Good morning, everyone. My name is Saini Kumar. Currently, I'm a critical care physician at Cleveland Clinic Mercy Hospital. I just graduated from my critical care fellowship from Memorial Sloan Kettering Cancer Center, so I have plenty of experience in oncological critical care. We're going to present cardiac immune-related events in lung cancer patients who received immune checkpoint inhibitors. I have no financial disclosures. So since the approval of ICIs in 2015 from FDA, immune checkpoint inhibitors actually have boomed. Like, in terms of clinical trials that are being published on ICIs have just exponentially increased. Now, you know, the categories of ICI normally falls into, like, CTLA-4, PD-1, and PD-L1. Now, all of these three categories are actually being utilized in lung cancer therapy, and even now, you know, the newer trials are looking at the dual therapy. So if you look at the graph, like, since from 2015, you know, until 2022, you know, there have been, like, more than a dozen of trials, especially, you know, focusing on different stages of lung cancer. Normally, they are very well given in the, you know, mid-to-late stages, but now the emphasis is even giving in early on, receiving, if they have high expression of any PD-1 or PD-L1. So, you know, our goal was to look up the, if there's, you know, because administration of ICIs, is it associated with a wide spectrum of immune-related adverse events? And we wanted to see, because there haven't been a lot of studies looking at the cardiac side effects of immune checkpoint inhibitors in terms of statics, in terms of, like, different other sub-categorization into cardiac condition. So we wanted to see if there is any ICIs in the cardiac side effects, and sub-categorizing into different categories. And then, you know, our research was conducted with the aid of extensive and expensive patient database, and that, you know, gives us comprehensive analysis and giving further findings. So methods that we utilized, you know, we used a clinical database called TINITICS. It's a national database that have, you know, like, de-identified data, electronic medical records from more than over 91 million patients. And then we conducted research, you know, patients who received immune checkpoint inhibitor for lung cancer from 2016 to 2022 when this abstract was submitted. And then we employed ICD-10 coding to look for the patients who received any immune checkpoint inhibitors and they have any nuanced atrial fibrillation or flutter, dilopin of pericarditis, dilopin of myocarditis, and dilopin of dilated cardiomyopathy. Then we evaluated for an excess one-year incidence of adverse events in one group compared to other group. And then, you know, because there's going to be a bias, because, you know, it's not a case-control study, so we used propensity matching score to address any unevenly distributed medical and demographic characteristics within the study group. So these are our results, you know, the initially focusing on patient cohort analysis. So we analyzed, you know, around like more than 35,000, around 35,000 of patients with lung cancer who received immune checkpoint inhibitor and that constituted 0.04% of whole patient cohort. And, you know, there were total more than 280,000 patients with lung cancer who did not receive immune checkpoint inhibitor, that representing around 0.31% of total population. What were the adverse events between ICI versus non-ICI groups? So the groups of the patient who received ICI, actually they experienced incidence of adverse events that are higher than the non-ICI group. So major focus is here on the cardiac toxicities. So incidence of atrial fibrillation was higher. It was 6.4% as compared to 4.9% in the non-ICI group and was statically significant given the amount of sample sizes is higher. And the odds ratio was 1.3 and, you know, P-value was statically significant. Similarly, the pericarditis was reported more frequently in patients who in the ICI group, 0.3%, as compared to 0.1% and that was statically significant. Now you may wonder, like, you know, it's only 0.3% to 0.1%, but if you look at the sample size, you know, that just describes like why it's statically significant. Similarly, the risk of myocarditis was also higher. You know, 0.3%, actually 0.04% and, you know, the odds ratio was 8 if you look at the, you know, atrial fibrillation and pericarditis, myocarditis actually had a higher incidence. Dilated cardiomyopathy was not like statically significant, but there were like some patients who have slightly higher risk of higher development of dilated cardiomyopathy. So after doing propensity matching analysis to control the potential confounding variable, you know, new onset atrial fibrillation was statically significantly higher, 6.4% compared to 5.3%, with odds ratio of 1.2. Pericarditis incidence was higher, 0.3% to 0.1%, with odds ratio of 2.1. And myocarditis continued to show a substantial difference after doing propensity matching analysis and that was 0.3% compared to 0.04%, with odds ratio of 9.2, that was higher than, you know, after propensity matching actually it's higher. Now then we, you know, try to look up because, you know, like given, you know, different three ICIs are fighting over each other with a CTLA-4, PD-1, PD-L1 