1 00:00:07,200 --> 00:00:15,670 Hello and welcome to Episode one of Gut Instinct Research Updates, bringing you the latest research in gastroenterology and Hepatology. 2 00:00:15,670 --> 00:00:16,650 I'm Tamzin Cogill. 3 00:00:16,650 --> 00:00:25,300 I'm a gastro registrar, MBA student in Oxford, and I'm interested in hepatology, particularly viral hepatitis and vaccine development. 4 00:00:25,300 --> 00:00:31,870 And I'm Michael Fitzpatrick, known as Fitz. Pretty much everyone, and as well as Tamzin Podcast's Sidekick. 5 00:00:31,870 --> 00:00:42,190 I'm a clinical lecturer in gastroenterology at Oxford with a research interest in G.I. immunology, coeliac disease and nutrition. 6 00:00:42,190 --> 00:00:48,520 Now, we've started this podcast to bring you some of the most interesting G.I. related papers that have come out recently. 7 00:00:48,520 --> 00:00:54,640 Hopefully saving you time and assuaging that terrible guilt of not reading enough journals. 8 00:00:54,640 --> 00:01:01,150 Now, each episode, we're going to talk you through to cracking primary research papers in some detail. 9 00:01:01,150 --> 00:01:07,870 One clinical and one translational. And we will give you our take on the research and what we think of it. 10 00:01:07,870 --> 00:01:10,540 Clearly, there are loads of great papers coming out every month. 11 00:01:10,540 --> 00:01:17,740 So in addition, we're going to give you a rapid fire rundown of what else there is out there in the gastro world. 12 00:01:17,740 --> 00:01:26,110 In our five and five section, where we will try and give you the key points of five papers in five minutes, we're aiming to give you some context, 13 00:01:26,110 --> 00:01:32,980 a bit of critical a play appraisal of the papers that we talk about and try not to take ourselves too seriously in the process. 14 00:01:32,980 --> 00:01:37,630 We both love gastro and and research fits is more interest in IBD. 15 00:01:37,630 --> 00:01:41,530 Small bowel disease and nutrition. Well, I'm more interested in liver disease. 16 00:01:41,530 --> 00:01:47,140 So we hope this podcast will have something for everyone. Now, obligatory disclaimer alert. 17 00:01:47,140 --> 00:01:55,000 Clearly nothing in this podcast constitutes medical advice. If you're a patient, you should consult your medical medical practitioner. 18 00:01:55,000 --> 00:02:01,810 For doctors, I'm sure you wouldn't base your medical management solely on what a couple of people told you on a podcast. 19 00:02:01,810 --> 00:02:06,450 Now, this is the first of our podcast. And if we're honest, we don't really know what we're doing. 20 00:02:06,450 --> 00:02:10,700 So please, we'd like to hear what you think about us right. 21 00:02:10,700 --> 00:02:17,980 As a glowing review on whatever platform your streaming this through. Connect to us via Twitter at G.I. Update. 22 00:02:17,980 --> 00:02:22,840 And we're both on Twitter also. Or e-mail us at Gut Instinct podcast. 23 00:02:22,840 --> 00:02:36,880 All one word at Gmail dot com. So, Thomson, shall we get started with the first paper? 24 00:02:36,880 --> 00:02:47,480 Let's go for it. Excellent, so I'm. I've got the clinical paper for this episode, and I think this is a really topical and important paper. 25 00:02:47,480 --> 00:02:56,990 But I am afraid that it involves the C word. And I know we had talks about not talking about Kofod during this podcast, but I think I think we got it. 26 00:02:56,990 --> 00:03:02,190 We're gonna have to mention it. I'm sorry. OK. You're forgiven this time. 27 00:03:02,190 --> 00:03:09,330 Thank you for forgiving me. So this paper is from Lancet's Gastroenterology and Hepatology from a couple of weeks ago, 28 00:03:09,330 --> 00:03:18,690 and it's entitled The Impacts of the Kovik 19 Pandemic on the Detection and Management of Colorectal Cancer in England, a population based study. 29 00:03:18,690 --> 00:03:26,400 Deep breath long title. So this is from a collaboration of several groups within Oxford, 30 00:03:26,400 --> 00:03:34,380 from the Department of Population Health and the Big Data Institute, as well as a variety of researchers from around the UK. 31 00:03:34,380 --> 00:03:42,210 So clearly during the Kofod pandemic, health surfaces have really had to adapt and change in order to deal with that 32 00:03:42,210 --> 00:03:47,370 sort of an influx of patients on the acute side and particularly critical care. 33 00:03:47,370 --> 00:03:50,550 And that's had a range of impacts on other services. 34 00:03:50,550 --> 00:03:57,360 And there's been real concern in the media, particularly about the impacts on cancer pathways in cancer care. 35 00:03:57,360 --> 00:04:01,530 And there's particular reasons we should be worried about colorectal cancer pathways. 36 00:04:01,530 --> 00:04:11,400 So first, bowel cancer is common. Colorectal cancer is the fourth most common cancer in the UK, with over 40000 diagnoses each year. 37 00:04:11,400 --> 00:04:21,690 Second, while colorectal cancer has a really excellent prognosis if diagnosed early stage one disease has a five year survival of over 90 percent. 38 00:04:21,690 --> 00:04:27,750 If diagnosed late, it's got a really poor prognosis with only a 10 percent five year survival. 39 00:04:27,750 --> 00:04:34,200 If diagnosed, stage four, so early diagnosis and early treatment really matters. 40 00:04:34,200 --> 00:04:42,800 And third, diagnosis relies on colonoscopy, a procedure that was severely affected during the pandemic. 41 00:04:42,800 --> 00:04:50,870 So health care leaders both regionally and nationally want to address the challenges that the Kovar have placed on on cancer pathways. 42 00:04:50,870 --> 00:05:00,260 But they need data. And something that I hadn't appreciated was that the usual related cancer data is collected for the official statistics. 43 00:05:00,260 --> 00:05:06,260 Takes a lot of time. So often it's 18 months to two years after. 44 00:05:06,260 --> 00:05:14,220 After the year in question, the data comes out. And clearly, we can't be discussing the impact of coated in twenty twenty two or twenty two. 45 00:05:14,220 --> 00:05:20,460 Twenty three. So how did this group solve that problem? 