It's the turn now of Masoudi and actually I think this is the first meeting really where we've embraced new nomenclature in a way that has really transformed the field and I'm really pleased to introduce Dr. Mazen Nureddin from Houston Methodist who's going to give us our final debrief on Masoudi. So over to you, thank you. Thank you very much Ms. Chairwoman, Mr. Chairman. Thank you for this great privilege. Ladies and gentlemen, this is the Masoudi brief 2023. It was an inspiring talk by my colleague Higg and I think everyone is inspired to go home and half an hour have lunch and celebrate Thanksgiving next week. So let's get it done. So those are the Masoud abstracts. There's a lot of good work out there and it keeps increasing. They were in the Masoud section, 538 related abstracts, 48 oral presentations and 498 posters. So as you can imagine I cannot present all of them but congratulations everyone on the great work that is ongoing. So I'm going to divide my talk into four tasks. I'm going to talk about studies that address pathophysiology. I'm going to talk about epidemiology and natural history. I'm going to talk about diagnostics and you're going to see me breaking them down and trying to move away from biopsy hopefully one day. You'll see stiffness measurements. You'll see composite scores that address at risk NASH and I'm also going to talk about machine learning which is evolving field. And finally I'm going to break the therapeutics into preventative weight loss related phase 2 data and phase 3 data. So let's see pathophysiology. Before I move to that, Ms. Chairwoman, you mentioned we're new to the nomenclature. We agreed to vote. Everyone voted. High degree of agreement. So we're moving on. No more NAVLT, it's MAZLED. No more NASH, it's MASH. And I would like to thank the ASLD as well as ESL and other societies for the tremendous efforts, the leadership, the task force, the MAZLED SIG and importantly the ASLD staff. So if you see them, please thank them for the tireless effort. So this study comes from Huang and colleagues using unbiased genome first approach of two agnostic biobanks that permitted deeper metabolic phenotyping of protein altering to variants in the well-known TMS6F2 that is associated with hepatic steatosis. This study examined 44,000 patients and what they found that this E167K homozygous were at increased risk of MAZLED, MASH and HCC related to it. The 156P heterozygous was associated with MAZLED and MASH as well. And then their conclusion that these two induces a loss of function effect on the protein structure, thus leading to liver disease. They also identified stopgain codon, which is a potential therapeutic utility toward MAZLED. Another study comes from Michigan. In this study, they had 16,000 patients, which is cross-ancestry genome wide association meta-analysis of imaging based MAZLED and whole genome sequence data across multiple human cohorts. This is important because a lot of studies came from European descent-wide patients. And what they found here, they were able to replicate the two genome-wide significant MAZLED variants, which is the PNLPA3 as well as the TMS6SF2 that we are more known. Importantly, they discovered two other genome-wide significant variants mentioned here in African ancestry certified analysis. We need more of these diversity data. A study from the IU group here, Vilar Gomez, studied the effect of PNLPA3, the 409, and its interaction when dietary factors such as caffeine and non-heavy alcohol consumption. They looked as outcome of liver-related death in the U.S. space population, and this is enhanced data, older data. And on the left, I want to show you that the association of PNLPA3 and environment factors and their relationship to liver-related death. And as you can see here, PNLPA3 and other factors such as BMI and nutritional factors had sub-hazard ratio that is listed here. On the right, you see this interaction between PNLPA3 and other dietary factors and their effect on liver-related death. And I want to point to the multiple factors such as BMI, cholesterol, caffeine, and the interaction with the G allele. So, you see it's changing the story here, giving this nice interaction between environmental factors, gene factors, and outcomes of the disease. So, the conclusion of their study that non-heavy alcohol consumption, high intake of saturated fat and cholesterol adversely, whereas higher caffeine, tea, drinking favorably modify the risk of liver-related associated with PNLPA349. Let's move to epidemiology and natural history. And I've covered oral and poster presentations, and this actually come from posters presentation similar to many significant studies that you, Zubair Younassi contributed to. He looked at the prevalence of measles in type 2 diabetics between 2016 and 2021, which is an update from previous analysis. And he saw increase by 23%. Now, this is alarming. The diabetics, 69% of them, they have measles. This is quite high. There's significant fibrosis in 40%, which is F2 and higher. And there's advanced fibrosis in 16%. And on the bottom, I show you the method of, that they analyze the measles either by imaging or other parameters. So, measles is on the rise globally, and we need better stratification, of course, screening the type 2 diabetics. So, it makes total sense in our guidelines. Also, another epidemiology study from the same