in the lung cancer treatment and we wanted to see like, you know, is there any difference between PD-1 and PD-L1 treatment subgroups? Interestingly, there was no significant differences in the adverse events in these subgroups. Atrial fibrillation was around 6.3% of the patient who received PD-1 drugs and 6.4% PD-L1. So it wasn't statically significant. Likewise, no difference in pericarditis. Myocarditis was also kind of similar and after propensity matching, you know, then we, between the different variable PD-1 and PD-L1, we did the propensity matching analysis too and there was no difference between the PD-1 and the PD-L1 patients. So conclusion, you know, among lung cancer patient receiving immune checkpoint inhibitor, you know, we see the higher observation since it's not a RCT, it's a purely a database observational study. There was a high incidence of atrial fibrillation, pericarditis, and particularly myocarditis among patients receiving ICI as compared to non-ICI in the lung cancer treatment. And even after doing the propensity analysis, you know, these findings have been true. And of particular interest, there was no difference between the PD-1 and PD-L1 therapy and that kind of suggesting that this is more related to the intrinsic nature of the drug therapy as compared to the sub-categorization of immune checkpoint inhibitor. Now what are the clinical implications? You know, since there are like no guidelines, if you open up the guidelines, you know, from American Society of Clinical Oncology, you know, Lung Cancer Association, in terms of patients who receive ICI, do they need a workup? Do they need to have, you know, as we know, like for giving chemotherapeutic drugs like anthracyclines, they need a baseline echocardiogram. So, you know, there haven't been like, do patients who receive ICI, do they need a significant cardiac workup? This, you know, this needs to be addressed in future, like, you know, there needs to be some studies done. But you know, from the study we can, you know, the implications are there's a high incidence of atrial fibrillation, pericarditis is observed, patients who received ICI and high incidence of myocarditis. And we know if you don't treat myocarditis, you know, it's very high mortality associated with that. And there's a further research should investigate underlying, like, why different ICIs, you know, like how we can prevent it, basically, and explore potential predictive biomarkers. Future direction, so just wanted to, you know, like, let's talk a little bit about, so, you know, there are no risk stratification model. Maybe in the future we have this stratification models where we can develop the risk models to identify patients who are going to be high risk. And that is going to be determined by if you have any underlying arrhythmias or cardiovascular, and what are the key biomarkers or genetic factors that influence development of these cardiotoxicities. And there's a, you know, there's a big research around, like, optimizing how we can approach therapeutic approaches, how we can balance the ICI anti-tumor effects with cardiovascular side effects. Are there any new treatment protocols that are going to come that is going to help not having these developed enough, like, do they need a prophylactic anti-arrhythmic in the high risk patients or not that need to be categorized? And what the long-term impact, there are no studies that look up, like, the, because since it's been only eight years, are there any studies that are going to be in future looking at the 10, 20 years data? And can we protocolize and personalize these approaches, and are there any preventive measures? These all, you know, questions remain unanswered, hopefully in Chase 2030, 2035, we'll have these answers. Thank you very much for your attention. Any questions? Thank you so much. Yeah. All right. Thank you. Okay. Do we have Omar? Hello. So I'm here to present a retrospective study about patients admitted with acute pancreatitis, with or without stable heart failure. My name is Omar Tamimi. I'm a hospital medicine physician over at Houston Methodist Hospital in Houston, Texas, and no financial relationships to disclose. So our objective was to evaluate the impact of chronic compensated congestive heart failure on patients admitted with acute pancreatitis. So what I mean by chronic compensated is we're not talking about a decompensated acute heart failure exacerbation patient. We're talking about somebody who has known either systolic or diastolic heart failure in their past medical history. Right now they're not in an acute exacerbation, but they are admitted with acute pancreatitis. So why? We know that the traditional mainstay of management for acute pancreatitis is fluids, fluids, pain control, and then more fluids. We use our clinical judgment to decide, but if somebody is admitted with acute pancreatitis, the traditional mainstay of management has been a lot of fluids. So the question was, in somebody who has a history of HFREF or HFPEF, how does that affect the outcomes, if at all? So our methods, the way we did it is we used the National Inpatient Sample. That's a publicly available, de-identified data set made by the Healthcare Cost and Utilization Project. And basically what this database does is it gathers