46 00:05:20,460 --> 00:05:27,610 Yes. I think this this group have been quite inventive by using some administrative datasets which have more kind 47 00:05:27,610 --> 00:05:35,560 of real time data or at least a much shorter lag in the data being released to compare referrals for cancer, 48 00:05:35,560 --> 00:05:43,600 as well as investigations, diagnoses and treatments for colorectal cancer in 2020 with the previous year. 49 00:05:43,600 --> 00:05:49,120 So they've extracted information from four NHS England datasets. 50 00:05:49,120 --> 00:05:52,270 So I will avoid using their long acronyms. 51 00:05:52,270 --> 00:06:01,600 But they include datasets that look at cancer waiting times on the two week weight referral pathways to diagnostic tests like colonoscopy, 52 00:06:01,600 --> 00:06:06,010 diagnoses of colorectal cancer, and then admitted patient care. 53 00:06:06,010 --> 00:06:11,120 So things like operations. What is radiotherapy databases? 54 00:06:11,120 --> 00:06:17,570 So the result was really tell us the tale of the initial pandemic impact and the peak. 55 00:06:17,570 --> 00:06:26,690 But I think more importantly, what happened afterwards. So if you remember, lockdown hit in about the last week of March 10, 20, 20. 56 00:06:26,690 --> 00:06:32,780 And so what we see is that in April, the number of of urgent cancer referrals from primary care. 57 00:06:32,780 --> 00:06:41,450 So the two week wait referrals for suspected colorectal cancer dropped by 63 percent in April 2020. 58 00:06:41,450 --> 00:06:47,990 And those referrals remain low in May. They started to pick up in June, which was when lockdown started to be relaxed. 59 00:06:47,990 --> 00:06:55,280 But it took until September until levels returned to to those seen in twenty nineteen. 60 00:06:55,280 --> 00:07:06,320 And the impact on Colon ASCAP was even more striking. So in 2019, colonoscopy numbers averaged around 46000 procedures a month in England. 61 00:07:06,320 --> 00:07:10,490 And that went down to just three and a half thousand in April 2020. 62 00:07:10,490 --> 00:07:15,130 So in 92 percent drop in colonoscopy procedures. 63 00:07:15,130 --> 00:07:23,450 And again, although numbers slowly rose over the following months, it took until October to get back to expected capacity. 64 00:07:23,450 --> 00:07:27,310 Most of the kind of Skippy's we do even on two week wait pathway, they're negative for cancer. 65 00:07:27,310 --> 00:07:33,310 So did this temporary drop in referrals and procedures lead to a reduction in diagnoses? 66 00:07:33,310 --> 00:07:44,440 Yeah, so a great question. And yes, so they looked at the number of new colorectal cancer diagnoses per month in 2020 and in 67 00:07:44,440 --> 00:07:51,100 the period of April to October where there was that big drop in referrals and colonoscopy. 68 00:07:51,100 --> 00:07:58,920 There were 22 percent fewer diagnoses of colorectal cancer than would be expected if we compare to last year's data. 69 00:07:58,920 --> 00:08:06,580 And that corresponds to about three and a half thousand fewer cases of colorectal cancer diagnose than would be expected. 70 00:08:06,580 --> 00:08:11,220 Now, consequently, there were fewer operations for Škoda rectal cancer during that period, 71 00:08:11,220 --> 00:08:18,020 and there were also some changes in the types of surgery performed and other and other treatments given. 72 00:08:18,020 --> 00:08:23,160 So during that that time, there were concerns about laparoscopic laparoscopic approaches. 73 00:08:23,160 --> 00:08:30,410 So they were more open operations that corresponded to a greater proportion of patients having a stoma formed at surgery. 74 00:08:30,410 --> 00:08:40,020 And there were also changes in radiotherapy treatments. So an increase in short course radiotherapy, particularly for rectal cancers. 75 00:08:40,020 --> 00:08:45,220 And is there any sign that we're sort of catching up and starting to diagnose any of the missing cases? 76 00:08:45,220 --> 00:08:48,910 No. Yeah, so from a clinical perspective, 77 00:08:48,910 --> 00:08:55,420 I agree that's that's definitely the biggest concern is where are those missing three and a half thousand 78 00:08:55,420 --> 00:09:02,800 diagnoses of cancer in that period in 2020 and up until the end of their data collection period, 79 00:09:02,800 --> 00:09:10,280 which was, I think, till the end of October 2020. There was no evidence of a compensatory increase in referrals. 80 00:09:10,280 --> 00:09:12,130 Scopes were diagnoses. 81 00:09:12,130 --> 00:09:21,430 So things have gone back to preprint pandemic levels that they haven't gone gone higher to catch up that those of those missing cases. 82 00:09:21,430 --> 00:09:25,840 So it raises the question of where are those patients that we would have expected 83 00:09:25,840 --> 00:09:30,770 to diagnose and which services would we have expected to pick up those cancers? 84 00:09:30,770 --> 00:09:37,420 Would they have been picked up on the two week wait pathway, symptomatic referrals, all bowel cancer screening programme? 85 00:09:37,420 --> 00:09:47,210 And what are the consequences for those salade diagnoses? So second, I think it really shows the enormous impact of the. 86 00:09:47,210 --> 00:09:59,180 The entirely necessary changes to the NHS during kov it so the fewer fewer consultations, referrals and scope's led to this reduction in diagnoses. 87 00:09:59,180 --> 00:10:09,020 And it shows how difficult it was to to get services back to normal once the lockdown and so on was was was eased in June. 88 00:10:09,020 --> 00:10:12,770 Now, for those for those who are listening from abroad, 89 00:10:12,770 --> 00:10:21,080 the UK has has had a very serious second wave of Kovik cases in the autumn and winter of this year. 90 00:10:21,080 --> 00:10:27,710 And we've gone into a second lock down from from the end of December onwards. 91 00:10:27,710 --> 00:10:32,480 But endoscopy services nationally have been much more protected than in the first wave. 92 00:10:32,480 --> 00:10:41,330 And it will be really important to see if we're maintaining enough capacity in the system in terms of procedures and and most importantly, 93 00:10:41,330 --> 00:10:45,710 diagnoses during during the second wave. 94 00:10:45,710 --> 00:10:51,860 And the researchers are laudably continuing to produce these data analysis each month on their website. 