group, here using the SRTR data and looking at the liver transplantation for HCC between 2013 and 2022. And you see mass in HCC going up from 10% to 31%, which is the higher contributor. Alcohol is another problem. The HCC for liver transplant went from 9% to 24%. A success story, thanks to many people here that contribute into the field, HCV, HCC dropped from 25% to 17%. So, we have done it there. Hopefully, we'll do it for MASH and alcohol HCC. So, MASH is currently the most common etiology of patients waitlisted for liver transplantation for HCC. This is a data came from Methodist Hospital that they look at disparities access to liver transplantation for measles-associated HCC, and they use the U.S. OPT-N-STAR data. And this is the conclusion, which is quite concerning. Hispanic underworld liver transplantation for measles HCC at significantly lower rates, waitlisting outcomes for Hispanics were significantly different across U.S. regions, and women were less likely to undergo liver transplantation due to measles HCC. This needs to be corrected. Let's look at diagnostics. Again, I'm going to break them down into screenings, stiffness measurement, the composite score, and I'm going to move to machine learning. Here, this study I show you from Daniel Huang of the UCSD group, and what they try to do in a prospective study from 396 degree, first degree relative, trying to identify score to identify advanced fibrosis in this population. And the score you see it on the right, it consists of age, type 2 diabetes, obesity, and family history. And the AUC of this score was on the high side, 0.94, much higher than the fib 4. So we'll see how it does in other studies, but this is very promising score, and they suggested it can be used for first degree relatives. This is a data from the Nalin IT Consortium, and this is data collected from multiple clinical trials that have been undergoing for MESH F2 and F3 and MESH cirrhosis. And in this database, you see here the amount of the data they have. Lab from 5,500 patients, fibroscan from 2,500 patients, MRI data from 2,400 patients, and liver biopsy from 2,300 patients. And the question is, what is the best parameters that will decrease our screening failure and will get us at-risk MESH patients, which are those patients with MESH and F2 and higher in clinical trials? We have a huge screening failure rate. So the message was, you need middle age group enriched by risk factors such as type 2 diabetes. They recommended excluding patients with fibroscan less than 8.5 and AST from the screening process, and they broke it down by the A1C of less than 6.5 or higher. So if it's 6.5, to increase your confidence, you need higher AST of 40 and the FAST score of 6.7. The FAST score is a score by transient allostrography that consists of the cap, stiffness plus AST, and it predicts at-risk MESH. If the A1C is on the higher side, 6.5, you can lower the AST to 30, and the FAST, you can lower it as well. So those are techniques that we're trying to use nowadays to decrease the high screening failure rate in our clinical trials, and this is a welcome effort. This is a very important study. It was in a poster session, but it's a multi-center cohort study from 16,000 patients, and what they did, they tried to determine the prognostic implication of one-off and repeated Agile score assessments. What is the Agile score? You have Agile 3 and Agile 4, and it's based on the transient allostrography and other parameters. The Agile 3, they look at those of 3 and higher, and Agile 4 look for the serotics, and here an example I put for you from the Agile 3 score. So they have three zones, low risk, indeterminate, and high risk as other scores, and then they looked at the incidence of liver-related events per 1,000 persons a year. So if you were in the low risk category, even if your stiffness increased by 30 percent, you still have lower incidence of liver-related events, 0.9 here, but look what happens if you go to the high risk and your fibroscan or transient allostrography increased by 30 percent. The incidence of liver-related events go up to 140. So there's the conclusion of this study that the Agile 3 and Agile 4 achieves the highest area under the curve of predicting outcome, it was 0.89 here, and they are very promising to be used in clinical practice to determine liver-related events. Another study along the same line, this is from the Litmus Consortium, and included 2,500 patients, and this was patient data meta-analysis of patients from patients, and they compared the FAST score to the Agile 3 score to Agile 4. So what you see here, the scores are predicting better, and it was more obvious for the Agile 3 and 4 compared to the FAST score, which makes sense because Agile 3 and Agile 4 catches up the advanced fibrosis and the cirrhotics, and the FAST is more at the lower stage. Moving on now, I'm going to switch a little bit to other diagnostic tests. We have now been trying to look at TRISC-MASH using serum tests. The example on the imaging will be the FAST score. So at TRISC-MASH, this is a score, it's called NIST-2, it's a serum-based test and has two parameters, and what they try to do here, try to assess the performance on multiple NITs in context of the BMI. So they broke the patient into those that are lean, overweight, or three classes of obesity, and the conclusion of this study was