information from all of the discharge records from patients using billing codes and ICD-10 codes. And it's publicly available, so anybody that was discharged who has many hospital claims, they use those records and it's available in that National Inpatient Sample. So we used the National Inpatient Sample. We used the year of 2020. So it looked at all hospitalizations from January 1st through December 31st in the year of 2020. We gathered all that discharge data. And aside from getting the baseline demographics, which I'll go through shortly, we identified the variables of patients who had a principal diagnosis of acute pancreatitis. What I mean by principal diagnosis is that when we looked back retrospectively after the patient was discharged, when they looked at the main reason why this patient was admitted to the hospital, it's because they had acute pancreatitis. And then we identified patients who had a secondary diagnosis of chronic CHF. So anything other than the principal diagnosis is by definition a secondary diagnosis. So again, patients admitted with acute pancreatitis, we divided them into either having comorbid CHF versus no history of CHF. So total in the entire database in 2020, there was 32,355,000 approximately total discharges. And discharges could mean anything, discharge to home, discharge to SNF. It could even mean a death, but it's just a discharge. Of these 32 million, about 252,595 had acute pancreatitis as their principal diagnosis for being admitted to the hospital. Of these 252,000, 13,555 had a mention, had comorbid CHF. And I'm talking about, again, we're not talking about acute decompensated CHF as a secondary diagnosis. We're talking about CHF that was not coded. It was coded as a chronic congestive heart failure. Either it could be systolic or it could be diastolic. The rest of them did not have comorbid CHF. So the results. So when looking initially at the baseline characteristics, we can see here that on the left you have the patients who did have CHF and on the right without CHF. And there were slightly more females. Patients who had CHF also were, on average, were older. They were 65 rather than 50. There was a higher proportion of African-Americans and a less proportion of Hispanics. And there was a higher proportion of patients who had the CHF. There was a higher proportion of patients with a lower median household income of less than 49,000. And looking at some more baseline demographics, these are all things we adjusted for. So these are all adjusted in the multivariate regression. Insurance status, much higher proportion of Medicare, probably because they're much older in age when we look back. But higher proportion of Medicare insurance status. Higher proportion of things that are known to be associated with the CHF. So hypertension, coronary artery disease, diabetes, morbid obesity, and CKD. And so these are the demographics that we identified and the comorbidities that we identified when doing our multivariate analysis. So the outcomes, we looked at the primary outcome was in-hospital mortality. Did the patient die or not in the hospital? And when we adjusted, we actually found that the adjusted odd ratio was 1.2, but it crossed the 1 mark and it wasn't clinically significant. There was a p-value of 0.31, indicating that those patients who did have a comorbid CHF had no change in the in-hospital mortality when they were admitted for acute pancreatitis. Now mechanical ventilation, there was an odds ratio of 1.47. So they had a significantly increased probability or increased odds of requiring mechanical ventilation. We defined that as either non-invasive mechanical ventilation or invasive. The mean time from admission to mechanical ventilation, when we say early mechanical ventilation, we define that as mechanical ventilation within 24 hours of admission. And patients who had CHF were much more likely to require early mechanical ventilation compared to patients who did not have CHF. But vasopressor use, there was no change between the two groups. Some other predictors of in-hospital mortality. So, again, we're talking about all patients admitted with acute pancreatitis. Age was associated with it, the male sex, Charleston Comorbidity Index, morbid obesity, and ESRD. Hypertension was protective. This is probably because if you're coding for hypertension in the hospital, you're probably not hypotensive by definition. So that's probably the reason why it looks like it's protective for in-hospital mortality. Mean total hospitalization charges. There was no difference when we adjusted for the multivariate regression. And same thing with the mean length of stay between the two groups. Although the crude length of stay was higher, there was no adjusted difference. The p-value was 0.75, and in total hospitalization charges was 0.92. So the conclusion, I think the takeaway message here is that in patients admitted to the hospital with a principal diagnosis of acute pancreatitis, those patients who also had comorbid CHF, either systolic or diastolic, had no difference in mortality compared to those patients who did not have CHF. Yes, they had a higher probability of mechanical ventilation, but that did not translate into a higher mortality. And the clinical implication is aggressive fluid resuscitation