95 00:10:51,860 --> 00:10:56,600 And the data for November shows that procedures are remaining at at 2090 levels, 96 00:10:56,600 --> 00:11:05,960 but will be interesting to see what happens in December and January figures because that's when things have have have really hit the hospitals. 97 00:11:05,960 --> 00:11:10,010 So we'll have to wait a couple of months to see the full impact on that. 98 00:11:10,010 --> 00:11:18,860 I think finally, this this paper shows just how powerful real world big data analysis is to kind of guide health care policy and resource use. 99 00:11:18,860 --> 00:11:22,640 And I think the researchers have really done a cracking job in that. 100 00:11:22,640 --> 00:11:28,220 I think the main deficits of this paper can't really be blamed, the researchers themselves. 101 00:11:28,220 --> 00:11:32,060 But the limitations of the data sets that they're looking at. 102 00:11:32,060 --> 00:11:36,470 So for me, there's two questions that remain unanswered. The first is, 103 00:11:36,470 --> 00:11:44,720 how do those changes in diagnoses or potential delays to diagnoses correspond to 104 00:11:44,720 --> 00:11:49,680 the stage of disease at diagnosis or to surrogate endpoints of curative surgery? 105 00:11:49,680 --> 00:11:53,220 So resection margins or lymph node involvement and so on. 106 00:11:53,220 --> 00:11:58,610 So we can kind of see what the impact is going to be on on on patients prognosis. 107 00:11:58,610 --> 00:12:07,730 And the second is that these data sets cannot be linked to individual level factors like age or ethnicity or socio economic status status. 108 00:12:07,730 --> 00:12:13,550 And that means that those researchers can't look at things like potential health inequalities, which I think is really important, 109 00:12:13,550 --> 00:12:20,010 as we know that this pandemic has had a disproportionate effect on certain groups in society. 110 00:12:20,010 --> 00:12:35,620 Thanks, fits. That was a really, really interesting and topical paper. We're going to re summarise the key points at the end of the podcast. 111 00:12:35,620 --> 00:12:40,400 So times in what have you got for me from the the translational side of things? 112 00:12:40,400 --> 00:12:49,100 So this week I've got a hepatology flavoured translational paper looking at the immune system in acute on chronic liver failure. 113 00:12:49,100 --> 00:13:00,230 It was published last month in January 2021 and Guts by Coche, which stands for the Chinese group on the study of severe hepatitis B, fantastic times. 114 00:13:00,230 --> 00:13:06,230 And you're going to have to remind me, I've been thinking about T cells, I think, for far too long. 115 00:13:06,230 --> 00:13:13,340 Can you remind me what Accutron chronic liver failure is, how it's defined and why it's important? 116 00:13:13,340 --> 00:13:20,300 Yes, all very good questions. So ACL F stands for Acute on chronic liver failure. 117 00:13:20,300 --> 00:13:27,140 And it basically describes a syndrome in which a patient with an acute decompensation of chronic liver disease 118 00:13:27,140 --> 00:13:34,580 develops organ failure associated with a very high short term mortality rate and diagnostic criteria, 119 00:13:34,580 --> 00:13:41,210 and score calculators to predict mortality have been developed and links to these will be on the show notes. 120 00:13:41,210 --> 00:13:46,730 So obviously understanding the pathogenesis of ACL life is really important so that we can identify novel targets 121 00:13:46,730 --> 00:13:53,600 for therapy in these patients and that might modify the disease process and improve the mortality at the moment. 122 00:13:53,600 --> 00:14:01,790 We know that ACL is initiated by a pesticide damage and that its development is associated with systemic inflammation. 123 00:14:01,790 --> 00:14:06,740 So within the scoring system to diagnose ACL F, for example, 124 00:14:06,740 --> 00:14:11,630 the white cell count being high school's points and some other research has shown 125 00:14:11,630 --> 00:14:17,660 that in patients with ACL F as opposed to decompensated chronic liver disease alone, 126 00:14:17,660 --> 00:14:25,380 they have raised pro inflammatory cytokines. And there's also been some analysis at the transcriptase MC level. 127 00:14:25,380 --> 00:14:29,330 So the transcription of proteins in immune cells, 128 00:14:29,330 --> 00:14:36,200 in the blood from people who have acute and chronic liver failure, and that's been compared to healthy people. 129 00:14:36,200 --> 00:14:38,750 And there are some differences have been found. 130 00:14:38,750 --> 00:14:47,240 But what we don't understand is what some of those differences might be in their mean cells between persons with compensated cirrhosis, 131 00:14:47,240 --> 00:14:55,640 decompensated cirrhosis and ACL F and what makes ACL F different and causes that really high mortality. 132 00:14:55,640 --> 00:15:03,110 So the aim of this study was to describe the transcription profile of what the paper calls PDM sees. 133 00:15:03,110 --> 00:15:10,310 And these are peripheral blood, one of nuclear cells. So the immune cells in the blood across different stages of liver disease, 134 00:15:10,310 --> 00:15:21,550 right from healthy people through to ACL F in order to try and identify the molecular pathways that might then serve as norful, therapeutic targets. 135 00:15:21,550 --> 00:15:24,070 So how did they do this? 136 00:15:24,070 --> 00:15:33,260 They already had a large cohort study underway, which is the Coche study and their studies, the Chinese group on the study of severe hepatitis B. 137 00:15:33,260 --> 00:15:38,260 So all patients in the study have had. It's got over a thousand people in it overall. 138 00:15:38,260 --> 00:15:49,000 They selected 340 patients from that and also recruited an additional 60 people who were healthy, making a total cohort of 400. 