NIST-2, FIB4, APRI, and ELF were not significantly impacted by BMI, and this is important in a disease that has different BMIs and sizes of patients. The VCTE was significantly impacted in class 3 obesity, which is the highest, something we kind of knew, and there's more data looking into that, and this study used Excel probe as well. So the results suggested a need for BMI-adapted cutoff for nafl-fibrosis score, which we use less, but we might need that for VCTE. This is another blood serum test published in Hepatology called the MASSIF score, which is a metabolomic-based score, similar to the other test that looks at TRISC-MASH, and in this cohort that was biopsy-proven cohort, what they did, they followed the guidelines. And it was the FIB4 first, and then those that they fell in the indeterminate zone, you follow the path. So the current path, you go for VCTE, and here they offered an alternative, which is the MASSIF, which is a blood-based test. So in the indeterminate zone, there was about 45 percent of the population. If you do the VCTE, the correct classification is 46 percent. It increases to 54 percent if you do the blood test. It's called MASSIF, not statistically significant. The misclassification, if you do VCTE, is 12 percent. If you do the MASSIF test, it goes down 6 percent, also not statistically significant. But here we suggested that this blood test can be alternative to a machine-based test when you do AHA and ASLD guidelines, and we'll see more data in the future. The chair here, Dr. Kim, did a nice study that where he looked at the performance of FIB4 and transient allostrography in the enhanced data. And he also compared it now we're moving to machine learning to the safe score that was created by him and the Stanford group. And this is the story. In this general population screening, if you do FIB4, I will show you the example, if it's less than 1.3, there were 63 percent of patients with KPA more than 12. So there's discrepancy here. And when he did the safe score, it went down significantly. So the FIB4 is a great first test that now we have it in our guidelines, but we have to continue to test its performance. We had a poster agreeing with Dr. Kim, but we screened for high-risk population, and there was also discrepancy between FIB4 and transient allostrography. And that's why people keep doing and looking into other scores. This is another score that came from Michigan, and the ELMS score, Vincent Chan here found six parameter, and I'll show you what's in the right. So when they did the FIB4 first, in that cohort, there were 38 percent in the indeterminate zone. Out of those, 56% had liver-related events. When he followed by the ELMS score, that gray zone dropped to 25%. And those that you could miss their liver-related events, the number also dropped. So things to keep an eye on. And there are scores that still need to be compared against each other and narrow it down and make it less complex for people over time. This is another machine learning, and here we're finishing machine learning. This is already published in hepatology that tell you F2, F3, and F4 at risk NASH. But the story here is the following. They compared the two steps, FIP4, followed by transient osteography together, their performance together, and compared to that test alone. And they performed similarly in terms of correct classification and the indeterminate zone. And the message is, and needs to be confirmed, that single test can replace these two tests using machine learning and AI. Moving on to therapeutics, and I break it to preventative weight loss, phase two and phase three data. So this is a nice study for statins. And as you know, there's a huge interest in statins. And from the UK Biobank, there was about 200,000 patients. And it was a nice result where they showed the statin users had 15.4 reduced risk of developing new liver disease. The hazard ratio was 0.846. An interesting analysis, actually, they looked at those that they needed statin. It was indicated, but they were not given that statin. And there was 23% increase of new liver disease. So if they need their statins, we should give it to them. So again, this is the summary, and I think that indication, when a lot of doctors take the patients of the statins because elevated liver enzymes, we need to make sure that they know this message. This is an interesting propensity match analysis from large databases. It came from Kaktani and colleagues. And here, they looked at multiple medications, especially as GL2 inhibitors, they compared the DPP4 inhibitors and GLP1s. And what they found that the SGLT2 inhibitors were associated with lower incidence of major adverse liver and renal outcomes. And what they found that, in addition to liver and renal outcomes, that the SGL2 were better than DPP4s, the SGL2 inhibitors were equivalent to GLP1s in terms of outcomes. Other data are needed to replicate these findings. I don't have a slide on another study, but I wanna mention under the preventative, although it's intervention, there was a study from the Harvard group, from Tracy Simon, when they randomized patient to aspirin for six months, and aspirin reduced liver fat fraction over that six months. Something noteworthy, and we look forward to the mechanistic studies. This is an interesting study from the French group that presented the bariatric data before, and