is widely recommended for acute pancreatitis when using our clinical judgment. But the study mainly highlights that acute pancreatitis in CHF does not lead to increased mortality, and we should not be kind of, of course we should use it in our clinical decision making, but we should not be shy to really treat the acute pancreatitis with fluids. So I think that's the take-home message. Of course, this is a retrospective study, and so we'd better have a prospective study in the future. Any questions? We are, yeah, we are able to differentiate them into HFPEF and systolic. When I did, because I was trying to figure out how to do this, when I did the systolic and, you know, systolic and basically HFPEF both gave me the same answers, but because there was limited word count, really, I went up with CHF as a whole. But yes, when we subdivided them, there was no, did not affect the results, really, in terms of the outcome. Yeah, yeah, yeah, so when we were looking at in acute pancreatitis, neither systolic nor HFPEF, when we subdivided them, led to an increased mortality compared to patients without heart failure. But when comparing systolic to diastolic, I did not compare the two in that way. I just compared them to whether they had heart failure or not, correct? Yeah. Sure. So I'm curious, did you, I guess it's hard in the National Inpatient Survey, but would you expect, now with that New England study that came out in 2022, advising more moderate fluid resuscitation versus aggressive, and kind of straying away from bolus fluids and pancreatitis, would you expect, probably if you start to date stratify some of this data, that you would see differences from old practices? And I know it takes on average about five years for things to actually pan out, but would you expect that some of this may change in the near future then? Right, yeah, and I think that's kind of why I kept saying clinical judgment, because obviously there's a big heterogeneity in patients who get admitted with pancreatitis, and some of them, of course, they have a SIRS, their heart rate is in the 130s, and obviously they're borderline hypotensive, and those were much more aggressive. Patients sometimes come in with acute pancreatitis based on lipase and mild abdominal pain, and maybe were not so aggressive with the fluids. Unfortunately, that's one of the big limitations of the database. We don't know how much fluid each person got, and that's a big thing. We just know that the physician used their clinical judgment when you're doing it, and so we don't know. But yes, that study was very interesting, and so that's why I think the take-home message is yes, there could be some change in that, but I think the big take-home message is that we know here that clinical judgment was used, and so we're clinically indicated, I think, that we should use that. We do have a way to differentiate the cause of acute pancreatitis based on ICD-10 codes. This was not done in the study. Any other questions? No? Okay. Thank you. Thank you. This is you? Good morning, everyone, and thank you for participating in today's session. Today I'm going to be presenting our research entitled SNP Associated with Decreased Global Longitudinal Strain and Heart Failure. My name is Renuka Reddy, and I'm a PGY-5 pulmonary and critical care fellow at the University of Florida. I have no financial disclosures. These are our lesson objectives today, namely to present our research identifying a genetic variant associated with cardiac strain and heart failure patients and discussing potential applications of this research. So I'd say most of us in this room typically assess left ventricular systolic function by measuring ejection fraction on echocardiography. But this is really a subjective visual measurement, and it's open to different readings by different cardiologists. On the contrary to this, we have global longitudinal strain, which is a more objective measure of systolic function. Turning your attention to the graphic, the first line shows that global longitudinal strain is measured using two-dimensional speckle tracking echocardiography, in which we obtain the peak longitudinal strain of 18 different subsegments of the left ventricle. And these values are plotted in the second line of the graphic on the polar plot that we see there. We take the average of these 18 values to obtain one distinct value for global longitudinal strain. And then global longitudinal strain reflects the function of subendocardial longitudinally oriented fibers. And these fibers are more susceptible to damage for wall stress and ischemic events. So even without abnormal contraction patterns, these fibers can be negatively impaired. So global longitudinal strain is thought to be a more sensitive measure of systolic function because it's able to detect subclinical left ventricular dysfunction even before the decline in ejection fraction occurs. Global longitudinal strain, as we would expect, to be reduced in heart failure with a reduced ejection fraction. Interestingly, it's also reduced in patients with heart failure with preserved ejection fraction. Decreased global longitudinal strain has also been associated with poor cardiovascular outcomes. So thus far, there's been limited research done on genetic predictors of cardiac strain. So