine there's not a ton of research I think there's some but I think that's something an active research of mine I want to look in diabetics and whether insulin resistance nothing else it would provide a very good comparator in your three panel if you had a pre something that was reflected you know significant insulin resistance in there to counterpoise because it's either gonna show something intermediate it's gonna show nothing or it's gonna show a correlation one of the three and any one of those would be informative yeah I certainly agree thank you thank you dr. Huck I want to thank all the speakers for the excellent levels of the presentation along the audience

pre-diabetes

myocardial infarction

risk factor

early recognition

intervention

cardiovascular health

HIV

subclinical coronary microvascular dysfunction

diabetes

mineral corticoid blockade

coronary flow reserve

increased risk