here, we're all familiar with this bariatric data, followed 15 years survival, and you know that the biopsies at various stages, some patients dropped, but still we had enough data. And at baseline, there were 8.6 patients with biopsy-proven MASH. What they found is, we all know this story, that MASH and F2 and higher had significant lower survival rate. But what's interesting, that the resolution of MASH without worsening fibrosis was predictive of survival, which is kind of a new story here. Also, fibrosis regression was observed, mainly after MASH resolution, which makes sense. However, here, what I wanna point, that these patients that had MASH resolution, they had also fibrosis improvement. So I think this data needs to be replicated, and we need to tease out the story better. This is important. Our APPs are really important, and we have been talking in this meeting about involving them more, especially those that in GI society. And this is a nice study by them, which is advanced practice providers, they took over and managed weight loss, compared to the standard of care. And look at the difference. They did much better job, which I don't think anyone is surprised here. There are 78% that let weight loss compared to the standard of care of 40%. A lot of our GIs wanna spend more time on scope. Our APPs are essential to the management of MASL and MASH, so let's empower them. This is also a nice story that has been developing over the years, mainly from the Mayo Clinic, when you do gastric sleeve along with liver transplantation. So this is a retrospective analysis, mainly from the Mayo Clinic, along with John Hopkins, where they did, in this retrospective analysis, they had 73 patients that had liver transplantation and gastric sleeve, and they compared them to 185 patients that just had liver transplantation. And on the top, not surprising, you see that those had gastric sleeve, had less weight. But what also they found, their steatosis gets better on non-invasive testing, and there was a trend to improvement in fibrosis. More data are needed. So this is the summary of the study. They suggested that it's excellent choice for BMI more than 40. There's not much problems with this, including morbidity and mortality. And this combination therapy or combination surgical approach, it helps the steatosis, maybe fibrosis, but also at least the resolution of diabetes and hypertension. This is another interesting study. It's secondary analysis, and this is from the retrutodide data. And retrutodide is a novel triple agonist of GIP, GLP-1, and glucagon receptors. So this is obesity phase two study. It was 48 weeks, and they used two doses, eight milligram and 12 milligram. And in a sub-analysis, they had patients with MRI-PDFF of more than 10%. Look at how many people lost the fat in the liver at the end of the trial. There were more than 85 participants, or 85% of participants lost the fat in this eight milligram and 12 milligram. And this signal was associated with improvement of other parameters, such as body weights, waist circumflex, CKAT, and Pro-C3. And I look forward to see more inflammation, fibrosis data in such populations. Moving on to some of the lay breakers. This is echizobitate, if I'm pronouncing it correctly, and this is an engineered fatty acid. And in this phase two B-design trial, the patients were randomized to placebo, 300 milligram and 600 milligram. The entry criteria were NAS and four and higher as others, and they required F1 at least and higher. The primary endpoint was NAS resolution. Long story short, and I summarize it here, there were multiple, multiple analysis and sub-analysis, MITTT, per protocol. The primary endpoint was not met. Nevertheless, in the sub-population, especially in type two diabetes, there was a signal suggested that you wanna move forward, or you might move forward in the type two diabetics, and we'll see where this go in the future. So here again, I summarize it, did not meet the primary endpoint, but there's a signal in type two diabetics. This is a lay breaker that we presented. It's a DWORD trial, and the unique thing about this study is it's a 12-week placebo randomized control trial that had single agent and multiple agent. And in this seven arm, there was a placebo arm, and there was three treatment arms of THR beta called TURN501, and there are three doses, one three and six milligram on the TH beta. Another arm, they had FXR, and there were two arms that they had combination of THR beta plus FXR, and THR beta six milligram plus FXR. So they're comparing mono to combo here. The primary endpoint was MRI-PDFF, and the monotherapy, THR beta. Let's look at the results. There was 45% reduction in the THR beta 501 and the six milligram meeting the primary endpoint of the study. What I show you on the right, that this drop was as fast as six weeks on all doses and was statistically significant and continued until week 12. They also met many primary key endpoints, such as the CT1 in both the monotherapy and the combination therapy. Nevertheless, the combination therapy did not add that much, but the slope was still decreasing, so we don't know beyond week 12. In addition, there was robust hepatic target engagement, and one of the best thing that we have shown in this study that the THR beta was extremely well-tolerated with a very low side effect, suggesting its compatibility