our study aimed to identify a SNP associated with cardiac strain in patients with left-sided heart failure using both a genome-wide association study and a candidate gene analysis. So we performed a study by genotyping 337 patients with chronic heart failure with either preserved or reduced ejection fraction. And these patients were recruited from a database at the University of Illinois' Health Science System. We performed a linear regression model. And we used this model to assess associations between roughly 1.8 million SNPs and global longitudinal strain. This model was adjusted for several COVIDs that have been associated with poorer outcomes in heart failure patients. And these are mainly things like demographics, elements of their past medical history, and some of their medications. The threshold for genome-wide significance was a p-value of less than 5 times 10 to the negative 8. So after we did the initial candidate gene analysis and identified our top SNP, we then decided to perform a secondary candidate gene analysis by which we wanted to validate a mechanism by which our SNP could lead to decreased global longitudinal strain. And we hypothesized that our SNP could lead to decreased global longitudinal strain by different expression of membrane proteins. So this led to us selecting five different candidate genes through four different molecular pathways that lead to different expression of membrane proteins known to be different in heart failure patients. And we used the same linear regression model that I had previously described to assess 212 putatively functional SNPs within these five candidate genes. We used a false discovery rate of less than 0.05 to be considered significant. So these are the results of our genome-wide association study. So this is a Manhattan plot displaying the data. And on the x-axis, we see the different chromosomes. And on the y-axis, we have the negative 10 times the log of the p-value. And on the Manhattan plot, each individual dot represents a unique genetic variant, a different SNP in the genome. And then we have the red line there that delineates the p-value of 1 times 10 to the negative 8, our significance level. So as we can see, our SNP that we detected is located on chromosome 20. And the unique SNP that we found is in the red lettering on the slide at the bottom. And then we also have this displayed on our Manhattan plot. It's circled on the top right corner. And as we can see, the SNP that we identified is well above the red line, indicating it's a very significant finding. So this is a graphic of chromosome 20. And we can see this light blue line displaying our SNP. And we can see that it's associated within the ATP9A gene. So the ATP9A gene encodes a protein that plays a role in endosome to plasma membrane recycling. And this gene has a moderate to high expression pattern in the cardiovascular system, particularly with increased expression in the left ventricle. And this gene, or sorry, endosome trafficking is known to play a key role in the expression of different membrane proteins. And heart failure patients are known to have varied expression of membrane proteins, such as the sodium calcium exchanger. So thus we hypothesize that the gene could lead to reduced global longitudinal strain through varied expressions of membrane proteins. And this was kind of the basis for our selection of the candidate genes that I had already described. So overall, within the candidate genes, we did not find any significant associations between any of the SNPs to validate our hypothesis. So although we were not able to validate a specific mechanism, but through our candidate gene analysis, it still remains possible that decreased global longitudinal strain could occur in heart failure patients through varied expressions of membrane proteins. But we need more studies to determine the exact mechanism by which this could occur. The next step in this process would be to determine how the variant affects the structure and function and expression patterns of the ATP9A gene, and then take it from there. So overall, we need further genetic studies to look at different genetic variants and their implications in cardiac strain and heart failure, as this has the potential to help with risk assessment, outcome prognostication, and potentially the development of therapeutic targets in our heart failure patients. These are my references. Thank you so much for your time. Does anyone have any questions? So that's not something that we have assessed thus far, but it would be interesting to look at. So I'm not sure about that specifically, it's not something I'm familiar with. I mean, I think it depends on the kind of mechanism that we see that the SNP uncovers and then potentially based on that, depending on what we find, I mean, potentially we could develop therapeutic targets against that. No, actually, the next step that we had planned for this project is we have a mouse model of heart failure patients, so we were kind of going to go and look at the expression of this protein in that model to see if we can kind of validate this project with that. Okay. Thank you. Thank you. All right. Is this one yours? Yeah. And then you can just put it up there with the disease control. Yeah. I guess we'll just advance