139 00:15:49,000 --> 00:15:56,800 And then they evaluated cross sectionally individuals at different stages of hepatitis B. 140 00:15:56,800 --> 00:16:03,850 So from healthy controls without infection to people with chronic hepatitis B infection. 141 00:16:03,850 --> 00:16:09,190 But no cirrhosis. Those with cirrhosis. But compensated stable cirrhosis. 142 00:16:09,190 --> 00:16:18,220 Decompensated cirrhosis and HCF. So these 400 participants were then randomised into a sequence in group. 143 00:16:18,220 --> 00:16:22,450 So. Sixty five of them in that group versus a validation group. 144 00:16:22,450 --> 00:16:30,820 And in the sequencing group, they took immune cells from these 65 individuals and they underwent a process called 145 00:16:30,820 --> 00:16:38,200 RNA Seke or RNA sequencing to identify what these immune cells were transcribing. 146 00:16:38,200 --> 00:16:44,830 And then they validated some of the things they found. Using PCR in the validation cohort. 147 00:16:44,830 --> 00:16:53,830 Okay, so they've got their cohort of patients. And then they're looking for difference in the gene expression between between the different groups. 148 00:16:53,830 --> 00:16:58,810 So individuals healthy with chronic liver disease, but not with ACL. 149 00:16:58,810 --> 00:17:02,850 And then those with ACL. That's correct. Yes. 150 00:17:02,850 --> 00:17:08,920 Okay. So for those who might be less familiar with molecular biology, what is already a Sikh, 151 00:17:08,920 --> 00:17:13,750 and how does that compare to other techniques like Q PCR that you've mentioned as well? 152 00:17:13,750 --> 00:17:18,970 So already seek is basically a technique that identifies genes that are being transcribed 153 00:17:18,970 --> 00:17:25,770 at a particular time by either a particular group of cells or within individual cells. 154 00:17:25,770 --> 00:17:34,240 And for the purposes of this study, the authors collected Devean cells in the blood from the participants and they extracted 155 00:17:34,240 --> 00:17:39,940 the MRN a being beat by each of the cells from each donor and pulled that together. 156 00:17:39,940 --> 00:17:48,250 So it was pooled RNA from all the cells from one donor and then pooled RNA from all the cells of donor two and so on, 157 00:17:48,250 --> 00:17:56,830 because the signal of the MRSA from those cells is quite weak to start with in order to be able to find out what TISM read it. 158 00:17:56,830 --> 00:17:59,890 As such, that signal needs to be amplified. 159 00:17:59,890 --> 00:18:08,110 So that step sort of uses polymerase chain reaction and amplifies all the M RNA that's been extracted from these cells. 160 00:18:08,110 --> 00:18:16,810 And then the results of that are sequenced, usually using a kind of technique where nucleotides are attached to fluorescent markers. 161 00:18:16,810 --> 00:18:22,060 And then the sequence of the first markers can be read and decoded into the nucleotide sequence. 162 00:18:22,060 --> 00:18:26,590 So, for example, it might be, I don't know, blue, red, green, yellow. 163 00:18:26,590 --> 00:18:30,190 And that might mean a. T. C. G. 164 00:18:30,190 --> 00:18:38,830 And that can be then mapped back onto the genome to understand which proteins are being transcribed from that set of cells. 165 00:18:38,830 --> 00:18:40,950 And then once you've got that, 166 00:18:40,950 --> 00:18:49,600 the expression or levels of the different M RNA molecules can be assessed and compared between different groups of cells from different donors. 167 00:18:49,600 --> 00:18:58,000 So it allows you to compare the gene expression of all of the of all of the genes in a kind of an unbiased way. 168 00:18:58,000 --> 00:19:05,490 Exactly. So you don't go in with a hypothesis and say, right, I want to try and detect this particular MRN. 169 00:19:05,490 --> 00:19:09,820 And and just amplify that particular one that you're interested in by PCR. 170 00:19:09,820 --> 00:19:13,990 You actually amplify and detect everything that that cell is making. 171 00:19:13,990 --> 00:19:25,090 And that means that it's a sort of unbiased way of finding out what's going on at the molecular level within cells. 172 00:19:25,090 --> 00:19:29,650 OK, great, thanks. So what did they find that was different between these different groups? 173 00:19:29,650 --> 00:19:36,850 They found firstly that in ACL F there is a particular transcriptional signature of Amien cells. 174 00:19:36,850 --> 00:19:44,080 And they they look different to cells from people that have other liver disease phenotypes. 175 00:19:44,080 --> 00:19:52,900 And a particular thing that they noticed that they found interesting was that there was an increase in differentially express 176 00:19:52,900 --> 00:20:02,220 genes that were associated with metabolism and that progressively increased it in a spectrum from healthy to A.S.A. left. 177 00:20:02,220 --> 00:20:08,720 So in ACL left, there were more genes that were differentially expressed to do with metabolism. 178 00:20:08,720 --> 00:20:10,660 And what else did they find? How else are they different? 179 00:20:10,660 --> 00:20:16,870 Well, there were also differential expression of genes that were associated with viral activation. 180 00:20:16,870 --> 00:20:30,550 So an example might be genes that regulate viral replication, immune processes such as in a to be activated cell surface receptors, 181 00:20:30,550 --> 00:20:35,680 inflammatory processes, in particular, inflammatory cytokines. 182 00:20:35,680 --> 00:20:44,040 And as I've mentioned, metabolism, genes associated with dysregulation of glycogen, metabolism, cholesterol, 183 00:20:44,040 --> 00:20:50,620 a flux and HDL metabolism were all found to be differentially expressed in immune 184 00:20:50,620 --> 00:20:58,280 cells from individuals with ACL F as opposed to other liver disease phenotypes. 185 00:20:58,280 --> 00:21:02,570 So these sort of broad classifications that they had discovered, 186 00:21:02,570 --> 00:21:10,910 they then went on to pick out the genes that were differentially expressed the most and 187 00:21:10,910 --> 00:21:16,500 that they thought would therefore be the most important potentially in the pathogenesis. 