with combining with other agents. This is also a unique analysis from a phase two B study that already published now in the New England Journal, and this study with Bicosa-Fermin, which is FGF21, the study was meant for F2 and F3 patients, yet they had subpopulation of patients that they had cirrhosis that were entered the study, and what they showed here, very small numbers, 11 patients in the improvement arm, translating to 45%, that they improved their fibrosis stage by one stage. What some say, well, it's a very small numbers, the optimistic part will say, well, there are also improvement in the non-invasive testing, but the authors concluded rightly that the results need to be validated and dedicated to study for compensated cirrhotic patients. Also, still with the cirrhotics, this is a study that looked at aldofermin, which is FGF19, had placebo and three treatment arms with different doses, this was biopsy-proven study for stage four, the primary endpoint was not biopsy, it was the ELF score, the secondary endpoint was fibrosis improvement, and what they showed here on the left, they met the ELF score endpoint by drop of 0.5, there was trend into improving fibrosis, that was not statistically significant, non-invasive test, and biomarkers moved the right direction. So there's optimism that we started seeing signals in NASH cirrhotics, and here the biomarker improved as well as other parameters. Staying with the cirrhotics, this is another study addressing efrefuxferbin, and this is an FGAF21, and in this study you see here the analysis, placebo versus two doses of the drug, and this is in term analysis of a biopsy at week 36. There's still another biopsy at week 96, so we're waiting for these results, and here's the story. On the left, you see the biopsy results, which was not statistically significant, rather there was a trend. When they split it by cryptogenic cirrhosis in the middle that thought to be more difficult, it was more striking effect. Again, this is interim analysis. On the right, you see statistical significant NASH resolution, which along with the improvement in the biomarkers, we're hopeful that the 96-week biopsy will be positive and will be reversing fibrosis in cirrhotic patients. That's the conclusion of the study. Improved biomarkers, it led to NASH resolutions, and we're gonna wait for the biopsy studies. And now finishing up with phase three study results. As you know, it's interim at the TH beta agonist, that the data were presented before the top-line data, and this is the design. Placebo versus 100 milligram, 80 milligram, and they met the primary endpoint. Here I show you that MRI-PDFF predicted NASH resolution, and for the first time, MRI-PDFF predicted fibrosis reduction. They did have the serum sex hormone binding lobulin also predicted NASH resolution fibrosis reduction, but this is more targeted engagement. So those are the results. There were improvement in other biomarkers, but sometimes no improvement, yet histology is improved. Last study, this is using Q fibrosis by histoindex, and what shows you here using the Q fibrosis, you even have more striking effect on one stage, two stage, as well as you show better improvement and more worsening on the placebo. So I'm gonna here point multiple studies that you're gonna see them on the slides. Again, everyone has done really good job, and I could not get more happier with these abstracts. So conclusion, MASLD and MASH are the new names. We have deeper understanding of the epidemiology and pathophysiology. There's ongoing progress in non-invasive testing. We have many positive phase two studies that they're entering into phase three studies. We have a positive combination trials. The cirrhosis, there's a positive signal. We need larger sample size or longer duration, and there was a positive three study here. We see the responder analysis and machine learning analysis, and the future are bright, and that's where we're gonna be next year. Thank you very much. Thank you. I'd like to thank all of the debrief presenters who presented such a succinct review of all the voluminous data that we enjoyed at this meeting. The slides and the recordings of this presentation will be available for download and viewing respectively early next week. So check Monday, please, and also the other four debriefs that are online only, they will be available after Thanksgiving. Before we conclude, I would like to remind you that there will be another chance for us to get together soon, and that will be at our Emerging Trend Conference in Las Vegas. The date for that is March 8 and 9, and that is on the theme of precision medicine in measled. DDW, in May, we were invigorating the liver part of the conference, including a basic science emerging topic conference for two days on the theme of microbiome in liver disease. I hope that you agree that this meeting was energetic and full of new material, so we would like to carry on this momentum to next year. It will be TLM 2024, November 15 to 19, in San Diego in my home state of California. So with that, on behalf of ASLD and Nora Terrell, I would like to thank you again for making this TLM 23 a wonderful success. I wish everybody a safe journey home, and I will see you soon. Thank you.

MASL 2023 conference

nomenclature changes

MASLD

MASH

non-alcoholic fatty liver disease

therapeutics

precision medicine