this slide. Okay. Yeah. Awesome. Thank you. Hello, and thank you for joining us in this session. My name is Kristen Corey. I am from Duke University Hospital. I have no financial disclosures. And I will be discussing preliminary results from our project, looking at metabolomic signatures that predict major adverse cardiovascular events along COVID and death in patients after having COVID-19. So it's been well observed that patients who have had COVID-19 infection are at an increased risk and have higher incidence of cardiovascular disease. In this study in particular, Z et al., were able to demonstrate through the U.S. veteran population that there is a significant excess burden of cardiovascular disease in these patients, particularly in those who have had severe infection as opposed to mild disease. This pattern is also similarly seen in what we call post-acute sequelae of COVID-19, or what's commonly known as long COVID. So this culmination of symptoms that are described by patients. Here you can see the increased cumulative burden of this syndrome in patients who were hospitalized with their COVID-19 infection as opposed to having mild disease. So really what we were trying to do with this study was to determine the underlying molecular mechanisms that are driving these adverse outcomes that we see after patients have having COVID-19 infection, and then using that molecular signature to really see if we could predict the adverse events and see who is at highest risk for developing them. So we were able to use two biorepositories at our institution, the first one being of mild disease outpatients, outpatient individuals with COVID-19 infection, and then the second one, this ICU biorepository, largely just ICU patients requiring critical care. We were able to align our computational phenotypes, especially our outcomes of interest with the cardiovascular diseases with that Z. et al. paper that I had mentioned in the background. And then similarly, we're able to define our computational phenotype for long COVID by this Nel Bondian et al. paper, which was really the first paper to describe long COVID in depth. I will say that this criteria for long COVID, this has changed significantly over the course of the pandemic, and still is a changing landscape without a rigorous definition for this syndrome in particular. So here are some statistics, descriptive statistics of our cohort, and as you can see, the case group is significantly different in certain demographics such as age, and then comorbidities such as heart failure, type 2 diabetes, and hypertension. So really, the analysis, the first portion was to look at the statistical association between the targeted metabolomics and our outcomes of interest, the cardiovascular disease, death, and long COVID. And so we ended up running a principal component analysis, taking the factors from that principal component analysis and running that with the multivariable logistic regression, and then adjusting for appropriate clinical covariates, which I have here. The second portion of our analysis was looking at a prediction task, so being able to actually predict these events. We decided to use lasso modeling just so we could see which metabolites came out as important features in the prediction task, and then using a standard 10 cross-fold validation testing on a held-out test set, and then bootstrapping about 1,000 times for our confidence intervals. So here's the results of that multivariable logistic regressions, and we were able to demonstrate through the principal component analysis that factor 1 was statistically significant for being associated with the composite outcome of cardiovascular, adverse cardiovascular events, long COVID, and death. The metabolites that were heavily loaded on factor 1 were medium-chain and long-chain acyl carnitines. So what's super interesting is that in the second half of our analysis, looking at this prediction task, we were able to also determine that the same metabolites, so medium-chain and long-chain acyl carnitines, also predict cardiovascular events and death with high accuracy and precision. So here on the left, you can see these are, this is a rock curve figure, and the blue line is the predictor, predicting composite outcome with an AUC of 0.92, and then the red curve is the cardiovascular events model with an AUC of 0.88. Unfortunately, the long COVID line, the green line, has an AUC of 0.5, so did not perform well at all. Interestingly, the two models, the composite outcome and the cardiovascular events prediction models, they both had pretty high precision with precision recall curves above 0.5. And then here, just pulling out the different coefficients where you can see the different features that come out as really important for making that prediction task, particularly in the cardiovascular events, history of arrhythmia, history of heart failure, and then severity of disease by using the surrogate, the protocol. And then for both of the models, these medium-chain and long-chain acyl carnitines come out as important features. So basically, through this analysis, we were able to not only demonstrate the statistical significance and association of these medium- and long-chain acyl carnitines with our outcomes of interest, but also be able to predict