188 00:21:16,500 --> 00:21:24,750 So for that, they looked at in more detail and again, I had to look up what some of the functions of these genes were. 189 00:21:24,750 --> 00:21:30,450 So something called Thrummed responded one, which is a protein that is involved in. 190 00:21:30,450 --> 00:21:37,740 Sell, sell, sell, matrix interactions so binds to collagen, fibrinogen, laminin, that kind of thing. 191 00:21:37,740 --> 00:21:47,760 Militarising kinase, which is involved in extracellular matrix, cytoplasm signalling and also phagocytes acis of apoptotic cells. 192 00:21:47,760 --> 00:21:55,750 Semaphore Inbee. And these are proteins that are involved in immune regulation and PPA or gamma. 193 00:21:55,750 --> 00:22:08,080 And this was the only one I've actually heard of. So in relation to insulin metabolism that but it also has roles in lipid metabolism as well. 194 00:22:08,080 --> 00:22:18,370 So they took these four genes that they had found to be expressed more highly in immune cells from individuals with ACL. 195 00:22:18,370 --> 00:22:23,710 And they specifically looked for them in the immune cells from the validation cohort. 196 00:22:23,710 --> 00:22:28,540 So the three hundred and thirty five patients that they hadn't sequenced, 197 00:22:28,540 --> 00:22:34,540 they took their immune cells probe to them for these particular proteins and amplified 198 00:22:34,540 --> 00:22:40,630 them by PCR to see if they were there at the transcript payment level again. 199 00:22:40,630 --> 00:22:52,140 So detecting the MRSA and they found that by PCR in the validation cohort, these four genes were also speculated. 200 00:22:52,140 --> 00:22:56,700 They also looked to see whether they were expressed at the protein level. 201 00:22:56,700 --> 00:23:02,310 So to do this, they took liver biopsies from persons with ACL left, 202 00:23:02,310 --> 00:23:11,430 but also at the different stages of liver disease in the cohort and stained them with immunohistochemistry for these four proteins. 203 00:23:11,430 --> 00:23:20,740 And again, this confirmed the findings that they were up regulated in AC Alef and therefore might have an important role in the pathogenesis. 204 00:23:20,740 --> 00:23:26,310 While say I mean, this this paper, as there's so much data in this, it's just an enormous amount of work. 205 00:23:26,310 --> 00:23:30,270 As is often the case in these these big kind of translational papers. 206 00:23:30,270 --> 00:23:35,190 It's kind of it's almost quite overwhelming and tinkly figure three and better figure 207 00:23:35,190 --> 00:23:40,310 for it makes me feel like Neoh looking at the code in The Matrix for the first time. 208 00:23:40,310 --> 00:23:45,990 Well, I feel for the person who isn't gater to pore over figure three. 209 00:23:45,990 --> 00:23:50,350 What do you think of the key take home findings in particular? 210 00:23:50,350 --> 00:23:55,110 What might be the findings for kind of clinical practise down the line? Yeah. 211 00:23:55,110 --> 00:23:56,790 Excellent points. 212 00:23:56,790 --> 00:24:08,540 So I think, number one, the authors have identified that immune cells in AC alef express different proteins than in less severe liver disease. 213 00:24:08,540 --> 00:24:13,590 To a lot of these genes are associated with immuno metabolism. 214 00:24:13,590 --> 00:24:25,170 Three, they've identified four of these proteins that could potentially be used as either biomarkers of ACL F or as targets for therapy. 215 00:24:25,170 --> 00:24:31,130 And they've sort of proposed a model of pathogenesis using their findings. 216 00:24:31,130 --> 00:24:37,940 Where there is some kind of exacerbation of the hepatitis B virus itself. 217 00:24:37,940 --> 00:24:46,940 And that was detected through the genes that were up regulated to do with viral replication and processing, for example. 218 00:24:46,940 --> 00:24:52,280 And then there is an excessive innate immune response to the virus and inflammation, 219 00:24:52,280 --> 00:25:01,440 which leads to a metabolic order within the immune cells and then ultimately leads to Voltio organ failure. 220 00:25:01,440 --> 00:25:05,850 Now, there's a lot of questions that remain from this paper. 221 00:25:05,850 --> 00:25:17,000 And I mean, I could just list a few of them. The technique they've used doesn't actually tell us which immune cell types are most district related. 222 00:25:17,000 --> 00:25:23,490 So which ones are expressing those genes that they found to be up regulated? 223 00:25:23,490 --> 00:25:30,810 How does the dysregulated, Amien, resort response result in this metabolic disorder? 224 00:25:30,810 --> 00:25:36,720 And how does that in turn lead to multi organ failure and death? This paper doesn't explain that. 225 00:25:36,720 --> 00:25:40,110 And is this a common vinyl pathway in ACL or from other causes? 226 00:25:40,110 --> 00:25:46,560 Because, of course, all the individuals enrolled in this study had chronic hepatitis B virus. 227 00:25:46,560 --> 00:25:55,710 But as we know, especially in Europe and North America, a lot of patients will have alcoholic related liver disease, for example. 228 00:25:55,710 --> 00:26:04,710 So on that point times, and do they speculate about whether it could be a common pathway or whether they think this is actually HPV specific? 229 00:26:04,710 --> 00:26:10,440 Because some of the genes that were up circulated are to do with the virus itself. 230 00:26:10,440 --> 00:26:16,080 Those ones in particular won't be relevant for liver disease if other aetiology. 231 00:26:16,080 --> 00:26:22,440 But because this is the first piece of work that has really looked in this much detail, 232 00:26:22,440 --> 00:26:30,360 they don't really know whether this is going to be a common pathway and in liver disease of other aetiologies. 233 00:26:30,360 --> 00:26:41,370 I think that's what these kind of papers can do, is generate new hypotheses that need to be tested in other ways using other methods. 234 00:26:41,370 --> 00:26:50,130 So, for example, this paper can't tell us which cells are over or under producing the proteins that they found that might be important. 235 00:26:50,130 --> 00:26:56,520 And what they're actually doing, because often they have several functions, whether they're expressed on the surface of cells. 