these events and determine who is at highest risk for developing these events through these metabolites. I think really through the analysis, what is driving the results is less so of a long COVID outcome through the composite and really the relationship between these metabolites and cardiovascular disease and death. So we know that these metabolites, they have been associated with cardiovascular disease at baseline in the past and have even been proven to show improvement in predictive performance in machine learning models. And they're really secondary to fatty acid oxidation, which is mainly seen in metabolic disorders such as type 2 diabetes, which we know is highly relevant to COVID-19 and the severity of disease that people can get. And we know that these specific metabolites are really heavily involved in activating inflammatory pathways. So kind of putting this all together and linking it to COVID-19, we know that it causes a widespread systemic inflammation and endoplasmic reticular stress. And so we believe through our findings that COVID-19 is potentially potentiating elevations in these particular metabolites and contributing to the mechanism behind adverse cardiovascular events that happen post-infection. That being said, we did not find an apparent association between the targeted metabolites and long COVID. So really those results just driven by cardiovascular events and death. The limitations of our study, so, you know, a pretty small sample size at a single institution. We had, you know, difficulty in phenotyping long COVID. It is still kind of a changing definition. So I think that probably impacted our analysis. And then we still need validation and functional studies to further kind of tease out this relationship a bit more. So the goals set for this project include a longitudinal analysis. What's nice about the biorepositories is they do have a longitudinal component to them. And then like adding multi-omic approaches. We do have Olin proteomics data, so are able to do that as next steps. I'd just like to thank my lab and all of our collaborators for making this work possible. And I'll open it up to any questions. Thank you. It's a great question. I don't have the answer to that, particularly in this study. So what I thought was really interesting about this study was that I thought that, I think our initial hypothesis was that COVID-19 would be causing cardiovascular disease in some other unknown way. And what it seems to be, at least with the metabolomic signatures, it seems to be just potentiating what already causes cardiovascular disease. So just heightening that response in this hyper-inflammatory way. And I find that very fascinating. But yeah, as you said, more studies need to be conducted to kind of tease out the relationship between metabolic disorders and infections. Any other questions? Okay, great job. Thank you. I'm the chief resident at Metraz Medical Center in Framingham and I'm very honored to be here to, oh, sorry about that, to present our work about biomarkers of cardiovascular stress and left ventricle hypertrophy, the cross-sectional relations and prognostic sickness. To begin with, I have no financial disclosures. Just a background, cardiac stress biomarkers have been largely studied and most of what have been shown in the literature is that GDF15, ST2, high-sensitive troponin and BNP has been associated with left ventricle hypertrophy and also with increased risk for cardiovascular disease. And aside from that, it also has been shown in the recent literature that a combination of certain cardiac stress biomarkers, such as high-sensitive troponin and also BNP, can produce a malignant phenotype, which characterizes for a population of higher risk for cardiovascular disease, such as heart failure. GDF15 is an inhibitory cytokine that's mainly present when you have an inflammatory scenario and it has been shown to be very associated with myocardial injury. And ST2 is an interleukin-1 receptor family member that's also been present in myocardial fibrosis and also inflammation. And as you all might know, high-sensitive troponin and BNP are well-known cardiac stress biomarkers that are related with myocardial injury and myocardial remodeling. So the interest for this study was to see whether these biomarkers would have any change depending on the type of LVH geometry. And for that, we had two main objectives on our study, to determine if the presence of LVH would be associated with higher levels of cardiac stress biomarkers and if that combination with LVH would give also a higher risk for heart failure. To conduct this, I did a look into the Framingham Heart Study and we did a cross-sectional analysis using the six examinations. So initially we had 3,532 participants, but after we applied our exclusion criteria, removing participants who had elevated creatinine levels above two, who had prevalent history of heart failure, who didn't have any of the available covariates, which I'm gonna show in the next slide, or had a significant valve disease, or non-available biomarkers or inadequate echocardiographic measurements, we ended up with 2,425 participants. Here is a table with the main covariates of our interest, and you can see that our population was mainly middle-aged adults with age 20, sorry, age 