236 00:26:56,520 --> 00:27:01,370 So therefore, are they accessible to drugs and other studies need to be done. 237 00:27:01,370 --> 00:27:08,060 And to answer these questions. And the other thing that I think is important to mention that is probably quite obvious, 238 00:27:08,060 --> 00:27:14,630 especially to a clinical audience, is that these kind of investigations cannot prove causality. 239 00:27:14,630 --> 00:27:19,280 They can only find associations. And that's why we need to build on them. 240 00:27:19,280 --> 00:27:23,160 And in order to understand what they really do mean. 241 00:27:23,160 --> 00:27:26,750 Yeah, I think that's a really good point. I mean, I really like this paper. 242 00:27:26,750 --> 00:27:31,880 I think the bit that I really like is the fact that they've got this huge cohort of patients. 243 00:27:31,880 --> 00:27:37,580 You know, they really take a lot of time to get a really clinically relevant large cohort in patients, 244 00:27:37,580 --> 00:27:42,030 because we know that in these sequencing studies, 245 00:27:42,030 --> 00:27:48,470 you know, there's often a lot of variation between patients, humans, unlike mice or annoyingly heterogeneous. 246 00:27:48,470 --> 00:27:55,610 And so they've got such a big cohort. They've really tried to validate some of their biomarkers and things like that. 247 00:27:55,610 --> 00:28:04,970 And, you know, full credit to them for that. But as you say, in a way, it it's poses more questions than it gives answers. 248 00:28:04,970 --> 00:28:20,360 But, yeah, it's great to see. It's great to see a study like that's being done in actually a really, really sick and unwell greif with patients. 249 00:28:20,360 --> 00:28:29,300 So now we get to move on to the bit that I think is putting the most ambitious of our podcast, which is to is the five in five section, 250 00:28:29,300 --> 00:28:36,440 where we are going to try and summarise five other interesting recent publications in five minutes. 251 00:28:36,440 --> 00:28:42,800 As with the main page papers, we'll put the titles and the links to all the papers in the show notes. 252 00:28:42,800 --> 00:28:49,580 And so here goes. If we if we get this in five minutes, I would be absolutely stunned. 253 00:28:49,580 --> 00:28:53,030 So I've got three for you, Tamsin, and I think you've got you've got two as well. 254 00:28:53,030 --> 00:29:01,130 So the first I'm going to mention is this paper just out in gastroenterology by teams in Cambridge and Imperial. 255 00:29:01,130 --> 00:29:07,730 The first authors are Gas, Breteau and Pain, and it's entitled Transcription and DNA Methylation. 256 00:29:07,730 --> 00:29:16,730 Patterns of blood derived CDH t cells are associated with age and inflammatory bowel disease, but do not predict prognosis. 257 00:29:16,730 --> 00:29:22,190 Another very long title, the background to this paper is really important. 258 00:29:22,190 --> 00:29:34,590 So back in 2011, Li McKinnie and Lyons had a paper in JCI which showed a transcription of signature of circulating seeds. 259 00:29:34,590 --> 00:29:40,280 Eight T cells predicted prognosis in patients with IBD. 260 00:29:40,280 --> 00:29:47,450 And clearly this was huge because if we can have an accurate biomarker of severe inflammatory bowel disease, 261 00:29:47,450 --> 00:29:53,360 this would be enormously helpful in identifying patients for aggressive escalation of therapy. 262 00:29:53,360 --> 00:29:57,110 And that work has led to further research. 263 00:29:57,110 --> 00:30:05,210 A spin out company has been formed and they are developing a commercial biomarker based on this gene signature. 264 00:30:05,210 --> 00:30:10,100 Unfortunately, this paper put someone somewhat of a dampener on that work. 265 00:30:10,100 --> 00:30:14,590 So this group have looked at that, have looked at the transcriptional signatures, 266 00:30:14,590 --> 00:30:18,710 so the gene expression signatures and then the methi relation profiles. 267 00:30:18,710 --> 00:30:26,630 So these are epigenetic changes on the DNA of cells which control what can and can't be expressed. 268 00:30:26,630 --> 00:30:34,950 Of of CDH cells in the blood of two cohorts of IBD patients, one paediatric and one adult. 269 00:30:34,950 --> 00:30:44,000 And they found differences in the gene expression and methylation profiles which were associated with age and with sex, 270 00:30:44,000 --> 00:30:55,460 and also whether the individuals had disease or not. But they were unable to validate this previously described gene signature with disease prognosis. 271 00:30:55,460 --> 00:31:07,370 And this finding has led to a pretty frosty series of letters in the journal between the authors of the 2011 paper and the authors of this one. 272 00:31:07,370 --> 00:31:14,750 I think the key take home is that, I mean, clearly this is disappointing for clinicians and for patients who'd hoped that this might be a 273 00:31:14,750 --> 00:31:20,090 great biomarker for prognosis to not IBD and could be something really useful in the clinic. 274 00:31:20,090 --> 00:31:26,120 But I think it also offers a sort of salutary lesson about biomarker development and validation. 275 00:31:26,120 --> 00:31:31,580 And that's discussed quite nicely in the accompanying editorial. So that's number one. 276 00:31:31,580 --> 00:31:40,010 And I think I'm way over time already. So we'll go on for the second. This one is about microscopic colitis, something a bit different. 277 00:31:40,010 --> 00:31:44,510 Specifically about the risks of cancer associated with it. 278 00:31:44,510 --> 00:31:54,920 This was published in JCC a couple of a few weeks ago by David Bergman and Yona Slewed fixin' from the Karolinska Institute. 279 00:31:54,920 --> 00:32:03,290 And they've used a Swedish pathology register, which is then linked to patient data to compare a huge Kobold 11000 patients with 280 00:32:03,290 --> 00:32:10,700 microscopic colitis with 50000 unaffected comparative subjects over a 25 year period. 281 00:32:10,700 --> 00:32:15,620 They've got quite decent follow up for these patients, just under seven years on average. 282 00:32:15,620 --> 00:32:19,070 And once they've adjusted for a variety of other variables, 283 00:32:19,070 --> 00:32:28,190 microscopic colitis was associated with a small increased risk of cancer with a hazard ratio of one point zero eight. 284 00:32:28,190 --> 00:32:31,580 But I think the interesting devil's in the detail here. 285 00:32:31,580 --> 00:32:39,470 So first, that increased risk was almost entirely confined to the first year of the after diagnosis, 286 00:32:39,470 --> 00:32:46,250 which suggests that this association is due to an association with either microscopic colitis, 287 00:32:46,250 --> 00:32:51,920 with some malignancies or a surveillance bias where patients who are being 288 00:32:51,920 --> 00:32:59,090 investigated for microscopic colitis and an Okkult cancer is diagnosed in parallel. 289 00:32:59,090 --> 00:33:07,130 Second, microscopic colitis was in so associated with an increased risk of lung cancer and lymphoma. 290 00:33:07,130 --> 00:33:12,110 And surprisingly to me, a decreased risk of colorectal cancer, 291 00:33:12,110 --> 00:33:21,170 which suggests that the mucosal information in microscopic colitis does not lead to colorectal cancer. 292 00:33:21,170 --> 00:33:30,020 And indeed, you know, the number suggests that it may even be protective. So they're very different from IBD and IBD related malignancies. 293 00:33:30,020 --> 00:33:38,810 On a practical level, I think it will prompt me to think about the possibility of synchronous non GI malignancy at the time of diagnosis. 294 00:33:38,810 --> 00:33:39,590 But at the same time, 295 00:33:39,590 --> 00:33:50,030 I think I will be less worried about about the any kind of colorectal cancer risk from ongoing inflammation in microscopic colitis. 296 00:33:50,030 --> 00:33:59,070 And the third one is hopefully a quick one. This is a paper about real world outcomes of Vitalis, about treatment in IBD, 297 00:33:59,070 --> 00:34:05,040 which is from Marcus Neurath and Sebastian Zoeller, who are based in Langan in Germany. 298 00:34:05,040 --> 00:34:12,810 So this is a single cohort of one hundred eighty one patients treated with Pheto at a single specialist centre. 299 00:34:12,810 --> 00:34:20,220 And really just encouraging results that it's at about a third of patients achieved clinical remission after four months of treatment. 300 00:34:20,220 --> 00:34:25,530 And those benefits were maintained for the best part of three years of follow up. 301 00:34:25,530 --> 00:34:31,320 And this is really reassuring because this cohort of patients looks very much like 302 00:34:31,320 --> 00:34:36,750 those that get treated in in kind of in real life rather than in clinical trials. 303 00:34:36,750 --> 00:34:42,460 So the patients had been had had a diagnosis of of IBD for many years. 304 00:34:42,460 --> 00:34:47,790 So eight years or 13 years for UC and Corden's respectively. 305 00:34:47,790 --> 00:34:53,940 And almost all of them had received at least one a TANF before, had required courses of steroids. 306 00:34:53,940 --> 00:35:02,990 So in a kind of a real world, patients setting vido shows, you know, really solid results for patients with IBD. 307 00:35:02,990 --> 00:35:07,010 Nice. Nice three. I liked those are all very, very good. 308 00:35:07,010 --> 00:35:11,480 I'll try and through my two. So they're both hepatology papers. 309 00:35:11,480 --> 00:35:16,610 So the first is another hepatitis B paper and asking whether we should treat 310 00:35:16,610 --> 00:35:22,880 patients who have a minimal minimally raised altie and a high and happy viral load. 311 00:35:22,880 --> 00:35:32,180 And this is these are the results from the Torch B trial, which is a really important multicenter randomised trial from Prof. 312 00:35:32,180 --> 00:35:40,490 Jiao Tao Lin in Taiwan. And that's been published within the last couple of weeks in The Lancet infectious diseases. 313 00:35:40,490 --> 00:35:47,210 So the background to this. So so easel guidelines currently recommend treatment for people who have chronic 314 00:35:47,210 --> 00:35:53,710 happy with raised viral load and an alti that is above the upper limit of normal. 315 00:35:53,710 --> 00:36:01,830 But the American guidelines and the Asian Pacific guidelines recommend treatment only when heavy DNA is raised. 316 00:36:01,830 --> 00:36:07,020 And the Alti is two times above the upper limit of normal. 317 00:36:07,020 --> 00:36:18,480 So there's this kind of question about whether people with an alty of between one and two times the upper limit of normal should be treated or not, 318 00:36:18,480 --> 00:36:25,290 whether it actually benefits them in terms of Hep B outcomes down the line. 319 00:36:25,290 --> 00:36:33,480 So in this trial, individuals are randomised to having to not avail once a day or placebo for three years. 320 00:36:33,480 --> 00:36:46,110 And the cohort of patients had chronic hep be with a raised HPV, DNA and a raised alti of between one and two times the upper limit of normal. 321 00:36:46,110 --> 00:36:51,880 So the primary outcomes are interesting. And for this trial, because they were actually histological. 322 00:36:51,880 --> 00:36:58,150 So the primary outcomes were a change in liver neck inflammation by measure by the nodal school, 323 00:36:58,150 --> 00:37:03,540 which Fitz thought was a delicious Austrian snack blood. 324 00:37:03,540 --> 00:37:12,310 That's Kinnaird, apparently. But but in this context, it's actually a score of netcode. 325 00:37:12,310 --> 00:37:19,570 Inflammation in the liver and or a change in the Firebrace is great for the Ishaq's go on pedal of a biopsy. 326 00:37:19,570 --> 00:37:26,820 And the important finding from this trial really is that they found that to not reduce the risk of fibrosis progression. 327 00:37:26,820 --> 00:37:39,970 It didn't change Niekro inflammation. But overall, this supports its use in those with a minimally raised alti and a raised happy DNA. 328 00:37:39,970 --> 00:37:42,970 So this is a great trial and I was really glad to see that out. 329 00:37:42,970 --> 00:37:53,950 There's still obviously lots of questions about other populations of people with chronic hepi, for example, those who have raised happy DNA, 330 00:37:53,950 --> 00:38:03,160 viral load, but a normal loyalty and whether treatment with Niks could actually reduce their risk of fibrosis and therefore cirrhosis. 