57, 58, and we had a slightly higher prevalent cardiovascular disease in the male population. With our study, with our participants redefined, we measured the cardiac stress biomarkers on their serum for ST2, GDF15, and high-sensitive troponin and BNP. And we also got the measurements for the echocardiographic TTE and classified them into normal concentric remodeling and eccentric hypertrophy and concentric hypertrophy. And after that, we applied a ANCOVA to explore the interactions between the biomarkers and the different types of LV geometry. And then later, we also applied a Cox hazard proportional regression to understand the interaction between the risk for heart failure or cardiovascular death with the combination of these biomarkers and the presence of LVH. Oops, I'm sorry. And as you can see here in our results, the biomarkers levels, they varied across the different types of LV geometry. However, the lowest levels of biomarkers, as you would expect, it's in the normal type of geometry. And the highest, we saw it in the concentric hypertrophy. Most of the biomarkers were all in the concentric hypertrophy with the most elevated levels except for BNP. And we think that it's probably because concentric hypertrophy might have a more inflammatory panel because most of the biomarkers that we were looking into, they are associated with inflammatory response. And furthermore, what we saw also is that the presence of these biomarkers did confer a higher risk for heart failure. And as you can see, some of them were even more, were even giving more higher risk for heart failure independent of the presence of LVH. To be more specific, we had that on BNP and GADF15. And we also saw with our Cox regression that when you have at least one elevated biomarker in the presence of LVH, that conferred even a higher risk for heart failure or cardiovascular death. So you can see here in this table who, the population had LVH and at least one elevated biomarker had a hazard ratio of three with a confidence interval of 1.71 to 5.27. So this concludes that in our study, we were able to at least assess that some biomarkers were associated with higher risk of heart failure and that was even regardless of LVH. We also saw that the combination of LVH with at least one biomarkers elevated, that could lead to a higher risk for heart failure. And we also saw that the distribution of this biomarker levels, they varied through the different types of LVH, which could indicate that for each type of remodeling, there is a different type of response. And we saw it also that this combination of LVH with different cardiac stress biomarkers levels that are elevated could produce also a malignant phenotype that could serve in the future as another tool to identify high risk profile and perhaps helps on target interventions. Of course, there are some limitations from our study. I think the main one is that this was a cross-sectional study, so we could only evaluate so much, so the causality and temporality were somewhat limited. And the other fact is that this was a single point time assessment for the cardiac stress biomarker levels and the same goes for their TTE measurements. So we couldn't fully appreciate the dynamic remodeling feature of LVH. But I think the study was definitely have some strength because this was a large cohort, so we definitely have some good reproducibility. And the fact that most of our participants had information regarding their biomarkers and also their LVH, I think that also strengthens our study. I think for future, it's definitely that we might have to explore more in depth of the same biomarkers in different cohorts, especially because Framingham Heart was, as I said initially, it's middle-aged adults. It's mostly white, European people. But I think it definitely moves the needle forward to figuring out what subsets of population have a higher risk for heart failure, especially in the LVH population. And then I want to acknowledge everyone who supported me, especially the Framingham Heart Study, Boston University, and NIH for funding me. And with that, I want to open up for questions. or is it available? Yeah, I think GDF15ST2 so far I've only seen in research. I think probably if we are able to see more associations, this is a cross-sectional, probably could be one of the new biomarkers, but I'll be very happy to see if that happens. Yeah, yeah, so when we did, let me go here. So we adjusted for all those risk factors for the covariates that we were interested in. So we adjusted for, for example, use of antihypersensitive medication, presence of diabetes, and prevalence of EVD. I think moving forward, we could potentially include more covariates, but that definitely would require more different analysis. But I think that's one way to at least make this more, a better association and not having so much confounders. At least in this examination of six, I didn't see that much. They have it in the new offspring though. I think that's another possibility for us to see on the next one. We picked the six because that was the one that we had the most data available. Okay. Thank you. Thank you so much.

cardiac stress biomarkers

left ventricular hypertrophy

LVH

predictive value

heart failure

GDF15

ST2

high-sensitivity troponin

BNP