331 00:38:03,160 --> 00:38:18,510 And actually see long term. And finally, final paper is a paper looking at budesonide treatment in primary Bellary cholangitis. 332 00:38:18,510 --> 00:38:24,440 So this was recently published in the Journal of Hepatology, led by Gideon Hirshfield. 333 00:38:24,440 --> 00:38:33,390 And this trial enrolled 62 individuals at PPC across multiple centres in North America or Europe who had raised ILP. 334 00:38:33,390 --> 00:38:41,550 So above one point five times the upper limit of normal and either inflammation or fibrosis liver biopsy. 335 00:38:41,550 --> 00:38:49,860 And despite being treated with as a toxic Kotick acid for at least six months. 336 00:38:49,860 --> 00:38:58,680 So participants remain Donoso, but were randomised to receive nine milligrams a day of budesonide or placebo for 36 months. 337 00:38:58,680 --> 00:39:06,600 So quite high dose budesonide for quite a long time. And the primary outcome, again, was histological. 338 00:39:06,600 --> 00:39:13,650 So improvement in inflammation and no progression of fibrosis, a liver biopsy was the primary outcome. 339 00:39:13,650 --> 00:39:22,630 Unfortunately, this wasn't achieved. And although some of the secretary outcomes, so by chemical improvements, 340 00:39:22,630 --> 00:39:31,980 a reduction in ILP and a normalisation of a reburn was significantly higher in the budesonide group compared to the placebo group. 341 00:39:31,980 --> 00:39:34,380 Now, this trial, I thought, was really interesting, 342 00:39:34,380 --> 00:39:46,910 partly because of the results and partly because I just think it exposes some of the real difficulties in conducting trials in rare diseases like PPC. 343 00:39:46,910 --> 00:39:53,460 And actually, the trial in the end was underpowered for the primary outcome firstly. 344 00:39:53,460 --> 00:39:58,600 So really, in a way, the jury's still out as to whether he doesn't what could be helpful. 345 00:39:58,600 --> 00:40:07,230 And secondly, they actually had to open up their inclusion criteria. 346 00:40:07,230 --> 00:40:15,840 Halfway through the trial, because the inclusion criteria at the beginning was so strict that they weren't able to recruit enough patients. 347 00:40:15,840 --> 00:40:19,420 So is food for thought. And I think the jury's still out. 348 00:40:19,420 --> 00:40:33,640 And but it really highlights the need and the difficulties of conducting trials in these kind of rare diseases. 349 00:40:33,640 --> 00:40:40,720 So shall we. Shall we recap what we have learnt? Just a quick bullet point of each of the papers that we have. 350 00:40:40,720 --> 00:40:45,130 The way that we've presented. So. So the first paper that I talked about. 351 00:40:45,130 --> 00:40:49,840 So the Kofod 19 pandemic has had a major impact on colorectal cancer. 352 00:40:49,840 --> 00:40:57,130 Pathway's in the U.K. with huge reductions in referrals and colonoscopy procedures in the first wave. 353 00:40:57,130 --> 00:41:03,310 And that led to a 22 percent reduction in diagnosed diagnoses corresponding to about three and 354 00:41:03,310 --> 00:41:09,730 a half thousand fewer patients diagnosed with colorectal cancer than we would have expected. 355 00:41:09,730 --> 00:41:16,060 Acute on chronic liver failure is associated with changes in gene expression and immune cell populations, 356 00:41:16,060 --> 00:41:24,460 particularly in immuno metabolism, which might help us to develop new biomarkers and potentially new treatments in the future. 357 00:41:24,460 --> 00:41:30,130 And then on to the five and five. Well, talking about biomarkers, disappointingly, 358 00:41:30,130 --> 00:41:41,020 a previously reported gene expression biomarker of of IBD progression could not be validated in two further cohorts by an independent group. 359 00:41:41,020 --> 00:41:48,010 And I think it highlights that we need to be cautious and I would argue probably pretty sceptical about novel 360 00:41:48,010 --> 00:41:56,660 transcriptional biomarkers for disease and prognosis until we've seen multiple independent validation studies. 361 00:41:56,660 --> 00:42:02,450 Then we have the paper on microscopic colitis, which is associated with a small overall risk of cancer, 362 00:42:02,450 --> 00:42:07,070 which mainly seems to be lung cancer and lymphoma at the time of diagnosis. 363 00:42:07,070 --> 00:42:12,240 But interestingly, a lower risk of colorectal cancer over time. 364 00:42:12,240 --> 00:42:22,620 And finally, I presented a paper about some real world real world data about vitalism abuse in inflammatory bowel disease, 365 00:42:22,620 --> 00:42:26,970 which shows decent proportions in terms of clinical remission, 366 00:42:26,970 --> 00:42:35,730 even in patients who've who are several years post diagnosis, have had previous M.S., TANF agents and required steroids. 367 00:42:35,730 --> 00:42:40,830 And my final two treatment of patients with minimally raised altie and high viral leading 368 00:42:40,830 --> 00:42:48,000 chronic hepatitis B with turn-off of their reduces the risk of fibrosis progression. 369 00:42:48,000 --> 00:43:03,240 Treatment of individuals of PPC, resistance to URSO, adding budesonide does not improve liver histology. 370 00:43:03,240 --> 00:43:08,610 Fantastic. I think we've got to the end of our first episode of G.I. Instinct. 371 00:43:08,610 --> 00:43:15,870 Your G.I. research update. We're got to put all of the details of the papers that we've discussed in the show notes. 372 00:43:15,870 --> 00:43:23,820 Please let us know what you think of the format. Any ideas you've got, any coup papers you've seen that you think we should be talking about? 373 00:43:23,820 --> 00:43:26,880 We would love to hear from you. Please leave us a review. 374 00:43:26,880 --> 00:43:35,940 Get in touch on Twitter at GeoEye Update or drop us an e-mail at Gut Instincts podcasts all at one word at Gevalt dot com. 375 00:43:35,940 --> 00:44:12,405 Thank you very much for listening and goodbye.